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– Question answers 
 –  providing a  Visual representation and detailed explanation , screenshot the solution 
 –  recommended to use RStudio for all  
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– NEED PLAGIARISM REPORT
– INTEXT CITATION 
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Assessment 1
Basic Graphs with ggplot2
For assessment 1 you are to complete the problems by providing one of the following:
1. Visual representation of the information,
2. A detailed explanation of the answer,
3. Visual representation and detailed explanation.
When providing a visual representation, screenshot the solution.

It is recommended to use RStudio for all assessments.

1 – Run ggplot(data = mpg). What do you see?

Import the necessary package, tidyverse, and load the mpg data from the ggplot2 package.

2 – How many rows are in mpg? How many columns?

To get the dimensions of a data matrix, we can simply use the function ‘dim()’.

3 – What does the drv variable describe? Read the help for ?mpg to find out.

4 -Make a scatterplot of hwy vs cyl.

5 – What happens if you make a scatterplot of class vs drv? Why is the plot not useful?

Link

Graphical Primitives

Data Visualization
with ggplot2

Cheat Sheet

RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • [email protected] • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org • ggplot2 0.9.3.1 • Updated: 3/15

Geoms – Use a geom to represent data points, use the geom’s aesthetic properties to represent variables. Each function returns a layer.
One Variable

a + geom_area(stat = “bin”)
x, y, alpha, color, fill, linetype, size
b + geom_area(aes(y = ..density..), stat = “bin”)

a + geom_density(kernel = “gaussian”)
x, y, alpha, color, fill, linetype, size, weight
b + geom_density(aes(y = ..county..))

a + geom_dotplot()
x, y, alpha, color, fill

a + geom_freqpoly()
x, y, alpha, color, linetype, size
b + geom_freqpoly(aes(y = ..density..))

a + geom_histogram(binwidth = 5)
x, y, alpha, color, fill, linetype, size, weight
b + geom_histogram(aes(y = ..density..))

Discrete
b <- ggplot(mpg, aes(fl))

b + geom_bar()
x, alpha, color, fill, linetype, size, weight

Continuous
a <- ggplot(mpg, aes(hwy))

Two Variables

Continuous Function

Discrete X, Discrete Y
h <- ggplot(diamonds, aes(cut, color))

h + geom_jitter()
x, y, alpha, color, fill, shape, size

Discrete X, Continuous Y
g <- ggplot(mpg, aes(class, hwy))

g + geom_bar(stat = "identity")
x, y, alpha, color, fill, linetype, size, weight

g + geom_boxplot()
lower, middle, upper, x, ymax, ymin, alpha,
color, fill, linetype, shape, size, weight

g + geom_dotplot(binaxis = "y",
stackdir = "center")
x, y, alpha, color, fill

g + geom_violin(scale = "area")
x, y, alpha, color, fill, linetype, size, weight

Continuous X, Continuous Y
f <- ggplot(mpg, aes(cty, hwy))

f + geom_blank()

f + geom_jitter()
x, y, alpha, color, fill, shape, size

f + geom_point()
x, y, alpha, color, fill, shape, size

f + geom_quantile()
x, y, alpha, color, linetype, size, weight

f + geom_rug(sides = "bl")
alpha, color, linetype, size

f + geom_smooth(model = lm)
x, y, alpha, color, fill, linetype, size, weight

f + geom_text(aes(label = cty))
x, y, label, alpha, angle, color, family, fontface,
hjust, lineheight, size, vjust

Three Variables

m + geom_contour(aes(z = z))
x, y, z, alpha, colour, linetype, size, weight

seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2))
m <- ggplot(seals, aes(long, lat))

j <- ggplot(economics, aes(date, unemploy))
j + geom_area()

x, y, alpha, color, fill, linetype, size

j + geom_line()
x, y, alpha, color, linetype, size

j + geom_step(direction = "hv")
x, y, alpha, color, linetype, size

Continuous Bivariate Distribution
i <- ggplot(movies, aes(year, rating))
i + geom_bin2d(binwidth = c(5, 0.5))

xmax, xmin, ymax, ymin, alpha, color, fill,
linetype, size, weight

i + geom_density2d()
x, y, alpha, colour, linetype, size

i + geom_hex()
x, y, alpha, colour, fill size

e + geom_segment(aes(
xend = long + delta_long,
yend = lat + delta_lat))
x, xend, y, yend, alpha, color, linetype, size

e + geom_rect(aes(xmin = long, ymin = lat,
xmax= long + delta_long,
ymax = lat + delta_lat))
xmax, xmin, ymax, ymin, alpha, color, fill,
linetype, size

c + geom_polygon(aes(group = group))
x, y, alpha, color, fill, linetype, size

e <- ggplot(seals, aes(x = long, y = lat))

m + geom_raster(aes(fill = z), hjust=0.5,
vjust=0.5, interpolate=FALSE)
x, y, alpha, fill

m + geom_tile(aes(fill = z))
x, y, alpha, color, fill, linetype, size

k + geom_crossbar(fatten = 2)
x, y, ymax, ymin, alpha, color, fill, linetype,
size

k + geom_errorbar()
x, ymax, ymin, alpha, color, linetype, size,
width (also geom_errorbarh())

k + geom_linerange()
x, ymin, ymax, alpha, color, linetype, size

k + geom_pointrange()
x, y, ymin, ymax, alpha, color, fill, linetype,
shape, size

Visualizing error
df <- data.frame(grp = c("A", "B"), fit = 4:5, se = 1:2)

k <- ggplot(df, aes(grp, fit, ymin = fit-se, ymax = fit+se))

d + geom_path(lineend="butt",
linejoin="round’, linemitre=1)
x, y, alpha, color, linetype, size

d + geom_ribbon(aes(ymin=unemploy – 900,
ymax=unemploy + 900))
x, ymax, ymin, alpha, color, fill, linetype, size

d <- ggplot(economics, aes(date, unemploy))

c <- ggplot(map, aes(long, lat))

data <- data.frame(murder = USArrests$Murder,
state = tolower(rownames(USArrests)))

map <- map_data("state")
l <- ggplot(data, aes(fill = murder))

l + geom_map(aes(map_id = state), map = map) +
expand_limits(x = map$long, y = map$lat)
map_id, alpha, color, fill, linetype, size

Maps

AB
C

Basics

Build a graph with qplot() or ggplot()

ggplot2 is based on the grammar of graphics, the
idea that you can build every graph from the same
few components: a data set, a set of geoms—visual
marks that represent data points, and a coordinate
system.

To display data values, map variables in the data set
to aesthetic properties of the geom like size, color,
and x and y locations.

Graphical Primitives

Data Visualization
with ggplot2

Cheat Sheet

RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • [email protected] • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org • ggplot2 0.9.3.1 • Updated: 3/15

Geoms – Use a geom to represent data points, use the geom’s aesthetic properties to represent variables

Basics

One Variable

a + geom_area(stat = "bin")
x, y, alpha, color, fill, linetype, size
b + geom_area(aes(y = ..density..), stat = "bin")

a + geom_density(kernal = "gaussian")
x, y, alpha, color, fill, linetype, size, weight
b + geom_density(aes(y = ..county..))

a+ geom_dotplot()
x, y, alpha, color, fill

a + geom_freqpoly()
x, y, alpha, color, linetype, size
b + geom_freqpoly(aes(y = ..density..))

a + geom_histogram(binwidth = 5)
x, y, alpha, color, fill, linetype, size, weight
b + geom_histogram(aes(y = ..density..))

Discrete
a <- ggplot(mpg, aes(fl))

b + geom_bar()
x, alpha, color, fill, linetype, size, weight

Continuous
a <- ggplot(mpg, aes(hwy))

Two Variables

Discrete X, Discrete Y
h <- ggplot(diamonds, aes(cut, color))

h + geom_jitter()
x, y, alpha, color, fill, shape, size

Discrete X, Continuous Y
g <- ggplot(mpg, aes(class, hwy))

g + geom_bar(stat = "identity")
x, y, alpha, color, fill, linetype, size, weight

g + geom_boxplot()
lower, middle, upper, x, ymax, ymin, alpha,
color, fill, linetype, shape, size, weight

g + geom_dotplot(binaxis = "y",
stackdir = "center")
x, y, alpha, color, fill

g + geom_violin(scale = "area")
x, y, alpha, color, fill, linetype, size, weight

Continuous X, Continuous Y
f <- ggplot(mpg, aes(cty, hwy))

f + geom_blank()

f + geom_jitter()
x, y, alpha, color, fill, shape, size

f + geom_point()
x, y, alpha, color, fill, shape, size

f + geom_quantile()
x, y, alpha, color, linetype, size, weight

f + geom_rug(sides = "bl")
alpha, color, linetype, size

f + geom_smooth(model = lm)
x, y, alpha, color, fill, linetype, size, weight

f + geom_text(aes(label = cty))
x, y, label, alpha, angle, color, family, fontface,
hjust, lineheight, size, vjust

