Categorical Data in the Tidyverse
Get ready to categorize! In this course, you will work with non-numerical data, such as job titles or survey responses, using the Tidyverse landscape.Start Course for Free
4 Hours13 Videos44 Exercises13,161 Learners
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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!
Introduction to Factor VariablesFree
In this chapter, you’ll learn all about factors. You’ll discover the difference between categorical and ordinal variables, how R represents them, and how to inspect them to find the number and names of the levels. Finally, you’ll find how forcats, a tidyverse package, can improve your plots by letting you quickly reorder variables by their frequency.Introduction to qualitative variables50 xpRecognizing factor variables100 xpQualitative variables in theory50 xpUnderstanding your qualitative variables50 xpGetting number of levels100 xpExamining number of levels100 xpExamining levels100 xpMaking better plots50 xpReordering a variable by its frequency100 xpOrdering one variable by another100 xp
Manipulating Factor Variables
You’ll continue to dive into the forcats package, learning how to change the order and names of levels and even collapse them into one another.Reordering factors50 xpChanging the order of factor levels100 xpTricks of fct_relevel()100 xpRenaming factor levels50 xpDistinguishing between forcats functions50 xpRenaming a few levels100 xpWhen you have a typo50 xpCollapsing factor levels50 xpManually collapsing levels100 xpLumping variables by proportion100 xpPreserving the most common levels100 xp
Creating Factor Variables
Having gotten a good grasp of forcats, you’ll expand out to the rest of the tidyverse, learning and reviewing functions from dplyr, tidyr, and stringr. You’ll refine graphs with ggplot2 by changing axes to percentage scales, editing the layout of the text, and more.Examining common themed variables50 xpGrouping and reshaping similar columns100 xpSummarizing data100 xpCreating an initial plot100 xpTricks of ggplot250 xpEditing plot text100 xpReordering graphs100 xpChanging and creating variables with case_when()50 xpcase_when() with single variable100 xpcase_when() from multiple columns100 xp
Case Study on Flight Etiquette
In this final chapter, you’ll take all that you’ve learned and apply it in a case study. You’ll learn more about working with strings and summarizing data, then replicate a publication quality 538 plot.Case study introduction50 xpChanging characters to factors100 xpTidying data100 xpData preparation and regex50 xpCleaning up strings100 xpDichotomizing variables100 xpSummarizing data100 xpRecreating the plot50 xpCreating an initial plot100 xpFixing labels100 xpFlipping things around100 xpFinalizing the chart100 xpEnd of course recap50 xp
In the following tracksTidyverse Fundamentals with R
PrerequisitesReshaping Data with tidyr
Emily RobinsonSee More
Data Scientist at DataCamp
Emily is a senior data scientist at Warby Parker. Follow her at @robinson_es on Twitter and on her blog, Hooked on Data
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