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Fundamentals of R

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  1. Welcome to Fundamentals of R
  2. RMarkdown
    Why Use RMarkdown?
  3. RMarkdown Overview
  4. YAML
  5. Text
  6. Code Chunks
  7. Wrapping Up
  8. Data Wrangling and Analysis
    Getting Started
  9. The Tidyverse
  10. select
  11. mutate
  12. filter
  13. summarize
  14. group_by
  15. count
  16. arrange
  17. Create a New Data Frame
  18. Crosstabs
  19. Wrapping Up
  20. Data Visualization
    An Important Workflow Tip
  21. The Grammar of Graphics
  22. Scatterplots
  23. Histograms
  24. Bar Charts
  25. color and fill
  26. scales
  27. Text and Labels
  28. Plot Labels
  29. Themes
  30. Facets
  31. Save Plots
  32. Wrapping Up
  33. Wrapping Up
    You Did It!
Fundamentals of R group_by
Lesson 14 of 33
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group_by

Your Turn

Complete the group_by sections of the data-wrangling-and-analysis-exercises.Rmd file.

Solutions

Video

Code

Learn More

General Data Wrangling and Analysis Resources

Because most material that discusses data wrangling and analysis with the dplyr packges does so in a way that covers all of the verbs discussed in this course, I have chosen not to separate them by lesson. Instead, here are some helpful resources for learning more about all of the tidyverse verbs discussed in this course:

Chapter 5 of R for Data Science

RStudio Cloud primer on working with data

Tidyverse for Beginners by Danielle Navarro

Learning Statistics with R by Danielle Navarro

Introduction to the Tidyverse by Alison Hill

A gRadual intRoduction to data wRangling by Chester Ismay and Ted Laderas

Working in the Tidyverse by Desi Quintans and Jeff Powell

Christine Monnier video tutorials on dplyr

Have any questions? Put them below and we will help you out!

  1. Daniel Sossa March 13, 2021

    Hello, I have a question. how could I get the subtotals by group on the same DF that we obtain when we use de group by + summarise?

    We get something like this, but i would like to see on the same table the sub totals (by gender) and the grand total (which should by 10.000)

    female Looking 6.940299 135
    female NotWorking 7.094077 1732
    female Working 6.909353 2086
    female NA NaN 1067
    male Looking 7.147727 176
    male NotWorking 7.101619 1115
    male Working 6.736634 2527
    male NA 6.000000 1162

  3. Blayne Beacham September 27, 2021

    Hey, I am getting the follow message in my console when I run anything with groupby

    ## `summarise()` has grouped output by ‘gender’. You can override using the `.groups` argument.

    The output seems fine and I googled to find a way to suppress it (message = FALSE) but just want to double check that nothing is wrong.

      1. Blayne Beacham September 28, 2021

        That’s perfect. Good to know that I can sort of toggle between keeping the group or not, depending on the need.

        So, if I’m getting it correctly, I should basically add .groups = “keep” when I want all groups to show (which is probably the vast majority of the time) but if I wanted a single specific detail, like a max, I would want the groups to be dropped, right?

        And at a much more basic level, I shouldn’t even really worry about it (i.e. keep default arguments) if I don’t really care about the message popping up, right? (…as I can usually suppress it with message = FALSE)

        1. David Keyes September 28, 2021

          Yeah, I’d say mostly just don’t even worry about it. I never set anything, as I’ve been using R long enough that it wasn’t an option when I started so I just don’t think in that way. I’d say go with whatever works best for you. And yes, no need to worry about the message.

          2. Charlie Hadley September 29, 2021

            Hey David and Blayne,

            I wanted to chime in on the .groups argument of summarise(). It’s currently labelled “lifecycle:experimental” which means theoretically how it works could change, and there’s a small chance the feature would be removed. I’m grumpy about this feature and deliberately don’t use it, but that’s my personal opinion.

  5. Kathleen Griesbach September 30, 2021

    Hello! I was doing the exercises without a problem, but all of the sudden at this stage (when I try to use “group by” am getting a repeated error message, “Error: attempt to use zero-length variable name.” And I just closed and reopened but now nothing seems to be working.
    nhanes %>%
    group_by(gender) %>%
    Error: attempt to use zero-length variable name

    (I know that I’m a bit behind this week, so don’t expect a quick answer!)

    In my code, also, the variables and such use capital letters rather than underscores. I believe you mentioned this syntax difference in class but cannot remember whether it is an issue. Thank you!

    1. David Keyes October 1, 2021

      It’s hard to say without seeing your full code, but I’m guessing this is because you didn’t use the clean_names() function when you read in your nhanes object and so the variable name is Gender with a capital G. If you want to post your full code as a GitHub Gist and then post the link in response I can confirm if that’s the issue.

      1. Kathleen Griesbach October 4, 2021

        Thank you, David!
        I think the problem might have been me just messing up the “`{r] by accident…I did rerun the clean_names() function and load all my packages again today. And things seem to be working now. Thanks so much.

  6. Sara Cifuentes March 30, 2022

    Hi,
    I follow the instructions (below in line 305). I added the function “filter” because you said ” (whether or not respondents are working)”; however, in the solution, you don’t use “filter”. Maybe I didn’t understand the assignment?

    We can use `group_by` with multiple groups.

    Use `group_by` for `gender` and `work` (whether or not respondents are working) before calculating mean hours of sleep.

    “`{r}
    nhanes %>%
    group_by(gender, work) %>%
    dplyr::filter (work %in% c(“Working”, “NotWorking”)) %>%
    summarize(mean_sleep = mean(sleep_hrs_night, na.rm = TRUE))
    “`

    Thank you very much for your help.

    1. Charlie Hadley March 31, 2022

      Hi Sara,
      In this video David uses group_by() to summarise the data by their working status, meaning that when summarise() is used we calculate the values for all different working statuses. We would need to use filter() if we were interested in only specific values in the work column. Does that help?
      Cheers, Charlie

      2. Tatiana Bustos July 27, 2022

        Can you explain this more with an example of the code where we would filter work? I am thinking this would be appropriate to add to the last solutions code —
        filter (work == “Looking” | work == “Not Working” | work == “Working”)
        Would this go before group by? Before summarize? Does the order matter?

        1. David Keyes July 28, 2022

          That code should work. You can put it before the group_by() and/or summarize(). If you filter first, the result of the group_by() and/or summarize() will only include rows that are not filtered out.

  7. G Mendez April 8, 2022

    Hi David –

    Quick question. Let’s say I had state level data with numerical values per state. Let’s say I wanted to assign each state to relevant regions, like northeast, pacific, etc. How can I first assign the states to the object, then perform a group by, to then summarize the numerical values aggregated to the regions I assigned them to? Thanks