# 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'))


## Help

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


# 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

# 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

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

Listening on http://127.0.0.1:6023


## cols4all

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

c4a_types() # understand abbreviation

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

c4a_overview() # how many palettes per series x types

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

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

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

## *paletteer package

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

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

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

## 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)

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

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

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

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

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

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.

## 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/.

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

## Heatmap for dual channels

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

# Themes and background for ggplot2

## Background

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

dev.copy(png,'myplot.png')
dev.off()
par(op)

• Transparent background with ggplot2
library(ggplot2)
data("airquality")

p <- ggplot(airquality, aes(Solar.R, Temp)) +
geom_point() +
geom_smooth() +
# set transparency
theme(
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
panel.background = element_rect(fill = "transparent",colour = NA),
plot.background = element_rect(fill = "transparent",colour = NA)
)
p
ggsave("airquality.png", p, bg = "transparent")

• ggplot2 theme background color and grids
ggplot() + geom_bar(aes(x=, fill=y)) +
theme(panel.background=element_rect(fill='purple')) +
theme(plot.background=element_blank())

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

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

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

ggplot() + geom_bar() +
theme_minimal() # no edge

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

ggplot() + geom_bar() +
theme_dark()


ggthmr 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


## Rotate x-axis labels, change colors

Counter-clockwise

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


## 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

## ggthemes package

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

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


# Common plots

## Scatterplot

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

## 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 '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

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)  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(). 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 sina plot from the ggforce package. library(ggplot2) ggplot(midwest, aes(state, area)) + geom_violin() + ggforce::geom_sina() ## 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 • 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 ## GGally::ggpairs ## 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 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$gene, horiz = T, las=1, main='base R', xlab = "Coefficients") # GGplot2 dat %>% ggplot(aes(y=gene, x=coef)) + geom_col(fill = 'gray') + theme(axis.ticks.y = element_blank()) + theme(panel.background = element_blank(), axis.line.x = element_line(colour = 'black')) + labs(x="Coefficients", y = '', title = "ggplot2")  , ### Proportion barplot ### Back to back barplot ### Pyramid Chart ### Flip x and y axes coord_flip() ### Rotate x-axis labels ggplot(mydf) + geom_col(aes(x = model, y=value, fill = method), position="dodge")+ theme(axis.text.x = element_text(angle = 45, hjust=1))  ### Starts at zero scale_y_continuous(expand = c(0,0), limits = c(0, YourLimit))  ### Add patterns ### Barplot with color gradient ### Barplot with only horizontal gridlines ### Barplot with text at the end ## Waterfall plot • Waterfall charts in ggplot2 with waterfalls package • ggplot2: Waterfall Charts geom_rect() • Waterfall Charts in Oncology Trials - Ride the Wave. Drug response • Collected data is compared to the data taken at baseline to determine if drug has some activity or not. Also each patient is assigned in to different categories based on overall response • Y-axis = % of change from baseline in the tumor size for each patient • We want to create this plot by grouping different patients based on their overall response category (eg 'Earth Death' or 'Complete Response') and fill the bars of such patients with different colors so it is easy to identify different groups. • Understanding Waterfall Plots • A waterfall plot for drug BYL719 and color it based on the mutation status of the CDK13 gene, see Xeva vignette. ## Polygon and map plot ## geom_step: Step function Connect observations: geom_path(), geom_step() Example: KM curves (without legend) library(survival) sf <- survfit(Surv(time, status) ~ x, data = aml) sf str(sf) # the first 10 forms one strata and the rest 10 forms the other ggplot() + geom_step(aes(x=c(0, sf$time[1:10]), y=c(1, sf$surv[1:10])), col='red') + scale_x_continuous('Time', limits = c(0, 161)) + scale_y_continuous('Survival probability', limits = c(0, 1)) + geom_step(aes(x=c(0, sf$time[11:20]), y=c(1, sf$surv[11:20])), col='black') # cf: plot(sf, col = c('red', 'black'), mark.time=FALSE)  Same example but with legend (see Construct a manual legend for a complicated plot) cols <- c("NEW"="#f04546","STD"="#3591d1") ggplot() + geom_step(aes(x=c(0, sf$time[1:10]), y=c(1, sf$surv[1:10]), col='NEW')) + scale_x_continuous('Time', limits = c(0, 161)) + scale_y_continuous('Survival probability', limits = c(0, 1)) + geom_step(aes(x=c(0, sf$time[11:20]), y=c(1, sf$surv[11:20]), col='STD')) + scale_colour_manual(name="Treatment", values = cols)  To control the line width, use the size parameter; e.g. geom_step(aes(x, y), size=.5). The default size is .5 (where to find this info?). To allow different line types, use the linetype parameter. The first level is solid line, the 2nd level is dashed, ... We can change the default line types by using the scale_linetype_manual() function. See Line Types in R: The Ultimate Guide for R Base Plot and GGPLOT. ## Coefficients, intervals, errorbars ## Comparing similarities / differences between groups # 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 # 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"))  ## aes_string() ## group # 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. ## ggcharts ## ggeasy ## ggx https://github.com/brandmaier/ggx Create ggplot in natural language # Interactive ## plotly ## ggiraph ggiraph: Make 'ggplot2' Graphics Interactive # ggconf: Simpler Appearance Modification of 'ggplot2' # Plotting individual observations and group means # subplot ## Adding/Inserting an image to ggplot2 See also ggbernie which uses a different way ggplot2::layer() and a self-defined geom (geometric object). # Easy way to mix/combine multiple graphs on the same page ## annotation_custom • predcurvePlot.R from TreatmentSelection. One issue is the font size is large for the text & labels at the bottom. The 2nd issue is the bottom part of the graph/annotation (marker value scale) can be truncated if the window size is too large. If the window is too small, the bottom part can overlap with the top part. p <- p + theme(plot.margin = unit(c(1,1,4,1), "lines")) # hard coding p <- p + annotation_custom() # axis for marker value scale p <- p + annotation_custom() # label only  • Similar plot but without using base R graphic. One issue is the text is not below the scale (this can be fixed by par(mar) & mtext(text, side=1, line=4)) and the 2nd issue is the same as ggplot2's approach. axis(1,at= breaks, label = round(quantile(x1, prob = breaks/100), 1),pos=-0.26) # hard coding  • Another common problem is the plot saved by pdf() or png() can be truncated too. I have a better luck with png() though. ## grid ## gridExtra ### Force a regular plot object into a Grob for use in grid.arrange gridGraphics package ### make one panel blank/create a placeholder # Method 1: Blank ggplot() + theme_void() # Method 2: Display N/A ggplot() + theme_void() + geom_text(aes(0,0,label='N/A'))  ### Overall title ### Remove vertical/horizontal grids but keep ticks ## patchwork ### Common legend 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 + theme(legend.position = "bottom") + plot_layout(guides = "collect") # two legends on the RHS # Method 2: p1 + p2 + plot_layout(guides = "collect") # two legends 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 ## egg • egg (ggarrange()): Extensions for 'ggplot2', to Align Plots, Plot insets, and Set Panel Sizes. Same author of gridExtra package. egg depends on gridExtra. ### Common x or y labels # Base R plot vs ggplot2 • My summary base-R ggplot2 plot(x, y, col) geom_point(aes(x, y, color, shape)) xlim scale_x_continuous(limits) log="x" scale_x_continuous(trans="log10") xlab mtext("Var", cex, line, adj, las, side) scale_x_discrete(name="sample size") labs(x) xlab() main labs(x, y, title, colour) ggtitle() axis(2, labels) scale_y_continuous(labels, breaks) scale_x_discrete(labels) ? scale_color_discrete('new color title') ? scale_shape_discrete('new shape title') col scale_color_manual(name, values = NamedVector) pch, cex geom_point(pch, size) plot(mpg, disp, col=factor(cyl)) legend("topleft", legend = sort(unique(cyl)), col=1:3, pch=1) # discrete case ggplot(mtcars, aes(mpg, disp, color = factor(cyl))) + geom_point() + labs(color = "Number of Cylinders") text() geom_text() ? theme(title = element_text(size=8), legend.title = element_blank(), legend.position = "none", legend.key = element_blank(), plot.title = element_text(hjust = 0.5), plot.sybtitle = element_text(size = 8)) las in plot(), barplot() text(x, y, labs, srt=45) theme(axis.text.x = element_text(angle = 90)) matplot() geom_line() + geom_point() plot(type = 'l'), points() geom_line() + geom_point() barplot() geom_bar() par(mfrow) facet_grid() # labs for x and y axes ## x and y labels https://stackoverflow.com/questions/10438752/adding-x-and-y-axis-labels-in-ggplot2 or the Labels part of the cheatsheet You can set the labels with xlab() and ylab(), or make it part of the scale_*.* call. labs(x = "sample size", y = "ngenes (glmnet)") scale_x_discrete(name="sample size") scale_y_continuous(name="ngenes (glmnet)", limits=c(100, 500))  ## Change tick mark labels ## name-value pairs See several examples (color, fill, size, ...) from opioid prescribing habits in texas. # Prevent sorting of x labels 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")

