Category: Visualizing Data

When producing so called infographics, it is rather common to use images rather than a mere grid as background. In this blog post, I will show how to use a background image with ggplot2.

Packages required

The following code will install load and / or install the R packages required for this blog post.

if (!require("pacman")) install.packages("pacman")
pacman::p_load(jpeg, png, ggplot2, grid, neuropsychology)

Choosing the data

The data set I will be using in this blog post is named diamonds and part of the ggplot2 package. It contains information about – surprise, surprise – diamonds, e.g. price and cut (Fair, Good, Very Good, Premium, Ideal). Using the tapply-function, we create a table returning the maximum prices per cut. Since we need the data to be organized in a data frame, we must transform the table using the data.frame-function.

mydata <- data.frame(price = tapply(diamonds$price, diamonds$cut, max))
mydata$cut <- rownames(mydata)

Importing the background image

The file format of the background image we will be using in this blog post is JPG. Since the image imitates a blackboard, we name it “blackboard.jpg”. The image file must be imported using the readJPEG-function of the jpeg package. The imported image will be saved into an object named image.

imgage <- jpeg::readJPEG("blackboard.jpg")

To import other image file formats, different packages and functions must be used. The next code snippet shows how to import PNG images.

image <- png::readPNG("blackboard.png")

Drawing the plot

In the next step, we actually draw a bar chart with a backgriund image. To make blackboard.jpg the background image, we need to combine the annotation_custom-function of the ggplot2 package and the rasterGrob-function of the grid package.

ggplot(mydata, aes(cut, price, fill = -price)) +
  ggtitle("Bar chart with background image") +
  scale_fill_continuous(guide = FALSE) +
  annotation_custom(rasterGrob(imgage, 
                               width = unit(1,"npc"), 
                               height = unit(1,"npc")), 
                               -Inf, Inf, -Inf, Inf) +
  geom_bar(stat="identity", position = "dodge", width = .75, colour = 'white') +
  scale_y_continuous('Price in $', limits = c(0, max(mydata$price) + max(mydata$price) / 4)) +
  scale_x_discrete('Cut') +
  geom_text(aes(label = round(price), ymax = 0), size = 7, fontface = 2, 
            colour = 'white', hjust = 0.5, vjust = -1) 

Adding opacity

Using the specification alpha = 0.5, we add 50% opacity to the bars. alpha ranges between 0 and 1, with higher values indicating greater opacity.

ggplot(mydata, aes(cut, price, fill = -price)) +
  theme_neuropsychology() +
  ggtitle("Bar chart with background image") +
  scale_fill_continuous(guide = FALSE) +
  annotation_custom(rasterGrob(imgage, 
                               width = unit(1,"npc"), 
                               height = unit(1,"npc")), 
                               -Inf, Inf, -Inf, Inf) +
  geom_bar(stat="identity", position = "dodge", width = .75, colour = 'white', alpha = 0.5) +
  scale_y_continuous('Price in $', limits = c(0, max(mydata$price) + max(mydata$price) / 4)) +
  scale_x_discrete('Cut') +
  geom_text(aes(label = round(price), ymax = 0), size = 7, fontface = 2, 
            colour = 'white', hjust = 0.5, vjust = -1) 

The recently published R package neuropsychology contains a theme named theme_neuropsychology(). This theme may be used to get bigger axis titles as well as bigger axis and legend text.

R makes it easy to combine different kinds of plots into one overall graph. This may be useful to visualize both basic measures of central tendency (median, quartiles etc.) and the distribution of a certain variable. Moreover, so called cut-off values can be added to the graph.

In this blog post, I show how to combine box and jitter plots using the ggplot2 package.

First of all, we need to install and load the R packages required for the following steps. Since we want to do the installation and loading using the pacman package, we need to check whether this package has been installed already. If not, it will be installed and loaded. If yes, it will just be loaded (line 1). Furthermore we need the R packages ggplot2 and Hmisc. This time, the p_load function checks whether these packages have been installed already and either installs and loads or just loads them (line 2).

if (!require("pacman")) install.packages("pacman")
pacman::p_load(ggplot2, Hmisc)

In a second step, we create three random variables (var.scale, var.group, var.cutoff) with n=300.

