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Data Manipulation with dplyr

Learn to transform and manipulate your data using dplyr.

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4 Hours13 Videos46 Exercises92,258 Learners3850 XPData Analyst TrackData Manipulation TrackData Scientist TrackR Programmer Track

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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
    The counties dataset
    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
    Mutate
    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 top_n verb
    50 xp
    Selecting a county from each region
    100 xp
    Finding the highest-income state in each region
    100 xp
    Using summarize, top_n, 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
    Using top_n with babynames
    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 Data Manipulation Data ScientistR Programmer

Datasets

countiesbabynames

Collaborators

amy-4121b590-cc52-442a-9779-03eb58089e08
Amy Peterson

Prerequisites

Introduction to the Tidyverse
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DataCamp Content Creator

Course Instructor

DataCamp offers interactive R, Python, Spreadsheets, SQL and shell courses. All on topics in data science, statistics, and machine learning. Learn from a team of expert teachers in the comfort of your browser with video lessons and fun coding challenges and projects.
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