A grammar for communicating data visualization:
These are strung together like words in a sentence.
ggplot2
The R package ggplot2
implements a grammar of graphics along these lines. First, let’s load ggplot2
:
> library(ggplot2)
Now let’s set a theme (more on this later):
> theme_set(theme_bw())
ggplot()
aes()
geom_*()
facet_*()
scale_*()
theme()
labs()
The *
is a placeholder for a variety of terms that we will consider.
Perhaps the most important aspect of ggplot2
is to understand the “geoms”. We will cover the following:
geom_bar()
geom_boxplot()
geom_violin()
geom_histogram()
geom_density()
geom_line()
geom_point()
geom_smooth()
geom_hex()
The most basic ggplot2
plot is made with something like:
ggplot(data = <DATA FRAME>) +
geom_*(mapping = aes(x = <VAR X>, y = <VAR Y>))
where <DATA FRAME>
is a data frame and <VAR X>
and <VAR Y>
are variables (i.e., columns) from this data frame. Recall geom_*
is a placeholder for a geometry such as geom_boxplot
.
There’s a complex “layers” construct occurring in the ggplot2
package. However, for our purposes, it suffices to note that the different parts of the plots are layered together through the +
operator:
> ggplot(data = mpg) +
+ geom_point(mapping = aes(x = displ, y = hwy, color=drv)) +
+ geom_smooth(mapping = aes(x = displ, y = hwy, color=drv)) +
+ scale_color_brewer(palette = "Set1", name = "Drivetrain") +
+ labs(title = "Highway MPG By Drivetrain and Displacement",
+ x = "Displacement", y = "Highway MPG")
aes()
Call
In the previous slide, we saw that the same aes()
call was made for two geom
’s. When this is the case, we may more simply call aes()
from within ggplot()
:
> ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color=drv)) +
+ geom_point() +
+ geom_smooth() +
+ scale_color_brewer(palette = "Set1", name = "Drivetrain") +
+ labs(title = "Highway MPG By Drivetrain and Displacement",
+ x = "Displacement", y = "Highway MPG")
There may be cases where different geom
’s are layered and require different aes()
calls. This is something to keep in mind as we go through the specifics of the ggplot2
package.
Wickham, H. (2010) A Layered Grammar of Graphics. Journal of Computational and Graphical Statistics, 19 (1): 3–28.
This paper designs an implementation of The Grammar of Graphics by Leland Wilkinson (published in 2005).
help(package="ggplot2")
mpg
Load the mpg
data set:
> library("dplyr") # why load dplyr?
> data("mpg", package="ggplot2")
> head(mpg)
Source: local data frame [6 x 11]
manufacturer model displ year cyl trans drv cty
(chr) (chr) (dbl) (int) (int) (chr) (chr) (int)
1 audi a4 1.8 1999 4 auto(l5) f 18
2 audi a4 1.8 1999 4 manual(m5) f 21
3 audi a4 2.0 2008 4 manual(m6) f 20
4 audi a4 2.0 2008 4 auto(av) f 21
5 audi a4 2.8 1999 6 auto(l5) f 16
6 audi a4 2.8 1999 6 manual(m5) f 18
Variables not shown: hwy (int), fl (chr), class (chr)
diamonds
Load the diamonds
data set:
> data("diamonds", package="ggplot2")
> head(diamonds)
Source: local data frame [6 x 10]
carat cut color clarity depth table price x y
(dbl) (fctr) (fctr) (fctr) (dbl) (dbl) (int) (dbl) (dbl)
1 0.23 Ideal E SI2 61.5 55 326 3.95 3.98
2 0.21 Premium E SI1 59.8 61 326 3.89 3.84
3 0.23 Good E VS1 56.9 65 327 4.05 4.07
4 0.29 Premium I VS2 62.4 58 334 4.20 4.23
5 0.31 Good J SI2 63.3 58 335 4.34 4.35
6 0.24 Very Good J VVS2 62.8 57 336 3.94 3.96
Variables not shown: z (dbl)
The geom_bar()
layer forms a barplot and only requires an x
assignment in the aes()
call:
> ggplot(data = diamonds) +
+ geom_bar(mapping = aes(x = cut))
Color in the bars by assigning fill
in geom_bar()
, but outside of aes()
:
> ggplot(data = diamonds) +
+ geom_bar(mapping = aes(x = cut), fill = "tomato")
Color within the bars according to a variable by assigning fill
in geom_bar()
inside of aes()
:
> ggplot(data = diamonds) +
+ geom_bar(mapping = aes(x = cut, fill = cut))
When we use fill = clarity
within aes()
, we see that it shows the proportion of each clarity
value within each cut
value:
