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: https://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.

Prerequisites

Load required packages and set the theme function theme_pubr() [in ggpubr] as the default theme:

library(ggplot2)
library(ggpubr)
theme_set(theme_pubr())

One categorical variable

ggplot(diamonds, aes(cut)) +
  geom_bar(fill = "#0073C2FF") +
  theme_pubclean()

# Compute the frequency
library(dplyr)
df <- diamonds %>%
  group_by(cut) %>%
  summarise(counts = n())
df

## # A tibble: 5 x 2
##         cut counts
##         
## 1      Fair   1610
## 2      Good   4906
## 3 Very Good  12082
## 4   Premium  13791
## 5     Ideal  21551

# Create the bar plot. Use theme_pubclean() [in ggpubr]
ggplot(df, aes(x = cut, y = counts)) +
  geom_bar(fill = "#0073C2FF", stat = "identity") +
  geom_text(aes(label = counts), vjust = -0.3) + 
  theme_pubclean()

df <- df %>%
  arrange(desc(cut)) %>%
  mutate(prop = round(counts*100/sum(counts), 1),
         lab.ypos = cumsum(prop) - 0.5*prop)
head(df, 4)

## # A tibble: 4 x 4
##         cut counts  prop lab.ypos
##              
## 1     Ideal  21551  40.0     20.0
## 2   Premium  13791  25.6     52.8
## 3 Very Good  12082  22.4     76.8
## 4      Good   4906   9.1     92.5

ggplot(df, aes(x = "", y = prop, fill = cut)) +
  geom_bar(width = 1, stat = "identity", color = "white") +
  geom_text(aes(y = lab.ypos, label = prop), color = "white")+
  coord_polar("y", start = 0)+
  ggpubr::fill_palette("jco")+
  theme_void()

ggpie(
  df, x = "prop", label = "prop",
  lab.pos = "in", lab.font = list(color = "white"), 
  fill = "cut", color = "white",
  palette = "jco"
)

ggplot(df, aes(cut, prop)) +
  geom_linerange(
    aes(x = cut, ymin = 0, ymax = prop), 
    color = "lightgray", size = 1.5
    )+
  geom_point(aes(color = cut), size = 2)+
  ggpubr::color_palette("jco")+
  theme_pubclean()

ggdotchart(
  df, x = "cut", y = "prop",
  color = "cut", size = 3,      # Points color and size
  add = "segment",              # Add line segments
  add.params = list(size = 2), 
  palette = "jco",
  ggtheme = theme_pubclean()
)

One continuous variable

Different types of graphs can be used to visualize the distribution of a continuous variable, including: density and histogram plots.

set.seed(1234)
wdata = data.frame(
        sex = factor(rep(c("F", "M"), each=200)),
        weight = c(rnorm(200, 55), rnorm(200, 58))
        )

head(wdata, 4)

##   sex weight
## 1   F   53.8
## 2   F   55.3
## 3   F   56.1
## 4   F   52.7

library("dplyr")
mu <- wdata %>% 
  group_by(sex) %>%
  summarise(grp.mean = mean(weight))
mu

## # A tibble: 2 x 2
##      sex grp.mean
##       
## 1      F     54.9
## 2      M     58.1

a <- ggplot(wdata, aes(x = weight))

# y axis scale = ..density.. (default behaviour)
a + geom_density() +
  geom_vline(aes(xintercept = mean(weight)), 
             linetype = "dashed", size = 0.6)
  
# Change y axis to count instead of density
a + geom_density(aes(y = ..count..), fill = "lightgray") +
  geom_vline(aes(xintercept = mean(weight)), 
             linetype = "dashed", size = 0.6,
             color = "#FC4E07")

# Change line color by sex
a + geom_density(aes(color = sex)) +
  scale_color_manual(values = c("#868686FF", "#EFC000FF"))

# Change fill color by sex and add mean line
# Use semi-transparent fill: alpha = 0.4
a + geom_density(aes(fill = sex), alpha = 0.4) +
      geom_vline(aes(xintercept = grp.mean, color = sex),
             data = mu, linetype = "dashed") +
  scale_color_manual(values = c("#868686FF", "#EFC000FF"))+
  scale_fill_manual(values = c("#868686FF", "#EFC000FF"))

library(ggpubr)

# Basic density plot with mean line and marginal rug
ggdensity(wdata, x = "weight", 
          fill = "#0073C2FF", color = "#0073C2FF",
          add = "mean", rug = TRUE)
     
# Change outline and fill colors by groups ("sex")
# Use a custom palette
ggdensity(wdata, x = "weight",
   add = "mean", rug = TRUE,
   color = "sex", fill = "sex",
   palette = c("#0073C2FF", "#FC4E07"))

a + geom_histogram(bins = 30, color = "black", fill = "gray") +
  geom_vline(aes(xintercept = mean(weight)), 
             linetype = "dashed", size = 0.6)

# Change line color by sex
a + geom_histogram(aes(color = sex), fill = "white", 
                   position = "identity") +
  scale_color_manual(values = c("#00AFBB", "#E7B800")) 

# change fill and outline color manually 
a + geom_histogram(aes(color = sex, fill = sex),
                         alpha = 0.4, position = "identity") +
  scale_fill_manual(values = c("#00AFBB", "#E7B800")) +
  scale_color_manual(values = c("#00AFBB", "#E7B800"))

# Histogram with density plot
a + geom_histogram(aes(y = ..density..), 
                   colour="black", fill="white") +
  geom_density(alpha = 0.2, fill = "#FF6666") 
     

# Color by groups
a + geom_histogram(aes(y = ..density.., color = sex), 
                   fill = "white",
                   position = "identity")+
  geom_density(aes(color = sex), size = 1) +
  scale_color_manual(values = c("#868686FF", "#EFC000FF"))

library(ggpubr)

# Basic histogram plot with mean line and marginal rug
gghistogram(wdata, x = "weight", bins = 30, 
            fill = "#0073C2FF", color = "#0073C2FF",
            add = "mean", rug = TRUE)
     
# Change outline and fill colors by groups ("sex")
# Use a custom palette
gghistogram(wdata, x = "weight", bins = 30,
   add = "mean", rug = TRUE,
   color = "sex", fill = "sex",
   palette = c("#0073C2FF", "#FC4E07"))

theme_set(theme_pubclean())

# Basic frequency polygon
a + geom_freqpoly(bins = 30) 

# Basic area plots, which can be filled by color
a + geom_area( stat = "bin", bins = 30,
               color = "black", fill = "#00AFBB")

# Frequency polygon: 
# Change line colors and types by groups
a + geom_freqpoly( aes(color = sex, linetype = sex),
                   bins = 30, size = 1.5) +
  scale_color_manual(values = c("#00AFBB", "#E7B800"))

# Area plots: change fill colors by sex
# Create a stacked area plots
a + geom_area(aes(fill = sex), color = "white", 
              stat ="bin", bins = 30) +
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))

a + geom_dotplot(aes(fill = sex), binwidth = 1/4) +
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))

ggplot(wdata, aes(x = factor(1), y = weight)) +
  geom_boxplot(width = 0.4, fill = "white") +
  geom_jitter(aes(color = sex, shape = sex), 
              width = 0.1, size = 1) +
  scale_color_manual(values = c("#00AFBB", "#E7B800")) + 
  labs(x = NULL)   # Remove x axis label

# Another option for geom = "point"
a + stat_ecdf(aes(color = sex,linetype = sex), 
              geom = "step", size = 1.5) +
  scale_color_manual(values = c("#00AFBB", "#E7B800"))+
  labs(y = "f(weight)")

# Change point shapes by groups
ggplot(wdata, aes(sample = weight)) +
  stat_qq(aes(color = sex)) +
  scale_color_manual(values = c("#00AFBB", "#E7B800"))+
  labs(y = "Weight")

library(ggpubr)
ggqqplot(wdata, x = "weight",
   color = "sex", 
   palette = c("#0073C2FF", "#FC4E07"),
   ggtheme = theme_pubclean())

install.packages("ggridges")

library(ggplot2)
library(ggridges)
theme_set(theme_ridges())

ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(aes(fill = Species)) +
  scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

ggplot(iris, aes(x = Sepal.Length, y = Species)) +
  geom_density_ridges(scale = 0.9) 

ggplot(
  lincoln_weather, 
  aes(x = `Mean Temperature [F]`, y = `Month`)
  ) +
  geom_density_ridges_gradient(
    aes(fill = ..x..), scale = 3, size = 0.3
    ) +
  scale_fill_gradientn(
    colours = c("#0D0887FF", "#CC4678FF", "#F0F921FF"),
    name = "Temp. [F]"
    )+
  labs(title = 'Temperatures in Lincoln NE') 

browseVignettes("ggridges")

library(ggpubr)

# Load data
dfm <- mtcars
# Convert the cyl variable to a factor
dfm$cyl <- as.factor(dfm$cyl)
# Add the name colums
dfm$name <- rownames(dfm)
# Inspect the data
head(dfm[, c("name", "wt", "mpg", "cyl")])

##                                name   wt  mpg cyl
## Mazda RX4                 Mazda RX4 2.62 21.0   6
## Mazda RX4 Wag         Mazda RX4 Wag 2.88 21.0   6
## Datsun 710               Datsun 710 2.32 22.8   4
## Hornet 4 Drive       Hornet 4 Drive 3.21 21.4   6
## Hornet Sportabout Hornet Sportabout 3.44 18.7   8
## Valiant                     Valiant 3.46 18.1   6

ggbarplot(dfm, x = "name", y = "mpg",
          fill = "cyl",               # change fill color by cyl
          color = "white",            # Set bar border colors to white
          palette = "jco",            # jco journal color palett. see ?ggpar
          sort.val = "asc",          # Sort the value in dscending order
          sort.by.groups = TRUE,     # Don't sort inside each group
          x.text.angle = 90,           # Rotate vertically x axis texts
          ggtheme = theme_pubclean()
          )+
  font("x.text", size = 8, vjust = 0.5)

ggdotchart(dfm, x = "name", y = "mpg",
           color = "cyl",                                
           palette = c("#00AFBB", "#E7B800", "#FC4E07"), 
           sorting = "asc", sort.by.groups = TRUE,                      
           add = "segments",                            
           add.params = list(color = "lightgray", size = 2), 
           group = "cyl",                                
           dot.size = 4,                                 
           ggtheme = theme_pubclean()
           )+
  font("x.text", size = 8, vjust = 0.5)

Conclusion

• Create a bar plot of a grouping variable:

ggplot(diamonds, aes(cut)) +
  geom_bar(fill = "#0073C2FF") +
  theme_minimal()

• Visualize a continuous variable:

Start by creating a plot, named a, that we’ll be finished by adding a layer.

a <- ggplot(wdata, aes(x = weight))

Possible layers include:

Key arguments to customize the plots:

• color, size, linetype: change the line color, size and type, respectively
• fill: change the areas fill color (for bar plots, histograms and density plots)
• alpha: create a semi-transparent color.

Prerequisites

Load required packages and set the theme function theme_pubclean() [in ggpubr] as the default theme:

library(dplyr) 
library(ggplot2)
library(ggpubr)
theme_set(theme_pubclean())

Grouped categorical variables

• Plot types: grouped bar plots of the frequencies of the categories. Key function: geom_bar().
• Demo dataset: diamonds [in ggplot2]. The categorical variables to be used in the demo example are:
  • cut: quality of the diamonds cut (Fair, Good, Very Good, Premium, Ideal)
  • color: diamond colour, from J (worst) to D (best).

