Ggplot2

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ggplot2

Books

The Grammar of Graphics

  • Data: Raw data that we'd like to visualize
  • Geometrics: shapes that we use to visualize data
  • Aesthetics: Properties of geometries (size, color, etc)
  • Scales: Mapping between geometries and aesthetics

Scatterplot aesthetics

geom_point(). The aesthetics is geom dependent.

  • x, y
  • shape
  • color
  • size. It is not always to put 'size' inside aes(). See an example at Legend layout.
  • alpha
library(ggplot2)
library(tidyverse)
set.seed(1)
x1 <- rbinom(100, 1, .5) - .5
x2 <- c(rnorm(50, 3, .8)*.1, rnorm(50, 8, .8)*.1)
x3 <- x1*x2*2
# x=1:100, y=x1, x2, x3
tibble(x=1:length(x1), T=x1, S=x2, I=x3) %>% 
  tidyr::pivot_longer(-x) %>% 
  ggplot(aes(x=x, y=value)) + 
  geom_point(aes(color=name))

# Cf
matplot(1:length(x1), cbind(x1, x2, x3), pch=16, 
        col=c('cornflowerblue', 'springgreen3', 'salmon'))

Online tutorials

Help

> library(ggplot2)
Need help? Try Stackoverflow: https://stackoverflow.com/tags/ggplot2

Gallery

Some examples

Examples from 'R for Data Science' book - Aesthetic mappings

ggplot(data = mpg) + 
  geom_point(mapping = aes(x = displ, y = hwy))
  # the 'mapping' is the 1st argument for all geom_* functions, so we can safely skip it.
# template
ggplot(data = <DATA>) + 
  <GEOM_FUNCTION>(mapping = aes(<MAPPINGS>))

# add another variable through color, size, alpha or shape
ggplot(data = mpg) + 
  geom_point(aes(x = displ, y = hwy, color = class))

ggplot(data = mpg) + 
  geom_point(aes(x = displ, y = hwy, size = class))

ggplot(data = mpg) + 
  geom_point(aes(x = displ, y = hwy, alpha = class))

ggplot(data = mpg) + 
  geom_point(aes(x = displ, y = hwy, shape = class))

ggplot(data = mpg) + 
  geom_point(aes(x = displ, y = hwy), color = "blue")

# add another variable through facets
ggplot(data = mpg) + 
  geom_point(aes(x = displ, y = hwy)) + 
  facet_wrap(~ class, nrow = 2)

# add another 2 variables through facets
ggplot(data = mpg) + 
  geom_point(aes(x = displ, y = hwy)) + 
  facet_grid(drv ~ cyl)

Examples from 'R for Data Science' book - Geometric objects, lines and smoothers

How to Add a Regression Line to a ggplot? 

# Points
ggplot(data = mpg) + 
  geom_point(aes(x = displ, y = hwy)) # we can add color to aes()

# Line plot
ggplot() +
  geom_line(aes(x, y))  # we can add color to aes()

# Smoothed
# 'size' controls the line width
ggplot(data = mpg) + 
  geom_smooth(aes(x = displ, y = hwy), size=1) 

# Points + smoother, add transparency to points, remove se
# We add transparency if we need to make smoothed line stands out
#                    and points less significant
# We move aes to the '''mapping''' option in ggplot()
ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) + 
  geom_point(alpha=1/10) +
  geom_smooth(se=FALSE)    

# Colored points + smoother
ggplot(data = mpg, aes(x = displ, y = hwy)) + 
  geom_point(aes(color = class)) + 
  geom_smooth()

Examples from 'R for Data Science' book - Transformation, bar plot

# y axis = counts
# bar plot
ggplot(data = diamonds) + 
  geom_bar(aes(x = cut))
# Or
ggplot(data = diamonds) + 
  stat_count(aes(x = cut))

# y axis = proportion
ggplot(data = diamonds) + 
  geom_bar(aes(x = cut, y = ..prop.., group = 1))

# bar plot with 2 variables
ggplot(data = diamonds) + 
  geom_bar(aes(x = cut, fill = clarity))

facet_wrap and facet_grid to create a panel of plots

  • The statement facet_grid() can be defined without a data. For example 
    mylayout <- list(ggplot2::facet_grid(cat_y ~ cat_x))
mytheme <- c(mylayout, 
             list(ggplot2::theme_bw(), ggplot2::ylim(NA, 1)))
# we haven't defined cat_y, cat_x variables
ggplot() + geom_line() + 
  mylayout
  • Multiclass predictive modeling for #TidyTuesday NBER papers
  • changing the facet_wrap labels using labeller in ggplot2. The solution is to create a labeller function as a function of a variable x (or any other name as long as it's not the faceting variables' names) and then coerce to labeller with as_labeller.

lattice::xyplot

df <- data.frame(x = rnorm(100), y = rnorm(100), group = sample(c("A", "B"), 100, replace = TRUE))

# Use the xyplot() function to create the plot
# with each group represented by a different color
# result is 1 plot only
# no annotation
xyplot(y ~ x, data = df, groups = group)

df <- data.frame(x = rnorm(100), y = rnorm(100), 
                 group = sample(c("A", "B"), 100, replace = TRUE), 
                 time = sample(c("T1", "T2"), 100, replace = TRUE))

# 2 plots grouped by time
# two colors (defined by group) was used in each plot 
# no annotation
xyplot(y ~ x | time, groups = group, data = df)

For more complicated plot, we can use the panel parameter.

Color palette

Top color palettes

Display color palettes

  • Use barplot() 
    pal <- c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00")
# pal <- sample(colors(), 10) # randomly pick 10 colors 

barplot(rep(1, length(pal)), col = pal, space = 0, 
        axes = FALSE, border = NA)
par()$usr
# [1] -0.20  5.20 -0.01  1.00

    Palettebarplot.png

  • Use heatmap() 
    pal <- c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00")
pal <- matrix(pal, nr=2) # acknowledge a nice warning message
#      [,1]      [,2]      [,3]     
# [1,] "#E41A1C" "#4DAF4A" "#FF7F00"
# [2,] "#377EB8" "#984EA3" "#E41A1C"
pal_matrix <- matrix(seq_along(pal), nr=nrow(pal), nc=ncol(pal))
heatmap(pal_matrix, col = pal, Rowv = NA, Colv = NA, scale = "none", 
         ylab = "", xlab = "", main = "", margins = c(5, 5))
# 2 rows, 3 columns with labeling on two axes
par()$usr
# [1] 0 1 0 1

    Paletteheatmap.png

  • Use image() 
    pal <- palette() # R 4.0 has a new default palette
                 # The old colors are highly saturated and vary enormousely
                 # in terms of luminance
# [1] "black"   "#DF536B" "#61D04F" "#2297E6" "#28E2E5" "#CD0BBC" "#F5C710"
# [8] "gray62"
pal_matrix <- matrix(seq_along(pal), nr=1)
image(pal_matrix, col = pal, axes = FALSE)
# 8 rows, 1 column, but no labeling
# Starting from bottom, left.

par()$usr  # change with the data dim
text(0, (par()$usr[4]-par()$usr[3])/8*c(0:7), 
     labels = pal)

    Rpalette.png

  • Use scales::show_col() 
    scales::show_col(palette())

    Paletteshowcol.png

colors()

In R, colors() is a function that returns a character vector of color names available in R.

To obtain the hexadecimal codes for all colors obtained by colors() 

rgb_values <- col2rgb(colors())

# Convert the RGB values to hexadecimal codes
hex_codes <- apply(rgb_values, 2, 
                   function(x) rgb(x[1], x[2], x[3], 
                   maxColorValue = 255))

# View the first few hexadecimal codes
head(hex_codes)

palette()

rainbow

  • ?rainbow
  • Below compare the effects of 's' and 'v' parameters. s (saturation) and v (value): These parameters control the color intensity and brightness, respectively. See also HSL and HSV from wikipedia.
    • Saturation (s): Determines how vivid or muted the colors are. A value of 1 (default) means fully saturated colors, while lower values reduce the intensity.
    • Value (v): Controls the brightness. A value of 1 (default) results in full brightness, while lower values make the colors darker.

