Data Visualization with
: : CHEATSHEET
Basics
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.
BRIE!
data geom
x- F -y-A
coordinate system
plot
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 color - F size-A
coordinate system
plot
Complete the template below to build a graph.
ggplot (data = S22ED)+ ^mjmm«|m^>lmapping = aes(Q2Q32IZ^9
stat = <STAT> .position = amMHiwp) +
<COORDINATE_FUNCTION>
<FACET FUNCTION>
<SCALE FUNCTION>
<THEME FUNCTIONS
required
Not required, sensible defaults supplied
aesthetic mappings
ggplotfdata = mpg, aes(x = cty, y = hwy)) Begins a plot that you finish by adding layers to. Add one geom function per layer.
qplotfx = 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
ggsavef'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.
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Studio
Geoms
Use a geom function to represent data points, use the geom's aesthetic properties to represent variables. Each function returns a layer.
GRAPHICAL PRIMITIVES
a <- ggplot(economics, aes(date, unemploy)) b <- ggplotjseals, aes(x = long, y = lat))
| a + geom blank()
| (Useful for expanding limits)
b + geom_curve(aes(yend = lat +1,
xend=long+l,curvature=z)) - x, xend, y, yend, alpha, angle, color, curvature, linetype, size
a + geom_path(lineend-'butt", linejoin- 'round", linemitre=l)
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 + 1J) - 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
-I
b + geom_abline(aes(intercept=0, slope=l)) b + geom_hline(aes(yintercept = lat)) b + geom_vline(aes(xintercept = long))
b + geom_segment(aes(yend=lat+l, xend=long+l)) b + geom_spoke(aes(angle = 1:1155, radius = 1))
ONE VARIABLE continuous
c<-ggplot(mpg, aes(hwy)); c2 <- ggplot(mpg)
c + geom area(stat = "bin")
I x, y, alpha, color, fill, linetype, size
c + geom_density(kernel = "gaussian")
x, y, alpha, color, fill, group, linetype, size, weight
|« c + geom_dotplot() |f«f   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
TWO VARIABLES
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 = Im), x, y, alpha, color, fill, group, linetype, size, weight
e + geom_text(aes(label = cty), nudge_x = 1 nudge_y = 1, check_overlap = TRUEJ, 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, IttBl 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
continuous bivariate distribution
h <- ggplot(diamonds, aes(carat, price))
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
continuous function
i <- ggplot(economics, aes(date, unemploy))
i  i + geom area()
x, y, alpha, color, fill, linetype, size
i + geom_line()
Jirlfffi x' ^' a'Pna> co'or> grouP. linetype, size
i + geom step(direction = "hv")
jJH 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))
B™i j + geom_crossbar(fatten = 2) j x, y, ymax, ymin, alpha, color, fill, group, linetype, □ size
j + geom_errorbar(), x, ymax ymin, alpha, color, group, linetype, size, width (also H geom_errorbarh())
j + geom linerangeO
x, ymin, ymax, alpna, color, group, linetype, size j + geom pointrangeO
x y, ymin, ymax, alpha, color, fill, group, linetype,
IS'
shape, size
maps
data <-data.frame(murder = USArrests$Murder, state = tolower(rownames(USArrests))) map <- map_data("state") k <- ggplotjdata, aes(fill = murder))
k + geom_map(aes(map id = state), map = map) + expand limits(x = mapSlong, y = mapSlat), map_id, alpha, color, fill, linetype, size
THREE VARIABLES
seals$z <- with(seals, sqrt(delta_longA2 + delta_latA2))l <- ggplot(seals, aes(long, lat))
I + geom contour(aes(z = z)) ■ 1 + geom_raster(aes(fill -z), hjust=0.5, vjust=0.5,
x, y, z, alpha, colour, group, linetype, I interpolate=FALSE)
size, weight t^M x, y, alpha, fill
I + geom_tile(aes(fill = z)), x, y, alpha, color, fill, linetype, size, width
RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • info@rstudio.com • 844-448-1212 • rstudio.com • Learn more at http://ggplot2.tidyverse.org> ggplot2 2.1.0 • Updated: 2016-11
discrete
d <- ggplot(mpg, aes(fl)) d + geom_bar()
x, alpha, color, fill, linetype, size, weight
Stats
An alternative way to build a layer
A stat builds new variables to plot (e.g., count, prop).
data
stat
coordinate system
plot
geom
x - x
y- ..count-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.
stat function J geommappings
I i + stat_density2d(aes(fill = ..level..), V geom = "polygon").
variable created by stat
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 = TRU E, n = 100) x, y, color, size ..level..
e + stat_ellipse(level = 0.95, segments = 51, type = "t")
I + stat_contour(aes(z = z)) x, y, z, order | ..level..