> ggplot(data = diamonds) +
+ geom_bar(mapping = aes(x = cut, fill = clarity))
By setting position = "dodge"
outside of aes()
, it shows bar charts for the clarity
values within each cut
value:
> ggplot(data = diamonds) +
+ geom_bar(mapping= aes(x = cut, fill = clarity), position = "dodge")
By setting position = "fill"
, it shows the proportion of clarity
values within each cut
value and no longer shows the cut
values:
> ggplot(data = diamonds) +
+ geom_bar(mapping=aes(x = cut, fill = clarity), position = "fill") +
+ labs(x = "cut", y = "relative proporition within cut")
The geom_boxplot()
layer forms a boxplot and requires both x
and y
assignments in the aes()
call, even when plotting a single boxplot:
> ggplot(data = mpg) +
+ geom_boxplot(mapping = aes(x = 1, y = hwy))
Color in the boxes by assigning fill
in geom_boxplot()
, but outside of aes()
:
> ggplot(data = mpg) +
+ geom_boxplot(mapping = aes(x = 1, y = hwy), fill="lightblue") +
+ labs(x=NULL)
Show a boxplot for the y
values occurring within each x
factor level by making these assignments in aes()
:
> ggplot(data = mpg) +
+ geom_boxplot(mapping = aes(x = factor(cyl), y = hwy))
By assigning the fill
argument within aes()
, we can color each boxplot according to the x-axis factor variable:
> ggplot(data = mpg) +
+ geom_boxplot(mapping = aes(x = factor(cyl), y = hwy,
+ fill = factor(cyl)))
The geom_jitter()
function plots the data points and randomly jitters them so we can better see all of the points:
> ggplot(data = mpg, mapping = aes(x=factor(cyl), y=hwy)) +
+ geom_boxplot(fill = "lightblue") +
+ geom_jitter(width = 0.2)
A violin plot, called via geom_violin()
, is similar to a boxplot, except shows a density plot turned on its side and reflected across its vertical axis:
> ggplot(data = mpg) +
+ geom_violin(mapping = aes(x = drv, y = hwy))
Add a geom_jitter()
to see how the original data points relate to the violin plots:
> ggplot(data = mpg, mapping = aes(x = drv, y = hwy)) +
+ geom_violin(adjust=1.2) +
+ geom_jitter(width=0.2, alpha=0.5)
Boxplots made from the diamonds
data:
> ggplot(diamonds) +
+ geom_boxplot(mapping = aes(x=color, y=price))
The analogous violin plots made from the diamonds
data:
> ggplot(diamonds) +
+ geom_violin(mapping = aes(x=color, y=price))
We can create a histogram using the geom_histogram()
layer, which requires an x
argument only in the aes()
call:
> ggplot(diamonds) +
+ geom_histogram(mapping = aes(x=price))
We can change the bin width directly in the histogram, which is an intuitive parameter to change based on visual inspection:
> ggplot(diamonds) +
+ geom_histogram(mapping = aes(x=price), binwidth = 1000)
Instead of counts on the y-axis, we may instead want the area of the bars to sum to 1, like a probability density:
> ggplot(diamonds) +
+ geom_histogram(mapping = aes(x=price, y=..density..), binwidth=1000)
When we use fill = cut
within aes()
, we see that it shows the counts of each cut
value within each price
bin:
> ggplot(diamonds) +
+ geom_histogram(mapping = aes(x=price, fill = cut), binwidth = 1000)
Display a density plot using the geom_density()
layer:
> ggplot(diamonds) +
+ geom_density(mapping = aes(x=price))
Employ the arguments color="blue"
and fill="lightblue"
outside of the aes()
call to include some colors:
> ggplot(diamonds) +
+ geom_density(mapping = aes(x=price), color="blue", fill="lightblue")
By utilizing color=clarity
we plot a density of price
stratified by each clarity
value:
> ggplot(diamonds) +
+ geom_density(mapping = aes(x=price, color=clarity))
Overlay a density plot and a histogram together:
> ggplot(diamonds, mapping = aes(x=price)) +
+ geom_histogram(aes(y=..density..), color="black", fill="white") +
+ geom_density(fill="lightblue", alpha=.5)
babynames
Revisited
Let’s first create a data frame that captures the number of times “John” is registered in males per year:
> library("babynames")
> john <- babynames %>% filter(sex=="M", name=="John")
> head(john)