Three Variables

i + geom_contour(aes(z = z))
x, y, z, alpha, colour, linetype, size, weight

seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2))
i <- ggplot(seals, aes(long, lat))

g <- ggplot(economics, aes(date, unemploy))
Continuous Function

g + geom_area()
x, y, alpha, color, fill, linetype, size

g + geom_line()
x, y, alpha, color, linetype, size

g + geom_step(direction = "hv")
x, y, alpha, color, linetype, size

Continuous Bivariate Distribution
h <- ggplot(movies, aes(year, rating))
h + geom_bin2d(binwidth = c(5, 0.5))

xmax, xmin, ymax, ymin, alpha, color, fill,
linetype, size, weight

h + geom_density2d()
x, y, alpha, colour, linetype, size

h + geom_hex()
x, y, alpha, colour, fill size

d + geom_segment(aes(
xend = long + delta_long,
yend = lat + delta_lat))
x, xend, y, yend, alpha, color, linetype, size

d + geom_rect(aes(xmin = long, ymin = lat,
xmax= long + delta_long,
ymax = lat + delta_lat))
xmax, xmin, ymax, ymin, alpha, color, fill,
linetype, size

c + geom_polygon(aes(group = group))
x, y, alpha, color, fill, linetype, size

d<- ggplot(seals, aes(x = long, y = lat))

i + geom_raster(aes(fill = z), hjust=0.5,
vjust=0.5, interpolate=FALSE)
x, y, alpha, fill

i + geom_tile(aes(fill = z))
x, y, alpha, color, fill, linetype, size

e + geom_crossbar(fatten = 2)
x, y, ymax, ymin, alpha, color, fill, linetype,
size

e + geom_errorbar()
x, ymax, ymin, alpha, color, linetype, size,
width (also geom_errorbarh())

e + geom_linerange()
x, ymin, ymax, alpha, color, linetype, size

e + geom_pointrange()
x, y, ymin, ymax, alpha, color, fill, linetype,
shape, size

Visualizing error
df <- data.frame(grp = c("A", "B"), fit = 4:5, se = 1:2)

e <- ggplot(df, aes(grp, fit, ymin = fit-se, ymax = fit+se))

g + geom_path(lineend="butt",
linejoin="round’, linemitre=1)
x, y, alpha, color, linetype, size

g + geom_ribbon(aes(ymin=unemploy – 900,
ymax=unemploy + 900))
x, ymax, ymin, alpha, color, fill, linetype, size

g <- ggplot(economics, aes(date, unemploy))

c <- ggplot(map, aes(long, lat))

data <- data.frame(murder = USArrests$Murder,
state = tolower(rownames(USArrests)))

map <- map_data("state")
e <- ggplot(data, aes(fill = murder))

e + geom_map(aes(map_id = state), map = map) +
expand_limits(x = map$long, y = map$lat)
map_id, alpha, color, fill, linetype, size

Maps

F M A

=
1

2

3

0
0 1 2 3 4

4

1

2

3

0
0 1 2 3 4

4

+

data geom coordinate
system

plot

+

F M A

=
1

2

3

0
0 1 2 3 4

4

1

2

3

0
0 1 2 3 4

4

data geom coordinate
system

plot
x = F
y = A
color = F
size = A

1

2

3

0
0 1 2 3 4

4

plot

+

F M A

=
1

2

3

0
0 1 2 3 4

4

data geom coordinate
systemx = F

y = A

x = F
y = A

Graphical Primitives

Data Visualization
with ggplot2

Cheat Sheet

RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • [email protected] • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org • ggplot2 0.9.3.1 • Updated: 3/15

Geoms – Use a geom to represent data points, use the geom’s aesthetic properties to represent variables

Basics

One Variable

a + geom_area(stat = "bin")
x, y, alpha, color, fill, linetype, size
b + geom_area(aes(y = ..density..), stat = "bin")

a + geom_density(kernal = "gaussian")
x, y, alpha, color, fill, linetype, size, weight
b + geom_density(aes(y = ..county..))

a+ geom_dotplot()
x, y, alpha, color, fill

a + geom_freqpoly()
x, y, alpha, color, linetype, size
b + geom_freqpoly(aes(y = ..density..))

a + geom_histogram(binwidth = 5)
x, y, alpha, color, fill, linetype, size, weight
b + geom_histogram(aes(y = ..density..))

Discrete
a <- ggplot(mpg, aes(fl))

b + geom_bar()
x, alpha, color, fill, linetype, size, weight

Continuous
a <- ggplot(mpg, aes(hwy))

Two Variables

Discrete X, Discrete Y
h <- ggplot(diamonds, aes(cut, color))

h + geom_jitter()
x, y, alpha, color, fill, shape, size

Discrete X, Continuous Y
g <- ggplot(mpg, aes(class, hwy))

g + geom_bar(stat = "identity")
x, y, alpha, color, fill, linetype, size, weight

g + geom_boxplot()
lower, middle, upper, x, ymax, ymin, alpha,
color, fill, linetype, shape, size, weight

g + geom_dotplot(binaxis = "y",
stackdir = "center")
x, y, alpha, color, fill

g + geom_violin(scale = "area")
x, y, alpha, color, fill, linetype, size, weight

Continuous X, Continuous Y
f <- ggplot(mpg, aes(cty, hwy))

f + geom_blank()

f + geom_jitter()
x, y, alpha, color, fill, shape, size

f + geom_point()
x, y, alpha, color, fill, shape, size

f + geom_quantile()
x, y, alpha, color, linetype, size, weight

f + geom_rug(sides = "bl")
alpha, color, linetype, size

f + geom_smooth(model = lm)
x, y, alpha, color, fill, linetype, size, weight

f + geom_text(aes(label = cty))
x, y, label, alpha, angle, color, family, fontface,
hjust, lineheight, size, vjust

Three Variables

i + geom_contour(aes(z = z))
x, y, z, alpha, colour, linetype, size, weight

seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2))
i <- ggplot(seals, aes(long, lat))

g <- ggplot(economics, aes(date, unemploy))
Continuous Function

g + geom_area()
x, y, alpha, color, fill, linetype, size

g + geom_line()
x, y, alpha, color, linetype, size

g + geom_step(direction = "hv")
x, y, alpha, color, linetype, size

Continuous Bivariate Distribution
h <- ggplot(movies, aes(year, rating))
h + geom_bin2d(binwidth = c(5, 0.5))

xmax, xmin, ymax, ymin, alpha, color, fill,
linetype, size, weight

h + geom_density2d()
x, y, alpha, colour, linetype, size

h + geom_hex()
x, y, alpha, colour, fill size

d + geom_segment(aes(
xend = long + delta_long,
yend = lat + delta_lat))
x, xend, y, yend, alpha, color, linetype, size

d + geom_rect(aes(xmin = long, ymin = lat,
xmax= long + delta_long,
ymax = lat + delta_lat))
xmax, xmin, ymax, ymin, alpha, color, fill,
linetype, size

c + geom_polygon(aes(group = group))
x, y, alpha, color, fill, linetype, size

d<- ggplot(seals, aes(x = long, y = lat))

i + geom_raster(aes(fill = z), hjust=0.5,
vjust=0.5, interpolate=FALSE)
x, y, alpha, fill

i + geom_tile(aes(fill = z))
x, y, alpha, color, fill, linetype, size

e + geom_crossbar(fatten = 2)
x, y, ymax, ymin, alpha, color, fill, linetype,
size

e + geom_errorbar()
x, ymax, ymin, alpha, color, linetype, size,
width (also geom_errorbarh())

e + geom_linerange()
x, ymin, ymax, alpha, color, linetype, size

e + geom_pointrange()
x, y, ymin, ymax, alpha, color, fill, linetype,
shape, size

Visualizing error
df <- data.frame(grp = c("A", "B"), fit = 4:5, se = 1:2)

e <- ggplot(df, aes(grp, fit, ymin = fit-se, ymax = fit+se))

g + geom_path(lineend="butt",
linejoin="round’, linemitre=1)
x, y, alpha, color, linetype, size

g + geom_ribbon(aes(ymin=unemploy – 900,
ymax=unemploy + 900))
x, ymax, ymin, alpha, color, fill, linetype, size

g <- ggplot(economics, aes(date, unemploy))

c <- ggplot(map, aes(long, lat))

data <- data.frame(murder = USArrests$Murder,
state = tolower(rownames(USArrests)))

map <- map_data("state")
e <- ggplot(data, aes(fill = murder))

e + geom_map(aes(map_id = state), map = map) +
expand_limits(x = map$long, y = map$lat)
map_id, alpha, color, fill, linetype, size

Maps

F M A

=
1

2

3

0
0 1 2 3 4

4

1

2

3

0
0 1 2 3 4

4

+

data geom coordinate
system

plot

+

F M A

=
1

2

3

0
0 1 2 3 4

4

1

2

3

0
0 1 2 3 4

4

data geom coordinate
system

plot
x = F
y = A
color = F
size = A

1

2

3

0
0 1 2 3 4

4

plot

+

F M A

=
1

2

3

0
0 1 2 3 4

4

data geom coordinate
systemx = F

y = A

x = F
y = A

ggsave("plot.png", width = 5, height = 5)
Saves last plot as 5’ x 5’ file named "plot.png" in
working directory. Matches file type to file extension.

qplot(x = cty, y = hwy, color = cyl, data = mpg, geom = "point")
Creates a complete plot with given data, geom, and
mappings. Supplies many useful defaults.

ggplot(data = mpg, aes(x = cty, y = hwy))
Begins a plot that you finish by adding layers to. No
defaults, but provides more control than qplot().

ggplot(mpg, aes(hwy, cty)) +
geom_point(aes(color = cyl)) +
geom_smooth(method ="lm") +
coord_cartesian() +
scale_color_gradient() +
theme_bw()

data

aesthetic mappings

add layers,
elements with +

layer = geom +
default stat +
layer specific

mappings

additional
elements

data geom

Add a new layer to a plot with a geom_*()
or stat_*() function. Each provides a geom, a
set of aesthetic mappings, and a default stat

and position adjustment.

