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.

Contents:

#### Related Book

GGPlot2 Essentials for Great Data Visualization in R## 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)
```

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`aes()`

.

## 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.

- use
- 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.

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