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Introduction

ggplot2.violinplot is an easy to use function custom function to plot and customize easily a violin plot using ggplot2 and R software. ggplot2.violinplot function is from easyGgplot2 R package. An R script is available in the next section to install the package.

The violin plot is similar to box plots, except that they also show the kernel probability density of the data at different value. Typically violin plots will include a marker for the median of the data and a box indicating the interquartile range, as in standard box plots (wiki).

The aim of this tutorial is to show you step by step, how to plot and customize a violin plot using ggplot2.violinplot function [easyGgplot2 package].

At the end of this tutorial you will be able to draw, with few R code, the following plots:

r data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical software

ggplot2.violinplot function is described in detail at the end of this document.

Install and load easyGgplot2 package

easyGgplot2 R package can be installed as follow :

install.packages("devtools")
library(devtools)
install_github("easyGgplot2", "kassambara")

Load the package using this R code :

library(easyGgplot2)

Data format

The data must be a numeric vector or a data.frame (columns are variables and rows are observations).

ToothGrowth data is used in the following examples.

# create a numeric vector
numVector<-rnorm(100)
head(numVector)
## [1] -1.089173  0.004361  0.650597  0.423704  1.147449  0.837064
# data.frame
df <- ToothGrowth
head(df)
##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5
## 4  5.8   VC  0.5
## 5  6.4   VC  0.5
## 6 10.0   VC  0.5

ToothGrowth describes the effect of Vitamin C on Tooth growth in Guinea pigs. The response is the length (len) of teeth in each of 10 guinea pigs at each of three dose levels of Vitamin C (0.5, 1, and 2 mg) with each of two delivery methods (orange juice or ascorbic acid).

The data is a data frame with 60 observations on 3 variables. * [,1] len numeric Tooth length. * [,2] supp factor Supplement type (VC or OJ). * [,3] dose numeric Dose in milligrams.


Basic violin plot

# Violin plot from a single numeric vector 
ggplot2.violinplot(data=numVector)

# Basic violin plot from the vector "len"
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;)

# Change the orientation: Horizontal violin plot
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                orientation="horizontal")

# set trim to FALSE. If TRUE (default), trim the tails of 
  # the violins to the range of the data.
#If FALSE, don&#39;t trim the tails
ggplot2.violinplot(data=df, xName=&#39;dose&#39;, yName=&#39;len&#39;,
                   trim=FALSE)

r data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical softwarer data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical softwarer data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical softwarer data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical software

Violin plot with mean point and dots

Each dot represents one observation and the mean point corresponds to the mean value of the observations in a given group.

# Violin plot with mean point
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
      addMean=TRUE, meanPointShape=23, meanPointSize=3,
      meanPointColor="black", meanPointFill="blue")

#Violin plot with centered dots
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
      addDot=TRUE, dotSize=1, dotPosition="center")

# Violin plot with jittered dots. 
#jitter: degree of jitter in x direction
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
      addDot=TRUE, dotSize=1.7,
      dotPosition="jitter", jitter=0.2)

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Change the violin plot line type and point shape

Different point shapes and line types can be used in the plot. By default, ggplot2 uses solid line type and circle shape.

# Change the violin plot line color and line type
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                colour="red", linetype="dotted")

r data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical software

Customize your violin plot

Parameters

The arguments that can be used to customize x and y axis are listed below :

Parameters Description
mainTitle the title of the plot
mainTitleFont a vector of length 3 indicating respectively the size, the style (“italic”, “bold”, “bold.italic”) and the color of x and y axis titles. Default value is: mainTitleFont=c(14, “bold”, “black”).
xShowTitle, yShowTitle if TRUE, x and y axis titles will be shown. Set the value to FALSE to hide axis labels. Default values are TRUE.
xtitle, ytitle x and y axis labels. Default values are NULL.
xtitleFont, ytitleFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis titles. Possible values for the style:“plain”, “italic”, “bold”, “bold.italic”. Color can be specified as an hexadecimal code (e.g: “#FFCC00”) or by the name (e.g : “red”, “green”). Default values are xtitleFont=c(14,"bold", "black"), ytitleFont=c(14,"bold", "black").
xlim, ylim limit for the x and y axis. Default values are NULL.
xScale, yScale x and y axis scales. Possible values : c(“none”, “log2”, “log10”). e.g: yScale=“log2”. Default values are NULL.
xShowTickLabel, yShowTickLabel if TRUE, x and y axis tick mark labels will be shown. Default values are TRUE.
xTickLabelFont, yTickLabelFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis tick label fonts. Default value are xTickLabelFont=c(12, "bold", "black"), yTickLabelFont=c(12, "bold", "black").
xtickLabelRotation, ytickLabelRotation Rotation angle of x and y axis tick labels. Default value are 0.
hideAxisTicks if TRUE, x and y axis ticks are hidden. Default value is FALSE.
axisLine a vector of length 3 indicating respectively the size, the line type and the color of axis lines. Default value is c(0.5, "solid", "#E5E5E5").

For more details follow this link : ggplot2.customize.

Main title and axis labels

# Change main title and axis titles
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        mainTitle="Plot of length \n by dose",
        xtitle="Dose (mg)", ytitle="Length")

# Customize title styles. Possible values for the font style :
  # &#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
      xtitle="Dose (mg)", ytitle="Length",
      mainTitle="Plot of length \n by dose",
      mainTitleFont=c(14,"bold.italic", "red"),
      xtitleFont=c(14,"bold", "#993333"),
      ytitleFont=c(14,"bold", "#993333"))

# Hide x an y axis titles
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
     xShowTitle=FALSE, yShowTitle=FALSE)

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Axis ticks

# Axis ticks labels and orientaion
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
      xShowTitle=FALSE, yShowTitle=FALSE,
      xTickLabelFont=c(14,"bold", "#993333"),
      yTickLabelFont=c(14,"bold", "#993333"),
      xtickLabelRotation=45, ytickLabelRotation=45)

# Hide axis tick labels
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        xShowTitle=FALSE, yShowTitle=FALSE,
        xShowTickLabel=FALSE, yShowTickLabel=FALSE)

# Hide axis ticks
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
      xShowTitle=FALSE, yShowTitle=FALSE,
      xShowTickLabel=FALSE, yShowTickLabel=FALSE,
      hideAxisTicks=TRUE)

# AxisLine : a vector of length 3 indicating the size,
  #the line type and the color of axis lines
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, 
      axisLine=c(1, "solid", "darkblue"))

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Background and colors

Change violin plot background and fill colors

# change background color to "white". Default is "gray"
ggplot2.violinplot(data=df, xName=&#39;dose&#39;, yName=&#39;len&#39;,
                backgroundColor="white")

# Change background color to "lightblue" and grid color to "white"
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                backgroundColor="lightblue", gridColor="white")

# Change plot fill color
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                backgroundColor="white", fill=&#39;#FFAAD4&#39;)

# remove grid; remove top and right borders around the plot;
# change  axis lines
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                backgroundColor="white", fill=&#39;#FFAAD4&#39;,
                removePanelGrid=TRUE,removePanelBorder=TRUE,
                axisLine=c(0.5, "solid", "black"))

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Change violin plot color according to the group

Colors can be specified as a hexadecimal RGB triplet, such as "#FFCC00" or by names (e.g : "red" ). You can also use other color scales, such as ones taken from the RColorBrewer package. The different color systems available in R have been described in detail here.

To change violin plot color according to the group, you have to specify the name of the data column containing the groups using the argument groupName. Use the argument groupColors, to specify colors by hexadecimal code or by name. In this case, the length of groupColors should be the same as the number of the groups. Use the argument brewerPalette, to specify colors using RColorBrewerpalette.

# Color the violin plot accoording to the groupName "dose"
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                   groupName=&#39;dose&#39;)

# Change group colors using hexadecimal colors
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        groupName=&#39;dose&#39;, 
        groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;))

# Change group colors using brewer palette: "Paired"
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, 
        groupName=&#39;dose&#39;, brewerPalette="Paired")

r data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical softwarer data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical softwarer data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical software


Color can also be changed by using names as follow :

# Change group colors using color names
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, 
        groupName=&#39;dose&#39;,
        groupColors=c(&#39;aquamarine3&#39;,&#39;chartreuse1&#39;,&#39;goldenrod1&#39;))


Legend

Legend position

# Change the legend position to "top" 
  # (possible values: "left","top", "right", "bottom")
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, 
          groupName=&#39;dose&#39;, legendPosition="top")

# legendPosition can be also a numeric vector c(x, y)
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        groupName=&#39;dose&#39;, legendPosition=c(0.8,0.2))

r data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical softwarer data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical software

It is also possible to position the legend inside the plotting area. You have to indicate the x, y coordinates of legend box. x and y values must be between 0 and 1. c(0,0) corresponds to "bottom left" and c(1,1) corresponds to "top right" position.

Legend background color, title and text font styles

# Change legend background color, title and text font styles
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
    groupName=&#39;dose&#39;,
    # legendTitleFont=c(size, style, color)
    legendTitle="Dose (mg)", legendTitleFont=c(10, "bold", "blue"),
    #legendTextFont=c(size, style, color)
    legendTextFont=c(10, "bold.italic", "red"),
    # legendBackground: c(fill, lineSize, lineType, lineColor)
    legendBackground=c("lightblue", 0.5, "solid", "darkblue" )
    )

r data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical software

Change the order of items in the legend, remove plot legend

# Change the order of items in the legend
 # legendItemOrder : character vector indicating 
# the order of items in the legends.
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        groupName=&#39;dose&#39;,
        legendItemOrder=c("2", "1", "0.5"))

# Remove plot legend
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        groupName=&#39;dose&#39;, showLegend=FALSE)

r data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical softwarer data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical software

Axis scales

Possible values for y axis scale are “none”, “log2” and log10. Default value is “none”.

# Change y axis limit
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                groupName=&#39;dose&#39;, ylim=c(0,50))

# y Log scale. Possible values="none", "log2" and "log10"
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                groupName=&#39;dose&#39;, yScale="log2")

r data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical softwarer data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical software

Create a customized plots with few R code

# Customized violinplot with centered dot plot
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, 
    groupName=&#39;dose&#39;,
    groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;), showLegend=FALSE,
    backgroundColor="white", xtitle="Dose (mg)", ytitle="length", 
    mainTitle="Plot of length \n by dose",
    addDot=TRUE, dotSize=1)

#Remove grid; Remove Top and right border around the plot
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
    groupName=&#39;dose&#39;,
    groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;), showLegend=FALSE,
    backgroundColor="white", xtitle="Dose (mg)", ytitle="length", 
    mainTitle="Plot of length \n by dose",
    addDot=TRUE, dotSize=1,
    removePanelGrid=TRUE,removePanelBorder=TRUE,
    axisLine=c(0.5, "solid", "black"))

r data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical softwarer data visualization with ggplot2 violin plot : tutorial on how to use ggplot2.violinplot function to easily make a violin plot using ggplot2 and R statistical software

Violin plot with multiple groups

The data format is:

##     len supp dose
## 1   4.2   VC  0.5
## 2  11.5   VC  0.5
## 3   7.3   VC  0.5
## 11 16.5   VC  1.0
## 12 16.5   VC  1.0
## 13 15.2   VC  1.0
## 14 17.3   VC  1.0
## 15 22.5   VC  1.0
## 21 23.6   VC  2.0
## 22 18.5   VC  2.0
## 23 33.9   VC  2.0
## 24 25.5   VC  2.0
## 31 15.2   OJ  0.5
## 32 21.5   OJ  0.5
## 33 17.6   OJ  0.5
## 34  9.7   OJ  0.5
## 41 19.7   OJ  1.0
## 42 23.3   OJ  1.0
## 43 23.6   OJ  1.0
## 44 26.4   OJ  1.0
## 51 25.5   OJ  2.0
## 52 26.4   OJ  2.0
## 53 22.4   OJ  2.0
## 54 24.5   OJ  2.0
# plot of variable &#39;len&#39; by xName &#39;dose&#39;. The plot is colored by the groupName &#39;supp&#39;
# position = interval between box plot of the same group
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
    groupName=&#39;supp&#39;, position=position_dodge(0.8), 
    backgroundColor="white", groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;),
    legendPosition="top")  

# add dots
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, 
    groupName=&#39;supp&#39;, position=position_dodge(0.8),
    backgroundColor="white", groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;),
    addDot=TRUE, dotSize=1,legendPosition="top")

Violin plot with multiple groupsViolin plot with multiple groups

Facet : split a plot into a matrix of panels

The facet approach splits a plot into a matrix of panels. Each panel shows a different subset of the data.

Faceting with one variable

# Facet according to the supp variable
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, 
        groupName=&#39;supp&#39;, legendPosition="top",
        faceting=TRUE, facetingVarNames="supp") 

# Change the direction. possible values are "vertical", horizontal".
# Default is vertical.
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        groupName=&#39;supp&#39;, legendPosition="top",
        faceting=TRUE, facetingVarNames="supp",
        facetingDirection="horizontal")

ggplot2 violin plot and facet approch, one variableggplot2 violin plot and facet approch, one variable

Facet with two variables

# Facet by two variables: dose and supp.
# Rows are dose and columns are supp
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
    groupName=&#39;supp&#39;, legendPosition="top",
    faceting=TRUE, facetingVarNames=c("dose","supp"))

# Facet by two variables: reverse the order of the 2 variables
# Rows are supp and columns are dose
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
    groupName=&#39;supp&#39;, legendPosition="top",
    faceting=TRUE, facetingVarNames=c("supp", "dose"))

ggplot2 vilolin plot and facet approch, two variablesggplot2 vilolin plot and facet approch, two variables

Facet scales

By default, all the panels have the same scale (facetingScales="fixed"). They can be made independent, by setting scales to free, free_x, or free_y.

# Facet with free scales
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
            groupName=&#39;supp&#39;, legendPosition="top",
            faceting=TRUE, facetingVarNames=c("dose", "supp"),
            facetingScales="free")

ggplot2 violin plot and facet approch, facet scale

As you can see in the above plot, y axis have different scales in the different panels.

Facet label apperance

# Change facet text font
# Possible values for the font style:&#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;,
        legendPosition="top",
        faceting=TRUE, facetingVarNames=c("dose","supp"),
        facetingFont=c(12, &#39;bold.italic&#39;, "red"))

# Change the apperance of the rectangle around facet label
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;,
        legendPosition="top",
        faceting=TRUE, facetingVarNames=c("dose","supp"),
        facetingRect=list(background="white", lineType="solid",
                          lineColor="black", lineSize=1.5)
        )

ggplot2 violin plot and facet approch, facet labelggplot2 violin plot and facet approch, facet label

ggplot2.violinplot function

Description

Plot easily a violin plot plot with R package easyGgplot2.

usage

ggplot2.violinplot(data, xName=NULL, yName=NULL, groupName=NULL,
        addMean=FALSE, meanPointShape=23, meanPointSize=4,
        meanPointColor="black", meanPointFill="blue",
        addDot=FALSE, dotSize=1, dotPosition=c("center", "jitter"),
        jitter=0.2, groupColors=NULL, brewerPalette=NULL,...)

Arguments

Arguments Descriptions
data data.frame or a numeric vector. Columns are variables and rows are observations.
xName The name of column containing x variable (i.e groups). Default value is NULL.
yName The name of column containing y variable. If yName=NULL, data should be a numeric vector.
groupName The name of column containing group variable. This variable is used to color plot according to the group.
addMean if TRUE, the mean point is added on the plot for each group. Default value is FALSE.
meanPointShape, meanPointSize The shape and size of mean point.
meanPointColor border color of the mean point. Default value is “black”.
meanPointFill Fill color of mean point. This parameter is used only when meanPointShape=21 to 25. Default value is “blue”
addDot if TRUE, dotplot is added on the violinplot. Default value is FALSE.
dotSize The size of dots.
dotPosition Possible values are “center” and “jitter”. Default value is “center”.
jitter Degree of jitter in x direction. Default value is 0.2.
groupColors Color of groups. groupColors should have the same length as groups.
brewerPalette This can be also used to indicate group colors. In this case the parameter groupColors should be NULL. e.g: brewerPalette=“Paired”.
…. Other arguments passed on to ggplot2.customize custom function or to geom_dotplot and to geom_violin functions from ggplot2 package.

The other arguments which can be used are described at this link : ggplot2 customize. They are used to customize the plot (axis, title, background, color, legend, ….) generated using ggplot2 or easyGgplot2 R package.

Examples

df <- ToothGrowth
ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                mainTitle="Plot of length according\n to the dose",
                xtitle="Dose (mg)", ytitle="Length")

# Or use this
plot<-ggplot2.violinplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;)
plot<-ggplot2.customize(plot, mainTitle="Plot of length according\n to the dose",
                        xtitle="Dose (mg)", ytitle="Length")
print(plot)


Easy ggplot2 ebook

Note that an eBook is available on easyGgplot2 package here.

By Alboukadel Kassambara

Copyright 2014 Alboukadel Kassambara. All rights reserved.

Published by STHDA (https://www.sthda.com/english).

September 2014 : First edition.

Licence : This document is under creative commons licence (http://creativecommons.org/licenses/by-nc-sa/3.0/).

Contact : Alboukadel Kassambara alboukadel.kassambara@gmail.com


Infos

This analysis was performed using R (ver. 3.1.0), easyGgplot2 (ver 1.0.0) and ggplot2 (ver 1.0.0).

]]> Fri, 06 Feb 2015 21:17:17 +0100 <![CDATA[ggplot2 dot plot : Easy function for making a dot plot]]> https://www.sthda.com/english/wiki/ggplot2-dot-plot-easy-function-for-making-a-dot-plot https://www.sthda.com/english/wiki/ggplot2-dot-plot-easy-function-for-making-a-dot-plot

Introduction

ggplot2.dotplot is an easy to use function for making a dot plot with R statistical software using ggplot2 package. The aim of this tutorial, is to show you how to make a dot plot and to personalize the different graphical parameters including main title, axis labels, legend, background and colors. ggplot2.dotplot function is from easyGgplot2 R package. An R script is available in the next section to install the package.

At the end of this tutorial you will be able to draw, with few R code, the following plot :

r data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical software

ggplot2.dotplot function is described in detail at the end of this document.

Install and load easyGgplot2 package

easyGgplot2 R package can be installed as follow :

install.packages("devtools")
library(devtools)
install_github("easyGgplot2", "kassambara")

Load the package using this R code :

library(easyGgplot2)

Data format

The data must be a numeric vector or a data.frame (columns are variables and rows are observations).

ToothGrowth data is used in the following examples.

# create a numeric vector
numVector<-rnorm(100)
head(numVector)
## [1]  1.2768 -1.6126 -0.1056  0.4091  0.8999 -0.7300
# data.frame
df <- ToothGrowth
head(df)
##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5
## 4  5.8   VC  0.5
## 5  6.4   VC  0.5
## 6 10.0   VC  0.5

ToothGrowth describes the effect of Vitamin C on Tooth growth in Guinea pigs. The response is the length (len) of teeth in each of 10 guinea pigs at each of three dose levels of Vitamin C (0.5, 1, and 2 mg) with each of two delivery methods (orange juice or ascorbic acid).

The data is a data frame with 60 observations on 3 variables. * [,1] len numeric Tooth length. * [,2] supp factor Supplement type (VC or OJ). * [,3] dose numeric Dose in milligrams.


Basic dot plot

# Dot plot from a single numeric vector 
ggplot2.dotplot(data=numVector)

# Basic dot plot from the vector "len"
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;)

# change dotsize and stack ratio
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, 
                stackratio=1.5, dotsize=1.2)

# Change the orientation: Horizontal dot plot
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                orientation="horizontal")

# Dot plot with box plot
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                addBoxplot=TRUE)

# dot plot with notched box plot
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                addBoxplot=TRUE,notch=TRUE)

r data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical software

notch: If TRUE, make a notched box plot. The notch displays a confidence interval around the median which is normally based on the median +/- 1.57 x IQR/sqrt of n. Notches are used to compare groups; if the notches of two boxes do not overlap, this is strong evidence that the medians differ.

