# 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

**Launch RStudio**as described here: Running RStudio and setting up your working directory**Prepare your data**as described here: Best practices for preparing your data and save it in an external .txt tab or .csv files**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")
```

# See also

# Infos

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

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