Reproducible examples for getting help in R

ID 529: Data Management and Analytic Workflows in R

Amanda Hernandez

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Thursday, January 12^th, 2023

Best practices for getting help in R

• Don’t ask for someone else to put in more work helping you than you’re willing to put in

  • Before you ask for help did you:

    • Google your error message?

    • Check your spelling? Check your commas?

  • Ask your question in a way that minimizes the amount of time someone has to spend to answer it

• Don’t be a meanie

• Pass it on! We’ve all benefited from the time/wisdom of a colleague/stranger – be that for someone else when you can!

  • Sometimes it’s helpful just to have someone to talk things through with!

Reproducible Examples

Help us help you! Please continue to ask questions in the #student-questions channel on Slack!

Artwork by @allison_horst

What is a reproducible example?

Reproducible examples:

• Use a minimal/basic/small dataset (it doesn’t have to be interesting, it just has to work!)

• Are able to be run on any computer (does not depend on data you have on your computer)

Think: what is the simplest version of the data that I need in order to reproduce the error

Advantages of reproducible examples:

• Anyone, anywhere in the world can help you!

  • They can run your code on their computer to recreate the issue

  • They can help, even if they’re not subject matter experts

• You are less likely to get yelled at by a collaborator or on stack overflow

• You might even solve your own problem along the way!

Making a reproducible example

this is sometimes called a “reprex” 😎

Option #1: Recreate your issue with simple dummy data

dat <- data.frame(x = c(rep(1:2, 3)),
                  y = c(rep(c("a", "b", "c"), 2)))

Option #2: Recreate your issue with built-in datasets

• Try mtcars or iris

  • Be familiar with what is in these datasets! They’re commonly used for examples in R4DS and online.

glimpse(mtcars)

Rows: 32
Columns: 11
$ mpg  <dbl> 21.0, 21.0, 22.8, 21.4, 18.7, 18.1, 14.3, 24.4, 22.8, 19.2, 17.8,…
$ cyl  <dbl> 6, 6, 4, 6, 8, 6, 8, 4, 4, 6, 6, 8, 8, 8, 8, 8, 8, 4, 4, 4, 4, 8,…
$ disp <dbl> 160.0, 160.0, 108.0, 258.0, 360.0, 225.0, 360.0, 146.7, 140.8, 16…
$ hp   <dbl> 110, 110, 93, 110, 175, 105, 245, 62, 95, 123, 123, 180, 180, 180…
$ drat <dbl> 3.90, 3.90, 3.85, 3.08, 3.15, 2.76, 3.21, 3.69, 3.92, 3.92, 3.92,…
$ wt   <dbl> 2.620, 2.875, 2.320, 3.215, 3.440, 3.460, 3.570, 3.190, 3.150, 3.…
$ qsec <dbl> 16.46, 17.02, 18.61, 19.44, 17.02, 20.22, 15.84, 20.00, 22.90, 18…
$ vs   <dbl> 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 1, 1, 1, 0,…
$ am   <dbl> 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0,…
$ gear <dbl> 4, 4, 4, 3, 3, 3, 3, 4, 4, 4, 4, 3, 3, 3, 3, 3, 3, 4, 4, 4, 3, 3,…
$ carb <dbl> 4, 4, 1, 1, 2, 1, 4, 2, 2, 4, 4, 3, 3, 3, 4, 4, 4, 1, 2, 1, 1, 2,…

glimpse(iris)

Rows: 150
Columns: 5
$ Sepal.Length <dbl> 5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.…
$ Sepal.Width  <dbl> 3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.…
$ Petal.Length <dbl> 1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.…
$ Petal.Width  <dbl> 0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.…
$ Species      <fct> setosa, setosa, setosa, setosa, setosa, setosa, setosa, s…

Option #2b: Recreate your issue with other common datasets

• mtcars and iris are built in data sets, but other good ones are starwars (in the dplyr:: package), penguins (in the palmerpenguins:: package), and flights (in the nycflights13:: package)

• These might provide you with a little more complexity (some missingness, more data, different classes), while still being relatively simple/intuitive

glimpse(dplyr::starwars)

