Stylistic and code consistency rules

[malcolmbarrett]

This is a sort of internal style guide for various decisions we need to make. All of these are open to discussion, but I want to be consistent for all of them. This issue also serves as a final check when the first edition is done.

Code

• Use grkstyle::grk_style_dir() to style code
• Use tibble() instead of data.frame()
• Use summarize() instead of summarise()
• Use if_else() instead of ifelse()
• When grouping by a single variable, use group_by() over .by
• When grouping by more than one variable when you don't need persistent grouping, use .by or group_by() with .groups = "drop" instead of ungroup(). Prefer .by unless group_by() is more readable for the code, e.g., a very long or complex summarize() statement
• augment(data = ...) vs. augment(newdata = ...): For cases when we are supplying the original data frame (which we need to do to keep the variables that weren't in the model), use the data argument of augment() instead of newdata. newdata should only be used when it's actually a new data frame, as we do in g-computation cloning.
• Use |> instead of %>%
• Use slice_sample() instead of sample_n(), sample_frac() (because it supercedes these per the documentation)
• For comments, use all lowercase (except proper nouns) and one # at beginning
• Use explicit testing of 0 and 1, e.g. if x = 1, write if (x == 1) rather than if (x)
• For case_when(), use .default = default_value rather than TRUE ~ default_value

Quarto

• Prefer cross-references over writing that we discussed/will discuss something somewhere else in the book
• For code we are not showing, prefer code-fold: true over echo: false. This way, we can de-emphasize the code while providing easy access for those interested
• Use sentence breaks (a line break after every sentence) for easier git usage. You can do this automatically by changing your settings in RStudio to use sentence breaks and turning visual mode on and off.
• For package names, write it as `{pkg}` (which will automatically link to the package website) the first time you mention it in a chapter; thereafter, don't use formatting. Just write: pkg.

Writing

• Check that anytime we type "casual" we mean it. Common typo!
• Defined terms are in bold; italics used for emphasis, not definitions.
• Prefer "exposure" and "outcome" as generic terms for the cause and effect over e.g., treated vs untreated, but use your judgment
• Write it as "Extra Magic Hours" and "Extra Magic Morning/Evening" (all caps)
• Write times as 9 AM instead of 9am
• "data frame" and "data set" (with a space between)

Figures and tables

• All figures and tables have a thorough caption (what is actually in the fig/tbl, not just the idea behind it)
• Use gt() for code-based tables instead of kable(). Otherwise, use markdown tables.
• No time labels for EMM DAG since we don't actually know. Putting this here because we reuse this code in several places so we should check for consistency

I think we may have some other things to add for this one to make sure these are consistent

[malcolmbarrett]

One thing from #310 that might be good: prefer ifelse()/if_else() over case_when() when just a single condition

The big one in this PR, though, is ifelse() vs. if_else(). I hold this opinion lightly, but I have a preference for ifelse() because I don't believe if_else() is that helpful in practice. As opposed to say, map_dbl(), it doesn't really feel like I'm declaring the type. I have to know the type of the inputs. And I also still need to know R's conversion rules to understand the violation. But if_else() is sort of a lonely function in this respect because lots of functions can do implicit conversion besides ifelse(), so I'm usually already on the defensive. So if_else() is a function that has never won me over. But again, I can be convinced and am more concerned about consistency

[tgerke]

Suggestion to use if_else instead of ifelse. Reasons:

• if_else ensures type consistency
• if_else handles NA better
• if_else is faster
  To my knowledge, reasons to use ifelse would include shortcutting TRUE/FALSE arguments with integers (which I don't think is the best pedagogy) and a desire to avoid evaluating the false clause when the statement is true (also not the best pedagogy / has a code smell). ifelse could also be good if aiming for minimal dependency applications, but we've already bought into library(tidyverse) as a first move in this book. Happy to learn if there are better reasons though.

Some good discussion here: https://stackoverflow.com/questions/50646133/dplyr-if-else-vs-base-r-ifelse

[malcolmbarrett]

shortcutting TRUE/FALSE arguments with integers (which I don't think is the best pedagogy)

I agree with this and we should add explicit testing to our list (as you changed in #310)

Edit: added to the checklist

[malcolmbarrett]

@LucyMcGowan can be the tie-breaker for ifelse() vs. if_else(). I'll be ok with either

[tgerke]

One thing from #310 that might be good: prefer ifelse()/if_else() over case_when() when just a single condition

The big one in this PR, though, is ifelse() vs. if_else(). I hold this opinion lightly, but I have a preference for ifelse() because I don't believe if_else() is that helpful in practice. As opposed to say, map_dbl(), it doesn't really feel like I'm declaring the type. I have to know the type of the inputs. And I also still need to know R's conversion rules to understand the violation. But if_else() is sort of a lonely function in this respect because lots of functions can do implicit conversion besides ifelse(), so I'm usually already on the defensive. So if_else() is a function that has never won me over. But again, I can be convinced and am more concerned about consistency

Afaik it's not relevant to applications we're using in this book, but ifelse can have some pretty severe gotchas that I've experienced due to hidden type casting. That SO thread I linked has a good example using dates, and there are others.

Reading from the beginning, this is the first time I've encountered ifelse in the book and I would surely notice it in the 6 other files where it occurs (did a quick search to make sure I'm not proposing a drastic PITA)

[tgerke]

@LucyMcGowan can be the tie-breaker for ifelse() vs. if_else(). I'll be ok with either

Great plan!

[tgerke]

New topic: suggest replacing TRUE ~ lines in case_when with the much clearer .default =

[malcolmbarrett]

ifelse() might be the easiest find-replace ever luckily. Identical syntax and no other use of the letters "ifelse"

[malcolmbarrett]

@LucyMcGowan voted for if_else(), adding it to the checklist now and will submit a PR

[malcolmbarrett]

Note: https://github.com/ropenscilabs/aeolus will auto wrap markdown lines