This tutorial has been forked from awesome classes developed by Adam Wilson here: http://adamwilson.us/RDataScience/

The R Script associated with this page is available here. Download this file and open it (or copy-paste into a new script) with RStudio so you can follow along.

0.1 Data

In this module, we’ll primarily use the mtcars data object. The data was extracted from the 1974 Motor Trend US magazine, and comprises fuel consumption and 10 aspects of automobile design and performance for 32 automobiles (1973–74 models).

A data frame with 32 observations on 11 variables.

Column name	Description
mpg	Miles/(US) gallon
cyl	Number of cylinders
disp	Displacement (cu.in.)
hp	Gross horsepower
drat	Rear axle ratio
wt	Weight (lb/1000)
qsec	1/4 mile time
vs	V/S
am	Transmission (0 = automatic, 1 = manual)
gear	Number of forward gears
carb	Number of carburetors


Here's what the data look like:

```r
library(ggplot2);library(knitr)
kable(head(mtcars))

	mpg c	yl d	isp	hp d	rat	wt	qsec v	s a	m g	ear c	arb
Mazda RX4	21.0	6	160	110	3.90	2.620	16.46	0	1	4	4
Mazda RX4 Wag	21.0	6	160	110	3.90	2.875	17.02	0	1	4	4
Datsun 710	22.8	4	108	93	3.85	2.320	18.61	1	1	4	1
Hornet 4 Drive	21.4	6	258	110	3.08	3.215	19.44	1	0	3	1
Hornet Sportabout	18.7	8	360	175	3.15	3.440	17.02	0	0	3	2
Valiant	18.1	6	225	105	2.76	3.460	20.22	1	0	3	1

1 Base graphics

1.1 Base `plot()`

R has a set of ‘base graphics’ that can do many plotting tasks (scatterplots, line plots, histograms, etc.)

plot(y=mtcars$mpg,x=mtcars$wt)

Or you can use the more common formula notation:

plot(mpg~wt,data=mtcars)

And you can customize with various parameters:

plot(mpg~wt,data=mtcars,
  ylab="Miles per gallon (mpg)",
  xlab="Weight (1000 pounds)",
  main="Fuel Efficiency vs. Weight",
  col="red"
  )

Or switch to a line plot:

plot(mpg~wt,data=mtcars,
  type="l",
  ylab="Miles per gallon (mpg)",
  xlab="Weight (1000 pounds)",
  main="Fuel Efficiency vs. Weight",
  col="blue"
  )

See ?plot for details.

1.2 Histograms

Check out the help for basic histograms.

?hist

Plot a histogram of the fuel efficiencies in the mtcars dataset.

hist(mtcars$mpg)

2 `ggplot2`

The grammar of graphics: consistent aesthetics, multidimensional conditioning, and step-by-step plot building.

Data: The raw data
geom_: The geometric shapes representing data
aes(): Aesthetics of the geometric and statistical objects (color, size, shape, and position)
scale_: Maps between the data and the aesthetic dimensions

data
+ geometry,
+ aesthetic mappings like position, color and size
+ scaling of ranges of the data to ranges of the aesthetics

2.0.1 Additional settings

stat_: Statistical summaries of the data that can be plotted, such as quantiles, fitted curves (loess, linear models), etc.
coord_: Transformation for mapping data coordinates into the plane of the data rectangle
facet_: Arrangement of data into grid of plots
theme: Visual defaults (background, grids, axes, typeface, colors, etc.)

For example, a simple scatterplot: alt text

Add variable colors and sizes: alt text

2.1 Simple scatterplot

First, create a blank ggplot object with the data and x-y geometry set up.

