Articles - R Graphics Essentials

Install R and RStudio

RStudio is an integrated development environment for R that makes using R easier. R and RStudio can be installed on Windows, MAC OSX and Linux platforms.

1. R can be downloaded and installed from the Comprehensive R Archive Network (CRAN) webpage (http://cran.r-project.org/)
2. After installing R software, install also the RStudio software available at: http://www.rstudio.com/products/RStudio/.
3. Launch RStudio and start use R inside R studio.

Install and load required R packages

An R package is a collection of functionalities that extends the capabilities of base R. To use the R code provide in this book, you should install the following R packages:

• tidyverse packages, which are a collection of R packages that share the same programming philosophy. These packages include:
  • readr: for importing data into R
  • dplyr: for data manipulation
  • ggplot2 and ggpubr for data visualization.
• ggpubr package, which makes it easy, for beginner, to create publication ready plots.

1. Install the tidyverse package. Installing tidyverse will install automatically readr, dplyr, ggplot2 and more. Type the following code in the R console:

install.packages("tidyverse")

2. Install the ggpubr package.

• We recommend to install the latest developmental version of ggpubr as follow:

if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")

• If the above R code fails, you can install the latest stable version on CRAN:

install.packages("ggpubr")

3. Load required packages. After installation, you must first load the package for using the functions in the package. The function library() is used for this task. An alternative function is require(). For example, to load ggplot2 and ggpubr packages, type this:

library("ggplot2")
library("ggpubr")

Now, we can use R functions, such as ggscatter() [in the ggpubr package] for creating a scatter plot.

If you want to learn more about a given function, say ggscatter(), type this in R console: ?ggscatter.

Data format

Your data should be in rectangular format, where columns are variables and rows are observations (individuals or samples).

• Column names should be compatible with R naming conventions. Avoid column with blank space and special characters. Good column names: long_jump or long.jump. Bad column name: long jump.

• Avoid beginning column names with a number. Use letter instead. Good column names: sport_100m or x100m. Bad column name: 100m.

• Replace missing values by NA (for not available)

For example, your data should look like this:

  manufacturer model displ year cyl      trans drv
1         audi    a4   1.8 1999   4   auto(l5)   f
2         audi    a4   1.8 1999   4 manual(m5)   f
3         audi    a4   2.0 2008   4 manual(m6)   f
4         audi    a4   2.0 2008   4   auto(av)   f

Read more at: Best Practices in Preparing Data Files for Importing into R

Import your data in R

First, save your data into txt or csv file formats and import it as follow (you will be asked to choose the file):

library("readr")
# Reads tab delimited files (.txt tab)
my_data <- read_tsv(file.choose())
# Reads comma (,) delimited files (.csv)
my_data <- read_csv(file.choose())
# Reads semicolon(;) separated files(.csv)
my_data <- read_csv2(file.choose())

Read more about how to import data into R at this link: http://www.sthda.com/english/wiki/importing-data-into-r

Demo data sets

R comes with several demo data sets for playing with R functions. The most used R demo data sets include: USArrests, iris and mtcars. To load a demo data set, use the function data() as follow. The function head() is used to inspect the data.

data("iris")   # Loading
head(iris, n = 3)  # Print the first n = 3 rows

##   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

To learn more about iris data sets, type this:

?iris

After typing the above R code, you will see the description of iris data set: this iris data set gives the measurements in centimeters of the variables sepal length and width and petal length and width, respectively, for 50 flowers from each of 3 species of iris. The species are Iris setosa, versicolor, and virginica.

Data manipulation

After importing your data in R, you can easily manipulate it using the dplyr package (Wickham et al. 2017), which can be installed using the R code: install.packages("dplyr").

After loading dplyr, you can use the following R functions:

• filter(): Pick rows (observations/samples) based on their values.
• distinct(): Remove duplicate rows.
• arrange(): Reorder the rows.
• select(): Select columns (variables) by their names.
• rename(): Rename columns.
• mutate(): Add/create new variables.
• summarise(): Compute statistical summaries (e.g., computing the mean or the sum)
• group_by(): Operate on subsets of the data set.

We’ll show you how these functions work in the different chapters of this book.

R graphics systems

There are different graphic packages available in R for visualizing your data: 1) R base graphs, 2) Lattice Graphs (Sarkar 2016) and 3) ggplot2 (Wickham and Chang 2017).

