Latest revision as of 13:21, 11 October 2024

Books

ggplot2: Elegant Graphics for Data Analysis by Hadley Wickham, Chapter 13 Guides: legends and axes
R for Data Science Chapter 28 Graphics for communication
R Graphics Cookbook, 2nd Edition by Winston Chang. Lots of recipes. For example, the Axes chapter talks how to set/hide tick marks.
The Hitchhiker's Guide to Ggplot2 in R
ggplot2 book and its source code. Before I build the (pdf version) of the book, I need to follow this suggestion by running the following in R before calling make.
Fundamentals of Data Visualization by Claus O. Wilke. The R code is in the Technical Notes section. The book is interesting. It educates how to produce meaningful and easy to read plots. The FAQs says the figure source code is not available.
Data Visualization for Social Science
R Graph Essentials Essentials by David Lillis. Chapters 3 and 4.
Data Visualization: A practical introduction by Kieran Healy
ggplot2 Grammar Guide

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

https://ggplot2.tidyverse.org/ which gives a link to two chapters in R for Data Science book
The Complete ggplot2 Tutorial from http://r-statistics.co
A curated list of awesome ggplot2 tutorials, packages etc.
https://www.lynda.com/RStudio-tutorials/Data-Visualization-R-ggplot2/672258-2.html
https://uc-r.github.io/ggplot_intro from UC Business Analytics R Programming Guide
R graphics with ggplot2 workshop notes
Graphics in R with ggplot2 Aug 2020
ggplot2 - Essentials from sthda.
A ggplot2 Tutorial for Beautiful Plotting in R
Chapter 7 ggplot2 from Introduction to Data Science Data Analysis and Prediction Algorithms with R, Rafael A. Irizarry
Plotting anything with ggplot2 - ggplot2 workshop part 1 (youtube) by Thomas Lin Pedersen

Help

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

Gallery

Some examples

Top 50 ggplot2 Visualizations - The Master List from http://r-statistics.co.
http://blog.diegovalle.net/2015/01/the-74-most-violent-cities-in-mexico.html
R Graph Catalog

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

facet_wrap(, nrow=4, ncol=3) in ggplot2 provides a solution similar to par(mfrow=c(4, 3)) in base R.
http://www.cookbook-r.com/Graphs/Facets_(ggplot2)/
Another example Polls v results
Ordering bars within their clumps in a bar chart

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

R -> Colors
http://www.cookbook-r.com/Graphs/Colors_(ggplot2)/
ggplot2 colors : How to change colors automatically and manually?
ggpubr package which was used by survminer. The colors c("#00AFBB", "#FC4E07") are very similar to the colors used in ggsurvplot(). Colorblind-friendly palette
Ten simple rules to colorize biological data visualization
a MEGA thread about all the ways you can choose a palette May 2021
How to select Colors for Data Visualizations?

Top color palettes

Top R Color Palettes to Know for Great Data Visualization

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

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

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)

Use scales::show_col()
```
scales::show_col(palette())
```

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.

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

ColorNameR - A tool for transforming coordinates in a color space to common color names using data from the Royal Horticultural Society and the International Union for the Protection of New Varieties of Plants.
ColorHexa
https://pinetools.com/invert-color

colorspace package

https://colorspace.r-forge.r-project.org/ More vignettes than CRAN have.
- Approximating Palettes from Other Packages
- it supports R's base graphics and also ggplot2 (eg scale_fill_discrete_qualitative(palette) , notice the part discrete_quantitative is specific to colorspace package). See my ggplot2 page.
CRAN colorspace: A Toolbox for Manipulating and Assessing Colors and Palettes
Some examples. The palette selections are different from scale_fill_XXX(). Note that the number of classes can be arbitrary in scale_fill_discrete_qualitative().
Note
- why it does not "Set 1"?
- the "Dark 2" colors are not the same as in RColorBrewer.

cols4all

https://github.com/mtennekes/cols4all. You can use cols4all palettes in ggplot2.

