# 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.

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))
``## [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).