How to Perform Univariate Analysis in R?

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

The analysis of univariate data is thus the simplest form of analysis since the information deals with only one quantity that changes.

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

# create a vector with 10 elements
data = c(1: 10)

# display
print(data)

Output:

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

Summary Statistics

Summary statistics include:

Minimum:

Get the Minimum element from the data.

Syntax:

min(data)

# minimum
print(min(data))

Output

[1] 1

Maximum:

Get the Maximum element from the data.

Syntax:

max(data)

# maximum
print(max(data))

Output:

[1] 10

Mean:

Get the mean of the given elements from the data.

Syntax:

mean(data)

# mean
print(mean(data))

Output

[1] 5.5

Median:

Get the median of the given elements from the data.

Syntax:

median(data)

# median
print(median(data))

Output

[1] 5.5

Inter Quartile Range:

Get the IQR of the given elements from the data.

Syntax:

IQR(data)

# IQR
print(IQR(data))

Output

[1] 4.5

Standard Deviation:

Get the standard deviation of the given elements from the data.

Syntax:

sd(data)

# standard deviation
print(sd(data))

Output

[1] 3.02765

Range:

Get a range of the elements from the data.

Syntax:

max(data)-min(data)

# range
print(max(data)-min(data))

Output

[1] 9

Frequency Table

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

Syntax:

table(data)

Example:

# create a vector with 10 elements
data = c(1: 10)

# display
print(data)

# display frequency table
print(table(data))

Output:

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

display frequency table

data
 1  2  3  4  5  6  7  8  9 10 
 1  1  1  1  1  1  1  1  1  1

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:

# create a vector with 10 elements
data = c(1: 10)

# display
print(data)

# display boxplot
print(boxplot(data))

Output:

$stats
     [,1]
[1,]  1.0
[2,]  3.0
[3,]  5.5
[4,]  8.0
[5,] 10.0

$n
[1] 10

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

$out
numeric(0)

$group
numeric(0)

$names
[1] "1"

Histogram

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

Syntax:

hist(data)

Example:

# 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"

Density plot

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

Syntax:

plot(density(data))

Example:

# 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