Source: local data frame [6 x 5]
year sex name n prop
(dbl) (chr) (chr) (int) (dbl)
1 1880 M John 9655 0.08154561
2 1881 M John 8769 0.08098075
3 1882 M John 9557 0.07831552
4 1883 M John 8894 0.07907113
5 1884 M John 9388 0.07648564
6 1885 M John 8756 0.07551661
We can geom_lines()
to plot a line showing the popularity of “John” over time:
> ggplot(data = john) +
+ geom_line(mapping = aes(x=year, y=prop), size=1.5)
Now let’s look at a name that occurs nontrivially in males and females:
> kelly <- babynames %>% filter(name=="Kelly")
> ggplot(data = kelly) +
+ geom_line(mapping = aes(x=year, y=prop, color=sex), size=1.5)
The layer geom_point()
produces a scatterplot, and the aes()
call requires x
and y
assignment:
> ggplot(data = mpg) +
+ geom_point(mapping = aes(x = displ, y = hwy))
Give the points a color:
> ggplot(data = mpg) +
+ geom_point(mapping = aes(x = displ, y = hwy), color = "blue")
Color the points according to a factor variable by including color = class
within the aes()
call:
> ggplot(data = mpg) +
+ geom_point(mapping = aes(x = displ, y = hwy, color = class))
Increase the size of points with size=2
outside of the aes()
call:
> ggplot(data = mpg) +
+ geom_point(mapping = aes(x = displ, y = hwy, color = class), size=2)
Vary the size of the points according to the class
factor variable:
> ggplot(data = mpg) +
+ geom_point(mapping = aes(x = displ, y = hwy, size = class))
Vary the transparency of the points according to the class
factor variable by setting alpha=class
within the aes()
call:
> ggplot(data = mpg) +
+ geom_point(mapping = aes(x = displ, y = hwy, alpha = class))
Vary the shape of the points according to the class
factor variable by setting alpha=class
within the aes()
call (maximum 6 possible shapes – oops!):
> ggplot(data = mpg) +
+ geom_point(mapping = aes(x = displ, y = hwy, shape = class))
Color the points according to the cut
variable by setting color=cut
within the aes()
call:
> ggplot(data = diamonds) +
+ geom_point(mapping = aes(x=carat, y=price, color=cut), alpha=0.7)
Color the points according to the clarity
variable by setting color=clarity
within the aes()
call:
> ggplot(data = diamonds) +
+ geom_point(mapping=aes(x=carat, y=price, color=clarity), alpha=0.3)
Override the alpha=0.3
in the legend:
> ggplot(data = diamonds) +
+ geom_point(mapping=aes(x=carat, y=price, color=clarity), alpha=0.3) +
+ guides(color = guide_legend(override.aes = list(alpha = 1)))
The price
variable seems to be significantly right-skewed:
> ggplot(diamonds) +
+ geom_boxplot(aes(x=color, y=price))
We can try to reduce this skewness by rescaling the variables. We first try to take the log(base=10)
of the price
variable via scale_y_log10()
:
> ggplot(diamonds) +
+ geom_boxplot(aes(x=color, y=price)) +
+ scale_y_log10()
Let’s repeat this on the analogous violing plots:
> ggplot(diamonds) +
+ geom_violin(aes(x=color, y=price)) +
+ scale_y_log10()
The relationship between carat
and price
is very nonlinear. Let’s explore different transformations to see if we can find an approximately linear relationship.
> ggplot(data = diamonds) +
+ geom_point(mapping=aes(x=carat, y=price, color=clarity), alpha=0.3)
First try to take the squareroot of the the price
variable:
> ggplot(data = diamonds) +
+ geom_point(aes(x=carat, y=price, color=clarity), alpha=0.3) +
+ scale_y_sqrt()
Now let’s try to take log(base=10)
on both the carat
and price
variables:
> ggplot(data = diamonds) +
+ geom_point(aes(x=carat, y=price, color=clarity), alpha=0.3) +
+ scale_y_log10(breaks=c(1000,5000,10000)) +
+ scale_x_log10(breaks=1:5)
Forming a violin plot of price
stratified by clarity
and transforming the price
variable yields an interesting relationship in this data set:
> ggplot(diamonds) +
+ geom_violin(aes(x=clarity, y=price, fill=clarity), adjust=1.5) +
+ scale_y_log10()
Recall the scatterplot showing the relationship between highway mpg and displacement. How can we plot a smoothed relationship between these two variables?
