Common R Packages for Data Analysis

Check out the R libraries that are designed specifically for data analysis.

R data analysis packages

Data analysis is a complex and multifaceted process and includes a wide range of subdisciplines like data cleaning, transformation, and visualization. R is a powerful programming language that makes it easier for us to carry out these tasks. However, to fully leverage the capabilities of R, we must use packages to supplement the built-in functions.

This lesson provides instructions on accessing the following practical and popular libraries used in this course: ggplot2, tidyr, readr, stringr, dplyr, readxl, and tidyverse.


The ggplot2 library is a popular tool utilized by other programming languages, such as Python, that enables the plotting of graphs in various forms. Some of the plot formats are listed here:

  • Line plots
  • Bar plots
  • Scatter plots
  • Histograms
  • Density plots

The ggplot2 library also allows customizing a wide range of features associated with graphs, including line color, title, and element size.

Run the following code to load the library:

library(ggplot2) # Load ggplot2 library


The tidyr library provides functions that help us do the following:

  • Convert data frames into long or wide forms
  • Combine or split data frames
  • Deal with null values
  • Unravel dictionaries

We frequently rely on the tidyr package to facilitate data cleaning and manipulation, which are often a dominant part of data analysis when data is untidy.

Run the following code to load the library:

library(tidyr) # Load tidyr library


The readr package allows us to read data from a wide range of sources, including:

  • CSV
  • TSV
  • Excel
  • JSON
  • Text files
  • Other files with delimiters

This is an essential package since built-in functions in R only support working with a limited number of data file types. The readr package successfully solves this issue.

Run the following code to load the library:

library(readr) # Load readr library


The stringr library focuses on the manipulation of strings. Here are some functionalities that stringr provides:

  • Slice and dice or concatenate strings
  • Search/find/replace patterns in strings
  • Create new string patterns
  • Modify letter cases

Run the following code to load the library:

library(stringr) # Load stringr library


The dplyr library makes data frame manipulation easier with its dedicated pipeline operand %>%.

The %>% operand is one of the most popular operands in data analysis thanks to its user-friendly structure. With the pipeline operand, we can work on a specific piece of data by sequentially adding the steps we need to execute.

The dplyr package provides the following functions for tabular data:

  • Select

  • Create

  • Mutate

  • Summarize

  • Filter

Run the following code to load the library:

library(dplyr) # Load dplyr library


The readxl package allows us to do the following:

  • Read data from Excel files
  • Save data in Excel files in a practical way

The syntax structure is almost identical to the built-in functions.

Run the following code to load the library:

library(readxl) # Load readxl library


The tidyverse library provides a set of subpackages that make data manipulation and analysis easier, making it an essential library for data science. Loading this package gives access to powerful functions for various practices, such as data wrangling, visualization, and statistical modeling. Each package mentioned above is a component of tidyverse.

Run the following code to load the library:

library(tidyverse) # Load the tidyverse library

Remember that in addition to the libraries listed above, there are many others out there, some of which may offer similar functionalities. For the sake of simplicity, we will only use the listed ones in this course. Feel free to explore the other options available and find the libraries that best suit your needs.