Loved by learners at thousands of companies
In this course, you'll learn to work with data using tools from the tidyverse in R. By data, we mean your own data, other people's data, messy data, big data, small data - any data with rows and columns that comes your way! By work, we mean doing most of the things that sound hard to do with R, and that need to happen before you can analyze or visualize your data. But work doesn't mean that it is not fun - you will see why so many people love working in the tidyverse as you learn how to explore, tame, tidy, and transform your data. Throughout this course, you'll work with data from a popular television baking competition called "The Great British Bake Off."
Explore your dataFree
You will start this course by learning how to read data into R. We'll begin with the readr package, and use it to read in data files organized in rows and columns. In the rest of the chapter, you'll learn how to explore your data using tools to help you view, summarize, and count values effectively. You'll see how each of these steps gives you more insights into your data.
Tame your data
In this chapter, you will learn some basics of data taming, like how to tame your variable types, names, and values.Cast column types50 xpCast a column to a date100 xpCast a column to a number100 xpCast a column as a factor100 xpRecode values50 xpRecode a character variable100 xpRecode a numeric variable100 xpSelect variables50 xpCombine functions with select50 xpRecode factor to plot100 xpSelect and reorder variables100 xpTame variable names50 xpReformat variables100 xpRename and subset variables100 xpRename and reorder variables100 xp
Tidy your data
Now that your data has been tamed, it is time to get tidy. In this chapter, you will get hands-on experience tidying data and combining multiple tidying functions together in a chain using the pipe operator.Introduction to Tidy Data50 xpTidy line-up50 xpPlot untidy data100 xpGather50 xpGather by hand50 xpGather & plot100 xpGather & plot non-sequential columns100 xpseparate()50 xpSeparate a column100 xpUnite columns100 xpspread()50 xpSpread rows into columns100 xpTidy multiple sets of columns50 xpMasterclass: Tidy I100 xpMasterclass: Tidy II100 xpMasterclass: Tidy III100 xp
Transform your data
In this chapter, you will learn how to tame specific types of variables that are known to be tricky to work with, such as dates, strings, and factors.Complex recoding with case_when50 xpCombine two variables100 xpAdd another bin100 xpFactors50 xpCast a factor and examine levels100 xpPlot factor counts100 xpDates50 xpCast characters as dates100 xpCalculate timespans100 xpStrings50 xpWrangle a character variable100 xpDetect a string pattern100 xpFinal thoughts50 xp
PrerequisitesIntroduction to Data Visualization with ggplot2
Professor and Data Scientist
Alison is an Associate Professor of Pediatrics at Oregon Health & Science University (OHSU) in Portland, Oregon, and the Assistant Director of OHSU’s Center for Spoken Language Understanding, home to the Computer Science graduate education program. She has studied health-related applications of Natural Language Processing-based methods, with a focus on pediatric populations with developmental disabilities like Autism Spectrum Disorders. Alison is also an experienced educator, with peer- and student-nominated awards for teaching. She teaches graduate-level data science courses on Statistics and Data Visualization using R.
What do other learners have to say?
I've used other sites—Coursera, Udacity, things like that—but DataCamp's been the one that I've stuck with.
Devon Edwards Joseph
Lloyds Banking Group
DataCamp is the top resource I recommend for learning data science.
Harvard Business School
DataCamp is by far my favorite website to learn from.
Decision Science Analytics, USAA