Easy Guides

R Base Graphs

Mon, 26 Sep 2016 06:55:40 +0200

Previously, we described the essentials of R programming and provided quick start guides for importing data into R.

This chapter contains articles describring how to visualize data using R base graphs.

How this chapter is organized?

Creating and saving graphs

Creating graphs
Saving graphs
File formats for exporting plots

create and save plots

Read more: —> Creating and Saving Graphs in R.

Generic plot types in R

Read more: —> Generic plot types in R.

Scatter plots

R base scatter plot: plot()
Enhanced scatter plots: car::scatterplot()
3D scatter plots

Scatter plot matrices

R base scatter plot matrices: pairs()
Use the R package psych

Box plots

R base box plots: boxplot()
Box plot with the number of observations: gplots::boxplot2()

Strip Charts: 1-D scatter Plots

Read more —> Strip Charts: 1-D scatter Plots.

Bar plots

Basic bar plots
- Change group names
- Change color
- Change main title and axis labels
Stacked bar plots
Grouped bar plots

Line plots

R base functions: plot() and lines()
Basic line plots
Plots with multiple lines

Pie charts

Create basic pie charts: pie()
Create 3D pie charts: plotix::pie3D()

Histogram and density plots

Create histogram plots: hist()
Create density plots: density()

## [1] 48.96467 56.38715 60.42221 43.27151 57.14562 57.53028

Read more —> Histogram and Density Plots.

QQ-plots: Quantile-Quantile plots

Use qqPlot() [in car package]:

Read more —> QQ-plots: Quantile-Quantile plots.

Dot charts

R base function: dotchart()
Dot chart of one numeric vector
Dot chart of a matrix

Plot group means and confidence intervals

R base graphical parameters

Add and customize titles
Add legends
Add texts
Add straight lines
Add an axis to a plot
Change axis scale : minimum, maximum and log scale
Customize tick mark labels
Change plotting symbols
Change line types
Change colors

Read more —> R base graphical Parameters.

Infos

This analysis has been performed using R statistical software (ver. 3.2.4).

QQ-plots: Quantile-Quantile plots - R Base Graphs

Sun, 25 Sep 2016 21:26:25 +0200

Pleleminary tasks
Example data
Create QQ plots
Related articles
See also
Infos

Previously, we described the essentials of R programming and provided quick start guides for importing data into R.

Here, we’ll describe how to create quantile-quantile plots in R. QQ plot (or quantile-quantile plot) draws the correlation between a given sample and the normal distribution. A 45-degree reference line is also plotted. QQ plots are used to visually check the normality of the data.

Pleleminary tasks

Launch RStudio as described here: Running RStudio and setting up your working directory
Prepare your data as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files
Import your data into R as described here: Fast reading of data from txt|csv files into R: readr package.

Example data

Here, we’ll use the built-in R data set named ToothGrowth.

# Store the data in the variable my_data
my_data <- ToothGrowth

Create QQ plots

The R base functions qqnorm() and qqplot() can be used to produce quantile-quantile plots:

qqnorm(): produces a normal QQ plot of the variable
qqline(): adds a reference line

qqnorm(my_data$len, pch = 1, frame = FALSE)
qqline(my_data$len, col = "steelblue", lwd = 2)

It’s also possible to use the function qqPlot() [in car package]:

library("car")
qqPlot(my_data$len)

As all the points fall approximately along this reference line, we can assume normality.

Infos

This analysis has been performed using R statistical software (ver. 3.2.4).

Plot Group Means and Confidence Intervals - R Base Graphs

Fri, 06 May 2016 07:17:35 +0200

Pleleminary tasks
Data
Plot group means
Related articles
See also
Infos

Previously, we described the essentials of R programming and provided quick start guides for importing data into R.

Here, we’ll describe how to create mean plots with confidence intervals in R.

Pleleminary tasks

Launch RStudio as described here: Running RStudio and setting up your working directory
Prepare your data as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files
Import your data into R as described here: Fast reading of data from txt|csv files into R: readr package.

Here, we’ll use the R built-in ToothGrowth data set.

Data

head(ToothGrowth)

##    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

Plot group means

The function plotmeans() [in gplots package] can be used.

library(gplots)
# Plot the mean of teeth length by dose groups
plotmeans(len ~ dose, data = ToothGrowth, frame = FALSE)

# Add mean labels (mean.labels = TRUE)
# Remove line connection (connect = FALSE)
plotmeans(len ~ dose, data = ToothGrowth, frame = FALSE,
          mean.labels = TRUE, connect = FALSE)

Infos

This analysis has been performed using R statistical software (ver. 3.2.4).

