Interactive Course

Data Manipulation with dplyr in R

Learn to transform and manipulate your data using dplyr.

  • 4 hours
  • 13 Videos
  • 46 Exercises
  • 1,798 Participants
  • 3,850 XP

Loved by learners at thousands of top companies:

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Course Description

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.

  1. Aggregating Data

    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 top_n.

  2. 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.

  1. 1

    Transforming Data with dplyr

    Free

    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.

  2. Aggregating Data

    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 top_n.

  3. 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 and transmute verbs.

  4. 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.

What do other learners have to say?

Devon

“I've used other sites, but DataCamp's been the one that I've stuck with.”

Devon Edwards Joseph

Lloyd's Banking Group

Louis

“DataCamp is the top resource I recommend for learning data science.”

Louis Maiden

Harvard Business School

Ronbowers

“DataCamp is by far my favorite website to learn from.”

Ronald Bowers

Decision Science Analytics @ USAA

Chris Cardillo
Chris Cardillo

Data Scientist at DataCamp

Chris is a Data Scientist at DataCamp, and actually learned programming for data science on DataCamp prior to joining the company. He is extremely passionate about helping others find the joy of coding to alleviate repetitive and time-consuming tasks. Previously, Chris was the Associate Director of Strategy at a digital advertising agency, and graduated with a B.S./M.B.A. from Drexel University in Philadelphia.

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Collaborators
  • Amy Peterson

    Amy Peterson

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