Say you've found a great dataset and would like to learn more about it. How can you start to answer the questions you have about the data? You can use dplyr to answer those questions—it can also help with basic transformations of your data. You'll also learn to aggregate your data and add, remove, or change the variables. Along the way, you'll explore a dataset containing information about counties in the United States. You'll finish the course by applying these tools to the babynames dataset to explore trends of baby names in the United States.
Transforming Data with dplyrFree
Learn verbs you can use to transform your data, including select, filter, arrange, and mutate. You'll use these functions to modify the counties dataset to view particular observations and answer questions about the data.Exploring data with dplyr50 xpUnderstanding your data50 xpSelecting columns100 xpThe filter and arrange verbs50 xpArranging observations100 xpFiltering for conditions100 xpFiltering and arranging100 xpThe mutate() verb50 xpCalculating the number of government employees100 xpCalculating the percentage of women in a county100 xpMutate, filter, and arrange100 xp
Now that you know how to transform your data, you'll want to know more about how to aggregate your data to make it more interpretable. You'll learn a number of functions you can use to take many observations in your data and summarize them, including count, group_by, summarize, ungroup, and slice_min/slice_max.The count verb50 xpCounting by region100 xpCounting citizens by state100 xpMutating and counting100 xpThe group_by, summarize, and ungroup verbs50 xpSummarizing100 xpSummarizing by state100 xpSummarizing by state and region100 xpThe slice_min and slice_max verbs50 xpSelecting a county from each region100 xpFinding the lowest-income state in each region100 xpUsing summarize, slice_max, and count together100 xp
Selecting and Transforming Data
Learn advanced methods to select and transform columns. Also, learn about select helpers, which are functions that specify criteria for columns you want to choose, as well as the rename verb.
Case Study: The babynames Dataset
Work with a new dataset that represents the names of babies born in the United States each year. Learn how to use grouped mutates and window functions to ask and answer more complex questions about your data. And use a combination of dplyr and ggplot2 to make interesting graphs to further explore your data.The babynames data50 xpFiltering and arranging for one year100 xpFinding the most popular names each year100 xpVisualizing names with ggplot2100 xpGrouped mutates50 xpFinding the year each name is most common100 xpAdding the total and maximum for each name100 xpVisualizing the normalized change in popularity100 xpWindow functions50 xpUsing ratios to describe the frequency of a name100 xpBiggest jumps in a name100 xpCongratulations!50 xp
In the following tracksData Analyst with RData Manipulation with RData Scientist with RData Scientist Professional with RR Programmer
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PrerequisitesIntroduction to the Tidyverse
James ChapmanSee More
Curriculum Manager, DataCamp
James is a Curriculum Manager at DataCamp, where he collaborates with experts from industry and academia to create courses on AI, data science, and analytics. He has led nine DataCamp courses on diverse topics in Python, R, AI developer tooling, and Google Sheets. He has a Master's degree in Physics and Astronomy from Durham University, where he specialized in high-redshift quasar detection. In his spare time, he enjoys restoring retro toys and electronics.
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