In our demo example, we’ll plot only a subset of the data (color J and D). The different steps are as follow:

• Filter the data to keep only diamonds which colors are in (“J”, “D”).
• Group the data by the quality of the cut and the diamond color
• Count the number of records by groups
• Create the bar plot

1. Filter and count the number of records by groups:

df <- diamonds %>%
  filter(color %in% c("J", "D")) %>%
  group_by(cut, color) %>%
  summarise(counts = n()) 
head(df, 4)

## # A tibble: 4 x 3
## # Groups:   cut [2]
##     cut color counts
##      
## 1  Fair     D    163
## 2  Fair     J    119
## 3  Good     D    662
## 4  Good     J    307

2. Creare the grouped bar plots:
   • Key function: geom_bar(). Key argument: stat = "identity" to plot the data as it is.
   • Use the functions scale_color_manual() and scale_fill_manual() to set manually the bars border line colors and area fill colors.

# Stacked bar plots of y = counts by x = cut,
# colored by the variable color
ggplot(df, aes(x = cut, y = counts)) +
  geom_bar(
    aes(color = color, fill = color),
    stat = "identity", position = position_stack()
    ) +
  scale_color_manual(values = c("#0073C2FF", "#EFC000FF"))+
  scale_fill_manual(values = c("#0073C2FF", "#EFC000FF"))

# Use position = position_dodge() 
p <- ggplot(df, aes(x = cut, y = counts)) +
  geom_bar(
    aes(color = color, fill = color),
    stat = "identity", position = position_dodge(0.8),
    width = 0.7
    ) +
  scale_color_manual(values = c("#0073C2FF", "#EFC000FF"))+
  scale_fill_manual(values = c("#0073C2FF", "#EFC000FF"))
p

Alternatively, you can easily create a dot chart with the ggpubr package:

ggdotchart(df, x = "cut", y ="counts",
           color = "color", palette = "jco", size = 3, 
           add = "segment", 
           add.params = list(color = "lightgray", size = 1.5),
           position = position_dodge(0.3),
           ggtheme = theme_pubclean()
           )

Or, if you prefer the ggplot2 verbs, type this:

ggplot(df, aes(cut, counts)) +
  geom_linerange(
    aes(x = cut, ymin = 0, ymax = counts, group = color), 
    color = "lightgray", size = 1.5,
    position = position_dodge(0.3)
    )+
  geom_point(
    aes(color = color),
    position = position_dodge(0.3), size = 3
    )+
  scale_color_manual(values = c("#0073C2FF", "#EFC000FF"))+
  theme_pubclean()

3. Add labels to the dodged bar plots:

p + geom_text(
  aes(label = counts, group = color), 
  position = position_dodge(0.8),
  vjust = -0.3, size = 3.5
)

4. Add labels to a stacked bar plots. 3 steps required to compute the position of text labels:
   • Sort the data by cut and color columns. As position_stack() reverse the group order, color column should be sorted in descending order.
   • Calculate the cumulative sum of counts for each cut category. Used as the y coordinates of labels. To put the label in the middle of the bars, we’ll use cumsum(counts) - 0.5 * counts.
   • Create the bar graph and add labels

# Arrange/sort and compute cumulative summs
 df <- df %>%
  arrange(cut, desc(color)) %>%
  mutate(lab_ypos = cumsum(counts) - 0.5 * counts) 
head(df, 4)

## # A tibble: 4 x 4
## # Groups:   cut [2]
##     cut color counts lab_ypos
##          
## 1  Fair     J    119     59.5
## 2  Fair     D    163    200.5
## 3  Good     J    307    153.5
## 4  Good     D    662    638.0

# Create stacked bar graphs with labels
ggplot(df, aes(x = cut, y = counts)) +
  geom_bar(aes(color = color, fill = color), stat = "identity") +
  geom_text(
    aes(y = lab_ypos, label = counts, group = color),
    color = "white"
  ) + 
  scale_color_manual(values = c("#0073C2FF", "#EFC000FF"))+
  scale_fill_manual(values = c("#0073C2FF", "#EFC000FF")) 

Alternatively, you can easily create the above plot using the function ggbarplot() [in ggpubr]:

ggbarplot(df, x = "cut", y = "counts",
          color = "color", fill = "color",
          palette = c("#0073C2FF", "#EFC000FF"),
          label = TRUE, lab.pos = "in", lab.col = "white",
          ggtheme = theme_pubclean()
          )

6. Alternative to bar plots. Instead of the creating a bar plot of the counts, you can plot two discrete variables with discrete x-axis and discrete y-axis. Each individual points are shown by groups. For a given group, the number of points corresponds to the number of records in that group.

Key function: geom_jitter(). Arguments: alpha, color, fill, shape and size.

In the example below, we’ll plot a small fraction (1/5) of the diamonds dataset.

diamonds.frac <- dplyr::sample_frac(diamonds, 1/5)
ggplot(diamonds.frac, aes(cut, color)) +
  geom_jitter(aes(color = cut), size = 0.3)+
  ggpubr::color_palette("jco")+
  ggpubr::theme_pubclean()

Grouped continuous variables

In this section, we’ll show to plot a grouped continuous variable using box plot, violin plot, strip chart and alternatives.

We’ll also describe how to add automatically p-values comparing groups.

In this section, we’ll set the theme theme_bw() as the default ggplot theme:

theme_set(
  theme_bw()
)

data("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

# Default plot
e <- ggplot(ToothGrowth, aes(x = dose, y = len))
e + geom_boxplot()

# Notched box plot with mean points
e + geom_boxplot(notch = TRUE, fill = "lightgray")+
  stat_summary(fun.y = mean, geom = "point",
               shape = 18, size = 2.5, color = "#FC4E07")

# Color by group (dose)
e + geom_boxplot(aes(color = dose))+
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

# Change fill color by group (dose)
e + geom_boxplot(aes(fill = dose)) +
  scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

# Choose which items to display: group "0.5" and "2"
e + geom_boxplot() + 
  scale_x_discrete(limits=c("0.5", "2"))

# Change the default order of items
e + geom_boxplot() +
  scale_x_discrete(limits=c("2", "0.5", "1"))

e2 <- e + geom_boxplot(
  aes(fill = supp),
  position = position_dodge(0.9) 
  ) +
  scale_fill_manual(values = c("#999999", "#E69F00"))
e2

e2 + facet_wrap(~supp)

# Add mean points +/- SD
# Use geom = "pointrange" or geom = "crossbar"
e + geom_violin(trim = FALSE) + 
  stat_summary(
    fun.data = "mean_sdl",  fun.args = list(mult = 1), 
    geom = "pointrange", color = "black"
    )
    
# Combine with box plot to add median and quartiles
# Change color by groups
e + geom_violin(aes(fill = dose), trim = FALSE) + 
  geom_boxplot(width = 0.2)+
  scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))+
  theme(legend.position = "none")

e + geom_violin(
  aes(color = supp), trim = FALSE,
  position = position_dodge(0.9) 
  ) +
  geom_boxplot(
    aes(color = supp), width = 0.15,
    position = position_dodge(0.9)
    ) +
  scale_color_manual(values = c("#00AFBB", "#E7B800"))

# Violin plots with mean points +/- SD
e + geom_dotplot(
  binaxis = "y", stackdir = "center",
  fill = "lightgray"
  ) + 
  stat_summary(
    fun.data = "mean_sdl", fun.args = list(mult=1), 
    geom = "pointrange", color = "red"
    )

# Combine with box plots
e + geom_boxplot(width = 0.5) + 
  geom_dotplot(
    binaxis = "y", stackdir = "center",
    fill = "white"
    ) 

# Dot plot + violin plot + stat summary
e + geom_violin(trim = FALSE) +
  geom_dotplot(
    binaxis='y', stackdir='center',
    color = "black", fill = "#999999"
    ) +
  stat_summary(
    fun.data="mean_sdl",  fun.args = list(mult=1), 
    geom = "pointrange", color = "#FC4E07", size = 0.4
    )

# Color dots by groups
e + geom_boxplot(width = 0.5, size = 0.4) +
  geom_dotplot(
    aes(fill = supp), trim = FALSE,
    binaxis='y', stackdir='center'
  )+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))

# Change the position : interval between dot plot of the same group
e + geom_boxplot(
  aes(color = supp), width = 0.5, size = 0.4,
  position = position_dodge(0.8)
  ) +
  geom_dotplot(
    aes(fill = supp, color = supp), trim = FALSE,
    binaxis='y', stackdir='center', dotsize = 0.8,
    position = position_dodge(0.8)
  )+
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))+
  scale_color_manual(values = c("#00AFBB", "#E7B800"))

e + geom_jitter(
  aes(shape = dose, color = dose), 
  position = position_jitter(0.2),
  size = 1.2
  ) +
  stat_summary(
    aes(color = dose),
    fun.data="mean_sdl",  fun.args = list(mult=1), 
    geom = "pointrange",  size = 0.4
    )+
  scale_color_manual(values =  c("#00AFBB", "#E7B800", "#FC4E07"))

e + geom_jitter(
  aes(shape = supp, color = supp), 
  position = position_jitterdodge(jitter.width = 0.2, dodge.width = 0.8),
  size = 1.2
  ) +
  stat_summary(
    aes(color = supp),
    fun.data="mean_sdl",  fun.args = list(mult=1), 
    geom = "pointrange",  size = 0.4,
    position = position_dodge(0.8)
    )+
  scale_color_manual(values =  c("#00AFBB", "#E7B800"))

library(ggforce)
# Create some data
d1 <- data.frame(
  y = c(rnorm(200, 4, 1), rnorm(200, 5, 2), rnorm(400, 6, 1.5)),
  group = rep(c("Grp1", "Grp2", "Grp3"), c(200, 200, 400))
  )

# Sinaplot
ggplot(d1, aes(group, y)) +
  geom_sina(aes(color = group), size = 0.7)+
  scale_color_manual(values =  c("#00AFBB", "#E7B800", "#FC4E07"))

theme_set(ggpubr::theme_pubr())

df <- ToothGrowth
df$dose <- as.factor(df$dose)
head(df, 3)

##    len supp dose
## 1  4.2   VC  0.5
## 2 11.5   VC  0.5
## 3  7.3   VC  0.5

library(dplyr)
df.summary <- df %>%
  group_by(dose) %>%
  summarise(
    sd = sd(len, na.rm = TRUE),
    len = mean(len)
  )
df.summary

## # A tibble: 3 x 3
##     dose    sd   len
##     
## 1    0.5  4.50  10.6
## 2      1  4.42  19.7
## 3      2  3.77  26.1

# Initialize ggplot with data
f <- ggplot(
  df.summary, 
  aes(x = dose, y = len, ymin = len-sd, ymax = len+sd)
  )

# Vertical line with point in the middle
f + geom_pointrange()

# Standard error bars
f + geom_errorbar(width = 0.2) +
  geom_point(size = 1.5)

# Horizontal error bars with mean points
# Change the color by groups
ggplot(
  df.summary, 
  aes(x = len, y = dose, xmin = len-sd, xmax = len+sd)
  ) +
  geom_point(aes(color = dose)) +
  geom_errorbarh(aes(color = dose), height=.2)+
  theme_light()