I + stat_summary_hex(aes(z = z), bins = 30, fun = max) x,y, z, fill | ..value..
I + 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 = "Im", formula = y - x, se=T, level=0.95)x,y | ..se.., ..x.., ..y.., ..ymin.., ..ymax..
ggplotf) + stat_function(aes(x -
dnorm, args = list(sd=0.5)) x ..x
-3:3), n = 99, fun = ,..y..
e + stat_identity(na.rm = TRUE)
ggplotf) + stat_qq(aes(sample=l:100), dist = qt, clparam=list(df=5)j sample, x,y | ..sample.., ..theoretic;
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))
@Stud
10
Scales
Scales map data values to the visual values of an aesthetic. To change a mapping, add a new scale.
■ ll
(n<- d + geom_bar(aes(fill = fl)))
aesthetic T prepackaged T scale-specific to adjust 1  scale to use I arguments
n + scale_fill_manual(
values - cfskyblue", "royalblue", "blue", "navy"), limits - cf"d", "e", "p", "r"), breaks -cf"d", "e", "p",' name = "fuel", labels = c("D", "E", "P", "R"))
r"),
titletousein labelstouse T breaksto use in legend/axis 1 in legend/axis ^ legend/axis
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&YLOCATION SCALES
Use with x or y aesthetics (x shown here)
scale_x_logl0() - Plot x on loglO 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))
ll ll
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 = "reoP')
COLOR AND FILL SCALES (CONTINUOUS)
II ll Ml I o <- c + geom dotplot(aes(fill = ..x..)) ,||||   o + scale_fill_distiller(palette = "Blues")
I •   o + scale_fill_gradient(low-red', high-'yellow")
•ilsi •iL
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.colorsO, 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))
0 1 2 3 4 S 6 7 8 9 1011 1213 14 151617 18 19 20 21 22 23 24 25
□ oA+xOVH*^®^fflBIESDOAoo o onOAv p + scale_radius(range = c(l,6)) p + scale_size_area(max_size = 6)
Coordinate Systems Faceting
r<- d + geom_bar()
r + coord_cartesian(xlim = c(0,5))
.■I
..ll
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", directional)
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.
it + coord quickmapO
n + coord_map(proiection = "ortho", orientation=c{41, -74, 0J)projection, onenztation, 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.
ill
III
s <- ggplotfmpg, aes(fl, fill = drv))
s + geom barfposition = "dodge")
Arrange elements side by side
s + geom_bar(position = "fill")
Stack elements on top of one another, normalize height
iVt"" e + geom_point(position = "jitter") .»•7*   Addrandom noise to X and Y position of each element to avoid overplotting
f n     e + geom label(position = "nudge")
|| \\lfj11 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))
Themes
m
r+ theme bw()
white background h grid lines
wit
I~| r + theme_gray() Grey background (default theme)
r + theme_dark()
dark for contrast
u
r + theme classic)) r + theme_light()
Ir + theme linedrawO r + theme minimal)) Minimal themes
...I
r + theme_void()
Empty theme
Facets divide a plot into subplots based on the values of one or more discrete variables.
t <- ggplotfmpg, aesfcty, hwy)) + geom point()
Hill t + facet_grid(.~fl)
facet into columns based on fl
t + facet_grid(year - .)
facet into rowsDased on year
t + facet_grid(year - fl)
facet into Both rows and columns
H5™ 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 la be Her to adjust facet labels
t + facet_grid(. - fl, labeller = label_both)
fhc fl: d fl: e fl: p fl: r
t + facet_grid(fl-., labeller = label_bquote(alpha A (fl))) qc ad ae op ar
t + facet_grid(. - fl, labeller = label parsed)
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(ge,om = "text", x = 8, y = 9, label = "A")
geom to place I manual values for geom's aesthetics
Use scale functions to update legend labels
Legends
n + theme(legend.position = "bottom")
Place legend at "bottom", "top", "left", or "right"
n + guidesffill = "none")
Set fegend type for each aesthetic: colorbar, legend, or none (no legend)
n + scale fill discretelname = "Title",
labels = cT/'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))
RStudio® is a trademark of RStudio, Inc. • CC BY RStudio • info@rstudio.com • 844-448-1212 • rstudio.com • Learn more at http://ggplot2.tidyverse.org> ggplot2 2.1.0 • Updated: 2016-11