Splitting Strings and Generating Frequency Tables in R

Question

I have a column of firm names in an R dataframe that goes something like this:

"ABC Industries"  
"ABC Enterprises"  
"123 and 456 Corporation"  
"XYZ Company"

And so on. I'm trying to generate frequency tables of every word that appears in this column, so for example, something like this:

Industries   10  
Corporation  31  
Enterprise   40  
ABC          30  
XYZ          40  

I'm relatively new to R, so I was wondering of a good way to approach this. Should I be splitting the strings and placing every distinct word into a new column? Is there a way to split up a multi-word row into multiple rows with one word?

Answer 1

If you wanted to, you could do it in a one-liner:

R> text <- c("ABC Industries", "ABC Enterprises", 
+            "123 and 456 Corporation", "XYZ Company")
R> table(do.call(c, lapply(text, function(x) unlist(strsplit(x, " ")))))

        123         456         ABC         and     Company 
          1           1           2           1           1 
Corporation Enterprises  Industries         XYZ 
          1           1           1           1 
R> 

Here I use strsplit() to break each entry intro components; this returns a list (within a list). I use do.call() so simply concatenate all result lists into one vector, which table() summarises.

Answer 2

Here is another one-liner. It uses paste() to combine all of the column entries into a single long text string, which it then splits apart and tabulates:

text <- c("ABC Industries", "ABC Enterprises", 
         "123 and 456 Corporation", "XYZ Company")

table(strsplit(paste(text, collapse=" "), " "))

Answer 3

You can use the package tidytext and dplyr:

set.seed(42)

text <- c("ABC Industries", "ABC Enterprises", 
       "123 and 456 Corporation", "XYZ Company")

data <- data.frame(category = sample(text, 100, replace = TRUE),
                   stringsAsFactors = FALSE)

library(tidytext)
library(dplyr)

data %>%
  unnest_tokens(word, category) %>%
  group_by(word) %>%
  count()

#> # A tibble: 9 x 2
#> # Groups:   word [9]
#>          word     n
#>         <chr> <int>
#> 1         123    29
#> 2         456    29
#> 3         abc    45
#> 4         and    29
#> 5     company    26
#> 6 corporation    29
#> 7 enterprises    21
#> 8  industries    24
#> 9         xyz    26