Paul Love has completed

# Data Manipulation with dplyr

4 hours
3,700 XP

## 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

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
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.

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.

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.

In the following tracks

Associate Data ScientistData Analyst Data Manipulation R Developer

Collaborators

James 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.