last_plot()
Returns the last plot

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RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • [email protected] • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org • ggplot2 0.9.3.1 • Updated: 3/15

Stats – An alternative way to build a layer Coordinate Systems

r + coord_cartesian(xlim = c(0, 5))
xlim, ylim
The default cartesian coordinate system

r + coord_fixed(ratio = 1/2)
ratio, xlim, ylim
Cartesian coordinates with fixed aspect
ratio between x and y units

r + coord_flip()
xlim, ylim
Flipped Cartesian coordinates

r + coord_polar(theta = "x", direction=1 )
theta, start, direction
Polar coordinates

r + coord_trans(ytrans = "sqrt")
xtrans, ytrans, limx, limy
Transformed cartesian coordinates. Set
extras and strains to the name
of a window function.

r <- b + geom_bar()

Scales Faceting

t <- ggplot(mpg, aes(cty, hwy)) + geom_point()

Position Adjustments

s + geom_bar(position = "dodge")
Arrange elements side by side

s + geom_bar(position = "fill")
Stack elements on top of one another,
normalize height

s + geom_bar(position = "stack")
Stack elements on top of one another

f + geom_point(position = "jitter")
Add random noise to X and Y position
of each element to avoid overplotting

s <- ggplot(mpg, aes(fl, fill = drv))

Labels
t + ggtitle("New Plot Title")

Add a main title above the plot
t + xlab("New X label")

Change the label on the X axis
t + ylab("New Y label")

Change the label on the Y axis
t + labs(title =" New title", x = "New x", y = "New y")

All of the above

Legends

Zooming

Themes

Facets divide a plot into subplots based on the values
of one or more discrete variables.

t + facet_grid(. ~ fl)
facet into columns based on fl

t + facet_grid(year ~ .)
facet into rows based on year

t + facet_grid(year ~ fl)
facet into both rows and columns

t + facet_wrap(~ fl)
wrap facets into a rectangular layout

Set scales to let axis limits vary across facets
t + facet_grid(y ~ x, scales = "free")

x and y axis limits adjust to individual facets
• "free_x" – x axis limits adjust
• "free_y" – y axis limits adjust

Set labeller to adjust facet labels
t + facet_grid(. ~ fl, labeller = label_both)

t + facet_grid(. ~ fl, labeller = label_bquote(alpha ^ .(x)))

t + facet_grid(. ~ fl, labeller = label_parsed)

Position adjustments determine how to arrange
geoms that would otherwise occupy the same space.

Each position adjustment can be recast as a function
with manual width and height arguments

s + geom_bar(position = position_dodge(width = 1))

r + theme_classic()
White background
no gridlines

r + theme_minimal()
Minimal theme

t + coord_cartesian(
xlim = c(0, 100), ylim = c(10, 20))

With clipping (removes unseen data points)
t + xlim(0, 100) + ylim(10, 20)
t + scale_x_continuous(limits = c(0, 100)) +

scale_y_continuous(limits = c(0, 100))

t + theme(legend.position = "bottom")
Place legend at "bottom", "top", "left", or "right"

t + guides(color = "none")
Set legend type for each aesthetic: colorbar, legend,
or none (no legend)

t + scale_fill_discrete(name = "Title",
labels = c("A", "B", "C"))
Set legend title and labels with a scale function.

Each stat creates additional variables to map aesthetics
to. These variables use a common ..name.. syntax.
stat functions and geom functions both combine a stat
with a geom to make a layer, i.e. stat_bin(geom="bar")
does the same as geom_bar(stat="bin")

+
x ..count..

=
1

2

3

0
0 1 2 3 4

4

1

2

3

0
0 1 2 3 4

4

data geom coordinate
system

plot
x = x
y = ..count..

fl cty cyl

stat

ggplot() + stat_function(aes(x = -3:3),
fun = dnorm, n = 101, args = list(sd=0.5))
x | ..y..

f + stat_identity()
ggplot() + stat_qq(aes(sample=1:100), distribution = qt,

dparams = list(df=5))
sample, x, y | ..x.., ..y..

f + stat_sum()
x, y, size | ..size..

f + stat_summary(fun.data = "mean_cl_boot")
f + stat_unique()

i + stat_density2d(aes(fill = ..level..),
geom = "polygon", n = 100)

stat function
layer specific

mappings
variable created

by transformation

geom for layer parameters for stat

a + stat_bin(binwidth = 1, origin = 10)
x, y | ..count.., ..ncount.., ..density.., ..ndensity..

a + stat_bindot(binwidth = 1, binaxis = "x")
x, y, | ..count.., ..ncount..

a + stat_density(adjust = 1, kernel = "gaussian")
x, y, | ..count.., ..density.., ..scaled..

f + stat_bin2d(bins = 30, drop = TRUE)
x, y, fill | ..count.., ..density..

f + stat_binhex(bins = 30)
x, y, fill | ..count.., ..density..

f + stat_density2d(contour = TRUE, n = 100)
x, y, color, size | ..level..

m + stat_contour(aes(z = z))
x, y, z, order | ..level..

m+ stat_spoke(aes(radius= z, angle = z))
angle, radius, x, xend, y, yend | ..x.., ..xend.., ..y.., ..yend..

m + stat_summary_hex(aes(z = z), bins = 30, fun = mean)
x, y, z, fill | ..value..

m + stat_summary2d(aes(z = z), bins = 30, fun = mean)
x, y, z, fill | ..value..

g + stat_boxplot(coef = 1.5)
x, y | ..lower.., ..middle.., ..upper.., ..outliers..

g + stat_ydensity(adjust = 1, kernel = "gaussian", scale = "area")
x, y | ..density.., ..scaled.., ..count.., ..n.., ..violinwidth.., ..width..

f + stat_ecdf(n = 40)
x, y | ..x.., ..y..

f + stat_quantile(quantiles = c(0.25, 0.5, 0.75), formula = y ~ log(x),
method = "rq")
x, y | ..quantile.., ..x.., ..y..

f + stat_smooth(method = "auto", formula = y ~ x, se = TRUE, n = 80,
fullrange = FALSE, level = 0.95)
x, y | ..se.., ..x.., ..y.., ..ymin.., ..ymax..

1D distributions

2D distributions

3 Variables

Comparisons

Functions

General Purpose

Scales control how a plot maps data values to the visual
values of an aesthetic. To change the mapping, add a
custom scale.

n <- b + geom_bar(aes(fill = fl))
n

n + scale_fill_manual(
values = c("skyblue", "royalblue", "blue", "navy"),
limits = c("d", "e", "p", "r"), breaks =c("d", "e", "p", "r"),
name = "fuel", labels = c("D", "E", "P", "R"))

scale_ aesthetic
to adjust

prepackaged
scale to use

scale specific
arguments

range of values to
include in mapping

title to use in
legend/axis

labels to use in
legend/axis

breaks to use in
legend/axis

General Purpose scales
Use with any aesthetic:

alpha, color, fill, linetype, shape, size
scale_*_continuous() – map cont’ values to visual values
scale_*_discrete() – map discrete values to visual values
scale_*_identity() – use data values as visual values
scale_*_manual(values = c()) – map discrete values to

manually chosen visual values

X and Y location scales

Color and fill scales

Shape scales

Size scales

Use with x or y aesthetics (x shown here)
scale_x_date(labels = date_format("%m/%d"),

breaks = date_breaks("2 weeks")) – treat x
values as dates. See ?strptime for label formats.

scale_x_datetime() – treat x values as date times. Use
same arguments as scale_x_date().

scale_x_log10() – Plot x on log10 scale
scale_x_reverse() – Reverse direction of x axis
scale_x_sqrt() – Plot x on square root scale

Discrete Continuous
n <- b + geom_bar(

aes(fill = fl))
o <- a + geom_dotplot(

aes(fill = ..x..))
n + scale_fill_brewer(

palette = "Blues")
For palette choices:
library(RcolorBrewer)
display.brewer.all()

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

o + scale_fill_gradient(
low = "red",
high = "yellow")

o + scale_fill_gradient2(
low = "red", hight = "blue",
mid = "white", midpoint = 25)

o + scale_fill_gradientn(
colours = terrain.colors(6))

Also: rainbow(), heat.colors(),
topo.colors(), cm.colors(),
RColorBrewer::brewer.pal()

p <- f + geom_point(
aes(shape = fl))

p + scale_shape(
solid = FALSE)

p + scale_shape_manual(
values = c(3:7))
Shape values shown in
chart on right

Manual Shape values

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

**
.

oo
OO

00
++

||
%%
##

Manual shape values

q install.packages(“tidyverse”)

> iris %>% Select iris data of species
filter(Species==”virginica”) “virginica”
> iris %>% Select iris data of species
filter(Species==”virginica”, “virginica” and sepal length
Sepal.Length > 6) greater than 6.

dplyr

Filter

> library(tidyverse)

Useful Functions

Arrange

Mutate

Summarize

> tidyverse_conflicts() Conflicts between tidyverse and other
packages
> tidyverse_deps() List all tidyverse dependencies
> tidyverse_logo() Get tidyverse logo, using ASCII or unicode
characters
> tidyverse_packages() List all tidyverse packages
> tidyverse_update() Update tidyverse packages

Loading in the data
> library(datasets) Load the datasets package
> library(gapminder) Load the gapminder package
> attach(iris) Attach iris data to the R search path

filter() allows you to select a subset of rows in a data frame.

