Parsing a string into a nested data.table

Question

I have data in a table in which one cell in every row is a multiline string, which is formatted a a bit like a document with references at the end of it. For example, one of those strings looks like:

item A...1
item B...2
item C...3
item D...2
1=foo
2=bar
3=baz

My eventual goal is to extract foo/bar/baz into columns and count the matching items. So for the above, I'd end up with a row including:

foo | bar | baz
----+-----+----
1   | 2   | 1

I tried to start by extracting the "reference" mappings, as a nested data.table looking like this:

code | reason
-----+-------
1    | foo
2    | bar
3    | baz

Here's how I tried to do it, using data.table and stringr.

encounter_alerts[, whys := lapply(
  str_extract_all(text, regex('^[0-9].*$', multiline = TRUE)),
  FUN = function (s) { fread(text = s, sep = '=', header = FALSE, col.names = c('code', 'reason')) }
)]

I am very confused by the error message I get when I try to do this:

Error in fread(text = s, sep = "=", header = FALSE, col.names = c("code",  :
  file not found: 1=foo

I am explicitly using text rather than file so I'm not sure how it's trying to interpret the line of text as a filename!

When I test this with a single row, it seems to work fine:

> fread(text = str_extract_all(encounter_alerts[989]$text, regex('^[0-9].*$', multiline = TRUE))[[1]], sep = '=', header = FALSE, col.names = c('code', 'reason'))
   code reason
1:    1    foo
2:    2    bar

What am I doing wrong? Is there a better way to do this?

Thanks!

Answer 1

Note: Edited after reading comments

From your comment, I tried to reproduce what I understand your data might look like.

library(tidyverse)

df <- tibble(
  strings = c("item A...1
item B...2
item C...3
item D...2
1=foo
2=bar
3=baz",
"item A...2
item B...2
item C...3
item D...1
1=toto
2=foo
3=lala",
"item A...3
item B...3
item C...3
item D...1
1=tutu
3=ttt")
)

Code:

get_ref <- function(string) {
  string %>%
    str_split("\n") %>%
    unlist() %>% 
    str_subset("=") %>%
    str_split_fixed("=", 2) %>%
    as_tibble() %>%
    rename(code = V1, reason = V2)
}

list1 <- map(df$strings, get_ref)

get_value <- function(string) {
  string %>%
      str_split("\n") %>%
      unlist() %>% 
      str_subset("\\.\\.\\.") %>%
      str_replace_all(".*\\.\\.\\.", "") %>%
      as_tibble() %>%
    rename(code = value)
}

list2 <- map(df$strings, get_value)

get_result <- function(df1, df2) {
  left_join(df1, df2) %>%
    count(reason) %>%
    spread(reason, n)
}

result <- map2_df(list1, list2, get_result)

result[is.na(result)] <- 0

result

Result

# A tibble: 3 x 7
    bar   baz   foo  lala  toto   ttt  tutu
  <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
1     2     1     1     0     0     0     0
2     0     0     2     1     1     0     0
3     0     0     0     0     0     3     1