p <- ggplot(mtcars, aes(x=wt, y=mpg))
summary(p)

## data: mpg, cyl, disp, hp, drat, wt, qsec,
##   vs, am, gear, carb [32x11]
## mapping:  x = wt, y = mpg
## faceting: <ggproto object: Class FacetNull, Facet>
##     compute_layout: function
##     draw_back: function
##     draw_front: function
##     draw_labels: function
##     draw_panels: function
##     finish_data: function
##     init_scales: function
##     map: function
##     map_data: function
##     params: list
##     render_back: function
##     render_front: function
##     render_panels: function
##     setup_data: function
##     setup_params: function
##     shrink: TRUE
##     train: function
##     train_positions: function
##     train_scales: function
##     vars: function
##     super:  <ggproto object: Class FacetNull, Facet>

p + geom_point()

Or you can do both at the same time:

ggplot(mtcars, aes(x=wt, y=mpg)) + 
  geom_point()

2.1.1 Aesthetic map: color by # of cylinders

p + 
  geom_point(aes(colour = factor(cyl)))

2.1.2 Set shape using # of cylinders

p + 
  geom_point(aes(shape = factor(cyl)))

2.1.3 Adjust size by `qsec`

p + 
  geom_point(aes(size = qsec))

2.1.4 Color by cylinders and size by `qsec`

p + 
  geom_point(aes(colour = factor(cyl),size = qsec))

2.1.5 Multiple aesthetics

p + 
  geom_point(aes(colour = factor(cyl),size = qsec,shape=factor(gear)))

2.1.6 Add a linear model

p + geom_point() + 
  geom_smooth(method="lm")

2.1.7 Add a LOESS smooth

p + geom_point() + 
  geom_smooth(method="loess")

2.1.8 Change scale color

p + geom_point(aes(colour = cyl)) + 
  scale_colour_gradient(low = "blue")

2.1.9 Change scale shapes

p + geom_point(aes(shape = factor(cyl))) + 
  scale_shape(solid = FALSE)

2.1.10 Set aesthetics to fixed value

ggplot(mtcars, aes(wt, mpg)) + 
  geom_point(colour = "red", size = 3)

2.1.11 Transparancy: alpha=0.2

d <- ggplot(diamonds, aes(carat, price))
d + geom_point(alpha = 0.2)

Varying alpha useful for large data sets

2.1.12 Transparancy: alpha=0.1

d + 
  geom_point(alpha = 0.1)

2.1.13 Transparancy: alpha=0.01

d + 
  geom_point(alpha = 0.01)

2.2 Building ggplots

alt text

2.3 Other Plot types

alt text

Edit plot p to include:

points
A smooth (‘loess’) curve
a “rug” to the plot

p <- ggplot(mtcars, aes(x=wt, y=mpg))

p+
  geom_point()+
  geom_smooth()+
  geom_rug()

alt text

2.3.1 Discrete X, Continuous Y

p <- ggplot(mtcars, aes(factor(cyl), mpg))
p + geom_point()

2.3.2 Discrete X, Continuous Y + geom_jitter()

p + 
  geom_jitter()

2.3.3 Discrete X, Continuous Y + geom_violin()

p + 
  geom_violin()

2.3.4 Discrete X, Continuous Y + geom_violin()

p + 
  geom_violin() + geom_jitter(position = position_jitter(width = .1))

alt text

2.3.5 Three Variables

alt text

Will return to this when we start working with raster maps.

2.3.6 Stats

Visualize a data transformation

alt text

Each stat creates additional variables with a common ..name.. syntax
Often two ways: stat_bin(geom="bar") OR geom_bar(stat="bin")

alt text

2.3.7 2D kernel density estimation

Old Faithful Geyser Data on duration and waiting times.

library("MASS")

## 
## Attaching package: 'MASS'

## The following object is masked from 'package:dplyr':
## 
##     select

## The following objects are masked from 'package:raster':
## 
##     area, select

data(geyser)
m <- ggplot(geyser, aes(x = duration, y = waiting))

alt text photo: Greg Willis

See ?geyser for details.

m + 
  geom_point()

m + 
  geom_point() +  stat_density2d(geom="contour")

Check ?geom_density2d() for details

m + 
  geom_point() +  stat_density2d(geom="contour") +
  xlim(0.5, 6) + ylim(40, 110)

Update limits to show full contours. Check ?geom_density2d() for details

m + stat_density2d(aes(fill = ..level..), geom="polygon") + 
  geom_point(col="red")

Check ?geom_density2d() for details

alt text

2.3.7 Your turn

Edit plot m to include:

The point data (with red points) on top
A binhex plot of the Old Faithful data

Experiment with the number of bins to find one that works.

See ?stat_binhex for details.