Rainbow default.png Rainbow s05.png Rainbow v05.png

Color blind

colorblindcheck: Check Color Palettes for Problems with Color Vision Deficiency

Color picker

https://github.com/daattali/colourpicker 

> library(colourpicker)
> plotHelper(colours=5)

Listening on http://127.0.0.1:6023

Color names, Complementary/Inverted colors

colorspace package

cols4all

c4a_gui() # it will create a shiny interface (but R will not be used at the same time)

c4a_types() # understand abbreviation

c4a_series() # 16 series like brewer, hcl, tableau, viridis, etc

c4a_overview() # how many palettes per series x types

c4a_palettes(type = "div", series = "hcl") # What palettes are available

# Give me the colors
c4a("hcl.purple_green", 11)
c4a("brewer.accent", 2)    # the 1st one on the website

# Plot the colors
c4a_plot("hcl.purple_green", 11, include.na = TRUE)

*paletteer package

paletteer_d("RColorBrewer::RdBu")
#67001FFF #B2182BFF #D6604DFF #F4A582FF #FDDBC7FF #F7F7F7FF 
#D1E5F0FF #92C5DEFF #4393C3FF #2166ACFF #053061FF 

paletteer_d("ggsci::uniform_startrek")
#CC0C00FF #5C88DAFF #84BD00FF #FFCD00FF #7C878EFF #00B5E2FF #00AF66FF 

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
      geom_point() +
      scale_color_paletteer_d("ggsci::uniform_startrek")
# the next is the same as above
ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
     geom_point() +
     scale_color_manual(values = c("setosa" = "#CC0C00FF", 
                                   "versicolor" = "#5C88DAFF", 
                                   "virginica" = "#84BD00FF"))

ggsci

ggokabeito

ggokabeito: Colorblind-friendly, qualitative 'Okabe-Ito' Scales for ggplot2 and ggraph. It seems to only support up to 9 classes/colors. It will give an error message if we have too many classes; e.g. Error: Insufficient values in manual scale. 15 needed but only 9 provided.) 

# Bad
ggplot(mpg, aes(hwy, color = class, fill = class)) +
     geom_density(alpha = .8)

# Bad (single color)
ggplot(mpg, aes(hwy, color = class, fill = class)) +
     geom_density(alpha = .8) +
     scale_fill_brewer(name = "Class") +
     scale_color_brewer(name = "Class")

# Bad
ggplot(mpg, aes(hwy, color = class, fill = class)) +
     geom_density(alpha = .8) +
     scale_fill_brewer(name = "Class", palette ="Set1") +
     scale_color_brewer(name = "Class", palette ="Set1")

# Nice
ggplot(mpg, aes(hwy, color = class, fill = class)) +
     geom_density(alpha = .8) +
     scale_fill_okabe_ito(name = "Class") +
     scale_color_okabe_ito(name = "Class")

Pride palette

Show Pride on Your Plots. gglgbtq package

unikn

Colour related aesthetics: colour, fill and alpha

https://ggplot2.tidyverse.org/reference/aes_colour_fill_alpha.html

Scatterplot with large number of points: alpha

smoothScatter with ggplot2 

ggplot(aes(x, y)) +
    geom_point(alpha=.1)

For base R, we can use the alpha parameter rgb(,,,alpha), 

plot(x, y, col=rgb(0,0,0, alpha=.1))
polygon(df, col=adjustcolor(c("red", "blue"), alpha.f=.3))

Combine colors and shapes in legend

  • https://ggplot2-book.org/scales.html#scale-details In order for legends to be merged, they must have the same name. 
    df <- data.frame(x = 1:3, y = 1:3, z = c("a", "b", "c"))
ggplot(df, aes(x, y)) + geom_point(aes(shape = z, colour = z), size=4)
  • How to Work with Scales in a ggplot2 in R. This solution is better since it allows to change the legend title. Just make sure the title name we put in both scale_* functions are the same. 
    ggplot(mtcars, aes(x=hp, y=mpg)) +
   geom_point(aes(shape=factor(cyl), colour=factor(cyl))) +
   scale_shape_discrete("Cylinders") + # change the legend title from 'factor(cyl)' to 'Cylinders'
   scale_colour_discrete("Cylinders")  # combine shape and colour in one legend; avoid another legend for colour
  • GGPLOT Point Shapes Best Tips
  • Simulated data 
    df <- data.frame(x = rnorm(100), y = rnorm(100),
                 Treatment = rep(c("Before", "After"), each = 50),
                 Response = rep(c("Sensitive", "Resistant"), each = 50),
                 Subject = rep(1:50, times = 2))

ggplot(df, aes(x = x, y = y, shape = Treatment, color = Response)) +
  geom_point() +
  geom_line(aes(group = Subject), alpha = 0.5) +  # Add lines connecting the same subject
  scale_shape_manual(values = c(16, 17)) +  # You can choose different shapes
  scale_color_manual(values = c("blue", "red")) +  # You can choose different colors
  theme_minimal() +
  labs(title = "Scatterplot with Different Shapes and Colors",
       x = "X-axis label",
       y = "Y-axis label",
       shape = "Treatment",
       color = "Response")

ggplot2::scale functions and scales packages

  • Scales control the mapping from data to aesthetics. They take your data and turn it into something that you can see, like size, colour, position or shape.
  • Scales also provide the tools that let you read the plot: the axes and legends.
  • scales 1.2.0

ggplot2::scale_* - axes/axis, legend

https://ggplot2-book.org/scales.html and reference of all scale_* functions. Modifies the scales of the axes, such as the x- and y-axes, color, size, etc.

Naming convention: scale_AestheticName_NameDataType where

  • AestheticName can be x, y, color, fill, size, shape, ...
  • NameDataType can be continuous, discrete, manual or gradient.
  • Table of common functions
scale_AestheticName_NameDataType
scale_x_continuous
scale_x_discrete
scale_x_log10
scale_color_continuous,
scale_color_gradient
scale_color_discrete
scale_color_brewer
scale_color_manual
scale_color_paletteer_d
scale_shape_discrete
scale_fill_brewer,
scale_fill_continuous,
scale_fill_discrete,
scale_fill_gradient
scale_fill_grey,
scale_fill_hue
scale_fill_manual,
scale_colour_viridis_d


Examples:

  • See Figure 12.1: Axis and legend components on the book ggplot2: Elegant Graphics for Data Analysis 
    # Set x-axis label
scale_x_discrete("Car type")   # or a shortcut xlab() or labs()
scale_x_continuous("Displacement")

# Set legend title
scale_colour_discrete("Drive\ntrain")    # or a shortcut labs()

# Change the default color
scale_color_brewer()

# Change the axis scale
scale_x_sqrt()

# Change breaks and their labels
scale_x_continuous(breaks = c(2000, 4000), labels = c("2k", "4k"))

# Relabel the breaks in a categorical scale
scale_y_discrete(labels = c(a = "apple", b = "banana", c = "carrot"))
  • See an example at geom_linerange where we have to specify the limits parameter in order to make "8" < "16" < "20"; otherwise it is 16 < 20 < 8. 
    Browse[2]> order(coordinates$chr)
[1] 3 4 1 2
Browse[2]> coordinates$chr 
[1] "20" "8"  "16" "16"
  • Differences of scale_color_gradient() and scale_color_continuous()
    • scale_color_gradient() (more common than scale_color_continuous) is used to map a continuous variable to a color gradient. It takes two arguments: low and high, which specify the colors for the minimum and maximum values of the variable, respectively. The gradient is automatically generated between these two colors.
    ggplot(data = diamonds, aes(x = carat, y = price, color = depth)) +
  geom_point() +
  scale_color_gradient(low = "blue", high = "red")
    • scale_color_continuous() (useful if we want to specify the labels to display on legend) does not automatically generate the color scale. Instead, it requires the user to specify the values to which the colors should be mapped. The limits argument sets the minimum and maximum values for the variable, and the breaks argument specifies the values at which breaks occur.
    ggplot(data = diamonds, aes(x = carat, y = price, color = depth)) +
     geom_point() +
     scale_color_continuous(name = "Depth", 
                            limits = c(40, 80), 
                            breaks = c(40, 60, 80),
                            labels = c("Shallow", "Moderate", "Deep"), # display on legend
                            type = "gradient")

ylim and xlim in ggplot2 in axes

https://stackoverflow.com/questions/3606697/how-to-set-limits-for-axes-in-ggplot2-r-plots or the Zooming part of the cheatsheet

Use one of the following

  • + scale_x_continuous(limits = c(-5000, 5000))
  • + coord_cartesian(xlim = c(-5000, 5000))
  • + xlim(-5000, 5000)

Emulate ggplot2 default color palette

Paletteggplot2.png

The above can be created by R >= 4.0.0 using the command scales::show_col(palette.colors(palette = "ggplot2")). We should ignore the 1st color (black). Also if n>=5, the colors do not match with the result of show_col(hue_pal()(5)) .

Answer 1 It is just equally spaced hues around the color wheel. Emulate ggplot2 default color palette 

gg_color_hue <- function(n) {
  hues = seq(15, 375, length = n + 1)
  hcl(h = hues, l = 65, c = 100)[1:n]
}

n = 4
cols = gg_color_hue(n)

dev.new(width = 4, height = 4)
plot(1:n, pch = 16, cex = 2, col = cols)

Answer 2 (better, it shows the color values in HEX). It should be read from left to right and then top to down.

scales package 

library(scales)
show_col(hue_pal()(4)) # ("#F8766D", "#7CAE00", "#00BFC4", "#C77CFF")
                       # (Salmon, Christi, Iris Blue, Heliotrope)
show_col(hue_pal()(3)) # ("#F8766D", "#00BA38", "#619CFF")
                       # (Salmon, Dark Pastel Green, Cornflower Blue)
show_col(hue_pal()(2)) # ("#F8767D", "#00BFC4") = (salmon, iris blue) 
           # see https://www.htmlcsscolor.com/ for color names

See also the last example in ggsurv() where the KM plots have 4 strata. The colors can be obtained by scales::hue_pal()(4) with hue_pal()'s default arguments.

