ASSESSMENT 3

– Annotation with ggplot2

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– exercises

– providing a Visual representation and detailed explanation , screenshot the solution

– recommended to use RStudio for all

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Assignment 3

Annotation with ggplot2

1 – What’s gone wrong with this code? Why are the points not blue?

ggplot(data = mpg) +

geom_point(mapping = aes(x = displ, y = hwy, color = “blue”))

2 – Which variables in mpg are categorical? Which variables are continuous? (Hint: type ?mpg to read the documentation for the dataset). How can you see this information when you run mpg?

We can type ?mpg and deduce which variables are categorical and which are continuous based on the descriptions. Or we can use str() to get the types of the variables.

3 – Map a continuous variable to color, size, and shape. How do these aesthetics behave differently for categorical vs. continuous variables?

When mapping a continuous variable, displ, to color, ggplot creats a gradient color scale to represent the values of the continous variable. By default, ggplot creates a color gradient scale from light blue to dark blue, where light blue reresents lower values and dark blue represents higher values.

Similiarly, when mapping a continuous variable to shape, ggplot displays larger values with circles with larger area.

ggplot(data = mpg) +

geom_point(mapping = aes(x = displ, y = hwy, size = displ))

4 – What happens if you map the same variable to multiple aesthetics?

We can map the same variable to multiple aesthetics, as long as the the aesethetics are compatiable with the type of the variables (categorical/continuous). For example, we can map drv, which is a categorical variable, to both color and shape.

5 – What does the stroke aesthetic do? What shapes does it work with? (Hint: use ?geom_point)

stroke only works with shapes 21 – 24, which also have a fill argument, which controls the color of the fill. size argument controls the size of the fill part, stroke controls the size of the stroke, and color contools the color of the stroke. For example:

6 – What happens if you map an aesthetic to something other than a variable name, like aes(colour = displ < 5)?

ggplot(data = mpg) +

geom_point(mapping = aes(x = displ, y = hwy, color = displ < 5))

R For Data Science Cheat Sheet
Tidyverse for Beginners

Learn More R for Data Science Interactively at www.datacamp.com

Tidyverse

DataCamp
Learn R for Data Science Interactively

The tidyverse is a powerful collection of R packages that are actually
data tools for transforming and visualizing data. All packages of the
tidyverse share an underlying philosophy and common APIs.

The core packages are:

• ggplot2, which implements the grammar of graphics. You can use it
to visualize your data.

• dplyr is a grammar of data manipulation. You can use it to solve the
most common data manipulation challenges.

• tidyr helps you to create tidy data or data where each variable is in a
column, each observation is a row end each value is a cell.

• readr is a fast and friendly way to read rectangular data.

• purrr enhances R’s functional programming (FP) toolkit by providing a
complete and consistent set of tools for working with functions and
vectors.

• tibble is a modern re-imaginging of the data frame.

• stringr provides a cohesive set of functions designed to make
working with strings as easy as posssible

• forcats provide a suite of useful tools that solve common problems
with factors.

You can install the complete tidyverse with:

Then, load the core tidyverse and make it available in your current R
session by running:

Note: there are many other tidyverse packages with more specialised usage. They are not
loaded automatically with library(tidyverse), so you’ll need to load each one with its own call
to library().

ggplot2

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

> by_species <- iris %>%
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 • info@rstudio.com • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org • ggplot2 0.9.3.1 • Updated: 3/1

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 • info@rstudio.com • 844-448-1212 • rstudio.com Learn more at docs.ggplot2.org • ggplot2 0.9.3.1 • Updated: 3/

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

0 1 2 3

4
1
2
3

0
0 1 2 3 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 • 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 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 “, width = 5, height = 5)
Saves last plot as 5’ x 5’ file named “plot ” 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

**
.

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

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)

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”)