> iris %>% Sort in ascending order of
arrange(Sepal.Length) sepal length
> iris %>% Sort in descending order of
arrange(desc(Sepal.Length)) sepal length

arrange() sorts the observations in a dataset in ascending or descending order
based on one of its variables.

> iris %>% Filter for species “virginica”
filter(Species==”virginica”) %>% then arrange in descending
arrange(desc(Sepal.Length)) order of sepal length

Combine multiple dplyr verbs in a row with the pipe operator %>%:

mutate() allows you to update or create new columns of a data frame.

> iris %>% Change Sepal.Length to be
mutate(Sepal.Length=Sepal.Length*10) in millimeters
> iris %>% Create a new column
mutate(SLMm=Sepal.Length*10) called SLMm

Combine the verbs filter(), arrange(), and mutate():
> iris %>%
filter(Species==”Virginica”) %>%
mutate(SLMm=Sepal.Length*10) %>%
arrange(desc(SLMm))

> iris %>% Summarize to find the
summarize(medianSL=median(Sepal.Length)) median sepal length
> iris %>% Filter for virginica then
filter(Species==”virginica”) %>% summarize the median
summarize(medianSL=median(Sepal.Length)) sepal length

summarize() allows you to turn many observations into a single data point.

> iris %>%
filter(Species==”virginica”) %>%
summarize(medianSL=median(Sepal.Length),
maxSL=max(Sepal.Length))

You can also summarize multiple variables at once:

group_by() allows you to summarize within groups instead of summarizing the
entire dataset:

> iris %>% Find median and max
group_by(Species) %>% sepal length of each
summarize(medianSL=median(Sepal.Length), species
maxSL=max(Sepal.Length))
> iris %>% Find median and max
filter(Sepal.Length>6) %>% petal length of each
group_by(Species) %>% species with sepal
summarize(medianPL=median(Petal.Length), length > 6
maxPL=max(Petal.Length))

Scatter plot

> iris_small %
filter(Sepal.Length > 5)
> ggplot(iris_small, aes(x=Petal.Length, Compare petal
y=Petal.Width)) + width and length
geom_point()

Scatter plots allow you to compare two variables within your data. To do this with
ggplot2, you use geom_point()

Additional Aesthetics

> ggplot(iris_small, aes(x=Petal.Length,
y=Petal.Width,
color=Species)) +
geom_point()

• Color

• Size
> ggplot(iris_small, aes(x=Petal.Length,
y=Petal.Width,
color=Species,
size=Sepal.Length)) +
geom_point()

Faceting
> ggplot(iris_small, aes(x=Petal.Length,
y=Petal.Width)) +
geom_point()+
facet_wrap(~Species)

Line Plots

Bar Plots

Histograms

Box Plots

> by_year %
group_by(year) %>%
summarize(medianGdpPerCap=median(gdpPercap))
> ggplot(by_year, aes(x=year,
y=medianGdpPerCap))+
geom_line()+
expand_limits(y=0)

> by_species %
filter(Sepal.Length>6) %>%
group_by(Species) %>%
summarize(medianPL=median(Petal.Length))
> ggplot(by_species, aes(x=Species,
y=medianPL)) +
geom_col()

> ggplot(iris_small, aes(x=Petal.Length))+
geom_histogram()

> ggplot(iris_small, aes(x=Species,
y=Sepal.Width))+
geom_boxplot()

Graphical Primitives

Data Visualization
with ggplot2

Cheat Sheet

RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • [email protected] • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org • ggplot2 0.9.3.1 • Updated: 3/15

Geoms – Use a geom to represent data points, use the geom’s aesthetic properties to represent variables. Each function returns a layer.
One Variable

a + geom_area(stat = “bin”)
x, y, alpha, color, fill, linetype, size
b + geom_area(aes(y = ..density..), stat = “bin”)

a + geom_density(kernel = “gaussian”)
x, y, alpha, color, fill, linetype, size, weight
b + geom_density(aes(y = ..county..))

a + geom_dotplot()
x, y, alpha, color, fill

a + geom_freqpoly()
x, y, alpha, color, linetype, size
b + geom_freqpoly(aes(y = ..density..))

a + geom_histogram(binwidth = 5)
x, y, alpha, color, fill, linetype, size, weight
b + geom_histogram(aes(y = ..density..))

Discrete
b <- ggplot(mpg, aes(fl))

b + geom_bar()
x, alpha, color, fill, linetype, size, weight

Continuous
a <- ggplot(mpg, aes(hwy))

Two Variables

Continuous Function

Discrete X, Discrete Y
h <- ggplot(diamonds, aes(cut, color))

h + geom_jitter()
x, y, alpha, color, fill, shape, size

Discrete X, Continuous Y
g <- ggplot(mpg, aes(class, hwy))

g + geom_bar(stat = "identity")
x, y, alpha, color, fill, linetype, size, weight

g + geom_boxplot()
lower, middle, upper, x, ymax, ymin, alpha,
color, fill, linetype, shape, size, weight

g + geom_dotplot(binaxis = "y",
stackdir = "center")
x, y, alpha, color, fill

g + geom_violin(scale = "area")
x, y, alpha, color, fill, linetype, size, weight

Continuous X, Continuous Y
f <- ggplot(mpg, aes(cty, hwy))

f + geom_blank()

f + geom_jitter()
x, y, alpha, color, fill, shape, size

f + geom_point()
x, y, alpha, color, fill, shape, size

f + geom_quantile()
x, y, alpha, color, linetype, size, weight

f + geom_rug(sides = "bl")
alpha, color, linetype, size

f + geom_smooth(model = lm)
x, y, alpha, color, fill, linetype, size, weight

f + geom_text(aes(label = cty))
x, y, label, alpha, angle, color, family, fontface,
hjust, lineheight, size, vjust

Three Variables

m + geom_contour(aes(z = z))
x, y, z, alpha, colour, linetype, size, weight

seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2))
m <- ggplot(seals, aes(long, lat))

j <- ggplot(economics, aes(date, unemploy))
j + geom_area()

x, y, alpha, color, fill, linetype, size

j + geom_line()
x, y, alpha, color, linetype, size

j + geom_step(direction = "hv")
x, y, alpha, color, linetype, size

Continuous Bivariate Distribution
i <- ggplot(movies, aes(year, rating))
i + geom_bin2d(binwidth = c(5, 0.5))

xmax, xmin, ymax, ymin, alpha, color, fill,
linetype, size, weight

i + geom_density2d()
x, y, alpha, colour, linetype, size

i + geom_hex()
x, y, alpha, colour, fill size

e + geom_segment(aes(
xend = long + delta_long,
yend = lat + delta_lat))
x, xend, y, yend, alpha, color, linetype, size

e + geom_rect(aes(xmin = long, ymin = lat,
xmax= long + delta_long,
ymax = lat + delta_lat))
xmax, xmin, ymax, ymin, alpha, color, fill,
linetype, size

c + geom_polygon(aes(group = group))
x, y, alpha, color, fill, linetype, size

e <- ggplot(seals, aes(x = long, y = lat))

m + geom_raster(aes(fill = z), hjust=0.5,
vjust=0.5, interpolate=FALSE)
x, y, alpha, fill

m + geom_tile(aes(fill = z))
x, y, alpha, color, fill, linetype, size

k + geom_crossbar(fatten = 2)
x, y, ymax, ymin, alpha, color, fill, linetype,
size

k + geom_errorbar()
x, ymax, ymin, alpha, color, linetype, size,
width (also geom_errorbarh())

k + geom_linerange()
x, ymin, ymax, alpha, color, linetype, size

k + geom_pointrange()
x, y, ymin, ymax, alpha, color, fill, linetype,
shape, size

Visualizing error
df <- data.frame(grp = c("A", "B"), fit = 4:5, se = 1:2)

k <- ggplot(df, aes(grp, fit, ymin = fit-se, ymax = fit+se))

d + geom_path(lineend="butt",
linejoin="round’, linemitre=1)
x, y, alpha, color, linetype, size

d + geom_ribbon(aes(ymin=unemploy – 900,
ymax=unemploy + 900))
x, ymax, ymin, alpha, color, fill, linetype, size

d <- ggplot(economics, aes(date, unemploy))

c <- ggplot(map, aes(long, lat))

data <- data.frame(murder = USArrests$Murder,
state = tolower(rownames(USArrests)))

map <- map_data("state")
l <- ggplot(data, aes(fill = murder))

l + geom_map(aes(map_id = state), map = map) +
expand_limits(x = map$long, y = map$lat)
map_id, alpha, color, fill, linetype, size

Maps

AB
C

Basics

Build a graph with qplot() or ggplot()

ggplot2 is based on the grammar of graphics, the
idea that you can build every graph from the same
few components: a data set, a set of geoms—visual
marks that represent data points, and a coordinate
system.

To display data values, map variables in the data set
to aesthetic properties of the geom like size, color,
and x and y locations.