R has a function called colorName() to convert a hex code to color name; see roloc package on CRAN.

How to change the default color palette in geom_XXX

  • Simple custom colour palettes with R ggplot graphs
  • Change the color palette for all plots
    • Create a Custom Theme 
      # Define a custom theme with a specific color palette
custom_theme <- theme_minimal() +
  scale_fill_manual(values = c("red", "blue", "green", "purple")) +
  scale_color_manual(values = c("red", "blue", "green", "purple"))

# Set the custom theme as the default
theme_set(custom_theme)
    • ggthemr package
    • rcartocolor package
  • Change the color palette for the current plot only:
    • Using scale_fill_manual() and scale_color_manual() 
      library(ggplot2)

data <- data.frame(
  category = c("A", "B", "C", "D"),
  value = c(3, 5, 2, 8)
)

ggplot(data, aes(x = category, y = value, fill = category)) +
  geom_bar(stat = "identity") +
  scale_fill_manual(values = c("red", "blue", "green", "purple")) +
  theme_minimal()
    • Using scale_fill_brewer() and scale_color_brewer() 
      library(ggplot2)
library(RColorBrewer)

ggplot(data, aes(x = category, y = value, fill = category)) +
  geom_bar(stat = "identity") +
  scale_fill_brewer(palette = "Set3") +
  theme_minimal()
    • Using scale_fill_viridis() and scale_color_viridis() 
      library(ggplot2)
library(viridis)

ggplot(data, aes(x = category, y = value, fill = category)) +
  geom_bar(stat = "identity") +
  scale_fill_viridis(discrete = TRUE) +
  theme_minimal()
    • Using scale_fill_hue() and scale_color_hue() 
      ggplot(data, aes(x = category, y = value, fill = category)) +
  geom_bar(stat = "identity") +
  scale_fill_hue(h = c(0, 360), l = 65, c = 100) +
  theme_minimal()
  • How to change the color in geom_point or lines in ggplot 
    ggplot() + 
  geom_point(data = data, aes(x = time, y = y, color = sample),size=4) +
  scale_color_manual(values = c("A" = "black", "B" = "red"))

ggplot(data = data, aes(x = time, y = y, color = sample)) + 
  geom_point(size=4) + 
  geom_line(aes(group = sample)) + 
  scale_color_manual(values = c("A" = "black", "B" = "red"))

transform scales

How to make that crazy Fox News y axis chart with ggplot2 and scales

Class variables

  • "Set1" is a good choice. See RColorBrewer::display.brewer.all()
  • For ordinal variable, brewer.pal(n, "Spectral") is good. But the middle color is too light. So I modify the middle color 
    brewer.pal(5, "Spectral")
cols[3] <- "#D4C683" # middle of "#FDAE61" and "#ABDDA4"

Red, Green, Blue alternatives

  • Red: "maroon"

Heatmap for single channel

How to Make a Heatmap of Customers in R, source code on github. geom_tile() and geom_text() were used. Heatmap in ggplot2 from https://r-charts.com/.

https://scales.r-lib.org/ 

# White <----> Blue
RColorBrewer::display.brewer.pal(n = 8, name = "Blues")

Heatmap for dual channels

http://www.sthda.com/english/wiki/colors-in-r 

library(RColorBrewer)
# Red <----> Blue
display.brewer.pal(n = 8, name = 'RdBu')
# Hexadecimal color specification 
brewer.pal(n = 8, name = "RdBu")

plot(1:8, col=brewer_pal(palette = "RdBu")(8), pch=20, cex=4)

# Blue <----> Red
plot(1:8, col=rev(brewer_pal(palette = "RdBu")(8)), pch=20, cex=4)

Twopalette.svg

Don't rely on color to explain the data

ggpattern

Don't use very bright or low-contrast colors, accessibility

Create your own scale_fill_FOO and scale_color_FOO

Custom colour palettes for {ggplot2}

Themes and background for ggplot2

Background

  • Export plot in .png with transparent background in base R plot. 
    x = c(1, 2, 3)
op <- par(bg=NA)
plot (x)

dev.copy(png,'myplot.png')
dev.off()
par(op)
  • Transparent background with ggplot2 
    library(ggplot2)
data("airquality")

p <- ggplot(airquality, aes(Solar.R, Temp)) +
     geom_point() +
     geom_smooth() +
     # set transparency
     theme(
        panel.grid.major = element_blank(), 
        panel.grid.minor = element_blank(),
        panel.background = element_rect(fill = "transparent",colour = NA),
        plot.background = element_rect(fill = "transparent",colour = NA)
        )
p
ggsave("airquality.png", p, bg = "transparent")
  • ggplot2 theme background color and grids 
    ggplot() + geom_bar(aes(x=, fill=y)) +
           theme(panel.background=element_rect(fill='purple')) + 
           theme(plot.background=element_blank())

ggplot() + geom_bar(aes(x=, fill=y)) + 
           theme(panel.background=element_blank()) + 
           theme(plot.background=element_blank()) # minimal background like base R
           # the grid lines are not gone; they are white so it is the same as the background

ggplot() + geom_bar(aes(x=, fill=y)) + 
           theme(panel.background=element_blank()) + 
           theme(plot.background=element_blank()) +
           theme(panel.grid.major.y = element_line(color="grey"))
           # draw grid line on y-axis only

ggplot() + geom_bar() +
           theme_bw()  # very similar to theme_light()
                       # have grid lines
ggplot() + geom_bar() +
           theme_classic() # similar to base R graphic
                       # no borders on top and right
 
ggplot() + geom_bar() +
           theme_minimal() # no edge

ggplot() + geom_bar() +
           theme_void() # no grid, no edge

ggplot() + geom_bar() +
           theme_dark()

ggthmr

ggthmr package

Font size

  • https://ggplot2.tidyverse.org/reference/theme.html
  • Change Font Size of ggplot2 Plot in R (5 Examples) | Axis Text, Main Title & Legend
    Change Font Size of All Text Elements theme(text = element_text(size = 20))
    Change Font Size of Axis Text
    X-axis only    		 theme(axis.text = element_text(size = 20))
    theme(axis.text.x = element_text(size = 20))
    Change Font Size of Axis Titles
    X-axis only    		 theme(axis.title = element_text(size = 20))
    theme(axis.title.x = element_text(size = 20))
    Change Font Size of Main Title theme(plot.title = element_text(size = 20))
    Change Font Size of Legend Text
    Title    		 theme(legend.text = element_text(size = 20))
    theme(legend.title = element_text(size = 20))
  • What is the default font for ggplot2 theme_get()$text and windowsFonts() / X11Fonts()
  • Fonts from Cookbook for R For example to make the subtitle font size smaller 
    my_ggp + theme(plot.sybtitle = element_text(size = 8)) 
# Default font size seems to be 11 for title/subtitle

Remove x and y axis titles

ggplot2 title : main, axis and legend titles 

theme(
  plot.title = element_blank(),
  axis.title.x = element_blank(),
  axis.title.y = element_blank())

Rotate x-axis labels, alignment (hjust)

Counter-clockwise 

theme(axis.text.x = element_text(angle = 90, size=5, hjust=1) # default hjust=0.5

customize ggplot2 axis labels with different colors

Add axis on top or right hand side

Remove labels

Plotting with ggplot: : adding titles and axis names

ggthemes package

https://cran.r-project.org/web/packages/ggthemes/index.html 

ggplot() + geom_bar() +
           theme_solarized()   # sun color in the background

theme_excel()
theme_wsj()
theme_economist()
theme_fivethirtyeight()

rsthemes

rsthemes

thematic

thematic, Top R tips and news from RStudio Global 2021

Common plots

Scatterplot

Handling overlapping points (slides) and the ebook Fundamentals of Data Visualization by Claus O. Wilke.

Scatterplot with histograms

aes(color)

groups

Geom smooth ex.png

Bubble Chart

Ellipse

ggside: scatterplot + marginal density plot

ggextra: scatterplot + marginal histogram/density

https://github.com/daattali/ggExtra

Line plots

Ridgeline plots, mountain diagram

Histogram

Histograms is a special case of bar plots. Instead of drawing each unique individual values as a bar, a histogram groups close data points into bins. 

ggplot(data = txhousing, aes(x = median)) +
  geom_histogram()  # adding 'origin =0' if we don't expect negative values.
                    # adding 'bins=10' to adjust the number of bins
                    # adding 'binwidth=10' to adjust the bin width

Histogram vs barplot from deeply trivial.

Multiple variables

Boxplot

Be careful that if we added scale_y_continuous(expand = c(0,0), limits = c(0,1)) to the code, it will change the boxplot if some data is outside the range of (0, 1). The console gives a warning message in this case.

