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.

Exploring data with dplyr

Understanding your data

Selecting columns

The filter and arrange verbs

Arranging observations

Filtering for conditions

Filtering and arranging

The mutate() verb

Calculating the number of government employees

Calculating the percentage of women in a county

Mutate, filter, and arrange

Transforming Data with dplyr

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.

The count verb

Counting by region

Counting citizens by state

Mutating and counting

The group_by, summarize, and ungroup verbs

Summarizing

Summarizing by state

Summarizing by state and region

The slice_min and slice_max verbs

Selecting a county from each region

Finding the lowest-income state in each region

Using summarize, slice_max, and count together

Aggregating 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.

Selecting

Select helpers

The rename verb

Renaming a column after count

Renaming a column as part of a select

The relocate verb

Using relocate

Choosing among the four verbs

Selecting and Transforming Data

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.

The babynames data

Filtering and arranging for one year

Finding the most popular names each year

Visualizing names with ggplot2

Grouped mutates

Finding the year each name is most common

Adding the total and maximum for each name

Visualizing the normalized change in popularity

Window functions

Using ratios to describe the frequency of a name

Biggest jumps in a name

Congratulations!

Case Study: The babynames Dataset

2015 US Census

US Baby Name Records

Course Glossary

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.

The videos contain live transcripts you can reveal by clicking "Show transcript" at the bottom left of the videos.

The course glossary can be found on the right in the resources section.

To obtain CPE credits you need to complete the course and reach a score of 70% on the qualified assessment. You can navigate to the assessment by clicking on the CPE credits callout on the right.

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? Use dplyr to answer those questions.<br><br><h2>First steps: Transforming data with dplyr</h2>
This course is designed to teach users how to efficiently manipulate and transform data using the dplyr package in R.<br><br>First, explore fundamental data transformation techniques, including the use of key dplyr verbs like select, filter, arrange, and mutate. These functions will teach you how to modify datasets by selecting specific columns, filtering rows based on conditions, sorting data, and creating new calculated columns​​.<br><br>
<h2>Aggregating data with dplyr</h2>Next, the course covers data aggregation, teaching users how to summarize and condense data for better interpretation.<br><br> You’ll understand how to make your data more interpretable and manageable​​. Functions such as count, group_by, and summarize are introduced to perform operations that aggregate many observations into meaningful summaries, essential for data analysis and reporting.<br><br><h2>Selecting and transforming data</h2>Finally, you will learn advanced data selection and transformation techniques, such as using select helpers and the rename verb. You will also get to apply your skills to a real-world case study and practice grouped mutates, window functions, and data visualization with ggplot2.<br><br>By the end of the course, you will have developed robust data manipulation skills using dplyr, enabling more efficient and effective data analysis​​—a vital capability for any data analyst or scientist.

Introduction to the Tidyverse

Learn how to use dplyr to transform and aggregate data, then add, remove, or change variables. You'll then apply your skills to a real-world case study.

Data Manipulation with dplyr

Build Tidyverse skills by learning how to transform and manipulate data with dplyr.

Visualizing names with ggplot2

Data Manipulation with dplyr

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