Efficiently convert a date column in data.table

Question

I have a large data set with many columns containing dates in two different formats:

\"1996-01-04\" \"1996-01-05\" \"1996-01-08\" \"1996-01-09\" \"1996-01-10         


        

Answer 1

If there are any duplicated date fields in your dataset, then one way you could do is by setting up de-duplicated reference table then do the mapping on the smaller dataset. This will be faster than converting the date fields on all records.

Data

df <- data.frame(
  X1 = c("1996-01-04", "1996-01-05", "1996-01-08", "1996-01-09", "1996-01-10", rep("1996-01-11", 100)), 
  X2 = c("02/01/1996", "03/01/1996", "04/01/1996", "05/01/1996", "08/01/1996", rep("09/01/1996", 100)), 
  stringsAsFactors = FALSE)

Create unique Date rows for mapping

date_mapping <- function(date_col){

  ref_df <- data.frame(date1 = unique(date_col), stringsAsFactors = FALSE)

  if(all(grepl("/", ref_df$date1))) {
    ref_df$date2 <- as.Date(ref_df$date1, format = "%d/%m/%Y")

  } else {
    ref_df$date2 <- as.Date(ref_df$date1)  
  }

  date_col_mapped <- ref_df[match(date_col, ref_df$date1), "date2"]

  return(date_col_mapped)

}


date_mapping(df$X1)
date_mapping(df$X2)