Geoms
Data Visualization
Graphical Primitives
with ggplot2
with ggplot2
ggplot2 is based on the
grammar
Cheat
Sheet of graphics, the
idea that you can build every graph from the same
Basics
few components: a data
set, a set of geoms—visual
marks that represent data points, and a coordinate
system. F M A
3
+
F MA
=
2
1
0
4
1
y, alpha, color, fill,
size
x, xend, y, yend, alpha,
color, curvature, Two Variablesh +x,geom_bin2d(binwidth
= c(5,shape,
0.5))
f + angle,
geom_blank()
One Variable
a + geom_area(stat
= "bin")
linetype,
size
xmax, xmin, ymax, ymin, alpha, color, fill,
Continuous X, Continuous Y
Continuous Bivariate Distribution
x, y, alpha, Continuous
color, fill, linetype, size
b + geom_area(aes(y
= ..density..),
stat = "bin")
a <- ggplot(mpg,
aes(hwy))
4
0
3
1
3
4
geom_point()
he<-+ggplot(movies,
aes(year, rating))
h + geom_density2d()
hx,+x,
geom_bin2d(binwidth
= c(5,
0.5)) size, stroke
y, alpha,
shape,
y, alpha,
colour, color,
linetype, fill,
size
linetype, size, weight
f <- ggplot(mpg, aes(cty, hwy))
f + geom_jitter()
f x,
+ geom_blank()
y, alpha, color, fill, shape, size
b += "gaussian")
geom_path(lineend="butt",
a + geom_density(kernal
ax,+y,geom_area(stat
= "bin")
linejoin="round’
, linemitre=1)
alpha, color, fill, linetype,
size, weight
xmax, xmin, ymax, ymin, alpha, color, fill,
alpha, color, fill, linetype,
size
bx,+y,geom_density(aes(y
= ..county..))
size, weight
h +linetype,
geom_hex()
ehx,++y,geom_density2d()
geom_quantile()
alpha,
colour, fill size
f + geom_point()
x, y, alpha, color, group,
linetype, size
f x,
+ geom_jitter()
y, alpha, color, fill, shape, size
b + geom_area(aes(y = ..density..), stat = "bin")
a+ geom_dotplot()
ax,+y,geom_density(kernal
= "gaussian")
alpha, color, fill
x, y, alpha, colour, linetype, size
x, y, alpha, color, fill, shape, size
x,Continuous
y, alpha, color,
group, linetype, size, weight
Function
h + geom_hex() aes(date, unemploy))
g <- ggplot(economics,
b + geom_polygon(aes(group
= group))
f + geom_quantile()
x, y, alpha, color, fill, linetype, size, weight
b + geom_density(aes(y = ..county..))
f x,
+ geom_point()
y, alpha, color, linetype, size, weight
x, y, alpha, color, fill, group,
linetype,
sizesize
x, y, alpha,
color, fill, shape,
x, y, alpha, color, linetype, size
x, y, alpha, color, fill
b + geom_freqpoly(aes(y = ..density..))
1
y, alpha, colour, fill size
ge+x,+
geom_area()
geom_rug(sides
f + geom_rug(sides = "bl")
a + geom_rect(aes(xmin
=color,
long,
ymin
f alpha,
+ geom_quantile()
linetype,
size = lat,
a + geom_histogram(binwidth = 5)
geom
x=F
y=A
+
2
2
3
4
coordinate
system
1
=
+
=
+
=
2
2
plot
= "bl")
+
h + geom_hex()
x, y, alpha, colour, fill, size
Continuous Function
i <- ggplot(economics, aes(date, unemploy))
i + geom_area()
i + geom_line()
x, y, alpha, color, group, linetype, size
1
AB
C
=
=
set of aesthetic mappings, and a default stat
and position adjustment.
