Category: R

John Mount shows a few techniques for legible function chaining with R:

The dot intermediate convention is very succinct, and we can use it with base R transforms to get a correct (and performant) result. Like all conventions: it is just a matter of teaching, learning, and repetition to make this seem natural, familiar and legible.

My preference is to use dplyr + magrittr because I really do like that pipe operator.  John’s point is well-taken, however:  you don’t need to use the tidyverse to write clean R code, and there can be value in using the base functionality.

John Mount recommends setting stringsAsFactors = FALSE for data frames in R:

R often uses a concept of factors to re-encode strings. This can be too early and too aggressive. Sometimes a string is just a string.

Tibbles have this set by default.  For an explanation as to why it defaults to TRUE for data frames, Roger Peng has the story.

David Smith notes that R 3.4.4 is now generally available:

R 3.4.4 has been released, and binaries for Windows, Mac, Linux and now available for download on CRAN. This update (codenamed “Someone to Lean On” — likely a Peanuts reference, though I couldn’t find which one with a quick search) is a minor bugfix release, and shouldn’t cause any compatibility issues with scripts or packages written for prior versions of R in the 3.4.x series.

Read on to see the change list.

Eric Cai demonstrates the difference between levels() and unique() when dealing with factors in R:

The new data set “iris2” does not have any rows containing “setosa” as a possible value of “Species”, yet the levels() function still shows “setosa” in its output.

According to the user G5W in Stack Overflow, this is a desirable behaviour for the levels() function.  Here is my interpretation of the intent behind the creators of base R: The possible values of a factor are fundamental attributes of that variable, which should not be altered because of changes in the data.

There’s some back-and-forth in the comments; my takeaway is that both are useful functions depending upon what, exactly, you want to learn.

John Mount has a couple of tips around using for loops in R.  First up, pre-allocate lists to make certain types of iterative processing faster:

Another R tip. Use vector(mode = "list") to pre-allocate lists.

result <- vector(mode = "list", 3)
print(result)
#> [[1]]
#> NULL
#> 
#> [[2]]
#> NULL
#> 
#> [[3]]
#> NULL

The above used to be critical for writing performant R code (R seems to have greatly improved incremental list growth over the years). It remains a convenient thing to know.

Also, use loop indices when iterating through for loops:

Below is an R annoyance that occurs again and again: vectors lose class attributes when you iterate over them in a for()-loop.

d <- c(Sys.time(), Sys.time())
print(d)
#> [1] "2018-02-18 10:16:16 PST" "2018-02-18 10:16:16 PST"

for(di in d) {
  print(di)
}
#> [1] 1518977777
#> [1] 1518977777

Notice we printed numbers, not dates/times.

Very useful information.

Steph Locke explains some tricky behavior with window functions in R:

So looking at the code I wrote, you may have expectedc2 to hold NA, 3, 5, ... where it’s taking the current value and the prior value to make a window of width 2. Another reasonable alternative is that you may have expected c2 to hold NA, NA, 3, ... where it’s summing up the prior two values. But hey, it’s kinda working like cumsum() right so that’s ok! But wait, check out c3. I gave c3 a window of width 3 and it gave me NA, 6, 9, ... which looks like it’s summing the prior value, the current value, and the next value. …. That’s weird right?

It turns out the default behaviour for these rolling calculations is to center align the window, which means the window sits over the current value and tries it’s best to fit over the prior and next values equally. In the case of us giving it an even number it decided to put the window over the next values more than the prior values.

Knowing your window is critical when using a window function, and knowing that some functions have different default windows than others helps you be prepared.