How this chapter is organized?

R Base Graphs

1. R Base Plots
   • Creating and Saving Graphs in R
   • 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
   • Dot Charts
   • Plot Group Means and Confidence Intervals
2. R Base Graphical Parameters
   • Add and customize titles
   • Add and customize legends
   • Add texts
   • Add straight lines: vertical, horizontal and regression lines
   • Add an axis
   • Change axis scale: minimum, maximum and log scale
   • Customize tick mark labels
   • Change point shapes
   • Change line types
   • Change colors

Read more: —> R base Graphs.

Lattice Graphs

• xyplot(): Scatter plot
• cloud(): 3D scatter plot
• Box plot, Dot plot, Strip plot
• Density plot and Histogram

Read more: —> Lattice Graphs.

Ggplot2

1. ggplots
   • qplot(): Quick plot with ggplot2
   • Box plots
   • Violin plots
   • Dot plots
   • Stripcharts
   • Density plots
   • Histogram plots
   • Scatter plots
   • Bar plots
   • Line plots
   • Error bars
   • Pie chart
   • QQ plots
   • ECDF plots

2. ggsave(): Save a ggplot

3. ggplot2 Graphical Parameters
   • Main title, axis labels and legend title
   • Legend position and appearance
   • Change colors automatically and manually
   • Point shapes, colors and size
   • Add text annotations to a graph
   • Line types
   • Themes and background colors
   • Axis scales and transformations
   • Axis ticks: customize tick marks and labels, reorder and select items
   • Add straight lines to a plot: horizontal, vertical and regression lines
   • Rotate a plot: flip and reverse
   • Faceting: split a plot into a matrix of panels
4. Cheat Sheets
   • Be Awesome in ggplot2: A Practical Guide to be Highly Effective

Read more: —> ggplot2 essentials.

3D Graphics

3d scatter plot rgl

1. Static 3D Scatter Plots
   • Simple 3D Scatter Plots: scatterplot3d Package
   • Advanced 3D Graphs: plot3D Package
2. Interactive 3D Graphs
   • Interactive 3D Scatter Plots
   • Guide to RGL 3D Visualization System

Read more: —> 3D Graphics.

Others

• The Elements of Choosing Colors for Great Data Visualization in R

Infos

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

Pleleminary tasks

1. Launch RStudio as described here: Running RStudio and setting up your working directory

2. Prepare your data as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files

3. Import your data into R as described here: Fast reading of data from txt|csv files into R: readr package.

Briefly, if your data is saved in an external .txt tab or .csv files, use the following script to import the data into R:

# If .txt tab file use this:
my_data <- read.delim(file.choose())

# or if .csv file:
my_data <- read.csv(file.choose())

In the following sections, we’ll use R built-in data sets.

Installing and loading the lattice package

# Install
install.packages("lattice")

# Load
library("lattice")

Main functions in the lattice package

Note that, other functions (ecdfplot() and mapplot()) are available in the latticeExtra package.

xyplot(): Scatter plot

• R function: The R function xyplot() is used to produce bivariate scatter plots or time-series plots. The simplified format is as follow:

xyplot(y ~ x, data)

• Data set: mtcars

my_data <- iris
head(my_data)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

• Basic scatter plot: y ~ x

# Default plot
xyplot(Sepal.Length ~ Petal.Length, data = my_data)

# Color by groups
xyplot(Sepal.Length ~ Petal.Length, group = Species, 
       data = my_data, auto.key = TRUE)

# Show points ("p"), grids ("g") and smoothing line
# Change xlab and ylab
xyplot(Sepal.Length ~ Petal.Length, data = my_data,
       type = c("p", "g", "smooth"),
       xlab = "Miles/(US) gallon", ylab = "Weight (1000 lbs)")

• Multiple panels by groups: y ~ x | group

xyplot(Sepal.Length ~ Petal.Length | Species, 
       group = Species, data = my_data,
       type = c("p", "smooth"),
       scales = "free")

cloud(): 3D scatter plot

• Data set: iris

my_data <- iris
head(my_data)

##   Sepal.Length Sepal.Width Petal.Length Petal.Width Species
## 1          5.1         3.5          1.4         0.2  setosa
## 2          4.9         3.0          1.4         0.2  setosa
## 3          4.7         3.2          1.3         0.2  setosa
## 4          4.6         3.1          1.5         0.2  setosa
## 5          5.0         3.6          1.4         0.2  setosa
## 6          5.4         3.9          1.7         0.4  setosa

• Scatter 3D plot: z ~ x * y

# Basic 3D scatter plot
cloud(Sepal.Length ~ Sepal.Length * Petal.Width, 
       data = iris)

# Color by groups; auto.key = TRUE to show legend
cloud(Sepal.Length ~ Sepal.Length * Petal.Width, 
       group = Species, data = iris,
       auto.key = TRUE)

