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R Cheat Sheet, Data Visualization with ggplot2, Cheat Sheet of Advanced Computer Programming

Goddard CollegeAdvanced Computer Programming

R color cheat sheet or ggplot2 function in R language. Explore data visualization with grammar of graphics function in R base

Typology: Cheat Sheet

2020/2021

Uploaded on 04/23/2021

anuradha
anuradha 🇺🇸

4.6

(9)

240 documents

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bg1
Geoms - Use a geom function to represent data points, use the geom’s aesthetic properties to represent variables. Each function returns a layer.
Three Variables
l + geom_contour(aes(z = z))
x, y, z, alpha, colour, group, linetype, size,
weight
seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2))
l <- ggplot(seals, aes(long, lat))
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
Two Variables
Discrete X, Discrete Y
g <- ggplot(diamonds, aes(cut, color))
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
Continuous X, Continuous Y
e <- ggplot(mpg, aes(cty, hwy))
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
A
B
C
A
B
C
Continuous Function
i <- ggplot(economics, aes(date, unemploy))
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
Continuous Bivariate Distribution
h <- ggplot(diamonds, aes(carat, price))
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
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))
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
Maps
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
Data Visualization
with ggplot2
Cheat Sheet
RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • info@rstudio.com • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org and www.ggplot2-exts.org • ggplot2 2.1.0 • Updated: 11/16
ggplot(data = mpg, aes(x = cty, y = hwy))
Begins a plot that you finish by adding layers to.
Add one geom function per layer.
Basics
Complete the template below to build a graph.
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.
To display values, map variables in the data to visual
properties of the geom (aesthetics) 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 • info@rstudio.com • 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
0123 4
4
1
2
3
0
0123 4
4
data geom coordinate
system plot
x = F
y = A
color = F
size = A
1
2
3
0
0123 4
4
plot
+
F M A
=
1
2
3
0
0123 4
4
data geom coordinate
system
x = 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 • info@rstudio.com • 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
0123 4
4
1
2
3
0
0123 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
0123 4
4
plot
+
F M A
=
1
2
3
0
0123 4
4
data geom coordinate
system
x = 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, data = mpg, geom = "point")
Creates a complete plot with given data, geom, and
mappings. Supplies many useful defaults.
aesthetic mappings
data
geom
last_plot()
Returns the last plot
ggplot(data = <DATA >) +
<GEOM_FUNCTION> (
mapping = aes(<MAPPINGS> ),
stat = <STAT> ,
position = <POSITION>
) +
<COORDINATE_FUNCTION> +
<FACET_FUNCTION> +
<SCALE_FUNCTION> +
<THEME_FUNCTION>
<THEME_FUNCTION>
<SCALE_FUNCTION>
<FACET_FUNCTION>
<COORDINATE_FUNCTION>
<POSITION>
<STAT>
<MAPPINGS>
<GEOM_FUNCTION>
<DATA>
Required
Not
required,
sensible
defaults
supplied
Graphical Primitives
a <- ggplot(economics, aes(date, unemploy))
b <- ggplot(seals, aes(x = long, y = lat))
a + geom_blank()
(Useful for expanding limits)
b + geom_curve(aes(yend = lat + 1,
xend=long+1,curvature=z)) - 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
Line Segments
common aesthetics: x, y, alpha, color, linetype, size
b + geom_abline(aes(intercept=0, slope=1))
b + geom_hline(aes(yintercept = lat))
b + geom_vline(aes(xintercept = long))
b + geom_segment(aes(yend=lat+1, xend=long+1))
b + geom_spoke(aes(angle = 1:1155, radius = 1))
One Variable
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
Continuous
c <- ggplot(mpg, aes(hwy)); c2 <- ggplot(mpg)
pf2

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

Three Variables

l + geom_contour(aes(z = z))

x, y, z, alpha, colour, group, linetype, size, weight seals$z <- with(seals, sqrt(delta_long^2 + delta_lat^2)) l <- ggplot(seals, aes(long, lat))

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

Two Variables

Discrete X, Discrete Y

g <- ggplot(diamonds, aes(cut, color))

g + geom_count()

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

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

Continuous X, Continuous Y

e <- ggplot(mpg, aes(cty, hwy))

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

AB

C

A

B

C

Continuous Function

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

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

Continuous Bivariate Distribution

h <- ggplot(diamonds, aes(carat, price))

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

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)) 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

Maps

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

Data Visualization

with ggplot

Cheat Sheet

RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • info@rstudio.com • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org and www.ggplot2-exts.org • ggplot2 2.1.0 • Updated: 11/1 6 ggplot(data = mpg, aes(x = cty, y = hwy)) Begins a plot that you finish by adding layers to. Add one geom function per layer.

Basics

Complete the template below to build a graph. 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. To display values, map variables in the data to visual properties of the geom (aesthetics) like size, color, and x and y locations. Graphical Primitives

Data Visualization

with ggplot

Cheat Sheet

RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • info@rstudio.com • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org • ggplot2 0.9.3.1 • Updated: 3/1 5 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 = 12 3 (^00 1 2 3 ) 4 1 2 3 (^00 1 2 3 ) 4

data geom coordinate system plot

F M A = 12 3 (^00 1 2 3 ) 4 1 2 3 (^00 1 2 3 ) 4 data geom coordinate system plot x = F y = A color = F size = A 1 2 3 (^00 1 2 3 ) 4 plot

F M A

1 2 3 (^00 1 2 3 ) 4 data geom coordinate x = F y = A system x = F y = A Graphical Primitives

Data Visualization

with ggplot

Cheat Sheet

RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • info@rstudio.com • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org • ggplot2 0.9.3.1 • Updated: 3/1 5 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 = 12 3 (^00 1 2 3 ) 4 1 2 3 (^00 1 2 3 ) 4

data geom coordinate system plot

F M A = 12 3 (^00 1 2 3 ) 4 1 2 3 (^00 1 2 3 ) 4 data geom coordinate system plot x = F y = A color = F size = A 1 2 3 (^00 1 2 3 ) 4 plot

F M A 12 = 3 (^00 1 2 3 ) 4 data geom coordinate x = F y = A system 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, data = mpg, geom = "point")

Creates a complete plot with given data, geom, and mappings. Supplies many useful defaults. aesthetic mappings data geom last_plot() Returns the last plot ggplot(data = ) + <GEOM_FUNCTION> ( mapping = aes( ) , stat = , position = ) + <COORDINATE_FUNCTION> + <FACET_FUNCTION> + <SCALE_FUNCTION> + <THEME_FUNCTION><THEME_FUNCTION> <SCALE_FUNCTION> <FACET_FUNCTION> <COORDINATE_FUNCTION> <GEOM_FUNCTION> Required Not required, sensible defaults supplied

Graphical Primitives

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

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

a + geom_blank()

(Useful for expanding limits)

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

xend=long+1,curvature=z)) - 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

Line Segments

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

b + geom_abline(aes(intercept=0, slope=1))

b + geom_hline(aes(yintercept = lat))

b + geom_vline(aes(xintercept = long))

b + geom_segment(aes(yend=lat+1, xend=long+1))

b + geom_spoke(aes(angle = 1:1155, radius = 1))

One Variable

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

Continuous

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

RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • info@rstudio.com • 844-448-1212 • rstudio.com

Coordinate Systems

60 long lat

π + coord_quickmap()

π + coord_map(projection = "ortho",

orientation=c(41, -74, 0))

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

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.

r <- d + geom_bar()

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 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

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

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)) A B

Themes

r + theme_classic() r + theme_light() r + theme_linedraw() r + theme_minimal() Minimal themes r + theme_void() Empty theme 0 50 100 150 c d e fl p r count 0 50 100 150 c d e fl p r count 0 50 100 150 c d e fl p r count 0 50 100 150 c d e fl p r r + theme_bw() count White background with grid lines r + theme_gray() Grey background (default theme) r + theme_dark() (^0) dark for contrast 50 100 150 c d e fl p r count

Zooming

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)) Without clipping (preferred)

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.

Faceting

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

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(drv ~ 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(. ~ fl, labeller = label_both) t + facet_grid(fl ~ ., labeller = label_bquote(alpha ^ .(fl))) t + facet_grid(. ~ fl, labeller = label_parsed) fl: c fl: d fl: e fl: p fl: r c d e p r ↵c^ ↵d^ ↵e^ ↵p^ ↵r Learn more at docs.ggplot2.org and www.ggplot2-exts.org • ggplot2 2.1.0 • Updated: 11/1 6 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() Stats - An alternative way to build a layer

x (^) ..count..

3 (^00 1 2 3 ) 4 1 2 3 (^00 1 2 3 ) 4 data geom^ coordinate system x = x plot y = ..count.. fl cty cyl stat A stat builds new variables to plot (e.g., count, prop). 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 geom mappings variable created by stat geom to use 1D distributions 2D distributions 3 Variables Comparisons Functions General Purpose

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") Use scale functions to update legend labels t + annotate(geom = "text", x = 8, y = 9, label = "A") geom to place manual values for geom’s aesthetics 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() Scales 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 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 most aesthetics scale_continuous() - map cont’ values to visual ones scalediscrete() - map discrete values to visual ones scaleidentity() - use data values as visual ones scalemanual(values = c()) - map discrete values to manually chosen visual ones scaledate(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 and Y location scales

Color and fill scales (Discrete)

Shape and size 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 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") p + scale_shape() + scale_size() p + scale_shape_manual(values = c(3:7))

Color and fill scales (Continuous)

o <- c + geom_dotplot(aes(fill = ..x..)) p <- e + geom_point(aes(shape = fl, size = cyl)) p + scale_radius(range = c(1,6)) p + scale_size_area(max_size = 6) Maps to radius of circle, or area c(-1, 26) 0: 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 Manual shape values