• var.scale is a numeric variable with a mean value of about 50 and a standard deviation of about 17.
• var.group is a factor variable comprising the groups male dnd female.
• var.cutoff was calculated based on var.scale using predefined cut-off values (0 – 40 == low, 41 –60 = medium, >60 == high).

var.scale <- round(rnorm(300, 50, 17))
var.group <- rbinom(300, 1, .5)
var.group <- factor(var.group, 
                     levels = c(0:1), 
                     labels = c("male", "female"))

var.cutoff <- ifelse(var.scale <= 40, 1, 
                     ifelse(var.scale > 40 & var.scale <= 60, 2, 3))

var.cutoff <- factor(var.cutoff, 
                     levels = c(3:1), 
                     labels = c("high", "medium", "low"))

The describe() function of the Hmisc package returns some basic measures of central tendency.

Hmisc::describe(var.scale)

## var.scale 
##       n missing  unique    Info    Mean     .05     .10     .25     .50 
##     300       0      71       1   51.25   24.00   30.90   41.00   50.00 
##     .75     .90     .95 
##   63.25   70.00   76.00 
## 
## lowest :   8  10  14  16  17, highest:  85  97 100 102 104

Hmisc::describe(var.group)

## var.group 
##       n missing  unique 
##     300       0       2 
## 
## male (141, 47%), female (159, 53%)

Hmisc::describe(var.cutoff)

## var.cutoff 
##       n missing  unique 
##     300       0       3 
## 
## high (87, 29%), medium (141, 47%), low (72, 24%)

Since the ggplot2 package requires the variables to be in a data frame, we have to create a new data frame df comprising our predefined variables using the data.frame() function.

df <- data.frame(var.scale, var.cutoff, var.group)

Using the functions xlab(), ylab() and ggtitle(), axis labels and plot title will be defined.

Box plots will be created using the geom_boxplot() function, with width specifying the boxes' width :-).

Jitter plots will be created using the geom_jitter() function. In addition, specifications have been made for colour and position and size of the dots.

ggplot(df) +
  xlab("Group") +
  ylab("Scale") +
  ggtitle("Combination of Box and Jitter Plot") + 
  geom_boxplot(aes(var.group, var.scale), 
               width=0.5) + 
  geom_jitter(aes(var.group, var.scale, colour = var.cutoff), 
              position = position_jitter(width = .15, height=-0.7),
              size=2) +
  scale_y_continuous(limits=c(0, 101), 
                     breaks = seq(0, 110, 10)) +
  scale_color_manual(name="Legend", 
                     values=c("red", "blue3", "green3")) 

Finally, we are going to format both Y-axis and legend using the functions scale_y_continuous() and scale_color_manual().

Manchmal kann es sinnvoll sein, die graphische Darstellung einer statistischen Größe auf ein Hintergrundbild zu applizieren. Mit R lässt sich das folgendermaßen bewerkstelligen:

Zuerst liest man mit der readJPEG()-Funktion aus dem jpeg-Paket das ausgewählte Hintergrundbild ein (im Beispiel heißt es blackboard.jpg).

library(jpeg)
imgage <- readJPEG("blackboard.jpg")

Danach lädt man die Pakete ggplot2 und grid, wobei mit dem ersten Paket die Grafik erzeugt und mit dem zweiten Paket das Hintergrundbild – hier eine Tafel – eingebunden wird.

library(ggplot2)
library(grid)

ggplot(diamonds) +
  annotation_custom(rasterGrob(imgage, 
                               width=unit(1,"npc"), 
                               height=unit(1,"npc")), 
                    -Inf, Inf, -Inf, Inf) +
  geom_bar(aes(clarity), fill="white", colour="red") +
  xlab("") +
  ylab("") +
  ggtitle("Säulendiagramm mit Hintergrundbild") +
  scale_fill_continuous(guide = FALSE)

PS: Der Dataframe diamonds sowie die darin enthaltene Variable clarity werden mit dem Paket ggplot2 geladen.