Dot plot with mean point

Each dot represents one observation and the mean point corresponds to the mean value of the observations in a given group.

# Dot plot with mean point
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        addMean=TRUE, meanPointShape=23, meanPointSize=4,
        meanPointColor="black", meanPointFill="blue")

#Change the dot plot border color
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                colour="red")

r data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical software

Customize your dot plot

Parameters

The arguments that can be used to customize x and y axis are listed below :

Parameters Description
mainTitle the title of the plot
mainTitleFont a vector of length 3 indicating respectively the size, the style (“italic”, “bold”, “bold.italic”) and the color of x and y axis titles. Default value is: mainTitleFont=c(14, “bold”, “black”).
xShowTitle, yShowTitle if TRUE, x and y axis titles will be shown. Set the value to FALSE to hide axis labels. Default values are TRUE.
xtitle, ytitle x and y axis labels. Default values are NULL.
xtitleFont, ytitleFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis titles. Possible values for the style:“plain”, “italic”, “bold”, “bold.italic”. Color can be specified as an hexadecimal code (e.g: “#FFCC00”) or by the name (e.g : “red”, “green”). Default values are xtitleFont=c(14,"bold", "black"), ytitleFont=c(14,"bold", "black").
xlim, ylim limit for the x and y axis. Default values are NULL.
xScale, yScale x and y axis scales. Possible values : c(“none”, “log2”, “log10”). e.g: yScale=“log2”. Default values are NULL.
xShowTickLabel, yShowTickLabel if TRUE, x and y axis tick mark labels will be shown. Default values are TRUE.
xTickLabelFont, yTickLabelFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis tick label fonts. Default value are xTickLabelFont=c(12, "bold", "black"), yTickLabelFont=c(12, "bold", "black").
xtickLabelRotation, ytickLabelRotation Rotation angle of x and y axis tick labels. Default value are 0.
hideAxisTicks if TRUE, x and y axis ticks are hidden. Default value is FALSE.
axisLine a vector of length 3 indicating respectively the size, the line type and the color of axis lines. Default value is c(0.5, "solid", "#E5E5E5").

For more details follow this link : ggplot2.customize.

Main title and axis labels

# Change main title and axis titles
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                mainTitle="Plot of length \n by dose",
                xtitle="Dose (mg)", ytitle="Length")

# Customize title styles. Possible values for the font style :
  # &#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                xtitle="Dose (mg)", ytitle="Length",
                mainTitle="Plot of length \n by dose",
                mainTitleFont=c(14,"bold.italic", "red"),
                xtitleFont=c(14,"bold", "#993333"),
                ytitleFont=c(14,"bold", "#993333"))

# Hide x an y axis titles
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                xShowTitle=FALSE, yShowTitle=FALSE)

r data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical software

Axis ticks

# Axis ticks labels and orientaion
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                xShowTitle=FALSE, yShowTitle=FALSE,
                xTickLabelFont=c(14,"bold", "#993333"),
                yTickLabelFont=c(14,"bold", "#993333"),
                xtickLabelRotation=45, ytickLabelRotation=45)

# Hide axis tick labels
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                xShowTitle=FALSE, yShowTitle=FALSE,
                xShowTickLabel=FALSE, yShowTickLabel=FALSE)

# Hide axis ticks
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                xShowTitle=FALSE, yShowTitle=FALSE,
                xShowTickLabel=FALSE,yShowTickLabel=FALSE,
                hideAxisTicks=TRUE)

# AxisLine : a vector of length 3 indicating the size,
  #the line type and the color of axis lines
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                axisLine=c(1, "solid", "darkblue"))

r data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical software

Background and colors

Change dot plot background and fill colors

# change background color to "white". Default is "gray"
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                backgroundColor="white")

# change background color to "lightblue" and grid color to "white"
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                backgroundColor="lightblue", gridColor="white")

# Change plot fill color
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                backgroundColor="white", fill=&#39;#FFAAD4&#39;)

# remove grid; remove top and right borders around the plot;
# change  axis lines
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                backgroundColor="white", fill=&#39;#FFAAD4&#39;,
                removePanelGrid=TRUE,removePanelBorder=TRUE,
                axisLine=c(0.5, "solid", "black"))

r data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical software

Change dot plot color according to the group

Colors can be specified as a hexadecimal RGB triplet, such as "#FFCC00" or by names (e.g : "red" ). You can also use other color scales, such as ones taken from the RColorBrewer package. The different color systems available in R have been described in detail here.

To change dot plot color according to the group, you have to specify the name of the data column containing the groups using the argument groupName. Use the argument groupColors, to specify colors by hexadecimal code or by name. In this case, the length of groupColors should be the same as the number of the groups. Use the argument brewerPalette, to specify colors using RColorBrewerpalette.

# Color the dot plot accoording to the groupName "dose"
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;)

#Change group colors using hexadecimal colors
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
                groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;))

#Change group colors using brewer palette: "Paired"
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
                brewerPalette="Paired")

r data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical software


Color can also be changed by using names as follow :

#Change group colors using color names
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
       groupName=&#39;dose&#39;,
       groupColors=c(&#39;aquamarine3&#39;,&#39;chartreuse1&#39;,&#39;goldenrod1&#39;))


Legend

Legend position

# Change the legend position to "top" 
  # (possible values: "left","top", "right", "bottom")
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
                legendPosition="top")

# legendPosition can be also a numeric vector c(x, y)
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
                legendPosition=c(0.8,0.2))

r data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical software

It is also possible to position the legend inside the plotting area. You have to indicate the x, y coordinates of legend box. x and y values must be between 0 and 1. c(0,0) corresponds to "bottom left" and c(1,1) corresponds to "top right" position.

Legend background color, title and text font styles

# Change legend background color, title and text font styles
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
        # legendTitleFont=c(size, style, color)
        legendTitle="Dose (mg)",
        legendTitleFont=c(10, "bold", "blue"),
        #legendTextFont=c(size, style, color)
        legendTextFont=c(10, "bold.italic", "red"),
        # legendBackground: c(fill, lineSize, lineType, lineColor)
        legendBackground=c("lightblue", 0.5, "solid", "darkblue" )
        )

r data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical software

Change the order of items in the legend, remove plot legend

# Change the order of items in the legend
 # legendItemOrder : character vector indicating 
# the order of items in the legends.
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
                legendItemOrder=c("2", "1", "0.5"))
    
# Remove plot legend
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
                showLegend=FALSE)

r data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical software

Axis scales

Possible values for y axis scale are “none”, “log2” and log10. Default value is “none”.

# Change y axis limit
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
                ylim=c(0,50))

# y Log scale. Possible values="none", "log2" and "log10"
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
                yScale="log2")

r data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical software

Create a customized plots with few R code

#Customized dot plot
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
    groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;), showLegend=FALSE,
    backgroundColor="white", xtitle="Dose (mg)", ytitle="length", 
    mainTitle="Plot of length \n by dose")

# Customized dot plot with notched box plot
# Remove grid; Remove Top and right border around the plot
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
    groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;), showLegend=FALSE,
    backgroundColor="white", xtitle="Dose (mg)", ytitle="length", 
    mainTitle="Plot of length \n by dose",
    addBoxplot=TRUE, notch=TRUE,
    removePanelGrid=TRUE,removePanelBorder=TRUE,
    axisLine=c(0.5, "solid", "black"))

r data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical softwarer data visualization with ggplot2 dot plot : tutorial on how to use ggplot2.dotplot function for easily making a dot plot using ggplot2 and R statistical software

# Customized dot plot, add box plot, pink fill color.
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
    groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;), showLegend=FALSE,
    backgroundColor="white", xtitle="Dose (mg)", ytitle="length", 
    mainTitle="Plot of length \n by dose",
    addBoxplot=TRUE, boxplotFill="pink")

# Customized dot plot, add box plot, 
#fill box plot accoording to the groups.
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
    groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;), showLegend=FALSE,
    backgroundColor="white", xtitle="Dose (mg)", ytitle="length", 
    mainTitle="Plot of length \n by dose",
    addBoxplot=TRUE, boxplotFill=NULL)

ggplot2 dotplotggplot2 dotplot

By default, box plot fill color is “white”. To change the fill color with the argument groupColors, set boxplotFill value to NULL.

Dot plot with multiple groups

The data format is:

##     len supp dose
## 1   4.2   VC  0.5
## 2  11.5   VC  0.5
## 3   7.3   VC  0.5
## 11 16.5   VC  1.0
## 12 16.5   VC  1.0
## 13 15.2   VC  1.0
## 14 17.3   VC  1.0
## 15 22.5   VC  1.0
## 21 23.6   VC  2.0
## 22 18.5   VC  2.0
## 23 33.9   VC  2.0
## 24 25.5   VC  2.0
## 31 15.2   OJ  0.5
## 32 21.5   OJ  0.5
## 33 17.6   OJ  0.5
## 34  9.7   OJ  0.5
## 41 19.7   OJ  1.0
## 42 23.3   OJ  1.0
## 43 23.6   OJ  1.0
## 44 26.4   OJ  1.0
## 51 25.5   OJ  2.0
## 52 26.4   OJ  2.0
## 53 22.4   OJ  2.0
## 54 24.5   OJ  2.0
# plot of variable &#39;len&#39; by xName &#39;dose&#39;. The plot is colored by the groupName &#39;supp&#39;
# position = interval between dot plot of the same group
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;,
      position=position_dodge(0.8), 
      backgroundColor="white",groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;)
    )  
 
# Change the interval between dotplot of the same group : 
  # position=position_dodge(0); overlap
#add box plot
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;,
      position=position_dodge(0.0), 
      backgroundColor="white", groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;),
      addBoxplot=TRUE, boxplotFill="white")

Dot plot with multiple groupsDot plot with multiple groups

Facet : split a plot into a matrix of panels

The facet approach splits a plot into a matrix of panels. Each panel shows a different subset of the data.

Facet with one variable

#Facet according to the supp variable
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;,
      legendPosition="top",
      faceting=TRUE, facetingVarNames="supp") 

# Change the direction. possible values are "vertical", horizontal".
# default is vertical.
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;,
        legendPosition="top",
        faceting=TRUE, facetingVarNames="supp",
        facetingDirection="horizontal")

ggplot2 dot plot and facet approch, one variableggplot2 dot plot and facet approch, one variable

Facet with two variables

# Facet by two variables: dose and supp.
# Rows are dose and columns are supp
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;,
          legendPosition="top", faceting=TRUE,
          facetingVarNames=c("dose","supp"))

# Facet by two variables: reverse the order of the 2 variables
# Rows are supp and columns are dose
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;,
        legendPosition="top", faceting=TRUE,
        facetingVarNames=c("supp", "dose"))

ggplot2 dot plot and facet approch, two variablesggplot2 dot plot and facet approch, two variables

Facet scales

By default, all the panels have the same scale (facetingScales="fixed"). They can be made independent, by setting scales to free, free_x, or free_y.

# Facet with free scales
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        groupName=&#39;supp&#39;, legendPosition="top",
        faceting=TRUE, facetingVarNames=c("dose", "supp"),
        facetingScales="free")

ggplot2 dotplot and facet approch, free scale

As you can see in the above plot, y axis have different scales in the different panels.

Facet label apperance

# Change facet text font. Possible values for the font style:
  #&#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;,
        legendPosition="top",
        faceting=TRUE, facetingVarNames=c("dose","supp"),
        facetingFont=c(12, &#39;bold.italic&#39;, "red"))

# Change the apperance of the rectangle around facet label
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;,
            legendPosition="top",
            faceting=TRUE, facetingVarNames=c("dose","supp"),
            facetingRect=list(background="white", lineType="solid",
                              lineColor="black", lineSize=1.5)
            )

ggplot2 dot plot and facet approch, facet labelggplot2 dot plot and facet approch, facet label

ggplot2.dotplot function

Description

Plot easily a dot plot plot with R package easyGgplot2.

usage

ggplot2.dotplot(data, xName=NULL, yName=NULL, groupName=NULL,
          position=position_dodge(0.8),
          addMean=FALSE, meanPointShape=5, meanPointSize=4,
          meanPointColor="black", meanPointFill="blue",
          addBoxplot=FALSE, boxplotFill="white",
          boxplotColor="black", boxplotLineWeight=0.5,
          groupColors=NULL, brewerPalette=NULL,...)

Arguments

Arguments Descriptions
data data.frame or a numeric vector. Columns are variables and rows are observations.
xName The name of column containing x variable (i.e groups). Default value is NULL.
yName The name of column containing y variable. If yName=NULL, data should be a numeric vector.
groupName The name of column containing group variable. This variable is used to color plot according to the group.
position The position adjustment to use for overlapping points. Default value is position_dodge(0.8).
addMean if TRUE, the mean point is added on the plot for each group. Default value is FALSE.
meanPointShape, meanPointSize The shape and size of mean point.
meanPointColor border color of the mean point. Default value is “black”.
meanPointFill Fill color of mean point. This parameter is used only when meanPointShape=21 to 25. Default value is “blue”
addBoxplot If TRUE, boxplot is added on the dotplot. Default value is FALSE.
boxplotFill Fill color of the boxplot. Default value is white.
boxplotColor Boxplot line color. Default value is black.
boxplotLineWeight Boxplot line weight. Default value is 0.5.
groupColors Color of groups. groupColors should have the same length as groups.
brewerPalette This can be also used to indicate group colors. In this case the parameter groupColors should be NULL. e.g: brewerPalette=“Paired”.
…. Other arguments passed on to ggplot2.customize custom function or to geom_dotplot functions from ggplot2 package.

The other arguments which can be used are described at this link : ggplot2 customize. They are used to customize the plot (axis, title, background, color, legend, ….) generated using ggplot2 or easyGgplot2 R package.

Examples

df <- ToothGrowth
ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                mainTitle="Plot of length according\n to the dose",
                xtitle="Dose (mg)", ytitle="Length")

#Or use this
plot<-ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;)
plot<-ggplot2.customize(plot, mainTitle="Plot of length according\n to the dose",
                        xtitle="Dose (mg)", ytitle="Length")
print(plot)

Easy ggplot2 ebook

Note that an eBook is available on easyGgplot2 package here.

By Alboukadel Kassambara

Copyright 2014 Alboukadel Kassambara. All rights reserved.

Published by STHDA (https://www.sthda.com/english).

September 2014 : First edition.

Licence : This document is under creative commons licence (http://creativecommons.org/licenses/by-nc-sa/3.0/).

Contact : Alboukadel Kassambara alboukadel.kassambara@gmail.com


Infos

This analysis was performed using R (ver. 3.1.0), easyGgplot2 (ver 1.0.0) and ggplot2 (ver 1.0.0).

]]> Fri, 06 Feb 2015 21:15:56 +0100 <![CDATA[ggplot2.stripchart : Easy one dimensional scatter plot using ggplot2 and R software]]> https://www.sthda.com/english/wiki/ggplot2-stripchart-easy-one-dimensional-scatter-plot-using-ggplot2-and-r-software https://www.sthda.com/english/wiki/ggplot2-stripchart-easy-one-dimensional-scatter-plot-using-ggplot2-and-r-software

Introduction

ggplot2.stripchart is an easy to use function (from easyGgplot2 package), to produce a stripchart using ggplot2 plotting system and R software. Stripcharts are also known as one dimensional scatter plots (or dot plots). These plots are suitable compared to box plots when sample sizes are small.

The aim of this tutorial is to show you step by step, how to plot and customize a stripchart using ggplot2.stripchart function.

At the end of this tutorial you will be able to draw, with few R code, the following plots :

Data visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical software

ggplot2.stripchart function is described in detail at the end of this document.

Install and load easyGgplot2 package

easyGgplot2 R package can be installed as follow :

install.packages("devtools")
library(devtools)
install_github("easyGgplot2", "kassambara")

Load the package using this R code :

library(easyGgplot2)

Data format

The data must be a numeric vector or a data.frame (columns are variables and rows are observations).

ToothGrowth data is used in the following examples.

# create a numeric vector
numVector<-rnorm(100)
head(numVector)
## [1] -0.7958  0.1623  2.1102  0.5006  0.2491 -1.5663
# data.frame
df <- ToothGrowth
head(df)
##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5
## 4  5.8   VC  0.5
## 5  6.4   VC  0.5
## 6 10.0   VC  0.5

ToothGrowth describes the effect of Vitamin C on Tooth growth in Guinea pigs. The response is the length (len) of teeth in each of 10 guinea pigs at each of three dose levels of Vitamin C (0.5, 1, and 2 mg) with each of two delivery methods (orange juice or ascorbic acid).

The data is a data frame with 60 observations on 3 variables. * [,1] len numeric Tooth length. * [,2] supp factor Supplement type (VC or OJ). * [,3] dose numeric Dose in milligrams.


Basic stripchart

# Stripchart from a single numeric vector 
ggplot2.stripchart(data=numVector)

# Basic stripchart from the vector "len"
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;)

#change dot size
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, 
                size=3)

# Change the orientation: Horizontal stripchart
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                orientation="horizontal")

# Stripchart with box plot
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                addBoxplot=TRUE)

# stripchart with notched box plot
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                addBoxplot=TRUE,notch=TRUE)

Data visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical software

notch: If TRUE, make a notched box plot. The notch displays a confidence interval around the median which is normally based on the median +/- 1.57 x IQR/sqrt of n. Notches are used to compare groups; if the notches of two boxes do not overlap, this is strong evidence that the medians differ.

To change the size of dots, use the argument ‘size’ (e.g : size = 4)

Stripchart with mean point

Each dot represents one observation and the mean point corresponds to the mean value of the observations in a given group.

# Stripchart with mean point
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
      addMean=TRUE, meanPointShape=23, meanPointSize=4,
      meanPointColor="black", meanPointFill="blue")

# Change the stripchart color
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                colour="red")

Data visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical software

Change the stripchart point shape

Different point shapes and line types can be used in the plot. By default, ggplot2 uses solid line type and circle shape.

# Change point shape
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                shape=18)
# Change point shape
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                shape=17)

Data visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical software

Customize your stripchart

Parameters

The arguments that can be used to customize x and y axis are listed below :

Parameters Description
mainTitle the title of the plot
mainTitleFont a vector of length 3 indicating respectively the size, the style (“italic”, “bold”, “bold.italic”) and the color of x and y axis titles. Default value is: mainTitleFont=c(14, “bold”, “black”).
xShowTitle, yShowTitle if TRUE, x and y axis titles will be shown. Set the value to FALSE to hide axis labels. Default values are TRUE.
xtitle, ytitle x and y axis labels. Default values are NULL.
xtitleFont, ytitleFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis titles. Possible values for the style:“plain”, “italic”, “bold”, “bold.italic”. Color can be specified as an hexadecimal code (e.g: “#FFCC00”) or by the name (e.g : “red”, “green”). Default values are xtitleFont=c(14,"bold", "black"), ytitleFont=c(14,"bold", "black").
xlim, ylim limit for the x and y axis. Default values are NULL.
xScale, yScale x and y axis scales. Possible values : c(“none”, “log2”, “log10”). e.g: yScale=“log2”. Default values are NULL.
xShowTickLabel, yShowTickLabel if TRUE, x and y axis tick mark labels will be shown. Default values are TRUE.
xTickLabelFont, yTickLabelFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis tick label fonts. Default value are xTickLabelFont=c(12, "bold", "black"), yTickLabelFont=c(12, "bold", "black").
xtickLabelRotation, ytickLabelRotation Rotation angle of x and y axis tick labels. Default value are 0.
hideAxisTicks if TRUE, x and y axis ticks are hidden. Default value is FALSE.
axisLine a vector of length 3 indicating respectively the size, the line type and the color of axis lines. Default value is c(0.5, "solid", "#E5E5E5").

For more details follow this link : ggplot2.customize.