# Combine with jitter points
ggplot(df, aes(dose, len)) +
  geom_jitter(
    position = position_jitter(0.2), color = "darkgray"
    ) + 
  geom_pointrange(
    aes(ymin = len-sd, ymax = len+sd),
    data = df.summary
    )

# Combine with violin plots
ggplot(df, aes(dose, len)) +
  geom_violin(color = "darkgray", trim = FALSE) + 
  geom_pointrange(
    aes(ymin = len-sd, ymax = len+sd),
    data = df.summary
    )

# (1) Line plot
ggplot(df.summary, aes(dose, len)) +
  geom_line(aes(group = 1)) +
  geom_errorbar( aes(ymin = len-sd, ymax = len+sd),width = 0.2) +
  geom_point(size = 2)

# (2) Bar plot
ggplot(df.summary, aes(dose, len)) +
  geom_bar(stat = "identity", fill = "lightgray", 
           color = "black") +
  geom_errorbar(aes(ymin = len, ymax = len+sd), width = 0.2) 

df.sum2 <- df.summary
df.sum2$dose <- as.numeric(df.sum2$dose)
ggplot(df.sum2, aes(dose, len)) +
  geom_line() +
  geom_errorbar( aes(ymin = len-sd, ymax = len+sd),width = 0.2) +
  geom_point(size = 2)

# (1) Create a line plot of means + 
# individual jitter points + error bars 
ggplot(df, aes(dose, len)) +
  geom_jitter( position = position_jitter(0.2),
               color = "darkgray") + 
  geom_line(aes(group = 1), data = df.summary) +
  geom_errorbar(
    aes(ymin = len-sd, ymax = len+sd),
    data = df.summary, width = 0.2) +
  geom_point(data = df.summary, size = 2)

# (2) Bar plots of means + individual jitter points + errors
ggplot(df, aes(dose, len)) +
  geom_bar(stat = "identity", data = df.summary,
           fill = NA, color = "black") +
  geom_jitter( position = position_jitter(0.2),
               color = "black") + 
  geom_errorbar(
    aes(ymin = len-sd, ymax = len+sd),
    data = df.summary, width = 0.2) 

library(dplyr)
df.summary2 <- df %>%
  group_by(dose, supp) %>%
  summarise(
    sd = sd(len),
    len = mean(len)
  )
df.summary2

## # A tibble: 6 x 4
## # Groups:   dose [?]
##     dose   supp    sd   len
##      
## 1    0.5     OJ  4.46 13.23
## 2    0.5     VC  2.75  7.98
## 3      1     OJ  3.91 22.70
## 4      1     VC  2.52 16.77
## 5      2     OJ  2.66 26.06
## 6      2     VC  4.80 26.14

# (1) Pointrange: Vertical line with point in the middle
ggplot(df.summary2, aes(dose, len)) +
  geom_pointrange(
    aes(ymin = len-sd, ymax = len+sd, color = supp),
    position = position_dodge(0.3)
    )+
  scale_color_manual(values = c("#00AFBB", "#E7B800"))


# (2) Standard error bars
ggplot(df.summary2, aes(dose, len)) +
  geom_errorbar(
    aes(ymin = len-sd, ymax = len+sd, color = supp),
    position = position_dodge(0.3), width = 0.2
    )+
  geom_point(aes(color = supp), position = position_dodge(0.3)) +
  scale_color_manual(values = c("#00AFBB", "#E7B800")) 

# (1) Line plot + error bars
ggplot(df.summary2, aes(dose, len)) +
  geom_line(aes(linetype = supp, group = supp))+
  geom_point()+
  geom_errorbar(
    aes(ymin = len-sd, ymax = len+sd, group = supp),
     width = 0.2
    )

# (2) Bar plots + upper error bars.
ggplot(df.summary2, aes(dose, len)) +
  geom_bar(aes(fill = supp), stat = "identity",
           position = position_dodge(0.8), width = 0.7)+
  geom_errorbar(
    aes(ymin = len, ymax = len+sd, group = supp),
    width = 0.2, position = position_dodge(0.8)
    )+
  scale_fill_manual(values = c("grey80", "grey30"))

library(ggpubr)
# Create line plots of means
ggline(ToothGrowth, x = "dose", y = "len", 
       add = c("mean_sd", "jitter"),
       color = "supp", palette = c("#00AFBB", "#E7B800"))

# Create bar plots of means
ggbarplot(ToothGrowth, x = "dose", y = "len", 
          add = c("mean_se", "jitter"),
          color = "supp", palette = c("#00AFBB", "#E7B800"),
          position = position_dodge(0.8))

# Create line plots
ggplot(df, aes(dose, len)) +
  geom_jitter(
    aes(color = supp),
    position = position_jitter(0.2)
    ) + 
  geom_line(
    aes(group = supp, color = supp),
    data = df.summary2
    ) +
  geom_errorbar(
    aes(ymin = len-sd, ymax = len+sd, color = supp),
    data = df.summary2, width = 0.2
    )+
  scale_color_manual(values = c("#00AFBB", "#E7B800"))

library(ggpubr)
compare_means(len ~ supp, data = ToothGrowth,
              method = "t.test")

## # A tibble: 1 x 8
##     .y. group1 group2      p  p.adj p.format p.signif method
##                     
## 1   len     OJ     VC 0.0606 0.0606    0.061       ns T-test

# Create a simple box plot and add p-values
p <- ggplot(ToothGrowth, aes(supp, len)) +
  geom_boxplot(aes(color = supp)) +
  scale_color_manual(values = c("#00AFBB", "#E7B800"))
p + stat_compare_means(method = "t.test")

# Display the significance level instead of the p-value
# Adjust label position
p + stat_compare_means(
  aes(label = ..p.signif..), label.x = 1.5, label.y = 40
  )

ggpaired(ToothGrowth, x = "supp", y = "len",
         color = "supp", line.color = "gray", line.size = 0.4,
         palette = "jco")+
  stat_compare_means(paired = TRUE)

# Perorm pairwise comparisons
compare_means(len ~ dose,  data = ToothGrowth)

## # A tibble: 3 x 8
##     .y. group1 group2        p    p.adj p.format p.signif   method
##                           
## 1   len    0.5      1 7.02e-06 1.40e-05  7.0e-06     **** Wilcoxon
## 2   len    0.5      2 8.41e-08 2.52e-07  8.4e-08     **** Wilcoxon
## 3   len      1      2 1.77e-04 1.77e-04  0.00018      *** Wilcoxon

# Visualize: Specify the comparisons you want
my_comparisons <- list( c("0.5", "1"), c("1", "2"), c("0.5", "2") )
ggboxplot(ToothGrowth, x = "dose", y = "len",
          color = "dose", palette = "jco")+ 
  stat_compare_means(comparisons = my_comparisons)+ 
  stat_compare_means(label.y = 50)                   

# Use only p.format as label. Remove method name.
ggplot(ToothGrowth, aes(supp, len)) +
  geom_boxplot(aes(color = supp))+
  facet_wrap(~dose) +
  scale_color_manual(values = c("#00AFBB", "#E7B800")) +
  stat_compare_means(label = "p.format")

ggplot(ToothGrowth, aes(dose, len)) +
  geom_boxplot(aes(color = supp))+
  scale_color_manual(values = c("#00AFBB", "#E7B800")) +
  stat_compare_means(aes(group = supp), label = "p.signif")

# Box plot facetted by "dose"
p <- ggpaired(ToothGrowth, x = "supp", y = "len",
          color = "supp", palette = "jco", 
          line.color = "gray", line.size = 0.4,
          facet.by = "dose", short.panel.labs = FALSE)
# Use only p.format as label. Remove method name.
p + stat_compare_means(label = "p.format", paired = TRUE)

Conclusion

1. Visualize the distribution of a grouped continuous variable: the grouping variable on x-axis and the continuous variable on y axis.

The possible ggplot2 layers include:

• geom_boxplot() for box plot
• geom_violin() for violin plot
• geom_dotplot() for dot plot
• geom_jitter() for stripchart
• geom_line() for line plot
• geom_bar() for bar plot

Examples of R code: start by creating a plot, named e, and then finish it by adding a layer:

ToothGrowth$dose <- as.factor(ToothGrowth$dose)
e <- ggplot(ToothGrowth, aes(x = dose, y = len))

2. Create mean and median plots with error bars: the grouping variable on x-axis and the summarized continuous variable (mean/median) on y-axis.

• Compute summary statistics and initialize ggplot with summary data:

# Summary statistics
library(dplyr)
df.summary <- ToothGrowth %>%
  group_by(dose) %>%
  summarise(
    sd = sd(len, na.rm = TRUE),
    len = mean(len)
  )
# Initialize ggplot with data
f <- ggplot(
  df.summary, 
  aes(x = dose, y = len, ymin = len-sd, ymax = len+sd)
  )

• Possible error plots:

3. Combine error bars with violin plots, dot plots, line and bar plots:

# Combine with violin plots
ggplot(ToothGrowth, aes(dose, len))+
  geom_violin(trim = FALSE) +
  geom_pointrange(aes(ymin = len-sd, ymax = len + sd),
                  data = df.summary)

# Combine with dot plots
ggplot(ToothGrowth, aes(dose, len))+
  geom_dotplot(stackdir = "center", binaxis = "y",
               fill = "lightgray", dotsize = 1) +
  geom_pointrange(aes(ymin = len-sd, ymax = len + sd),
                  data = df.summary)

# Combine with line plot
ggplot(df.summary, aes(dose, len))+
  geom_line(aes(group = 1)) +
  geom_pointrange(aes(ymin = len-sd, ymax = len + sd))

# Combine with bar plots
ggplot(df.summary, aes(dose, len))+
  geom_bar(stat = "identity", fill = "lightgray") +
  geom_pointrange(aes(ymin = len-sd, ymax = len + sd))

See also

• ggpubr: Publication Ready Plots. https://goo.gl/7uySha
• Facilitating Exploratory Data Visualization: Application to TCGA Genomic Data. https://goo.gl/9LNsRi
• Add P-values and Significance Levels to ggplots. https://goo.gl/VH7Yq7
• Plot Means/Medians and Error Bars. https://goo.gl/zRwAeV

Prerequisites

1. Install cowplot package. Used to arrange multiple plots. Will be used here to create a scatter plot with marginal density plots. Install the latest developmental version as follow:

devtools::install_github("wilkelab/cowplot")

2. Install ggpmisc for adding the equation of a fitted regression line on a scatter plot:

install.packages("ggpmisc")

3. Load required packages and set ggplot themes:

• Load ggplot2 and ggpubr R packages
• Set the default theme to theme_minimal() [in ggplot2]

library(ggplot2)
library(ggpubr)
theme_set(
  theme_minimal() +
    theme(legend.position = "top")
  )

4. Prepare demo data sets:

Dataset: mtcars. The variable cyl is used as grouping variable.