Rows: 87
Columns: 14
$ name       <chr> "Luke Skywalker", "C-3PO", "R2-D2", "Darth Vader", "Leia Or…
$ height     <int> 172, 167, 96, 202, 150, 178, 165, 97, 183, 182, 188, 180, 2…
$ mass       <dbl> 77.0, 75.0, 32.0, 136.0, 49.0, 120.0, 75.0, 32.0, 84.0, 77.…
$ hair_color <chr> "blond", NA, NA, "none", "brown", "brown, grey", "brown", N…
$ skin_color <chr> "fair", "gold", "white, blue", "white", "light", "light", "…
$ eye_color  <chr> "blue", "yellow", "red", "yellow", "brown", "blue", "blue",…
$ birth_year <dbl> 19.0, 112.0, 33.0, 41.9, 19.0, 52.0, 47.0, NA, 24.0, 57.0, …
$ sex        <chr> "male", "none", "none", "male", "female", "male", "female",…
$ gender     <chr> "masculine", "masculine", "masculine", "masculine", "femini…
$ homeworld  <chr> "Tatooine", "Tatooine", "Naboo", "Tatooine", "Alderaan", "T…
$ species    <chr> "Human", "Droid", "Droid", "Human", "Human", "Human", "Huma…
$ films      <list> <"The Empire Strikes Back", "Revenge of the Sith", "Return…
$ vehicles   <list> <"Snowspeeder", "Imperial Speeder Bike">, <>, <>, <>, "Imp…
$ starships  <list> <"X-wing", "Imperial shuttle">, <>, <>, "TIE Advanced x1",…

Option #2b: Recreate your issue with other common datasets

• mtcars and iris are built in data sets, but other good ones are starwars (in the dplyr:: package), penguins (in the palmerpenguins:: package), and flights (in the nycflights13:: package)

• These might provide you with a little more complexity (some missingness, more data, different classes), while still being relatively simple/intuitive

glimpse(palmerpenguins::penguins)

Rows: 344
Columns: 8
$ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
$ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
$ sex               <fct> male, female, female, NA, female, male, female, male…
$ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

Option #2b: Recreate your issue with other common datasets

• mtcars and iris are built in data sets, but other good ones are starwars (in the dplyr:: package), penguins (in the palmerpenguins:: package), and flights (in the nycflights13:: package)

• These might provide you with a little more complexity (some missingness, more data, different classes), while still being relatively simple/intuitive

glimpse(nycflights13::flights)

Rows: 336,776
Columns: 19
$ year           <int> 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2013, 2…
$ month          <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ day            <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1…
$ dep_time       <int> 517, 533, 542, 544, 554, 554, 555, 557, 557, 558, 558, …
$ sched_dep_time <int> 515, 529, 540, 545, 600, 558, 600, 600, 600, 600, 600, …
$ dep_delay      <dbl> 2, 4, 2, -1, -6, -4, -5, -3, -3, -2, -2, -2, -2, -2, -1…
$ arr_time       <int> 830, 850, 923, 1004, 812, 740, 913, 709, 838, 753, 849,…
$ sched_arr_time <int> 819, 830, 850, 1022, 837, 728, 854, 723, 846, 745, 851,…
$ arr_delay      <dbl> 11, 20, 33, -18, -25, 12, 19, -14, -8, 8, -2, -3, 7, -1…
$ carrier        <chr> "UA", "UA", "AA", "B6", "DL", "UA", "B6", "EV", "B6", "…
$ flight         <int> 1545, 1714, 1141, 725, 461, 1696, 507, 5708, 79, 301, 4…
$ tailnum        <chr> "N14228", "N24211", "N619AA", "N804JB", "N668DN", "N394…
$ origin         <chr> "EWR", "LGA", "JFK", "JFK", "LGA", "EWR", "EWR", "LGA",…
$ dest           <chr> "IAH", "IAH", "MIA", "BQN", "ATL", "ORD", "FLL", "IAD",…
$ air_time       <dbl> 227, 227, 160, 183, 116, 150, 158, 53, 140, 138, 149, 1…
$ distance       <dbl> 1400, 1416, 1089, 1576, 762, 719, 1065, 229, 944, 733, …
$ hour           <dbl> 5, 5, 5, 5, 6, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 5, 6, 6, 6…
$ minute         <dbl> 15, 29, 40, 45, 0, 58, 0, 0, 0, 0, 0, 0, 0, 0, 0, 59, 0…
$ time_hour      <dttm> 2013-01-01 05:00:00, 2013-01-01 05:00:00, 2013-01-01 0…

Option #3: Modify your own data

There may be something specific to your data that is hard to recreate with another dataset.