Graphical Primitives

Data Visualization
with ggplot2

Cheat Sheet

RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • [email protected] • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org • ggplot2 0.9.3.1 • Updated: 3/15

Geoms – Use a geom to represent data points, use the geom’s aesthetic properties to represent variables

Basics

One Variable

a + geom_area(stat = "bin")
x, y, alpha, color, fill, linetype, size
b + geom_area(aes(y = ..density..), stat = "bin")

a + geom_density(kernal = "gaussian")
x, y, alpha, color, fill, linetype, size, weight
b + geom_density(aes(y = ..county..))

a+ geom_dotplot()
x, y, alpha, color, fill

a + geom_freqpoly()
x, y, alpha, color, linetype, size
b + geom_freqpoly(aes(y = ..density..))

a + geom_histogram(binwidth = 5)
x, y, alpha, color, fill, linetype, size, weight
b + geom_histogram(aes(y = ..density..))

Discrete
a <- ggplot(mpg, aes(fl))

b + geom_bar()
x, alpha, color, fill, linetype, size, weight

Continuous
a <- ggplot(mpg, aes(hwy))

Two Variables

Discrete X, Discrete Y
h <- ggplot(diamonds, aes(cut, color))

h + geom_jitter()
x, y, alpha, color, fill, shape, size

Discrete X, Continuous Y
g <- ggplot(mpg, aes(class, hwy))

g + geom_bar(stat = "identity")
x, y, alpha, color, fill, linetype, size, weight

g + geom_boxplot()
lower, middle, upper, x, ymax, ymin, alpha,
color, fill, linetype, shape, size, weight

g + geom_dotplot(binaxis = "y",
stackdir = "center")
x, y, alpha, color, fill

g + geom_violin(scale = "area")
x, y, alpha, color, fill, linetype, size, weight

Continuous X, Continuous Y
f <- ggplot(mpg, aes(cty, hwy))

f + geom_blank()

f + geom_jitter()
x, y, alpha, color, fill, shape, size

f + geom_point()
x, y, alpha, color, fill, shape, size

f + geom_quantile()
x, y, alpha, color, linetype, size, weight

f + geom_rug(sides = "bl")
alpha, color, linetype, size

f + geom_smooth(model = lm)
x, y, alpha, color, fill, linetype, size, weight

f + geom_text(aes(label = cty))
x, y, label, alpha, angle, color, family, fontface,
hjust, lineheight, size, vjust

Three Variables

i + geom_contour(aes(z = z))
x, y, z, alpha, colour, linetype, size, weight

seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2))
i <- ggplot(seals, aes(long, lat))

g <- ggplot(economics, aes(date, unemploy))
Continuous Function

g + geom_area()
x, y, alpha, color, fill, linetype, size

g + geom_line()
x, y, alpha, color, linetype, size

g + geom_step(direction = "hv")
x, y, alpha, color, linetype, size

Continuous Bivariate Distribution
h <- ggplot(movies, aes(year, rating))
h + geom_bin2d(binwidth = c(5, 0.5))

xmax, xmin, ymax, ymin, alpha, color, fill,
linetype, size, weight

h + geom_density2d()
x, y, alpha, colour, linetype, size

h + geom_hex()
x, y, alpha, colour, fill size

d + geom_segment(aes(
xend = long + delta_long,
yend = lat + delta_lat))
x, xend, y, yend, alpha, color, linetype, size

d + geom_rect(aes(xmin = long, ymin = lat,
xmax= long + delta_long,
ymax = lat + delta_lat))
xmax, xmin, ymax, ymin, alpha, color, fill,
linetype, size

c + geom_polygon(aes(group = group))
x, y, alpha, color, fill, linetype, size

d<- ggplot(seals, aes(x = long, y = lat))

i + geom_raster(aes(fill = z), hjust=0.5,
vjust=0.5, interpolate=FALSE)
x, y, alpha, fill

i + geom_tile(aes(fill = z))
x, y, alpha, color, fill, linetype, size

e + geom_crossbar(fatten = 2)
x, y, ymax, ymin, alpha, color, fill, linetype,
size

e + geom_errorbar()
x, ymax, ymin, alpha, color, linetype, size,
width (also geom_errorbarh())

e + geom_linerange()
x, ymin, ymax, alpha, color, linetype, size

e + geom_pointrange()
x, y, ymin, ymax, alpha, color, fill, linetype,
shape, size

Visualizing error
df <- data.frame(grp = c("A", "B"), fit = 4:5, se = 1:2)

e <- ggplot(df, aes(grp, fit, ymin = fit-se, ymax = fit+se))

g + geom_path(lineend="butt",
linejoin="round’, linemitre=1)
x, y, alpha, color, linetype, size

g + geom_ribbon(aes(ymin=unemploy – 900,
ymax=unemploy + 900))
x, ymax, ymin, alpha, color, fill, linetype, size

g <- ggplot(economics, aes(date, unemploy))

c <- ggplot(map, aes(long, lat))

data <- data.frame(murder = USArrests$Murder,
state = tolower(rownames(USArrests)))

map <- map_data("state")
e <- ggplot(data, aes(fill = murder))

e + geom_map(aes(map_id = state), map = map) +
expand_limits(x = map$long, y = map$lat)
map_id, alpha, color, fill, linetype, size

Maps

F M A

=
1

2

3

0
0 1 2 3 4

4

1

2

3

0
0 1 2 3 4

4

+

data geom coordinate
system

plot

+

F M A

=
1

2

3

0
0 1 2 3 4

4

1

2

3

0
0 1 2 3 4

4

data geom coordinate
system

plot
x = F
y = A
color = F
size = A

1

2

3

0
0 1 2 3 4

4

plot

+

F M A

=
1

2

3

0
0 1 2 3 4

4

data geom coordinate
systemx = F

y = A

x = F
y = A

Graphical Primitives

Data Visualization
with ggplot2

Cheat Sheet

RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • [email protected] • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org • ggplot2 0.9.3.1 • Updated: 3/15

Geoms – Use a geom to represent data points, use the geom’s aesthetic properties to represent variables

Basics

One Variable

a + geom_area(stat = "bin")
x, y, alpha, color, fill, linetype, size
b + geom_area(aes(y = ..density..), stat = "bin")

a + geom_density(kernal = "gaussian")
x, y, alpha, color, fill, linetype, size, weight
b + geom_density(aes(y = ..county..))

a+ geom_dotplot()
x, y, alpha, color, fill

a + geom_freqpoly()
x, y, alpha, color, linetype, size
b + geom_freqpoly(aes(y = ..density..))

a + geom_histogram(binwidth = 5)
x, y, alpha, color, fill, linetype, size, weight
b + geom_histogram(aes(y = ..density..))

Discrete
a <- ggplot(mpg, aes(fl))

b + geom_bar()
x, alpha, color, fill, linetype, size, weight

Continuous
a <- ggplot(mpg, aes(hwy))

Two Variables

Discrete X, Discrete Y
h <- ggplot(diamonds, aes(cut, color))

h + geom_jitter()
x, y, alpha, color, fill, shape, size

Discrete X, Continuous Y
g <- ggplot(mpg, aes(class, hwy))

g + geom_bar(stat = "identity")
x, y, alpha, color, fill, linetype, size, weight

g + geom_boxplot()
lower, middle, upper, x, ymax, ymin, alpha,
color, fill, linetype, shape, size, weight

g + geom_dotplot(binaxis = "y",
stackdir = "center")
x, y, alpha, color, fill

g + geom_violin(scale = "area")
x, y, alpha, color, fill, linetype, size, weight

Continuous X, Continuous Y
f <- ggplot(mpg, aes(cty, hwy))

f + geom_blank()

f + geom_jitter()
x, y, alpha, color, fill, shape, size

f + geom_point()
x, y, alpha, color, fill, shape, size

f + geom_quantile()
x, y, alpha, color, linetype, size, weight

f + geom_rug(sides = "bl")
alpha, color, linetype, size

f + geom_smooth(model = lm)
x, y, alpha, color, fill, linetype, size, weight

f + geom_text(aes(label = cty))
x, y, label, alpha, angle, color, family, fontface,
hjust, lineheight, size, vjust

Three Variables

i + geom_contour(aes(z = z))
x, y, z, alpha, colour, linetype, size, weight

seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2))
i <- ggplot(seals, aes(long, lat))

g <- ggplot(economics, aes(date, unemploy))
Continuous Function

g + geom_area()
x, y, alpha, color, fill, linetype, size

g + geom_line()
x, y, alpha, color, linetype, size

g + geom_step(direction = "hv")
x, y, alpha, color, linetype, size

Continuous Bivariate Distribution
h <- ggplot(movies, aes(year, rating))
h + geom_bin2d(binwidth = c(5, 0.5))

xmax, xmin, ymax, ymin, alpha, color, fill,
linetype, size, weight

h + geom_density2d()
x, y, alpha, colour, linetype, size

h + geom_hex()
x, y, alpha, colour, fill size

d + geom_segment(aes(
xend = long + delta_long,
yend = lat + delta_lat))
x, xend, y, yend, alpha, color, linetype, size

d + geom_rect(aes(xmin = long, ymin = lat,
xmax= long + delta_long,
ymax = lat + delta_lat))
xmax, xmin, ymax, ymin, alpha, color, fill,
linetype, size

c + geom_polygon(aes(group = group))
x, y, alpha, color, fill, linetype, size

d<- ggplot(seals, aes(x = long, y = lat))

i + geom_raster(aes(fill = z), hjust=0.5,
vjust=0.5, interpolate=FALSE)
x, y, alpha, fill

i + geom_tile(aes(fill = z))
x, y, alpha, color, fill, linetype, size

e + geom_crossbar(fatten = 2)
x, y, ymax, ymin, alpha, color, fill, linetype,
size

e + geom_errorbar()
x, ymax, ymin, alpha, color, linetype, size,
width (also geom_errorbarh())

e + geom_linerange()
x, ymin, ymax, alpha, color, linetype, size

e + geom_pointrange()
x, y, ymin, ymax, alpha, color, fill, linetype,
shape, size