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

The paletteer package offers direct access to 1759 color palettes, from 50 different packages!
paletteer, paletteer_d() function for getting discrete palette by package and name.
Interactive https://emilhvitfeldt.github.io/r-color-palettes/discrete.html and choose 'sort by length'
Palettes sorted by type (Sequential/Diverging/Qualitative)
*More examples with a gallery

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

unikn: Enabling corporate design elements in R (with colors and color-related functions). The curve plot is interesting.
12 ggplot extensions for snazzier R graphics

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:

scale_x_discrete, scale_y_continuous
Create your own discrete scale:
- scale_colour_manual(),
- scale_fill_manual(values),
- scale_size_manual(),
- scale_shape_manual(),
- scale_linetype_manual(),
- scale_alpha_manual(),
- scale_discrete_manual()

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

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)

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

Getting started with theme() 2023/11/23
ggplot2 Theme Elements Demonstration

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

Remove x and y axis titles

ggplot2 title : main, axis and legend titles

Rotate x-axis labels, change colors

Counter-clockwise

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

customize ggplot2 axis labels with different colors

Add axis on top or right hand side

Specify a secondary axis, sec_axis(). This new function was added in ggplot2 2.2.0; see here.

Create secondary x-axis in ggplot2. dup_axis(name, breaks, labels). Note that ggplot2 uses breaks while base R plot uses at. See R → Include labels on the top axis/margin: axis().

# Bottom x-axis is the quantiles and the top x-axis is the original values

Fn <- ecdf(mtcars$mpg)
mtcars %>% dplyr::mutate(quantile = Fn(mpg)) %>%
  ggplot(aes(x= quantile, y= disp)) +
  geom_point() + 
  scale_x_continuous(name = "quantile of mpg", 
                     breaks=c(.25, .5, .75, 1.0),
                     labels = c("0.25", "0.50", "0.75", "1.00"),
                     sec.axis = dup_axis(name = "mpg",
                                         breaks = c(.25, .5, .75, 1.0),
                                         labels = quantile(mtcars$mpg, c(.25, .5, .75, 1.0))))

How to add line at top panel border of ggplot2

mtcars %>% 
  ggplot(aes(x= mpg, y= disp)) +
  geom_point() +
  annotate(geom = 'segment', y = Inf, yend = Inf, color = 'green', 
           x = -Inf, xend = Inf, size = 4)

ggplot2: Secondary Y axis
Dual Y axis with R and ggplot2

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

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

How To Make Scatterplot with Marginal Histograms in R?
ggpubr::ggscatterhist()
Scatter Plot Matrices
Example 8.41: Scatterplot with marginal histograms (old fashion, based on layout())

aes(color)

Discrete colors. ?scale_colour_brewer. How to fix 'continuous value supplied to discrete scale' in with scale_color_brewer. Change ggplot2 Color & Fill Using scale_brewer Functions & RColorBrewer Package in R

ggplot(mpg, aes(x = hwy, y = cty)) +
  geom_point(aes(color = class), palette = "Set2")

ggplot(mpg, aes(x = displ, y = hwy, colour = manufacturer)) +
  geom_point() +
  scale_colour_brewer(palette = "Set3")

Continuous colors. The default color scale is ?scale_colour_gradient with prespecified 'low' and 'high' colors. ?scale_colour_continuous.

ggplot(mpg, aes(x = displ, y = hwy, color = cty)) + 
  geom_point(size = 2) + 
  scale_color_continuous("City Miles Per Gallon")
# scale_color_continuous("City MPG Rating", low = "springgreen3", high = "red")

ggplot2 colors : How to change colors automatically and manually? (mainly the scatterplot and box plots)
Colour related aesthetics: colour, fill, and alpha
how to change the color in geom_point or lines in ggplot.
- color is used outside aes(): the color parameter can be used to specify the color name (eg 'red')
- color is used inside aes(): it is used to specify the category/level of colors. It does not work as expected if we try to specify colors explicitly; e.g. aes(color=c("red", "red", "green")). In this case, the color names becomes a factor.
```
ggplot() + 
  geom_point(data = data, aes(x = time, y = y, color = sample),size=4) +
  scale_color_manual(values = c("A" = "black", "B" = "red"))
```
How to highlight data in ggplot2

groups

How To Add Regression Line per Group to Scatterplot in ggplot2? geom_smooth()
Multiple fitted lines in one plot