Main title and axis labels

# Change main title and axis titles
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                mainTitle="Plot of length \n by dose",
                xtitle="Dose (mg)", ytitle="Length")

# Customize title styles. Possible values for the font style :
  # &#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                xtitle="Dose (mg)", ytitle="Length",
                mainTitle="Plot of length \n by dose",
                mainTitleFont=c(14,"bold.italic", "red"),
                xtitleFont=c(14,"bold", "#993333"),
                ytitleFont=c(14,"bold", "#993333"))

# Hide x an y axis titles
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                xShowTitle=FALSE, yShowTitle=FALSE)

Data visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical software

Axis ticks

# Axis ticks labels and orientaion
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        xShowTitle=FALSE, yShowTitle=FALSE,
        xTickLabelFont=c(14,"bold", "#993333"),
        yTickLabelFont=c(14,"bold", "#993333"),
        xtickLabelRotation=45, ytickLabelRotation=45)

# Hide axis tick labels
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
      xShowTitle=FALSE, yShowTitle=FALSE,
      xShowTickLabel=FALSE, yShowTickLabel=FALSE)

# Hide axis ticks
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        xShowTitle=FALSE, yShowTitle=FALSE,
        xShowTickLabel=FALSE, yShowTickLabel=FALSE,
        hideAxisTicks=TRUE)

# AxisLine : a vector of length 3 indicating the size,
  #the line type and the color of axis lines
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, 
         axisLine=c(1, "solid", "darkblue"))

Data visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical software

Background and colors

Change stripchart background and fill colors

# change background color to "white". Default is "gray"
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                backgroundColor="white")

# Change background color to "lightblue" and grid color to "white"
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                backgroundColor="lightblue", gridColor="white")

# Change plot fill color
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                backgroundColor="white", fill=&#39;#FFAAD4&#39;)

# remove grid; remove top and right borders around the plot;
# change  axis lines
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                backgroundColor="white", fill=&#39;#FFAAD4&#39;,
                removePanelGrid=TRUE,removePanelBorder=TRUE,
                axisLine=c(0.5, "solid", "black"))

Data visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical software

Change stripchart color according to the group

Colors can be specified as a hexadecimal RGB triplet, such as "#FFCC00" or by names (e.g : "red" ). You can also use other color scales, such as ones taken from the RColorBrewer package. The different color systems available in R have been described in detail here.

To change stripchart color according to the group, you have to specify the name of the data column containing the groups using the argument groupName. Use the argument groupColors, to specify colors by hexadecimal code or by name. In this case, the length of groupColors should be the same as the number of the groups. Use the argument brewerPalette, to specify colors using RColorBrewerpalette.

# Color the stripchart accoording to the groupName "dose"
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        groupName=&#39;dose&#39;)

# Change group colors using hexadecimal colors
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        groupName=&#39;dose&#39;,
        groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;))

# Change group colors using brewer palette: "Paired"
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        groupName=&#39;dose&#39;,brewerPalette="Paired")

Data visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical software


Color can also be changed by using names as follow :

# Change group colors using color names
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
      groupName=&#39;dose&#39;,
      groupColors=c(&#39;aquamarine3&#39;,&#39;chartreuse1&#39;,&#39;goldenrod1&#39;))


Legend

Legend position

# Change the legend position to "top" 
  # (possible values: "left","top", "right", "bottom")
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        groupName=&#39;dose&#39;, legendPosition="top")

# legendPosition can be also a numeric vector c(x, y)
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        groupName=&#39;dose&#39;, legendPosition=c(0.8,0.2))

Data visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical software

It is also possible to position the legend inside the plotting area. You have to indicate the x, y coordinates of legend box. x and y values must be between 0 and 1. c(0,0) corresponds to “bottom left” and c(1,1) corresponds to “top right” position.

Legend background color, title and text font styles

# Change legend background color, title and text font styles
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
    groupName=&#39;dose&#39;,
    #legendTitleFont=c(size, style, color)
    legendTitle="Dose (mg)", legendTitleFont=c(10, "bold", "blue"),
    #legendTextFont=c(size, style, color)
    legendTextFont=c(10, "bold.italic", "red"),
    #legendBackground: c(fill, lineSize, lineType, lineColor)
    legendBackground=c("lightblue", 0.5, "solid", "darkblue" )
    )

Data visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical software

Change the order of items in the legend, remove plot legend

# Change the order of items in the legend
 # legendItemOrder : character vector indicating 
# the order of items in the legends.
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
            groupName=&#39;dose&#39;,
            legendItemOrder=c("2", "1", "0.5"))

# Remove plot legend
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
              groupName=&#39;dose&#39;, showLegend=FALSE)

Data visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical software

Axis scales

Possible values for y axis scale are “none”, “log2” and log10. Default value is “none”.

# Change y axis limit
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                groupName=&#39;dose&#39;, ylim=c(0,50))

# y Log scale. yScale="log2". 
# Possible value="none", "log2" and "log10"
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
              groupName=&#39;dose&#39;, yScale="log2")

Data visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical software

Create a customized plots with few R code

# Customized stripchart
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
    groupName=&#39;dose&#39;,
    groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;), showLegend=FALSE,
    backgroundColor="white", xtitle="Dose (mg)", ytitle="length", 
    mainTitle="Plot of length \n by dose")

# Customized stripchart with notched box plot
# Remove grid; Remove Top and right border around the plot
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
    groupName=&#39;dose&#39;,
    groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;), showLegend=FALSE,
    backgroundColor="white", xtitle="Dose (mg)", ytitle="length", 
    mainTitle="Plot of length \n by dose",
    addBoxplot=TRUE, notch=TRUE,
    removePanelGrid=TRUE,removePanelBorder=TRUE,
    axisLine=c(0.5, "solid", "black"))

# Customized stripchart, change point color, 
# fill box plot accoording to the groups
#Change the color of points to "black"
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
    groupName=&#39;dose&#39;,
    groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;), showLegend=FALSE,
    backgroundColor="white", xtitle="Dose (mg)", ytitle="length", 
    mainTitle="Plot of length \n by dose",
    addBoxplot=TRUE, boxplotFill=NULL, notch=TRUE, colour="black")

Data visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical software

# Customized stripchart, add box plot, pink fill color.
# Change the color of points to "black"
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
    groupName=&#39;dose&#39;,
    groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;), showLegend=FALSE,
    backgroundColor="white", xtitle="Dose (mg)", ytitle="length", 
    mainTitle="Plot of length \n by dose",
    addBoxplot=TRUE, boxplotFill="pink", colour="black")

# Customized stripchart, add box plot, 
#fill box plot accoording to the groups.
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
    groupName=&#39;dose&#39;,
    groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;),
    showLegend=FALSE,
    backgroundColor="white", xtitle="Dose (mg)", ytitle="length", 
    mainTitle="Plot of length \n by dose",
    addBoxplot=TRUE, boxplotFill=NULL, colour="black")

Data visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical softwareData visualization with ggplot2 stripchart : tutorial on how to use ggplot2.stripchart function to produce a one dimensional scatter plots (or dot plots) using ggplot2 and R statistical software

By default, box plot fill color is “white”. To change the fill color with the argument groupColors, set boxplotFill value to NULL.

Stripchart with multiple groups

The data format is :

##     len supp dose
## 1   4.2   VC  0.5
## 2  11.5   VC  0.5
## 3   7.3   VC  0.5
## 11 16.5   VC  1.0
## 12 16.5   VC  1.0
## 13 15.2   VC  1.0
## 14 17.3   VC  1.0
## 15 22.5   VC  1.0
## 21 23.6   VC  2.0
## 22 18.5   VC  2.0
## 23 33.9   VC  2.0
## 24 25.5   VC  2.0
## 31 15.2   OJ  0.5
## 32 21.5   OJ  0.5
## 33 17.6   OJ  0.5
## 34  9.7   OJ  0.5
## 41 19.7   OJ  1.0
## 42 23.3   OJ  1.0
## 43 23.6   OJ  1.0
## 44 26.4   OJ  1.0
## 51 25.5   OJ  2.0
## 52 26.4   OJ  2.0
## 53 22.4   OJ  2.0
## 54 24.5   OJ  2.0
# plot of variable &#39;len&#39; by xName &#39;dose&#39;. The plot is colored by the groupName &#39;supp&#39;
# position = interval between dot plot of the same group
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
      groupName=&#39;supp&#39;, 
      position=position_jitter(0.2), 
      backgroundColor="white", 
      groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;)
)  
 
# Change the interval between stripchart of the same group:
  # position = interval between dot plot of the same group
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
      groupName=&#39;supp&#39;, 
      position=position_dodge(0.8), 
      backgroundColor="white", groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;))

# Change the interval between stripchart of the same group
#position=position_dodge(0.8), add box plot
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
      groupName=&#39;supp&#39;, 
      position=position_dodge(0.8), 
      backgroundColor="white", groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;),
      addBoxplot=TRUE, boxplotFill="white")

Stripchart with multiple groupsStripchart with multiple groupsStripchart with multiple groups

Facet : split a plot into a matrix of pannels

The facet approach splits a plot into a matrix of panels. Each panel shows a different subset of the data.

Facet with one variable

#Facet according to the supp variable
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
            groupName=&#39;supp&#39;,
            legendPosition="top",
            faceting=TRUE, facetingVarNames="supp") 

# Change the direction. possible values are "vertical", "horizontal".
# default is vertical.
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
            groupName=&#39;supp&#39;,
            legendPosition="top",
            faceting=TRUE, facetingVarNames="supp", 
            facetingDirection="horizontal")

ggplot2 stripchart and facet approch, one variableggplot2 stripchart and facet approch, one variable

Facet with two variables

# Facet by two variables: dose and supp.
# Rows are dose and columns are supp
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
            groupName=&#39;supp&#39;, 
            legendPosition="top",
            faceting=TRUE, facetingVarNames=c("dose","supp"))

# Facet by two variables: reverse the order of the 2 variables
# Rows are supp and columns are dose
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
            groupName=&#39;supp&#39;,
            legendPosition="top",
            faceting=TRUE, facetingVarNames=c("supp", "dose"))

ggplot2 stripchart and facet approch, two variablesggplot2 stripchart and facet approch, two variables

Facet scales

By default, all the panels have the same scale (facetingScales="fixed"). They can be made independent, by setting scales to free, free_x, or free_y.

# Facet with free scales
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
            groupName=&#39;supp&#39;,
            legendPosition="top",
            faceting=TRUE, facetingVarNames=c("dose", "supp"), 
            facetingScales="free")

ggplot2 stripchart and facet approch, free scale

As you can see in the above plot, y axis have different scales in the different panels.

Facet label apperance

# Change facet text font. Possible values for the font style:
  #&#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
            groupName=&#39;supp&#39;,
            legendPosition="top",
            faceting=TRUE, facetingVarNames=c("dose","supp"),
            facetingFont=c(12, &#39;bold.italic&#39;, "red"))

# Change the apperance of the rectangle around facet label
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
            groupName=&#39;supp&#39;,
            legendPosition="top",
            faceting=TRUE, facetingVarNames=c("dose","supp"),
            facetingRect=list(background="white", lineType="solid",
                              lineColor="black", lineSize=1.5)
            )

ggplot2 stripchart and facet approch, facet labelggplot2 stripchart and facet approch, facet label

ggplot2.stripchart function

Description

Plot easily a stripchart plot using easyGgplot2 R package.

usage

ggplot2.stripchart(data, xName=NULL, yName=NULL, groupName=NULL,
        position=position_jitter(0.2),
        addMean=FALSE, meanPointShape=5, meanPointSize=4,
        meanPointColor="black", meanPointFill="blue",
        addBoxplot=FALSE, boxplotFill="white", 
        boxplotColor="black", boxplotLineWeight=0.5,
        groupColors=NULL, brewerPalette=NULL,...)

Arguments

Arguments Descriptions
data data.frame or a numeric vector. Columns are variables and rows are observations.
xName The name of column containing x variable (i.e groups). Default value is NULL.
yName The name of column containing y variable. If yName=NULL, data should be a numeric vector.
groupName The name of column containing group variable. This variable is used to color plot according to the group.
position The position adjustment to use for overlapping points. Default value is position_jitter(0.2).
addMean if TRUE, the mean point is added on the plot for each group. Default value is FALSE.
meanPointShape, meanPointSize The shape and size of mean point.
meanPointColor border color of the mean point. Default value is “black”.
meanPointFill Fill color of mean point. This parameter is used only when meanPointShape=21 to 25. Default value is “blue”
addBoxplot if TRUE, box plot is added on the stripchart. Default value is FALSE.
boxplotFill Fill color of the box plot. Default value is white.
dotPosition Possible values are “center” and “jitter”. Default value is “center”.
boxplotColor Box plot line color. Default value is black.
boxplotLineWeight Box plot line weight. Default value is 0.5.
groupColors Color of groups. groupColors should have the same length as groups.
brewerPalette This can be also used to indicate group colors. In this case the parameter groupColors should be NULL. e.g: brewerPalette=“Paired”.
…. Other arguments passed on to ggplot2.customize custom function or to geom_jitter and to geom_boxplot functions from ggplot2 package.

The other arguments which can be used are described at this link : ggplot2 customize. They are used to customize the plot (axis, title, background, color, legend, ….) generated using ggplot2 or easyGgplot2 R package.

Examples

df <- ToothGrowth
ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
        mainTitle="Plot of length according\n to the dose",
        xtitle="Dose (mg)", ytitle="Length")

#Or use this
plot<-ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;)
plot<-ggplot2.customize(plot,
        mainTitle="Plot of length according\n to the dose",
        xtitle="Dose (mg)", ytitle="Length")
print(plot)

ggplot2.jitter can be also used instead of ggplot2.stripchart.


Easy ggplot2 ebook

Note that an eBook is available on easyGgplot2 package here.

By Alboukadel Kassambara

Copyright 2014 Alboukadel Kassambara. All rights reserved.

Published by STHDA (https://www.sthda.com/english).

September 2014 : First edition.

Licence : This document is under creative commons licence (http://creativecommons.org/licenses/by-nc-sa/3.0/).

Contact : Alboukadel Kassambara alboukadel.kassambara@gmail.com


Infos

This analysis was performed using R (ver. 3.1.0), easyGgplot2 (ver 1.0.0) and ggplot2 (ver 1.0.0).

]]> Fri, 06 Feb 2015 21:14:17 +0100 <![CDATA[ggplot2 boxplot : Easy box and whisker plots maker function]]> https://www.sthda.com/english/wiki/ggplot2-boxplot-easy-box-and-whisker-plots-maker-function https://www.sthda.com/english/wiki/ggplot2-boxplot-easy-box-and-whisker-plots-maker-function

Introduction

ggplot2.boxplot is a function, to plot easily a box plot (also known as a box and whisker plot) with R statistical software using ggplot2 package. It can also be used to customize quickly the plot parameters including main title, axis labels, legend, background and colors. ggplot2.boxplot function is from easyGgplot2 R package. An R script is available in the next section to install the package.

The aim of this ggplot2 tutorial is to show you step by step, how to make and customize a box plot using ggplot2.boxplot function.

At the end of this article you will be able to draw, with few R code, the following plot:

r data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical software

ggplot2.boxplot function is described in detail at the end of this document.

Install and load easyGgplot2 package

easyGgplot2 R package can be installed as follow :

install.packages("devtools")
library(devtools)
install_github("easyGgplot2", "kassambara")

Load the package using this R code :

library(easyGgplot2)

Data format

The data must be a numeric vector or a data.frame (columns are variables and rows are observations).

ToothGrowth data is used in the following examples.

# create a numeric vector
numVector<-rnorm(100)
head(numVector)
## [1] -1.1381  0.5408 -0.9359  0.4949 -1.1467 -0.1464
# data.frame
df <- ToothGrowth
head(df)
##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5
## 4  5.8   VC  0.5
## 5  6.4   VC  0.5
## 6 10.0   VC  0.5

ToothGrowth describes the effect of Vitamin C on Tooth growth in Guinea pigs. The response is the length (len) of teeth in each of 10 guinea pigs at each of three dose levels of Vitamin C (0.5, 1, and 2 mg) with each of two delivery methods (orange juice or ascorbic acid).

The data is a data frame with 60 observations on 3 variables. * [,1] len numeric Tooth length. * [,2] supp factor Supplement type (VC or OJ). * [,3] dose numeric Dose in milligrams.


Basic box plot

# Box plot from a single numeric vector 
ggplot2.boxplot(data=numVector)

# Basic box plot from the vector "len"
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;)

# Change the orientation: Horizontal box plot
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                orientation="horizontal")

# Notched box plot
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                notch=TRUE)

r data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical software

notch: If TRUE, make a notched box plot. The notch displays a confidence interval around the median which is normally based on the median +/- 1.57 x IQR/sqrt of n. Notches are used to compare groups; if the notches of two boxes do not overlap, this is strong evidence that the medians differ.

Box plot with mean point and dots

Each dot represents one observation and the mean point corresponds to the mean value of the observations in a given group.

# Box plot with mean point
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                addMean=TRUE, meanPointShape=23, meanPointSize=2.5,
                meanPointColor="black", meanPointFill="blue")

# Box plot with centered dots
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                addDot=TRUE, dotSize=1, dotPosition="center")

# Box plot with jittered dots. jitter: degree of jitter in x direction
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                addDot=TRUE, dotSize=1.7, dotPosition="jitter",
                jitter=0.2)

r data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical software

Change the box plot line type and point shape

Different point shapes and line types can be used in the plot. By default, ggplot2 uses solid line type and circle shape.

# Change the box plot line color and line type
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                colour="red", linetype="dotted")

# Change outlier color, shape and size
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                outlier.colour="red", outlier.shape=8,
                outlier.size=4)

r data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical software

Customize your box plot

Parameters

The arguments that can be used to customize x and y axis are listed below :

Parameters Description
mainTitle the title of the plot
mainTitleFont a vector of length 3 indicating respectively the size, the style (“italic”, “bold”, “bold.italic”) and the color of x and y axis titles. Default value is: mainTitleFont=c(14, “bold”, “black”).
xShowTitle, yShowTitle if TRUE, x and y axis titles will be shown. Set the value to FALSE to hide axis labels. Default values are TRUE.
xtitle, ytitle x and y axis labels. Default values are NULL.
xtitleFont, ytitleFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis titles. Possible values for the style:“plain”, “italic”, “bold”, “bold.italic”. Color can be specified as an hexadecimal code (e.g: “#FFCC00”) or by the name (e.g : “red”, “green”). Default values are xtitleFont=c(14,"bold", "black"), ytitleFont=c(14,"bold", "black").
xlim, ylim limit for the x and y axis. Default values are NULL.
xScale, yScale x and y axis scales. Possible values : c(“none”, “log2”, “log10”). e.g: yScale=“log2”. Default values are NULL.
xShowTickLabel, yShowTickLabel if TRUE, x and y axis tick mark labels will be shown. Default values are TRUE.
xTickLabelFont, yTickLabelFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis tick label fonts. Default value are xTickLabelFont=c(12, "bold", "black"), yTickLabelFont=c(12, "bold", "black").
xtickLabelRotation, ytickLabelRotation Rotation angle of x and y axis tick labels. Default value are 0.
hideAxisTicks if TRUE, x and y axis ticks are hidden. Default value is FALSE.
axisLine a vector of length 3 indicating respectively the size, the line type and the color of axis lines. Default value is c(0.5, "solid", "#E5E5E5").

For more details follow this link : ggplot2.customize.