Visualizing error
df <- data.frame(grp = c("A", "B"), fit = 4:5, se = 1:2)

e <- ggplot(df, aes(grp, fit, ymin = fit-se, ymax = fit+se))

g + geom_path(lineend="butt",
linejoin="round’, linemitre=1)
x, y, alpha, color, linetype, size

g + geom_ribbon(aes(ymin=unemploy – 900,
ymax=unemploy + 900))
x, ymax, ymin, alpha, color, fill, linetype, size

g <- ggplot(economics, aes(date, unemploy))

c <- ggplot(map, aes(long, lat))

data <- data.frame(murder = USArrests$Murder,
state = tolower(rownames(USArrests)))

map <- map_data("state")
e <- ggplot(data, aes(fill = murder))

e + geom_map(aes(map_id = state), map = map) +
expand_limits(x = map$long, y = map$lat)
map_id, alpha, color, fill, linetype, size

Maps

F M A

=
1

2

3

0
0 1 2 3 4

4

1

2

3

0
0 1 2 3 4

4

+

data geom coordinate
system

plot

+

F M A

=
1

2

3

0
0 1 2 3 4

4

1

2

3

0
0 1 2 3 4

4

data geom coordinate
system

plot
x = F
y = A
color = F
size = A

1

2

3

0
0 1 2 3 4

4

plot

+

F M A

=
1

2

3

0
0 1 2 3 4

4

data geom coordinate
systemx = F

y = A

x = F
y = A

ggsave("plot.png", width = 5, height = 5)
Saves last plot as 5’ x 5’ file named "plot.png" in
working directory. Matches file type to file extension.

qplot(x = cty, y = hwy, color = cyl, data = mpg, geom = "point")
Creates a complete plot with given data, geom, and
mappings. Supplies many useful defaults.

ggplot(data = mpg, aes(x = cty, y = hwy))
Begins a plot that you finish by adding layers to. No
defaults, but provides more control than qplot().

ggplot(mpg, aes(hwy, cty)) +
geom_point(aes(color = cyl)) +
geom_smooth(method ="lm") +
coord_cartesian() +
scale_color_gradient() +
theme_bw()

data

aesthetic mappings

add layers,
elements with +

layer = geom +
default stat +
layer specific

mappings

additional
elements

data geom

Add a new layer to a plot with a geom_*()
or stat_*() function. Each provides a geom, a
set of aesthetic mappings, and a default stat

and position adjustment.

last_plot()
Returns the last plot

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Stats – An alternative way to build a layer Coordinate Systems

r + coord_cartesian(xlim = c(0, 5))
xlim, ylim
The default cartesian coordinate system

r + coord_fixed(ratio = 1/2)
ratio, xlim, ylim
Cartesian coordinates with fixed aspect
ratio between x and y units

r + coord_flip()
xlim, ylim
Flipped Cartesian coordinates

r + coord_polar(theta = "x", direction=1 )
theta, start, direction
Polar coordinates

r + coord_trans(ytrans = "sqrt")
xtrans, ytrans, limx, limy
Transformed cartesian coordinates. Set
extras and strains to the name
of a window function.

r <- b + geom_bar()

Scales Faceting

t <- ggplot(mpg, aes(cty, hwy)) + geom_point()

Position Adjustments

s + geom_bar(position = "dodge")
Arrange elements side by side

s + geom_bar(position = "fill")
Stack elements on top of one another,
normalize height

s + geom_bar(position = "stack")
Stack elements on top of one another

f + geom_point(position = "jitter")
Add random noise to X and Y position
of each element to avoid overplotting

s <- ggplot(mpg, aes(fl, fill = drv))

Labels
t + ggtitle("New Plot Title")

Add a main title above the plot
t + xlab("New X label")

Change the label on the X axis
t + ylab("New Y label")

Change the label on the Y axis
t + labs(title =" New title", x = "New x", y = "New y")

All of the above

Legends

Zooming

Themes

Facets divide a plot into subplots based on the values
of one or more discrete variables.

t + facet_grid(. ~ fl)
facet into columns based on fl

t + facet_grid(year ~ .)
facet into rows based on year

t + facet_grid(year ~ fl)
facet into both rows and columns

t + facet_wrap(~ fl)
wrap facets into a rectangular layout

Set scales to let axis limits vary across facets
t + facet_grid(y ~ x, scales = "free")

x and y axis limits adjust to individual facets
• "free_x" – x axis limits adjust
• "free_y" – y axis limits adjust

Set labeller to adjust facet labels
t + facet_grid(. ~ fl, labeller = label_both)

t + facet_grid(. ~ fl, labeller = label_bquote(alpha ^ .(x)))

t + facet_grid(. ~ fl, labeller = label_parsed)

Position adjustments determine how to arrange
geoms that would otherwise occupy the same space.

Each position adjustment can be recast as a function
with manual width and height arguments

s + geom_bar(position = position_dodge(width = 1))

r + theme_classic()
White background
no gridlines

r + theme_minimal()
Minimal theme

t + coord_cartesian(
xlim = c(0, 100), ylim = c(10, 20))

With clipping (removes unseen data points)
t + xlim(0, 100) + ylim(10, 20)
t + scale_x_continuous(limits = c(0, 100)) +

scale_y_continuous(limits = c(0, 100))

t + theme(legend.position = "bottom")
Place legend at "bottom", "top", "left", or "right"

t + guides(color = "none")
Set legend type for each aesthetic: colorbar, legend,
or none (no legend)

t + scale_fill_discrete(name = "Title",
labels = c("A", "B", "C"))
Set legend title and labels with a scale function.

Each stat creates additional variables to map aesthetics
to. These variables use a common ..name.. syntax.
stat functions and geom functions both combine a stat
with a geom to make a layer, i.e. stat_bin(geom="bar")
does the same as geom_bar(stat="bin")

+
x ..count..

=
1

2

3

0
0 1 2 3 4

4

1

2

3

0
0 1 2 3 4

4

data geom coordinate
system

plot
x = x
y = ..count..

fl cty cyl

stat

ggplot() + stat_function(aes(x = -3:3),
fun = dnorm, n = 101, args = list(sd=0.5))
x | ..y..

f + stat_identity()
ggplot() + stat_qq(aes(sample=1:100), distribution = qt,

dparams = list(df=5))
sample, x, y | ..x.., ..y..

f + stat_sum()
x, y, size | ..size..

f + stat_summary(fun.data = "mean_cl_boot")
f + stat_unique()

i + stat_density2d(aes(fill = ..level..),
geom = "polygon", n = 100)

stat function
layer specific

mappings
variable created

by transformation

geom for layer parameters for stat

a + stat_bin(binwidth = 1, origin = 10)
x, y | ..count.., ..ncount.., ..density.., ..ndensity..

a + stat_bindot(binwidth = 1, binaxis = "x")
x, y, | ..count.., ..ncount..

a + stat_density(adjust = 1, kernel = "gaussian")
x, y, | ..count.., ..density.., ..scaled..

f + stat_bin2d(bins = 30, drop = TRUE)
x, y, fill | ..count.., ..density..

f + stat_binhex(bins = 30)
x, y, fill | ..count.., ..density..

f + stat_density2d(contour = TRUE, n = 100)
x, y, color, size | ..level..

m + stat_contour(aes(z = z))
x, y, z, order | ..level..

m+ stat_spoke(aes(radius= z, angle = z))
angle, radius, x, xend, y, yend | ..x.., ..xend.., ..y.., ..yend..

m + stat_summary_hex(aes(z = z), bins = 30, fun = mean)
x, y, z, fill | ..value..

m + stat_summary2d(aes(z = z), bins = 30, fun = mean)
x, y, z, fill | ..value..

g + stat_boxplot(coef = 1.5)
x, y | ..lower.., ..middle.., ..upper.., ..outliers..

g + stat_ydensity(adjust = 1, kernel = "gaussian", scale = "area")
x, y | ..density.., ..scaled.., ..count.., ..n.., ..violinwidth.., ..width..

f + stat_ecdf(n = 40)
x, y | ..x.., ..y..

f + stat_quantile(quantiles = c(0.25, 0.5, 0.75), formula = y ~ log(x),
method = "rq")
x, y | ..quantile.., ..x.., ..y..

f + stat_smooth(method = "auto", formula = y ~ x, se = TRUE, n = 80,
fullrange = FALSE, level = 0.95)
x, y | ..se.., ..x.., ..y.., ..ymin.., ..ymax..