Bubble Chart

Ellipse

ggplot2::stat_ellipse()
How can a data ellipse be superimposed on a ggplot2 scatterplot?. Hint: use the ellipse package.

ggside: scatterplot + marginal density plot

ggextra: scatterplot + marginal histogram/density

https://github.com/daattali/ggExtra

Line plots

http://www.sthda.com/english/wiki/ggplot2-line-plot-quick-start-guide-r-software-and-data-visualization
Multi-Line Chart by D3. Download the tarball. The index.html shows the interactive plot on FF but not Chrome or safari. See ES6 module support in Chrome 62/Chrome Canary 64, does not work locally. Chrome is blocking it because local files cannot have cross origin requests. it should work in chrome if you put it on a server.
- This and this are examples where X is a continuous variable.
- Click "..." and compare code.
How to Make Stunning Line Charts in R: A Complete Guide with ggplot2

Ridgeline plots, mountain diagram

ggridges: Ridgeline plots in ggplot2
Elegant Visualization of Density Distribution in R Using Ridgeline
An example from Scientific Reports.
CP 1919 / PSR B1919+21 Dataset

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)

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)).

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

Add P-values and Significance Levels to ggplots

ggpubr::stat_compare_means()

library(ggpubr)
my_comparisons <- list( c("6", "8"), c("4", "6"), c("4", "8") )
ggboxplot(mtcars, x = "cyl", y = "mpg",
           color = "cyl", add = "jitter", palette = "jco") + 
     stat_compare_means(comparisons = my_comparisons)+ # method="t.test", default is "wilcox.test"
     stat_compare_means(label.y = 45) # y-axis loc of overall p-value

How to Perform Multiple Paired T-tests in R
- ggpubr::stat_pvalue_manual()

Add Significance Level and Stars to Plot in R

ggsignif::geom_signif()

library(ggsignif)
ggplot(mtcars, aes(factor(cyl), mpg)) +
   geom_boxplot() +
   geom_signif(
     comparisons = list(
       c("6","8"),
       c("4","6"), c("4","8")
     ),
     map_signif_level=TRUE, 
     y_position = c(34, 35, 36)
   )

How to draw the boxplot with significant level?
- ggsignif package or geom_line() function.
Paper examples
- Fig 5A,B
- Fig 2B
Manually do it - signibox package (small).

Violin plot and sina plot

https://en.wikipedia.org/wiki/Violin_plot. It is similar to a box plot, with the addition of a rotated kernel density plot on each side.
geom_violin()
Violin plot with mean/median in ggplot2, stat_summary()

sina plot from the ggforce package.

library(ggplot2)
ggplot(midwest, aes(state, area)) + geom_violin() + ggforce::geom_sina()

An example

geom_density: Kernel density plot

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

ggplot(iris, aes(x = Sepal.Length, fill = Species, col = Species)) +
       geom_density(alpha = 0.4)

And two densities (black & red colors)

mydata <- data.frame(var1 = rnorm(100), var2 = rnorm(100, mean = 2))

# Create the plot
ggplot(data = mydata, aes(x = var1)) +
  geom_density() +
  geom_density(aes(x = var2), color = "red")

As you can see the default colors are so terrible. A better choice is ggokabeito color scales.
Density plot + histogram
https://learnr.wordpress.com/2009/03/16/ggplot2-plotting-two-or-more-overlapping-density-plots-on-the-same-graph/
Your Lopsided Model is Out to Get You & WVPlots package
http://www.cookbook-r.com/Graphs/Plotting_distributions_(ggplot2)/

Overlay histograms with density plots

library(ggplot2); library(tidyr)
x <- data.frame(v1=rnorm(100), v2=rnorm(100,1,1), 
                v3=rnorm(100, 0,2))
data <- pivot_longer(x, cols=1:3)
ggplot(data, aes(x=value, fill=name)) +
  geom_histogram(aes(y=..density..), alpha=.25) + 
  stat_density(geom="line", aes(color=name, linetype=name))
ggplot(data, aes(x=value, fill=name, col =name)) +
  geom_density(alpha = .4)