linetype, size
RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • info@rstudio.com • 844-448-1212 • rstudio.com
aesthetic mappings
x, y, alpha, colour, group, linetype, size
x, y, alpha, color, fill, linetype, size
Function
x, Continuous
y, alpha, color,
linetype, size
One Variable
+
h + geom_density2d()
x, y, alpha, color, fill, linetype, size
g <- ggplot(economics, aes(date, unemploy))
x, y,ymax
alpha, color,
size, weight
g + geom_line()
ax,+y,geom_freqpoly()
xmax=
long + delta_long,
= latlinetype,
+ delta_lat)
)
alpha, color, fill, linetype,
size, weight
f + geom_smooth(model = lm)
gx,+y,geom_area()
alpha, color, linetype, size
x,
y,
alpha,
color,
linetype,
size
xmax,
xmin,
ymax,
ymin,
alpha,
color,
fill,
linetype,
size
b + geom_histogram(aes(y = ..density..))
f x,
+ geom_rug(sides
= "bl") size, weight
e x,+y,geom_smooth(method
= lm)
y, alpha, color, fill, linetype,
alpha, color, fill, linetype, size
b + geom_freqpoly(aes(y = ..density..))
0
0
alpha,
color,
linetype,
size
Discrete
1
0
1
0
3
4
3
4
g + x,
geom_step(direction
=
"hv")
2
2 variables
To display data
values,
map
in
the
data
set
a + geom_histogram(binwidth
=
5)
y, alpha, color, fill, group, linetype, size, weight
baes(fl))
+ geom_ribbon(
aes(ymin=unemploy
- 900,
data geom coordinate
plot
f + geom_text(aes(label = cty))
gx,+y,geom_line()
alpha, color, linetype, size
<- ggplot(mpg,
4
x, y,aalpha,
color, fill, linetype,
size, weight
F M
A x = F 4 ofsystem
to aesthetic
properties
the geom
like size, color,
f x,
+ )geom_smooth(model
= lm)
y, label, alpha, angle, color,
family, fontface,
y=A
x,
y,
alpha, color, linetype, size
ymax=unemploy
+
900)
3
3
+ geom_histogram(aes(y = ..density..))
b +bgeom_bar()
hjust,
lineheight,
vjust size, weight
x, y, alpha,
color,size,
fill, linetype,
and x and y locations.2
e
+
geom_text(aes(label
= cty), nudge_x = 1,
Visualizing error
x,
ymax,
ymin,
alpha,
color,
fill,
group,
linetype,
size
2
x, alpha, color,
fill,
linetype,
size,
weight
Discrete
g + geom_step(direction
= "hv")
df <- data.frame(grp
= c("A", "B"), fit
= 4:5, se = 1:2)
1
1
f + geom_text(aes(label = cty))
nudge_y
=
1,
check_overlap
x,
y,
alpha,
color,
linetype,
size
a
<ggplot(mpg,
aes(fl))
e <- ggplot(df, aes(grp, fit, ymin = fit-se, ymax = fit+se)) = TRUE)
F MA
04
04
a + geom_segment(aes(yend=lat
+ delta_lat,
Discrete
X, Continuous
Y
x, y, label, alpha,
angle,
color, family,
fontface,
1
0
1
0
3
4
3
4
2
2
3
x, y, label, alpha, angle, color, family, fontface,
3
b
+
geom_bar()
g <ggplot(mpg,
Graphical Primitives
hjust,
lineheight,aes(class,
size, vjust hwy))
data geom coordinate
plot
xend = long + delta_long))
e + geom_crossbar(fatten
2)
Visualizing
error=size,
2
2
x, alpha, color, fill, linetype, size, weight
system
x=F
hjust,
lineheight,
vjust
y=A
df <- data.frame(grp
= c("A",
"B"),
fit =fill,4:5,
se = 1:2)
x, y, ymax, ymin,
alpha,
color,
linetype,
1
g +color,
geom_bar(stat
= "identity")
x,
xend,
y,
yend,
alpha,
linetype,
size
color = F 1
size
c
<ggplot(map,
aes(long,
lat))
size = A
e
<ggplot(df,
aes(grp,
fit,
ymin
=
fit-se,
ymax
= fit+se))
0
0
x,Discrete
y, alpha, color,
fill, linetype, size,
weight
X,
Continuous
Y
1
0
1
0
3
4
3
4
2
2
e + geom_errorbar()
c
+
geom_polygon(aes(group
=
group))
g
<ggplot(mpg,
aes(class,
hwy))
Graphical
Primitives
data geom coordinate
plot
a + geom_spoke(aes(yend
= lat + delta_lat,
Discrete
Y
ex,+ymax,
geom_crossbar(fatten
= 2)
ymin, alpha,X,