# Load data
data("mtcars")
df <- mtcars

# Convert cyl as a grouping variable
df$cyl <- as.factor(df$cyl)

# Inspect the data
head(df[, c("wt", "mpg", "cyl", "qsec")], 4)

##                  wt  mpg cyl qsec
## Mazda RX4      2.62 21.0   6 16.5
## Mazda RX4 Wag  2.88 21.0   6 17.0
## Datsun 710     2.32 22.8   4 18.6
## Hornet 4 Drive 3.21 21.4   6 19.4

Basic scatter plots

Key functions:

• geom_point(): Create scatter plots. Key arguments: color, size and shape to change point color, size and shape.
• geom_smooth(): Add smoothed conditional means / regression line. Key arguments:
  • color, size and linetype: Change the line color, size and type.
  • fill: Change the fill color of the confidence region.

b <- ggplot(df, aes(x = wt, y = mpg))

# Scatter plot with regression line
b + geom_point()+
  geom_smooth(method = "lm") 
     
# Add a loess smoothed fit curve
b + geom_point()+
  geom_smooth(method = "loess") 

Change the point shape, by specifying the argument shape, for example:

b + geom_point(shape = 18)

To see the different point shapes commonly used in R, type this:

ggpubr::show_point_shapes()

Create easily a scatter plot using ggscatter() [in ggpubr]. Use stat_cor() [ggpubr] to add the correlation coefficient and the significance level.

# Add regression line and confidence interval
# Add correlation coefficient: stat_cor()
ggscatter(df, x = "wt", y = "mpg",
          add = "reg.line", conf.int = TRUE,    
          add.params = list(fill = "lightgray"),
          ggtheme = theme_minimal()
          )+
  stat_cor(method = "pearson", 
           label.x = 3, label.y = 30) 

Multiple groups

• Change point colors and shapes by groups.
• Add marginal rug: geom_rug().

# Change color and shape by groups (cyl)
b + geom_point(aes(color = cyl, shape = cyl))+
  geom_smooth(aes(color = cyl, fill = cyl), method = "lm") +
  geom_rug(aes(color =cyl)) +
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))+
  scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

# Remove confidence region (se = FALSE)
# Extend the regression lines: fullrange = TRUE
b + geom_point(aes(color = cyl, shape = cyl)) +
  geom_rug(aes(color =cyl)) +
  geom_smooth(aes(color = cyl), method = lm, 
              se = FALSE, fullrange = TRUE)+
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))+
  ggpubr::stat_cor(aes(color = cyl), label.x = 3)

• Split the plot into multiple panels. Use the function facet_wrap():

b + geom_point(aes(color = cyl, shape = cyl))+
  geom_smooth(aes(color = cyl, fill = cyl), 
              method = "lm", fullrange = TRUE) +
  facet_wrap(~cyl) +
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))+
  scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07")) +
  theme_bw()

• Add concentration ellipse around groups. R function stat_ellipse(). Key arguments:
  • type: The type of ellipse. The default “t” assumes a multivariate t-distribution, and “norm” assumes a multivariate normal distribution. “euclid” draws a circle with the radius equal to level, representing the euclidean distance from the center.
  • level: The confidence level at which to draw an ellipse (default is 0.95), or, if type=“euclid”, the radius of the circle to be drawn.

b + geom_point(aes(color = cyl, shape = cyl))+
  stat_ellipse(aes(color = cyl), type = "t")+
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

Instead of drawing the concentration ellipse, you can: i) plot a convex hull of a set of points; ii) add the mean points and the confidence ellipse of each group. Key R functions: stat_chull(), stat_conf_ellipse() and stat_mean() [in ggpubr]:

# Convex hull of groups
b + geom_point(aes(color = cyl, shape = cyl)) +
  stat_chull(aes(color = cyl, fill = cyl), 
             alpha = 0.1, geom = "polygon") +
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07")) +
  scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07")) 

# Add mean points and confidence ellipses
b + geom_point(aes(color = cyl, shape = cyl)) +
  stat_conf_ellipse(aes(color = cyl, fill = cyl), 
                    alpha = 0.1, geom = "polygon") +
  stat_mean(aes(color = cyl, shape = cyl), size = 2) + 
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07")) +
  scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07")) 

• Easy alternative using ggpubr. See this article: Perfect Scatter Plots with Correlation and Marginal Histograms

# Add group mean points and stars
ggscatter(df, x = "wt", y = "mpg",
          color = "cyl", palette = "npg",
          shape = "cyl", ellipse = TRUE, 
          mean.point = TRUE, star.plot = TRUE,
          ggtheme = theme_minimal())

# Change the ellipse type to 'convex'
ggscatter(df, x = "wt", y = "mpg",
          color = "cyl", palette = "npg",
          shape = "cyl",
          ellipse = TRUE, ellipse.type = "convex",
          ggtheme = theme_minimal())

Add point text labels

Key functions:

• geom_text() and geom_label(): ggplot2 standard functions to add text to a plot.
• geom_text_repel() and geom_label_repel() [in ggrepel package]. Repulsive textual annotations. Avoid text overlapping.

First install ggrepel (ìnstall.packages("ggrepel")), then type this:

library(ggrepel)

# Add text to the plot
.labs <- rownames(df)
b + geom_point(aes(color = cyl)) +
  geom_text_repel(aes(label = .labs,  color = cyl), size = 3)+
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

# Draw a rectangle underneath the text, making it easier to read.
b + geom_point(aes(color = cyl)) +
  geom_label_repel(aes(label = .labs,  color = cyl), size = 3)+
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

Bubble chart

In a bubble chart, points size is controlled by a continuous variable, here qsec. In the R code below, the argument alpha is used to control color transparency. alpha should be between 0 and 1.

b + geom_point(aes(color = cyl, size = qsec), alpha = 0.5) +
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07")) +
  scale_size(range = c(0.5, 12))  # Adjust the range of points size

Color by a continuous variable

• Color points according to the values of the continuous variable: “mpg”.
• Change the default blue gradient color using the function scale_color_gradientn() [in ggplot2], by specifying two or more colors.

b + geom_point(aes(color = mpg), size = 3) +
  scale_color_gradientn(colors = c("#00AFBB", "#E7B800", "#FC4E07"))

Add marginal density plots

The function ggMarginal() [in ggExtra package] (Attali 2017), can be used to easily add a marginal histogram, density or box plot to a scatter plot.

First, install the ggExtra package as follow: install.packages("ggExtra"); then type the following R code:

# Create a scatter plot
p <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point(aes(color = Species), size = 3, alpha = 0.6) +
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

# Add density distribution as marginal plot
library("ggExtra")
ggMarginal(p, type = "density")

# Change marginal plot type
ggMarginal(p, type = "boxplot")

One limitation of ggExtra is that it can’t cope with multiple groups in the scatter plot and the marginal plots.

A solution is provided in the function ggscatterhist() [ggpubr]:

library(ggpubr)
# Grouped Scatter plot with marginal density plots
ggscatterhist(
  iris, x = "Sepal.Length", y = "Sepal.Width",
  color = "Species", size = 3, alpha = 0.6,
  palette = c("#00AFBB", "#E7B800", "#FC4E07"),
  margin.params = list(fill = "Species", color = "black", size = 0.2)
  )

# Use box plot as marginal plots
ggscatterhist(
  iris, x = "Sepal.Length", y = "Sepal.Width",
  color = "Species", size = 3, alpha = 0.6,
  palette = c("#00AFBB", "#E7B800", "#FC4E07"),
  margin.plot = "boxplot",
  ggtheme = theme_bw()
  )

Continuous bivariate distribution

In this section, we’ll present some alternatives to the standard scatter plots. These include:

• Rectangular binning. Rectangular heatmap of 2d bin counts
• Hexagonal binning: Hexagonal heatmap of 2d bin counts.
• 2d density estimation

1. Rectangular binning:

Rectangular binning is a very useful alternative to the standard scatter plot in a situation where you have a large data set containing thousands of records.

Rectangular binning helps to handle overplotting. Rather than plotting each point, which would appear highly dense, it divides the plane into rectangles, counts the number of cases in each rectangle, and then plots a heatmap of 2d bin counts. In this plot, many small hexagon are drawn with a color intensity corresponding to the number of cases in that bin.

Key function: geom_bin2d(): Creates a heatmap of 2d bin counts. Key arguments: bins, numeric vector giving number of bins in both vertical and horizontal directions. Set to 30 by default.

2. Hexagonal binning: Similar to rectangular binning, but divides the plane into regular hexagons. Hexagon bins avoid the visual artefacts sometimes generated by the very regular alignment of `geom_bin2d().

Key function: geom_hex()

3. Contours of a 2d density estimate. Perform a 2D kernel density estimation and display results as contours overlaid on the scatter plot. This can be also useful for dealing with overplotting.

Key function: geom_density_2d()

• Create a scatter plot with rectangular and hexagonal binning:

# Rectangular binning
ggplot(diamonds, aes(carat, price)) +
  geom_bin2d(bins = 20, color ="white")+
  scale_fill_gradient(low =  "#00AFBB", high = "#FC4E07")+
  theme_minimal()

# Hexagonal binning
ggplot(diamonds, aes(carat, price)) +
  geom_hex(bins = 20, color = "white")+
  scale_fill_gradient(low =  "#00AFBB", high = "#FC4E07")+
  theme_minimal()

• Create a scatter plot with 2d density estimation:

# Add 2d density estimation
sp <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point(color = "lightgray")
sp + geom_density_2d()
    

# Use different geometry and change the gradient color
sp + stat_density_2d(aes(fill = ..level..), geom = "polygon") +
  scale_fill_gradientn(colors = c("#FFEDA0", "#FEB24C", "#F03B20"))

Zoom in a scatter plot

• Key function: facet_zomm() [in ggforce] (Pedersen 2016).
• Demo data set: iris. The R code below zoom the points where Species == "versicolor".

library(ggforce)
ggplot(iris, aes(Petal.Length, Petal.Width, colour = Species)) +
  geom_point() +
  ggpubr::color_palette("jco") + 
  facet_zoom(x = Species == "versicolor")+
  theme_bw()

To zoom the points, where Petal.Length < 2.5, type this:

ggplot(iris, aes(Petal.Length, Petal.Width, colour = Species)) +
  geom_point() +
  ggpubr::color_palette("jco") + 
  facet_zoom(x = Petal.Length < 2.5)+
  theme_bw()

Add trend lines and equations

In this section, we’ll describe how to add trend lines to a scatter plot and labels (equation, R2, BIC, AIC) for a fitted lineal model.