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

graphics::pairs()
- How to Create and Interpret Pairs Plots in R. pairs()
- Mastering Data Visualization with Pairs Plots in Base R. Adding colors and regression lines,.
All vignettes launched by GGally::vig_ggally()
Kmeans Clustering of Penguins
Multiple regression lines in ggpairs
A Brief Introduction to ggpairs
How to show only the lower triangle in ggpairs?

barplot/bar plot

Ordered barplot and facet

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")

,

Proportion barplot

Grouped, stacked and percent stacked barplot in ggplot2 geom_bar(position = "fill", stat = "identity")
Powerful Bar Plot for Presentations

Back to back barplot

https://community.rstudio.com/t/back-to-back-barplot/17106. Comment: the colors should be opposite but not.
https://stackoverflow.com/a/55015174 (different scale on positive/negative sides. Cool!)
https://learnr.wordpress.com/2009/09/24/ggplot2-back-to-back-bar-charts/ (change negative values to positive values, slow to load the page)
Pyramid plot in R
How to basic: bar plots. Hint: use geom_col() twice.

Pyramid Chart

ggcharts::pyramid_chart()

Flip x and y axes

coord_flip()

Rotate x-axis labels

How To Rotate x-axis Text Labels in ggplot2?
What do hjust and vjust do when making a plot using ggplot?
- 0 means left-justified 1 means right-justified.
- Left-justified means the starting point (left edge) of the text is placed at the specified x-coordinate. So text appeared on the right side of the point.
- Right-justified means the end point (right edge) of the text is placed at the specified x-coordinate. So text appeared on the left side of the point.
- Default hjust/vjust is 0.5

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

ggpattern package
ggpartten填充柱状图

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")

Base R approach.

Barplot with color gradient

horizontal barplot with color gradient from top to bottom of the graphic
ggplot2 heatmap
scale_fill_gradient(), scale_colour_brewer()/scale_fill_distiller(), scale_fill_viridis(). To reverse the colors, use the direction parameter; see here.

Barplot with only horizontal gridlines

Barplot with text at the end

Barplot with number of observation
A Quick How-to on Labelling Bar Graphs in ggplot2
How to label a barplot bar with positive and negative bars with ggplot2 (Looks good but 2012)
plitting a stacked bar plot simple
Examples from publications
- https://twitter.com/simocristea/status/1603055034081505280/photo/1. Draw a panel of barplots with common labels?

Polygon and map plot

https://ggplot2.tidyverse.org/reference/geom_polygon.html
Base R method. ?polygon.

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

Plotting two models with regression coefficients with geom_pointrange() - Vertical intervals: lines, crossbars & errorbars.
Grouping and staggering estimates with geom_point

Comparing similarities / differences between groups

Special plots

5 Extremely Useful Plots For Data Scientists That You Never Knew Existed.
- Chord Diagram
- Sunburst Chart
- Hexbin Plot
- Sankey Diagram
- Stream Graph/ Theme River

Dot plot & forest plot

Wikipedia
- https://en.wikipedia.org/wiki/Dot_plot_(statistics),
- https://en.wikipedia.org/wiki/Forest_plot
Tutorial: How to read a forest plot
Dotplot – the single most useful yet largely neglected dataviz type
ggplot2 dot plot : Quick start guide - R software and data visualization
foresplot package
Forest Plot, ordering and summarizing multiple variables
How to Create a Forest Plot in R. A forest plot (sometimes called a “blobbogram”) is used in a meta-analysis to visualize the results of several studies in one plot.
Doing Meta-Analysis with R: A Hands-On Guide ebook where the meta package was used.
survminer::ggforest()*: Draws forest plot for CoxPH model. See Survminer Cheatsheet to Create Easily Survival Plots & Hazard ratio forest plot: ggforest() from survminer
- survivalAnalysis::forest_plot(). Builds upon the 'survminer' package for Kaplan-Meier plots and provides a customizable implementation for forest plots.
- Multi-omics analysis identifies therapeutic vulnerabilities in triple-negative breast cancer subtypes 2021
forestmodel*: Forest Plots from Regression Models. ggforest (survminer) only selected covariates
forestploter

