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Categorical Data in the Tidyverse
4.8(172)16k studentsBEGINNEREnglish
Korshub's take
The Korshub Take
Working with non-numerical variables like job titles and demographic categories is essential in data science. This beginner-friendly course focuses on handling factor variables in R using the forcats package alongside the broader tidyverse ecosystem, working with real datasets like FiveThirtyEight flight records and Kaggle survey data.
Who It's For
Data science learners using R who want to master factor variable manipulation and produce clean, accurate categorical visualizations.
Key Takeaways
- Factor Manipulation: Master the forcats package to reorder, collapse, and modify factor levels easily in R.
- Tidyverse Integration: Combine forcats with ggplot2, dplyr, stringr, and tidyr for seamless data wrangling and visualization pipelines.
- Real-World Practice: Apply factor tools to authentic datasets to solve common categorical data representation challenges.
About this course
As a data scientist, you will often find yourself working with non-numerical data, such as job titles, survey responses, or demographic information. R has a special way of representing them, called factors, and this course will help you master working with them using the tidyverse package forcats. We’ll also work with other tidyverse packages, including ggplot2, dplyr, stringr, and tidyr and use real world datasets, such as the fivethirtyeight flight dataset and Kaggle’s State of Data Science and ML Survey. Following this course, you’ll be able to identify and manipulate factor variables, quickly and efficiently visualize your data, and effectively communicate your results. Get ready to categorize!
Price
Free
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