Main title and axis labels

# Change main title and axis titles
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                mainTitle="Plot of length \n by dose",
                xtitle="Dose (mg)", ytitle="Length")

# Customize title styles. Possible values for the font style :
  # &#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
              xtitle="Dose (mg)", ytitle="Length",
              mainTitle="Plot of length \n by dose",
              mainTitleFont=c(14,"bold.italic", "red"),
              xtitleFont=c(14,"bold", "#993333"), 
              ytitleFont=c(14,"bold", "#993333"))

# Hide x an y axis titles
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                xShowTitle=FALSE, yShowTitle=FALSE)

r data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical software

Axis ticks

# Axis ticks labels and orientaion
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                xTickLabelFont=c(14,"bold", "#993333"),
                yTickLabelFont=c(14,"bold", "#993333"),
                xtickLabelRotation=45, ytickLabelRotation=45)

# Hide axis tick labels
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                xShowTickLabel=FALSE, yShowTickLabel=FALSE)

# Hide axis ticks
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                xShowTickLabel=FALSE, yShowTickLabel=FALSE,
                hideAxisTicks=TRUE)

# AxisLine : a vector of length 3 indicating the size,
  #the line type and the color of axis lines
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                axisLine=c(1, "solid", "darkblue"))

r data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical software

Background and colors

Change box plot background and fill colors

# change background color to "white". Default is "gray"
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                backgroundColor="white")

# change background color to "lightblue" and grid color to "white"
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                backgroundColor="lightblue", gridColor="white")

# change plot fill color
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                backgroundColor="white", fill=&#39;#FFAAD4&#39;)

# remove grid; remove top and right borders around the plot;
# change  axis lines
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                backgroundColor="white", fill=&#39;#FFAAD4&#39;,
                removePanelGrid=TRUE,removePanelBorder=TRUE,
                axisLine=c(0.5, "solid", "black"))

r data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical software

Change box plot color according to the group

Colors can be specified as a hexadecimal RGB triplet, such as "#FFCC00" or by names (e.g : "red" ). You can also use other color scales, such as ones taken from the RColorBrewer package. The different color systems available in R have been described in detail here.

To change box plot color according to the group, you have to specify the name of the data column containing the groups using the argument groupName. Use the argument groupColors, to specify colors by hexadecimal code or by name. In this case, the length of groupColors should be the same as the number of the groups. Use the argument brewerPalette, to specify colors using RColorBrewerpalette.

# color the box plot accoording to the groupName "dose"
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;)

# change group colors using hexadecimal colors
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
                groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;))

# change group colors using brewer palette: "Paired"
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
                brewerPalette="Paired")

r data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical software


Color can also be changed by using names as follow :

# change group colors using color names
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                groupName=&#39;dose&#39;,
                groupColors=c(&#39;blue&#39;,&#39;red&#39;,&#39;green&#39;))

Legend

Legend position

# Change the legend position to "top" 
  # (possible values: "left","top", "right", "bottom")
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
                legendPosition="top")

# legendPosition can be also a numeric vector c(x, y)
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
                legendPosition=c(0.8,0.2))

r data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical software

It is also possible to position the legend inside the plotting area. You have to indicate the x, y coordinates of legend box. x and y values must be between 0 and 1. c(0,0) corresponds to "bottom left" and c(1,1) corresponds to "top right" position.

Legend background color, title and text font styles

# Change legend background color, title and text font styles
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
        # legendTitleFont=c(size, style, color)
        legendTitle="Dose (mg)",
        legendTitleFont=c(10, "bold", "blue"),
        #legendTextFont=c(size, style, color)
        legendTextFont=c(10, "bold.italic", "red"),
        # legendBackground: c(fill, lineSize, lineType, lineColor)
        legendBackground=c("lightblue", 0.5, "solid", "darkblue" )
        )

r data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical software

Change the order of items in the legend, remove plot legend

# Change the order of items in the legend
 # legendItemOrder : character vector indicating 
# the order of items in the legends.
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
                legendItemOrder=c("2", "1", "0.5"))
    
# Remove plot legend
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
                showLegend=FALSE)

r data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical software

Axis scales

Possible values for y axis scale are “none”, “log2” and log10. Default value is “none”.

# Change y axis limit
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
                ylim=c(0,50))

# y Log scale. Possible values="none", "log2" and "log10"
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
                yScale="log2")

r data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical software

Create a customized plots with few R code

# Customized boxplot with centered dot plot
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
        groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;), showLegend=FALSE,
        backgroundColor="white", xtitle="Dose (mg)", 
        ytitle="length", mainTitle="Plot of length \n by dose",
        addDot=TRUE, dotSize=1)

# Remove grid; Remove Top and right border around the plot
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;dose&#39;,
        groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;), showLegend=FALSE,
        backgroundColor="white", xtitle="Dose (mg)", ytitle="length", 
        mainTitle="Plot of length \n by dose",
        addDot=TRUE, dotSize=1,
        removePanelGrid=TRUE,removePanelBorder=TRUE,
        axisLine=c(0.5, "solid", "black"))

r data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical softwarer data visualization with ggplot2 boxplot : tutorial on how to use ggplot2.boxplot function to easily make a box plot using ggplot2 in R statistical software

Box plot with multiple groups

The data format is:

##     len supp dose
## 1   4.2   VC  0.5
## 2  11.5   VC  0.5
## 3   7.3   VC  0.5
## 11 16.5   VC  1.0
## 12 16.5   VC  1.0
## 13 15.2   VC  1.0
## 14 17.3   VC  1.0
## 15 22.5   VC  1.0
## 21 23.6   VC  2.0
## 22 18.5   VC  2.0
## 23 33.9   VC  2.0
## 24 25.5   VC  2.0
## 31 15.2   OJ  0.5
## 32 21.5   OJ  0.5
## 33 17.6   OJ  0.5
## 34  9.7   OJ  0.5
## 41 19.7   OJ  1.0
## 42 23.3   OJ  1.0
## 43 23.6   OJ  1.0
## 44 26.4   OJ  1.0
## 51 25.5   OJ  2.0
## 52 26.4   OJ  2.0
## 53 22.4   OJ  2.0
## 54 24.5   OJ  2.0
# plot of variable &#39;len&#39; by xName &#39;dose&#39;. The plot is colored by the groupName &#39;supp&#39;
# position = interval between box plot of the same group
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;, 
      position=position_dodge(0.8),
      backgroundColor="white", groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;),
      legendPosition="top")  

# add dots
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;, 
        position=position_dodge(0.8),
        backgroundColor="white", groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;),
        addDot=TRUE, dotSize=1,legendPosition="top")

Boxplot with multiple groupsBoxplot with multiple groups

Facet : split a plot into a matrix of panels

The facet approach splits a plot into a matrix of panels. Each panel shows a different subset of the data.

Facet with one variable

# Facet according to the supp variable
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;,
        legendPosition="top", 
        faceting=TRUE, facetingVarNames="supp") 

# Change the direction. possible values are "vertical", horizontal".
# default is vertical.
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;,
        legendPosition="top",
        faceting=TRUE, facetingVarNames="supp",
        facetingDirection="horizontal")

ggplot2 boxplot and facet approch, one variableggplot2 boxplot and facet approch, one variable

Facet with two variables

# Facet by two variables: dose and supp.
# Rows are dose and columns are supp
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;,
        legendPosition="top", faceting=TRUE,
        facetingVarNames=c("dose","supp"))

# Facet by two variables: reverse the order of the 2 variables
# Rows are supp and columns are dose
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;,
        legendPosition="top", faceting=TRUE,
        facetingVarNames=c("supp", "dose"))

ggplot2 boxplot and facet approch, two variablesggplot2 boxplot and facet approch, two variables

Facet scales

By default, all the panels have the same scale (facetingScales="fixed"). They can be made independent, by setting scales to free, free_x, or free_y.

# Facet with free scales
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;, legendPosition="top",
            faceting=TRUE, facetingVarNames=c("dose", "supp"),
            facetingScales="free")

ggplot2 boxplot and facet approch, free scale

As you can see in the above plot, y axis have different scales in the different panels.

Facet label apperance

# Change facet text font. Possible values for the font style:
  #&#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;,
        legendPosition="top",
        faceting=TRUE, facetingVarNames=c("dose","supp"),
        facetingFont=c(12, &#39;bold.italic&#39;, "red"))

# Change the apperance of the rectangle around facet label
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;, groupName=&#39;supp&#39;,
      legendPosition="top",
      faceting=TRUE, facetingVarNames=c("dose","supp"),
      facetingRect=list(background="white", lineType="solid",
                        lineColor="black", lineSize=1.5)
      )

ggplot2 boxplot and facet approch, facet labelggplot2 boxplot and facet approch, facet label

ggplot2.boxplot function

Description

Plot easily a boxplot with R package easyGgplot2.

usage

ggplot2.boxplot(data, xName=NULL, yName=NULL, groupName=NULL,
        addMean=FALSE, meanPointShape=23, meanPointSize=4,
        meanPointColor="black", meanPointFill="blue",
        addDot=FALSE, dotSize=1,
        dotPosition=c("center", "jitter"), jitter=0.2,
        groupColors=NULL, brewerPalette=NULL,...)

Arguments

Arguments Descriptions
data data.frame or a numeric vector. Columns are variables and rows are observations.
xName The name of column containing x variable (i.e groups). Default value is NULL.
yName The name of column containing y variable. If yName=NULL, data should be a numeric vector.
groupName The name of column containing group variable. This variable is used to color plot according to the group.
addMean if TRUE, the mean point is added on the plot for each group. Default value is FALSE.
meanPointShape, meanPointSize The shape and size of mean point.
meanPointColor Border color of the mean point. Default value is “black”.
meanPointFill Fill color of mean point. This parameter is used only when meanPointShape=21 to 25. Default value is “blue”
addDot If TRUE, dotplot is added on the boxplot. Default value is FALSE.
dotSize The size of dots.
dotPosition Possible values are “center” and “jitter”. Default value is “center”.
jitter Degree of jitter in x direction. Default value is 0.2.
groupColors Color of groups. groupColors should have the same length as groups.
brewerPalette This can be also used to indicate group colors. In this case the parameter groupColors should be NULL. e.g: brewerPalette=“Paired”.
…. Other arguments passed on to ggplot2.customize function or to geom_dotplot and to geom_boxplot functions from ggplot2 package.

The other arguments which can be used are described at this link : ggplot2 customize. They are used to customize the plot (axis, title, background, color, legend, ….) generated using ggplot2 or easyGgplot2 R package.

Examples

library(easyGgplot2)
df <- ToothGrowth
ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                mainTitle="Plot of length according\n to the dose",
                xtitle="Dose (mg)", ytitle="Length")

#Or use this
plot<-ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;)
plot<-ggplot2.customize(plot, mainTitle="Plot of length according\n to the dose",
                        xtitle="Dose (mg)", ytitle="Length")
print(plot)


Easy ggplot2 ebook

Note that an eBook is available on easyGgplot2 package here.

By Alboukadel Kassambara

Copyright 2014 Alboukadel Kassambara. All rights reserved.

Published by STHDA (https://www.sthda.com/english).

September 2014 : First edition.

Licence : This document is under creative commons licence (http://creativecommons.org/licenses/by-nc-sa/3.0/).

Contact : Alboukadel Kassambara alboukadel.kassambara@gmail.com


Infos

This analysis was performed using R (ver. 3.1.0), easyGgplot2 (ver 1.0.0) and ggplot2 (ver 1.0.0).

]]> Fri, 06 Feb 2015 21:11:34 +0100 <![CDATA[ggplot2.scatterplot : Easy scatter plot using ggplot2 and R statistical software]]> https://www.sthda.com/english/wiki/ggplot2-scatterplot-easy-scatter-plot-using-ggplot2-and-r-statistical-software https://www.sthda.com/english/wiki/ggplot2-scatterplot-easy-scatter-plot-using-ggplot2-and-r-statistical-software

Introduction

ggplot2.scatterplot is an easy to use function to make and customize quickly a scatter plot using R software and ggplot2 package. ggplot2.scatterplot function is from easyGgplot2 R package. An R script is available in the next section to install the package.

The aim of this tutorial is to show you step by step, how to plot and customize a scatter plot using ggplot2.scatterplot function.

At the end of this tutorial you will be able to draw, with few R code, the following plots:

Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.

ggplot2.scatterplot function is described in detail at the end of this document.

Install and load easyGgplot2 package

easyGgplot2 R package can be installed as follow :

install.packages("devtools")
library(devtools)
install_github("easyGgplot2", "kassambara")

Load the package using this R code :

library(easyGgplot2)

Data format

The data must be a data.frame (columns are variables and rows are observations).

mtcars data is used in the following examples.

df <- mtcars[, c("mpg", "cyl", "wt", "qsec", "vs")]
head(df)
##                    mpg cyl    wt  qsec vs
## Mazda RX4         21.0   6 2.620 16.46  0
## Mazda RX4 Wag     21.0   6 2.875 17.02  0
## Datsun 710        22.8   4 2.320 18.61  1
## Hornet 4 Drive    21.4   6 3.215 19.44  1
## Hornet Sportabout 18.7   8 3.440 17.02  0
## Valiant           18.1   6 3.460 20.22  1

mtcars : Motor Trend Car Road Tests.

Description: The data comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973 - 74 models).

Format: A data frame with 32 observations on 4 variables.

  • [, 1] mpg Miles/(US) gallon
  • [, 2] cyl Number of cylinders
  • [, 3] wt Weight (lb/1000)
  • [, 4] qsec 1/4 mile time
  • [, 5] vs V/S


Basic scatter plot plot

# Basic scatter plot of mpg according to cyl
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;)

# Change point size
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, size=3)

# change point size according to a numeric variable (qsec)
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
                    mapping=aes(size = qsec))

Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.

Scatterplot with regression line

#Add linear regression line
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, 
                    addRegLine=TRUE, regLineColor="blue")
                    
#Add the 95% confidence region
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
            addRegLine=TRUE, regLineColor="blue",
            addConfidenceInterval=TRUE)
                    
#Use loess (local fiting as smooth method)
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
            addRegLine=TRUE, regLineColor="blue",
            addConfidenceInterval=TRUE, smoothingMethod="loess")

ggplot2 scatterplotggplot2 scatterplotggplot2 scatterplot

Change the line type and the point shapes of the scatter plot

Different point shapes and line types can be used in the plot. By default, ggplot2 uses solid line type and circle shape.

# Change the scatter plot line type;
# change point shape, size and fill color
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
        addRegLine=TRUE, regLineColor="darkred",
        linetype="dashed", shape=23, size=3, fill="blue")

Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.

Scatter plot plot with multiple groups

We will set color/shape by another variable (cyl)

# plot of variable &#39;mpg&#39; according to xName &#39;wt&#39;.
# The plot is colored by the groupName &#39;cyl&#39;
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
                    groupName="cyl")

# Change group colors
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, 
        groupName=&#39;cyl&#39;, size=3,
        backgroundColor="white",
        groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;, &#39;#56B4E9&#39;))  

# Use unique color for all groups
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, 
            groupName=&#39;cyl&#39;, size=3,
            backgroundColor="white", setColorByGroupName=FALSE)

Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.

# Add regression line and confidence interval
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
      groupName=&#39;cyl&#39;, size=3, backgroundColor="white",
      groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;, &#39;#56B4E9&#39;),
      addRegLine=TRUE, addConfidenceInterval=TRUE)  

# Extend the regression lines beyond the domain of the data
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, 
      groupName=&#39;cyl&#39;, size=3, backgroundColor="white",
      groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;, &#39;#56B4E9&#39;),
      addRegLine=TRUE, fullrange=TRUE)  

# Set point shape by groupName
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
      groupName=&#39;cyl&#39;, size=3, backgroundColor="white",
      groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;, &#39;#56B4E9&#39;),
      setShapeByGroupName=TRUE)

Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.

Customize your scatter plot

Parameters

The arguments that can be used to customize x and y axis are listed below :

Parameters Description
mainTitle the title of the plot
mainTitleFont a vector of length 3 indicating respectively the size, the style (“italic”, “bold”, “bold.italic”) and the color of x and y axis titles. Default value is: mainTitleFont=c(14, “bold”, “black”).
xShowTitle, yShowTitle if TRUE, x and y axis titles will be shown. Set the value to FALSE to hide axis labels. Default values are TRUE.
xtitle, ytitle x and y axis labels. Default values are NULL.
xtitleFont, ytitleFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis titles. Possible values for the style:“plain”, “italic”, “bold”, “bold.italic”. Color can be specified as an hexadecimal code (e.g: “#FFCC00”) or by the name (e.g : “red”, “green”). Default values are xtitleFont=c(14,"bold", "black"), ytitleFont=c(14,"bold", "black").
xlim, ylim limit for the x and y axis. Default values are NULL.
xScale, yScale x and y axis scales. Possible values : c(“none”, “log2”, “log10”). e.g: yScale=“log2”. Default values are NULL.
xShowTickLabel, yShowTickLabel if TRUE, x and y axis tick mark labels will be shown. Default values are TRUE.
xTickLabelFont, yTickLabelFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis tick label fonts. Default value are xTickLabelFont=c(12, "bold", "black"), yTickLabelFont=c(12, "bold", "black").
xtickLabelRotation, ytickLabelRotation Rotation angle of x and y axis tick labels. Default value are 0.
hideAxisTicks if TRUE, x and y axis ticks are hidden. Default value is FALSE.
axisLine a vector of length 3 indicating respectively the size, the line type and the color of axis lines. Default value is c(0.5, "solid", "#E5E5E5").

For more details follow this link : ggplot2.customize.

Main title and axis labels

# Change main title and axis titles
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
      mainTitle="Miles per gallon \n according to the weight",
      xtitle="Weight (lb/1000)", ytitle="Miles/(US) gallon")

# Customize title styles. Possible values for the font style :
  # &#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
    mainTitle="Miles per gallon \n according to the weight",
    xtitle="Weight (lb/1000)", ytitle="Miles/(US) gallon",
    mainTitleFont=c(14,"bold.italic", "red"),
    xtitleFont=c(14,"bold", "#993333"), 
    ytitleFont=c(14,"bold", "#993333"))

# Hide x an y axis titles
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
                xShowTitle=FALSE, yShowTitle=FALSE)

Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.

Axis ticks

# Axis ticks labels and orientaion
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
          xShowTitle=FALSE, yShowTitle=FALSE,
          xTickLabelFont=c(14,"bold", "#993333"),
          yTickLabelFont=c(14,"bold", "#993333"),
          xtickLabelRotation=45, ytickLabelRotation=45)

# Hide axis tick labels
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
          xShowTitle=FALSE, yShowTitle=FALSE,
          xShowTickLabel=FALSE, yShowTickLabel=FALSE)

# Hide axis ticks
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
        xShowTitle=FALSE, yShowTitle=FALSE,
        xShowTickLabel=FALSE, yShowTickLabel=FALSE,
        hideAxisTicks=TRUE)

# AxisLine : a vector of length 3 indicating the size,
  #the line type and the color of axis lines
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, 
        axisLine=c(1, "solid", "darkblue"))

Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.

Background and colors

Change scatter plot plot background and point colors

# change background color to "white". Default is "gray"
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
                backgroundColor="white")

# Change background color to "lightblue" and grid color to "white"
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
          backgroundColor="lightblue", gridColor="white")

# Change point color
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
          backgroundColor="white", color=&#39;#FFAAD4&#39;)

# Remove grid; Remove Top and right border around the plot
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
          backgroundColor="white", color=&#39;#FFAAD4&#39;,
          removePanelGrid=TRUE,removePanelBorder=TRUE,
          axisLine=c(0.5, "solid", "black"))

Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.

Change scatter plot color according to the group

Colors can be specified as a hexadecimal RGB triplet, such as "#FFCC00" or by names (e.g : "red" ). You can also use other color scales, such as ones taken from the RColorBrewer package. The different color systems available in R have been described in detail here.

To change scatter plot color according to the group, you have to specify the name of the data column containing the groups using the argument groupName. Use the argument groupColors, to specify colors by hexadecimal code or by name. In this case, the length of groupColors should be the same as the number of the groups. Use the argument brewerPalette, to specify colors using RColorBrewerpalette.

# Change point color according to another variable
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, 
                size=3, mapping=aes(colour = qsec))                 

# change point color transparency (alpha) according 
# to another variable. alpha is the transparency degree of color. 
#The value can variate from 0 (total transparency) 
  # to 1 (no transparency)
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, size=3, 
                  mapping=aes(alpha = qsec), color="darkgreen")

#Change point color according to a factor variable (group)
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, 
                    size=3, groupName="cyl")
                    
#Change group colors using hexadecimal colors
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
              groupName="cyl", size=3,
              groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;))
                     
# Change group colors using brewer palette: "Paired"
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, size=3,
                    groupName="cyl", brewerPalette="Paired")

Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.