Base R method

  • Box Plots - R Base Graphs 
    # Use default color palette
colors <- palette()[1:6] # "black"   "#DF536B" "#61D04F" "#2297E6" "#28E2E5" "#CD0BBC"

# Boxplot with default colors
boxplot(count ~ spray, data = InsectSprays, col = colors)
  • If we like to add jitters to the boxplot, we can use points() + jitter(); this this example. However, we need to hide outliers created by boxplot() by adding outline = FALSE 
    boxplot(count ~ spray, data = InsectSprays, col = colors, outline = FALSE)
# par("usr")[1:2] confirms the locations of x-axis are 1, 2, 3, ...
set.seed(1)
points(jitter(as.integer(InsectSprays$spray) ), InsectSprays$count, pch=16)
  • We can follow this to use the reorder() function to reorder the groups on the x-axis by their group mean/median.
  • If we like to rotate the boxplot by 90 degrees, we can add , horizontal = TRUE to boxplot() function. 
    InsectSprays$newFac <- with(InsectSprays, reorder(spray, count, FUN=median))
boxplot(count ~ newFac, data = InsectSprays, col = "lightgray", horizontal = TRUE, outline = FALSE)
set.seed(1); points(InsectSprays$count, jitter(as.integer(InsectSprays$newFac) ),  pch=16)
  • Another base plot approach to create a jittered boxplot is to use boxplot() + stripchart(). See Stripchart in R, How to Create a Strip Chart in R. Consider to add outline = FALSE to boxplot() to avoid drawing outliers in boxplot() when stripchart() has been added. 
    ylim <- range(df$estimate, na.rm = TRUE)
boxplot(estimate~type, data=df, xlab=NULL, ylab=NULL, ylim=ylim, outline=F)
set.seed(1)
stripchart(estimate~type, data=df, method = "jitter",
		pch=19, col=c("salmon", "orange", "yellowgreen", "green"),
		vertical=TRUE, add=TRUE)

Color fill/scale_fill_XXX

n <- 100
k <- 12
set.seed(1234)
cond <- factor(rep(LETTERS[1:k], each=n))
rating <- rnorm(n*k)
dat <- data.frame(cond = cond, rating = rating)

p <- ggplot(dat, aes(x=cond, y=rating, fill=cond)) + 
     geom_boxplot() 

p + scale_fill_hue() + labs(title="hue default") # Same as only p 
p + scale_fill_hue(l=40, c=35) + labs(title="hue options")
p + scale_fill_brewer(palette="Dark2") + labs(title="Dark2")
p + colorspace::scale_fill_discrete_qualitative(palette = "Dark 3") + labs(title="Dark 3")
p + scale_fill_brewer(palette="Accent") + labs(title="Accent")
p + scale_fill_brewer(palette="Pastel1") + labs(title="Pastel1")
p + scale_fill_brewer(palette="Set1") + labs(title="Set1")
p + scale_fill_brewer(palette="Spectral") + labs(title ="Spectral") 
p + scale_fill_brewer(palette="Paired") + labs(title="Paired")
# cbbPalette <- c("#000000", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7")
# p + scale_fill_manual(values=cbbPalette)

Scalefill.png

ColorBrewer palettes RColorBrewer::display.brewer.all() to display all brewer palettes.

Reference from ggplot2. scale_fill_binned, scale_fill_brewer, scale_fill_continuous, scale_fill_date, scale_fill_datetime, scale_fill_discrete, scale_fill_distiller, scale_fill_gradient, scale_fill_gradientc, scale_fill_gradientn, scale_fill_grey, scale_fill_hue, scale_fill_identity, scale_fill_manual, scale_fill_ordinal, scale_fill_steps, scale_fill_steps2, scale_fill_stepsn, scale_fill_viridis_b, scale_fill_viridis_c, scale_fill_viridis_d

Jittering - plot the data on top of the boxplot

  • What is a boxplot
  • Quick look 
    # Only 1 variable
ggplot(data.frame(Wi), aes(y = Wi)) + 
  geom_boxplot()

# Two variable, one of them is a factor
ggplot() + geom_jitter(mapping = aes(x, y))

# Box plot
ggplot() + geom_boxplot(mapping = aes(x, y))
  • geom_jitter()
  • geom_jitter can affect both X and Y values. 
    tibble(x=1:4, y=1:4) %>% ggplot(aes(x, y)) + geom_jitter()
  • https://stackoverflow.com/a/17560113
  • How to make scatterplot with geom_jitter plot reproducible? 
    set.seed(1); data %>%
  ggplot() +
  geom_jitter(aes(T.categ, sex, colour = status))
  • Boxplot with jittered data points in ggplot2 
    • # df2 is n x 2 
ggplot(df2, aes(x=nboot, y=boot)) +
  geom_boxplot(outlier.shape=NA) + #avoid plotting outliers twice
  geom_jitter(aes(color=nboot), position=position_jitter(width=.2, height=0, seed=1)) +
  labs(title="", y = "", x = "nboot")

    If we omit the outlier.shape=NA option in geom_boxplot(), we will get the following plot where some outliers will appear twice. (Another option is outlier.color = NA; see extra point at boxplot with jittered points (ggplot2)).

    Jitterboxplot.png

  • Change colors 
    set.seed(123)
data <- data.frame(
  Group = rep(c("A", "B", "C"), each = 20),
  Value = c(rnorm(20, mean = 5), rnorm(20, mean = 7), rnorm(20, mean = 6))
)

ggplot(data, aes(x=Group, y=Value)) +
  geom_boxplot(outlier.shape=NA) + #avoid plotting outliers twice
  geom_jitter(aes(color=Group), position=position_jitter(width=.2, height=0, seed=1)) +
  scale_color_manual(values = c("red", "blue", "green")) +
     # c("#F8767D", "#00BFC4")  (salmon, iris blue)
     # c("#F8766D", "#00BA38", "#619CFF") (Salmon, Dark Pastel Green, Cornflower Blue)
     # c("#F8766D", "#7CAE00", "#00BFC4", "#C77CFF") (Salmon, Christi, Iris Blue, Heliotrope)
  labs(title="", y = "", x = "Group")
  • Base plot approach Batch effects and confounders

Groups of boxplots

  • How to Make Grouped Boxplot with Jittered Data Points in ggplot2. Use the color parameter in ggplot(aes()).
  • Boxplot With Jittered Points in R
  • How To Make Grouped Boxplots with ggplot2?, A review of Longitudinal Data Analysis in R. Use the fill parameter such as 
    mydata %>%
  ggplot(aes(x=Factor1, y=Response, fill=factor(Factor2))) +   
  geom_boxplot()
  • Another method is to use ggpubr::ggboxplot(). Papers TumorPurity. 
    ggboxplot(df, "dose", "len",
           fill = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07"), add.params=list(size=0.1),
           notch=T, add = "jitter", outlier.shape = NA, shape=16,
           size = 1/.pt, x.text.angle = 30, 
           ylab = "Silhouette Values", legend="right",
           ggtheme = theme_pubr(base_size = 8)) +
     theme(plot.title = element_text(size=8,hjust = 0.5), 
           text = element_text(size=8), 
           title = element_text(size=8),
           rect = element_rect(size = 0.75/.pt),
           line = element_line(size = 0.75/.pt),
           axis.text.x = element_text(size = 7),
           axis.line = element_line(colour = 'black', size = 0.75/.pt),
           legend.title = element_blank(),
           legend.position = c(0,1), 
           legend.justification = c(0,1),
           legend.key.size = unit(4,"mm"))

p-values on top of boxplots

Violin plot and sina plot

geom_density: Kernel density plot

A panel of density plots

  • Common xlim for all subplots 
    ggplot(data = mpg, aes(x = hwy)) +
     geom_density() +
     facet_wrap(~ class)
  • Each subplot has its own xlim 
    ggplot(data = mpg, aes(x = hwy)) +
     geom_density() +
     facet_wrap(~ class, scales = "free_x")

Bivariate analysis with ggpair

Correlation in R: Pearson & Spearman with Matrix Example

GGally::ggpairs

barplot/bar plot

ggplot2 geom_col()/geom_bar() vs base R barplot()

  • geom_col(): This function is more closely aligned with barplot() in base R, as barplot() also directly uses the values provided to it for the heights of the bars.
  • geom_bar(): This function is more for counting occurrences and creating histograms, similar to using table() with barplot().
  • Example with Counts from a Categorical Variable 
    # Sample data
category <- c("A", "B", "A", "C", "B", "A")

# base R
# Create a table of counts
counts <- table(category)
barplot(counts,
        main = "Bar Plot of Counts",
        xlab = "Category",
        ylab = "Count",
        col = c("red", "blue", "green"))

