R has a lot of string functions, many of them can be found with ls("package:base", pattern="str"). Additionally, there are add-on packages such as stringr, gsubfn and brew that enhance R string processing capabilities. As a statistical language and environment, R has an edge compared to other programming languages when it comes to text mining algorithms or natural language processing. There is even a taskview for this on CRAN.

I am currently playing with markdown files in R, which eventually will result in a new version of mdtools, and collected or created some string functions I like to present in this blogpost. The source code of the functions is at the end of the post, first I show how to use these functions.

Head and tail for strings

The idea for the first two functions I had earlier, and I had to learn that providing a S3 method for head and tail is not an good idea. But strhead and strtail did prove as handy. Here are some usage examples:

> strhead("egghead", 3)
[1] "egg"
> strhead("beagle", -1) # negative index
[1] "beagl"
> strtail(c("bowl", "snowboard"), 3) # vector-able in the first argument
[1] "owl" "ard"

These functions are only syntactic sugar, hopefully easy to memorize because of their similarity to existing R functions. For packages, they are probably not worth introducing an extra dependency. I thought about defining an replacement function like substr does, but I did not try it because head and tail do not have replacement functions.

Bare minimum template

With sprintf, format and pretty, there are powerful functions for formatting strings. However, sometimes I miss the named template syntax as in Python or in Makefiles. So I implemented this in R. Here are some usage examples:

> strsubst(
+   "$(WHAT) is $(HEIGHT) meters high.", 
+   list(
+     WHAT="Berlin's teletower",
+     HEIGHT=348
+   )
+ )
[1] "Berlin's teletower is 348 meters high."
> d <- strptime("2012-03-18", "%Y-%m-%d")
> strsubst(c(
+   "Be careful with dates.",
+   "$(NO_CONV) shows a list.",
+   "$(CONV) is more helpful."),
+   list(
+     NO_CONV=d,
+     CONV= as.character(d)
+   )
+ )
[1] "Be careful with dates."                                                                                        
[2] "list(sec = 0, min = 0, hour = 0, mday = 18, mon = 2, year = 112, wday = 0, yday = 77, isdst = 0) shows a list."
[3] "2012-03-18 is more helpful."                                                                                   

The first argument can be string or a vector of strings such as the output of readLines. The second argument can be any indexable object (i.e. with working [ operator) such as lists. Environments are not indexable hence won’t work.

Parse raw text

Frequently, I need to extract parts from raw text data. For instance, few weeks ago I had to parse a SPSS script (some variable labels were hard-coded theree and not in the .sav file). The script contained lines VARIABLE LABELS some_var "<some_label>". I was interested in some_var and <some_label>. The examples from the R documentation on regexpr gave me the direction and led me to the strparse function that is applied as follows:

> lines <- c(
+     'VARIABLE LABELS weight "weight".',
+     'VARIABLE LABELS altq "Year of birth".',
+     'VARIABLE LABELS hhg "Household size".',
+     'missing values all (-1).',
+     'EXECUTE.'
+ )
> pat <- 'VARIABLE LABELS (?<name>[^\\s]+) \\"(?<lbl>.*)\\".$'
> matches <- grepl(pat, lines, perl=TRUE)
> strparse(pat, lines[matches])
name     lbl             
[1,] "weight" "weight"        
[2,] "altq"   "Year of birth" 
[3,] "hhg"    "Household size"

The function returns a vector if one line was parsed and a matrix otherwise. It supports named groups.

Recoding with regular expressions

Sometimes I need to recode a vector of strings in a way that I find all mathces for a particular regular expression and replace these matches with one string. The I match all remaining strings with a second regular expression and replace the hits with a second replacement. And so on. I wrote the strrecode function to support this operation. The function can be seen as an generalisation of the gsub function. It is the only function without test code. Here is a made-up example analysing process information from the task manager:

> dat <- data.frame(
+     wtitle=c(paste(c("Inbox", "Starred", "All"), "- Google Mail"), paste("file", 1:4, "- Notepad++")),
+     pid=sample.int(9999,7),
+     exe=c(rep("chrome.exe",3), rep("notepad++.exe", 4))
+ )
> dat <- transform(
+     dat,
+     usage=strrecode(c("Google Mail$|Microsoft Outlook$", " - Notepad\\+\\+$|Microsoft Word$"), c("Mail", "Text"), dat$wtitle)
+ )
> dat
wtitle  pid           exe usage
1   Inbox - Google Mail 6810    chrome.exe  Mail
2 Starred - Google Mail 2488    chrome.exe  Mail
3     All - Google Mail 4086    chrome.exe  Mail
4    file 1 - Notepad++ 2946 notepad++.exe  Text
5    file 2 - Notepad++  112 notepad++.exe  Text
6    file 3 - Notepad++ 1176 notepad++.exe  Text
7    file 4 - Notepad++ 8881 notepad++.exe  Text

Interested in the source code of these helper functions? Read on.