1. Load packages and create a basic scatter plot facetted by groups:

# Load packages and set theme
library(ggpubr)
library(ggpmisc)

theme_set(
  theme_bw() +
    theme(legend.position = "top")
  )

# Scatter plot
p <- ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point(aes(color = Species), size = 3, alpha = 0.6) +
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07")) +
  scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))+
  facet_wrap(~Species)

2. Add regression line, correlation coefficient and equantions of the fitted line. Key functions:
   • stat_smooth() [ggplot2]
   • stat_cor() [ggpubr]
   • stat_poly_eq()[ggpmisc]

formula <- y ~ x
p + 
  stat_smooth( aes(color = Species, fill = Species), method = "lm") +
  stat_cor(aes(color = Species), label.y = 4.4)+
  stat_poly_eq(
    aes(color = Species, label = ..eq.label..),
    formula = formula, label.y = 4.2, parse = TRUE)

3. Fit polynomial equation:

• Create some data:

set.seed(4321)
x <- 1:100
y <- (x + x^2 + x^3) + rnorm(length(x), mean = 0, sd = mean(x^3) / 4)
my.data <- data.frame(x, y, group = c("A", "B"), 
                      y2 = y * c(0.5,2), block = c("a", "a", "b", "b"))

• Fit polynomial regression line and add labels:

# Polynomial regression. Sow equation and adjusted R2
formula <- y ~ poly(x, 3, raw = TRUE)
p <- ggplot(my.data, aes(x, y2, color = group)) +
  geom_point() +
  geom_smooth(aes(fill = group), method = "lm", formula = formula) +
  stat_poly_eq(
    aes(label =  paste(..eq.label.., ..adj.rr.label.., sep = "~~~~")),
    formula = formula, parse = TRUE
    )
ggpar(p, palette = "jco")

Conclusion

1. Create a basic scatter plot:

b <- ggplot(mtcars, aes(x = wt, y = mpg))

Possible layers, include:

• geom_point() for scatter plot
• geom_smooth() for adding smoothed line such as regression line
• geom_rug() for adding a marginal rug
• geom_text() for adding textual annotations

2. Continuous bivariate distribution:

c <- ggplot(diamonds, aes(carat, price))

Possible layers include:

• geom_bin2d(): Rectangular binning.
• geom_hex(): Hexagonal binning.
• geom_density_2d(): Contours from a 2d density estimate

See also

• ggpubr: Publication Ready Plots. https://goo.gl/7uySha
• Perfect Scatter Plots with Correlation and Marginal Histograms. https://goo.gl/3o4ddg

Demo data set and R package

library("magrittr") # for piping %>%
head(iris, 3)

##   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

Create a 3d scatter plot

You can create a 3d scatter plot using the R package scatterplot3d (Ligges, Maechler, and Schnackenberg 2017), which contains a function of the same name.

• Install: install.packages("scatterplot3d")

• Create a basic 3d scatter plot:

library(scatterplot3d)
scatterplot3d(
  iris[,1:3], pch = 19, color = "steelblue",
   grid = TRUE, box = FALSE,
   mar = c(3, 3, 0.5, 3)        
  )

• See more examples at: https://www.sthda.com/english/wiki/3d-graphics

Create a scatter plot matrix

To create a scatter plot of each possible pairs of variables, you can use the function ggpairs() [in GGally package, an extension of ggplot2](Schloerke et al. 2016) . It produces a pairwise comparison of multivariate data.

• Install: install.packages("GGally")

• Create a simple scatter plot matrix. The plot contains the:
  • Scatter plot and the correlation coefficient between each pair of variables
  • Density distribution of each variable

library(GGally)
library(ggplot2)
ggpairs(iris[,-5])+ theme_bw()

• Create a scatter plot matrix by groups. The plot contains the :
  • Scatter plot and the correlation coefficient, between each pair of variables, colored by groups
  • Density distribution and the box plot, of each continuous variable, colored by groups

p <- ggpairs(iris, aes(color = Species))+ theme_bw()
# Change color manually.
# Loop through each plot changing relevant scales
for(i in 1:p$nrow) {
  for(j in 1:p$ncol){
    p[i,j] <- p[i,j] + 
        scale_fill_manual(values=c("#00AFBB", "#E7B800", "#FC4E07")) +
        scale_color_manual(values=c("#00AFBB", "#E7B800", "#FC4E07"))  
  }
}
p

An alternative to the function ggpairs() is provided by the R base plot function chart.Correlation() [in PerformanceAnalytics packages]. It displays the correlation coefficient and the significance levels as stars.

For example, type the following R code, after installing the PerformanceAnalytics package:

# install.packages("PerformanceAnalytics")
library("PerformanceAnalytics")
my_data <- mtcars[, c(1,3,4,5,6,7)]
chart.Correlation(my_data, histogram=TRUE, pch=19)

Correlation analysis

Recall that, correlation analysis is used to investigate the association between two or more variables. Read more at: Correlation analyses in R.

1. Compute correlation matrix between pairs of variables using the R base function cor()
2. Visualize the output. Two possibilities:
   • Use the function ggcorrplot() [in ggcorplot package]. Extension to the ggplot2 system. See more examples at: https://www.sthda.com/english/wiki/ggcorrplot-visualization-of-a-correlation-matrix-using-ggplot2.
   • Use the function corrplot() [in corrplot package]. R base plotting system. See examples at: https://www.sthda.com/english/wiki/visualize-correlation-matrix-using-correlogram.

Here, we’ll present only the ggcorrplot package (Kassambara 2016), which can be installed as follow: install.packages("ggcorrplot").

library("ggcorrplot")
# Compute a correlation matrix
my_data <- mtcars[, c(1,3,4,5,6,7)]
corr <- round(cor(my_data), 1)
# Visualize
ggcorrplot(corr, p.mat = cor_pmat(my_data),
           hc.order = TRUE, type = "lower",
           color = c("#FC4E07", "white", "#00AFBB"),
           outline.col = "white", lab = TRUE)

In the plot above:

• Positive correlations are shown in blue and negative correlation in red
• Variables that are associated are grouped together.
• Non-significant correlation are marked by a cross (X)

Principal component analysis

Principal component analysis (PCA) is a multivariate data analysis approach that allows us to summarize and visualize the most important information contained in a multivariate data set.

PCA reduces the data into few new dimensions (or axes), which are a linear combination of the original variables. You can visualize a multivariate data by drawing a scatter plot of the first two dimensions, which contain the most important information in the data. Read more at: https://goo.gl/kabVHq

• Demo data set: iris
• Compute PCA using the R base function prcomp()
• Visualize the output using the factoextra R package (an extension to ggplot2) (Kassambara and Mundt 2017)

library("factoextra")
my_data <- iris[, -5] # Remove the grouping variable
res.pca <- prcomp(my_data, scale = TRUE)
fviz_pca_biplot(res.pca, col.ind = iris$Species,
                palette = "jco", geom = "point")

In the plot above:

• Dimension (Dim.) 1 and 2 retained about 96% (73% + 22.9%) of the total information contained in the data set.
• Individuals with a similar profile are grouped together
• Variables that are positively correlated are on the same side of the plots. Variables that are negatively correlated are on the opposite side of the plots.

Cluster analysis

Cluster analysis is one of the important data mining methods for discovering knowledge in multidimensional data. The goal of clustering is to identify pattern or groups of similar objects within a data set of interest. Read more at: https://www.sthda.com/english/articles/25-cluster-analysis-in-r-practical-guide/.

This section describes how to compute and visualize hierarchical clustering, which output is a tree called dendrogram showing groups of similar individuals.

• Computation. R function: hclust(). It takes a dissimilarity matrix as an input, which is calculated using the function dist().
• Visualization: fviz_dend() [in factoextra]
• Demo data sets: USArrests

Before cluster analysis, it’s recommended to scale (or normalize) the data, to make the variables comparable. R function: scale(), applies scaling on the column of the data (variables).

library(factoextra)
USArrests %>%
  scale() %>%                           # Scale the data
  dist() %>%                            # Compute distance matrix
  hclust(method = "ward.D2") %>%        # Hierarchical clustering
  fviz_dend(cex = 0.5, k = 4, palette = "jco") # Visualize and cut 
                                              # into 4 groups

A heatmap is another way to visualize hierarchical clustering. It’s also called a false colored image, where data values are transformed to color scale. Heat maps allow us to simultaneously visualize groups of samples and features. You can easily create a pretty heatmap using the R package pheatmap.

In heatmap, generally, columns are samples and rows are variables. Therefore we start by scaling and then transpose the data before creating the heatmap.

library(pheatmap)
USArrests %>%
  scale() %>%                  # Scale variables
  t() %>%                      # Transpose 
  pheatmap(cutree_cols = 4)    # Create the heatmap

Conclusion

For a multivariate continuous data, you can perform the following analysis or visualization depending on the complexity of your data:

• 3D scatter plot : scatterplot3d() [scatterplot3d]
• Create a scatter plot matrix: ggpairs [GGally]
• Correlation matrix analysis and visualization: cor()[stats] and ggcorrplot() [ggcorrplot] for the visualization.
• Principal component analysis: prcomp() [stats] and fviz_pca() [factoextra]
• Cluster analysis: hclust() [stats] and fviz_dend() [factoextra]

See also

Visualizing Multivariate Categorical Data

Prerequisites

Load required R packages and set the default theme:

library(ggplot2)
library(ggpubr)
theme_set(theme_pubr())

Bar plots of contingency tables

Demo data set: HairEyeColor (distribution of hair and eye color and sex in 592 statistics students)

• Prepare and inspect the data:

data("HairEyeColor")
df <- as.data.frame(HairEyeColor)
head(df)

##    Hair   Eye  Sex Freq
## 1 Black Brown Male   32
## 2 Brown Brown Male   53
## 3   Red Brown Male   10
## 4 Blond Brown Male    3
## 5 Black  Blue Male   11
## 6 Brown  Blue Male   50

• Create the bar graph:
  • Hair color on x-axis
  • Change bar fill by Eye color
  • Split the graph into multiple panel by Sex

ggplot(df, aes(x = Hair, y = Freq))+
  geom_bar(
    aes(fill = Eye), stat = "identity", color = "white",
    position = position_dodge(0.9)
    )+
  facet_wrap(~Sex) + 
  fill_palette("jco")

Balloon plot

Balloon plot is an alternative to bar plot for visualizing a large categorical data. We’ll use the function ggballoonplot() [in ggpubr], which draws a graphical matrix of a contingency table, where each cell contains a dot whose size reflects the relative magnitude of the corresponding component.

Demo data sets: Housetasks (a contingency table containing the frequency of execution of 13 house tasks in the couple.)

housetasks <- read.delim(
  system.file("demo-data/housetasks.txt", package = "ggpubr"),
  row.names = 1
  )
head(housetasks, 4)

##            Wife Alternating Husband Jointly
## Laundry     156          14       2       4
## Main_meal   124          20       5       4
## Dinner       77          11       7      13
## Breakfeast   82          36      15       7

• Create a simple balloon plot of a contingency table. Change the fill color by the values in the cells.

ggballoonplot(housetasks, fill = "value")+
  scale_fill_viridis_c(option = "C")

• Visualize a grouped frequency table. Demo data set: HairEyeColor. Create a multi-panel plot by Sex

df <- as.data.frame(HairEyeColor)
ggballoonplot(df, x = "Hair", y = "Eye", size = "Freq",
              fill = "Freq", facet.by = "Sex",
              ggtheme = theme_bw()) +
  scale_fill_viridis_c(option = "C")

Mosaic plot

A mosaic plot is basically an area-proportional visualization of observed frequencies, composed of tiles (corresponding to the cells) created by recursive vertical and horizontal splits of a rectangle. The area of each tile is proportional to the corresponding cell entry, given the dimensions of previous splits.

Mosaic graph can be created using either the function mosaicplot() [in graphics] or the function mosaic() [in vcd package]. Read more at: Visualizing Multi-way Contingency Tables with vcd.

Example of mosaic plot:

library(vcd)
mosaic(HairEyeColor, shade = TRUE, legend = TRUE) 

Correspondence analysis

Correspondence analysis can be used to summarize and visualize the information contained in a large contingency table formed by two categorical variables.

