From 太極
Jump to navigation Jump to search



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
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)) + 

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

Online tutorials


> library(ggplot2)
Need help? Try Stackoverflow:


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) +

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

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() + 
  • 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.


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

# Use the xyplot() function to create the plot
# with each group represented by a different color
# result is 1 plot only
# no annotation
xyplot(y ~ x, data = df, groups = group)
df <- data.frame(x = rnorm(100), y = rnorm(100), 
                 group = sample(c("A", "B"), 100, replace = TRUE), 
                 time = sample(c("T1", "T2"), 100, replace = TRUE))

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

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

Color palette

Top color palettes

Display color palettes

  • Use barplot()
    pal <- c("#E41A1C", "#377EB8", "#4DAF4A", "#984EA3", "#FF7F00")
    # pal <- sample(colors(), 10) # randomly pick 10 colors 
    barplot(rep(1, length(pal)), col = pal, space = 0, 
            axes = FALSE, border = NA)
    # [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
    # [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()



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



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

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

Color blind

colorblindcheck: Check Color Palettes for Problems with Color Vision Deficiency

Color picker

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

Listening on

Color names, Complementary/Inverted colors

colorspace package


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, = TRUE)

*paletteer package

#67001FFF #B2182BFF #D6604DFF #F4A582FF #FDDBC7FF #F7F7F7FF 
#D1E5F0FF #92C5DEFF #4393C3FF #2166ACFF #053061FF 

#CC0C00FF #5C88DAFF #84BD00FF #FFCD00FF #7C878EFF #00B5E2FF #00AF66FF 

ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
      geom_point() +
# 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"))



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


Colour related aesthetics: colour, fill and alpha

Scatterplot with large number of points: alpha

smoothScatter with ggplot2

ggplot(aes(x, y)) +

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

  • 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 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


  • 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()
    # Set legend title
    scale_colour_discrete("Drive\ntrain")    # or a shortcut labs()
    # Change the default color
    # Change the axis scale
    # 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 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) = 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

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 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

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

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

Heatmap for dual channels

# 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


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


  • Export plot in .png with transparent background in base R plot.
    x = c(1, 2, 3)
    op <- par(bg=NA)
    plot (x)
  • Transparent background with ggplot2
    p <- ggplot(airquality, aes(Solar.R, Temp)) +
         geom_point() +
         geom_smooth() +
         # set transparency
            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)
    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')) + 
    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() +


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


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

Remove labels

Plotting with ggplot: : adding titles and axis names

ggthemes package

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





thematic, Top R tips and news from RStudio Global 2021

Common plots


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

Scatterplot with histograms



Geom smooth ex.png

Bubble Chart


ggside: scatterplot + marginal density plot

ggextra: scatterplot + marginal histogram/density

Line plots

Ridgeline plots, mountain diagram


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.


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
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)) + 

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)) + 
    # 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()
  • 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
  • Another base plot approach. 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)
    stripchart(estimate~type, data=df, method = "jitter",
    		pch=19, col=c("salmon", "orange", "yellowgreen", "green"),
    		vertical=TRUE, add=TRUE)

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))) +   
  • Another method is to use ggpubr::ggboxplot(). Papers TumorPurity.
    ggboxplot(df, "dose", "len",
               fill = "dose", palette = c("#00AFBB", "#E7B800", "#FC4E07"), add.params=list(size=0.1),
               notch=T, add = "jitter", outlier.shape = NA, shape=16,
               size = 1/.pt, x.text.angle = 30, 
               ylab = "Silhouette Values", legend="right",
               ggtheme = theme_pubr(base_size = 8)) +
         theme(plot.title = element_text(size=8,hjust = 0.5), 
               text = element_text(size=8), 
               title = element_text(size=8),
               rect = element_rect(size = 0.75/.pt),
               line = element_line(size = 0.75/.pt),
               axis.text.x = element_text(size = 7),
               axis.line = element_line(colour = 'black', size = 0.75/.pt),
               legend.title = element_blank(),
               legend.position = c(0,1), 
               legend.justification = c(0,1),
               legend.key.size = unit(4,"mm"))

p-values on top of boxplots

Violin plot and sina plot

geom_density: Kernel density plot

A panel of density plots

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

Bivariate analysis with ggpair

Correlation in R: Pearson & Spearman with Matrix Example


barplot/bar plot

Ordered barplot and facet

  • ?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
       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")


    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$y on x-axis. Fortunately,
    #   ggplot2 will draw barplot vertically or horizontally depending the 2 variables' types
    # The reason of using "-order" is to make the 1st name appears on the top
    ggplot(df, aes(x=y, y=reorder(fac, -order))) + geom_col()
    ggplot(df, aes(x=reorder(x, desc(y)), y=y)), geom_col()
  • Predict #TidyTuesday giant pumpkin weights with workflowsets. fct_reorder()
  • Reordering and facetting for ggplot2. tidytext::reorder_within() was used.
  • Chapter2 of data.table cookbook. reorder(fac, value) was used.
  • PCA and UMAP with tidymodels
  • A simple example
    dat <- structure(list(gene = c("CAPN9", "CSF3R", "HPN", "KCNA5", "MTMR7", 
    "NRG3", "SMTNL2", "TMPRSS6"), coef = c(-1.238, -0.892, -0.224, 
    -0.057, 0.133, 0.377, 0.436, 0.804)), row.names = c("4976", "6467", 
    "12355", "13373", "18143", "19010", "23805", "25602"), class = "data.frame")
    # Base R plot
    barplot(dat$coef, names = dat$gene, horiz = T, las=1,
            main='base R', xlab = "Coefficients")
    # GGplot2
    dat %>% ggplot(aes(y=gene, x=coef)) + geom_col(fill = 'gray') + 
        theme(axis.ticks.y = element_blank()) + 
        theme(panel.background = element_blank(), 
              axis.line.x = element_line(colour = 'black')) +
        labs(x="Coefficients", y = '', title = "ggplot2")