> ggplot(data = mpg) +
+ geom_point(mapping = aes(x = displ, y = hwy))
Plot a smoother with geom_smooth()
using the default settings (other than removing the error bands):
> ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
+ geom_point() +
+ geom_smooth(se=FALSE)
The default smoother here is a “loess” smoother. Let’s compare that to the least squares regresson line:
> ggplot(data = mpg, mapping = aes(x = displ, y = hwy)) +
+ geom_point() +
+ geom_smooth(aes(colour = "loess"), method = "loess", se = FALSE) +
+ geom_smooth(aes(colour = "lm"), method = "lm", se = FALSE)
Now let’s plot a smoother to the points stratified by the drv
variable:
> ggplot(data=mpg, mapping = aes(x = displ, y = hwy, linetype = drv)) +
+ geom_point() +
+ geom_smooth(se=FALSE)
Instead of different line types, let’s instead differentiate them by line color:
> ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color=drv)) +
+ geom_point() +
+ geom_smooth(se=FALSE)
diamonds
data set has 53940 observations per variableHere is an example of an overplotted scatterplot:
> ggplot(data = diamonds, mapping = aes(x=carat, y=price)) +
+ geom_point()
Let’s reduce the alpha
of the points:
> ggplot(data = diamonds, mapping = aes(x=carat, y=price)) +
+ geom_point(alpha=0.1)
Let’s further reduce the alpha
:
> ggplot(data = diamonds, mapping = aes(x=carat, y=price)) +
+ geom_point(alpha=0.01)
We can bin the points into hexagons, and report how many points fall within each bin. We use the geom_hex()
layer to do this:
> ggplot(data = diamonds, mapping = aes(x=carat, y=price)) +
+ geom_hex()
Let’s try to improve the color scheme:
> ggplot(data = diamonds, mapping = aes(x=carat, y=price)) +
+ geom_hex() +
+ scale_fill_gradient2(low="lightblue", mid="purple", high="black",
+ midpoint=3000)
We can combine the scale transformation used earlier with the “hexbin” plotting method:
> ggplot(data = diamonds, mapping = aes(x=carat, y=price)) +
+ geom_hex(bins=20) +
+ scale_x_log10(breaks=1:5) + scale_y_log10(breaks=c(1000,5000,10000))
Here’s how you can change the axis labels and give the plot a title:
> ggplot(data = mpg) +
+ geom_boxplot(mapping = aes(x = factor(cyl), y = hwy)) +
+ labs(title="Highway MPG by Cylinders",x="Cylinders",y="Highway MPG")
You can remove the legend to a plot by the following:
> ggplot(data = diamonds) +
+ geom_bar(mapping = aes(x = cut, fill = cut)) +
+ theme(legend.position="none")
The legend can be placed on the “top”, “bottom”, “left”, or “right”:
> ggplot(data = diamonds) +
+ geom_bar(mapping = aes(x = cut, fill = cut)) +
+ theme(legend.position="bottom")
The legend can be moved inside the plot itself:
> ggplot(data = diamonds) +
+ geom_bar(mapping = aes(x = cut, fill = cut)) +
+ theme(legend.position=c(0.15,0.75))
Change the name of the legend:
> ggplot(data = diamonds) +
+ geom_bar(mapping = aes(x = cut, fill = cut)) +
+ scale_fill_discrete(name="Diamond\nCut")
Change the labels within the legend:
> ggplot(data = diamonds) +
+ geom_bar(mapping = aes(x = cut, fill = cut)) +
+ scale_fill_discrete(labels=c("F", "G", "VG", "P", "I"))
Here is the histogram of the displ
variable from the mpg
data set:
> ggplot(mpg) + geom_histogram(mapping=aes(x=displ), binwidth=0.25)
The facet_wrap()
layer allows us to stratify the displ
variable according to cyl
, and show the histograms for the strata in an organized fashion:
> ggplot(mpg) +
+ geom_histogram(mapping=aes(x=displ), binwidth=0.25) +
+ facet_wrap(~ cyl)
Here is facet_wrap()
applied to displ
startified by the drv
variable:
> ggplot(mpg) +
+ geom_histogram(mapping=aes(x=displ), binwidth=0.25) +
+ facet_wrap(~ drv)
We can stratify by two variable simultaneously by using the facet_grid()
layer:
> ggplot(mpg) +
+ geom_histogram(mapping=aes(x=displ), binwidth=0.25) +
+ facet_grid(drv ~ cyl)
Let’s carry out a similar faceting on the diamonds
data over the next four plots:
> ggplot(diamonds) +
+ geom_histogram(mapping=aes(x=price), binwidth=500)
Stratify price
by clarity
:
> ggplot(diamonds) +
+ geom_histogram(mapping=aes(x=price), binwidth=500) +
+ facet_wrap(~ clarity)
Stratify price
by clarity
