Histogram and Density Plots - R Base Graphs


Previously, we described the essentials of R programming and provided quick start guides for importing data into R.


Here, we’ll describe how to create histogram and density plots in R.


Pleleminary tasks

  1. Launch RStudio as described here: Running RStudio and setting up your working directory

  2. Prepare your data as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files

  3. Import your data into R as described here: Fast reading of data from txt|csv files into R: readr package.

Create some data

The data set contains the value of weight by sex for 200 individuals.

set.seed(1234)
x <- c(rnorm(200, mean=55, sd=5),
     rnorm(200, mean=65, sd=5))
head(x)
## [1] 48.96467 56.38715 60.42221 43.27151 57.14562 57.53028

Create histogram plots: hist()

  • A histogram can be created using the function hist(), which simplified format is as follow:
hist(x, breaks = "Sturges")

  • x: a numeric vector
  • breaks: breakpoints between histogram cells.


  • Create histograms
hist(x, col = "steelblue", frame = FALSE)

# Change the number of breaks
hist(x, col = "steelblue", frame = FALSE,
     breaks = 30)

Create density plots: density()

The function density() is used to estimate kernel density.

# Compute the density data
dens <- density(mtcars$mpg)
# plot density
plot(dens, frame = FALSE, col = "steelblue", 
     main = "Density plot of mpg") 

# Fill the density plot using polygon()
plot(dens, frame = FALSE, col = "steelblue", 
     main = "Density plot of mpg") 
polygon(dens, col = "steelblue")

Infos

This analysis has been performed using R statistical software (ver. 3.2.4).


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