# ggplot2
df <- as.data.frame(table(category)) 
colnames(df) <- c("category", "count"); df
#   category count
# 1        A     3
# 2        B     2
# 3        C     1
ggplot(df, aes(x = category, y = count, fill = category)) + 
  geom_col() + 
  scale_fill_manual(values = c("red", "blue", "green"))
ggplot(df, aes(x = category, y = count, fill = category)) + 
  geom_bar(stat = "identity") + 
  scale_fill_manual(values = c("red", "blue", "green"))

df2 <- data.frame(
  category = c("A", "B", "A", "C", "B", "A")
)

# Creating the bar plot
ggplot(df2, aes(x = category)) +
  geom_bar() +
  labs(title = "Bar Plot Using geom_bar()",
       x = "Category",
       y = "Count") +
  theme_minimal()
  • Example with Precomputed Values and different colors for each bar 
    # Sample data frame with precomputed values
df2 <- data.frame(
  category = c("A", "B", "C"),
  count = c(3, 2, 1)
)

# ggplot2
ggplot(df2, aes(x = category, y = count, fill = category)) + 
  geom_bar(stat = "identity") + 
  scale_fill_manual(values = c("red", "blue", "green"))
# OR
ggplot(df2, aes(x = category, y = count, fill = category)) + 
  geom_col() + 
  scale_fill_manual(values = c("red", "blue", "green"))

# base R
colors <- c("red", "blue", "green")
barplot(count ~ category,
        data = df2, 
        main = "Bar Plot with Different Colors",
        xlab = "Category",
        ylab = "Count",
        col = colors)

Ordered barplot and facet

  • Simple example 
    df <- data.frame(trt = c("a", "b", "c"), outcome = c(2.3, 1.9, 3.2))
ggplot(df, aes(outcome, reorder(trt, outcome), fill = trt)) + 
  geom_col() + 
  scale_fill_brewer(palette = "Set2") +
  labs(x="Outcome", y="Treatment", title ="") + 
  theme_minimal()
  • Reorder a variable with ggplot2
  • ‘reorder()’ gets an argument ‘decreasing’ which it passes to ‘sort()’ for level creation. 2021-11-23
  • How to Reorder bars in barplot with ggplot2 in R. fct_reorder() and reorder().
  • ?reorder. This, as relevel(), is a special case of simply calling factor(x, levels = levels(x)[....]). 
    R> bymedian <- with(InsectSprays, reorder(spray, count, median))
# bymedian will replace spray (a factor) 
# The data is not changed except the order of levels (a factor) 
# In this case, the order is determined by the median of count from each spray level
#   from small to large.

R> InsectSprays[1:3, ]
  count spray
1    10     A
2     7     A
3    20     A
R> bymedian
 [1] A A A A A A A A A A A A B B B B B B B B B B B B C C C C C C C C C C C C D D D D D D D
[44] D D D D D E E E E E E E E E E E E F F F F F F F F F F F F
attr(,"scores")
   A    B    C    D    E    F 
14.0 16.5  1.5  5.0  3.0 15.0 
Levels: C E D A F B
R> InsectSprays$spray
 [1] A A A A A A A A A A A A B B B B B B B B B B B B C C C C C C C C C C C C D D D D D D D
[44] D D D D D E E E E E E E E E E E E F F F F F F F F F F F F
Levels: A B C D E F
R> boxplot(count ~ bymedian, data = InsectSprays,
         xlab = "Type of spray", ylab = "Insect count",
         main = "InsectSprays data", varwidth = TRUE,
         col = "lightgray")

    Scatterplot 

    tibble(y=sample(6), x=letters[1:6]) %>% 
  ggplot(aes(reorder(x, -y), y)) + geom_point(size=4)
  • Sorting the x-axis in bargraphs using ggplot2 or this one from Deeply Trivial. reorder(fac, value) was used. 
    ggplot(df, aes(x=reorder(x, -y), y=y)) + geom_bar(stat = 'identity')

df$order <- 1:nrow(df)
# Assume df$y is a continuous variable and df$fac is a character/factor variable
#   and we want to show factor according to the way they appear in the data
#   (not following R's order even the variable is of type "character" not "factor")
# We like to plot df$fac on the y-axis and df$y on x-axis. Fortunately,
#   ggplot2 will draw barplot vertically or horizontally depending the 2 variables' types
# The reason of using "-order" is to make the 1st name appears on the top
ggplot(df, aes(x=y, y=reorder(fac, -order))) + geom_col()

ggplot(df, aes(x=reorder(x, desc(y)), y=y)), geom_col()
  • Predict #TidyTuesday giant pumpkin weights with workflowsets. fct_reorder()
  • Reordering and facetting for ggplot2. tidytext::reorder_within() was used.
  • Chapter2 of data.table cookbook. reorder(fac, value) was used.
  • PCA and UMAP with tidymodels
  • A simple example 
    dat <- structure(list(gene = c("CAPN9", "CSF3R", "HPN", "KCNA5", "MTMR7", 
"NRG3", "SMTNL2", "TMPRSS6"), coef = c(-1.238, -0.892, -0.224, 
-0.057, 0.133, 0.377, 0.436, 0.804)), row.names = c("4976", "6467", 
"12355", "13373", "18143", "19010", "23805", "25602"), class = "data.frame")

# Base R plot
par(mar=c(4,6,4,1))
barplot(dat$coef, names = dat$gene, horiz = T, las=1,
        main='base R', xlab = "Coefficients")

# GGplot2
dat %>% ggplot(aes(y=gene, x=coef)) + geom_col(fill = 'gray') + 
    theme(axis.ticks.y = element_blank()) + 
    theme(panel.background = element_blank(), 
          axis.line.x = element_line(colour = 'black')) +
    labs(x="Coefficients", y = '', title = "ggplot2")

    Barplot base.png, Barplot ggplot2.png

Proportion barplot

Back to back barplot

Pyramid Chart

ggcharts::pyramid_chart()

Flip x and y axes

coord_flip()

Rotate x-axis labels

ggplot(mydf) + geom_col(aes(x = model, y=value, fill = method), position="dodge")+
  theme(axis.text.x = element_text(angle = 45, hjust=1, size= 8))

Starts at zero

Starting bars and histograms at zero in ggplot2 

scale_y_continuous(expand = c(0,0), limits = c(0, YourLimit))

Add patterns

Barplot with colors for a 2nd variable

How to basic: bar plots

By default, the barplots are stacked on top of each other. Use geom_col(position = "dodge") if we want the barplots to be side-by-side. 

df <- data.frame(group = c("A", "A", "B", "B", "C", "C"), 
      count = c(3, 4, 5, 6, 7, 8), 
      fill = c("red", "blue", "red", "blue", "red", "blue"))
ggplot(df, aes(x = group, y = count, fill = fill)) + 
      geom_col(position = "dodge")

Ggplotbarplot.png

Base R approach.

Barplot with color gradient

Geomcolviridis.png

Barplot with only horizontal gridlines

Geom bar3.png Geom bar4.png

Barplot with text at the end

Geom bar1.png Geom bar2.png

Polygon and map plot

Polygon.png

geom_step: Step function

Connect observations: geom_path(), geom_step()

Example: KM curves (without legend) 

library(survival)
sf <- survfit(Surv(time, status) ~ x, data = aml)
sf
str(sf) # the first 10 forms one strata and the rest 10 forms the other
ggplot() + 
  geom_step(aes(x=c(0, sf$time[1:10]), y=c(1, sf$surv[1:10])), 
            col='red') + 
  scale_x_continuous('Time', limits = c(0, 161)) + 
  scale_y_continuous('Survival probability', limits = c(0, 1)) +
  geom_step(aes(x=c(0, sf$time[11:20]), y=c(1, sf$surv[11:20])), 
            col='black') 
# cf:  plot(sf, col = c('red', 'black'), mark.time=FALSE)

Same example but with legend (see Construct a manual legend for a complicated plot) 

cols <- c("NEW"="#f04546","STD"="#3591d1")
ggplot() + 
  geom_step(aes(x=c(0, sf$time[1:10]), y=c(1, sf$surv[1:10]), col='NEW')) +
  scale_x_continuous('Time', limits = c(0, 161)) + 
  scale_y_continuous('Survival probability', limits = c(0, 1)) +
  geom_step(aes(x=c(0, sf$time[11:20]), y=c(1, sf$surv[11:20]), col='STD')) + 
  scale_colour_manual(name="Treatment", values = cols)

To control the line width, use the size parameter; e.g. geom_step(aes(x, y), size=.5). The default size is .5 (where to find this info?).

To allow different line types, use the linetype parameter. The first level is solid line, the 2nd level is dashed, ... We can change the default line types by using the scale_linetype_manual() function. See Line Types in R: The Ultimate Guide for R Base Plot and GGPLOT.

