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# How to Perform Univariate Analysis in R?

• Last Updated : 19 Dec, 2021

In this article, we will discuss how to perform Univariate Analysis in R Programming Language. Univariate Analysis means doing Analysis on one variable.

## Summary Statistics

Summary statistics include:

• Minimum – Get the Minimum element

Syntax:

`min(data)`
• Maximum – Get the Maximum element

Syntax:

`max(data)`
• Mean – Get the mean of the given elements

Syntax:

`mean(data)`
• Median – Get the median of the given elements

Syntax:

`median(data)`
• Inter Quartile Range – Get the IQR of the given elements

Syntax:

`IQR(data)`
• Standard Deviation – Get the standard deviation of the given elements

Syntax:

`sd(data)`
• Range – Get range from the elements

Syntax:

`max(data)-min(data)`

Example: R program to create  a vector with 10 elements and display the Summary statistics.

## R

 `# create a vector with 10 elements ` `data = ``c``(1: 10) ` ` `  `# display ` `print``(data) ` ` `  ` `  `# minimum ` `print``(``min``(data)) ` ` `  `# maximum ` `print``(``max``(data)) ` ` `  `# mean ` `print``(``mean``(data)) ` ` `  `# median ` `print``(``median``(data)) ` ` `  `# IQR ` `print``(``IQR``(data)) ` ` `  `# range ` `print``(``max``(data)-``min``(data)) ` ` `  `# standard deviation ` `print``(``sd``(data)) `

Output:

```[1]  1  2  3  4  5  6  7  8  9 10
[1] 1
[1] 10
[1] 5.5
[1] 5.5
[1] 4.5
[1] 9
[1] 3.02765```

## Frequency Table

We can display the frequency table using table() method, This will return the count of element occurrence.

Syntax:

`table(data)`

Example:

## R

 `# create a vector with 10 elements ` `data = ``c``(1: 10) ` ` `  `# display ` `print``(data) ` ` `  `# display frequency table ` `print``(``table``(data)) `

Output:

## Visualization

Here we can visualize the data using some plots

### Boxplot

boxplot() function will result in a five-point summary(min, max, median, 1st quartile, 3rd quartile)

Syntax:

`boxplot(data)`

Example:

## R

 `# create a vector with 10 elements ` `data = ``c``(1: 10) ` ` `  `# display ` `print``(data) ` ` `  `# display boxplot ` `print``(``boxplot``(data)) `

Output:

```[1]  1  2  3  4  5  6  7  8  9 10
\$stats
[,1]
[1,]  1.0
[2,]  3.0
[3,]  5.5
[4,]  8.0
[5,] 10.0
attr(,"class")
1
"integer"

\$n
[1] 10

\$conf
[,1]
[1,] 3.001801
[2,] 7.998199

\$out
numeric(0)

\$group
numeric(0)

\$names
[1] "1"```

Output:

### Histogram

This will return the histogram of the data and the function used is hist()

Syntax:

`hist(data)`

Example:

## R

 `# create a vector with 10 elements ` `data = ``c``(1: 10) ` ` `  `# display ` `print``(data) ` ` `  `# display histogram ` `print``(``hist``(data)) `

Output:

```[1]  1  2  3  4  5  6  7  8  9 10
\$breaks
[1]  0  2  4  6  8 10

\$counts
[1] 2 2 2 2 2

\$density
[1] 0.1 0.1 0.1 0.1 0.1

\$mids
[1] 1 3 5 7 9

\$xname
[1] "data"

\$equidist
[1] TRUE

attr(,"class")
[1] "histogram"```

Output:

### Density plot

This will display the density plot . We have to use density() function along with plot() function.

Syntax:

`plot(density(data))`

Example:

## R

 `# create a vector with 10 elements ` `data = ``c``(1: 10) ` ` `  `# display ` `print``(data) ` ` `  `# display density plot ` `print``(``plot``(``density``(data))) `

Output:

```[1]  1  2  3  4  5  6  7  8  9 10
NULL```

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