In this section, we start by providing a quick overview of R base and lattice plots, and then we move to ggplot2 graphic system. The vast majority of plots generated in this book is based on the modern and flexible ggplot2 R package.

# (1) Create a scatter lot
plot(
  x = iris$Sepal.Length, y = iris$Sepal.Width,
  pch = 19, cex = 0.8, frame = FALSE,
  xlab = "Sepal Length",ylab = "Sepal Width"
  )
# (2) Create a box plot
boxplot(Sepal.Length ~ Species, data = iris,
        ylab = "Sepal.Length", 
        frame = FALSE, col = "lightgray")

library("lattice")
xyplot(
  Sepal.Length ~ Petal.Length, group = Species, 
  data = iris, auto.key = TRUE, pch = 19, cex = 0.5
  )

xyplot(
  Sepal.Length ~ Petal.Length | Species, 
  layout = c(3, 1),               # panel with ncol = 3 and nrow = 1
  group = Species, data = iris,
  type = c("p", "smooth"),        # Show points and smoothed line
  scales = "free"                 # Make panels axis scales independent
  )

library(ggplot2)
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width))+
  geom_point()
# Change point size, color and shape
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width))+
  geom_point(size = 1.2, color = "steelblue", shape = 21)

ggpubr::show_point_shapes()

# Control points color by groups
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width))+
  geom_point(aes(color = Species, shape = Species))
# Change the default color manually.
# Use the scale_color_manual() function
ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width))+
  geom_point(aes(color = Species, shape = Species))+
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width))+
  geom_point(aes(color = Species))+               
  geom_smooth(aes(color = Species, fill = Species))+
  facet_wrap(~Species, ncol = 3, nrow = 1)+
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))+
  scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

theme_set(
  theme_classic()
)

ggplot(iris, aes(x = Sepal.Length, y = Sepal.Width))+
  geom_point()

library(ggpubr)
# Density plot with mean lines and marginal rug
ggdensity(iris, x = "Sepal.Length",
   add = "mean", rug = TRUE,             # Add mean line and marginal rugs
   color = "Species", fill = "Species",  # Color by groups
   palette = "jco")                      # use jco journal color palette

# Groups that we want to compare
my_comparisons <- list(
  c("setosa", "versicolor"), c("versicolor", "virginica"),
  c("setosa", "virginica")
)
# Create the box plot. Change colors by groups: Species
# Add jitter points and change the shape by groups
ggboxplot(
  iris, x = "Species", y = "Sepal.Length",
  color = "Species", palette = c("#00AFBB", "#E7B800", "#FC4E07"),
  add = "jitter"
  )+
  stat_compare_means(comparisons = my_comparisons, method = "t.test")

Export R graphics

You can export R graphics to many file formats, including: PDF, PostScript, SVG vector files, Windows MetaFile (WMF), PNG, TIFF, JPEG, etc.

The standard procedure to save any graphics from R is as follow:

1. Open a graphic device using one of the following functions:

• pdf(“r-graphics.pdf”),
• postscript(“r-graphics.ps”),
• svg(“r-graphics.svg”),
• png(“r-graphics.png”),
• tiff(“r-graphics.tiff”),
• jpeg(“r-graphics.jpg”),
• win.metafile(“r-graphics.wmf”),
• and so on.

Additional arguments indicating the width and the height (in inches) of the graphics region can be also specified in the mentioned function.

2. Create a plot

3. Close the graphic device using the function dev.off()

For example, you can export R base plots to a pdf file as follow:

pdf("r-base-plot.pdf") 
# Plot 1 --> in the first page of PDF
plot(x = iris$Sepal.Length, y = iris$Sepal.Width)
# Plot 2 ---> in the second page of the PDF
hist(iris$Sepal.Length)
dev.off()

To export ggplot2 graphs, the R code looks like this:

# Create some plots
library(ggplot2)
myplot1 <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) + 
  geom_point()
myplot2 <- ggplot(iris, aes(Species, Sepal.Length)) + 
  geom_boxplot()
# Print plots to a pdf file
pdf("ggplot.pdf")
print(myplot1)     # Plot 1 --> in the first page of PDF
print(myplot2)     # Plot 2 ---> in the second page of the PDF
dev.off() 

Note that for a ggplot, you can also use the following functions to export the graphic:

• ggsave() [in ggplot2]. Makes it easy to save a ggplot. It guesses the type of graphics device from the file extension.
• ggexport() [in ggpubr]. Makes it easy to arrange and export multiple ggplots at once.