Coefficients, intervals, errorbars

Comparing similarities / differences between groups

comparing similarities / differences between groups

Special plots

Dot plot & forest plot

Lollipop plot

geom_segment() + geom_point()

ggpubr:: ggdotchart()

Correlation Analysis Different

Bump plot: plot ranking over time

https://github.com/davidsjoberg/ggbump

Gauge plots

Sankey diagrams

Horizon chart

Circos plots

Aesthetics

  • We can create a new aesthetic name in aes(aesthetic = variable) function; for example, the "text2" below. In this case "text2" name will not be shown; only the original variable will be used. 
    library(plotly)
g <- ggplot(tail(iris), aes(Petal.Length, Sepal.Length, text2=Species)) + geom_point()
ggplotly(g, tooltip = c("Petal.Length", "text2"))

Aesthetics finder

https://ggplot2tor.com/aesthetics/, video

aes_string()

group

https://ggplot2.tidyverse.org/reference/aes_group_order.html

GUI/Helper packages

ggedit & ggplotgui – interactive ggplot aesthetic and theme editor

esquisse (French, means 'sketch'): creating ggplot2 interactively

https://cran.rstudio.com/web/packages/esquisse/index.html

A 'shiny' gadget to create 'ggplot2' charts interactively with drag-and-drop to map your variables. You can quickly visualize your data accordingly to their type, export to 'PNG' or 'PowerPoint', and retrieve the code to reproduce the chart.

The interface introduces basic terms used in ggplot2:

  • x, y,
  • fill (useful for geom_bar, geom_rect, geom_boxplot, & geom_raster, not useful for scatterplot),
  • color (edges for geom_bar, geom_line, geom_point),
  • size,
  • facet, split up your data by one or more variables and plot the subsets of data together.

It does not include all features in ggplot2. At the bottom of the interface,

  • Labels & title & caption.
  • Plot options. Palette, theme, legend position.
  • Data. Remove subset of data.
  • Export & code. Copy/save the R code. Export file as PNG or PowerPoint.

ggcharts

https://cran.r-project.org/web/packages/ggcharts/index.html

ggeasy

ggx

https://github.com/brandmaier/ggx Create ggplot in natural language

Interactive

plotly

R web → plotly

ggiraph

ggiraph: Make 'ggplot2' Graphics Interactive

ggconf: Simpler Appearance Modification of 'ggplot2'

https://github.com/caprice-j/ggconf

Plotting individual observations and group means

https://drsimonj.svbtle.com/plotting-individual-observations-and-group-means-with-ggplot2

subplot

Adding/Inserting an image to ggplot2

Inserting an image to ggplot2: See annotation_custom.

See also ggbernie which uses a different way ggplot2::layer() and a self-defined geom (geometric object).

Easy way to mix/combine multiple graphs on the same page

annotation_custom

  • predcurvePlot.R from TreatmentSelection. One issue is the font size is large for the text & labels at the bottom. The 2nd issue is the bottom part of the graph/annotation (marker value scale) can be truncated if the window size is too large. If the window is too small, the bottom part can overlap with the top part. 
    p <- p + theme(plot.margin = unit(c(1,1,4,1), "lines"))  # hard coding
p <- p + annotation_custom() # axis for marker value scale
p <- p + annotation_custom() # label only
    • Similar plot but without using base R graphic. One issue is the text is not below the scale (this can be fixed by par(mar) & mtext(text, side=1, line=4)) and the 2nd issue is the same as ggplot2's approach. 
      axis(1,at= breaks, label = round(quantile(x1, prob = breaks/100), 1),pos=-0.26) # hard coding
    • Another common problem is the plot saved by pdf() or png() can be truncated too. I have a better luck with png() though.

grid

gridExtra

Force a regular plot object into a Grob for use in grid.arrange

gridGraphics package

make one panel blank/create a placeholder

# Method 1: Blank
ggplot() + theme_void()
# Method 2: Display N/A
ggplot() +
    theme_void() +
    geom_text(aes(0,0,label='N/A'))

Overall title

multiple ggplots overall title

Remove vertical/horizontal grids but keep ticks

removeGrid()

patchwork

Common legend

Add a common Legend for combined ggplots 

library(ggplot2)
library(patchwork)

p1 <- ggplot(df1, aes(x = x, y = y, colour = group)) + 
  geom_point(position = position_jitter(w = 0.04, h = 0.02), size = 1.8)
p2 <- ggplot(df2, aes(x = x, y = y, colour = group)) + 
  geom_point(position = position_jitter(w = 0.04, h = 0.02), size = 1.8)

# Method 1:
p1 + p2 + plot_layout(guides = "collect") + theme(legend.position = "bottom") 
                                          # one legend on the bottom
# Method 2:
p1 + p2 + plot_layout(guides = "collect") # one legend on the RHS
# Method 2:
p1 + theme(legend.position="none") + p2  # legend (based on p2) is on the RHS
# Method 3:
p1 + p2 + theme(legend.position="none")  # legend (based on p1) is in the middle!!

Overall title

Common Main Title for Multiple Plots in Base R & ggplot2 (2 Examples)

egg

Common x or y labels

Base R plot vs ggplot2

  • My summary
base-R ggplot2
plot(x, y, col) geom_point(aes(x, y, color, shape))
xlim scale_x_continuous(limits)
log="x" scale_x_continuous(trans="log10")
xlab
mtext("Var", cex, line, adj, las, side) 		scale_x_discrete(name="sample size")
labs(x)
xlab()
main labs(x, y, title, colour)
ggtitle()
axis(2, labels) scale_y_continuous(labels, breaks)
scale_x_discrete(labels)
? scale_color_discrete('new color title')
? scale_shape_discrete('new shape title')
col scale_color_manual(name,
values = NamedVector)
pch, cex geom_point(pch, size)
plot(mpg, disp, col=factor(cyl))
legend("topleft",
legend = sort(unique(cyl)),
col=1:3, pch=1)
# discrete case 		ggplot(mtcars,
aes(mpg, disp, color = factor(cyl))) +
geom_point() +
labs(color = "Number of Cylinders")
text() geom_text()
? theme(title = element_text(size=8),
legend.title = element_blank(),
legend.position = "none",
legend.key = element_blank(),
plot.title = element_text(hjust = 0.5),
plot.sybtitle = element_text(size = 8))
las in plot(), barplot()
text(x, y, labs, srt=45) 		theme(axis.text.x = element_text(angle = 90))
matplot() geom_line() + geom_point()
plot(type = 'l'), points() geom_line() + geom_point()
barplot() geom_bar()
par(mfrow) facet_grid()

labs for x and y axes

x and y labels

https://stackoverflow.com/questions/10438752/adding-x-and-y-axis-labels-in-ggplot2 or the Labels part of the cheatsheet

You can set the labels with xlab() and ylab(), or make it part of the scale_*.* call. 

labs(x = "sample size", y = "ngenes (glmnet)")

scale_x_discrete(name="sample size")
scale_y_continuous(name="ngenes (glmnet)", limits=c(100, 500))

Change tick mark labels

ggplot2 axis ticks : A guide to customize tick marks and labels

name-value pairs

See several examples (color, fill, size, ...) from opioid prescribing habits in texas.

Footnote

Add Footnote to ggplot2

Prevent sorting of x labels

See Change the order of a discrete x scale.

The idea is to set the levels of x variable. 

junk   # n x 2 table
colnames(junk) <- c("gset", "boot")
junk$gset <- factor(junk$gset, levels = as.character(junk$gset))
ggplot(data = junk, aes(x = gset, y = boot, group = 1)) + 
  geom_line() + 
  theme(axis.text.x=element_text(color = "black", angle=30, vjust=.8, hjust=0.8))

Legends

Legend title

  • labs() function 
    p <- ggplot(df, aes(x, y)) + geom_point(aes(colour = z))
p + labs(x = "X axis", y = "Y axis", colour = "Colour\nlegend")
       # Use color to represent the legend title

p <- ggplot(df) + geom_col(aes(x=x, y=y, fill=cat), position = "dodge") 
p + labs(x = "X", y = "Y", fill = "Category")
       # Use fill to represent the legend title
  • scale_colour_manual() 
    scale_colour_manual("Treatment", values = c("black", "red"))
  • scale_color_discrete() and scale_shape_discrete(). See Combine colors and shapes in legend. 
    df <- data.frame(x = 1:3, y = 1:3, z = c("a", "b", "c"))
ggplot(df, aes(x, y)) + geom_point(aes(shape = z, colour = z), size=5) + 
  scale_color_discrete('new title') + scale_shape_discrete('new title')

Remove NA factor level from color legend

Use na.translate = F in scale_color_XXX(). See ggplot: remove NA factor level in legend

Layout: move the legend from right to top/bottom of the plot or inside the plot or hide it

gg + theme(legend.position = "top")

# Useful in the boxplot case
gg + theme(legend.position="none")

gg + theme(legend.position = c(0.87, 0.25)) +
     guides(colour = guide_legend(nrow = 1))

# Customize the edge color and background color
gapminder %>%
  ggplot(aes(gdpPercap,lifeExp, color=continent)) +
  geom_point() +
  scale_x_log10()+
  theme(legend.position = c(0.87, 0.25),
        legend.background = element_rect(fill = "white", color = "black"))

Guide functions for finer control (legend, axis, color scales)