Lollipop plot

geom_segment() + geom_point()

A lollipop plot is basically a barplot, where the bar is transformed in a line and a dot.
r-charts.com/

r-graph-gallery.com, Most basic lollipop plot, Lollipop chart with conditional color

library(ggplot2)

# Create data
data <- data.frame(
  x=LETTERS[1:26],
  y=abs(rnorm(26))
)

# Horizontal version
ggplot(data, aes(x=x, y=y)) +
  geom_segment( aes(x=x, xend=x, y=0, yend=y), color="skyblue") +
  geom_point( color="blue", size=4, alpha=0.6) +
  theme_light() +
  coord_flip() +
  theme(
    panel.grid.major.y = element_blank(),
    panel.border = element_blank(),
    axis.ticks.y = element_blank()
  )

Note if we put color argument in geom_segment(), the color shape in the legend will be a solid circle with a cross line (looks funny). So it is better not to have multiple colors for the segment part in the lollipop plot.

Diverging Dot Plot and Lollipop Charts – Plotting Variance with ggplot2
How To Make Lollipop Plot in R with ggplot2?
Color annotation
Top 50 ggplot2 Visualizations - The Master List (With Full R Code) from r-statistics.co

ggpubr:: ggdotchart()

Dot charts, Lollipop chart

Correlation Analysis Different

corrmorant: Flexible Correlation Matrices Based on ggplot2
Correlation Analysis Different Types of Plots in R

Bump plot: plot ranking over time

https://github.com/davidsjoberg/ggbump

Gauge plots

Sankey diagrams

Wikipedia
Some examples by the networkD3 package

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()

aes_(). Define aesthetic mappings programmatically.
How to create a boxplot using ggplot2 with aes_string in R?

group

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

It seems the group parameter in aes() is used for coloring of lines. See How to change the color in geom_point or lines in ggplot.
geom_line in ggplot2.
ggplot2 manually specifying colour with geom_line
ggplot2 line types : How to change line types of a graph in R software?

GUI/Helper packages

ggedit & ggplotgui – interactive ggplot aesthetic and theme editor

https://www.r-statistics.com/2016/11/ggedit-interactive-ggplot-aesthetic-and-theme-editor/
https://github.com/gertstulp/ggplotgui/. It allows to change text (axis, title, font size), themes, legend, et al. A docker website was set up for the online version.

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

http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_(ggplot2)/. grid package is used.
gridExtra::grid.arrange() which has lots of reverse imports.
- Machine Learning Results in R: one plot to rule them all!
- It is used by the book Orchestrating Single-Cell Analysis with Bioconductor to visualize dimension reduction result among cells from the t-SNE algorithm.
Easy Way to Mix Multiple Graphs on The Same Page. Four packages are included: ggpubr (ggarrange()), cowplot (plot_grid()), gridExtra and grid.
- cowplot can mix ggplot2 and base graphics (require the gridGraphics package). It can also add 'A', 'B' to each subplot for easy annotation.
- draw_image() from cowplot can embed an image to a plot. See this example.
- How to combine Multiple ggplot Plots to make Publication-ready Plots
- Cannot convert object of class ggsurvplotggsurvlist into a grob ggpubr::ggarrange is just a wrapper around cowplot::plot_grid(). This does not solve the problem. Using survminer::arrange_ggsurvplots() does work.
- unable to use survfit when called from a function. Use surv_fit() instead survfit() with ggsurvplot() when ggsurvplot() is used within another function.
patchwork. plot_spacer() to create an empty plot.
Why you should master small multiple chart (facet_wrap()), facet_grid())
Download statistics and enter "gridExtra, cowplot, ggpubr, egg, grid" (the number of downloads is in this order).