Required package: FactoMineR for the analysis and factoextra for the visualization

library(FactoMineR)
library(factoextra)
res.ca <- CA(housetasks, graph = FALSE)
fviz_ca_biplot(res.ca, repel = TRUE)

From the graphic above, it’s clear that:

• Housetasks such as dinner, breakfeast, laundry are done more often by the wife
• Driving and repairs are done more frequently by the husband

Read more at: Correspondence analysis in R

Basic ggplot of time series

• Plot types: line plot with dates on x-axis
• Demo data set: economics [ggplot2] time series data sets are used.

In this section we’ll plot the variables psavert (personal savings rate) and uempmed (number of unemployed in thousands) by date (x-axis).

• Load required packages and set the default theme:

library(ggplot2)
theme_set(theme_minimal())
# Demo dataset
head(economics)

## # A tibble: 6 x 6
##         date   pce    pop psavert uempmed unemploy
##                    
## 1 1967-07-01   507 198712    12.5     4.5     2944
## 2 1967-08-01   510 198911    12.5     4.7     2945
## 3 1967-09-01   516 199113    11.7     4.6     2958
## 4 1967-10-01   513 199311    12.5     4.9     3143
## 5 1967-11-01   518 199498    12.5     4.7     3066
## 6 1967-12-01   526 199657    12.1     4.8     3018

• Create basic line plots

# Basic line plot
ggplot(data = economics, aes(x = date, y = pop))+
  geom_line(color = "#00AFBB", size = 2)

# Plot a subset of the data
ss <- subset(economics, date > as.Date("2006-1-1"))
ggplot(data = ss, aes(x = date, y = pop)) + 
  geom_line(color = "#FC4E07", size = 2)

• Control line size by the value of a continuous variable:

ggplot(data = economics, aes(x = date, y = pop)) +
  geom_line(aes(size = unemploy/pop), color = "#FC4E07")

Plot multiple time series data

Here, we’ll plot the variables psavert and uempmed by dates. You should first reshape the data using the tidyr package: - Collapse psavert and uempmed values in the same column (new column). R function: gather()[tidyr] - Create a grouping variable that with levels = psavert and uempmed

library(tidyr)
library(dplyr)
df <- economics %>%
  select(date, psavert, uempmed) %>%
  gather(key = "variable", value = "value", -date)
head(df, 3)

## # A tibble: 3 x 3
##         date variable value
##            
## 1 1967-07-01  psavert  12.5
## 2 1967-08-01  psavert  12.5
## 3 1967-09-01  psavert  11.7

# Multiple line plot
ggplot(df, aes(x = date, y = value)) + 
  geom_line(aes(color = variable), size = 1) +
  scale_color_manual(values = c("#00AFBB", "#E7B800")) +
  theme_minimal()

# Area plot
ggplot(df, aes(x = date, y = value)) + 
  geom_area(aes(color = variable, fill = variable), 
            alpha = 0.5, position = position_dodge(0.8)) +
  scale_color_manual(values = c("#00AFBB", "#E7B800")) +
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))

Set date axis limits

Key R function: scale_x_date()

# Base plot with date axis
p <- ggplot(data = economics, aes(x = date, y = psavert)) + 
     geom_line(color = "#00AFBB", size = 1)
p

# Set axis limits c(min, max)
min <- as.Date("2002-1-1")
max <- NA
p + scale_x_date(limits = c(min, max))

Format date axis labels

Key function: scale_x_date().

To format date axis labels, you can use different combinations of days, weeks, months and years:

• Weekday name: use %a and %A for abbreviated and full weekday name, respectively
• Month name: use %b and %B for abbreviated and full month name, respectively
• %d: day of the month as decimal number
• %Y: Year with century.
• See more options in the documentation of the function ?strptime

# Format : month/year
p + scale_x_date(date_labels = "%b/%Y")

Add trend smoothed line

Key function: stat_smooth()

p + stat_smooth(
  color = "#FC4E07", fill = "#FC4E07",
  method = "loess"
  )

ggplot2 extensions for ts objects

The ggfortify package is an extension to ggplot2 that makes it easy to plot time series objects (Horikoshi and Tang 2017). It can handle the output of many time series packages, including: zoo::zooreg(), xts::xts(), timeSeries::timSeries(), tseries::irts(), forecast::forecast(), vars:vars().

Another interesting package is the ggpmisc package (Aphalo 2017), which provides two useful methods for time series object:

• stat_peaks() finds at which x positions local y maxima are located, and
• stat_valleys() finds at which x positions local y minima are located.

Here, we’ll show how to easily:

• Visualize a time series object, using the data set AirPassengers (monthly airline passenger numbers 1949-1960).
• Identify shifts in mean and/or variance in a time series using the changepoint package.
• Detect jumps in a data using the strucchange package and the data set Nile (Measurements of the annual flow of the river Nile at Aswan).
• Detect peaks and valleys using the ggpmisc package and the data set lynx (Annual Canadian Lynx trappings 1821–1934).

First, install required R packages:

install.packages(
  c("ggfortify", "changepoint",
    "strucchange", "ggpmisc")
)

Then use the autoplot.ts() function to visualize time series objects, as follow:

library(ggfortify)
library(magrittr) # for piping %>%

# Plot ts objects
autoplot(AirPassengers)

# Identify change points in mean and variance
AirPassengers %>%
  changepoint:: cpt.meanvar() %>%  # Identify change points
  autoplot()

# Detect jump in a data
strucchange::breakpoints(Nile ~ 1) %>%
  autoplot()

Detect peaks and valleys:

library(ggpmisc)
ggplot(lynx, as.numeric = FALSE) + geom_line() + 
  stat_peaks(colour = "red") +
  stat_peaks(geom = "text", colour = "red", 
             vjust = -0.5, x.label.fmt = "%Y") +
  stat_valleys(colour = "blue") +
  stat_valleys(geom = "text", colour = "blue", angle = 45,
               vjust = 1.5, hjust = 1,  x.label.fmt = "%Y")+
  ylim(-500, 7300)

Prerequisites

Load required packages and set the theme function theme_light() [ggplot2] as the default theme:

library(ggplot2)
theme_set(
  theme_light() + theme(legend.position = "top")
  )

Create a box plot filled by groups:

# Load data and convert dose to a factor variable
data("ToothGrowth")
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
# Box plot
p <- ggplot(ToothGrowth, aes(x = dose, y = len)) + 
  geom_boxplot(aes(fill = supp), position = position_dodge(0.9)) +
  scale_fill_manual(values = c("#00AFBB", "#E7B800"))
p

Key R functions: facet_grid and facet_wrap

The following functions can be used for facets:

• p + facet_grid(supp ~ .): Facet in vertical direction based on the levels of supp variable.
• p + facet_grid(. ~ supp): Facet in horizontal direction based on the levels of supp variable.
• p + facet_grid(dose ~ supp): Facet in horizontal and vertical directions based on two variables: dose and supp.
• p + facet_wrap(~ fl): Place facet side by side into a rectangular layout

Using facet_grid

1. Facet with one discrete variable. Split by the levels of the group “supp”

# Split in vertical direction
p + facet_grid(supp ~ .)

# Split in horizontal direction
p + facet_grid(. ~ supp)

2. Facet with multiple variables. Split by the levels of two grouping variables: “dose” and “supp”

# Facet by two variables: dose and supp.
# Rows are dose and columns are supp
p + facet_grid(dose ~ supp)

# Facet by two variables: reverse the order of the 2 variables
# Rows are supp and columns are dose
p + facet_grid(supp ~ dose)

Note that, you can use the argument margins to add additional facets which contain all the data for each of the possible values of the faceting variables

p + facet_grid(dose ~ supp, margins=TRUE)

Using facet_wrap

facet_wrap: Facets can be placed side by side using the function facet_wrap() as follow :

p + facet_wrap(~ dose)

p + facet_wrap(~ dose, ncol=2)

Facet scales

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

p + facet_grid(dose ~ supp, scales = 'free')

Change facet labels

Change facet labels. The argument labeller can be used to change facet labels. Should be a function.

• In the following R code, facets are labelled by combining the name of the grouping variable with group levels. The labeller function label_both is used.

p + facet_grid(dose ~ supp, labeller = label_both)

• A simple way to modify facet label text, is to provide new labels as a named character vector:

# New facet label names for dose variable
dose.labs <- c("D0.5", "D1", "D2")
names(dose.labs) <- c("0.5", "1", "2")

# New facet label names for supp variable
supp.labs <- c("Orange Juice", "Vitamin C")
names(supp.labs) <- c("OJ", "VC")

# Create the plot
p + facet_grid(
  dose ~ supp, 
  labeller = labeller(dose = dose.labs, supp = supp.labs)
  )

• An alternative solution to change the facet labels, is to modify the data:

df <- ToothGrowth
# Modify the data
df$dose <- factor(df$dose, levels = c("0.5", "1", "2"), 
                  labels = c("D0.5", "D1", "D2"))
df$supp <- factor(df$supp, levels = c("OJ", "VC"),
                  labels = c("Orange Juice", "Vitamin C")
                  )
# Create the plot
ggplot(df, aes(x = dose, y = len)) + 
  geom_boxplot(aes(fill = supp)) +
  facet_grid(dose ~ supp)

Change facet labels appearance:

# Change facet text font. Possible values for the font style:
  #'plain', 'italic', 'bold', 'bold.italic'.
p + facet_grid(dose ~ supp)+
    theme(
      strip.text.x = element_text(
        size = 12, color = "red", face = "bold.italic"
        ),
      strip.text.y = element_text(
        size = 12, color = "red", face = "bold.italic"
        )
      )

Change facet background color. The rectangle around facet labels can be modified using the function element_rect().

p + facet_grid(dose ~ supp)+
 theme(
   strip.background = element_rect(
     color="black", fill="#FC4E07", size=1.5, linetype="solid"
     )
   )

See also

• Create and Customize Multi-panel ggplots: Easy Guide to Facet. https://goo.gl/eRKHV7

Prerequisites

Load required packages and set the theme function theme_pubr() [in ggpubr] as the default theme:

library(ggplot2)
library(ggpubr)
theme_set(theme_pubr())

Arrange on one page

• Create some basic plots as follow:

# 0. Define custom color palette and prepare the data
my3cols <- c("#E7B800", "#2E9FDF", "#FC4E07")
ToothGrowth$dose <- as.factor(ToothGrowth$dose)

# 1. Create a box plot (bp)
p <- ggplot(ToothGrowth, aes(x = dose, y = len))
bxp <- p + geom_boxplot(aes(color = dose)) +
  scale_color_manual(values = my3cols)

# 2. Create a dot plot (dp)
dp <- p + geom_dotplot(aes(color = dose, fill = dose), 
                       binaxis=&#39;y&#39;, stackdir=&#39;center&#39;) +
  scale_color_manual(values = my3cols) + 
  scale_fill_manual(values = my3cols)

# 3. Create a line plot
lp <- ggplot(economics, aes(x = date, y = psavert)) + 
  geom_line(color = "#E46726") 

• Combine multiple ggplot on one page. Use the function ggarrange()[ggpubr package], a wrapper around the function plot_grid() [cowplot package]. Compared to plot_grid(), ggarange() can arrange multiple ggplots over multiple pages.