color,Continuous
linetype,
size,
g + geom_boxplot()
x, y, alpha, color, fill, linetype, size
system
x=F
y=A
(also ymin,
geom_errorbarh())
x,<y, ymax,
alpha, color,
fill, linetype,
xend = long + delta_long))
glower,
+ geom_bar(stat
fwidth
ggplot(mpg,
aes(class,
hwy))
middle, upper,=x,"identity")
ymax, ymin, alpha,
color = F
c <- ggplot(map, aes(long, lat))
size = A
e +size
geom_linerange()
color,
fill, linetype,
shape,
size,
weight
x, y,color,
alpha,
color,
fill,
linetype,
size,
weight
x, y, angle, radius, alpha,
linetype,
size
ex,+ymin,
geom_errorbar()
ymax, alpha, color, linetype, size
c + geom_polygon(aes(group = group))
g + geom_dotplot(binaxis = "y",
g <- ggplot(economics, aes(date, unemploy))
f +x, ymax,
geom_bar(stat
= "identity")
ymin, alpha, color, linetype,
size,
gstackdir
+ geom_boxplot()
Build a graph with ggplot() or qplot()
x, y, alpha, color, fill, linetype, size
= "center")
(also geom_errorbarh())
e +width
geom_pointrange()
x,lower,
y, alpha,
color,upper,
fill x, ymax, ymin, alpha,
g + geom_path(lineend="butt",
middle,
y, alpha,
color,
linetype, size, weight
ex,+x,
fill, linetype, shape,
size, weight
y,geom_linerange()
ymin,
ymax, alpha,
color,fill,
fill, linetype,
linejoin="round’, linemitre=1)
g +color,
geom_violin(scale
= "area")
ggplot(data = mpg, aes(x = cty, y = hwy))
shape,
x, ymin,size
ymax, alpha, color, linetype, size
x, y, alpha, color, linetype, size
gx,+y,geom_dotplot(binaxis
=size,
"y", weight
alpha,
color,
fill,
linetype,
g <- ggplot(economics, aes(date, unemploy))
4
4
geom_ribbon(aes(ymin=unemploy - 900,
F M
A you finish
stackdir = "center")
Maps
Begins a plot
that
by adding
layers to. No gg+ymax=unemploy
f +geom_pointrange()
geom_boxplot()
Continuous
x, y, alpha, color, fill
+ 900))
+ geom_path(lineend="butt",
3
3
data e<-+data.frame(murder
= USArrests$Murder,
defaults, but provides more
control2 than qplot().
x,linejoin="round’
ymax, ymin, alpha,
color, fill, linetype, size
ymin,
ymax, alpha,
color, fill,
linetype,ymax, ymin, alpha,
, linemitre=1)
statex,=x,y,tolower(rownames(USArrests)))
gaes(hwy))
+ geom_violin(scale
= "area")
2
y,
lower,
middle,
upper,
Discrete X, Discrete
Y
c
<ggplot(mpg,
map
<map_data("state")
shape,
size
x, y, alpha, color, linetype, size
1
y, alpha, color, fill, linetype,
weight
1
h <-x,ggplot(diamonds,
aes(cut, size,
color))
color,
fill,
group,
linetype,
shape, size, weight
e <- ggplot(data,
aes(fill
= murder))
dataF M A
g + geom_ribbon(aes(ymin=unemploy - 900,
04
04
add
layers, d<Maps = state), map = map) +
e + geom_map(aes(map_id
c
+
geom_area(stat
=
"bin")
1
0
1
0
3
4
3
4
2
2
ggplot(seals,
aes(x
=
long,
y
=
lat))
h
+
geom_jitter()
ymax=unemploy
+
900)
)
3
dataf<-+data.frame(murder
= USArrests$Murder, = "y",
geom_dotplot(binaxis
elements with + x, ymax, ymin, alpha, color, fill, linetype, size
data geom 3 coordinate
plot
expand_limits(
x = map$long, y = map$lat)
state
= tolower(rownames(USArrests)))
x, y,Discrete
alpha, color,
shape, size
ggplot(mpg, aes(hwy,
cty))
2
x=F
X,fill,
Discrete
Y
system + 2
size
d + geom_segment(aes( x, y, alpha, color, fill, linetype,
alpha, color, fill, linetype, size
mapmap_id,
<-stackdir
map_data("state")
y=A
= "center")
1
h <- ggplot(diamonds, aes(cut, color))
geom_point(aes(color
= cyl)) 1 +
layer = geom + xend = long + delta_long,
e <- ggplot(data, aes(fill = murder))
= lat + delta_lat)) a + geom_area(aes(y = ..density..), stat = "bin")