If that is the case, you can consider sharing a small and stripped down version of your data so that someone else can reproduce it.

When asking for help: Writing a good request

Provide context:

• Go back to your pseudocode and use that as an outline for describing where something is going wrong:

  • What do you have? (describe the type of data or analysis)

  • What are you trying to accomplish?

  • What have you tried already?

  • What is the issue with what you’ve tried?

When asking for help: Providing useful code

• Cut anything unrelated to the issue

• Keep anything that is required, including loading specific packages

  • If a package is particularly obscure, consider using packagename::function()

• If you’re including your own dataset, use intuitive variable names and good coding practices

  • Write in lower case and use _ instead of .

  • Provide concise and meaningful variable names

Example: Creating blood pressure categories

Let’s imagine that we want to create a new variable that categorizes participants by blood pressure. We want to create column called bp_cat that groups them into the following bins:

• high BP (>140 mm Hg)

• low BP (< 90 mm Hg)

• normal BP (90-140 mm Hg)

# nhanes1314 <- data.frame(id = c(1:5),
#            mean_BP = c(88, 125, 130, 155, 75))

summary(nhanes$mean_BP)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max.    NA's 
  83.33  108.00  117.33  120.85  130.00  216.67      92 

nhanes$bp_cat <- case_when(nhanes$mean_BP > 140 ~ "high BP",
                           nhanes$mean_BP >= 90 | nhanes$mean_BP <= 140 ~ "normal BP",
                           nhanes$mean_BP <= 90 ~ "low BP"
)

table(nhanes$bp_cat, useNA = "ifany")

  high BP normal BP      <NA> 
      294      1953        92 

Drafting a message

Describe the type of data or analysis:

• I’m working with the 2013-2014 cycle of NHANES data provided by the ID529 teaching team.

What are you trying to accomplish?

• I have a numeric mean blood pressure measurements for all participants and I’d like to categorize them into clinically relevant groups (high, low, normal).

nhanes1314 <- data.frame(id = c(1:5),
           mean_BP = c(88, 125, 130, 155, 75))

Drafting a message

What have you tried already?

• I’m using a case_when() approach to group them based on a relational operator evaluating the mean_BP column.

nhanes1314$bp_cat <- case_when(nhanes1314$mean_BP > 140 ~ "high BP",
                           nhanes1314$mean_BP >= 90 |
                             nhanes1314$mean_BP <= 140 ~ "normal BP",
                           nhanes1314$mean_BP <= 90 ~ "low BP"
)

What is the issue with what you’ve tried?

• Measurements less than 90 should be flagged as “low BP” but are being categorized as “normal BP”

nhanes1314

  id mean_BP    bp_cat
1  1      88 normal BP
2  2     125 normal BP
3  3     130 normal BP
4  4     155   high BP
5  5      75 normal BP

Putting it all together

More best practices for posting code on Slack:

• What we don’t want to see:

  • Everything is named “dat” and “dat1” when intuitive names are more appropriate

  • Really long lines of code on one line

    • Try to limit to ~80 characters

    • Use new lines after a %>% or +

• what we do want to see:

  • Apply some of these best practices

  • Use Slack’s code and code block in your messages to help organize your message

  • updates: if you resolve your own question, post the solution as a thread on the original post. That way others can learn from what you did.

  • Make sure your question is clear – run it by a friend first, if needed!

  • Make sure your question isn’t redundant – people will just redirect you to the answer

How do I find a good reprex?

Check out the datasets that are built into R here: https://stat.ethz.ch/R-manual/R-patched/library/datasets/html/00Index.html

Key takeaways: How do I not get yelled (at on the internet or by collaborators)?

• Use the most minimally reproducible dataset

  • If relevant, you may want to include the session info, R version

• Don’t post screenshots of the code! (check out reprex:: for best practices)

• Don’t ask others to do more work than you’re willing to put in + help others the way you want to be helped!

• Read more: https://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example/16532098