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

Color palette

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

*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)

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") +
   scale_colour_discrete("Cylinders")
  • GGPLOT Point Shapes Best Tips

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

Naming convention: scale_AestheticName_NameDataType where

  • AestheticName can be x, y, color, fill, size, shape, ...
  • NameDataType can be continuous, discrete, manual or gradient.

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"))
  • 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"))
  • 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"

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

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

Answer 1 

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.

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()

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()

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

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

Rotate x-axis labels, change colors

Counter-clockwise 

theme(axis.text.x = element_text(angle = 90)

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)

Bubble Chart

Ellipse

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.

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 

dim(df) # 112436 x 2
mycol <- c("#F8766D", "#7CAE00", "#00BFC4", "#C77CFF")
# mycol defines colors of 4 levels in df$Method (a factor)
boxplot(df$value ~ df$Method, col = mycol, xlab="Method")

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

Groups of boxplots

mydata %>%
  ggplot(aes(x=Factor1, y=Response, fill=factor(Factor2))) +   
  geom_boxplot()

Another method is to use ggpubr::ggboxplot(). 

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

sina plot from the ggforce package. 

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

Violinplot.png

geom_density: Kernel density plot

  • 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

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