stat + d<-yend
geom_smooth(method 0 ="lm")
+ 0 0 1 2 default
x,
y,
alpha,
color,= state),
fill, map
group
e
+
geom_map(
aes(map_id
= map) +
Three
Variables
1
0
3
4
3
4
2
ggplot(seals, aes(x = long, y = lat))
h + geom_jitter()
data geom + coordinate
plotlayer specific x, xend, y, yend, alpha, color, linetype, size
coord_cartesian()
expand_limits(
x = map$long, y = map$lat)
x, y, alpha, color,
fill, shape, size+ delta_lat^2))
i
+
geom_raster(aes(fill
=
z),
hjust=0.5,
seals$z <- with(seals,
sqrt(delta_long^2
f
+
geom_violin(scale
=
"area")
x=F
system
geom_segment(aes(
mappings dd++geom_rect(aes(xmin
c =+long,
geom_density(kernel
= "gaussian")
map_id, alpha, color, fill, linetype, size
ymin = lat,
y=A
scale_color_gradient()
+
i <- ggplot(seals, aes(long, lat))
vjust=0.5, interpolate=FALSE)
xend =long
long++delta_long,
delta_long,
xmax=
x, y, alpha, color, fill, group, linetype, size,
theme_bw()
yend == lat
lat ++ delta_lat))
delta_lat)) x, y, alpha, color, fill, group, linetype, size, weight
additional ymax
Three Variables x, y, alpha, fill
i + geom_contour(aes(z = z))
weight
elements
i
+
geom_tile(aes(fill = z))
x,
xend,
y,
yend,
alpha,
color,
linetype,
size
xmax, xmin, ymax, ymin, alpha, color, fill,
i +x, geom_raster(aes(fill
= z),size
hjust=0.5,
seals$z <-x,with(seals,
+ delta_lat^2))
y, z, alpha, sqrt(delta_long^2
colour, linetype, size,
weight
y, alpha, color, fill, linetype,
size
dlinetype,
+ geom_rect(aes(xmin
= long,
ymin = lat,
c
+
geom_dotplot()
i <- ggplot(seals, aes(long, lat))
vjust=0.5, interpolate=FALSE)
Add a new layer to a plot with a geom_*()
xmax= long + delta_long,
x, y, alpha, fill
= lat + delta_lat)) x, y, alpha, color, fill
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trademark of RStudio,
Inc. • provides
CC BY RStudio • info@rstudio.com
• rstudio.com
Learn more at docs.ggplot2.org
• ggplot2
• Updated: 3/15Y
Discrete
X, 0.9.3.1
Discrete
or stat_*()
Each
a geom, a• 844-448-1212ymax
i + geom_contour(aes(z = z))
i + geom_tile(aes(fill = z))
xmax, xmin, ymax, ymin, alpha, color, fill,
data
x, y, alpha, color, fill, linetype, size, weight
a <- ggplot(mpg, aes(hwy))
a + geom_freqpoly()
a+ geom_dotplot()
2
0
0
3
a + geom_curve(aes(yend = lat + delta_lat,
Continuous X, Continuous Y
Continuous
Bivariate Distribution= 2, width = 2)
erepresent
+ggplot(movies,
geom_jitter(height
Geomsxend
- Use=a geom
data
points,
use the
geom’s
aesthetic properties to
variables
longto+represent
delta_long,
curvature
=hwy))
z))
h <aes(year, rating))
f <- ggplot(mpg,
aes(cty,
h + geom_bin2d(binwidth = c(0.25, 500))
Continuous
4
3
Continuous Bivariate Distribution
h <- ggplot(diamonds, aes(carat, price))
A C e + geom_label(aes(label = cty), nudge_x = 1,
nudge_y = 1, check_overlap = TRUE)
B
Geoms(Useful
- Use a geom
to represent data
points, use the geom’s aesthetic properties
to represent variables
for expanding
limits)
x, y, label, alpha, angle, color, family, fontface,
One Variable
Two Variables hjust, lineheight, size, vjust
with ggplot2
Cheat Sheet
Data Visualization
Basics
Continuous X, Continuous Y
e <- ggplot(mpg, aes(cty, hwy))
a + geom_blank()
Data Visualization
Basics
Two Variables
a <- ggplot(seals, aes(x = long, y = lat))
b <- ggplot(economics, aes(date, unemploy))
Cheat Sheet
4
- Use a geom to represent data points, use the geom’s aesthetic properties to represent variables. Each function returns a layer.
