What 1-2 letter object names conflict with existing R objects?

Question

To make my code more readable, I like to avoid names of objects that already exist when creating new objects. Because of the package-based nature of R, and because functions are

Answer 1

Been thinking about this more. Here's a list of one-letter object names in base R:

> var.names <- c(letters,LETTERS)
> var.names[sapply(var.names,exists)]
[1] "c" "q" "t" "C" "D" "F" "I" "T" "X"

And one- and two-letter object names in base R:

one.letter.names <- c(letters,LETTERS)

N <- length(one.letter.names)


first <- rep(one.letter.names,N)
second <- rep(one.letter.names,each=N)

two.letter.names <- paste(first,second,sep="")

var.names <- c(one.letter.names,two.letter.names)

> var.names[sapply(var.names,exists)]
[1] "c"  "d"  "q"  "t"  "C"  "D"  "F"  "I"  "J"  "N"  "T"  "X"  "bc" "gc"
[15] "id" "sd" "de" "Re" "df" "if" "pf" "qf" "rf" "lh" "pi" "vi" "el" "gl"
[29] "ll" "cm" "lm" "rm" "Im" "sp" "qq" "ar" "qr" "tr" "as" "bs" "is" "ls"
[43] "ns" "ps" "ts" "dt" "pt" "qt" "rt" "tt" "by" "VA" "UN"

That's a much bigger list than I initially suspected, although I would never think of naming a variable "if", so to a certain degree it makes sense.

Still doesn't capture object names not in base, or give any sense of which functions are best avoided. I think a better answer would either use expert opinion to figure out which functions are important (e.g. using c is probably worse than using qf) or use a data mining approach on a bunch of R code to see what short-named functions get used the most.

Answer 2

apropos is ideal for this:

apropos("^[[:alpha:]]{1,2}$")

With no packages loaded, this returns:

 [1] "ar" "as" "by" "c"  "C"  "cm" "D"  "de" "df" "dt" "el" "F"  "gc" "gl"
[15] "I"  "if" "Im" "is" "lh" "lm" "ls" "pf" "pi" "pt" "q"  "qf" "qr" "qt"
[29] "Re" "rf" "rm" "rt" "sd" "t"  "T"  "ts" "vi"

The exact contents will depend upon the search list. Try loading a few packages and re-running it if you care about conflicts with packages that you commonly use.

---

I loaded all the (>200) packages installed on my machine with this:

lapply(rownames(installed.packages()), require, character.only = TRUE)

And reran the call to apropos, wrapping it in unique, since there were a few duplicates.

one_or_two <- unique(apropos("^[[:alpha:]]{1,2}$"))

This returned:

  [1] "Ad" "am" "ar" "as" "bc" "bd" "bp" "br" "BR" "bs" "by" "c"  "C" 
 [14] "cc" "cd" "ch" "ci" "CJ" "ck" "Cl" "cm" "cn" "cq" "cs" "Cs" "cv"
 [27] "d"  "D"  "dc" "dd" "de" "df" "dg" "dn" "do" "ds" "dt" "e"  "E" 
 [40] "el" "ES" "F"  "FF" "fn" "gc" "gl" "go" "H"  "Hi" "hm" "I"  "ic"
 [53] "id" "ID" "if" "IJ" "Im" "In" "ip" "is" "J"  "lh" "ll" "lm" "lo"
 [66] "Lo" "ls" "lu" "m"  "MH" "mn" "ms" "N"  "nc" "nd" "nn" "ns" "on"
 [79] "Op" "P"  "pa" "pf" "pi" "Pi" "pm" "pp" "ps" "pt" "q"  "qf" "qq"
 [92] "qr" "qt" "r"  "Re" "rf" "rk" "rl" "rm" "rt" "s"  "sc" "sd" "SJ"
[105] "sn" "sp" "ss" "t"  "T"  "te" "tr" "ts" "tt" "tz" "ug" "UG" "UN"
[118] "V"  "VA" "Vd" "vi" "Vo" "w"  "W"  "y"

You can see where they came from with

lapply(one_or_two, find)