, but allow each y-axis range to be different by including the scale="free_y"
argument:
> ggplot(diamonds) +
+ geom_histogram(mapping=aes(x=price), binwidth=500) +
+ facet_wrap(~ clarity, scale="free_y")
Jointly stratify price
by cut
and clarify
:
> ggplot(diamonds) +
+ geom_histogram(mapping=aes(x=price), binwidth=500) +
+ facet_grid(cut ~ clarity) +
+ scale_x_continuous(breaks=9000)
broman
package – use brocolors(set="crayons")
> cbPalette <- c("#999999", "#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2",
+ "#D55E00", "#CC79A7")
scale_fill_manual()
scale_color_manual()
scale_fill_gradient()
scale_color_gradient()
Manually determine colors to fill the barplot using the color blind palette defined above, cbPalette
:
> ggplot(data = diamonds) +
+ geom_bar(mapping = aes(x = cut, fill = cut)) +
+ scale_fill_manual(values=cbPalette)
Manually determine point colors using the color blind palette defined above, cbPalette
:
> ggplot(data = mpg) +
+ geom_point(mapping = aes(x = displ, y = hwy, color = class), size=2) +
+ scale_color_manual(values=cbPalette)
Fill the histogram bars using a color gradient by their counts, where we determine the endpoint colors:
> ggplot(data = mpg) +
+ geom_histogram(aes(x=hwy, fill=..count..)) +
+ scale_fill_gradient(low="blue", high="red")
Color the points based on a gradient formed from the quantitative variable, displ
, where we we determine the endpoint colors:
> ggplot(data = mpg) +
+ geom_point(aes(x=hwy, y=cty, color=displ), size=2) +
+ scale_color_gradient(low="blue", high="red")
An example of using the palette “Set1” from the RColorBrewer
package, included in ggplot2
:
> ggplot(diamonds) +
+ geom_density(mapping = aes(x=price, color=clarity)) +
+ scale_color_brewer(palette = "Set1")
Another example of using the palette “Set1” from the RColorBrewer
package, included in ggplot2
:
> ggplot(data = mpg) +
+ geom_point(mapping = aes(x = displ, y = hwy, color = class)) +
+ scale_color_brewer(palette = "Set1")
Pieces of the plots can be saved as variables, which is particular useful to explortatory data analysis. These all produce the same plot:
> ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color=drv)) +
+ geom_point() +
+ geom_smooth(se=FALSE)
> p <- ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color=drv)) +
+ geom_point()
> p + geom_smooth(se=FALSE)
> p <- ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color=drv))
> p + geom_point() + geom_smooth(se=FALSE)
Try it yourself!
Plots can be saved to many formats using the ggsave()
function. Here are some examples:
> p <- ggplot(data = mpg, mapping = aes(x = displ, y = hwy, color=drv)) +
+ geom_point() +
+ geom_smooth(se=FALSE)
> ggsave(filename="my_plot.pdf", plot=p) # saves PDF file
> ggsave(filename="my_plot.png", plot=p) # saves PNG file
Here are the arguments that ggsave()
takes:
> str(ggsave)
function (filename, plot = last_plot(), device = NULL,
path = NULL, scale = 1, width = NA, height = NA, units = c("in",
"cm", "mm"), dpi = 300, limitsize = TRUE, ...)
From http://r4ds.had.co.nz/visualize.html. See also ggthemes
package.
Globally:
> theme_set(theme_minimal())
Locally:
> ggplot(data = diamonds) +
+ geom_bar(mapping = aes(x = cut)) +
+ theme_minimal()
> sessionInfo()
R version 3.2.3 (2015-12-10)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.11.3 (El Capitan)
locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
attached base packages:
[1] stats graphics grDevices utils datasets methods
[7] base
other attached packages:
[1] babynames_0.1 dplyr_0.4.3 ggplot2_2.0.0
[4] knitr_1.12.3 devtools_1.10.0
loaded via a namespace (and not attached):
[1] Rcpp_0.12.3 magrittr_1.5 munsell_0.4.3
[4] lattice_0.20-33 colorspace_1.2-6 R6_2.1.2
[7] stringr_1.0.0 highr_0.5.1 plyr_1.8.3
[10] tools_3.2.3 revealjs_0.5.1 parallel_3.2.3
[13] grid_3.2.3 gtable_0.1.2 DBI_0.3.1
[16] htmltools_0.3 lazyeval_0.1.10 yaml_2.1.13
[19] digest_0.6.9 assertthat_0.1 RColorBrewer_1.1-2
[22] reshape2_1.4.1 formatR_1.2.1 codetools_0.2-14
[25] memoise_1.0.0 evaluate_0.8 rmarkdown_0.9.5
[28] labeling_0.3 stringi_1.0-1 scales_0.3.0
[31] hexbin_1.27.1