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

Create a gradient image grid.raster() or rasterGrob()

redGradient <- matrix(hcl(0, 80, seq(50, 80, 10)), nrow=4, ncol=5)
# interpolated
grid.newpage()
grid.raster(redGradient)

Recipe for Infographics in R. See example of using rasterGrob() and annotation_custom() to place more images using a custom function.
How to add a background image to ggplot2 graphs
How to Add a Background Image in ggplot2 with R

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

egg (ggarrange()): Extensions for 'ggplot2', to Align Plots, Plot insets, and Set Panel Sizes. Same author of gridExtra package. egg depends on gridExtra.
- Showing a difference in means between two groups
- How can I make consistent-width plots in ggplot (with legends)?

Common x or y labels

how to add common x and y labels to a grid of plots. Another solution is on the egg package's vignette.

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()

Comparing ggplot2 and R Base Graphics

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

How to make a line chart with ggplot2
Colour palettes. Note some palette options like Accent from the Qualitative category will give a warning message In RColorBrewer::brewer.pal(n, pal) : n too large, allowed maximum for palette Accent is 8.

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

fct_reorder() .

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

Useful for adding confidence interval. geom_ribbon() Ribbons and area plots.
Shadowing your ggplot2 lines. Forecasting confidence interval in R use case
Example

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()

Connect Paired Points with Lines in Scatterplot

Connect Paired Points with Lines in Scatterplot in ggplot2? geom_line(aes(group = patient)) where the 'patient' variable has 2 same values for the same 'patient'; e.g. patient=0,0,1,1,2,2,3,3.
How to Connect Paired Points with Lines in Scatterplot in ggplot2 in R?

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

Plotting means and error bars (ggplot2) from Cookbook for R.
GGPlot Error Bars using geom_errorbar() and geom_segment()

Can ggplot2 do this? https://www.nature.com/articles/nature25173/figures/1
plotCI() from the plotrix package or geom_errorbar() from ggplot2 package
Vertical error bars
Horizontal error bars
Horizontal panel plot example and more
R does not draw error bars out of the box. R has arrows() to create the error bars. Using just arrows(x0, y0, x1, y1, code=3, angle=90, length=.05, col). See
- Building Barplots with Error Bars. Note that the segments() statement is not necessary.
- https://www.rdocumentation.org/packages/graphics/versions/3.4.3/topics/arrows
Toy example (see this nature paper)

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)

Forest plot example using geom_errorbarh()

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()

Rectangles. This is useful for creating heatmaps; .e.g DoHeatmap() & an example in Seurat.
Wordle Words and Expected Value

Waterfall plot

https://en.wikipedia.org/wiki/Waterfall_chart. A waterfall chart is a type of chart that represents how an initial value is affected by a series of intermediate positive or negative values.
Understanding Waterfall Plots
Waterfall charts in ggplot2 with waterfalls package
ggplot2: Waterfall Charts geom_rect()
Waterfall Charts in Oncology Trials - Ride the Wave. Drug response
- Collected data is compared to the data taken at baseline to determine if drug has some activity or not. Also each patient is assigned in to different categories based on overall response
- Y-axis = % of change from baseline in the tumor size for each patient
- We want to create this plot by grouping different patients based on their overall response category (eg 'Earth Death' or 'Complete Response') and fill the bars of such patients with different colors so it is easy to identify different groups.
A waterfall plot for drug BYL719 and color it based on the mutation status of the CDK13 gene, see Xeva vignette.

geom_linerange

https://ggplot2.tidyverse.org/reference/geom_linerange.html
A plot of genes on chromosomes. Since ggplot() is inside a function, we need to add print() in order to show the plot.
- See also Given the human gene TP53, retrieve the human chromosomal location of this gene and also retrieve the chromosomal location and RefSeq id of its homolog in mouse from the biomaRt package's vignette.
- Get gene location from gene symbol and ID
- Genomic coordinates to gene lists and vice versa — Annotating gene coordinates and gene lists
- Genomic coordinates of HGNC gene names where org.Hs.eg.db and TxDb.Hsapiens.UCSC.hg19.knownGene are used
- TxDb: Genes, Transcripts, and Genomic Locations which uses a gtf file and the GenomicFeatures package