Dot Charts - R Base Graphs

Fri, 06 May 2016 07:13:24 +0200

Pleleminary tasks
Data
R base function: dotchart()
Dot chart of one numeric vector
Dot chart of a matrix
Related articles
See also
Infos

Previously, we described the essentials of R programming and provided quick start guides for importing data into R.

Here, we’ll describe how to draw a Cleveland dot plot in R.

Pleleminary tasks

Launch RStudio as described here: Running RStudio and setting up your working directory
Prepare your data as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files
Import your data into R as described here: Fast reading of data from txt|csv files into R: readr package.

Here, we’ll use the R built-in mtcars data set.

Data

We’ll use mtcars data sets. We start by ordering the data set according to mpg variable.

mtcars <- mtcars[order(mtcars$mpg), ]

R base function: dotchart()

The function dotchart() is used to draw a cleveland dot plot.

dotchart(x, labels = NULL, groups = NULL, 
         gcolor = par("fg"), color = par("fg"))

x: numeric vector or matrix
labels: a vector of labels for each point.
groups: a grouping variable indicating how the elements of x are grouped.
gcolor: color to be used for group labels and values.
color: the color(s) to be used for points and labels.

Dot chart of one numeric vector

# Dot chart of a single numeric vector
dotchart(mtcars$mpg, labels = row.names(mtcars),
         cex = 0.6, xlab = "mpg")

# Plot and color by groups cyl
grps <- as.factor(mtcars$cyl)
my_cols <- c("#999999", "#E69F00", "#56B4E9")
dotchart(mtcars$mpg, labels = row.names(mtcars),
         groups = grps, gcolor = my_cols,
         color = my_cols[grps],
         cex = 0.6,  pch = 19, xlab = "mpg")

Dot chart of a matrix

dotchart(VADeaths, cex = 0.6,
         main = "Death Rates in Virginia - 1940")

Infos

This analysis has been performed using R statistical software (ver. 3.2.4).

Histogram and Density Plots - R Base Graphs

Fri, 06 May 2016 07:05:12 +0200

Pleleminary tasks
Create some data
Create histogram plots: hist()
Create density plots: density()
Related articles
See also
Infos

Previously, we described the essentials of R programming and provided quick start guides for importing data into R.

Here, we’ll describe how to create histogram and density plots in R.

Pleleminary tasks

Launch RStudio as described here: Running RStudio and setting up your working directory
Prepare your data as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files
Import your data into R as described here: Fast reading of data from txt|csv files into R: readr package.

Create some data

The data set contains the value of weight by sex for 200 individuals.

set.seed(1234)
x <- c(rnorm(200, mean=55, sd=5),
     rnorm(200, mean=65, sd=5))
head(x)

## [1] 48.96467 56.38715 60.42221 43.27151 57.14562 57.53028

Create histogram plots: hist()

A histogram can be created using the function hist(), which simplified format is as follow:

hist(x, breaks = "Sturges")

x: a numeric vector
breaks: breakpoints between histogram cells.

Create histograms

hist(x, col = "steelblue", frame = FALSE)

# Change the number of breaks
hist(x, col = "steelblue", frame = FALSE,
     breaks = 30)

Create density plots: density()

The function density() is used to estimate kernel density.

# Compute the density data
dens <- density(mtcars$mpg)
# plot density
plot(dens, frame = FALSE, col = "steelblue", 
     main = "Density plot of mpg")

# Fill the density plot using polygon()
plot(dens, frame = FALSE, col = "steelblue", 
     main = "Density plot of mpg") 
polygon(dens, col = "steelblue")

Infos

This analysis has been performed using R statistical software (ver. 3.2.4).

Pie Charts - R Base Graphs

Fri, 06 May 2016 06:57:51 +0200

Pleleminary tasks
Create some data
Create basic pie charts: pie()
Create 3D pie charts: plotix::pie3D()
Related articles
See also
Infos

Previously, we described the essentials of R programming and provided quick start guides for importing data into R.

Here, we’ll describe how to create pie charts in R. The R base function pie() can be used for this.

Pleleminary tasks

Launch RStudio as described here: Running RStudio and setting up your working directory
Prepare your data as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files
Import your data into R as described here: Fast reading of data from txt|csv files into R: readr package.

