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Alex Mirugwe has completed

Data Manipulation with dplyr

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4 hours
3,700 XP
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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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  1. 1

    Transforming Data with dplyr


    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.

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In other tracks

R Developer


Collaborator's avatar
Amy Peterson
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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