  • https://ggplot2-book.org/scales.html#guide-functions The guide functions, guide_colourbar() and guide_legend(), offer additional control over the fine details of the legend.
  • guide_legend() allows the modification of legends for scales, including fill, color, and shape. This function can be used in scale_fill_manual(), scale_fill_continuous(), ... functions. 
    scale_fill_manual(values=c("orange", "blue"), 
                  guide=guide_legend(title = "My Legend Title",
                                     nrow=1,  # multiple items in one row
                                     label.position = "top", # move the texts on top of the color key
                                     keywidth=2.5)) # increase the color key width

    The problem with the default setting is it leaves a lot of white space above and below the legend. To change the position of the entire legend to the bottom of the plot, we use theme(). 

    theme(legend.position = 'bottom')
  • guides()
    • Legend. For example, to remove the legend title:
    ggplot(mtcars, aes(x = mpg, y = disp, color = factor(cyl))) +
  geom_point() +
  guides(color = guide_legend(title = NULL))
    • Axis. For example, to change the angle of the x-axis labels:
    ggplot(mtcars, aes(x = mpg, y = disp)) +
  geom_point() +
  theme(axis.text.x = element_text(angle = 45, hjust = 1)) +
  guides(x = guide_axis(angle = 45))
    • Color scales. For example, to change the number of color breaks:
    ggplot(mtcars, aes(x = mpg, y = disp, color = hp)) +
  geom_point() +
  guides(color = guide_colorbar(nbin = 10))

Legend symbol background

ggplot() + geom_point(aes(x, y, color, size)) +
           theme(legend.key = element_blank())
           # remove the symbol background in legend

Construct a manual legend for a complicated plot

https://stackoverflow.com/a/17149021

Legend size

How to Change Legend Size in ggplot2 (With Examples) 

data <- data.frame(x = 1:5, y = 1:5, label = c("A", "B", "C", "D", "E"))
ggplot(data, aes(x, y, color = as.factor(label))) +
  geom_point() +
  labs(title = "Legend Size Example with Theme Modification",
       color = "Label") +
  theme(
    legend.text = element_text(size = 12), 
    legend.title = element_text(size = 14)
    )

ggtitle()

Centered title

See the Legends part of the cheatsheet. 

ggtitle("MY TITLE") +
  theme(plot.title = element_text(hjust = 0.5))

Subtitle

ggtitle("My title",
        subtitle = "My subtitle")

margins

https://stackoverflow.com/a/10840417

Aspect ratio

?coord_fixed 

p <- ggplot(mtcars, aes(mpg, wt)) + geom_point()
p + coord_fixed() # plot is compressed horizontally
p  # fill up plot region

Time series plot

Multiple lines plot https://stackoverflow.com/questions/14860078/plot-multiple-lines-data-series-each-with-unique-color-in-r 

set.seed(45)
nc <- 9
df <- data.frame(x=rep(1:5, nc), val=sample(1:100, 5*nc), 
                   variable=rep(paste0("category", 1:nc), each=5))
# plot
# http://colorbrewer2.org/#type=qualitative&scheme=Paired&n=9
ggplot(data = df, aes(x=x, y=val)) + 
    geom_line(aes(colour=variable)) + 
    scale_colour_manual(values=c("#a6cee3", "#1f78b4", "#b2df8a", "#33a02c", "#fb9a99", "#e31a1c", "#fdbf6f", "#ff7f00", "#cab2d6"))

Versus old fashion 

dat <- matrix(runif(40,1,20),ncol=4) # make data
matplot(dat, type = c("b"),pch=1,col = 1:4) #plot
legend("topleft", legend = 1:4, col=1:4, pch=1) # optional legend

calendR

Calendar plot in R using ggplot2

Github style calendar plot

geom_point()

See Scatterplot. 

df <- data.frame(x=1:3, y=1:3, color=c("red", "green", "blue"))
# Use I() to set aes values to the identify of a value from your data table
ggplot(df, aes(x,y, color=I(color))) + geom_point(size=10) # no color legend
# VS
ggplot(df, aes(x,y, color=color)) + geom_point(size=10) # color is like a class label

geom_bar(), geom_col(), stat_count()

https://ggplot2.tidyverse.org/reference/geom_bar.html

  • geom_bar: Counts the number of cases at each x position and makes the height of the bar proportional to the count (or sum of weights if supplied)
  • geom_col: Leaves the data as is and makes the height of the bar proportional to the value in the data
Function Default Statistic Purpose
geom_bar() stat_count() 
df2 <- data.frame(cat = c("A", "A", "A", "B", "B", 
   "B", "B", "B", "C", "C", "C", "C", "C", "C"))
ggplot(df2, aes(x = cat)) + geom_bar()
# Same as
# barplot(table(df2$cat))
geom_col() stat_identity() 
df <- data.frame(group = c("A", "B", "C"), 
                 count = c(3, 5, 6))
ggplot(df, aes(x = group, y = count)) + geom_col()
# Same as
# barplot(df$count, names.arg = df$group)

geom_col(position = 'dodge')  # same as 
geom_bar(stat = 'identity', position = 'dodge')

geom_bar() can not specify the y-axis. To specify y-axis, use geom_col(). 

ggplot() + geom_col(mapping = aes(x, y))

Add colors to the plot

df <- data.frame(group = c("A", "B", "C"), 
                 count = c(3, 5, 6), 
                 fill = c("red", "green", "blue"))
ggplot(df, aes(x = group, y = count, fill = fill)) + 
  geom_col()

Add numbers to the plot

An example

Simple example

Original Geom bar simple.png

fct_reorder() Geom bar reorder.png.

Ordered barplot and reorder()

Ordered barplot and facet

stat_function()

stat_summary()

https://ggplot2.tidyverse.org/reference/stat_summary.html

stat_smooth(), geom_smooth()

?geom_smooth, ?stat_smooth 

ggplot(data = mtcars, aes(x = wt, y = mpg)) + 
  geom_point() +
  stat_smooth(method = "glm", formula = "y ~ x", 
              method.args = list(family = poisson(link = "log")), 
              se = FALSE, color = "red") +
  labs(x = "Weight", y = "Miles per gallon")

To control the smoothness, use the "span" parameter. To disable the confidence interval, use "se = F". 

geom_smooth(method = 'loess', se = FALSE, span = 0.3)

geom_ribbon

set.seed(123)
df <- data.frame(
  X = seq(0, 100, by = 5),  # Pathologist estimate
  Y = seq(0, 100, by = 5) + rnorm(21, 0, 5)  # XXX prediction
)

# Choice 1: Calculate the lower and upper bounds of the confidence interval
df$lower_bound <- 0.863 * df$X  # 13.7% below X
df$upper_bound <- 1.137 * df$X  # 13.7% above X

# Choice 2: Constant width for the confidence band
c <- 13.7 
df$lower_bound <- df$X - c
df$upper_bound <- df$X + c

# Plotting
ggplot(df, aes(x = X, y = Y)) +
  geom_point() + 
  geom_ribbon(aes(ymin = lower_bound, ymax = upper_bound), fill = "blue", alpha = 0.2) + 
  geom_smooth(method = "lm", color = "red", se = FALSE) +
  labs(x = "Pathologist Estimate", y = "XXX Prediction") +
  theme_minimal()

= geom_area() =
[http://blog.fellstat.com/?p=440 The Pfizer-Biontech Vaccine May Be A Lot More Effective Than You Think]

= Square shaped plot =
<pre>
ggplot() + theme(aspect.ratio=1) # do not adjust xlim, ylim

xylim <- range(c(x, y))
ggplot() + coord_fixed(xlim=xylim, ylim=xylim)

geom_line()

See also aes(..., group, ...).

Connect Paired Points with Lines in Scatterplot

Use geom_line() to create a square bracket to annotate the plot

Barchart with Significance Tests

Interaction plot

Randomized block design

geom_segment()

Line segments, arrows and curves. See an example in geom_errorbar section below.