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

ggrepel package, ?geom_text_repel. Found on Some datasets for teaching data science by Rafael Irizarry.

p <- ggplot(dat, aes(wt, mpg, label = car)) +
  geom_point(color = "red")

p1 <- p + geom_text() + labs(title = "geom_text()") # Bad

p2 <- p + geom_text_repel(seed=1) + labs(title = "geom_text_repel()") # Good
                                          # Use 'seed' to fix the location of text

Note that we may need to add show.legend = FALSE in geom_text_repel() to get rid of "a" character in the legend. See Remove 'a' from legend when using aesthetics and geom_text

Annotations from the chapter Graphics for communication of R for Data Science by Grolemund & Hadley
ggplot2 texts : Add text annotations to a graph in R software. The functions geom_text() and annotate() can be used to add a text annotation at a particular coordinate/position.

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

geomtextpath- Create curved text in ggplot2

Build your own geom

Fonts, icons

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

?smoothScatter
Smooth scatter plot in R
smoothScatter with ggplot2
An example from DeMixT. As we can see, we can we the lines() or abline() to add lines.

Other tips/FAQs

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

Ten Simple Rules for Better Figures

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

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

bbplot package from github, How to customize ggplot with bbplot
BBC Visual and Data Journalism cookbook for R graphics from github
How the BBC Visual and Data Journalism team works with graphics in R

Add your brand to ggplot graph

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

Animation and gganimate

https://gganimate.com/
Animating Changes in Football Kits using R: rvest, tidyverse, xml2, purrr & magick
Animated Directional Chord Diagrams tweenr & magick
x-mas tRees with gganimate, ggplot, plotly and friends
Create animation in R: learn by examples (gganimate)
The USMS ePostal Over the Last 20+ Years (gganimate and bar charts)
R tip: Animations in R from IDG TECHtalk
A moving super mario. See gganimate (with a spooky twist)

ggstatsplot

ggstatsplot: ggplot2 Based Plots with Statistical Details

Write your own ggplot2 function: rlang

Some packages depend on ggplot2

dittoSeq from Bicoonductor

Meme

ggbernie and How to use an image as a point in ggplot? and Extending ggplot2

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

Ggplot2: Difference between revisions

Latest revision as of 13:21, 11 October 2024

Books

The Grammar of Graphics

Scatterplot aesthetics

Online tutorials

Help

Gallery

Some examples

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

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

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

facet_wrap and facet_grid to create a panel of plots

lattice::xyplot

Color palette

Top color palettes

Display color palettes

colors()

palette()

rainbow

Color blind

Color picker

Color names, Complementary/Inverted colors

colorspace package

cols4all

*paletteer package

ggsci

ggokabeito

Pride palette

unikn

Colour related aesthetics: colour, fill and alpha

Scatterplot with large number of points: alpha

Combine colors and shapes in legend

ggplot2::scale functions and scales packages

ggplot2::scale_* - axes/axis, legend

ylim and xlim in ggplot2 in axes

Emulate ggplot2 default color palette

How to change the default color palette in geom_XXX

transform scales

Class variables

Red, Green, Blue alternatives

Heatmap for single channel

Heatmap for dual channels

Don't rely on color to explain the data

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

Create your own scale_fill_FOO and scale_color_FOO

Themes and background for ggplot2

Background

ggthmr

Font size

Remove x and y axis titles

Rotate x-axis labels, change colors

Add axis on top or right hand side

Remove labels

ggthemes package

rsthemes

thematic

Common plots

Scatterplot

Scatterplot with histograms

aes(color)

groups

Bubble Chart

Ellipse

ggside: scatterplot + marginal density plot

ggextra: scatterplot + marginal histogram/density

Line plots

Ridgeline plots, mountain diagram

Histogram

Multiple variables

Boxplot

Base R method

Color fill/scale_fill_XXX

Jittering - plot the data on top of the boxplot

Groups of boxplots

p-values on top of boxplots

Violin plot and sina plot

geom_density: Kernel density plot

A panel of density plots

Bivariate analysis with ggpair