Color can also be changed by using names as follow :

# Change group colors using color names
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, size=3,
      groupName="cyl", 
      groupColors=c(&#39;aquamarine3&#39;,&#39;chartreuse1&#39;,&#39;goldenrod1&#39;))


Legend

Legend position

# Change the legend position to "top" 
  # (possible values: "left","top", "right", "bottom")
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, 
            groupName="cyl", legendPosition="top")

# legendPosition can be also a numeric vector c(x, y)
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
        groupName="cyl", legendPosition=c(0.8,0.2))

# Remove plot legend
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, 
                groupName="cyl", showLegend=FALSE)

Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.

It is also possible to position the legend inside the plotting area. You have to indicate the x, y coordinates of legend box. x and y values must be between 0 and 1. c(0,0) corresponds to “bottom left” and c(1,1) corresponds to “top right” position.

Legend background color, title and text font styles

# Change legend background color, title and text font styles
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, 
    groupName="cyl",
    #legendPosition=c("right", "left","top", "bottom")
    legendPosition="right",
    #legendTitleFont=c(size, style, color)
    legendTitle="Dose (mg)", legendTitleFont=c(10, "bold", "blue"),
    #legendTextFont=c(size, style, color)
    legendTextFont=c(10, "bold.italic", "red"),
    #legendBackground: c(fill, lineSize, lineType, lineColor)
    legendBackground=c("lightblue", 0.5, "solid", "darkblue" )
    )

Data visualization using ggplot2 scatter plot : tutorial on how to use ggplot2.scatterplot function to plot and customize quickly a scatter plot with R statistical software.

Axis scales

Possible values for x axis scale are “none”, “log2” and log10. Default value is “none”.

# Change y axis limit
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
                xlim=c(0,6) ,ylim=c(0,40))

# Log scale. yScale="log2". 
# Possible value="none", "log2" and "log10"
  #Default value is "none"
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;,
                xScale="log2", yScale="log2")

ggplot2 scatterplotggplot2 scatterplot

Create a customized plots with few R code

#Customized scatterplot
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, size=3,
        addRegLine=TRUE, regLineColor="black", 
        addConfidenceInterval=TRUE,
        backgroundColor="white", xtitle="Weight (lb/1000)",
        ytitle="Miles/(US) gallon",
        mainTitle="Miles per gallon \n according to the weight")

# Remove grid; Remove Top and right border around the plot
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, size=3,
        addRegLine=TRUE, regLineColor="black",
        addConfidenceInterval=TRUE,
        backgroundColor="white", 
        xtitle="Weight (lb/1000)", ytitle="Miles/(US) gallon",
        mainTitle="Miles per gallon \n according to the weight",
        removePanelGrid=TRUE,removePanelBorder=TRUE,
        axisLine=c(0.5, "solid", "black"))
                       
# Change point color according to the factor cyl
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, size=3,
        groupName="cyl", 
        groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;,&#39;#56B4E9&#39;),
        addRegLine=TRUE, fullrange=TRUE, setShapeByGroupName=TRUE,
        backgroundColor="white", 
        xtitle="Weight (lb/1000)", ytitle="Miles/(US) gallon",
        mainTitle="Miles by weight")

ggplot2 scatterplotggplot2 scatterplotggplot2 scatterplot

Faceting : split a plot into a matrix of pannels

The facet approach splits a plot into a matrix of panels. Each panel shows a different subset of the data.

Facet with one variable

# Facet according to the supp variable
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;, yName="mpg",
            faceting=TRUE, facetingVarNames="cyl") 

# Change the direction. possible values are "vertical", "horizontal".
# default is vertical.
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;, yName="mpg",
            faceting=TRUE, facetingVarNames="cyl", 
            facetingDirection="horizontal")

ggplot2 scatter plot and facet approch, one variableggplot2 scatter plot and facet approch, one variable

Facet with two variables

# Facet by two variables: cyl and vs.
#Rows are cyl and columns are vs
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;, yName="mpg",
                faceting=TRUE, facetingVarNames=c("cyl", "vs"))

# Facet by two variables: reverse the order of the 2 variables
# Rows are vs and columns are cyl
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;, yName="mpg",
                faceting=TRUE, facetingVarNames=c("vs", "cyl"))

ggplot2 scatter plot and facet approch, two variablesggplot2 scatter plot and facet approch, two variables

Facet scales

By default, all the panels have the same scale (facetingScales="fixed"). They can be made independent, by setting scales to free, free_x, or free_y.

#Facet with free scales
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;, yName="mpg",
        faceting=TRUE, facetingVarNames=c("vs", "cyl"),
        facetingScales="free")

ggplot2 scatter plot and facet approch, free scale

As you can see in the above plot, y axis have different scales in the different panels.

Facet label apperance

# Change facet text font. Possible values for the font style:
  #&#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;, yName="mpg",
              faceting=TRUE, facetingVarNames=c("vs", "cyl"),
              facetingFont=c(12, &#39;bold.italic&#39;, "red"))

# Change the apperance of the rectangle around facet label
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;, yName="mpg",
            faceting=TRUE, facetingVarNames=c("vs", "cyl"),
            facetingRect=list(background="white", lineType="solid", 
                              lineColor="black", lineSize=1.5)
            )

ggplot2 scatter plot and facet approch, facet labelggplot2 scatter plot and facet approch, facet label

ggplot2.scatterplot function

Description

Plot easily a scatter plot using easyGgplot2 R package.

usage

ggplot2.scatterplot(data, xName, yName, groupName=NULL,
        addRegLine=FALSE,regLineColor="blue",regLineSize=0.5,
        smoothingMethod=c("lm", "glm", "gam", "loess", "rlm"),
        addConfidenceInterval=FALSE, confidenceLevel= 0.95,
        confidenceIntervalFill="#C7C7C7",
        setColorByGroupName=TRUE, setShapeByGroupName=FALSE,
        groupColors=NULL, brewerPalette=NULL,...)

Arguments

Arguments Descriptions
data Data frame. Columns are variables and rows are observations.
xName The name of column containing x variable (i.e groups).
yName The name of column containing y variable.
groupName The name of column containing group variable. This variable is used to color plot according to the group.
addRegLine If TRUE, regression line is added. Default value is FALSE.
regLineColor Color of regression line. Default value is blue.
regLineSize Weight of regression line. Default value is 0.5.
smoothingMethod Smoothing method (function) to use, eg. lm, glm, gam, loess, rlm. For datasets with n < 1000 default is loess. For datasets with 1000 or more observations defaults to gam. lm for linear smooths, glm for generalized linear smooths, loess for local smooths, gam fits a generalized additive model.
addConfidenceInterval Display confidence interval around smooth? (FALSE by default).
confidenceLevel Level controlling confidence region. Default is 95%.
confidenceIntervalFill Fill color of confidence interval.
setColorByGroupName If TRUE, points are colored according the groups. Default value is TRUE.
setShapeByGroupName If TRUE, point shapes are different according to the group. Default value is FALSE.
groupColors Color of groups. groupColors should have the same length as groups.
brewerPalette This can be also used to indicate group colors. In this case the parameter groupColors should be NULL. e.g: brewerPalette=“Paired”.
…. Other parameters passed on to ggplot2.customize custom function or to geom_smooth and to geom_point functions from ggplot2 package.

The other arguments which can be used are described at this link : ggplot2 customize. They are used to customize the plot (axis, title, background, color, legend, ….) generated using ggplot2 or easyGgplot2 R package.

Examples

df <- mtcars
ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, size=3,
        mainTitle="Miles per gallon \n according to the weight",
        xtitle="Weight (lb/1000)", ytitle="Miles/(US) gallon")

#Or use this
plot<-ggplot2.scatterplot(data=df, xName=&#39;wt&#39;,yName=&#39;mpg&#39;, size=3)
plot<-ggplot2.customize(plot, 
          mainTitle="Miles per gallon \n according to the weight",
          xtitle="Weight (lb/1000)", ytitle="Miles/(US) gallon")
print(plot)


Easy ggplot2 ebook

Note that an eBook is available on easyGgplot2 package here.

By Alboukadel Kassambara

Copyright 2014 Alboukadel Kassambara. All rights reserved.

Published by STHDA (https://www.sthda.com/english).

September 2014 : First edition.

Licence : This document is under creative commons licence (http://creativecommons.org/licenses/by-nc-sa/3.0/).

Contact : Alboukadel Kassambara alboukadel.kassambara@gmail.com


Infos

This analysis was performed using R (ver. 3.1.0), easyGgplot2 (ver 1.0.0) and ggplot2 (ver 1.0.0).

]]> Fri, 06 Feb 2015 21:10:05 +0100 <![CDATA[ggplot2.multiplot : Put multiple graphs on the same page using ggplot2]]> https://www.sthda.com/english/wiki/ggplot2-multiplot-put-multiple-graphs-on-the-same-page-using-ggplot2 https://www.sthda.com/english/wiki/ggplot2-multiplot-put-multiple-graphs-on-the-same-page-using-ggplot2

Introduction

ggplot2.multiplot is an easy to use function to put multiple graphs on the same page using R statistical software and ggplot2 plotting methods. This function is from easyGgplot2 package.

Install and load easyGgplot2 package

easyGgplot2 R package can be installed as follow :

install.packages("devtools")
library(devtools)
install_github("easyGgplot2", "kassambara")

Load the package using this R code :

library(easyGgplot2)

Examples

# data.frame
df <- ToothGrowth
# Custom box plot with centered dot plot
plot1<-ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                groupName=&#39;dose&#39;,
                addDot=TRUE, dotSize=1, showLegend=FALSE)

# Custom dot plot with centered dot plot
plot2<-ggplot2.dotplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                groupName=&#39;dose&#39;,showLegend=FALSE)

# Custom strip chart with centered dot plot
plot3<-ggplot2.stripchart(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                      groupName=&#39;dose&#39;, showLegend=FALSE)

# Notched box plot
plot4<-ggplot2.boxplot(data=df, xName=&#39;dose&#39;,yName=&#39;len&#39;,
                notch=TRUE)

# Multiple graphs on the same page
ggplot2.multiplot(plot1,plot2,plot3,plot4, cols=2)

Data visualization using ggplot2 : tutorial on how to put multiple graphs on the same page

ggplot2.multiplot function

Description

Put multiple graphs on the same page

usage

library(easyGgplot2)
ggplot2.multiplot(..., plotlist=NULL, cols=2)

Arguments

Arguments Descriptions
…, plotList List of ggplot2 objects separated by a comma. (e.g: plot1, plot2, plot3)
cols Number of columns in layout


Easy ggplot2 ebook

Note that an eBook is available on easyGgplot2 package here.

By Alboukadel Kassambara

Copyright 2014 Alboukadel Kassambara. All rights reserved.

Published by STHDA (https://www.sthda.com/english).

September 2014 : First edition.

Licence : This document is under creative commons licence (http://creativecommons.org/licenses/by-nc-sa/3.0/).

Contact : Alboukadel Kassambara alboukadel.kassambara@gmail.com


Infos

This analysis was performed using R (ver. 3.1.0), easyGgplot2 (ver 1.0.0) and ggplot2 (ver 1.0.0).

]]> Fri, 06 Feb 2015 21:08:32 +0100 <![CDATA[ggplot2 histogram : Easy histogram graph with ggplot2 R package]]> https://www.sthda.com/english/wiki/ggplot2-histogram-easy-histogram-graph-with-ggplot2-r-package https://www.sthda.com/english/wiki/ggplot2-histogram-easy-histogram-graph-with-ggplot2-r-package

Introduction

ggplot2.histogram is an easy to use function for plotting histograms using ggplot2 package and R statistical software. In this ggplot2 tutorial we will see how to make a histogram and to customize the graphical parameters including main title, axis labels, legend, background and colors. ggplot2.histogram function is from easyGgplot2 R package. An R script is available in the next section to install the package.

At the end of this tutorial you will be able to draw, with few R code, the following plot:

r data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical software

ggplot2.histogram function is described in detail at the end of this document.

Install and load easyGgplot2 package

easyGgplot2 R package can be installed as follow :

install.packages("devtools")
library(devtools)
install_github("easyGgplot2", "kassambara")

Load the package using this R code :

library(easyGgplot2)

Data format

The data must be a numeric vector or a data.frame (columns are variables and rows are observations).

weight data, from easyGgplot2 package, will be used in the following examples.

# create a numeric vector
numVector<-rnorm(100)
head(numVector)
## [1]  0.5738  1.1956 -0.1904  0.4465  0.2567 -1.6642
# data.frame
head(weight)
##      sex weight
## 1 Female  63.79
## 2 Female  65.28
## 3 Female  66.08
## 4 Female  62.65
## 5 Female  65.43
## 6 Female  65.51

weight data contain the weight of 400 people (200 females and 200 males).


Basic histograms

# Histogram from a single numeric vector 
# ggplot2.histogram(data=numVector)

# Basic histogram plot from the vector "weight"
ggplot2.histogram(data=weight, xName=&#39;weight&#39;)

# Change the width of bars
ggplot2.histogram(data=weight, xName=&#39;weight&#39;, binwidth=0.1)

# Change y axis values to density
ggplot2.histogram(data=weight, xName=&#39;weight&#39;, scale="density")

r data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical software

Change the histogram orientation

# Horizontal histogram plot
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
                  orientation="horizontal")

# y Axis reversed
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
                  orientation="yAxisReversed")

r data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical software

Add mean line and density curve

# Add mean line to the histogram plot.
# Change fill color and line color
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
                  fill="white", color="black",
                  addMeanLine=TRUE, meanLineColor="red",
                  meanLineType="dashed", meanLineSize=1)

# Add density curve
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
                  fill="white", color="black",
                  addDensityCurve=TRUE, densityFill=&#39;#FF6666&#39;)

r data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical software

Change the line type of the histogram plot

Different point shapes and line types can be used in the plot. By default, ggplot2 uses solid line type and circle shape.

#Change the histogram line color and line type
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
                  fill="white", color="black",
                  linetype="longdash")

r data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical software

Histogram plot with multiple groups

# Multiple histograms on the same plot
# Color the histogram plot by the groupName "sex"
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
        groupName=&#39;sex&#39;, legendPosition="top")

# Histogram plots with semi-transparent fill.
# alpha is the transparency of the overlaid color
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
        groupName=&#39;sex&#39;, legendPosition="top",
        alpha=0.5 )

# Histogram plots with mean lines
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
    groupName=&#39;sex&#39;, legendPosition="top",
    alpha=0.5, addDensity=TRUE,
    addMeanLine=TRUE, meanLineColor="white", meanLineSize=1.5)

Histogram with multiple groupsHistogram with multiple groupsHistogram with multiple groups

You can change the position adjustment to use for overlapping points on the layer. Possible values for the argument position is “identity”, “stack”, “dodge”. This is shown in the following histograms. 

# Default value of position is "identity"
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
        groupName=&#39;sex&#39;, legendPosition="top",
        alpha=0.5, position="identity")

# Interleaved histograms
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
        groupName=&#39;sex&#39;, legendPosition="top",
        alpha=0.5, position="dodge")

#stacked histograms
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
        groupName=&#39;sex&#39;, legendPosition="top",
        alpha=0.5, position="stack")

Histogram with multiple groupsHistogram with multiple groupsHistogram with multiple groups

Customize your histogram plot

Parameters

The arguments that can be used to customize titles and x and y axis are listed below :

Parameters Description
mainTitle the title of the plot
mainTitleFont a vector of length 3 indicating respectively the size, the style (“italic”, “bold”, “bold.italic”) and the color of x and y axis titles. Default value is: mainTitleFont=c(14, "bold", "black").
xShowTitle, yShowTitle if TRUE, x and y axis titles will be shown. Set the value to FALSE to hide axis labels. Default values are TRUE.
xtitle, ytitle x and y axis labels. Default values are NULL.
xtitleFont, ytitleFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis titles. Possible values for the style:“plain”, “italic”, “bold”, “bold.italic”. Color can be specified as an hexadecimal code (e.g: “#FFCC00”) or by the name (e.g : “red”, “green”). Default values are xtitleFont=c(14,"bold", "black"), ytitleFont=c(14,"bold", "black").
xlim, ylim limit for the x and y axis. Default values are NULL.
xScale, yScale x and y axis scales. Possible values : c(“none”, “log2”, “log10”). e.g: yScale=“log2”. Default values are NULL.
xShowTickLabel, yShowTickLabel if TRUE, x and y axis tick mark labels will be shown. Default values are TRUE.
xTickLabelFont, yTickLabelFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis tick label fonts. Default value are xTickLabelFont=c(12, "bold", "black"), yTickLabelFont=c(12, "bold", "black").
xtickLabelRotation, ytickLabelRotation Rotation angle of x and y axis tick labels. Default value are 0.
hideAxisTicks if TRUE, x and y axis ticks are hidden. Default value is FALSE.
axisLine a vector of length 3 indicating respectively the size, the line type and the color of axis lines. Default value is c(0.5, "solid", "#E5E5E5").

For more details follow this link : ggplot2.customize.

Main title and axis labels

# basic plot
plot<-ggplot2.histogram(data=weight, xName=&#39;weight&#39;)
print(plot)

# Change main title and axis titles
ggplot2.customize(plot, mainTitle="Weight histo.",
                  xtitle="Weight (Kg)", ytitle="Histogram")

# Customize title styles. Possible values for the font style :
  # &#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.customize(plot,  mainTitle="Weight histo.",
                  mainTitleFont=c(14,"bold.italic", "red"),
                  xtitle="Weight (Kg)", ytitle="Histogram",
                  xtitleFont=c(14,"bold", "#993333"), 
                  ytitleFont=c(14,"bold", "#993333"))

# Hide x an y axis titles
ggplot2.customize(plot, xShowTitle=FALSE, yShowTitle=FALSE)

r data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical software

Axis ticks

# Axis ticks labels and orientaion
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
                xShowTitle=FALSE, yShowTitle=FALSE,
                xTickLabelFont=c(14,"bold", "#993333"),
                yTickLabelFont=c(14,"bold", "#993333"),
                xtickLabelRotation=45, ytickLabelRotation=45)

# Hide axis tick labels
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
                xShowTitle=FALSE, yShowTitle=FALSE,
                xShowTickLabel=FALSE, yShowTickLabel=FALSE)

# Hide axis ticks
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
                xShowTitle=FALSE, yShowTitle=FALSE,
                xShowTickLabel=FALSE, yShowTickLabel=FALSE,
                hideAxisTicks=TRUE)

# AxisLine : a vector of length 3 indicating the size,
  #the line type and the color of axis lines
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
                  axisLine=c(1, "solid", "darkblue"))

r data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical software

Background and colors

Change histogram plot background and fill colors

# Change background color to "white". Default is "gray"
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
        backgroundColor="white")

# Change background color to "lightblue" and grid color to "white"
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
        backgroundColor="lightblue", gridColor="white")

#Change plot fill color
#color =  color of the histogram border
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
         backgroundColor="white", color="black", fill=&#39;#FFAAD4&#39;)

# Remove grid; remove top and right borders around the plot;
# change  axis lines
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
        backgroundColor="white", color="black", fill=&#39;#FFAAD4&#39;,
        removePanelGrid=TRUE,removePanelBorder=TRUE,
        axisLine=c(0.5, "solid", "black"))

r data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical software

Fill the histogram by count value

# Fill the histogram according to the count value
ggplot2.histogram(data=weight, xName=&#39;weight&#39;)+
   geom_histogram(aes(fill = ..count..))
    
