Extracting a date from a column and adding the year if missing in R

Question

I am trying to extract dates from text and create a new column in a dataset. Dates are entered in different formats in column A1 (either mm-dd-yy or mm-dd). I need to find a way to identify the date in column A1 and then add the year if it is missing. Thus far, I have been able to extract the date regardless of the format; however, when I use as.Date on the new column A2, the date with mm-dd format becomes <NA> . I am aware that there might not be a direct solution for this situation, but a workaround (generalizable to a larger data set) would be great. The year would go from September 2019 to August 2020. Additionally, I am not sure why the format I use within the as.Date function is unable to control how the date gets displayed. This latter issue is not that important, but I am surprised by the behavior of the R function. A solution in tidyverse would be much appreciated.

library(tidyverse)
library(stringr)
    
db <- data.frame(A1 = c("review 11/18", "begins 12/4/19", "3/5/20", NA, "deadline 09/5/19", "9/3")) 

db %>% mutate(A2 = str_extract(A1, "[0-9/0-9]+")) 
#                A1      A2
#1     review 11/18   11/18
#2   begins 12/4/19 12/4/19
#3           3/5/20  3/5/20
#4             <NA>    <NA>
#5 deadline 09/5/19 09/5/19
#6              9/3     9/3
    
db %>% mutate(A2 = str_extract(A1, "[0-9/0-9]+")) %>% 
       mutate(A2 = A2 %>% as.Date(., "%m/%d/%y"))

 #               A1         A2
 #   1     review 11/18       <NA>
 #   2   begins 12/4/19 2019-12-04
 #   3           3/5/20 2020-03-05
 #   4             <NA>       <NA>
 #   5 deadline 09/5/19 2019-09-05
 #   6              9/3       <NA>

Answer 1

Well, this is neither a beautiful, concise or tidyverse solution but it does work and should be flexible in its modularity.

library(tidyverse)

db <- data.frame(A1 = c("review 11/18", "begins 12/4/19", "3/5/20", NA, "deadline 09/5/19", "9/3")) 
db <- db %>% mutate(A2 = str_extract(A1, "[0-9/0-9]+"), A2 = str_extract(A1, "[0-9/0-9]+"))

test1 <- unlist(lapply(str_split(db$A2, "/", n = 3), function(x) length(x)))
test2 <- lapply(str_split(db$A2, "/", n = 3), function(x) as.numeric(x))

if(test1 == 2){
  if(test2[[1]] >= 9){
    db$A2 <- ifelse(test = between(nchar(db$A2), 3, 5) & !is.na(db$A2), yes = paste0(db$A2, "/19"), no = db$A2)
  }
  if(test2[[1]] < 9){
    db$A2 <- ifelse(test = between(nchar(db$A2), 3, 5) & !is.na(db$A2), yes = paste0(db$A2, "/20"), no = db$A2)
  }
}

db <- db %>% mutate(A2 = A2 %>% as.Date(., "%m/%d/%y"))
db

                A1         A2
1     review 11/18 2019-11-18
2   begins 12/4/19 2019-12-04
3           3/5/20 2020-03-05
4             <NA>       <NA>
5 deadline 09/5/19 2019-09-05
6              9/3 2019-09-03

Answer 2

Perhaps:

library(tidyverse)

db <- data.frame(A1 = c("review 11/18", "begins 12/4/19", "3/5/20", NA, "deadline 09/5/19", "9/3")) 

#year from september to august 2019

(db <- 
 db %>% 
  mutate(A2 = str_extract(A1, '[\\d\\d/]+'),
         A2 = if_else(str_count(A2, '/') == 1 & as.numeric(str_extract(A2, '\\d+')) > 8, paste0(A2, '/19'), A2),
         A2 = if_else(str_count(A2, '/') == 1 & as.numeric(str_extract(A2, '\\d+')) <= 8, paste0(A2, '/20'), A2),
         A2 = as.Date(A2, "%m/%d/%y")) )             
#>                 A1         A2
#> 1     review 11/18 2019-11-18
#> 2   begins 12/4/19 2019-12-04
#> 3           3/5/20 2020-03-05
#> 4             <NA>       <NA>
#> 5 deadline 09/5/19 2019-09-05
#> 6              9/3 2019-09-03

^{Created on 2021-11-21 by the reprex package (v2.0.1)}

Answer 3

I like the rematch2 package for many regex scenarios.

The first pattern tries to match the full m/d/y values. The second patterns tried to match the partial m/d values (furthermore, it separates the month from the day, so it can determine if it should be 2019 or 2020).

Once those pieces are isolated, the rest is just a sequence of small steps.

db |> 
  rematch2::bind_re_match(from = A1, "^.*?(?<mdy>\\d{1,2}/\\d{1,2}/\\d{2})$") |> 
  rematch2::bind_re_match(from = A1, "^.*?(?<md_m>\\d{1,2})/(?<md_d>\\d{1,2})$") |> 
  dplyr::mutate(
    md_m  = as.integer(md_m),
    md_y  = dplyr::if_else(9L <= md_m, "19", "20"), # It's 2019 if the month is Sept or later
    md    = sprintf("%i/%s/%s", md_m, md_d, md_y),  # Assemble components
    md    = as.Date(md , "%m/%d/%y"),               # Convert data type
    mdy   = as.Date(mdy, "%m/%d/%y"),               # Convert data type
    
    date = dplyr::coalesce(mdy, md),                # Prefer the mdy if it's not missing
  )

Output:

                A1        mdy md_m md_d md_y         md       date
1     review 11/18       <NA>   11   18   19 2019-11-18 2019-11-18
2   begins 12/4/19 2019-12-04    4   19   20 2020-04-19 2019-12-04
3           3/5/20 2020-03-05    5   20   20 2020-05-20 2020-03-05
4             <NA>       <NA>   NA <NA> <NA>       <NA>       <NA>
5 deadline 09/5/19 2019-09-05    5   19   20 2020-05-19 2019-09-05
6              9/3       <NA>    9    3   19 2019-09-03 2019-09-03