Separate a shopping list into multiple columns

Possibily something like this, and should work with any item/quantity. It just assumes that the quantity follows the item.

Lets use a custom function which extracts item and quantity:

my_fun <- function(w) {
  items <- stringr::str_split(w, "\\d+", simplify = T)
  items <- items[items!=""] # dont now why but you get en empty spot each time
  quantities <- stringr::str_split(w, "\\D+", simplify = T)
  quantities <- quantities[quantities!=""]

  d <- data.frame(item = items, quantity=quantities, stringsAsFactors = F)


  return(d)

}

Example:

my_fun("apple2milk5")
# gives:
#    item quantity
# 1 apple        2
# 2  milk        5

Now we can apply the function to each id, using nest and map:

library(dplyr)
df_result <- df %>% 
  nest(item) %>% 
  mutate(res = purrr::map(data, ~my_fun(.x))) %>% 
  unnest(res)

df_results
# # A tibble: 9 x 3
# id item  quantity
# <int> <chr> <chr>   
# 1     1 apple 2       
# 2     1 milk  5       
# 3     2 milk  1       
# 4     3 juice 3       
# 5     3 apple 5       
# 6     4 egg   10      
# 7     4 juice 1       
# 8     5 egg   8       
# 9     5 milk  2 

Now we can use dcast() (probabily spread would work too):

data.table::dcast(df_result, id~item, value.var="quantity")

#     id apple  egg juice milk
#   1  1     2 <NA>  <NA>    5
#   2  2  <NA> <NA>  <NA>    1
#   3  3     5 <NA>     3 <NA>
#   4  4  <NA>   10     1 <NA>
#   5  5  <NA>    8  <NA>    2

Data:

df <- data.frame(id = 1:5, item = c("apple2milk5", "milk1", "juice3apple5", "egg10juice1", "egg8milk2"), stringsAsFactors = F)

tmp = lapply(strsplit(df$item, "(?<=\\d)(?=\\D)|(?<=\\D)(?=\\d)", perl = TRUE),
             function(x) {
                 d = split(x, 0:1)
                 setNames(as.numeric(d[[2]]), d[[1]])
             })
nm = unique(unlist(lapply(tmp, names)))

cbind(df, do.call(rbind, lapply(tmp, function(x) setNames(x[nm], nm))))
#  id         item apple milk juice egg
#1  1  apple2milk5     2    5    NA  NA
#2  2        milk1    NA    1    NA  NA
#3  3 juice3apple5     5   NA     3  NA
#4  4  egg10juice1    NA   NA     1  10
#5  5    egg8milk2    NA    2    NA   8

I just came up with a tidyverse solution. Use str_extract() to extract the quantities and set their names as product names. Then reduce(bind_rows) generates the expected outcome.

library(tidyverse)

df$item %>%
  map(~ set_names(str_extract_all(., "\\d+")[[1]], str_extract_all(., "\\D+")[[1]])) %>%
  reduce(bind_rows) %>%
  mutate_all(as.numeric) %>%
  bind_cols(df, .)

#   id         item apple milk juice egg
# 1  1  apple2milk5     2    5    NA  NA
# 2  2        milk1    NA    1    NA  NA
# 3  3 juice3apple5     5   NA     3  NA
# 4  4  egg10juice1    NA   NA     1  10
# 5  5    egg8milk2    NA    2    NA   8