# Change the scale
ggplot2.histogram(data=weight, xName=&#39;weight&#39;)+
  geom_histogram(aes(fill = ..count..))+
   scale_fill_gradient("Count", low = "green", high = "red")

r data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical software

Change histogram plot color according to the group

Colors can be specified as a hexadecimal RGB triplet, such as "#FFCC00" or by names (e.g : "red" ). You can also use other color scales, such as ones taken from the RColorBrewer package. The different color systems available in R have been described in detail here.

To change histogram plot color according to the group, you have to specify the name of the data column containing the groups using the argument groupName. Use the argument groupColors, to specify colors by hexadecimal code or by name. In this case, the length of groupColors should be the same as the number of the groups. Use the argument brewerPalette, to specify colors using RColorBrewerpalette.

# Change group colors using hexadecimal colors
# alpha is the transparency level of overlaid color. 
  #The value can variate from 0 (total transparency) 
  #to 1 (no transparency)
ggplot2.histogram(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
                legendPosition="top",
                groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;), alpha=0.5 )

# Change group colors using brewer palette: "Paired"
ggplot2.histogram(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
                legendPosition="top",
                brewerPalette="Paired",alpha=0.5)

r data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical software


Color can also be changed by using names as follow :

#Change group colors using color names
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
      groupName=&#39;sex&#39;, groupColors=c(&#39;aquamarine3&#39;,&#39;goldenrod1&#39;),
      alpha=0.5 )


Legend

Legend position

# Change the legend position to "top" 
  # (possible values: "left","top", "right", "bottom")
ggplot2.histogram(data=weight, xName=&#39;weight&#39;, 
                groupName=&#39;sex&#39;, alpha=0.5,
                legendPosition="top")

# legendPosition can be also a numeric vector c(x, y)
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
                groupName=&#39;sex&#39;, alpha=0.5,
                legendPosition=c(0.8,0.2))

#Remove legend
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
                groupName=&#39;sex&#39;, alpha=0.5,
                showLegend=FALSE)

r data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical software

It is also possible to position the legend inside the plotting area. You have to indicate the x, y coordinates of legend box. x and y values must be between 0 and 1. c(0,0) corresponds to "bottom left" and c(1,1) corresponds to "top right" position.

Legend background color, title and text font styles

# Change legend background color, title and text font styles
ggplot2.histogram(data=weight, xName=&#39;weight&#39;, 
        groupName=&#39;sex&#39;, alpha=0.5,
        # legendTitleFont=c(size, style, color)
        legendTitle="Groups", legendTitleFont=c(10, "bold", "blue"),
        #legendTextFont=c(size, style, color)
        legendTextFont=c(10, "bold.italic", "red"),
        #legendBackground: c(fill, lineSize, lineType, lineColor)
        legendBackground=c("lightblue", 0.5, "solid", "darkblue" )
        )

r data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical software

Axis scales

Possible values for x axis scale are “none”, “log2” and log10. Default value is “none”.

# Change x axis limit
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
                groupName=&#39;sex&#39;, alpha=0.5,
                showLegend=FALSE, xlim=c(60,72))

# x Log scale. Possible values="none", "log2" and "log10"
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
                groupName=&#39;sex&#39;, alpha=0.5,
                showLegend=FALSE, xScale="log2")

r data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical software

Create a customized plots with few R code

# basic plot
plot<-ggplot2.histogram(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
              addMeanLine=TRUE, showLegend=FALSE,
              groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;), alpha=0.5,
              backgroundColor="white")
#print(plot)

# Customized histogram
plot<-ggplot2.customize(plot, xtitle="Weight (Kg)", ytitle="Count",
                showLegend=FALSE, 
                mainTitle="Weight histogram \nby sex")                
print(plot)


# Remove grid; Remove Top and right border around the plot 
# add density curve and mean line
ggplot2.customize(plot,
        removePanelGrid=TRUE,removePanelBorder=TRUE,
        axisLine=c(0.5, "solid", "black"),
        addDensity=TRUE, showLegend=FALSE)

r data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical softwarer data visualization with ggplot2 histogram : tutorial on how to use ggplot2.histogram function to easily make histograms in R statistical software

The argument alpha is used to specify the transparency of colors.

Facet : split a plot into a matrix of panels

The facet approach splits a plot into a matrix of panels. Each panel shows a different subset of the data.

Facet with one variable

# Facet by "sex" variable
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
         groupName=&#39;sex&#39;, legendPosition="top",
        faceting=TRUE, facetingVarNames="sex") 

# Change the direction. possible values are "vertical", horizontal".
# default is vertical.
ggplot2.histogram(data=weight, xName=&#39;weight&#39;,
        groupName=&#39;sex&#39;, legendPosition="top",
        faceting=TRUE, facetingVarNames="sex",
        facetingDirection="horizontal")

ggplot2 histogram and facet approch, one variableggplot2 histogram and facet approch, one variable

Facet with two variables

The mtcars data is used in the following examples.

data(mtcars)
head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

mtcars (Motor Trend Car Road Tests) comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles.

# Facet by two variables: vs and am.
# Rows are vs and columns are am
ggplot2.histogram(data=mtcars, xName=&#39;mpg&#39;, groupName=&#39;vs&#39;,
        legendPosition="top",
        faceting=TRUE, facetingVarNames=c("vs", "am"))

#Facet by two variables: reverse the order of the 2 variables
#Rows are am and columns are vs
ggplot2.histogram(data=mtcars, xName=&#39;mpg&#39;, groupName=&#39;vs&#39;,
        legendPosition="top",
        faceting=TRUE, facetingVarNames=c("am", "vs"))

ggplot2 histogram and facet approch, two variablesggplot2 histogram and facet approch, two variables

Facet scales

By default, all the panels have the same scale (facetingScales="fixed"). They can be made independent, by setting scales to free, free_x, or free_y.

# Facet with free scales
ggplot2.histogram(data=mtcars, xName=&#39;mpg&#39;, groupName=&#39;vs&#39;,
      legendPosition="top",
      faceting=TRUE, facetingVarNames=c("vs", "am"),
      facetingScales="free")

ggplot2 histogram and facet approch, free scale

As you can see in the above plot, y axis have different scales in the different panels.

Facet label apperance

# Change facet text font. Possible values for the font style:
  #&#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.histogram(data=mtcars, xName=&#39;mpg&#39;, groupName=&#39;vs&#39;,
        legendPosition="top",
        faceting=TRUE, facetingVarNames=c("vs", "am"),
        facetingFont=c(12, &#39;bold.italic&#39;, "red"))

# Change the apperance of the rectangle around facet label
ggplot2.histogram(data=mtcars, xName=&#39;mpg&#39;, groupName=&#39;vs&#39;,
        legendPosition="top",
        faceting=TRUE, facetingVarNames=c("vs", "am"),
        facetingRect=list(background="white", lineType="solid",
                      lineColor="black", lineSize=1.5)
        )

ggplot2 histogram and facet approch, facet labelggplot2 histogram and facet approch, facet label

ggplot2.histogram function

Description

Plot easily a histogram plot with R package easyGgplot2.

usage

ggplot2.histogram(data, xName=NULL, groupName=NULL,
        position=c("identity", "stack", "dodge"),
        addMeanLine=FALSE, meanLineColor=NULL,
        meanLineType="dashed", meanLineSize=1,
        addDensityCurve=FALSE, densityFill="#FF6666",
        densityAlpha=0.2, 
        densityLineType="solid", densityLineColor="#2F2F2F",
        scale=c("frequency", "density"), 
        groupColors=NULL, brewerPalette=NULL,...)

Arguments

Arguments Descriptions
data data.frame or a numeric vector. Columns are variables and rows are observations.
xName The name of column containing x variable. Default value is NULL.
groupName The name of column containing group variable. This variable is used to color plot according to the group.
position Change the position adjustment to use for overlapping points on the layer. Possible values for the argument position is “identity”, “stack”, “dodge”. Default value is identity.
addMeanLine If TRUE, the mean line is added on the plot for each group. Default value is FALSE.
meanLineColor, meanLineType, meanLineSize mean line color, type and size.
addDensityCurve If true, add density curve. Default value is FALSE.
densityFill The fill color of density plot. The value is considered only when groupName=NULL. If groupName is specified, density curves are colored according groupColors or brewerPalette.
densityAlpha Degree of transparency of overlaid colors for density curves. Default is 0.2 (20%).
densityLineType, densityLineColor Line type and color for density curve.
scale Indicate whether y axis values are density or frequency. Default value is frequency.
groupColors Color of groups. groupColors should have the same length as groups.
brewerPalette This can be also used to indicate group colors. In this case the parameter groupColors should be NULL. e.g: brewerPalette=“Paired”.
…. Other arguments passed on to ggplot2.customize custom function or to geom_histogram and geom_density functions from ggplot2 package.

The other arguments which can be used are described at this link : ggplot2 customize. They are used to customize the plot (axis, title, background, color, legend, ….) generated using ggplot2 or easyGgplot2 R package.

Examples

library(easyGgplot2)
#plot
plot<-ggplot2.histogram(data=weight, xName=&#39;weight&#39;,groupName=&#39;sex&#39;,
                  groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;))
plot<-ggplot2.customize(plot, 
            mainTitle="Plot of weight histogram \nper sex",
            xtitle="Weight (Kg)", ytitle="Histogram")
print(plot)

Easy ggplot2 ebook

Note that an eBook is available on easyGgplot2 package here.

By Alboukadel Kassambara

Copyright 2014 Alboukadel Kassambara. All rights reserved.

Published by STHDA (https://www.sthda.com/english).

September 2014 : First edition.

Licence : This document is under creative commons licence (http://creativecommons.org/licenses/by-nc-sa/3.0/).

Contact : Alboukadel Kassambara alboukadel.kassambara@gmail.com


Infos

This analysis was performed using R (ver. 3.1.0), easyGgplot2 (ver 1.0.0) and ggplot2 (ver 1.0.0).

]]> Fri, 06 Feb 2015 20:54:13 +0100 <![CDATA[ggplot2.customize : How to personalize easily ggplot2 graphs in R statistical software]]> https://www.sthda.com/english/wiki/ggplot2-customize-how-to-personalize-easily-ggplot2-graphs-in-r-statistical-software https://www.sthda.com/english/wiki/ggplot2-customize-how-to-personalize-easily-ggplot2-graphs-in-r-statistical-software


Introduction

ggplot2.customize is an easy to use function, to customize plots (e.g : box and whisker plot, histogram, density plot, dotplot, scatter plot, line plot, …) generated with R ggplot2 package. ggplot2.customize function is from easyGgplot2 R package and it can be used to personalize graphical parameters including axis, title, background, color, legend and more. To be able to run the R script of this tutorial, you have to install easyGgplot2. This is explained in the next section.

The aim of this tutorial is to show you step by step, how to plot and customize a box plot using ggplot2.customize function.

Note that all the R scripts used in this tutorial to customize ggplot2 boxplot can also be applied to personalize the others graphics (including boxplot, histogram, density plot, dotplot, etc, …) generated by either ggplot2 or easyGgplot2 R packages.

At the end of this tutorial you will be able to draw, with few R code, the following plots :

ggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical software

The prototype of ggplot2.customize function is described at the end of this document.

Install and load easyGgplot2 package

easyGgplot2 R package can be installed as follow :

install.packages("devtools")
library(devtools)
install_github("easyGgplot2", "kassambara")

Load the package using this R code :

library(easyGgplot2)

Data format

The data must be a numeric vector or a data.frame (columns are variables and rows are observations).

ToothGrowth data is used in the following examples.

# data.frame
df <- ToothGrowth
head(df)
##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5
## 4  5.8   VC  0.5
## 5  6.4   VC  0.5
## 6 10.0   VC  0.5

ToothGrowth describes the effect of Vitamin C on Tooth growth in Guinea pigs. The response is the length (len) of teeth in each of 10 guinea pigs at each of three dose levels of Vitamin C (0.5, 1, and 2 mg) with each of two delivery methods (orange juice or ascorbic acid).

The data is a data frame with 60 observations on 3 variables. * [,1] len numeric Tooth length. * [,2] supp factor Supplement type (VC or OJ). * [,3] dose numeric Dose in milligrams.


Basic box plot

You can generate basic box plot using either ggplot2.boxplot [easyGgplot2 package] or geom_boxplot [ggplot2 package].

# Boxplot using ggplot2 package
p <- ggplot(ToothGrowth, aes(factor(dose), len))+geom_boxplot()
p

# Boxplot using easyGgplot2 package
p<-ggplot2.boxplot(data = ToothGrowth, xName = &#39;dose&#39;,yName = &#39;len&#39;)
p

ggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical software

Customize your box plot

Parameters

The arguments that can be used to customize x and y axis are listed below :

Parameters Description
xShowTitle, yShowTitle if TRUE, x and y axis titles will be shown. Set the value to FALSE to hide axis labels. Default values are TRUE.
xtitle, ytitle x and y axis labels. Default values are NULL.
xtitleFont, ytitleFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis titles. Possible values for the style:“plain”, “italic”, “bold”, “bold.italic”. Color can be specified as an hexadecimal code (e.g: “#FFCC00”) or by the name (e.g : “red”, “green”). Default values are xtitleFont = c(14,"bold", "black"), ytitleFont = c(14,"bold", "black").
xlim, ylim limit for the x and y axis. Default values are NULL.
xScale, yScale x and y axis scales. Possible values : c(“none”, “log2”, “log10”). e.g: yScale = “log2”. Default values are NULL.
xShowTickLabel, yShowTickLabel if TRUE, x and y axis tick mark labels will be shown. Default values are TRUE.
xTickLabelFont, yTickLabelFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis tick label fonts. Default value are xTickLabelFont = c(12, "bold", "black"), yTickLabelFont = c(12, "bold", "black").
xtickLabelRotation, ytickLabelRotation Rotation angle of x and y axis tick labels. Default value are 0.
hideAxisTicks if TRUE, x and y axis ticks are hidden. Default value is FALSE.
axisLine a vector of length 3 indicating respectively the size, the line type and the color of axis lines. Default value is c(0.5, "solid", "#E5E5E5").

Main title and axis labels

# Change main title and axis titles
ggplot2.customize(p,
                mainTitle = "Plot of length \n by dose",
                xtitle = "Dose (mg)", ytitle = "Length")

# Customize title styles. Possible values for the font style :
  # &#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.customize(p,
                xtitle = "Dose (mg)", ytitle = "Length",
                mainTitle = "Plot of length \n by dose",
                mainTitleFont = c(14,"bold.italic", "red"),
                xtitleFont = c(14,"bold", "#993333"), ytitleFont = c(14,"bold", "#993333"))

# Hide x an y axis titles
ggplot2.customize(p, xShowTitle = FALSE, yShowTitle = FALSE)

ggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical software

Axis ticks

# Axis ticks labels and orientaion
ggplot2.customize(p,
                xShowTitle = FALSE, yShowTitle = FALSE,
                xTickLabelFont = c(14,"bold", "#993333"),
                yTickLabelFont = c(14,"bold", "#993333"),
                xtickLabelRotation = 45, ytickLabelRotation = 45)

# Hide axis tick labels
ggplot2.customize(p,
                xShowTitle = FALSE, yShowTitle = FALSE,
                xShowTickLabel = FALSE, yShowTickLabel = FALSE)

# Hide axis ticks
ggplot2.customize(p,
                xShowTitle = FALSE, yShowTitle = FALSE,
                xShowTickLabel = FALSE, yShowTickLabel = FALSE,
                hideAxisTicks = TRUE)

# AxisLine : a vector of length 3 indicating the size,
  #the line type and the color of axis lines
ggplot2.customize(p, axisLine = c(1, "solid", "darkblue"))

ggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical software

Background and colors

Change box plot background and fill colors

# change background color to "white". Default is "gray"
ggplot2.customize(p,backgroundColor = "white")

# change background color to "lightblue" and grid color to "white"
ggplot2.customize(p, backgroundColor = "lightblue",
                  gridColor = "white")
             
# remove grid; remove top and right borders around the plot;
# change  axis lines
ggplot2.customize(p,
                backgroundColor = "white",
                removePanelGrid = TRUE,removePanelBorder = TRUE,
                axisLine = c(0.5, "solid", "black"))

ggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical software

Colors can be specified as a hexadecimal RGB triplet, such as "#FFCC00" or by names (e.g : "red" ). You can also use other color scales, such as ones taken from the RColorBrewer package. The different color systems available in R have been described in detail here.

Legend

Legend position

# Color the box plot accoording to the groupName "dose"
# Create legend
p1<- ggplot2.boxplot(data = ToothGrowth, 
          xName = "dose", yName = "len", groupName = "dose")
p1

# Change the legend position to "top" 
  # Possible values: "left","top", "right", "bottom"
ggplot2.customize(p1, legendPosition = "top")
                
# legendPosition can be also a numeric vector c(x, y)
ggplot2.customize(p1, legendPosition = c(0.8,0.2))

ggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical software

It is also possible to position the legend inside the plotting area. You have to indicate the x, y coordinates of legend box. x and y values must be between 0 and 1. c(0,0) corresponds to “bottom left” and c(1,1) corresponds to “top right” position.

Legend background color, title and text font styles

# Change legend background color, title and text font styles
ggplot2.customize(p1, 
        #legendTitleFont = c(size, style, color)
        legendTitle = "Dose (mg)",
        legendTitleFont = c(10, "bold", "blue"),
        #legendTextFont = c(size, style, color)
        legendTextFont = c(10, "bold.italic", "red"),
        #legendBackground: c(fill, lineSize, lineType, lineColor)
        legendBackground = c("lightblue", 0.5, "solid", "darkblue" )
        )

ggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical software

Change the order of items in the legend, remove plot legend

# Change the order of items in the legend
 # legendItemOrder : character vector indicating 
# the order of items in the legends.
ggplot2.customize(p1, legendItemOrder = c("2", "1", "0.5"))

# Remove plot legend
ggplot2.customize(p1, showLegend = FALSE)

ggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical software

Axis scales

Possible values for y axis scale are “none”, “log2” and log10. Default value is “none”.

# Change y axis limit
ggplot2.customize(p1, ylim = c(0,50))
                
# y Log scale. yScale = "log2". 
  # Possible value = "none", "log2" and "log10"
ggplot2.customize(p1,  yScale = "log2")

ggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical software

Create a customized plots with few R code

# Customized box plot with centered dot plot
ggplot2.customize(p1, showLegend = FALSE,
        backgroundColor = "white",
        xtitle = "Dose (mg)", ytitle = "length", 
        mainTitle = "Plot of length \n by dose")

# Remove grid; Remove Top and right border around the plot
ggplot2.customize(p1, showLegend = FALSE,
        backgroundColor = "white",
        xtitle = "Dose (mg)", ytitle = "length", 
        mainTitle = "Plot of length \n by dose",
        removePanelGrid = TRUE,removePanelBorder = TRUE,
        axisLine = c(0.5, "solid", "black"))

ggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical software

Facet : split a plot into a matrix of panels

The approach of facet splits a plot into a matrix of panels. Each panel displays a different subset of the data.

Facet with one variable

# Box plot with multiples groups
p2<- ggplot2.boxplot(data = ToothGrowth,
          xName = "dose", yName = "len", groupName = "supp")
p2

# Facet according to the supp variable
ggplot2.customize(p2, legendPosition = "top",
        faceting = TRUE, facetingVarNames = "supp") 

# Change the direction. Possible values are
  # "vertical", horizontal". default is vertical.
ggplot2.customize(p2, legendPosition = "top",
        faceting = TRUE, facetingVarNames = "supp",
        facetingDirection = "horizontal")

ggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical software

Facet with two variables

# Facet by two variables: dose and supp.
# Rows are dose and columns are supp
ggplot2.customize(p2, legendPosition = "top",
        faceting = TRUE, facetingVarNames = c("dose","supp"))

#Facet by two variables: reverse the order of the 2 variables
#Rows are supp and columns are dose
ggplot2.customize(p2, legendPosition = "top",
        faceting = TRUE, facetingVarNames = c("supp", "dose"))

ggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical software

Facet scales

By default, all the panels have the same scale (facetingScales="fixed"). They can be made independent, by setting scales to free, free_x, or free_y.