figure <- ggarrange(bxp, dp, lp,
                    labels = c("A", "B", "C"),
                    ncol = 2, nrow = 2)
figure

Annotate the arranged figure

Key R function: annotate_figure() [in ggpubr].

annotate_figure(
  figure,
  top = text_grob("Visualizing len",
                  color = "red", face = "bold", size = 14),
  bottom = text_grob("Data source: \n ToothGrowth", color = "blue",
                     hjust = 1, x = 1, face = "italic", size = 10),
  left = text_grob("Fig arranged using ggpubr",
                   color = "green", rot = 90),
  right = "I'm done, thanks :-)!",
  fig.lab = "Figure 1", fig.lab.face = "bold"
  )

Change column and row span of a plot

We’ll use nested ggarrange() functions to change column/row span of plots. For example, using the R code below:

• the line plot (lp) will live in the first row and spans over two columns
• the box plot (bxp) and the dot plot (dp) will be first arranged and will live in the second row with two different columns

ggarrange(
  lp,                # First row with line plot
  # Second row with box and dot plots
  ggarrange(bxp, dp, ncol = 2, labels = c("B", "C")), 
  nrow = 2, 
  labels = "A"       # Label of the line plot
  ) 

Use shared legend for combined ggplots

To place a common unique legend in the margin of the arranged plots, the function ggarrange() [in ggpubr] can be used with the following arguments:

• common.legend = TRUE: place a common legend in a margin
• legend: specify the legend position. Allowed values include one of c(“top”, “bottom”, “left”, “right”)

ggarrange(
  bxp, dp, labels = c("A", "B"),
  common.legend = TRUE, legend = "bottom"
  )

Mix table, text and ggplot2 graphs

In this section, we’ll show how to plot a table and text alongside a chart. The iris data set will be used.

We start by creating the following plots:

1. a density plot of the variable “Sepal.Length”. R function: ggdensity() [in ggpubr]
2. a plot of the summary table containing the descriptive statistics (mean, sd, … ) of Sepal.Length.
   • R function for computing descriptive statistics: desc_statby() [in ggpubr].
   • R function to draw a textual table: ggtexttable() [in ggpubr].
3. a plot of a text paragraph. R function: ggparagraph() [in ggpubr].

We finish by arranging/combining the three plots using the function ggarrange() [in ggpubr]

# Density plot of "Sepal.Length"
#::::::::::::::::::::::::::::::::::::::
density.p <- ggdensity(iris, x = "Sepal.Length", 
                       fill = "Species", palette = "jco")

# Draw the summary table of Sepal.Length
#::::::::::::::::::::::::::::::::::::::
# Compute descriptive statistics by groups
stable <- desc_statby(iris, measure.var = "Sepal.Length",
                      grps = "Species")
stable <- stable[, c("Species", "length", "mean", "sd")]
# Summary table plot, medium orange theme
stable.p <- ggtexttable(stable, rows = NULL, 
                        theme = ttheme("mOrange"))

# Draw text
#::::::::::::::::::::::::::::::::::::::
text <- paste("iris data set gives the measurements in cm",
              "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.",
             sep = " ")
text.p <- ggparagraph(text = text, face = "italic", size = 11, color = "black")

# Arrange the plots on the same page
ggarrange(density.p, stable.p, text.p, 
          ncol = 1, nrow = 3,
          heights = c(1, 0.5, 0.3))

Arrange over multiple pages

If you have a long list of ggplots, say n = 20 plots, you may want to arrange the plots and to place them on multiple pages. With 4 plots per page, you need 5 pages to hold the 20 plots.

The function ggarrange() [ggpubr] provides a convenient solution to arrange multiple ggplots over multiple pages. After specifying the arguments nrow and ncol,ggarrange()` computes automatically the number of pages required to hold the list of the plots. It returns a list of arranged ggplots.

For example the following R code,

multi.page <- ggarrange(bxp, dp, lp, bxp,
                        nrow = 1, ncol = 2)

returns a list of two pages with two plots per page. You can visualize each page as follow:

multi.page[[1]] # Visualize page 1
multi.page[[2]] # Visualize page 2

You can also export the arranged plots to a pdf file using the function ggexport() [ggpubr]:

ggexport(multi.page, filename = "multi.page.ggplot2.pdf")

See the PDF file: Multi.page.ggplot2

Export the arranged plots

R function: ggexport() [in ggpubr].

• Export the arranged figure to a pdf, eps or png file (one figure per page).

ggexport(figure, filename = "figure1.pdf")

• It’s also possible to arrange the plots (2 plot per page) when exporting them.

Export individual plots to a pdf file (one plot per page):

ggexport(bxp, dp, lp, bxp, filename = "test.pdf")

Arrange and export. Specify the nrow and ncol arguments to display multiple plots on the same page:

ggexport(bxp, dp, lp, bxp, filename = "test.pdf",
         nrow = 2, ncol = 1)

See also

• ggplot2 - Easy Way to Mix Multiple Graphs on The Same Page. https://goo.gl/WrieY4

Prerequisites

1. Load packages and set the default theme:

library(ggplot2)
library(ggpubr)
theme_set(
  theme_pubr() +
    theme(legend.position = "right")
  )

2. Create a box plot (bxp) and a scatter plot (sp) that we’ll customize in the next section:

• Box plot using the ToothGrowth dataset:

# Convert the variable dose from numeric to factor variable
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
bxp <- ggplot(ToothGrowth, aes(x=dose, y=len)) +
  geom_boxplot(aes(color = dose)) +
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

• Scatter plot using the cars dataset

sp <- ggplot(cars, aes(x = speed, y = dist)) + 
  geom_point()

Titles and axis labels

Key function: labs(). Used to change the main title, the subtitle, the axis labels and captions.

1. Add a title, subtitle, caption and change axis labels

bxp <- bxp + labs(title = "Effect of Vitamin C on Tooth Growth",
              subtitle = "Plot of length by dose",
              caption = "Data source: ToothGrowth",
              x = "Dose (mg)", y = "Teeth length")
bxp

2. Change the appearance of titles

• Key functions: theme() and element_text():

theme(
  plot.title = element_text(),
  plot.subtitle.title = element_text(),
  plot.caption = element_text()
)

• Arguments of the function element_text() include:
  • color, size, face, family: to change the text font color, size, face (“plain”, “italic”, “bold”, “bold.italic”) and family.
  • lineheight: change space between two lines of text elements. Number between 0 and 1. Useful for multi-line plot titles.
  • hjust and vjust: number in [0, 1], for horizontal and vertical adjustment of titles, respectively.
    • hjust = 0.5: Center the plot titles.
    • hjust = 1: Place the plot title on the right
    • hjust = 0: Place the plot title on the left
• Examples of R code:
  • Center main title and subtitle (hjust = 0.5)
  • Change color, size and face

bxp + theme(
  plot.title = element_text(color = "red", size = 12, 
                            face = "bold", hjust = 0.5),
  plot.subtitle = element_text(color = "blue", hjust = 0.5),
  plot.caption = element_text(color = "green", face = "italic")
)

3. Case of long titles. If the title is too long, you can split it into multiple lines using \n. In this case you can adjust the space between text lines by specifying the argument lineheight in the theme function element_text():

bxp + labs(title = "Effect of Vitamin C on Tooth Growth \n in Guinea Pigs")+
  theme(plot.title = element_text(lineheight = 0.9))

Axes: Limits, Ticks and Log

sp + coord_cartesian(xlim = c(5, 20), ylim = (0, 50))

# Use this
sp + scale_x_continuous(limits = c(5, 20)) + 
  scale_y_continuous(limits = c(0, 50))

# Or this shothand functions
sp + xlim(5, 20) + ylim(0, 50)

# set the intercept of x and y axes at (0,0)
sp + expand_limits(x = 0, y = 0)

# Expand plot limits
sp + expand_limits(x = c(5, 50), y = c(0, 150))

# Default plot
print(sp)

# Change axis limits using coord_cartesian()
sp + coord_cartesian(xlim =c(5, 20), ylim = c(0, 50))

# set the intercept of x and y axis at (0,0)
sp + expand_limits(x = 0, y = 0)

sp + scale_x_continuous(trans = "log2")

sp + scale_y_continuous(trans = "log2")

sp + coord_trans(x = "log2", y = "log2")

sp + scale_y_log10() + annotation_logticks()

# Set axis into log2 scale
# Possible values for trans : 'log2', 'log10','sqrt'
sp + scale_x_continuous(trans = 'log2') +
  scale_y_continuous(trans = 'log2')

# Format y axis tick mark labels to show exponents
require(scales)
sp + scale_y_continuous(
  trans = log2_trans(),
  breaks = trans_breaks("log2", function(x) 2^x),
  labels = trans_format("log2", math_format(2^.x))
  )

bxp <- ggplot(ToothGrowth, aes(x=dose, y=len)) +
  geom_boxplot(aes(color = dose)) +
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))+
  theme(legend.position = "none")

# Rotate x and y axis text by 45 degree
# face can be "plain", "italic", "bold" or "bold.italic"
bxp + theme(axis.text.x = element_text(face = "bold", color = "#993333", 
                           size = 12, angle = 45),
          axis.text.y = element_text(face = "bold", color = "blue", 
                           size = 12, angle = 45))

# Remove axis ticks and tick mark labels
bxp + theme(
  axis.text.x = element_blank(), # Remove x axis tick labels
  axis.text.y = element_blank(), # Remove y axis tick labels
  axis.ticks = element_blank()   # Remove ticks 
  ) 

bxp + theme( 
  axis.line.y = element_blank(),
  axis.line = element_line(
    color = "gray", size = 1, linetype = "solid"
    )
  )

# Change x axis label and the order of items
bxp + scale_x_discrete(name ="Dose (mg)", 
                    limits = c("2","1","0.5"))

# Rename / Change tick mark labels
bxp + scale_x_discrete(breaks = c("0.5","1","2"),
        labels = c("D0.5", "D1", "D2"))

# Choose which items to display
bxp + scale_x_discrete(limits = c("0.5", "2"))

# Default scatter plot
sp <- ggplot(cars, aes(x = speed, y = dist)) + 
  geom_point()
sp

# Break y axis by a specified value
# a tick mark is shown on every 50
sp + scale_y_continuous(breaks=seq(0, 150, 50))

# Tick marks can be spaced randomly
sp + scale_y_continuous(breaks=c(0, 50, 65, 75, 150))

Legends: Title, Position and Appearance

Start by creating a box plot using the ToothGrowth data set. Change the box plot fill color according to the grouping variable dose.

library(ggplot2)
ToothGrowth$dose <- as.factor(ToothGrowth$dose)
bxp <- ggplot(ToothGrowth, aes(x = dose, y = len))+ 
  geom_boxplot(aes(fill = dose)) +
  scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

# Default plot
bxp

# Change legend title and position
bxp +
  labs(fill = "Dose (mg)") +
  theme(legend.position = "top")

# Change the appearance of legend title and text labels
bxp + theme(
  legend.title = element_text(color = "blue", size = 10),
  legend.text = element_text(color = "red")
  )

# Change legend background color, key size and width
bxp + theme(
  # Change legend background color
  legend.background = element_rect(fill = "darkgray"),
  legend.key = element_rect(fill = "lightblue", color = NA),
  # Change legend key size and key width
  legend.key.size = unit(1.5, "cm"),
  legend.key.width = unit(0.5,"cm") 
  )