Cf annotate("segment", ...)

geom_errorbar(): error bars

set.seed(301)
x <- rnorm(10)
SE <- rnorm(10)
y <- 1:10

par(mfrow=c(2,1))
par(mar=c(0,4,4,4))
xlim <- c(-4, 4)
plot(x[1:5], 1:5, xlim=xlim, ylim=c(0+.1,6-.1), yaxs="i", xaxt = "n", ylab = "", pch = 16, las=1)
mtext("group 1", 4, las = 1, adj = 0, line = 1) # las=text rotation, adj=alignment, line=spacing
par(mar=c(5,4,0,4))
plot(x[6:10], 6:10, xlim=xlim, ylim=c(5+.1,11-.1), yaxs="i", ylab ="", pch = 16, las=1, xlab="")
arrows(x[6:10]-SE[6:10], 6:10, x[6:10]+SE[6:10], 6:10, code=3, angle=90, length=0)
mtext("group 2", 4, las = 1, adj = 0, line = 1)

Stklnpt.svg

  • Forest plot example using geom_errorbarh()

Geomerrorbarh.png

geom_rect(), geom_bar()

Note that we can use scale_fill_manual() to change the 'fill' colors (scheme/palette). The 'fill' parameter in geom_rect() is only used to define the discrete variable. 

ggplot(data=) +
  geom_bar(aes(x=, fill=)) +
  scale_fill_manual(values = c("orange", "blue"))

geom_raster() and geom_tile()

Waterfall plot

geom_linerange

Circle

Circle in ggplot2 ggplot(data.frame(x = 0, y = 0), aes(x, y)) + geom_point(size = 25, pch = 1)

Annotation

Add a horizontal/vertical line

geom_hline(), geom_vline() 

geom_hline(yintercept=1000)
geom_vline(xintercept=99)

text annotations, annotate() and geom_text(): ggrepel package

  • https://ggplot2-book.org/annotations.html 
    annotate("text", label="Toyota", x=3, y=100)
annotate("segment", x = 2.5, xend = 4, y = 15, yend = 25, colour = "blue", size = 2)

geom_text(aes(x, y, label), data, size, vjust, hjust, nudge_x)
  • Text annotations in ggplot2 
    p + geom_text(aes(x = -115, y = 25,
                  label = "Map of the United States"),
              stat = "unique")
p + geom_label(aes(x = -115, y = 25,
                   label = "Map of the United States"),
              stat = "unique") # include border around the text
  • Use the nudge_y parameter to avoid the overlap of the point and the text such as 
    ggplot() + geom_point() +
           geom_text(aes(x, y, label), color='red', data, nudge_y=1)
  • What do hjust and vjust do when making a plot using ggplot? 0 means left-justified 1 means right-justified. This is necessary if we have multiples lines in text. By default, it will center-justified.
  • Volcano plots, EnhancedVolcano package
  • Visualization of Volcano Plots in R
  • AI 
    library(ggplot2)
library(ggrepel)

set.seed(123)
data <- data.frame(
    gene = paste("Gene", 1:1000, sep = "_"),
    log2FoldChange = rnorm(1000),
    pvalue = runif(1000)
)
data$pvalue[1:20] <- runif(20, 0, .001)
data$padj <- p.adjust(data$pvalue, method = "BH") # Adjusted p-values

significant_genes <- subset(data, padj < 0.05 & abs(log2FoldChange) > 1)

ggplot(data, aes(x = log2FoldChange, y = -log10(padj))) +
    geom_point(aes(color = padj < 0.05 & abs(log2FoldChange) > 1), alpha = 0.5) +
    scale_color_manual(values = c("black", "red"), na.translate = F) +
    theme_minimal() +
    labs(title = "Volcano Plot", x = "Log2 Fold Change", y = "-Log10 Adjusted P-Value") +
    geom_label_repel(
        data = significant_genes,
        aes(label = gene),
        size=3,
        box.padding = 0.25,     # default
        point.padding = 1e-06,  # default
        max.overlaps = 10       # default
    )

Text wrap

ggplot2 is there an easy way to wrap annotation text? 

p <- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()

# Solution 1: Not work with Chinese characters
wrapper <- function(x, ...) paste(strwrap(x, ...), collapse = "\n")
# The a label
my_label <- "Some arbitrarily larger text"
# and finally your plot with the label
p + annotate("text", x = 4, y = 25, label = wrapper(my_label, width = 5))

# Solution 2: Not work with Chinese characters
library(RGraphics)
library(ggplot2)
p <- ggplot(mtcars, aes(x = wt, y = mpg)) + geom_point()
grob1 <-  splitTextGrob("Some arbitrarily larger text")
p + annotation_custom(grob = grob1,  xmin = 3, xmax = 4, ymin = 25, ymax = 25) 

# Solution 3: stringr::str_wrap()
my_label <- "太極者無極而生。陰陽之母也。動之則分。靜之則合。無過不及。隨曲就伸。人剛我柔謂之走。我順人背謂之黏。"
p <- ggplot() + geom_point() + xlim(0, 400) + ylim(0, 300) # 400x300 e-paper
p + annotate("text", x = 0, y = 200, hjust=0, size=5,
             label = stringr::str_wrap(my_label, width =30)) +
    theme_bw () + 
    theme(panel.grid.major = element_blank(), 
          panel.grid.minor = element_blank(), 
          panel.border = element_blank(),
          axis.title = element_blank(), 
          axis.text = element_blank(),
          axis.ticks = element_blank())

ggtext

ggtext: Improved text rendering support for ggplot2

ggforce - Annotate areas with ellipses

geom_mark_ellipse()

Other geoms

Exploring other {ggplot2} geoms

geomtextpath: Create curved text

geomtextpath- Create curved text in ggplot2

Build your own geom

Fonts, icons

Lines of best fit

Lines of best fit

Save the plots -- ggsave()

ggsave(). Note svglite package is required, see R Graphics Cookbook. The svglite package provides more standards-compliant output.

By default the units of width & height is inch no matter what output formats we choose.

(3/24/2022) If I save the plot in the svg format using RStudio GUI (Export -> As as Image...) or by the svg() function, the svg plot can't be converted to a png file by ImageMagick. But if I save the plot by using the ggsave() command, the svg plot can be converted to a png file. 

$ convert -resize 100% Rerrorbar.svg tmp.png
convert-im6.q16: non-conforming drawing primitive definition `path' @ error/draw.c/RenderMVGContent/4300.
$ convert -resize 100% Rerrorbar2.svg tmp.png # Works

(1/31/2022) For some reason, the text in legend in svg files generated by ggsave() looks fine in browsers but when I insert it into ppt, the word "Sensitive" becomes "Sensitiv e". However, the svg files generated by svg() command looks fine in browsers AND in ppt.

ggsave() will save a plot with the width/height based on the current graphical device if we don't specify them. That's why after we issue ggsave() it will tell us the image size (inch). So in order to have a fixed width/height, we need to specify them explicitly. See

My experience is ggsave() is better than png() because ggsave() makes the text larger when we save a file with a higher resolution. 

...
ggsave("filename.png", object, width=8, height=4)
# vs
png("filename.png", width=1200, height=600)
...
dev.off()

We can specify dpi to increase the resolution if we use the png format (svg is not affected); see Chapter 14.5 Outputting to Bitmap (PNG/TIFF) Files from R Graphics Cookbook. 

g1 <- ggplot(data = mydf) 
g1
ggsave("myfile.png", g1, height = 7, width = 8, units = "in", dpi = 300)

I got an error - Error in loadNamespace(name) : there is no package called ‘svglite’. After I install the package, everything works fine. 

ggsave("raw-output.bmp", p, width=4, height=3, dpi = 100)
# Will generate 4*100 x 3*100 pixel plot

Note:

  • For saving to "png" file, increasing dpi (from 72 to 300) will increase font & point size. dpi/ppi is not an inherent property of an image.
  • If we don't specify any parameters and without resizing the graphics device size, then "png" file created by ggsave() will contain much more pixels compared to "svg" file (e.g. 1200 vs 360).
  • How ggsave() decides width/height if a svg file was used in an Rmd file? A: 7x7 from my experiment. So the font/point size will be smaller compared to a 4x4 inch output.
  • When I created an svg file in Linux with 4x4 inch (width x height), the file is 360 x 360 pixels when I right click the file to get the properties of the file. But macOS cannot return this number nor am I able to find this number from the svg file??

Multiple pages in pdf

https://stackoverflow.com/a/53698682. The key is to save the plot in an object and use the print() function. 

pdf("FileName", onefile = TRUE)
for(i in 1:I) {
  p <- ggplot()
  print(p)
}
dev.off()

graphics::smoothScatter: scatter plots with lots of points

Other tips/FAQs

Tips and tricks for working with images and figures in R Markdown documents

Ten Simple Rules for Better Figures

Ten Simple Rules for Better Figures

Five ways to improve your chart axes

Five ways to improve your chart axes

Beyond Bar and Line Graphs

Beyond Bar and Line Graphs: Time for a New Data Presentation Paradigm

Recreating the Storytelling with Data look with ggplot

Recreating the Storytelling with Data look with ggplot

ggplot2 does not appear to work when inside a function

https://stackoverflow.com/a/17126172. Use print() or ggsave(). When you use these functions interactively at the command line, the result is automatically printed, but in source() or inside your own functions you will need an explicit print() statement.

BBC

Add your brand to ggplot graph

You Need to Start Branding Your Graphs. Here's How, with ggplot!

Animation and gganimate

ggstatsplot

ggstatsplot: ggplot2 Based Plots with Statistical Details

Write your own ggplot2 function: rlang

Some packages depend on ggplot2

dittoSeq from Bicoonductor

Meme

Python

plotnine: A Grammar of Graphics for Python.

plotnine is an implementation of a grammar of graphics in Python, it is based on ggplot2. The grammar allows users to compose plots by explicitly mapping data to the visual objects that make up the plot.

The Hitchhiker’s Guide to Plotnine

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