GGPlot Cheat Sheet for Great Customization - Articles (2024)

• Prerequisites
• Titles and axis labels
• Axes: Limits, Ticks and Log
  • Axis limits and scales
  • Log scale
  • Axis Ticks: Set and Rotate Text Labels
• Legends: Title, Position and Appearance
  • Change legend title and position
  • Change the appearance of legends
  • Rename legend labels and change the order of items
• Themes gallery
  • Use themes in ggplot2 package
• Background color and grid lines
• Add background image to ggplot2 graphs
• Colors
• Points shape, color and size
• Line types
• Rotate a ggplot
• Plot annotation
  • Add straight lines
  • Text annotation

The Book:

R Graphics Essentials for Great Data Visualization: +200 Practical Examples You Want to Know for Data Science

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 variableToothGrowth$dose <- as.factor(ToothGrowth$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 thissp + scale_x_continuous(limits = c(5, 20)) + scale_y_continuous(limits = c(0, 50))# Or this shothand functionssp + 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 limitssp + expand_limits(x = c(5, 50), y = c(0, 150))

# Default plotprint(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 exponentsrequire(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 labelsbxp + 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 itemsbxp + scale_x_discrete(name ="Dose (mg)", limits = c("2","1","0.5"))# Rename / Change tick mark labelsbxp + scale_x_discrete(breaks = c("0.5","1","2"), labels = c("D0.5", "D1", "D2"))# Choose which items to displaybxp + scale_x_discrete(limits = c("0.5", "2"))

# Default scatter plotsp <- ggplot(cars, aes(x = speed, y = dist)) + geom_point()sp# Break y axis by a specified value# a tick mark is shown on every 50sp + scale_y_continuous(breaks=seq(0, 150, 50))# Tick marks can be spaced randomlysp + 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 plotbxp# Change legend title and positionbxp + labs(fill = "Dose (mg)") + theme(legend.position = "top")

# Change the appearance of legend title and text labelsbxp + theme( legend.title = element_text(color = "blue", size = 10), legend.text = element_text(color = "red") )# Change legend background color, key size and widthbxp + 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 itemsbxp + scale_x_discrete(limits=c("2", "0.5", "1"))# Edit legend title and labels for the fill aestheticsbxp + 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 pointsscale_color_manual(name, labels, limits, breaks)# For linetypesscale_linetype_manual(name, labels, limits, breaks)# For point shapesscale_shape_manual(name, labels, limits, breaks)# For point sizescale_size_manual(name, labels, limits, breaks)# Opacity/transparencyscale_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 whitep + 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 imageimg.file <- system.file("http://www.sthda.com/english/sthda-upload/images/r-graphics-essentials/background-image.png", package = "ggpubr")img.file <- "http://www.sthda.com/english/sthda-upload/images/r-graphics-essentials/background-image.png"# Combine with ggplotlibrary(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 plotbp <- ggplot(iris, aes(Species, Sepal.Length))# Scatter plotsp <- 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 plotbp + geom_boxplot(fill = "#FFDB6D", color = "#C4961A") # Scatter plotsp + 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 plotbp <- bp + geom_boxplot(aes(fill = Species)) bp + scale_fill_manual(values = c("#00AFBB", "#E7B800", "#FC4E07"))# Scatter plotsp <- 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 colorggplot(iris, aes(Sepal.Length, Sepal.Width))+ geom_point(aes(color = Sepal.Length)) + scale_color_viridis(option = "D")# Discrete color. use the argument discrete = TRUEggplot(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 plotbp + scale_fill_brewer(palette = "Dark2")# Scatter plotsp + 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 packagebp + ggsci::scale_fill_jco()# Discrete color from wesanderson packagelibrary(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()

Note that, the point shape options from pch 21 to 25 are open symbols that can be filled by a color. Therefore, you can use the fill argument in geom_point() for these symbols.

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 plotggplot(iris, aes(Sepal.Length, Sepal.Width)) + geom_point(shape = 18, color = "#FC4E07", size = 3)+ theme_minimal()# Change point shapes and colors by groupsggplot(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 supplibrary(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 manuallyggplot(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 plotggplot(ToothGrowth, aes(factor(dose), len)) + geom_boxplot(fill = "lightgray") + theme_bw() + coord_flip()# Reverse y axisggplot(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 = 3sp + geom_hline(yintercept = 20, linetype = "dashed", color = "red") + geom_vline(xintercept = 3, color = "blue", size = 1)# Add regression linesp + 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 segmentrequire(grid)sp + geom_segment(x = 5, y = 30, xend = 3.5, yend = 25, arrow = arrow(length = unit(0.5, "cm")))# Add curvessp + geom_curve(aes(x = 2, y = 15, xend = 3, yend = 15))# Add rectanglesggplot(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 coordinatesp + annotate("text", x = 3, y = 30, label = "Scatter plot", color = "red", fontface = 2)