# Change the order of legend items
bxp + scale_x_discrete(limits=c("2", "0.5", "1"))

# Edit legend title and labels for the fill aesthetics
bxp + scale_fill_manual(
  values = c("#00AFBB", "#E7B800", "#FC4E07"),
  name = "Dose", 
  breaks = c("0.5", "1", "2"),
  labels = c("D0.5", "D1", "D2")
  )

# Color of lines and points
scale_color_manual(name, labels, limits, breaks)
# For linetypes
scale_linetype_manual(name, labels, limits, breaks)
# For point shapes
scale_shape_manual(name, labels, limits, breaks)
# For point size
scale_size_manual(name, labels, limits, breaks)
# Opacity/transparency
scale_alpha_manual(name, labels, limits, breaks)

Themes gallery

Start by creating a simple box plot:

bxp <- ggplot(ToothGrowth, aes(x = factor(dose), y = len)) + 
  geom_boxplot()

bxp + theme_gray(base_size = 14) 

bxp + theme_bw()

bxp + theme_linedraw()

bxp + theme_light()

bxp + theme_dark() 

bxp + theme_minimal()

bxp + theme_classic()

bxp + theme_void()

Background color and grid lines

• Create a simple box plot:

p <- ggplot(ToothGrowth, aes(factor(dose), len)) +
  geom_boxplot()

• Change the panel background (1) and the plot background (2) colors:

# 1. Change plot panel background color to lightblue
# and the color of major/grid lines to white
p + theme(
  panel.background = element_rect(fill = "#BFD5E3", colour = "#6D9EC1",
                                size = 2, linetype = "solid"),
  panel.grid.major = element_line(size = 0.5, linetype = 'solid',
                                colour = "white"), 
  panel.grid.minor = element_line(size = 0.25, linetype = 'solid',
                                colour = "white")
  )

# 2. Change the plot background color (not the panel)
p + theme(plot.background = element_rect(fill = "#BFD5E3"))

Add background image to ggplot2 graphs

1. Import the background image. Use either the function readJPEG() [in jpeg package] or the function `readPNG()[in png package] depending on the format of the background image.

2. Combine a ggplot with the background image. R function: background_image() [in ggpubr].

# Import the image
img.file <- system.file("https://www.sthda.com/english/sthda-upload/images/r-graphics-essentials/background-image.png",
                        package = "ggpubr")
img.file <- "https://www.sthda.com/english/sthda-upload/images/r-graphics-essentials/background-image.png"

# Combine with ggplot
library(ggpubr)
ggplot(iris, aes(Species, Sepal.Length))+
  background_image(img)+
  geom_boxplot(aes(fill = Species), color = "white", alpha = 0.5)+
  fill_palette("jco")

Colors

A color can be specified either by name (e.g.: “red”) or by hexadecimal code (e.g. : “#FF1234”). In this section, you will learn how to change ggplot colors by groups and how to set gradient colors.

0. Set ggplot theme to theme_minimal():

theme_set(
  theme_minimal() +
    theme(legend.position = "top")
  )

1. Initialize ggplots using the iris data set:

# Box plot
bp <- ggplot(iris, aes(Species, Sepal.Length))

# Scatter plot
sp <- ggplot(iris, aes(Sepal.Length, Sepal.Width))

2. Specify a single color. Change the fill color (in box plots) and points color (in scatter plots).

# Box plot
bp + geom_boxplot(fill = "#FFDB6D", color = "#C4961A") 

# Scatter plot
sp + geom_point(color = "#00AFBB")

3. Change colors by groups.

You can change colors according to a grouping variable by:

• Mapping the argument color to the variable of interest. This will be applied to points, lines and texts
• Mapping the argument fill to the variable of interest. This will change the fill color of areas, such as in box plot, bar plot, histogram, density plots, etc.

It’s possible to specify manually the color palettes by using the functions:

• scale_fill_manual() for box plot, bar plot, violin plot, dot plot, etc
• scale_color_manual() or scale_colour_manual() for lines and points

# Box plot
bp <- bp + geom_boxplot(aes(fill = Species)) 
bp + scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

# Scatter plot
sp <- sp + geom_point(aes(color = Species)) 
sp + scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))

Find below, two color-blind-friendly palettes, one with gray, and one with black (source: http://jfly.iam.u-tokyo.ac.jp/color/).

# The palette with grey:
cbp1 <- c("#999999", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

# The palette with black:
cbp2 <- c("#000000", "#E69F00", "#56B4E9", "#009E73",
          "#F0E442", "#0072B2", "#D55E00", "#CC79A7")

4. Use viridis color palettes. The viridis R package provides color palettes to make beautiful plots that are: printer-friendly, perceptually uniform and easy to read by those with colorblindness. Key functions scale_color_viridis() and scale_fill_viridis()

library(viridis)
# Gradient color
ggplot(iris, aes(Sepal.Length, Sepal.Width))+
  geom_point(aes(color = Sepal.Length)) +
  scale_color_viridis(option = "D")

# Discrete color. use the argument discrete = TRUE
ggplot(iris, aes(Sepal.Length, Sepal.Width))+
  geom_point(aes(color = Species)) +
  geom_smooth(aes(color = Species, fill = Species), method = "lm") + 
  scale_color_viridis(discrete = TRUE, option = "D")+
  scale_fill_viridis(discrete = TRUE) 

5. Use RColorBrewer palettes. Two color scale functions are available in ggplot2 for using the colorbrewer palettes:

• scale_fill_brewer() for box plot, bar plot, violin plot, dot plot, etc
• scale_color_brewer() for lines and points

For example:

# Box plot
bp + scale_fill_brewer(palette = "Dark2")

# Scatter plot
sp + scale_color_brewer(palette = "Dark2")

To display colorblind-friendly brewer palettes, use this R code:

library(RColorBrewer)
display.brewer.all(colorblindFriendly = TRUE)

6. Other discrete color palettes:
   • Scientific journal color palettes in the ggsci R package. Contains a collection of high-quality color palettes inspired by colors used in scientific journals, data visualization libraries, and more. For example:
     • scale_color_npg() and scale_fill_npg(): Nature Publishing Group
     • scale_color_aaas() and scale_fill_aaas(): American Association for the Advancement of Science
     • scale_color_lancet() and scale_fill_lancet(): Lancet journal
     • scale_color_jco() and scale_fill_jco(): Journal of Clinical Oncology
   • Wes Anderson color palettes in the wesanderson R package. Contains 16 color palettes from Wes Anderson movies.

For example:

# jco color palette from the ggsci package
bp + ggsci::scale_fill_jco()

# Discrete color from wesanderson package
library(wesanderson)
bp + scale_fill_manual(
  values = wes_palette("GrandBudapest1", n = 3)
  )

You can find more examples at ggsci package vignettes and at wesanderson github page

7. Set gradient colors. For gradient colors, you should map the map the argument color and/or fill to a continuous variable. In the following example, we color points according to the variable: Sepal.Length.

ggplot(iris, aes(Sepal.Length, Sepal.Width))+
  geom_point(aes(color = Sepal.Length)) +
  scale_color_gradientn(colours = c("blue", "yellow", "red"))+
  theme(legend.position = "right")

8. Design and use the power of color palette at https://goo.gl/F5g3Lb

Points shape, color and size

1. Common point shapes available in R:

ggpubr::show_point_shapes()+
  theme_void()

2. Change ggplot point shapes. The argument shape is used, in the function geom_point() [ggplot2], for specifying point shapes.

It’s also possible to change point shapes and colors by groups. In this case, ggplot2 will use automatically a default color palette and point shapes. You can change manually the appearance of points using the following functions:

• scale_shape_manual() : to change manually point shapes
• scale_color_manual() : to change manually point colors
• scale_size_manual() : to change manually the size of points

Create a scatter plot and change points shape, color and size:

# Create a simple scatter plot
ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point(shape = 18, color = "#FC4E07", size = 3)+
  theme_minimal()

# Change point shapes and colors by groups
ggplot(iris, aes(Sepal.Length, Sepal.Width)) +
  geom_point(aes(shape = Species, color = Species), size = 3) +
  scale_shape_manual(values = c(5, 16, 17)) +
  scale_color_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))+
  theme_minimal() +
  theme(legend.position = "top")

Line types

1. Common line types available in R:

ggpubr::show_line_types()+
  theme_gray()

2. Change line types. To change a single line, use for example linetype = "dashed".

In the following R code, we’ll change line types and colors by groups. To modify the default colors and line types, the function scale_color_manual() and scale_linetype_manual() can be used.

# Create some data.
# # Compute the mean of `len` grouped by dose and supp
library(dplyr)
df2 <- ToothGrowth %>%
  group_by(dose, supp) %>%
  summarise(len.mean = mean(len))
df2

## # A tibble: 6 x 3
## # Groups:   dose [?]
##     dose   supp len.mean
##        
## 1    0.5     OJ    13.23
## 2    0.5     VC     7.98
## 3      1     OJ    22.70
## 4      1     VC    16.77
## 5      2     OJ    26.06
## 6      2     VC    26.14

# Change manually line type and color manually
ggplot(df2, aes(x = dose, y = len.mean, group = supp)) +
  geom_line(aes(linetype = supp, color = supp))+
  geom_point(aes(color = supp))+
  scale_linetype_manual(values=c("solid", "dashed"))+
  scale_color_manual(values=c("#00AFBB","#FC4E07"))

Rotate a ggplot

Key functions:

• coord_flip(): creates horizontal plots
• scale_x_reverse() and scale_y_reverse(): reverse the axis

# Horizontal box plot
ggplot(ToothGrowth, aes(factor(dose), len)) +
  geom_boxplot(fill = "lightgray") +
  theme_bw() +
  coord_flip()

# Reverse y axis
ggplot(mtcars, aes(mpg))+
  geom_density(fill = "lightgray") +
  xlim(0, 40) +
  theme_bw()+
  scale_y_reverse()

Plot annotation

sp <- ggplot(data = mtcars, aes(x = wt, y = mpg)) + 
  geom_point()+theme_bw()

# Add horizontal line at y = 2O; and vertical line at x = 3
sp + geom_hline(yintercept = 20, linetype = "dashed", color = "red") + 
  geom_vline(xintercept = 3, color = "blue", size = 1)

# Add regression line
sp + geom_abline(intercept = 37, slope = -5, color="blue")+
  labs(title = "y = -5X + 37")

# Add a vertical line segment from
# point A(4, 15) to point B(4, 27)
sp + geom_segment(x = 4, y = 15, xend = 4, yend = 27)

# Add arrow at the end of the segment
require(grid)
sp + geom_segment(x = 5, y = 30, xend = 3.5, yend = 25,
                 arrow = arrow(length = unit(0.5, "cm")))

# Add curves
sp + geom_curve(aes(x = 2, y = 15, xend = 3, yend = 15))

# Add rectangles
ggplot(data = mtcars, aes(x = wt, y = mpg)) + 
  geom_rect(xmin = 3, ymin = -Inf, xmax = 4, ymax = Inf,
            fill = "lightgray") +
  geom_point() + theme_bw()

# Add text at a particular coordinate
sp + annotate("text", x = 3, y = 30, 
              label = "Scatter plot",
              color = "red", fontface = 2)