# Facet with free scales
ggplot2.customize(p2, legendPosition = "top",
        faceting = TRUE, facetingVarNames = c("dose", "supp"),
        facetingScales = "free")

ggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical software

y axis have different scales now in the different panels.

Facet label apperance

# Change facet text font. Possible values for the font style:
  #&#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.customize(p2, legendPosition = "top",
          faceting = TRUE, facetingVarNames = c("dose","supp"),
          facetingFont = c(12, &#39;bold.italic&#39;, "red"))

#Change the apperance of the rectangle around facet label
ggplot2.customize(p2, legendPosition = "top",
      faceting = TRUE, facetingVarNames = c("dose","supp"),
      facetingRect = list(background = "white", lineType = "solid", 
                        lineColor = "black", lineSize = 1.5)
        )

ggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical softwareggplot2 boxplot : ggplot2 tutorial on how to use ggplot2.customize function to personalize easily graphics (main title, axis labels, legend, background and color) generated by ggplot2 using r statistical software

ggplot2.customize function

Description

Customize easily a plot generated with either easyGgplot2 or ggplot2 R packages.

usage

ggplot2.boxplot(plot,...)

Arguments

plot : A plot object generated with ggplot2 or easyGgplot2 package (boxplot, density plot, histogram, dotplot, etc ….)

… : Any argument from the list of the arguments indicated below.

The other arguments which can be used are shown in the table below. They are used to customize the plot (axis, title,** background, color, legend**, ….) generated by easyGgplot2 and ggplot2.

Arguments Descriptions
Customize plot title
mainTitle Title of the plot.
mainTitleFont A vector of length 3 indicating respectively the size, the style and the color of main title. Possible values for font style are “plain”, “italic”, “bold”, “bold.italic”. Color can be specified as an hexadecimal code (e.g: “#FFCC00”) or by the name (e.g : “red”, “green”). Default value is: mainTitleFont=c(14, “bold”, “black”).
Customize x and y axis
xShowTitle, yShowTitle if TRUE, x axis and y axis titles will be shown. Default values are TRUE.
xtitle, ytitle x and y axis labels.
xtitleFont, ytitleFont A vector of length 3 indicating respectively the size, the style and the color of x and y axis titles. Default values are c(14,“bold”, “black”).
xlim, ylim Limit for the x and y axis. Default values are NULL.
xScale, yScale x and y axis scales. Possible values are c(“none”, “log2”, “log10”). Example: yScale = “log2”. Default values are “none”.
xShowTickLabel, yShowTickLabel if TRUE, x and y axis tick mark labels will be shown. Default values are TRUE.
xTickLabelFont, yTickLabelFont A vector of length 3 indicating respectively the size, the style and the color of x and y axis tick label fonts. Default values are c(12, “bold”, “black”).
xtickLabelRotation, ytickLabelRotation Rotation angle of x and y axis tick labels. Default values are 0.
hideAxisTicks if TRUE, x and y axis ticks are removed. Default value is FALSE.
axisLine A vector of length 3 indicating respectively the size, the line type and the color of axis lines. Default value is c(0.5, “solid”, “#E5E5E5”).
Customize plot background
removePanelBorder if TRUE, the top-right border of the plot are removed. Default value is FALSE.
removePanelGrid if TRUE, panel grids are removed. Default value is FALSE.
backgroundColor background color of plot panel. Default value is “gray”.
gridColor Color of plot panel grids. Default value is “white”.
Customize plot legend
showLegend if TRUE, plot legend will be shown. Default value is TRUE.
legendPosition Position of legend box. Default value is “right”. Possible values for legend position are “right”, “left”,“top”, “bottom”. LegendPosition can be also a numeric vector c(x, y) indicating the coordinate of legend box. x and y values must be between 0 and 1. c(0,0) corresponds to the “bottom left” and c(1,1) corresponds to the “top right” position.
legendBackground A vector of length 4 indicating boxFill, boxLineSize, boxLineType, boxLineColor. Default value is legendBackground = c(“#FFFFFF”, 0.5, “blank”, “black” ).
legendTextFont A vector of length 3 indicating respectively the size, the style and the color of legend text. Default value is c(10, “plain”, “black”). Possible values for font style are “plain”, “italic”, “bold”, “bold.italic”.
legendTitleFont A vector of length 3 indicating respectively the size, the style and the color of legend title. Default value is c(10, “plain”, “black”)
legendItemOrder Character vector indicating the order of items in the legends. Example: c(“2”, “1”, “0.5”)
Faceting : split a plot into a matrix of panels
faceting if TRUE, the data are split up by one or two variables and graphs are displayed in different panels. In this case, you have to indicate the facetingVarNames. Default value is FALSE.
facetingVarNames the names of the columns containing variables to use for faceting. Default value is NULL.
facetingDirection possibles values are “vertical” and “horizontal”. Default value is vertical. Default value is “vertical”.
facetingScales By default, all the panels have the same scale (facetingScales = "fixed"). They can be made independent, by setting scales to “free”, “free_x”, or “free_y”. Default value is “fixed”.
facetingFont Change facet label appearance. A vector of length 3 indicating respectively the size, the font style and the color of the text. Default value is facetingFont = c(size = 12, font = “plain”, color = “black”).
facetingRect Modify the appearance of the rectangle around the facet label.Default value is list(background = NULL, lineType = NULL, lineColor = NULL, lineSize = NULL).
facetingTextAngles Modify the facet label text orientation. It indicates the angles of the x and y texts (in [0, 360]). Default value is c(NULL, NULL).
Others
orientation Change the orientation of the plot. Possible values are c(“standard”, “horizontal”, “yAxisReversed”). Default value is “standard”.

Possible values for font style are “plain”, “italic”, “bold”, “bold.italic”.

Examples

plot<-ggplot2.boxplot(data = ToothGrowth, 
              xName = &#39;dose&#39;, yName = &#39;len&#39;)
plot<-ggplot2.customize(plot, mainTitle = "Plot of length \n by dose",
            xtitle = "Dose (mg)", ytitle = "Length")
print(plot)


Easy ggplot2 ebook

Note that an eBook is available on easyGgplot2 package here.

By Alboukadel Kassambara

Copyright 2014 Alboukadel Kassambara. All rights reserved.

Published by STHDA (https://www.sthda.com/english).

September 2014 : First edition.

Licence : This document is under creative commons licence (http://creativecommons.org/licenses/by-nc-sa/3.0/).

Contact : Alboukadel Kassambara alboukadel.kassambara@gmail.com


Infos

This analysis was performed using R (ver. 3.1.0), easyGgplot2 (ver 1.0.0) and ggplot2 (ver 1.0.0).

]]> Fri, 06 Feb 2015 20:52:12 +0100 <![CDATA[ggplot2 density : Easy density plot using ggplot2 and R statistical software]]> https://www.sthda.com/english/wiki/ggplot2-density-easy-density-plot-using-ggplot2-and-r-statistical-software https://www.sthda.com/english/wiki/ggplot2-density-easy-density-plot-using-ggplot2-and-r-statistical-software

Introduction

ggplot2.density is an easy to use function for plotting density curve using ggplot2 package and R statistical software. The aim of this ggplot2 tutorial is to show you step by step, how to make and customize a density plot using ggplot2.density function. This function can also be used to personalize the different graphical parameters including main title, axis labels, legend, background and colors.

ggplot2.density function is from easyGgplot2 R package. An R script is available in the next section to install the package.

At the end of this tutorial you will be able to draw, with few R code, the following plots:

r data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical software

ggplot2.density function is described in detail at the end of this document.

Install and load easyGgplot2 package

easyGgplot2 R package can be installed as follow :

install.packages("devtools")
library(devtools)
install_github("easyGgplot2", "kassambara")

Load the package using this R code :

library(easyGgplot2)

Data format

The data must be a numeric vector or a data.frame (columns are variables and rows are observations).

weight data, from easyGgplot2 package, will be used in the following examples.

# create a numeric vector
numVector<-rnorm(100)
head(numVector)
## [1] -0.8557 -0.6604  0.1977  2.0698 -1.1753 -0.5429
# data.frame
head(weight)
##      sex weight
## 1 Female  63.79
## 2 Female  65.28
## 3 Female  66.08
## 4 Female  62.65
## 5 Female  65.43
## 6 Female  65.51

weight data contain the weight of 400 people (200 females and 200 males).


Basic density plot

# Density of a single numeric vector 
#ggplot2.density(data=numVector)

# Basic density plot from the vector "weight"
ggplot2.density(data=weight, xName=&#39;weight&#39;)

# Add mean line to the density plot
# Change the density line color, linetype and line size
ggplot2.density(data=weight, xName=&#39;weight&#39;,
        addMeanLine=TRUE, meanLineColor="red",
        meanLineType="dashed", meanLineSize=1,
        colour="darkblue", linetype="dotted", size=1.5)

#Change the orientation: Horizontal density plot
ggplot2.density(data=weight, xName=&#39;weight&#39;,
          orientation="horizontal")

# y Axis reversed
ggplot2.density(data=weight, xName=&#39;weight&#39;,
        orientation="yAxisReversed")

r data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical software

Change the line type of the density plot

Different point shapes and line types can be used in the plot. By default, ggplot2 uses solid line type and circle shape.

# Change the density line color and line type
ggplot2.density(data=weight, xName=&#39;weight&#39;,linetype="longdash")

r data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical software

Density plot with multiple groups

# Multiple density curves on the same plot
# Color the density plot accoording to the groupName "grp"
ggplot2.density(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
                legendPosition="top")

# Density plots with semi-transparent fill.
# alpha is the transparency of the overlaid color
ggplot2.density(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
    legendPosition="top",
    alpha=0.5, fillGroupDensity=TRUE )

# Density plots with mean lines
ggplot2.density(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
                legendPosition="top",
                addMeanLine=TRUE)

Density with multiple groupsDensity with multiple groupsDensity with multiple groups

Customize your density plot

Parameters

The arguments that can be used to customize titles and x and y axis are listed below :

Parameters Description
mainTitle the title of the plot
mainTitleFont a vector of length 3 indicating respectively the size, the style (“italic”, “bold”, “bold.italic”) and the color of x and y axis titles. Default value is: mainTitleFont=c(14, "bold", "black").
xShowTitle, yShowTitle if TRUE, x and y axis titles will be shown. Set the value to FALSE to hide axis labels. Default values are TRUE.
xtitle, ytitle x and y axis labels. Default values are NULL.
xtitleFont, ytitleFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis titles. Possible values for the style:“plain”, “italic”, “bold”, “bold.italic”. Color can be specified as an hexadecimal code (e.g: “#FFCC00”) or by the name (e.g : “red”, “green”). Default values are xtitleFont=c(14,"bold", "black"), ytitleFont=c(14,"bold", "black").
xlim, ylim limit for the x and y axis. Default values are NULL.
xScale, yScale x and y axis scales. Possible values : c(“none”, “log2”, “log10”). e.g: yScale=“log2”. Default values are NULL.
xShowTickLabel, yShowTickLabel if TRUE, x and y axis tick mark labels will be shown. Default values are TRUE.
xTickLabelFont, yTickLabelFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis tick label fonts. Default value are xTickLabelFont=c(12, "bold", "black"), yTickLabelFont=c(12, "bold", "black").
xtickLabelRotation, ytickLabelRotation Rotation angle of x and y axis tick labels. Default value are 0.
hideAxisTicks if TRUE, x and y axis ticks are hidden. Default value is FALSE.
axisLine a vector of length 3 indicating respectively the size, the line type and the color of axis lines. Default value is c(0.5, "solid", "#E5E5E5").

For more details follow this link : ggplot2.customize.

Main title and axis labels

# Change main title and axis titles
ggplot2.density(data=weight, xName=&#39;weight&#39;,
        mainTitle="Density curve",
        xtitle="Weight (kg)", ytitle="Density")

# Customize title styles. Possible values for the font style :
  # &#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.density(data=weight, xName=&#39;weight&#39;,
        xtitle="Weight (kg)", ytitle="Density",
        mainTitle="Density curve",
        mainTitleFont=c(14,"bold.italic", "red"),
        xtitleFont=c(14,"bold", "#993333"),
        ytitleFont=c(14,"bold", "#993333"))

# Hide x an y axis titles
ggplot2.density(data=weight, xName=&#39;weight&#39;,
         xShowTitle=FALSE, yShowTitle=FALSE)

r data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical software

Axis ticks

# Axis ticks labels and orientaion
ggplot2.density(data=weight, xName=&#39;weight&#39;,
        xShowTitle=FALSE, yShowTitle=FALSE,
        xTickLabelFont=c(14,"bold", "#993333"),
        yTickLabelFont=c(14,"bold", "#993333"),
        xtickLabelRotation=45, ytickLabelRotation=45)

# Hide axis tick labels
ggplot2.density(data=weight, xName=&#39;weight&#39;,
        xShowTitle=FALSE, yShowTitle=FALSE,
        xShowTickLabel=FALSE, yShowTickLabel=FALSE)

# Hide axis ticks
ggplot2.density(data=weight, xName=&#39;weight&#39;,
        xShowTitle=FALSE, yShowTitle=FALSE,
        xShowTickLabel=FALSE,
        yShowTickLabel=FALSE,hideAxisTicks=TRUE)

# AxisLine : a vector of length 3 indicating the size,
  #the line type and the color of axis lines
ggplot2.density(data=weight, xName=&#39;weight&#39;,
      axisLine=c(1, "solid", "darkblue"))

r data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical software

Background and colors

Change density plot background and fill colors

# Change background color to "white". Default is "gray"
ggplot2.density(data=weight, xName=&#39;weight&#39;,
        backgroundColor="white")

# Change background color to "lightblue" and grid color to "white"
ggplot2.density(data=weight, xName=&#39;weight&#39;,
        backgroundColor="lightblue", gridColor="white")

# Change plot fill color
ggplot2.density(data=weight, xName=&#39;weight&#39;,
         backgroundColor="white", densityFill=&#39;#FFAAD4&#39;)

# Remove grid; remove top and right borders around the plot;
# change  axis lines
ggplot2.density(data=weight, xName=&#39;weight&#39;,
        backgroundColor="white", densityFill=&#39;#FFAAD4&#39;,
        removePanelGrid=TRUE,removePanelBorder=TRUE,
        axisLine=c(0.5, "solid", "black"))

r data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical software

Change density plot color according to the group

Colors can be specified as a hexadecimal RGB triplet, such as "#FFCC00" or by names (e.g : "red" ). You can also use other color scales, such as ones taken from the RColorBrewer package. The different color systems available in R have been described in detail here.

To change density plot color according to the group, you have to specify the name of the data column containing the groups using the argument groupName. Use the argument groupColors, to specify colors by hexadecimal code or by name. In this case, the length of groupColors should be the same as the number of the groups. Use the argument brewerPalette, to specify colors using RColorBrewerpalette.

# Change group colors using hexadecimal colors
# alpha is the transparency level of overlaid color. 
  #The value can variate from 0 (total transparency) 
  #to 1 (no transparency)
ggplot2.density(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
                groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;),
                fillGroupDensity=TRUE, alpha=0.5,
                legendPosition="top")

# Change group colors using brewer palette: "Paired"
ggplot2.density(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
                brewerPalette="Paired",
                fillGroupDensity=TRUE, alpha=0.5,
                legendPosition="top")

r data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical software


Color can also be changed by using names as follow :

# Change group colors using color names
ggplot2.density(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
          groupColors=c(&#39;aquamarine3&#39;,&#39;goldenrod1&#39;),
          fillGroupDensity=TRUE, alpha=0.5 )


Legend

Legend position

# Change the legend position to "top" 
  # (possible values: "left","top", "right", "bottom")
ggplot2.density(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
                legendPosition="top")

# legendPosition can be also a numeric vector c(x, y)
ggplot2.density(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
                legendPosition=c(0.8,0.2))

# Remove legend
ggplot2.density(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
                showLegend=FALSE)

r data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical software

It is also possible to position the legend inside the plotting area. You have to indicate the x, y coordinates of legend box. x and y values must be between 0 and 1. c(0,0) corresponds to "bottom left" and c(1,1) corresponds to "top right" position.

Legend background color, title and text font styles

# Change legend background color, title and text font styles
ggplot2.density(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
      # legendTitleFont=c(size, style, color)
      legendTitle="Groups", legendTitleFont=c(10, "bold", "blue"),
      #legendTextFont=c(size, style, color)
      legendTextFont=c(10, "bold.italic", "red"),
      #legendBackground: c(fill, lineSize, lineType, lineColor)
      legendBackground=c("lightblue", 0.5, "solid", "darkblue" )
      )

r data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical software

Axis scales

Possible values for x axis scale are “none”, “log2” and log10. Default value is “none”.

# Change x axis limit
ggplot2.density(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
                showLegend=FALSE, xlim=c(60,72))

# x Log scale. Possible values="none", "log2" and "log10"
ggplot2.density(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
                showLegend=FALSE, xScale="log2")

r data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical software

Create a customized plots with few R code

#Customized density
ggplot2.density(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
      groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;), showLegend=FALSE,
      backgroundColor="white",
      xtitle="Weight (kg)", ytitle="Density", 
      mainTitle="Weight density curve \nper sex",
      addMeanLine=TRUE
      )

#Remove grid; Remove Top and right border around the plot 
#and fill each density curve according to the group
ggplot2.density(data=weight, xName=&#39;weight&#39;, groupName=&#39;sex&#39;,
    groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;), showLegend=FALSE,
    backgroundColor="white",
    xtitle="Weight (kg)", ytitle="Density", 
    mainTitle="Weight density curve \nper sex",
    removePanelGrid=TRUE,removePanelBorder=TRUE,
    axisLine=c(0.5, "solid", "black"),
    fillGroupDensity=TRUE, alpha=0.5)

r data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical softwarer data visualization with ggplot2 density plot : tutorial on how to use ggplot2.density function to easily make density curve with R statistical software

The argument alpha is used to specify the transparency of colors.

Facet : split a plot into a matrix of pannels

The facet approach splits a plot into a matrix of panels. Each panel shows a different subset of the data.

Facet with one variable

#Facet according to the sex variable
ggplot2.density(data=weight, xName=&#39;weight&#39;,
        groupName=&#39;sex&#39;, legendPosition="top",
        faceting=TRUE, facetingVarNames="sex") 

# Change the direction. possible values are "vertical", horizontal".
# default is vertical.
ggplot2.density(data=weight, xName=&#39;weight&#39;, 
        groupName=&#39;sex&#39;, legendPosition="top",
        faceting=TRUE, facetingVarNames="sex",
        facetingDirection="horizontal")

ggplot2 density and facet approch, one variableggplot2 density and facet approch, one variable

Faceting with two variables

The mtcars data is used in the following examples.

data(mtcars)
head(mtcars)
##                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
## Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1

mtcars (Motor Trend Car Road Tests) comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles.

#Facet by two variables: vs and am.
#Rows are vs and columns are am
ggplot2.density(data=mtcars, xName=&#39;mpg&#39;, groupName=&#39;vs&#39;,
        legendPosition="top",
        faceting=TRUE, facetingVarNames=c("vs", "am"))

# Facet by two variables: reverse the order of the 2 variables
#Rows are am and columns are vs
ggplot2.density(data=mtcars, xName=&#39;mpg&#39;, groupName=&#39;vs&#39;,
        legendPosition="top",
        faceting=TRUE, facetingVarNames=c("am", "vs"))

ggplot2 density plot and facet approch, two variablesggplot2 density plot and facet approch, two variables

Facet scales

By default, all the panels have the same scale (facetingScales="fixed"). They can be made independent, by setting scales to free, free_x, or free_y.

# Facet with free scales
ggplot2.density(data=mtcars, xName=&#39;mpg&#39;, groupName=&#39;vs&#39;,
      legendPosition="top",
      faceting=TRUE, facetingVarNames=c("vs", "am"),
      facetingScales="free")

ggplot2 density plot and facet approch, free scale

As you can see in the above plot, y axis have different scales in the different panels.

