# Text Processing in R

On a regular basis, I have to process text in R. I invariably find that I need a function whose name or usage I can’t bring to mind. To help my future self, I’m writing this review of R’s built-in text processing functions. Hopefully, this review will also be of use to others.

Character Vectors == Arrays of Strings
The first source of confusion for me is the R type system. In R, a string is considered to be a character vector, but an R character vector would be an array of strings in any other programming language. Consider the following example:

 1 2  str = 'string' str[1] # This evaluates to 'string'.

To get access to the individual characters in an R string, you need to use the substr function:

 1 2  str = 'string' substr(str, 1, 1) # This evaluates to 's'.

For the same reason, you can’t use length to find the number of characters in a string. You have to use nchar instead.

But let’s go back to substr. The first argument to substr is a character vector, the second is the index of the first character you want, and the third is the index of the last character you want. So you can also use substr as follows:

 1 2 3  str = 'string' substr(str, 1, 2) == 'st' substr(str, 5, 6) == 'ng'

As you can see, substr lets you access the individual characters of a string using an indexing/slicing strategy.

To break strings apart into vectors of characters, you can use the strsplit function, which works a lot like the split function in Perl. Here’s an example:

 1  strsplit('0-0-1', '-') # Evaluates to list('0', '0', '1')

Putting Things Back Together Again
Now that you can pull strings apart, you need to be able to put the characters back together again into strings. You can do this using paste. paste is an idiosyncratic function: it is the only function for concatenation of strings in R, but it also handles the work of more sophisticated functions like Perl’s join. Try the following:

 1 2 3  str1 = 'first' str2 = 'second' print(paste(str1, str))

As you’ll see, there’s an odd space added to the output. That’s because paste has an optional argument that provides a separator used when combining strings that defaults to a single space. So,

 1  paste('first', 'second') == paste('first', 'second', sep = ' ')

You can get rid of the space by specifying a null separator instead.

 1  print(paste('first', 'second', sep = ''))

Changing Case
To change the case of strings or individual characters, you need to use the tolower and toupper functions. You can use these with substr to make a function that turns most common words into their title case form:

 1 2 3 4 5  pseudo.titlecase = function(str) { substr(str, 1, 1) = toupper(substr(str, 1, 1)) return(str) }

With a little more sophistication, you can make a full title case function à la John Gruber. The result of my attempt to do this is fairly long, so I’ve posted it to my GitHub account. I’ll probably see about adding it to the R repository at some point if I can incorporate enough features to make it worth using. If you’re interested in helping or using what I’ve written, you can check out the code here.

Finally, there is a chartr function that translates characters in the input into the corresponding characters you select. For instance, you might try this:

 1  chartr('abc', 'XYZ', 'abcabc') # Evaluates to "XYZXYZ".

This will remind Perl users of tr, which I personally never use. Nevertheless, it’s nice knowing that it’s there in R, albeit with a slightly different name.

Substring Containment
Finally, you might want to know if a string is contained in another string or set of strings. You can do this using the charmatch function:

 1 2  charmatch("m", c("mean", "mode")) # returns 0 charmatch("med", c("mean", "median")) # returns 2

I tend to use regular expressions by the time I would need substring matching, so I’m not sure if I would ever use charmatch in practice.

Future Ideas
For more sophisticated text processing, you would want to use regular expressions and the grep family of functions. I’ll have to read about them and write up something about their use in the future. R also implements an approximate regular expression matching system using Levenshtein edit distances, but I haven’t tried using that yet.

### 6 responses to “Text Processing in R”

1. Old posting. Indeed, R has regular expressions. See, for instance, http://en.wikibooks.org/wiki/R_Programming/Text_Processing

2. Nice post; very helpful. One of the few posts on text processing in R that lays things out in an easily understood format.

I’m wondering if you have any ideas on how to split a string into two part at the point where digits are first encountered. For example, I’d like to be able to split this sting in two : “pp to6/29/2010”. So one entry would be “pp to” and the second would be the date “6/29/2010”. Note that the text in the front could be of variable length so you can’t just instruct based on the character count.

Probably strsplit or substr but I’m not sure how to say … at the first digit encountered.

Either way, thanks for the great post

y

3. Nice post very helpful…
Hi eric u can do that by searching for the digit in ur string first.. \d+..
and then find out its indice and u may use substr and split the string into 2 parts…

4. Thanks for this useful post. I was stuck with similar problems and then googled out your post. :)

5. While substr and its kin are useful for one-off short things, I’d recommend getting familiar with the entire regex family, such as gsub. From an R window having accompanying Help installed, simply incant

?gsub

to see them. There’s also strsplit, especially if a regex is used to specify the splitting form.

Finally, check out Wickham’s package reshape2, and the interesting doBy package of Højsgaard and Halekoh.