data
geom
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.
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.
c + geom_freqpoly()
x, y, z, alpha, colour, linetype, size, weight
x, y, alpha, color, group, linetype, size
a + geom_freqpoly(aes(y = ..density..))
g <-x, ggplot(diamonds,
y, alpha, color, fill, linetype, size aes(cut, color))
g + geom_count()
Learn more at docs.ggplot2.org • ggplot2 0.9.3.1 • Updated: 3/15
x, y, alpha, color, fill, shape, size, stroke
c + geom_histogram(binwidth = 5)
x, y, alpha, color, fill, linetype, size, weight
a + geom_histogram(aes(y = ..density..))
Discrete
d <- ggplot(mpg, aes(fl))
d + geom_bar()
x, alpha, color, fill, linetype, size, weight
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i + geom_step(direction = "hv")
x, y, alpha, color, group, linetype, 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))
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
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$lat)
map_id, alpha, color, fill, linetype, size
Three Variables
seals$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
Learn more at docs.ggplot2.org • ggplot2 2.0.0 • Updated: 12/15
Stats - An alternative way to build a layer
Scales
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 = "count")
Scales control how a plot maps data values to the visual
values of an aesthetic. To change the mapping, add a
custom scale.
=
2
1
stat
geom
2
1
scale_
0
0
data
n <- b + geom_bar(aes(fill = fl))
n
0
1
2
3
4
coordinate
x=x
system
y = ..count..
0
1
2
plot
3
4
Each stat creates additional variables to map aesthetics
to. These variables use a common ..name.. syntax.
stat and geom functions both combine a stat with a
geom to make a layer, i.e. stat_count(geom="bar")
does the same as geom_bar(stat="count")
stat function
layer mappings
i + stat_density2d(aes(fill = ..level..),
geom = "polygon", n = 100) variable
geom for layer
parameters for stat
created by
transformation
c + stat_bin(binwidth = 1, origin = 10)
1D distributions
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 = TRUE)
2D distributions
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))
3 Variables
x, y, z, order | ..level..
l + stat_summary_hex(aes(z = z), bins = 30, fun = mean)
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)
Comparisons
x, y | ..lower.., ..middle.., ..upper.., ..width.. , ..ymin.., ..ymax..
f + stat_ydensity(adjust = 1, kernel = "gaussian", scale = "area")
x, y | ..density.., ..scaled.., ..count.., ..n.., ..violinwidth.., ..width..
e + stat_ecdf(n = 40)
Functions
x, y | ..x.., ..y..
e + stat_quantile(quantiles = c(0.25, 0.5, 0.75), formula = y ~ log(x),
method = "rq")
x, y | ..quantile..
e + stat_smooth(method = "auto", formula = y ~ x, se = TRUE, n = 80,
fullrange = FALSE, level = 0.95)
x, y | ..se.., ..x.., ..y.., ..ymin.., ..ymax..
ggplot() + stat_function(aes(x = -3:3),
General Purpose
fun = dnorm, n = 101, args = list(sd=0.5))
x | ..x.., ..y..
e + stat_identity(na.rm = TRUE)
ggplot() + stat_qq(aes(sample=1:100), distribution = qt,
dparams = 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()
aesthetic prepackaged
to adjust scale to use
title to use in
legend/axis
labels to use in
legend/axis
xlim, ylim
Flipped Cartesian coordinates
breaks to use in
legend/axis
r + coord_polar(theta = "x", direction=1 )
theta, start, direction
Polar coordinates
Use with any aesthetic:
alpha, color, fill, linetype, shape, size
r + coord_trans(ytrans = "sqrt")
xtrans, ytrans, limx, limy
Transformed cartesian coordinates. Set
xtrans and ytrans to the name
of a window function.
