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 seeddf <- 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 histogramggplot(df, aes(x=Average_income)) + geom_histogram() # Change the width of binsggplot(df, aes(x=Average_income)) + geom_histogram(binwidth=1) # Change colorsp<-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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