Data cleaning may not be the sexiest task in data science, but it’s an absolute requirement for anyone who wants to work in a data-related field.

In our Data Cleaning in R course, you will learn to perform common data cleaning tasks using the R programming language, and we’ll cover both the why and the how of data cleaning. 

In the first three lessons of the course, you will learn to identify data cleaning needs prior to analysis, use functionals for data cleaning, practice string manipulation, work with relational data, reshape data using tools from the tidyverse, and create correlation matrices to identify trends in your data. Finally, you will learn how to deal with missing values in your data set and build intuition to help you decide how to approach analyses when data is missing.

At the end of the course, you’ll put it all together to create a portfolio project and perform additional analysis through data visualization and correlation analysis to explore parents', students', and teachers' perceptions of NYC schools. You'll learn to work with survey data; specifically how to import, simplify, and reshape the data. You will also learn about R Notebooks and how you can use them to showcase your work.

Once you complete this project, you'll be able to showcase your data cleaning skills in your portfolio to prove to future employers you’ve got the skills needed to tackle working with messy data sets.

By the end of this course, you'll be able to:

  • Use new tools for manipulating data frames.
  • Deal with missing data.
  • Understand relational data and methods for joining data frames.
  • Reshape data using the tidyr package.

Learn Data Cleaning Using R

Data Cleaning With R

Learn to simplify data frames, change data types, and create new variables.

String Manipulation and Relational Data

Practice string manipulation and learn to work with relational data as you prepare six data frames for analysis.

Correlations and Reshaping Data

Continue learning techniques for data cleaning and analysis as you work with real-world data.

Dealing With Missing Data

Learn tools and build intuition you need to decide how to handle missing values in your data set.

NYC Schools Perceptions

Practice your data cleaning and analysis skills and learn to use R Notebooks as you explore survey data.