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
π + coord_map(projection = "ortho",
orientation=c(41, -74, 0))
60
Use with x or y aesthetics (x shown here)
scale_x_date(date_labels = "%m/%d"),
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
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
Color and fill scales
s + geom_bar(position = "fill")
Continuous
o <- c + geom_dotplot(
aes(fill = ..x..))
n + scale_fill_brewer(
palette = "Blues")
For palette choices:
library(RColorBrewer)
display.brewer.all()
o + scale_fill_gradient(
low = "red",
high = "yellow")
o + scale_fill_gradient2(
low = "red", high = "blue",
mid = "white", midpoint = 25)
n + scale_fill_grey(
start = 0.2, end = 0.8,
na.value = "red")
Stack elements on top of one another,
normalize height
e + geom_point(position = "jitter")
A
B
s + geom_bar(position = position_dodge(width = 1))
Manual Shape values
0
6
12
18
24
0
p + scale_shape(
solid = FALSE)
1
7
13
19
25
+
2
8
14
20
*
-
3
9
15
21
.
10
5
11
16
17
22
23
0
+
* |
o %
O #
o
O
Themes
r + theme_bw()
150
|
100
50
c
%
d
e
p
r
fl
r + theme_gray()
150
#
100
50
c
d
e
p
Grey background
(default theme)
r
fl
Size scales
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p + scale_size_area(
max_scale = 6)
Maps to area of circle (not radius)
t + facet_grid(year ~ .)
facet into rows based on year
t + facet_grid(year ~ fl)
facet into both rows and columns
150
r + theme_dark()
100
50
0
c
d
e
fl
p
r
dark for contrast
100
50
0
c
d
e
p
r
fl
150
100
50
0
c
d
e
fl
p
r
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)
fl: d
fl: e
fl: p
fl: r
t + facet_grid(fl ~ ., labeller = label_bquote(alpha ^ .(fl)))
↵c
↵e
↵p
↵r
↵d
t + facet_grid(. ~ fl, labeller = label_parsed)
c
d
e
p
r
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")
Use scale functions
to update legend
labels
Change the label on the Y axis
t + labs(title =" New title", x = "New x", y = "New y")
All of the above
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
r + theme_classic()
r + theme_light()
r + theme_linedraw()
r + theme_minimal()
150
White background
with grid lines
0
0
p + scale_radius(
range=c(1,6))
p + scale_size()
Nudge labels away from points
Each position adjustment can be recast as a function
with manual width and height arguments
Manual shape values
4
e + geom_label(position = "nudge")
Stack elements on top of one another
p <- e + geom_point(aes(
shape = fl, size = cyl))
p + scale_shape_manual(
values = c(3:7))
Shape values shown in
chart on right
Add random noise to X and Y position of each
element to avoid overplotting
s + geom_bar(position = "stack")
o + scale_fill_gradientn(
colours = terrain.colors(6))
Also: rainbow(), heat.colors(),
topo.colors(), cm.colors(),
RColorBrewer::brewer.pal()
Shape scales
facet into columns based on fl
fl: c
projection, orientation, xlim, ylim
Map projections from the mapproj package long
(mercator (default), azequalarea, lagrange, etc.)
X and Y location scales
n <- d + geom_bar(
aes(fill = fl))
t + facet_grid(. ~ fl)
r + coord_flip()
General Purpose scales
Discrete
t <- ggplot(mpg, aes(cty, hwy)) + geom_point()
ratio, xlim, ylim
Cartesian coordinates with fixed aspect
ratio between x and y units
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"))
range of values to
include in mapping
Facets divide a plot into subplots based on the values
of one or more discrete variables.
r + coord_fixed(ratio = 1/2)
scale specific
arguments
lat
3
count
3
count
+
4
xlim, ylim
The default cartesian coordinate system
count
..count..
r + coord_cartesian(xlim = c(0, 5))
count
x
r <- d + geom_bar()
count
fl cty cyl
4
Faceting
Coordinate Systems
Minimal themes
r + theme_void()
Empty theme
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))
Learn more at docs.ggplot2.org • ggplot2 2.0.0 • Updated: 12/15