Create some data

df <- data.frame(
  group = c("Male", "Female", "Child"),
  value = c(25, 25, 50)
  )

df

##    group value
## 1   Male    25
## 2 Female    25
## 3  Child    50

Create basic pie charts: pie()

The function pie() can be used to draw a pie chart.

pie(x, labels = names(x), radius = 0.8)

x: a vector of non-negative numerical quantities. The values in x are displayed as the areas of pie slices.
labels: character strings giving names for the slices.
radius: radius of the pie circle. If the character strings labeling the slices are long it may be necessary to use a smaller radius.

pie(df$value, labels = df$group, radius = 1)

# Change colors
pie(df$value, labels = df$group, radius = 1,
    col = c("#999999", "#E69F00", "#56B4E9"))

Create 3D pie charts: plotix::pie3D()

Te function pie3D()[in plotrix package] can be used to draw a 3D pie chart.

Install plotrix package:

install.packages("plotrix")

Use pie3D():

# 3D pie chart
library("plotrix")
pie3D(df$value, labels = df$group, radius = 1.5, 
      col = c("#999999", "#E69F00", "#56B4E9"))

# Explode the pie chart
pie3D(df$value, labels = df$group, radius = 1.5,
      col = c("#999999", "#E69F00", "#56B4E9"),
      explode = 0.1)

Infos

This analysis has been performed using R statistical software (ver. 3.2.4).

Line Plots - R Base Graphs

Fri, 06 May 2016 06:51:08 +0200

Pleleminary tasks
R base functions: plot() and lines()
Create some data
Basic line plots
Plots with multiple lines
Related articles
See also
Infos

Previously, we described the essentials of R programming and provided quick start guides for importing data into R.

Here, we’ll describe how to create line plots in R. The function plot() or lines() can be used to create a line plot.

Pleleminary tasks

Launch RStudio as described here: Running RStudio and setting up your working directory
Prepare your data as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files
Import your data into R as described here: Fast reading of data from txt|csv files into R: readr package.

R base functions: plot() and lines()

The simplified format of plot() and lines() is as follow.

plot(x, y, type = "l", lty = 1)

lines(x, y, type = "l", lty = 1)

x, y: coordinate vectors of points to join
type: character indicating the type of plotting. Allowed values are:
- “p” for points
- “l” for lines
- “b” for both points and lines
- “c” for empty points joined by lines
- “o” for overplotted points and lines
- “s” and “S” for stair steps
- “n” does not produce any points or lines
lty: line types. Line types can either be specified as an integer (0=blank, 1=solid (default), 2=dashed, 3=dotted, 4=dotdash, 5=longdash, 6=twodash) or as one of the character strings “blank”, “solid”, “dashed”, “dotted”, “dotdash”, “longdash”, or “twodash”, where “blank” uses ‘invisible lines’ (i.e., does not draw them).

Create some data

# Create some variables
x <- 1:10
y1 <- x*x
y2  <- 2*y1

We’ll plot a plot with two lines: lines(x, y1) and lines(x, y2).

Note that the function lines() can not produce a plot on its own. However, it can be used to add lines() on an existing graph. This means that, first you have to use the function plot() to create an empty graph and then use the function lines() to add lines.

Basic line plots

# Create a basic stair steps plot 
plot(x, y1, type = "S")

# Show both points and line
plot(x, y1, type = "b", pch = 19, 
     col = "red", xlab = "x", ylab = "y")

Plots with multiple lines

# Create a first line
plot(x, y1, type = "b", frame = FALSE, pch = 19, 
     col = "red", xlab = "x", ylab = "y")

# Add a second line
lines(x, y2, pch = 18, col = "blue", type = "b", lty = 2)

# Add a legend to the plot
legend("topleft", legend=c("Line 1", "Line 2"),
       col=c("red", "blue"), lty = 1:2, cex=0.8)

Infos

This analysis has been performed using R statistical software (ver. 3.2.4).

Bar Plots - R Base Graphs

Fri, 06 May 2016 06:43:10 +0200

Pleleminary tasks
Basic bar plots
Stacked bar plots
Grouped bar plots
Related articles
See also
Infos

Previously, we described the essentials of R programming and provided quick start guides for importing data into R.

Here, we’ll describe how to create bar plots in R. The function barplot() can be used to create a bar plot with vertical or horizontal bars.

Pleleminary tasks

Launch RStudio as described here: Running RStudio and setting up your working directory
Prepare your data as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files
Import your data into R as described here: Fast reading of data from txt|csv files into R: readr package.

Here, we’ll use the R built-in VADeaths data set.

