Selecting a county from each region

Previously, you used the walk column, which offers a percentage of people in each county that walk to work, to add a new column and count to find the total number of people who walk to work in each county.

Now, you're interested in finding the county within each region with the highest percentage of citizens who walk to work.

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

Deze oefening maakt deel uit van de cursus

Data Manipulation with dplyr

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Find the county in each region with the highest percentage of citizens who walk to work.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

counties_selected %>%
  # Group by region
  ___
  # Find the county with the highest percentage of people who walk to work
  ___

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Data Manipulation with dplyr

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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.

Exercise 1: Exploring data with dplyr Exercise 2: Understanding your data Exercise 3: Selecting columns Exercise 4: The filter and arrange verbs Exercise 5: Arranging observations Exercise 6: Filtering for conditions Exercise 7: Filtering and arranging Exercise 8: The mutate() verb Exercise 9: Calculating the number of government employees Exercise 10: Calculating the percentage of women in a county Exercise 11: Mutate, filter, and arrange

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.

Exercise 1: The count verb Exercise 2: Counting by region Exercise 3: Counting citizens by state Exercise 4: Mutating and counting Exercise 5: The group_by, summarize, and ungroup verbs Exercise 6: Summarizing Exercise 7: Summarizing by state Exercise 8: Summarizing by state and region Exercise 9: The slice_min and slice_max verbs Exercise 10: Selecting a county from each region

Huidige oefening

Exercise 11: Finding the lowest-income state in each region Exercise 12: Using summarize, slice_max, and count together

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.

Exercise 1: Selecting Exercise 2: Selecting columns Exercise 3: Select helpers Exercise 4: The rename verb Exercise 5: Renaming a column after count Exercise 6: Renaming a column as part of a select Exercise 7: The relocate verb Exercise 8: Using relocate Exercise 9: Choosing among the four verbs

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.

Exercise 1: The babynames data Exercise 2: Filtering and arranging for one year Exercise 3: Finding the most popular names each year Exercise 4: Visualizing names with ggplot2 Exercise 5: Grouped mutates Exercise 6: Finding the year each name is most common Exercise 7: Adding the total and maximum for each name Exercise 8: Visualizing the normalized change in popularity Exercise 9: Window functions Exercise 10: Using ratios to describe the frequency of a name Exercise 11: Biggest jumps in a name Exercise 12: Congratulations!