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== Examples from 'R for Data Science' book - Transformation ==
== Examples from 'R for Data Science' book - Transformation, bar plot ==
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# y axis = counts
# y axis = counts

Revision as of 10:26, 3 May 2020

ggplot2

Books

The Grammar of Graphics

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

Scatterplot aesthetics

  • x, y
  • shape
  • color
  • size. It is not always to put 'size' inside aes(). See an example at Legend layout.
  • alpha

Tutorials

Help

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

Extensions

http://www.ggplot2-exts.org/gallery/

Some examples

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

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

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

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

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

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

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

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

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

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

# 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
ggplot(data = mpg) + 
  geom_smooth(aes(x = displ, y = hwy))

# 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

Color palette

Color picker

https://github.com/daattali/colourpicker

Colour related aesthetics: colour, fill and alpha

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

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")

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.

ggplot2::scale - axes/axis, legend

https://ggplot2-book.org/scales.html

Naming convention: scale_AestheticName_NameDataType where

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

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"))

ylim and xlim in ggplot2 in axes

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

Use one of the following

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

Emulate ggplot2 default color palette

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

Answer 1 

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

n = 4
cols = gg_color_hue(n)

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

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

scales package 

library(scales)
show_col(hue_pal()(4))
show_col(hue_pal()(2)) # (salmon, iris blue) 
           # see https://www.htmlcsscolor.com/ for color names

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()

Heatmap for single channel

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

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

Heatmap for dual channels

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

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

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

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

Twopalette.svg

Themes and background for ggplot2

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()

ggplot() + geom_bar() +
           theme_minimal()

ggplot() + geom_bar() +
           theme_void()

ggplot() + geom_bar() +
           theme_dark()

ggthmr

ggthmr package

ggsci

https://nanx.me/ggsci/

Font size

Change Font Size of ggplot2 Plot in R (5 Examples) | Axis Text, Main Title & Legend

ggthemes package

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

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

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

Common plots

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

Line plots

Histogram

ggplot(data = txhousing, aes(x = median)) +
  geom_histogram()

Histogram vs barplot from deeply trivial.

Boxplot with jittering

ggplot(data.frame(Wi), aes(y = Wi)) + 
  geom_boxplot()
# 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.

Jitterboxplot.png

Violin plot

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

Violinplot.png

Kernel density plot

Back to back barplot

Bivariate analysis with ggpair

Correlation in R: Pearson & Spearman with Matrix Example

barplot

How to basic: bar plots

Ordered barplot and facet

Special plots

Bump plot: plot ranking over time

https://github.com/davidsjoberg/ggbump

Aesthetics

group

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

GUI

ggedit & ggplotgui – interactive ggplot aesthetic and theme editor

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

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

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

The interface introduces basic terms used in ggplot2:

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

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

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

ggcharts

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

plotly

R → plotly

ggconf: Simpler Appearance Modification of 'ggplot2'

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

Plotting individual observations and group means

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

subplot

Easy way to mix multiple graphs on the same page

gridExtra

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

gridGraphics package

make one panel blank/create a placeholder

https://stackoverflow.com/questions/20552226/make-one-panel-blank-in-ggplot2

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))

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))

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 hide it

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

gg + theme(legend.position="none")

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")

margins

https://stackoverflow.com/a/10840417

Time series plot

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

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

Versus old fashion 

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

Github style calendar plot

geom_bar(), geom_col(), stat_count()

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

geom_segment()

Line segments, arrows and curves

geom_errorbar(): error bars

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

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

Stklnpt.svg

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"))

Annotation

geom_hline(), geom_vline()

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

text annotations: ggrepel package

Fonts

Adding Custom Fonts to ggplot in R

Save the plots

ggsave() We can specify dpi to increase the resolution. For example, 

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

graphics::smoothScatter

smoothScatter with ggplot2

BBC

Add your brand to ggplot graph

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

Python

plotnine: A Grammar of Graphics for Python.

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

The Hitchhiker’s Guide to Plotnine

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