Data Visualization using GGPlot2

Key R functions

• Key function: geom_histgram() (for density plots).
• Key arguments to customize the plots:
  • color, size, linetype: change the line color, size and type, respectively
  • fill: change the areas fill color (for bar plots, histograms and density plots)
  • alpha: create a semi-transparent color.

Data preparation

Create some data (wdata) containing the weights by sex (M for male; F for female):

set.seed(1234)
wdata = data.frame(
        sex = factor(rep(c("F", "M"), each=200)),
        weight = c(rnorm(200, 55), rnorm(200, 58))
        )

head(wdata, 4)

##   sex weight
## 1   F   53.8
## 2   F   55.3
## 3   F   56.1
## 4   F   52.7

Compute the mean weight by sex using the dplyr package. First, the data is grouped by sex and then summarized by computing the mean weight by groups. The operator %>% is used to combine multiple operations:

library("dplyr")
mu <- wdata %>% 
  group_by(sex) %>%
  summarise(grp.mean = mean(weight))
mu

## # A tibble: 2 x 2
##   sex   grp.mean
##   <fct>    <dbl>
## 1 F         54.9
## 2 M         58.1

Loading required R package

Load the ggplot2 package and set the default theme to theme_classic() with the legend at the top of the plot:

library(ggplot2)
theme_set(
  theme_classic() +
    theme(legend.position = "top")
  )

Basic histogram plots

We start by creating a plot, named a, that we’ll finish in the next section by adding a layer using the function geom_histogram().

a <- ggplot(wdata, aes(x = weight))

The following R code creates some basic density plots with a vertical line corresponding to the mean value of the weight variable (geom_vline()):

# Basic density plots
a + geom_histogram(bins = 30, color = "black", fill = "gray") +
  geom_vline(aes(xintercept = mean(weight)), 
             linetype = "dashed", size = 0.6)

Change color by groups

The following R code will change the histogram plot line and fill color by groups. The functions scale_color_manual() and scale_fill_manual() are used to specify custom colors for each group.

We’ll proceed as follow:

• Change areas fill and add line color by groups (sex)
• Add vertical mean lines using geom_vline(). Data: mu, which contains the mean values of weights by sex (computed in the previous section).
• Change color manually:
  • use scale_color_manual() or scale_colour_manual() for changing line color
  • use scale_fill_manual() for changing area fill colors.
• Adjust the position of histogram bars by using the argument position. Allowed values: “identity”, “stack”, “dodge”. Default value is “stack”.

# Change line color by sex
a + geom_histogram(aes(color = sex), fill = "white",
                   position = "identity") +
  scale_color_manual(values = c("#00AFBB", "#E7B800")) 

# change fill and outline color manually 
a + geom_histogram(aes(color = sex, fill = sex),
                         alpha = 0.4, position = "identity") +
  scale_fill_manual(values = c("#00AFBB", "#E7B800")) +
  scale_color_manual(values = c("#00AFBB", "#E7B800"))

Combine histogram and density plots

• Plot histogram with density values on y-axis (instead of count values).
• Add density plot with transparent density plot

# Histogram with density plot
a + geom_histogram(aes(y = stat(density)), 
                   colour="black", fill="white") +
  geom_density(alpha = 0.2, fill = "#FF6666") 
     

# Color by groups
a + geom_histogram(aes(y = stat(density), color = sex), 
                   fill = "white",position = "identity")+
  geom_density(aes(color = sex), size = 1) +
  scale_color_manual(values = c("#868686FF", "#EFC000FF"))

Conclusion

This article describes how to create histogram plots using the ggplot2 package.