split tables with fixed columns

split_tables

split_tables <- function(data, cols_to_freeze, max_col = 7) {
   
   stopifnot(
      "columns to fix do not exist in data" =
      all(cols_to_freeze %in% names(data))
   )
   
   # we don't need to table wrap if the data is less than max_col
   if (ncol(data) <= max_col) {
      return(data)
   }
   
   # get the columns we'll append to each data.frame
   # needs to be a data.frame, even when only one column specified!
   if (length(cols_to_freeze) == 1) {
      cols_in_all <- data.frame(cols_to_freeze = data[,cols_to_keep])
   } else {
      cols_in_all <- data[,cols_to_freeze]
   }

   # this is going to be split based on the max_col
   other_cols <- data[ ,!(colnames(data) %in% cols_to_freeze)]
   
   # number of subtable columns allowed
   subtable_column_n <- max_col - ncol(cols_in_all)
   
   # use this to find the number of tables we'll need
   n_tables <- ceiling(ncol(other_cols)/subtable_column_n)
   
   # loop over the tables and iterate
   # what happens when this isnt a nice even number....
   all_tables <- list()
   
   col_diff <- max_col - length(cols_to_freeze)
   
   for (i in 1:n_tables) {
      # when i is the last table we need to only fill to max columns
      if (i == n_tables) {
         all_tables[[i]] <- cbind(
            cols_in_all,
            other_cols[(col_diff*(i-1)+1):length(other_cols)]
         )
      # otherwise iterate to the next table i
      } else {
         all_tables[[i]] <- cbind(
            cols_in_all,
            other_cols[col_diff*(i-1)+1:col_diff*i]
         )
      }
   }
   
   return(all_tables)
   
}

# theres only 11 columns in mtcars so the total is bigger
# return a DF. great.
split_tables(mtcars, c("mpg", "vs"), 12)
split_tables(mtcars, c("mpg", "vs"), 5)
split_tables(mtcars, c("mpg", "vs"), 7)