Box plot, Dot plot, Strip plot

• Data set: ToothGrowth

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

• Basic plot: Plot len by dose

# Basic box plot
bwplot(len ~ dose,  data = ToothGrowth,
       xlab = "Dose", ylab = "Length")

# Violin plot using panel = panel.violin
bwplot(len ~ dose,  data = ToothGrowth,
       panel = panel.violin,
       xlab = "Dose", ylab = "Length")
   
# Basic dot plot
dotplot(len ~ dose,  data = ToothGrowth,
        xlab = "Dose", ylab = "Length")

# Basic stip plot
stripplot(len ~ dose,  data = ToothGrowth,
          jitter.data = TRUE, pch = 19,
          xlab = "Dose", ylab = "Length")

• Plot with multiple groups: Additional argument layout is used: c(3, 1) specifying the number of column and row, respectively

# Box plot
bwplot(len ~ supp | dose,  data = ToothGrowth,
       layout = c(3, 1),
        xlab = "Dose", ylab = "Length")

# Violin plot
bwplot(len ~ supp | dose,  data = ToothGrowth,
       layout = c(3, 1), panel = panel.violin,
        xlab = "Dose", ylab = "Length")

# Dot plot
dotplot(len ~ supp | dose,  data = ToothGrowth,
       layout = c(3, 1),
        xlab = "Dose", ylab = "Length")

# Strip plot
stripplot(len ~ supp | dose,  data = ToothGrowth,
       layout = c(3, 1), jitter.data = TRUE,
        xlab = "Dose", ylab = "Length")

Density plot and Histogram

• Basic plots

densityplot(~ len, data = ToothGrowth,
            plot.points = FALSE)

histogram(~ len, data = ToothGrowth,
            breaks = 20)

• Plot with multiple groups

densityplot(~ len, groups = dose, data = ToothGrowth,
            plot.points = FALSE, auto.key = TRUE)

See also

1. R base plots
   • Creating and Saving Graphs 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
   • Dot Charts
   • Plot Group Means and Confidence Intervals
   • Graphical Parameters
2. ggplot2 Graphs

Infos

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

Understanding color wheel

(Image from Nancy Duarte, slide:ology)

The color wheel helps you to visualize the relationships between colors.

The wheel is divided into pie slices which have the following components:

• hue (true color): On the wheel above, the hue is four rings out of the center.
• tints: correspond to the colors toward the center of the wheel (= hue + white color)
• shades: corresponds to the ring of colors on the outside of the hue ( = hue + black color)

Defining the basics for choosing colors

1. Monochromatic: Variations of the same color.

2. Analogous: colors that are touching in the wheel creates narrow harmonious color scheme.

3. Complementary: Colors from the opposite ends of the wheel provide the most contrast.

(Image from Nancy Duarte, slide:ology)

4. Split Complementary: A variation of the complementary scheme that uses two colors on either side of a directly complementary color. These colors have high visual contrast but with less visual tension than purely complementary colors.

5. Triadic: Three colors equally spaced around the color wheel create vivid visual interest.

6. Tetradic: Two pairs of complementary colors. This scheme is popular because it offers strong visual contrast while retaining harmony.

(Image from Nancy Duarte, slide:ology)

colortools: R package for creating easily color schemes in R

The excellent R package colortools developed by Gaston Sanchez is an easy to use solution for generating color schemes in R.

install.packages("colortools")

library(colortools)

wheel("darkblue", num = 12)

##  [1] "#00008B" "#46008B" "#8B008B" "#8B0046" "#8B0000" "#8B4500" "#8B8B00"
##  [8] "#468B00" "#008B00" "#008B45" "#008B8B" "#00468B"

analogous("darkblue")

## [1] "#00008B" "#46008B" "#00468B"

complementary("steelblue")

## [1] "#4682B4" "#B47846"

splitComp("steelblue")

## [1] "#4682B4" "#B4464B" "#B4AF46"

tetradic("steelblue")

## [1] "#4682B4" "#7846B4" "#B47846" "#82B446"

square("steelblue")

## [1] "#4682B4" "#AF46B4" "#B47846" "#4BB446"

sequential("steelblue")

##  [1] "#B4B4B4FF" "#ABB0B4FF" "#A2ACB4FF" "#99A8B4FF" "#90A4B4FF"
##  [6] "#87A0B4FF" "#7E9BB4FF" "#7597B4FF" "#6C93B4FF" "#638FB4FF"
## [11] "#5A8BB4FF" "#5187B4FF" "#4883B4FF" "#3F7FB4FF" "#367BB4FF"
## [16] "#2D77B4FF" "#2473B4FF" "#1B6EB4FF" "#126AB4FF" "#0966B4FF"
## [21] "#0062B4FF"

Design your color scheme online

The online tool Colors Scheme Designer can be used:

Read more

• slide:ology: The Art and Science of Creating Great Presentations (by Nancy Duarte)
• R package colortools by Gaston Sanchez.

Infos

This analysis has been performed using R software (ver. 3.2.3) and colortools (ver. 0.1.5)