A histogram is a graphical representation that organizes a group of data points into user-specified ranges and an approximate representation of the distribution of numerical data.
In R language the histogram is built with the use of hist() function.
Syntax: hist(v,main,xlab,xlim,ylim,breaks,col,border)
Parameters:
- v:- It is a vector containing numeric values used in the histogram.
- main:-It indicates the title of the chart.
- col:- It is used to set the color of the bars.
- border:-It is used to set the border color of each bar.
- xlab:-It is used to give a description of the x-axis.
- xlim:-It is used to specify the range of values on the x-axis.
- ylim:-It is used to specify the range of values on the y-axis.
- breaks:-It is used to mention the width of each bar.
Return: It will return the histogram.
Example:-
R
v <- c(5,9,13,2,50,20,59,36,23,2,8,27,72,14) hist(v,xlab = "Weight",col = "red",border = "black") |
Output:
A density plot is a representation of the distribution of a numeric variable that uses a kernel density estimate to show the probability density function of the variable. In R Language we use the density() function which helps to compute kernel density estimates. And further with its return value, is used to build the final density plot.
Syntax: density(x)
Parameters:
- x: the data from which the estimate is to be computed
Returns:
It will return the kernel density.
Example:
Used dataset link:-Link
R
library(readxl)library(ggplot2) Salary_Data <- read_excel("Salary_Data.xls") den <- density(Salary_Data$YearsExperience) plot(den, frame = FALSE, col = "blue",main = "Density plot") |
Output:
We can also create a histogram and a density plot in the same frame.
Example:
R
hist(beaver1$temp, col="green", border="black", prob = TRUE, xlab = "temp", main = "GFG") lines(density(beaver1$temp), lwd = 2, col = "chocolate3") |
Output:
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