5 Must-Know R Packages for Data Analysis

R is a powerful and versatile programming language used widely in data analytics and statistics. A key strength of this programming language is the robust ecosystem of packages that support it. These enhance the capabilities of base R and make it easier to perform various analytical tasks.

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Here are five must-know R packages for data analysis in R.

1. dplyr

 
Dplyr is part of the tidyverse collection of packages designed for effective data manipulation. It provides intuitive functions to make working with data easier. These include filter to select rows from a dataset that meet certain conditions, mutate to create new columns, arrange to sort data, and summarize to create summary statistics.  

A key feature of dplyr is the pipe operator (%>%). This allows you to link multiple steps in your data analysis into a continuous flow. For example, you can start by filtering your dataset, then pipe the result of that into a mutate function to create new columns, then pipe that into arrange to sort the final result, all in a single line of code. Dplyr also works well with other packages discussed below like ggplot2 and tidyr, making it a key package for any time of data analysis project in R. 

2. ggplot2

 
The ggplot2 package is also part of the tidyverse collection. It allows you to make a wide variety of plots, including scatterplots, bar charts, and line graphs with minimal lines of code. Each plot is developed in layers where you first set up the axes, then overlay the points or lines you want to include, then add labels, legends, and other elements. This allows for the creation of very complex but highly customized visualizations. 

A benefit of ggplot2 is that this layering method allows you to create unique visualizations. For example, you can start with a box and whisker plot, then overlay a scatterplot over the top. The ggplot2 package also integrates very well with other tidyverse packages, making it a reliable part of any workflow.  

3. tidyr

 
The tidyr package is again part of the tidyverse collection. Its purpose is for data cleaning and organization, essentially taking data from a messy, raw format into a tidy version. Key functions include gather to turn wide data into long format, spread to turn long data into wide format, and unite to combine multiple columns into one. While it is a simple package, it is indispensable when working with complex datasets or combining multiple data sources.  

4. lubridate

 
Handing date and time variables can be notoriously tricky due to various formats and the intricacies of time zones. The lubridate package helps simplify these complexities by providing a set of functions custom built for working with date and time variables. The central feature are the date and time format functions, like ymd and mdy_hms, that automatically recognize common datetime formats and convert them into date objects that R can then directly manipulate.  

Functions like year or minute also allow you to extract specific components of a datetime object. You can use these to quickly create a new column that just contains the year of a variable, for example, or calculate the years elapsed between two dates. This package is critical for anyone working with events, time series data, or demographics data that contains date of birth.  

5. shiny

 
The shiny package is a powerful framework for building interactive visualizations and web applications directly in R. You can create dynamic data dashboards and visualizations without any knowledge of web development languages, like HTML or JavaScript. An application built in shiny will include both the user interface section, which defines the visual layout and appearance, and the server section, which defines how data is processed and updated in response to user interactions.  

Shiny is driven by reactive programming, so the visualizations will automatically update when users interact with it. For example, you can create a scatterplot that filters data based on user inputs. Shiny integrates well with other R packages, like ggplot2 and dplyr, to strengthen the analysis and visualziations.  

Summary

 
These R packages are essential tools for any data analyst, providing efficient ways to manipulate, visualize, and analyze data. Mastering these packages will significantly enhance your analysis capabilities and improve your productivity.
 
 

Mehrnaz Siavoshi holds a Masters in Data Analytics and is a full time biostatistician working on complex machine learning development and statistical analysis in healthcare. She has experience with AI and has taught university courses in biostatistics and machine learning at University of the People.