Data set: VADeaths

# Data set
VADeaths

##       Rural Male Rural Female Urban Male Urban Female
## 50-54       11.7          8.7       15.4          8.4
## 55-59       18.1         11.7       24.3         13.6
## 60-64       26.9         20.3       37.0         19.3
## 65-69       41.0         30.9       54.6         35.1
## 70-74       66.0         54.3       71.1         50.0

# Subset
x <- VADeaths[1:3, "Rural Male"]
x

## 50-54 55-59 60-64 
##  11.7  18.1  26.9

Basic bar plots

# Bar plot of one variable
barplot(x)

# Horizontal bar plot
barplot(x, horiz = TRUE)

Change group names

barplot(x, names.arg = c("A", "B", "C"))

Change color

# Change border and fill color using one single color
barplot(x, col = "white", border = "steelblue")

# Change the color of border.
#  Use different colors for each group
barplot(x, col = "white",
        border = c("#999999", "#E69F00", "#56B4E9"))

# Change fill color : single color
barplot(x, col = "steelblue")

# Change fill color: multiple colors
barplot(x, col = c("#999999", "#E69F00", "#56B4E9"))

Change main title and axis labels

# Change axis titles
# Change color (col = "gray") and remove frame
barplot(x, main = "Death Rates in Virginia",
        xlab = "Age", ylab = "Rate")

Stacked bar plots

barplot(VADeaths,
         col = c("lightblue", "mistyrose", "lightcyan", 
                 "lavender", "cornsilk"),
        legend = rownames(VADeaths))

Grouped bar plots

barplot(VADeaths,
         col = c("lightblue", "mistyrose", "lightcyan", 
                 "lavender", "cornsilk"),
        legend = rownames(VADeaths), beside = TRUE)

It’s also possible to add legends to a plot using the function legend() as follow.

# Define a set of colors
my_colors <- c("lightblue", "mistyrose", "lightcyan", 
                 "lavender", "cornsilk")
# Bar plot
barplot(VADeaths, col = my_colors, beside = TRUE)
# Add legend
legend("topleft", legend = rownames(VADeaths), 
       fill = my_colors, box.lty = 0, cex = 0.8)

box.lty = 0: Remove the box around the legend
cex = 0.8: legend text size

Infos

This analysis has been performed using R statistical software (ver. 3.2.4).

Strip charts: 1-D scatter plots - R Base Graphs

Thu, 05 May 2016 23:24:23 +0200

Pleleminary tasks
R base function: stripchart()
Create strip charts
Related articles
See also
Infos

Previously, we described the essentials of R programming and provided quick start guides for importing data into R.

Here, we’ll describe how to create strip charts (i.e., one dimensional scatter plots or dot plots) in R. These plots are a good alternative to box plots when sample sizes are small.

Pleleminary tasks

Launch RStudio as described here: Running RStudio and setting up your working directory
Prepare your data as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files
Import your data into R as described here: Fast reading of data from txt|csv files into R: readr package.

Here, we’ll use the R built-in ToothGrowth data set.

ToothGrowth$dose <- as.factor(ToothGrowth$dose)

# Print the first 6 rows
head(ToothGrowth, 6)

##    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

R base function: stripchart()

stripchart(x, data = NULL method = "overplot", jitter = 0.1)

x: the data from which the plots are to be produced. Allowed values are one or a list of numeric vector, each corresponding to a component plot.
data: a data.frame (or list) from which the variables in x should be taken.
Method: the method to be used to separate coincident points. Allowed values are one of “overplot”, “jitter” or “stack”.
jitter: when method = “jitter” is used, jitter gives the amount of jittering applied.

Create strip charts

# Plot len by dose
stripchart(len ~ dose, data = ToothGrowth,
         pch = 19, frame = FALSE)

# Vertical plot using method = "jitter"
stripchart(len ~ dose, data = ToothGrowth,
         pch = 19, frame = FALSE, vertical = TRUE,
         method = "jitter")

# Change point shapes (pch) and colors by groups
# add main title and axis labels
stripchart(len ~ dose, data = ToothGrowth,
         frame = FALSE, vertical = TRUE,
         method = "jitter", pch = c(21, 18, 16),
         col = c("#999999", "#E69F00", "#56B4E9"),
         main = "Length by dose", xlab = "Dose", ylab = "Length")

Infos

This analysis has been performed using R statistical software (ver. 3.2.4).

Box Plots - R Base Graphs

Thu, 05 May 2016 23:19:03 +0200

Pleleminary tasks
R base box plots: boxplot()
Box plot with the number of observations: gplots::boxplot2()
Summary
Related articles
See also
Infos

Previously, we described the essentials of R programming and provided quick start guides for importing data into R.

Here, we’ll describe how to create box plots in R.

Pleleminary tasks

Launch RStudio as described here: Running RStudio and setting up your working directory
Prepare your data as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files
Import your data into R as described here: Fast reading of data from txt|csv files into R: readr package.

