ggplot2 is an R Package that is dedicated to Data visualization. ggplot2 Package Improve the quality and the beauty (aesthetics ) of the graph. By Using ggplot2 we can make almost every kind of graph In RStudio

A histogram is an approximate representation of the distribution of numerical data. In a histogram, each bar groups numbers into ranges. Taller bars show that more data falls in that range. A histogram displays the shape and spread of continuous sample data.

Histograms roughly give us an idea about the probability distribution of a given variable by depicting the frequencies of observations occurring in certain ranges of values. Basically, Histograms are used to show distributions of a given variable while bar charts are used to compare variables. Histograms plot quantitative data with ranges of the data grouped into the intervals while bar charts plot categorical data.

**geom_histogram()** function is an in-built function of ggplot2 module.

**Approach**

- Import module
- Create dataframe
- Create histogram using function
- Display plot

**Example 1:**

## R

`set.seed` `(123)` ` ` `# In the above line,123 is set as the ` `# random number value` `# The main point of using the seed is to` `# be able to reproduce a particular sequence ` `# of 'random' numbers. and sed(n) reproduces` `# random numbers results by seed` `df <- ` `data.frame` `(` ` ` `gender=` `factor` `(` `rep` `(` `c` `(` ` ` `"Average Female income "` `, ` `"Average Male incmome"` `), each=20000)),` ` ` `Average_income=` `round` `(` `c` `(` `rnorm` `(20000, mean=15500, sd=500), ` ` ` `rnorm` `(20000, mean=17500, sd=600))) ` `) ` `head` `(df)` ` ` `# if already installed ggplot2 then use library(ggplot2)` `library` `(ggplot2)` ` ` `# Basic histogram` `ggplot` `(df, ` `aes` `(x=Average_income)) + ` `geom_histogram` `()` ` ` `# Change the width of bins` `ggplot` `(df, ` `aes` `(x=Average_income)) + ` ` ` ` ` `geom_histogram` `(binwidth=1)` ` ` `# Change colors` `p<-` `ggplot` `(df, ` `aes` `(x=Average_income)) + ` ` ` ` ` `geom_histogram` `(color=` `"white"` `, fill=` `"red"` `)` `p` |

**Output : **

**Example 2:**

## R

`plot_hist <- ` `ggplot` `(airquality, ` `aes` `(x = Ozone)) +` ` ` ` ` `# binwidth help to change the thickness (Width) of the bar ` ` ` `geom_histogram` `(` `aes` `(fill = ..count..), binwidth = 10)+` ` ` ` ` `# name = "Mean ozone(03) in ppm parts per million "` ` ` `# name is used to give name to axis ` ` ` `scale_x_continuous` `(name = ` `"Mean ozone(03) in ppm parts per million "` `,` ` ` `breaks = ` `seq` `(0, 200, 25),` ` ` `limits=` `c` `(0, 200)) +` ` ` `scale_y_continuous` `(name = ` `"Count"` `) +` ` ` ` ` `# ggtitle is used to give name to a chart` ` ` `ggtitle` `(` `"Frequency of mean ozone(03)"` `) +` ` ` `scale_fill_gradient` `(` `"Count"` `, low = ` `"green"` `, high = ` `"red"` `)` ` ` `plot_hist` |

**Output : **

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