Data Visualization with ggplot2 : : CHEAT SHEET
ggplot2is based on thegrammar of graphics, the idea
that you can build every graph from the same
components: adataset, acoordinate system,
andgeoms—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=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
+ =
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 = <DATA> ) +
<GEOM_FUNCTION> (mapping = aes( <MAPPINGS> ),
stat = <STAT> , position = <POSITION> ) +
<COORDINATE_FUNCTION> +
<FACET_FUNCTION> +
<SCALE_FUNCTION> +
<THEME_FUNCTION>