Here, we’ll use the R built-in ToothGrowth data set.

# Print the first 6 rows
head(ToothGrowth, 6)

##    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

R base box plots: boxplot()

Draw a box plot of teeth length (len):

Basic box plots

# Box plot of one variable
boxplot(ToothGrowth$len)

# Box plots by groups (dose)
# remove frame
boxplot(len ~ dose, data = ToothGrowth, frame = FALSE)

# Horizontal box plots
boxplot(len ~ dose, data = ToothGrowth, frame = FALSE,
        horizontal = TRUE)

# Notched box plots
boxplot(len ~ dose, data = ToothGrowth, frame = FALSE,
        notch = TRUE)

Notch is used to compare groups. In the notched boxplot, if two boxes’ notches do not overlap this is “strong evidence” their medians differ (Chambers et al., 1983, p. 62).

Change group names

boxplot(len ~ dose, data = ToothGrowth, frame = FALSE,
        names = c("D0.5", "D1", "D2"))

Change color

# Change the color of border using one single color
boxplot(len ~ dose, data = ToothGrowth, frame = FALSE,
        border = "steelblue")

# Change the color of border.
#  Use different colors for each group
boxplot(len ~ dose, data = ToothGrowth, frame = FALSE,
        border = c("#999999", "#E69F00", "#56B4E9"))

# Change fill color : single color
boxplot(len ~ dose, data = ToothGrowth, frame = FALSE,
        col = "steelblue")

# Change fill color: multiple colors
boxplot(len ~ dose, data = ToothGrowth, frame = FALSE,
        col = c("#999999", "#E69F00", "#56B4E9"))

Box plot with multiple groups

boxplot(len ~ supp*dose, data = ToothGrowth,
        col = c("white", "steelblue"), frame = FALSE)

Change main title and axis labels

# Change axis titles
# Change color (col = "gray") and remove frame
# Create notched box plot
boxplot(len ~ dose, data = ToothGrowth,
        main = "Plot of length by dose",
        xlab = "Dose (mg)", ylab = "Length",
        col = "lightgray", frame = FALSE)

Box plot with the number of observations: gplots::boxplot2()

The function boxplot2()[in gplots package] can be used to create a box plot annotated with the number of observations.

Install gplots:

install.packages("gplots")

Use boxplot2() [in gplots]:

library("gplots")
# Box plot with annotation
boxplot2(len ~ dose, data = ToothGrowth,
         frame = FALSE)

# Put the annotation at the top
boxplot2(len ~ dose, data = ToothGrowth,
         frame = FALSE, top = TRUE)

Summary

Create basic box plots:

boxplot(len ~ dose, data = ToothGrowth, frame = FALSE)

Box plots with number of observations:

gplots::boxplot2(len ~ dose, data = ToothGrowth,
                 frame = FALSE, top = TRUE)

Infos

This analysis has been performed using R statistical software (ver. 3.2.4).

Easy Guides

R Base Graphs

How this chapter is organized?

Creating and saving graphs

Generic plot types in R

Scatter plots

Scatter plot matrices

Box plots

Strip Charts: 1-D scatter Plots

Bar plots

Line plots

Pie charts

Histogram and density plots

QQ-plots: Quantile-Quantile plots

Dot charts

Plot group means and confidence intervals

R base graphical parameters

See also

Infos

QQ-plots: Quantile-Quantile plots - R Base Graphs

Pleleminary tasks

Example data

Create QQ plots

Related articles

See also

Infos

Plot Group Means and Confidence Intervals - R Base Graphs

Pleleminary tasks

Data

Plot group means

Related articles

See also

Infos

Dot Charts - R Base Graphs

Pleleminary tasks

Data

R base function: dotchart()

Dot chart of one numeric vector

Dot chart of a matrix

Related articles

See also

Infos

Histogram and Density Plots - R Base Graphs

Pleleminary tasks

Create some data

Create histogram plots: hist()

Create density plots: density()

Related articles

See also

Infos

Pie Charts - R Base Graphs

Pleleminary tasks

Create some data

Create basic pie charts: pie()

Create 3D pie charts: plotix::pie3D()

Related articles

See also

Infos

Line Plots - R Base Graphs

Pleleminary tasks

R base functions: plot() and lines()

Create some data

Basic line plots

Plots with multiple lines

Related articles

See also

Infos

Bar Plots - R Base Graphs

Pleleminary tasks

Basic bar plots

Change group names

Change color

Change main title and axis labels

Stacked bar plots

Grouped bar plots

Related articles

See also

Infos

Strip charts: 1-D scatter plots - R Base Graphs

Pleleminary tasks