• 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() +
scale_x_log10()+
theme(legend.position = c(0.87, 0.25),
legend.background = element_rect(fill = "white", color = "black"))


## Guide functions for finer control

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')


## Legend symbol background

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


# 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")


# 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

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

# 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()

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))


# 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)


# geom_segment()

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

Cf annotate("segment", ...)

# geom_errorbar(): error bars

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

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

# 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"))


# Circle

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

# Annotation

## geom_hline(), geom_vline()

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


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

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

geom_text(aes(x, y, label), data, size, vjust, hjust, nudge_x)

• Text annotations in ggplot2
p + geom_text(aes(x = -115, y = 25,
label = "Map of the United States"),
stat = "unique")
p + geom_label(aes(x = -115, y = 25,
label = "Map of the United States"),
stat = "unique") # include border around the text
• Use the nudge_y parameter to avoid the overlap of the point and the text such as
ggplot() + geom_point() +
geom_text(aes(x, y, label), color='red', data, nudge_y=1)

• What do hjust and vjust do when making a plot using ggplot? 0 means left-justified 1 means right-justified. This is necessary if we have multiples lines in text. By default, it will center-justified.
• Volcano plots, EnhancedVolcano package

## Text wrap

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())


# Other geoms

## geomtextpath

geomtextpath- Create curved text in ggplot2

# 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). For example,

g1 <- ggplot(data = mydf)
g1
ggsave("myfile.png", g1, height = 7, width = 8, units = "in", dpi = 500)

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()
`

# Other tips/FAQs

## 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.

# ggstatsplot

ggstatsplot: ggplot2 Based Plots with Statistical Details

# Some packages depend on ggplot2

dittoSeq from Bicoonductor

# 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.