1D distributions

2D distributions

3 Variables

Comparisons

Functions

General Purpose

Scales control how a plot maps data values to the visual
values of an aesthetic. To change the mapping, add a
custom scale.

n <- b + geom_bar(aes(fill = fl))
n

n + scale_fill_manual(
values = c("skyblue", "royalblue", "blue", "navy"),
limits = c("d", "e", "p", "r"), breaks =c("d", "e", "p", "r"),
name = "fuel", labels = c("D", "E", "P", "R"))

scale_ aesthetic
to adjust

prepackaged
scale to use

scale specific
arguments

range of values to
include in mapping

title to use in
legend/axis

labels to use in
legend/axis

breaks to use in
legend/axis

General Purpose scales
Use with any aesthetic:

alpha, color, fill, linetype, shape, size
scale_*_continuous() – map cont’ values to visual values
scale_*_discrete() – map discrete values to visual values
scale_*_identity() – use data values as visual values
scale_*_manual(values = c()) – map discrete values to

manually chosen visual values

X and Y location scales

Color and fill scales

Shape scales

Size scales

Use with x or y aesthetics (x shown here)
scale_x_date(labels = date_format("%m/%d"),

breaks = date_breaks("2 weeks")) – treat x
values as dates. See ?strptime for label formats.

scale_x_datetime() – treat x values as date times. Use
same arguments as scale_x_date().

scale_x_log10() – Plot x on log10 scale
scale_x_reverse() – Reverse direction of x axis
scale_x_sqrt() – Plot x on square root scale

Discrete Continuous
n <- b + geom_bar(

aes(fill = fl))
o <- a + geom_dotplot(

aes(fill = ..x..))
n + scale_fill_brewer(

palette = "Blues")
For palette choices:
library(RcolorBrewer)
display.brewer.all()

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

o + scale_fill_gradient(
low = "red",
high = "yellow")

o + scale_fill_gradient2(
low = "red", hight = "blue",
mid = "white", midpoint = 25)

o + scale_fill_gradientn(
colours = terrain.colors(6))

Also: rainbow(), heat.colors(),
topo.colors(), cm.colors(),
RColorBrewer::brewer.pal()

p <- f + geom_point(
aes(shape = fl))

p + scale_shape(
solid = FALSE)

p + scale_shape_manual(
values = c(3:7))
Shape values shown in
chart on right

Manual Shape values

0

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

23

24

25

**
.

oo
OO

00
++

||
%%
##

Manual shape values

q <- f + geom_point(
aes(size = cyl))

q + scale_size_area(max = 6)
Value mapped to area of circle
(not radius)

ggthemes – Package with additional ggplot2 themes

60

long

la
t

z + coord_map(projection = "ortho",
orientation=c(41, -74, 0))

projection, orientation, xlim, ylim
Map projections from the mapproj package
(mercator (default), azequalarea, lagrange, etc.)

fl: c fl: d fl: e fl: p fl: r

c d e p r

↵c ↵d ↵
e ↵p ↵r

Use scale functions
to update legend

labels

Without clipping (preferred)

0

50

100

150

c d e p r
fl

co
un
t

0

50

100

150

c d e p r
fl

co
un
t

0

50

100

150

c d e p r
fl

co
un
t

r + theme_bw()
White background
with grid lines

r + theme_grey()
Grey background
(default theme) 0

50

100

150

c d e p r
fl

co
un
t

Some plots visualize a transformation of the original data set.
Use a stat to choose a common transformation to visualize,
e.g. a + geom_bar(stat = "bin")

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Data Visualization with ggplot2 : : CHEAT SHEET

ggplot2 is based on the grammar of graphics, the idea
that you can build every graph from the same
components: a data set, a coordinate system,
and geoms—visual marks that represent data points.

Basics
GRAPHICAL PRIMITIVES

a + geom_blank()

(Useful for expanding limits)

b + geom_curve(aes(yend = lat + 1,

xend=long+1),curvature=1) – x, xend, y, yend,
alpha, angle, color, curvature, linetype, size

a + geom_path(lineend="butt", linejoin="round",
linemitre=1)

x, y, alpha, color, group, linetype, size

a + geom_polygon(aes(group = group))

x, y, alpha, color, fill, group, linetype, size

b + geom_rect(aes(xmin = long, ymin=lat, xmax=
long + 1, ymax = lat + 1)) – xmax, xmin, ymax,
ymin, alpha, color, fill, linetype, size

a + geom_ribbon(aes(ymin=unemploy – 900,
ymax=unemploy + 900)) – x, ymax, ymin,
alpha, color, fill, group, linetype, size

+ =

To display values, map variables in the data to visual
properties of the geom (aesthetics) like size, color, and x
and y locations.

+ =

data geom
x = F · y = A

coordinate
system

plot

data geom
x = F · y = A
color = F
size = A

coordinate
system

plot

Complete the template below to build a graph.
required

ggplot(data = mpg, aes(x = cty, y = hwy)) Begins a plot
that you finish by adding layers to. Add one geom
function per layer. 


qplot(x = cty, y = hwy, data = mpg, geom = “point")
Creates a complete plot with given data, geom, and
mappings. Supplies many useful defaults.

last_plot() Returns the last plot

ggsave("plot.png", width = 5, height = 5) Saves last plot
as 5’ x 5’ file named "plot.png" in working directory.
Matches file type to file extension.

F M A

F M A

aesthetic mappings data geom

LINE SEGMENTS

b + geom_abline(aes(intercept=0, slope=1))
b + geom_hline(aes(yintercept = lat))
b + geom_vline(aes(xintercept = long))

common aesthetics: x, y, alpha, color, linetype, size

b + geom_segment(aes(yend=lat+1, xend=long+1))
b + geom_spoke(aes(angle = 1:1155, radius = 1))

a <- ggplot(economics, aes(date, unemploy))
b <- ggplot(seals, aes(x = long, y = lat))

ONE VARIABLE continuous
c <- ggplot(mpg, aes(hwy)); c2 <- ggplot(mpg)

c + geom_area(stat = "bin")

x, y, alpha, color, fill, linetype, size

c + geom_density(kernel = "gaussian")

x, y, alpha, color, fill, group, linetype, size, weight

c + geom_dotplot() 

x, y, alpha, color, fill

c + geom_freqpoly() x, y, alpha, color, group,
linetype, size

c + geom_histogram(binwidth = 5) x, y, alpha,
color, fill, linetype, size, weight

c2 + geom_qq(aes(sample = hwy)) x, y, alpha,
color, fill, linetype, size, weight

discrete
d <- ggplot(mpg, aes(fl))

d + geom_bar() 

x, alpha, color, fill, linetype, size, weight

e + geom_label(aes(label = cty), nudge_x = 1,
nudge_y = 1, check_overlap = TRUE) x, y, label,
alpha, angle, color, family, fontface, hjust,
lineheight, size, vjust

e + geom_jitter(height = 2, width = 2) 

x, y, alpha, color, fill, shape, size

e + geom_point(), x, y, alpha, color, fill, shape,
size, stroke

e + geom_quantile(), x, y, alpha, color, group,
linetype, size, weight


e + geom_rug(sides = "bl"), x, y, alpha, color,
linetype, size

e + geom_smooth(method = lm), x, y, alpha,
color, fill, group, linetype, size, weight

e + geom_text(aes(label = cty), nudge_x = 1,
nudge_y = 1, check_overlap = TRUE), x, y, label,
alpha, angle, color, family, fontface, hjust,
lineheight, size, vjust

discrete x , continuous y
f <- ggplot(mpg, aes(class, hwy))

f + geom_col(), x, y, alpha, color, fill, group,
linetype, size

f + geom_boxplot(), x, y, lower, middle, upper,
ymax, ymin, alpha, color, fill, group, linetype,
shape, size, weight

f + geom_dotplot(binaxis = "y", stackdir =
"center"), x, y, alpha, color, fill, group

f + geom_violin(scale = "area"), x, y, alpha, color,
fill, group, linetype, size, weight

discrete x , discrete y
g <- ggplot(diamonds, aes(cut, color))

g + geom_count(), x, y, alpha, color, fill, shape,
size, stroke

THREE VARIABLES
seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2)); l <- ggplot(seals, aes(long, lat))

l + geom_contour(aes(z = z))

x, y, z, alpha, colour, group, linetype, 

size, weight

l + geom_raster(aes(fill = z), hjust=0.5, vjust=0.5,
interpolate=FALSE)

x, y, alpha, fill

l + geom_tile(aes(fill = z)), x, y, alpha, color, fill,
linetype, size, width

h + geom_bin2d(binwidth = c(0.25, 500))

x, y, alpha, color, fill, linetype, size, weight

h + geom_density2d()

x, y, alpha, colour, group, linetype, size

h + geom_hex()

x, y, alpha, colour, fill, size

i + geom_area()

x, y, alpha, color, fill, linetype, size

i + geom_line()

x, y, alpha, color, group, linetype, size

i + geom_step(direction = "hv")

x, y, alpha, color, group, linetype, size



j + geom_crossbar(fatten = 2)

x, y, ymax, ymin, alpha, color, fill, group, linetype,
size

j + geom_errorbar(), x, ymax, ymin, alpha, color,
group, linetype, size, width (also
geom_errorbarh())

j + geom_linerange()

x, ymin, ymax, alpha, color, group, linetype, size

j + geom_pointrange()

x, y, ymin, ymax, alpha, color, fill, group, linetype,
shape, size

continuous function
i <- ggplot(economics, aes(date, unemploy))

visualizing error
df <- data.frame(grp = c("A", "B"), fit = 4:5, se = 1:2)
j <- ggplot(df, aes(grp, fit, ymin = fit-se, ymax = fit+se))

maps
data <- data.frame(murder = USArrests$Murder,

state = tolower(rownames(USArrests)))

map <- map_data("state")

k <- ggplot(data, aes(fill = murder))

k + geom_map(aes(map_id = state), map = map)
+ expand_limits(x = map$long, y = map$lat),
map_id, alpha, color, fill, linetype, size

Not 

required,
sensible
defaults
supplied

Geoms Use a geom function to represent data points, use the geom’s aesthetic properties to represent variables. 
Each function returns a layer.
TWO VARIABLES
continuous x , continuous y
e <- ggplot(mpg, aes(cty, hwy))


continuous bivariate distribution
h <- ggplot(diamonds, aes(carat, price))

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ggplot (data = ) +
(mapping = aes( ),
stat = , position = ) +
+
+
+

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Scales Coordinate Systems
A stat builds new variables to plot (e.g., count, prop).

