A histogram plot is an alternative to Density plot for visualizing the distribution of a continuous variable. This chart represents the distribution of a continuous variable by dividing into bins and counting the number of observations in each bin.
This article describes how to create Histogram plots using the ggplot2 R package.
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.
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
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 (
# Basic density plots a + geom_histogram(bins = 30, color = "black", fill = "gray") + geom_vline(aes(xintercept = mean(weight)), linetype = "dashed", size = 0.6)
Note that, by default:
- By default,
geom_histogram()uses 30 bins - this might not be good default. You can change the number of bins (e.g.: bins = 50) or the bin width (e.g.: binwidth = 0.5)
- The y axis corresponds to the count of weight values. If you want to change the plot in order to have the density on y axis, specify the argument
y = ..density..in
Change color by groups
The following R code will change the histogram plot line and fill color by groups. The functions
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
mu, which contains the mean values of weights by sex (computed in the previous section).
- Change color manually:
scale_colour_manual()for changing line color
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"))
This article describes how to create histogram plots using the ggplot2 package.
Recommended for you
This section contains best data science and self-development resources to help you on your path.
Coursera - Online Courses and Specialization
- Course: Machine Learning: Master the Fundamentals by Stanford
- Specialization: Data Science by Johns Hopkins University
- Specialization: Python for Everybody by University of Michigan
- Courses: Build Skills for a Top Job in any Industry by Coursera
- Specialization: Master Machine Learning Fundamentals by University of Washington
- Specialization: Statistics with R by Duke University
- Specialization: Software Development in R by Johns Hopkins University
- Specialization: Genomic Data Science by Johns Hopkins University
Popular Courses Launched in 2020
- Google IT Automation with Python by Google
- AI for Medicine by deeplearning.ai
- Epidemiology in Public Health Practice by Johns Hopkins University
- AWS Fundamentals by Amazon Web Services
- The Science of Well-Being by Yale University
- Google IT Support Professional by Google
- Python for Everybody by University of Michigan
- IBM Data Science Professional Certificate by IBM
- Business Foundations by University of Pennsylvania
- Introduction to Psychology by Yale University
- Excel Skills for Business by Macquarie University
- Psychological First Aid by Johns Hopkins University
- Graphic Design by Cal Arts
Amazing Selling Machine
- Free Training - How to Build a 7-Figure Amazon FBA Business You Can Run 100% From Home and Build Your Dream Life! by ASM
Books - Data Science
- Practical Guide to Cluster Analysis in R by A. Kassambara (Datanovia)
- Practical Guide To Principal Component Methods in R by A. Kassambara (Datanovia)
- Machine Learning Essentials: Practical Guide in R by A. Kassambara (Datanovia)
- R Graphics Essentials for Great Data Visualization by A. Kassambara (Datanovia)
- GGPlot2 Essentials for Great Data Visualization in R by A. Kassambara (Datanovia)
- Network Analysis and Visualization in R by A. Kassambara (Datanovia)
- Practical Statistics in R for Comparing Groups: Numerical Variables by A. Kassambara (Datanovia)
- Inter-Rater Reliability Essentials: Practical Guide in R by A. Kassambara (Datanovia)
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data by Hadley Wickham & Garrett Grolemund
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Géron
- Practical Statistics for Data Scientists: 50 Essential Concepts by Peter Bruce & Andrew Bruce
- Hands-On Programming with R: Write Your Own Functions And Simulations by Garrett Grolemund & Hadley Wickham
- An Introduction to Statistical Learning: with Applications in R by Gareth James et al.
- Deep Learning with R by François Chollet & J.J. Allaire
- Deep Learning with Python by François Chollet