Data Wrangling with R


Data Wrangling with R

Authors: Dan Tofan, Mariah Weatherford, Okan Bulut, Emilee McWilliams

Data wrangling is the process of transforming and mapping data from one form into another, with the intent of making it more available for data analytics. This skill teaches... Read more

What You Will Learn

  • Data Wrangling with R conveys commonly performed data wrangling and transformation patterns using R.


  • Data Literacy
  • Mathematics
  • bR for Data Analysts
  • Visualizing Data with R
  • Importing Data with R


You’ll learn different ways to combine, group and merge your data.

Merging Data Sources with R

by Dan Tofan

Jul 31, 2019 / 1h 28m

1h 28m

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In your R data science projects, you need very often to work with data which is spread out across multiple data sources. For example, given two separate data sets on products and their sales, how can you merge them into a new data set? In this course, Merging Data Sources with R, you will gain the ability to merge data from different sources in a controlled way that enables you to keep only the data you need. First, you will learn to merge vectors, which includes using the paste() and append() methods. Next, you will discover how to join data sets with the merge() function, which includes left, inner, right and full outer joins, on data frames that can have one-to-one, one-to-many, or many-to-many relationships. Finally, you will explore how to join data sets with the dplyr package, which covers the previous joins plus anti and semi joins. When you are finished with this course, you will have the skills and knowledge of merging data from different sources, needed to do data wrangling with R.

Table of contents
  1. Course Overview
  2. Managing Vectors
  3. Joining Data Sets with the Merge() Function
  4. Joining Data Sets with dplyr

Splitting and Combining Data with R

by Mariah Weatherford

Oct 25, 2019 / 1h 57m

1h 57m

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Summarizing statistics across groups is invaluable for comparing categories of observations. In this course, Splitting and Combining Data with R, you'll explore splitting data into groups based on some criteria, applying functions or calculations to each group independently, and combining the results into a data structure. To begin, you’ll learn how to create custom categorical variables for grouping, and custom numeric variables to which you can apply functions. Next, with the criteria for grouping created, you will split the data, apply functions, and combine the data into a data structure. Finally, with the raw data transformed, you’ll discover how a grouped dataframe can then be ungrouped with summary statistics maintained, or keep the grouped dataframe intact with plotting functions for visualizing variation between groups. By the end of this course, you’ll have a better understanding of how to use R to build data pipelines with dplyr, manipulate strings and dates for feature engineering, and create customized ggplot charts. .

Table of contents
  1. Course Overview
  2. Creating Variables to Combine/Summarize Data
  3. Grouping and Summarizing Data
  4. Plotting Combined Data


Normalize data to make it appropriate for an analysis with dplyr and reshape long and wide data for further analysis.

Reshaping Data with R

by Okan Bulut

Feb 14, 2020 / 1h 59m

1h 59m

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Have you ever thought about the format of data you are dealing with? Is it long or wide? Which format is better for your analysis? In this course, Reshaping Data with R, you will gain the ability to reshape and aggregate data using the R programming language. First, you will learn how to transform wide-format data into long-format data. Next, you will discover how to reshape data from long format to wide format. Finally, you will explore how to aggregate data with and without group variables. When you are finished with this course, you will have the skills and knowledge of reshaping data needed to manage your data more effectively for statistical analysis and data visualization. Software required: R*

Table of contents
  1. Course Overview
  2. Introduction
  3. Reshaping Wide Data to Long Data
  4. Reshaping Long Data to Wide Data
  5. Reshaping Data Based on Aggregated Values
  6. Course Recap


Transform data encoding to enable further analysis.

Encoding Data with R

by Emilee McWilliams

Jan 15, 2020 / 37m


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Encoding Data can be time consuming and lacks proper data insights in the process. In this course, Encoding Data with R, you will gain the ability to encode data to utilize a data set, while being able to find data frequencies and insights that fit your data set and business goals. First, you will learn the factor() function for converting data types. Next, you will discover how to find data frequencies through the table() function. Finally, you will explore how to encode data for a indicator flag or for a potential model. When you are finished with this course, you will have the skills and knowledge to encode data to find insights quickly.

Table of contents
  1. Course Overview
  2. Converting Values to Factors in R
  3. Finding Frequency Data Summaries in R
  4. Preparing Qualitative Data for a Database in R
  5. Encoding Data for a Potential Model in R
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