The term univariate analysis refers to the analysis of one variable. You can remember this because the prefix “uni” means “one.”

There are three common ways to perform univariate analysis on one variable:

**1. Summary statistics** – Measures the center and spread of values.

**2. Frequency table** – Describes how often different values occur.

**3. Charts** – Used to visualize the distribution of values.

This tutorial provides an example of how to perform univariate analysis for the following variable:

#create variable with 15 values x <- c(1, 1, 2, 3.5, 4, 4, 4, 5, 5, 6.5, 7, 7.4, 8, 13, 14.2)

**Summary Statistics**

We can use the following syntax to calculate various summary statistics for our variable:

#find mean mean(x) [1] 5.706667 #find median median(x) [1] 5 #find range max(x) - min(x) [1] 13.2 #find interquartile range (spread of middle 50% of values) IQR(x) [1] 3.45 #find standard deviation sd(x) [1] 3.858287

**Frequency Table**

We can use the following syntax to produce a frequency table for our variable:

#produce frequency table table(x) 1 2 3.5 4 5 6.5 7 7.4 8 13 14.2 2 1 1 3 2 1 1 1 1 1 1

This tells us that:

- The value
**1**occurs 2 times - The value
**2**occurs 1 time - The value
**3.5**occurs 1 time

And so on.

**Charts**

We can produce a boxplot using the following syntax:

#produce boxplot boxplot(x)

We can produce a histogram using the following syntax:

#produce histogram hist(x)

We can produce a density curve using the following syntax:

#produce density curve plot(density(x))

Each of these charts give us a unique way to visualize the distribution of values for our variable.

You can find more R tutorials on this page.