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Data Manipulation with dplyr

4.3+
48 reviews
Beginner

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

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4 Hours13 Videos46 Exercises
116,202 Learners

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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. 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.

    Play Chapter Now
    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
    Select, 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 top_n.

    Play Chapter Now
    The count verb
    50 xp
    Counting by region
    100 xp
    Counting citizens by state
    100 xp
    Mutating and counting
    100 xp
    The group_by, summarize, and ungroup verbs
    50 xp
    Summarizing
    100 xp
    Summarizing by state
    100 xp
    Summarizing by state and region
    100 xp
    The slice_min and slice_max verbs
    50 xp
    Selecting a county from each region
    100 xp
    Finding the lowest-income state in each region
    100 xp
    Using summarize, slice_max, and count together
    100 xp
  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 and transmute verbs.

    Play Chapter Now
    Selecting
    50 xp
    Selecting columns
    100 xp
    Select helpers
    100 xp
    The rename verb
    50 xp
    Renaming a column after count
    100 xp
    Renaming a column as part of a select
    100 xp
    The transmute verb
    50 xp
    Choosing among verbs
    50 xp
    Using transmute
    100 xp
    Matching verbs to their definitions
    100 xp
    Choosing among the four verbs
    100 xp
  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
    The babynames data
    50 xp
    Filtering and arranging for one year
    100 xp
    Finding the most popular names each year
    100 xp
    Visualizing names with ggplot2
    100 xp
    Grouped mutates
    50 xp
    Finding the year each name is most common
    100 xp
    Adding the total and maximum for each name
    100 xp
    Visualizing the normalized change in popularity
    100 xp
    Window functions
    50 xp
    Using ratios to describe the frequency of a name
    100 xp
    Biggest jumps in a name
    100 xp
    Congratulations!
    50 xp

In the following tracks

Data Analyst with RData Manipulation with RData Scientist with RData Scientist Professional with RR Programmer

Datasets

2015 US CensusUS Baby Name Records

Collaborators

Collaborator's avatar
Amy Peterson

Prerequisites

Introduction to the Tidyverse
James Chapman HeadshotJames Chapman

Curriculum Manager, DataCamp

James is a Curriculum Manager at DataCamp. He has a Master's degree in Physics and Astronomy from Durham University, where he specialized in quasar detection and tutored Math and English. He joined DataCamp as a learner in 2018, and the data skills learned on DataCamp were quickly integrated into his scientific projects. In his spare time, he enjoys restoring retro toys and electronics.

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Don’t just take our word for it

*4.3
from 48 reviews
65%
23%
6%
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Sort by
  • Hedda G.
    about 1 month

    clear, easy to follow

  • Maricel S.
    about 2 months

    Good exercises to reinforce concept.

  • Victor C.
    4 months

    good exercise that not too easy or hard for beginner.

  • Asseliya T.
    5 months

    very helpful

  • Jiao L.
    6 months

    easy to understand and good practice!

"clear, easy to follow"

Hedda G.

"Good exercises to reinforce concept."

Maricel S.

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

Victor C.

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