R as it is – Win-Vector Blog

June 15, 2018John MountCoding, TutorialsR, R as it is, R tips, R tricks, wrapr

R Tip: Be Wary of “…”

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.

Continue reading R Tip: Be Wary of “…”

April 11, 2018April 11, 2018John MountOpinion, Programming, StatisticsR, R as it is, R tips, R tricks14 Comments

Neglected R Super Functions

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.

Continue reading Neglected R Super Functions

March 17, 2018March 18, 2018John MountCoding, TutorialsR, R as it is, R tips, R tricks4 Comments on R Tip: Use stringsAsFactors = FALSE

R Tip: Use `stringsAsFactors = FALSE`

R tip: use stringsAsFactors = FALSE.

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.

800px Sigmund Freud by Max Halberstadt cropped

It is often claimed Sigmund Freud said “Sometimes a cigar is just a cigar.”

Continue reading R Tip: Use stringsAsFactors = FALSE

March 16, 2018March 16, 2018John MountCoding, Opiniondata bases, PostgreSQL, R, R and big data, R as it is, RPostgres, RPostgreSQL4 Comments

Take Care If Trying the RPostgres Package

Take 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!

Continue reading Take Care If Trying the RPostgres Package

March 14, 2018John MountOpinion, Rants, StatisticsR, R as it is3 Comments

The Many Faces of R

Some days I see R as an eclectic programming language preferred by scientists.

“Programming languages as people.”

From Leftover Salad (David Marino).

Other days I see it more like the following.

Continue reading The Many Faces of R

March 8, 2018John MountCoding, Statistics, TutorialsR, R as it is, R tips, R tricks1 Comment on R Tip: Introduce Indices to Avoid for() Class Loss Issues

R Tip: Introduce Indices to Avoid `for()` Class Loss Issues

Here 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

February 27, 2018February 27, 2018John MountCoding, Programming, TutorialsR, R as it is, R tips, R tricks8 Comments on R Tip: Use drop = FALSE with data.frames

R Tip: Use `drop = FALSE` with `data.frame`s

Another R tip. Get in the habit of using drop = FALSE when indexing (using [ , ] on) data.frames.

Prince Rupert’s drops (img: Wikimedia Commons)

Continue reading R Tip: Use drop = FALSE with data.frames

February 23, 2018February 26, 2018John MountCoding, Opinion, Programming, Statistics, Tutorialsbase-R, piping with base R, R, R as it is, R tricks, rlang, subset6 Comments on Is R base::subset() really that bad?

Is `R` `base::subset()` really that bad?

Is R base::subset() really that bad?

Continue reading Is R base::subset() really that bad?

February 21, 2018February 22, 2018John MountAdministrativia, Coding, Statistics, TutorialsR, R as it is, R tips, R tricks7 Comments on R Tip: Use [[ ]] Wherever You Can

R Tip: Use `[[ ]]` Wherever You Can

R tip: use [[ ]] wherever you can.

In R the [[ ]] is the operator that (when supplied a simple scalar argument) pulls a single element out of lists (and the [ ] operator pulls out sub-lists).

For vectors [[ ]] and [ ] appear to be synonyms (modulo the issue of names). However, for a vector [[ ]] checks that the indexing argument is a scalar, so if you intend to retrieve one element this is a good way of getting an extra check and documenting intent. Also, when writing reusable code you may not always be sure if your code is going to be applied to a vector or list in the future.

It is safer to get into the habit of always using [[ ]] when you intend to retrieve a single element.

Example with lists:

list("a", "b")[1]
#> [[1]]
#> [1] "a"

list("a", "b")[[1]]
#> [1] "a"

Example with vectors:

c("a", "b")[1]
#> [1] "a"

c("a", "b")[[1]]
#> [1] "a"

The idea is: in situations where both [ ] and [[ ]] apply we rarely see [[ ]] being the worse choice.

Note on this article series.

This R tips series is short simple notes on R best practices, and additional packaged tools. The intent is to show both how to perform common tasks, and how to avoid common pitfalls. I hope to share about 20 of these about every other day to learn from the community which issues resonate and to also introduce some of features from some of our packages. It is an opinionated series and will sometimes touch on coding style, and also try to showcase appropriate Win-Vector LLC R tools.

September 22, 2017September 23, 2017John MountOpinion, Pragmatic Data Science, Pragmatic Machine Learning, Statistics, TutorialsBig Data, enterprise, part-time R user, R, R and big data, R as it is3 Comments

My advice on dplyr::mutate()

There are substantial differences between ad-hoc analyses (be they: machine learning research, data science contests, or other demonstrations) and production worthy systems. Roughly: ad-hoc analyses have to be correct only at the moment they are run (and often once they are correct, that is the last time they are run; obviously the idea of reproducible research is an attempt to raise this standard). Production systems have to be durable: they have to remain correct as models, data, packages, users, and environments change over time.

Demonstration systems need merely glow in bright light among friends; production systems must be correct, even alone in the dark.

“Character is what you are in the dark.”

John Whorfin quoting Dwight L. Moody.

I have found: to deliver production worthy data science and predictive analytic systems, one has to develop per-team and per-project field tested recommendations and best practices. This is necessary even when, or especially when, these procedures differ from official doctrine.

What I want to do is share a single small piece of Win-Vector LLC‘s current guidance on using the R package dplyr. Continue reading My advice on dplyr::mutate()