Here is simple modeling problem in R.
We want to fit a linear model where the names of the data columns carrying the outcome to predict (y), the explanatory variables (x1, x2), and per-example row weights (wt) are given to us as string values in variables.
Let’s try some "ugly corner cases" for data manipulation in R. Corner cases are examples where the user might be running to the edge of where the package developer intended their package to work, and thus often where things can go wrong.
Let’s see what happens when we try to stick a fork in the power-outlet.
Our group has done a lot of work with non-standard calling conventions in R.
Our tools work hard to eliminate non-standard calling (as is the purpose of wrapr::let()), or at least make it cleaner and more controllable (as is done in the wrapr dot pipe). And even so, we still get surprised by some of the side-effects and mal-consequences of the over-use of non-standard calling conventions in R.
Please read on for a recent example.
R Tip: be wary of “...“.
The following code example contains an easy error in using the R function unique().
vec1 <- c("a", "b", "c")
vec2 <- c("c", "d")
unique(vec1, vec2)
# [1] "a" "b" "c"
Notice none of the novel values from vec2 are present in the result. Our mistake was: we (improperly) tried to use unique() with multiple value arguments, as one would use union(). Also notice no error or warning was signaled. We used unique() incorrectly and nothing pointed this out to us. What compounded our error was R‘s “...” function signature feature.
In this note I will talk a bit about how to defend against this kind of mistake. I am going to apply the principle that a design that makes committing mistakes more difficult (or even impossible) is a good thing, and not a sign of carelessness, laziness, or weakness. I am well aware that every time I admit to making a mistake (I have indeed made the above mistake) those who claim to never make mistakes have a laugh at my expense. Honestly I feel the reason I see more mistakes is I check a lot more.
R has a lot of under-appreciated super powerful functions. I list a few of our favorites below.
Atlas, carrying the sky. Royal Palace (Paleis op de Dam), Amsterdam.
Photo: Dominik Bartsch, CC some rights reserved.
stringsAsFactors = FALSEstringsAsFactors = FALSETake care if trying the new RPostgres database connection package. By default it returns some non-standard types that code developed against other database drivers may not expect, and may not be ready to defend against.
Danger, Will Robinson!
Some days I see R as an eclectic programming language preferred by scientists.
“Programming languages as people.”
Other days I see it more like the following.
for() Class Loss Issuesfor() Class Loss IssuesHere is an R tip. Use loop indices to avoid for()-loops damaging classes.
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. To avoid this problem introduce an index, and loop over that, not over the vector contents.
for(ii in seq_along(d)) {
di <- d[[ii]]
print(di)
}
#> [1] "2018-02-18 10:16:16 PST"
#> [1] "2018-02-18 10:16:16 PST"
Continue reading R Tip: Introduce Indices to Avoid for() Class Loss Issues
drop = FALSE with data.framesdrop = FALSE with data.frames
