I need to validate a string against a character vector pattern. My current code is:
trim <- function (x) gsub("^\\s+|\\s+$", "", x)
# valid pattern is lowercase alphabet, '.', '!', and '?' AND
# the string length should be >= than 2
my.pattern = c(letters, '!', '.', '?')
check.pattern = function(word, min.size = 2)
{
word = trim(word)
chars = strsplit(word, NULL)[[1]]
all(chars %in% my.pattern) && (length(chars) >= min.size)
}
Example:
w.valid = 'special!'
w.invalid = 'test-me'
check.pattern(w.valid) #TRUE
check.pattern(w.invalid) #FALSE
This is VERY SLOW i guess…is there a faster way to do this? Regex maybe?
Thanks!
PS: Thanks everyone for the great answers. My objective was to build a 29 x 29 matrix,
where the row names and column names are the allowed characters. Then i iterate over each word of a huge text file and build a ‘letter precedence’ matrix. For example, consider the word ‘special’, starting from the first char:
row s, col p -> increment 1
row p, col e -> increment 1
row e, col c -> increment 1
... and so on.
The bottleneck of my code was the vector allocation, i was ‘appending’ instead of pre-allocate the final vector, so the code was taking 30 minutes to execute, instead of 20 seconds!
There are some built-in functions that can clean up your code. And I think you’re not leveraging the full power of regular expressions.
The blaring issue here is
strsplit. Comparing the equality of things character-by-character is inefficient when you have regular expressions. The pattern here uses the square bracket notation to filter for the characters you want.*is for any number of repeats (including zero), while the^and$symbols represent the beginning and end of the line so that there is nothing else there.nchar(word)is the same aslength(chars). Changing&&to&makes the function vectorized so you can input a vector of strings and get a logical vector as output.Next, using curly braces for number of repetitions and some
paste0, the pattern can use your min.size:Finally, you can internalize the regex from
trim: