CNC | C | C++ | Assembly | Python | R | Rust | Arduino | Solidworks | Embedded Systems

# R - Boxplots

Boxplots are a measure of how well distributed is the data in a data set. It divides the data set into three quartiles. This graph represents the minimum, maximum, median, first quartile and third quartile in the data set. It is also useful in comparing the distribution of data across data sets by drawing boxplots for each of them.
Boxplots are created in R by using the boxplot() function.

## Syntax

The basic syntax to create a boxplot in R is −
```boxplot(x, data, notch, varwidth, names, main)
```
Following is the description of the parameters used −
• x is a vector or a formula.
• data is the data frame.
• notch is a logical value. Set as TRUE to draw a notch.
• varwidth is a logical value. Set as true to draw width of the box proportionate to the sample size.
• names are the group labels which will be printed under each boxplot.
• main is used to give a title to the graph.

## Example

We use the data set "mtcars" available in the R environment to create a basic boxplot. Let's look at the columns "mpg" and "cyl" in mtcars.
```input <- mtcars[,c('mpg','cyl')]
```
When we execute above code, it produces following result −
```                   mpg  cyl
Mazda RX4         21.0   6
Mazda RX4 Wag     21.0   6
Datsun 710        22.8   4
Hornet 4 Drive    21.4   6
Valiant           18.1   6
```

## Creating the Boxplot

The below script will create a boxplot graph for the relation between mpg (miles per gallon) and cyl (number of cylinders).
```# Give the chart file a name.
png(file = "boxplot.png")

# Plot the chart.
boxplot(mpg ~ cyl, data = mtcars, xlab = "Number of Cylinders",
ylab = "Miles Per Gallon", main = "Mileage Data")

# Save the file.
dev.off()
```
When we execute the above code, it produces the following result −

## Boxplot with Notch

We can draw boxplot with notch to find out how the medians of different data groups match with each other.
The below script will create a boxplot graph with notch for each of the data group.
```# Give the chart file a name.
png(file = "boxplot_with_notch.png")

# Plot the chart.
boxplot(mpg ~ cyl, data = mtcars,
xlab = "Number of Cylinders",
ylab = "Miles Per Gallon",
main = "Mileage Data",
notch = TRUE,
varwidth = TRUE,
col = c("green","yellow","purple"),
names = c("High","Medium","Low")
)
# Save the file.
dev.off()
```
When we execute the above code, it produces the following result −

1. R - Overview
2. R - Environment Setup
3. R - Basic Syntax
4. R - Data Types
5. R - Variables
6. R - Operators
7. R - Decision Making
8. R - Loops
9. R - Functions
10. R - Strings
11. R - Vectors
12. R - Matrices
13. R - Arrays
14. R - Factors
15. R - Data Frames
16. R - Packages
17. R - Data Reshaping
18. R - CSV Files
19. R - Excel Files
20. R - Binary Files
21. R - XML Files
22. R - JSON Files
23. R - Web Data
24. R - Database
25. R - Pie Charts
26. R - Bar Charts
27. R - Boxplots
28. R - Histograms
29. R - Line Graphs
30. R - Scatterplots
31. R - Mean, Median and Mode
32. R - Linear Regression
33. R - Multiple Regression
34. R - Logistic Regression
35. R - Normal Distribution
36. R - Binomial Distribution
37. R - Poisson Regression
38. R - Analysis of Covariance
39. R - Time Series Analysis
40. R - Nonlinear Least Square
41. R - Decision Tree
42. R - Random Forest
43. R - Survival Analysis
44. R - Chi Square Tests