    Barplot base.png, Barplot ggplot2.png

Proportion barplot

Back to back barplot

Pyramid Chart


Flip x and y axes


Rotate x-axis labels

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

Starts at zero

Starting bars and histograms at zero in ggplot2

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

Add patterns

Barplot with colors for a 2nd variable

How to basic: bar plots

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

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


Base R approach.

Barplot with color gradient


Barplot with only horizontal gridlines

Geom bar3.png Geom bar4.png

Barplot with text at the end

Geom bar1.png Geom bar2.png

Polygon and map plot


geom_step: Step function

Connect observations: geom_path(), geom_step()

Example: KM curves (without legend)

sf <- survfit(Surv(time, status) ~ x, data = aml)
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])), 
# cf:  plot(sf, col = c('red', 'black'), mark.time=FALSE)

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

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

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

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

Coefficients, intervals, errorbars

Comparing similarities / differences between groups

comparing similarities / differences between groups

Special plots

Dot plot & forest plot

Lollipop plot

geom_segment() + geom_point()

ggpubr:: ggdotchart()

Correlation Analysis Different

Bump plot: plot ranking over time

Gauge plots

Sankey diagrams

Horizon chart

Circos plots


  • 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.
    g <- ggplot(tail(iris), aes(Petal.Length, Sepal.Length, text2=Species)) + geom_point()
    ggplotly(g, tooltip = c("Petal.Length", "text2"))

Aesthetics finder, video



GUI/Helper packages

ggedit & ggplotgui – interactive ggplot aesthetic and theme editor

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

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.



ggx Create ggplot in natural language



R web → plotly


ggiraph: Make 'ggplot2' Graphics Interactive

ggconf: Simpler Appearance Modification of 'ggplot2'

Plotting individual observations and group means


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


  • 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.



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() +

Overall title

multiple ggplots overall title

Remove vertical/horizontal grids but keep ticks



Common legend

Add a common Legend for combined ggplots


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)


Common x or y labels

Base R plot vs ggplot2

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

labs for x and y axes

x and y labels 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.

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))


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')

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))

# Customize the edge color and background color
gapminder %>%
  ggplot(aes(gdpPercap,lifeExp, color=continent)) +
  geom_point() +
  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)

  • 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

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") +
    legend.text = element_text(size = 12), 
    legend.title = element_text(size = 14)


Centered title

See the Legends part of the cheatsheet.

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


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


Aspect ratio


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

Time series plot

Multiple lines plot

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
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


Calendar plot in R using ggplot2

Github style calendar plot


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()

  • 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)) + 

Add numbers to the plot

An example

Ordered barplot and reorder()

Ordered barplot and facet



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)


The Pfizer-Biontech Vaccine May Be A Lot More Effective Than You Think

Square shaped plot

ggplot() + theme(aspect.ratio=1) # do not adjust xlim, ylim

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


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

Connect Paired Points with Lines in Scatterplot

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

Barchart with Significance Tests

Interaction plot

Randomized block design


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

Cf annotate("segment", ...)

geom_errorbar(): error bars

x <- rnorm(10)
SE <- rnorm(10)
y <- 1:10

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
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()

Waterfall plot



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


Add a horizontal/vertical line

geom_hline(), geom_vline()


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

    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
    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("grey", "red")) +
        theme_minimal() +
        labs(title = "Volcano Plot", x = "Log2 Fold Change", y = "-Log10 Adjusted P-Value") +
            data = significant_genes,
            aes(label = gene),
            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
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: Improved text rendering support for ggplot2

ggforce - Annotate areas with ellipses


Other geoms

Exploring other {ggplot2} geoms


geomtextpath- Create curved text in ggplot2

Build your own geom

Fonts, icons

Lines of best fit

Lines of best fit

Save the plots -- ggsave()

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

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

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

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

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

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

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

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

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) 
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


  • 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 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()

graphics::smoothScatter: scatter plots with lots of points

Other tips/FAQs

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

Ten Simple Rules for Better Figures

Ten Simple Rules for Better Figures

Recreating the Storytelling with Data look with ggplot

Recreating the Storytelling with Data look with ggplot

ggplot2 does not appear to work when inside a function 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.


Add your brand to ggplot graph

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

Animation and gganimate


ggstatsplot: ggplot2 Based Plots with Statistical Details

Write your own ggplot2 function: rlang

Some packages depend on ggplot2

dittoSeq from Bicoonductor



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