Facet label apperance

# Change facet text font. Possible values for the font style:
  #&#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.density(data=mtcars, xName=&#39;mpg&#39;, groupName=&#39;vs&#39;,
        legendPosition="top",
        faceting=TRUE, facetingVarNames=c("vs", "am"),
        facetingFont=c(12, &#39;bold.italic&#39;, "red"))

# Change the apperance of the rectangle around facet label
ggplot2.density(data=mtcars, xName=&#39;mpg&#39;, groupName=&#39;vs&#39;,
      legendPosition="top",
      faceting=TRUE, facetingVarNames=c("vs", "am"),
      facetingRect=list(background="white", lineType="solid",
                      lineColor="black", lineSize=1.5)
      )

ggplot2 density plot and facet approch, facet labelggplot2 density plot and facet approch, facet label

ggplot2.density function

Description

Plot easily a density plot with R package easyGgplot2.

usage

ggplot2.density(data, xName, groupName=NULL,
      addMeanLine=FALSE, meanLineColor=NULL,
      meanLineType="dashed", meanLineSize=1,
      densityFill=NULL,fillGroupDensity=FALSE,
      colorGroupDensityLine=FALSE,
      groupColors=NULL, brewerPalette=NULL,faceting=FALSE,...)

Arguments

Arguments Descriptions
data data.frame or a numeric vector. Columns are variables and rows are observations.
xName The name of column containing x variable. Default value is NULL.
groupName The name of column containing group variable. This variable is used to color plot according to the group.
addMeanLine If TRUE, the mean line is added on the plot for each group. Default value is FALSE.
meanLineColor, meanLineType, meanLineSize mean line color, type and size.
densityFill Fill color of density plot. This is only considered when groupName=NULL.
fillGroupDensity If TRUE, density curve of each group is filled. Default value is FALSE.
colorGroupDensityLine If TRUE, density curve line are colored. Default value is FALSE.
groupColors Color of groups. groupColors should have the same length as groups.
brewerPalette This can be also used to indicate group colors. In this case the parameter groupColors should be NULL. e.g: brewerPalette=“Paired”.
…. Other arguments passed on to ggplot2.customize custom function or to geom_density functions from ggplot2 package.

The other arguments which can be used are described at this link : ggplot2 customize. They are used to customize the plot (axis, title, background, color, legend, ….) generated using ggplot2 or easyGgplot2 R package.

Examples

library(easyGgplot2)
#plot
ggplot2.density(data=weight, xName=&#39;weight&#39;,groupName=&#39;sex&#39;,
          groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;),
          mainTitle="Density plot \nper sex",
          xtitle="Weight (kg)", ytitle="Density")

#Or use this
plot<-ggplot2.density(data=weight, xName=&#39;weight&#39;,groupName=&#39;sex&#39;,
                  groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;))
plot<-ggplot2.customize(plot, 
          mainTitle="Density plot \nper sex",
          xtitle="Weight (kg)", ytitle="Density")
print(plot)

Easy ggplot2 ebook

Note that an eBook is available on easyGgplot2 package here.

By Alboukadel Kassambara

Copyright 2014 Alboukadel Kassambara. All rights reserved.

Published by STHDA (https://www.sthda.com/english).

September 2014 : First edition.

Licence : This document is under creative commons licence (http://creativecommons.org/licenses/by-nc-sa/3.0/).

Contact : Alboukadel Kassambara alboukadel.kassambara@gmail.com


Infos

This analysis was performed using R (ver. 3.1.0), easyGgplot2 (ver 1.0.0) and ggplot2 (ver 1.0.0).

]]> Fri, 06 Feb 2015 20:50:43 +0100 <![CDATA[ggplot2.lineplot : Easy line plots in R software using ggplot2]]> https://www.sthda.com/english/wiki/ggplot2-lineplot-easy-line-plots-in-r-software-using-ggplot2 https://www.sthda.com/english/wiki/ggplot2-lineplot-easy-line-plots-in-r-software-using-ggplot2

Introduction

ggplot2.lineplot is an easy to use function to generate line plots in R software using ggplot2 plotting system. It can also be used to customize quickly the plot parameters including main title, axis labels, legend, background and colors. ggplot2.lineplot function is from easyGgplot2 package. An R script is available in the next section to install the package.

The aim of this article is to show you step by step, how to plot and customize a line plot using ggplot2.lineplot function.

At the end of this tutorial you will be able to draw, with few R code, the following plots:

Data visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical software

ggplot2.lineplot function is described in detail at the end of this document.

Install and load easyGgplot2 package

easyGgplot2 R package can be installed as follow :

install.packages("devtools")
library(devtools)
install_github("easyGgplot2", "kassambara")

Load the package using this R code :

library(easyGgplot2)

Data format

The data must be a numeric vector or a data.frame (columns are variables and rows are observations).

restaurant data from easyGgplot2 package will be used in the following example.

#data.frame
head(restaurant)
##      sex   time total_bill
## 1 Female  Lunch      13.53
## 2 Female Dinner      16.81
## 3   Male  Lunch      16.24
## 4   Male Dinner      17.42

Basic line plot

A subset of restaurant data is used :

df<-restaurant[c(1,4), c("time", "total_bill")]
head(df)
##     time total_bill
## 1  Lunch      13.53
## 4 Dinner      17.42
# Basic line plot of the values of "total_bill" variables
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;)

# Add points
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;, 
                 addPoint=TRUE)

# Use the arrow parameter to add an arrow to the line
# See ?grid::arrow for more details
rquery.loadPackages("grid")
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;,
                 arrow=arrow())

# Add closed arrow to the to end of the line
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;,
                 arrow=arrow(angle = 15, ends = "both",
                             type = "closed")
                 )

Data visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical software

Change the line type and the point shape of line plot

Different point shapes and line types can be used in the plot. By default, ggplot2 uses solid line type and circle shape.

# Change color of both line and points
# Change line type and point type, 
  #and use thicker line and larger points
# Change points to circles with white fill
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;,
             #line color, type and size
             color="red", linetype="dotted", size=1.5,
             addPoint=TRUE, pointSize=4, pointShape=21,
             pointFill="white", pointColor="red")

Data visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical software

Lineplot with more variables

This data will be used for the examples below:

df1 <- restaurant
head(df1)
##      sex   time total_bill
## 1 Female  Lunch      13.53
## 2 Female Dinner      16.81
## 3   Male  Lunch      16.24
## 4   Male Dinner      17.42
# Plot of variable &#39;total_bill&#39; according to xName &#39;time&#39;.
# The plot is colored by the groupName &#39;sex&#39;
# line type and point shape are set by the groupName &#39;sex&#39;
ggplot2.lineplot(data=df1, xName=&#39;time&#39;, yName="total_bill",
               groupName=&#39;sex&#39;, addPoint=TRUE,
               legendPosition="top")

# Use the same linetype and point shape 
# but different colors by group
ggplot2.lineplot(data=df1, xName=&#39;time&#39;, yName="total_bill",
                 groupName=&#39;sex&#39;,legendPosition="top",
                 linetype="solid",addPoint=TRUE, shape=19)

# Basic plot with black color and
# differents point shapes by group
ggplot2.lineplot(data=df1, xName=&#39;time&#39;, yName="total_bill", 
             groupName=&#39;sex&#39;,legendPosition="top",
             linetype="solid",addPoint=TRUE, color="black")

# Set point shape manually
ggplot2.lineplot(data=df1, xName=&#39;time&#39;, yName="total_bill",
       groupName=&#39;sex&#39;,legendPosition="top",
       linetype="solid",addPoint=TRUE, color="black",
       pointSize=3, pointFill="white")+
              scale_shape_manual(values=c(22,21))

Data visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical software

Customize your line plot

Parameters

The arguments that can be used to customize x and y axis are listed below :

Parameters Description
mainTitle the title of the plot
mainTitleFont a vector of length 3 indicating respectively the size, the style (“italic”, “bold”, “bold.italic”) and the color of x and y axis titles. Default value is: mainTitleFont=c(14, “bold”, “black”).
xShowTitle, yShowTitle if TRUE, x and y axis titles will be shown. Set the value to FALSE to hide axis labels. Default values are TRUE.
xtitle, ytitle x and y axis labels. Default values are NULL.
xtitleFont, ytitleFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis titles. Possible values for the style:“plain”, “italic”, “bold”, “bold.italic”. Color can be specified as an hexadecimal code (e.g: “#FFCC00”) or by the name (e.g : “red”, “green”). Default values are xtitleFont=c(14,"bold", "black"), ytitleFont=c(14,"bold", "black").
xlim, ylim limit for the x and y axis. Default values are NULL.
xScale, yScale x and y axis scales. Possible values : c(“none”, “log2”, “log10”). e.g: yScale=“log2”. Default values are NULL.
xShowTickLabel, yShowTickLabel if TRUE, x and y axis tick mark labels will be shown. Default values are TRUE.
xTickLabelFont, yTickLabelFont a vector of length 3 indicating respectively the size, the style and the color of x and y axis tick label fonts. Default value are xTickLabelFont=c(12, "bold", "black"), yTickLabelFont=c(12, "bold", "black").
xtickLabelRotation, ytickLabelRotation Rotation angle of x and y axis tick labels. Default value are 0.
hideAxisTicks if TRUE, x and y axis ticks are hidden. Default value is FALSE.
axisLine a vector of length 3 indicating respectively the size, the line type and the color of axis lines. Default value is c(0.5, "solid", "#E5E5E5").

For more details follow this link : ggplot2.customize.

Main title and axis labels

# Change main title and axis titles
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;,
                mainTitle="Total bill\n per time of day",
                xtitle="Time of day", ytitle="Total bill")

# Customize title styles. Possible values for the font style :
  # &#39;plain&#39;, &#39;italic&#39;, &#39;bold&#39;, &#39;bold.italic&#39;.
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;,
                mainTitle="Total bill\n per time of day",
                xtitle="Time of day", ytitle="Total bill",
                mainTitleFont=c(14,"bold.italic", "red"),
                xtitleFont=c(14,"bold", "#993333"),
                ytitleFont=c(14,"bold", "#993333"))

# Hide x an y axis titles
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;,
                xShowTitle=FALSE, yShowTitle=FALSE)

Data visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical software

Axis ticks

# Axis ticks labels and orientaion
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;,
            xShowTitle=FALSE, yShowTitle=FALSE,
            xTickLabelFont=c(14,"bold", "#993333"),
            yTickLabelFont=c(14,"bold", "#993333"),
            xtickLabelRotation=45, ytickLabelRotation=45)

# Hide axis tick labels
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;,
          xShowTitle=FALSE, yShowTitle=FALSE,
          xShowTickLabel=FALSE, yShowTickLabel=FALSE)

# Hide axis ticks
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;,
          xShowTitle=FALSE, yShowTitle=FALSE,
          xShowTickLabel=FALSE, yShowTickLabel=FALSE,
          hideAxisTicks=TRUE)

# AxisLine : a vector of length 3 indicating the size,
  #the line type and the color of axis lines
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;,
                 axisLine=c(1, "solid", "darkblue"))

Data visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical software

Background and colors

Change line plot background and fill colors

# change background color to "white". Default is "gray"
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;,
            backgroundColor="white")

# Change background color to "lightblue" and grid color to "white"
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;,
            backgroundColor="lightblue", gridColor="white")

# Remove grid; Remove Top and right border around the plot
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;,
          backgroundColor="white", fill=&#39;#FFAAD4&#39;, color="black",
          removePanelGrid=TRUE,removePanelBorder=TRUE,
          axisLine=c(0.5, "solid", "black"))

Data visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical software

Change line plot color according to the group

Colors can be specified as a hexadecimal RGB triplet, such as "#FFCC00" or by names (e.g : "red" ). You can also use other color scales, such as ones taken from the RColorBrewer package. The different color systems available in R have been described in detail here.

To change line plot color according to the group, you have to specify the name of the data column containing the groups using the argument groupName. Use the argument groupColors, to specify colors by hexadecimal code or by name. In this case, the length of groupColors should be the same as the number of the groups. Use the argument brewerPalette, to specify colors using RColorBrewerpalette.

# Color the line plot by groupName
ggplot2.lineplot(data=df1, xName=&#39;time&#39;, yName="total_bill",
          groupName=&#39;sex&#39;, size=2)

# Change group colors using hexadecimal colors;
ggplot2.lineplot(data=df1, xName=&#39;time&#39;, yName="total_bill",
          groupName=&#39;sex&#39;,size=2,
          backgroundColor="white", #background and line colors
          groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;))
                

# Change group colors using brewer palette: "Paired"
ggplot2.lineplot(data=df1, xName=&#39;time&#39;, yName="total_bill",
          groupName=&#39;sex&#39;,size=2,
          backgroundColor="white", #background and line colors
          brewerPalette="Paired")

# Change group colors using color names
ggplot2.lineplot(data=df1, xName=&#39;time&#39;, yName="total_bill",
          groupName=&#39;sex&#39;,size=2,
          backgroundColor="white", #background and line colors
          groupColors=c(&#39;aquamarine3&#39;,&#39;chartreuse1&#39;))

Data visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical software

Legend

Legend position

# Change the legend position to "top" 
  # (possible values: "left","top", "right", "bottom")
ggplot2.lineplot(data=df1, xName=&#39;time&#39;, yName="total_bill",
                groupName=&#39;sex&#39;,legendPosition="top")
                
# legendPosition can be also a numeric vector c(x, y)
ggplot2.lineplot(data=df1, xName=&#39;time&#39;, yName="total_bill",
                groupName=&#39;sex&#39;, legendPosition=c(0.8,0.2))
                
# Remove plot legend
ggplot2.lineplot(data=df1, xName=&#39;time&#39;, yName="total_bill",
                 groupName=&#39;sex&#39;,showLegend=FALSE)

Data visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical software

It is also possible to position the legend inside the plotting area. You have to indicate the x, y coordinates of legend box. x and y values must be between 0 and 1. c(0,0) corresponds to “bottom left” and c(1,1) corresponds to “top right” position.

Legend background color, title and text font styles

# Change legend background color, title and text font styles
ggplot2.lineplot(data=df1, xName=&#39;time&#39;, yName="total_bill",
        groupName=&#39;sex&#39;,
        #legendPosition=c("right", "left","top", "bottom")
        legendPosition="right",
        #legendTitleFont=c(size, style, color)
        legendTitle="Time", legendTitleFont=c(10, "bold", "blue"),
        #legendTextFont=c(size, style, color)
        legendTextFont=c(10, "bold.italic", "red"),
        #legendBackground: c(fill, lineSize, lineType, lineColor)
        legendBackground=c("lightblue", 0.5, "solid", "darkblue" )
        )

Data visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical software

Axis scales

Possible values for y axis scale are “none”, “log2” and log10. Default value is “none”.

# Change x axis limit
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;,
                 ylim=c(0,18))
                
# y Log scale. yScale="log2". 
# Possible value="none", "log2" and "log10"
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;,
                 yScale="log2")

Data visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical software

Create a customized plots with few R code

# Customized lineplot
ggplot2.lineplot(data=df1, xName="time", yName=&#39;total_bill&#39;,
        groupName="sex",size=2,
        addPoint=TRUE, pointSize=4, backgroundColor="white",
        color="black",
        xtitle="Time of day", ytitle="Total bill",
        mainTitle="Total bill per time of day")           
               
# Remove grid; Remove Top and right border around the plot;
# different colors per group 
ggplot2.lineplot(data=df1, xName="time", yName=&#39;total_bill&#39;, 
        groupName="sex",size=2,
        addPoint=TRUE, pointSize=4, backgroundColor="white", 
        groupColors=c(&#39;#999999&#39;,&#39;#E69F00&#39;),
        xtitle="Time of day", ytitle="Total bill",
        mainTitle="Total bill per time of day",            
        removePanelGrid=TRUE,removePanelBorder=TRUE,
        axisLine=c(0.5, "solid", "black"))

ggplot2 lineplotggplot2 lineplot

Line plot with a numeric x-axis

When the variable on the x-axis is numeric, it is sometimes useful to treat it as continuous, and sometimes useful to treat it as categorical. In this data set, the dose is a numeric variable with values 0.5, 1.0, and 2.0. It might be useful to treat these values as equal categories when making a graph.

This is derived from the ToothGrowth dataset included with R.

dfn <- read.table(header=T, text=&#39;
supp dose length
  OJ  0.5  13.23
  OJ  1.0  22.70
  OJ  2.0  26.06
  VC  0.5   7.98
  VC  1.0  16.77
  VC  2.0  26.14
&#39;)

# line plot with x-axis treated as continuous
ggplot2.lineplot(data=dfn, xName="dose", yName=&#39;length&#39;,
               groupName="supp", 
               addPoint=TRUE, xAxisType="continuous")

# With x-axis treated as categorical
ggplot2.lineplot(data=dfn, xName="dose", yName=&#39;length&#39;,
               groupName="supp", 
               addPoint=TRUE, xAxisType="categorical")

Data visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical softwareData visualization with ggplot2 line plots : tutorial on how to use ggplot2.lineplot function to easily make line graphs using ggplot2 and R statistical software

ggplot2.lineplot function

Description

Plot easily a line plot using easyGgplot2 R package.

usage

ggplot2.lineplot(data, xName, yName,  groupName=NULL,
         addPoint=FALSE, pointSize=1.5, pointShape=19,
         pointFill=NULL, pointColor="black",
         arrow=NULL, xAxisType=c("categorical", "continuous"),                     
         groupColors=NULL, brewerPalette=NULL,...)

Arguments

Arguments Descriptions
data data.frame or a numeric vector. Columns are variables and rows are observations.
xName The name of column containing x variable (i.e groups).
yName The name of column containing y variable.
groupName The name of column containing group variable. This variable is used to color plot according to the group.
addPoint If TRUE, points are added to the plot. Default value is FALSE.
pointSize, pointShape, pointFill, pointColor Modify point size, shape, fill and color.
arrow arrow is function from package grid. This function Describe arrows to add to a line. See ?grid::arrow for more details. Usage : arrow=arrow(). Default value is NULL.
xAxisType Indicate the type of x-axis. Possible values =c(“categorical”, “continuous”). Default value is “categorical”. When the variable on the x-axis is numeric, it is sometimes useful to treat it as continuous, and sometimes useful to treat it as categorical.
groupColors Color of groups. groupColors should have the same length as groups.
brewerPalette This can be also used to indicate group colors. In this case the parameter groupColors should be NULL. e.g: brewerPalette=“Paired”.
…. Other arguments passed on to ggplot2.customize custom function or to geom_line functions from ggplot2 package.

The other arguments which can be used are described at this link : ggplot2 customize. They are used to customize the plot (axis, title, background, color, legend, ….) generated using ggplot2 or easyGgplot2 R package.

Examples

library(easyGgplot2)
#data
df<-restaurant[c(1,4), c("time", "total_bill")]

#plot
ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;,
          mainTitle="Plot of total bill\n per time of day",
          xtitle="Time of day", ytitle="Total bill")

#Or use this
plot<-ggplot2.lineplot(data=df, xName="time", yName=&#39;total_bill&#39;)
plot<-ggplot2.customize(plot,  
          mainTitle="Plot of total bill\n per time of day",
          xtitle="Time of day", ytitle="Total bill")
print(plot)

Easy ggplot2 ebook

Note that an eBook is available on easyGgplot2 package here.

By Alboukadel Kassambara

Copyright 2014 Alboukadel Kassambara. All rights reserved.

Published by STHDA (https://www.sthda.com/english).

September 2014 : First edition.

Licence : This document is under creative commons licence (http://creativecommons.org/licenses/by-nc-sa/3.0/).

Contact : Alboukadel Kassambara alboukadel.kassambara@gmail.com


Infos

This analysis was performed using R (ver. 3.1.0), easyGgplot2 (ver 1.0.0) and ggplot2 (ver 1.0.0).

]]> Fri, 06 Feb 2015 20:49:06 +0100