m <- ggplot(geyser, aes(x = duration, y = waiting))

m + stat_binhex(bins=10) + 
  geom_point(col="red")

2.4 Specifying Scales

alt text

2.4.1 Discrete color: default

b=ggplot(mpg,aes(fl))+
  geom_bar( aes(fill = fl)); b

2.4.2 Discrete color: greys

b + scale_fill_grey( start = 0.2, end = 0.8, 
                   na.value = "red")

2.4.3 Continuous color: defaults

a <- ggplot(mpg, aes(x=hwy,y=cty,col=displ)) + 
  geom_point(); a

2.4.4 Continuous color: `gradient`

a +  scale_color_gradient( low = "red", 
                          high = "yellow")

2.4.5 Continuous color: `gradient2`

a + scale_color_gradient2(low = "red", high = "blue", 
                       mid = "white", midpoint = 4)

2.4.6 Continuous color: `gradientn`

a + scale_color_gradientn(
  colours = rainbow(10))

2.4.7 Discrete color: brewer

b + 
  scale_fill_brewer( palette = "Blues")

2.5 colorbrewer2.org

alt text

2.6 ColorBrewer: Diverging

alt text

2.7 ColorBrewer: Filtered

alt text

2.7 Your turn

Edit the contour plot of the geyser data:

Reduce the size of the points
Use a sequential brewer palette (select from colorbrewer2.org)
Add informative x and y labels

m <- ggplot(geyser, aes(x = duration, y = waiting)) +
  stat_density2d(aes(fill = ..level..), geom="polygon") + 
  geom_point(col="red")

Note: scale_fill_distiller() rather than scale_fill_brewer() for continuous data

m + stat_density2d(aes(fill = ..level..), geom="polygon") + 
  geom_point(size=.75)+
  scale_fill_distiller(palette="OrRd",
                       name="Kernel\nDensity")+
      xlim(0.5, 6) + ylim(40, 110)+
  xlab("Eruption Duration (minutes)")+
  ylab("Waiting time (minutes)")

Or use geom=tile for a raster representation.

m + stat_density2d(aes(fill = ..density..), geom="tile",contour=F) + 
  geom_point(size=.75)+
  scale_fill_distiller(palette="OrRd",
                       name="Kernel\nDensity")+
      xlim(0.5, 6) + ylim(40, 110)+
  xlab("Eruption Duration (minutes)")+
  ylab("Waiting time (minutes)")

2.8 Axis scaling

Create noisy exponential data

set.seed(201)
n <- 100
dat <- data.frame(
    xval = (1:n+rnorm(n,sd=5))/20,
    yval = 10^((1:n+rnorm(n,sd=5))/20)
)

Make scatter plot with regular (linear) axis scaling

sp <- ggplot(dat, aes(xval, yval)) + geom_point()
sp

Example from R Cookbook

log10 scaling of the y axis (with visually-equal spacing)

sp + scale_y_log10()

2.9 Coordinate Systems

alt text

2.10 Position

alt text

2.10.1 Stacked bars

ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar()

2.10.2 Dodged bars

ggplot(diamonds, aes(clarity, fill=cut)) + geom_bar(position="dodge")

3 Facets

Use facets to divide graphic into small multiples based on a categorical variable.

facet_wrap() for one variable:

ggplot(mpg, aes(x = cty, y = hwy, color = factor(cyl))) +
  geom_point()+
  facet_wrap(~year)

facet_grid(): two variables

ggplot(mpg, aes(x = cty, y = hwy, color = factor(cyl))) +
  geom_point()+
  facet_grid(year~cyl)

Small multiples (via facets) are very useful for visualization of timeseries (and especially timeseries of spatial data.)

4 Themes

Set default display parameters (colors, font sizes, etc.) for different purposes (for example print vs. presentation) using themes.

4.1 GGplot Themes

alt text

Quickly change plot appearance with themes.

4.1.1 More options in the `ggthemes` package.

library(ggthemes)

Or build your own!

