Skip to main content

Data Manipulation in R

Data manipulation is a crucial skill in data analysis. It involves cleaning, transforming, and restructuring data. R is a popular language used by data scientists and analysts worldwide due to its powerful packages that make data manipulation seamless. This tutorial will introduce you to data manipulation in R using the dplyr package.

Installing and Loading the dplyr Package​

The dplyr package in R is a powerful tool for data manipulation. It's not part of the standard R installation, so we need to install it first. You can install it by running the following command in your R console:

install.packages("dplyr")

After installing the package, you need to load it into your R environment each time you start a new R session. You can load it using the library() function like this:

library(dplyr)

Basic dplyr Functions​

dplyr provides several functions for the most common data manipulation tasks:

  • select(): This function is used to select columns in a data frame.

  • filter(): This function is used to extract subsets of rows from a data frame based on logical conditions.

  • mutate(): This function is used to add new variables/columns or transform existing variables.

  • summarise(): This function is used to summarise multiple values into a single value.

  • arrange(): This function is used to rearrange rows of a data frame.

Let's see each of them in action.

Select​

The select() function is used to choose which columns of a data frame to keep. For example, if we have a data frame named 'df' with columns 'x', 'y', 'z', and 'a', and we want to keep only 'x' and 'z', we would do:

df_new <- select(df, x, z)

Filter​

The filter() function is used to filter rows of a data frame that meet certain criteria. For example, if we want to find all rows in 'df' where 'x' is greater than 5, we would do:

df_filtered <- filter(df, x > 5)

Mutate​

The mutate() function is used to create new columns in a data frame or to modify existing ones. For example, if we want to create a new column 'b' in 'df' that is the sum of 'x' and 'y', we would do:

df_mutated <- mutate(df, b = x + y)

Summarise​

The summarise() function is used to summarise the values in a column into a single value. For example, if we want to find the average of 'x' in 'df', we would do:

df_summary <- summarise(df, avg_x = mean(x))

Arrange​

The arrange() function is used to reorder the rows of a data frame. For example, if we want to arrange 'df' in ascending order of 'x', we would do:

df_arranged <- arrange(df, x)

Conclusion​

In this tutorial, we have introduced you to the basics of data manipulation in R using the dplyr package. We have covered how to select, filter, mutate, summarise, and arrange data frames in R. With these tools, you'll be well on your way to becoming proficient in data manipulation in R. Happy coding!