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Data Manipulation with dplyr

4.4+
57 reviews
Beginner

Delve further into the Tidyverse by learning to transform and manipulate data with dplyr.

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4 Hours13 Videos44 Exercises
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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.
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In the following Tracks

Certification Available

Data Analyst with R

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Certification Available

Associate Data Scientist in R

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Data Manipulation with R

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  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.

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    Exploring data with dplyr
    50 xp
    Understanding your data
    50 xp
    Selecting columns
    100 xp
    The filter and arrange verbs
    50 xp
    Arranging observations
    100 xp
    Filtering for conditions
    100 xp
    Filtering and arranging
    100 xp
    The mutate() verb
    50 xp
    Calculating the number of government employees
    100 xp
    Calculating the percentage of women in a county
    100 xp
    Mutate, filter, and arrange
    100 xp
  2. 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 slice_min/slice_max.

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  3. 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 verb.

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  4. 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.

    Play Chapter Now
For Business

GroupTraining 2 or more people?

Get your team access to the full DataCamp library, with centralized reporting, assignments, projects and more

In the following Tracks

Certification Available

Data Analyst with R

Go To Track
Certification Available

Associate Data Scientist in R

Go To Track

Data Manipulation with R

Go To Track

In other tracks

R Developer

Collaborators

Collaborator's avatar
Amy Peterson

Audio Recorded By

James Chapman's avatar
James Chapman
James Chapman HeadshotJames Chapman

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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Don’t just take our word for it

*4.4
from 57 reviews
67%
23%
5%
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  • Adir P.
    3 months

    Another Great Course

  • Ulrich T.
    6 months

    The course is straight forward. The exercises are cool and easy to follow. Developing the graphics was at first somehow to be at the beginning but towards the end of the course, I became adept to it. A good course indeed. Looking forward for any available datacamp scholarship to complete R programming and take in other programming and coding lessons

  • Ricardo R.
    7 months

    A rich beginning to learn about the dplyr library.

  • Victor C.
    about 1 year

    good exercise that not too easy or hard for beginner.

  • Asseliya T.
    about 1 year

    very helpful

"Another Great Course"

Adir P.

"A rich beginning to learn about the dplyr library."

Ricardo R.

"good exercise that not too easy or hard for beginner."

Victor C.

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