4.1.2 Theme examples: default

p=ggplot(mpg, aes(x = cty, y = hwy, color = factor(cyl))) +
  geom_jitter() +
  labs(
    x = "City mileage/gallon",
    y = "Highway mileage/gallon",
    color = "Cylinders"
  )

4.1.3 Theme examples: default

4.1.4 Theme examples: Solarized

p + theme_solarized()

4.1.5 Theme examples: Solarized Dark

p +  theme_solarized(light=FALSE)

4.1.6 Theme examples: Excel

p + theme_excel()

4.1.7 Theme examples: The Economist

p + theme_economist()

## Warning: `panel.margin` is deprecated. Please use
## `panel.spacing` property instead

## Warning: `legend.margin` must be specified
## using `margin()`. For the old behavior use
## legend.spacing

5 Saving/exporting

5.1 Saving using the GUI

alt text

5.2 Saving using `ggsave()`

Save a ggplot with sensible defaults:

ggsave(filename, plot = last_plot(), scale = 1, width, height)

5.3 Saving using devices

Save any plot with maximum flexibility:

pdf(filename, width, height)  # open device
ggplot()                      # draw the plot(s)
dev.off()                     # close the device

Formats

pdf
jpeg
png
tif

and more…

5.3 Your turn

Save the p plot from above using png() and dev.off()
Switch to the solarized theme with light=FALSE
Adjust fontsize with base_size in the theme + theme_solarized(base_size=24)

5.3 Save a plot: Example 1

png("03_assets/test1.png",width=600,height=300)
p +  theme_solarized(light=FALSE)
dev.off()

5.3 Save a plot: Example 2

png("03_assets/test2.png",width=600,height=300)
p +  theme_solarized(light=FALSE, base_size=24)
dev.off()

5.4 GGPLOT2 Documentation

Perhaps R’s best documented package: docs.ggplot2.org

alt text

5.5 Colophon

Sources:

ggplot cheatsheet

Licensing:

Presentation: CC-BY-3.0
Source code: MIT

Getting Graphic

Base graphics and ggplot2

0.1 Data

1 Base graphics

1.1 Base plot()

1.2 Histograms

2 ggplot2

2.0.1 Additional settings

2.1 Simple scatterplot

2.1.1 Aesthetic map: color by # of cylinders

2.1.2 Set shape using # of cylinders

2.1.3 Adjust size by qsec

2.1.4 Color by cylinders and size by qsec

2.1.5 Multiple aesthetics

2.1.6 Add a linear model

2.1.7 Add a LOESS smooth

2.1.8 Change scale color

2.1.9 Change scale shapes

2.1.10 Set aesthetics to fixed value

2.1.11 Transparancy: alpha=0.2

2.1.12 Transparancy: alpha=0.1

2.1.13 Transparancy: alpha=0.01

2.2 Building ggplots

2.3 Other Plot types

2.3.1 Discrete X, Continuous Y

2.3.2 Discrete X, Continuous Y + geom_jitter()

2.3.3 Discrete X, Continuous Y + geom_violin()

2.3.4 Discrete X, Continuous Y + geom_violin()

2.3.5 Three Variables

2.3.6 Stats

2.3.7 2D kernel density estimation

2.3.7 Your turn

2.4 Specifying Scales

2.4.1 Discrete color: default

2.4.2 Discrete color: greys

2.4.3 Continuous color: defaults

2.4.4 Continuous color: gradient

2.4.5 Continuous color: gradient2

2.4.6 Continuous color: gradientn

2.4.7 Discrete color: brewer

2.5 colorbrewer2.org

2.6 ColorBrewer: Diverging

2.7 ColorBrewer: Filtered

2.7 Your turn

2.8 Axis scaling

2.9 Coordinate Systems

2.10 Position

2.10.1 Stacked bars

2.10.2 Dodged bars

3 Facets

4 Themes

4.1 GGplot Themes

4.1.1 More options in the ggthemes package.

4.1.2 Theme examples: default

4.1.3 Theme examples: default

4.1.4 Theme examples: Solarized

4.1.5 Theme examples: Solarized Dark

4.1.6 Theme examples: Excel

4.1.7 Theme examples: The Economist

5 Saving/exporting

5.1 Saving using the GUI

5.2 Saving using ggsave()

5.3 Saving using devices

5.3 Your turn

5.3 Save a plot: Example 1

5.3 Save a plot: Example 2

5.4 GGPLOT2 Documentation

5.5 Colophon

Base graphics and `ggplot2`

1.1 Base `plot()`

2 `ggplot2`

2.1.3 Adjust size by `qsec`

2.1.4 Color by cylinders and size by `qsec`

2.4.4 Continuous color: `gradient`

2.4.5 Continuous color: `gradient2`

2.4.6 Continuous color: `gradientn`

4.1.1 More options in the `ggthemes` package.

5.2 Saving using `ggsave()`