Stats An alternative way to build a layer

+ =
data geom

x = x ·

y = ..count..

coordinate
system

plot

fl cty cyl

x ..count..

stat

Visualize a stat by changing the default stat of a geom
function, geom_bar(stat=”count”) or by using a stat
function, stat_count(geom=”bar”), which calls a default
geom to make a layer (equivalent to a geom function).
Use ..name.. syntax to map stat variables to aesthetics.

i + stat_density2d(aes(fill = ..level..),
geom = “polygon”)

stat function geommappings

variable created by stat

geom to use

c + stat_bin(binwidth = 1, origin = 10)

x, y | ..count.., ..ncount.., ..density.., ..ndensity..
c + stat_count(width = 1) x, y, | ..count.., ..prop..
c + stat_density(adjust = 1, kernel = “gaussian”) 

x, y, | ..count.., ..density.., ..scaled..

e + stat_bin_2d(bins = 30, drop = T)

x, y, fill | ..count.., ..density..
e + stat_bin_hex(bins=30) x, y, fill | ..count.., ..density..
e + stat_density_2d(contour = TRUE, n = 100)

x, y, color, size | ..level..
e + stat_ellipse(level = 0.95, segments = 51, type = “t”)

l + stat_contour(aes(z = z)) x, y, z, order | ..level..
l + stat_summary_hex(aes(z = z), bins = 30, fun = max)

x, y, z, fill | ..value..
l + stat_summary_2d(aes(z = z), bins = 30, fun = mean)

x, y, z, fill | ..value..

f + stat_boxplot(coef = 1.5) x, y | ..lower.., 

..middle.., ..upper.., ..width.. , ..ymin.., ..ymax..
f + stat_ydensity(kernel = “gaussian”, scale = “area”) x, y |
..density.., ..scaled.., ..count.., ..n.., ..violinwidth.., ..width..

e + stat_ecdf(n = 40) x, y | ..x.., ..y..
e + stat_quantile(quantiles = c(0.1, 0.9), formula = y ~
log(x), method = “rq”) x, y | ..quantile..
e + stat_smooth(method = “lm”, formula = y ~ x, se=T,
level=0.95) x, y | ..se.., ..x.., ..y.., ..ymin.., ..ymax..

ggplot() + stat_function(aes(x = -3:3), n = 99, fun =
dnorm, args = list(sd=0.5)) x | ..x.., ..y..
e + stat_identity(na.rm = TRUE)
ggplot() + stat_qq(aes(sample=1:100), dist = qt,
dparam=list(df=5)) sample, x, y | ..sample.., ..theoretical..
e + stat_sum() x, y, size | ..n.., ..prop..
e + stat_summary(fun.data = “mean_cl_boot”)
h + stat_summary_bin(fun.y = “mean”, geom = “bar”)
e + stat_unique()

Scales map data values to the visual values of an
aesthetic. To change a mapping, add a new scale.

(n <- d + geom_bar(aes(fill = fl)))

n + scale_fill_manual(
values = c("skyblue", "royalblue", "blue", “navy"),
limits = c("d", "e", "p", "r"), breaks =c("d", "e", "p", “r"),
name = "fuel", labels = c("D", "E", "P", "R"))

scale_
aesthetic
to adjust

prepackaged
scale to use

scale-specific
arguments

title to use in
legend/axis

labels to use
in legend/axis

breaks to use in
legend/axis

range of
values to include

in mapping

GENERAL PURPOSE SCALES
Use with most aesthetics
scale_*_continuous() – map cont’ values to visual ones
scale_*_discrete() – map discrete values to visual ones
scale_*_identity() – use data values as visual ones
scale_*_manual(values = c()) – map discrete values to
manually chosen visual ones
scale_*_date(date_labels = "%m/%d"), date_breaks = "2
weeks") – treat data values as dates.
scale_*_datetime() – treat data x values as date times.
Use same arguments as scale_x_date(). See ?strptime for
label formats.

X & Y LOCATION SCALES
Use with x or y aesthetics (x shown here)
scale_x_log10() – Plot x on log10 scale
scale_x_reverse() – Reverse direction of x axis
scale_x_sqrt() – Plot x on square root scale

COLOR AND FILL SCALES (DISCRETE)
n <- d + geom_bar(aes(fill = fl))
n + scale_fill_brewer(palette = "Blues") 

For palette choices:
RColorBrewer::display.brewer.all()
n + scale_fill_grey(start = 0.2, end = 0.8, 

na.value = "red")

COLOR AND FILL SCALES (CONTINUOUS)
o <- c + geom_dotplot(aes(fill = ..x..))

o + scale_fill_distiller(palette = "Blues")

o + scale_fill_gradient(low="red", high="yellow")

o + scale_fill_gradient2(low="red", high=“blue",
mid = "white", midpoint = 25)

o + scale_fill_gradientn(colours=topo.colors(6))
Also: rainbow(), heat.colors(), terrain.colors(),
cm.colors(), RColorBrewer::brewer.pal()

SHAPE AND SIZE SCALES
p <- e + geom_point(aes(shape = fl, size = cyl))
p + scale_shape() + scale_size()
p + scale_shape_manual(values = c(3:7))

p + scale_radius(range = c(1,6))
p + scale_size_area(max_size = 6)

r <- d + geom_bar()
r + coord_cartesian(xlim = c(0, 5)) 

xlim, ylim

The default cartesian coordinate system
r + coord_fixed(ratio = 1/2) 

ratio, xlim, ylim

Cartesian coordinates with fixed aspect ratio
between x and y units

r + coord_flip() 

xlim, ylim

Flipped Cartesian coordinates
r + coord_polar(theta = "x", direction=1 ) 

theta, start, direction

Polar coordinates

r + coord_trans(ytrans = “sqrt") 

xtrans, ytrans, limx, limy

Transformed cartesian coordinates. Set xtrans and
ytrans to the name of a window function.

π + coord_quickmap()
π + coord_map(projection = "ortho",
orientation=c(41, -74, 0))projection, xlim, ylim
Map projections from the mapproj package
(mercator (default), azequalarea, lagrange, etc.)

Position Adjustments
Position adjustments determine how to arrange geoms
that would otherwise occupy the same space.

s <- ggplot(mpg, aes(fl, fill = drv))
s + geom_bar(position = "dodge")

Arrange elements side by side
s + geom_bar(position = "fill")

Stack elements on top of one another, 

normalize height
e + geom_point(position = "jitter")

Add random noise to X and Y position of each
element to avoid overplotting
e + geom_label(position = "nudge")

Nudge labels away from points


s + geom_bar(position = "stack")

Stack elements on top of one another

Each position adjustment can be recast as a function with
manual width and height arguments
s + geom_bar(position = position_dodge(width = 1))

A
B

Themes
r + theme_bw()

White background

with grid lines
r + theme_gray()

Grey background 

(default theme)
r + theme_dark()

dark for contrast

r + theme_classic()
r + theme_light()
r + theme_linedraw()
r + theme_minimal()

Minimal themes
r + theme_void()

Empty theme

Faceting
Facets divide a plot into 

subplots based on the 

values of one or more 

discrete variables.

t <- ggplot(mpg, aes(cty, hwy)) + geom_point()

t + facet_grid(cols = vars(fl))

facet into columns based on fl
t + facet_grid(rows = vars(year))

facet into rows based on year
t + facet_grid(rows = vars(year), cols = vars(fl))

facet into both rows and columns
t + facet_wrap(vars(fl))

wrap facets into a rectangular layout

Set scales to let axis limits vary across facets

t + facet_grid(rows = vars(drv), cols = vars(fl),
scales = "free")

x and y axis limits adjust to individual facets

"free_x" – x axis limits adjust

"free_y" – y axis limits adjust

Set labeller to adjust facet labels
t + facet_grid(cols = vars(fl), labeller = label_both)

t + facet_grid(rows = vars(fl),
labeller = label_bquote(alpha ^ .(fl)))

fl: c fl: d fl: e fl: p fl: r

↵c ↵d ↵
e ↵p ↵r

Labels
t + labs( x = "New x axis label", y = "New y axis label",

title ="Add a title above the plot", 

subtitle = "Add a subtitle below title",

caption = "Add a caption below plot",
= “New legend title”)
t + annotate(geom = “text”, x = 8, y = 9, label = “A”)

Use scale functions
to update legend
labels

geom to place manual values for geom’s aesthetics

Legends
n + theme(legend.position = “bottom”)

Place legend at “bottom”, “top”, “left”, or “right”
n + guides(fill = “none”)

Set legend type for each aesthetic: colorbar, legend, or
none (no legend)
n + scale_fill_discrete(name = “Title”, 

labels = c(“A”, “B”, “C”, “D”, “E”))

Set legend title and labels with a scale function.

Zooming
Without clipping (preferred)
t + coord_cartesian(

xlim = c(0, 100), ylim = c(10, 20))
With clipping (removes unseen data points)
t + xlim(0, 100) + ylim(10, 20)
t + scale_x_continuous(limits = c(0, 100)) +
scale_y_continuous(limits = c(0, 100))

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