1. Learn
    2. /
  2. Courses
    3. /
  3. Data Manipulation with dplyr
  • 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.

  • 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

    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

    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.

Initializing

Exercise

Counting by region

The counties dataset contains columns for region, state, population, and the number of citizens, which we selected and saved as the counties_selected table. In this exercise, you'll focus on the region column.

counties_selected <- counties %>%
  select(county, region, state, population, citizens)

Instructions

100 XP
  • Use count() to find the number of counties in each region, using a second argument to sort in descending order.