Hot Topics

Home » Security Bloggers Network » Convert Apple Card PDF Statements to Tidy Data (i.e. for CSV/Excel/database export)

Convert Apple Card PDF Statements to Tidy Data (i.e. for CSV/Excel/database export)

by hrbrmstr on January 13, 2020

I saw this CNBC article on an in-theory browser client-side-only conversion utility for taking Apple Card PDF statements and turning them into CSV files.

Since I (a) never trust any browser or site and (b) the article indicated that there is a $5 fee to avoid the “single random transaction removal”, I felt compelled to throw together an R script to do this for at least folks who are capable of setting up R so that all processing is guaranteed to be local.

FWIW the site does appear to do what it says on the tin (all processing is, indeed, local). That doesn’t mean one of your extensions isn’t spying on you, nor does it mean that the site could not turn evil someday (one its own or via an attacker compromise).

read_apple_card_statement <- function(path) {  require(stringi)  require(pdftools)  require(tidyverse)  # make sure the file exists  path <- path.expand(path[1])  if (!file.exists(path)) stop("File '", path, "' not found.", call.=FALSE)  pdf_text(path) %>% # read it in    stri_split_lines() %>% # turn \n to a separate character vector element    unlist() %>% # flatten it    stri_trim_both() %>% # get rid of leading/trailing spaces    keep(stri_detect_regex, "^([[:digit:]]{2}/[[:digit:]]{2}/[[:digit:]]{4})") %>% # find lines that start with a date    map_df(~{      rec <- as.list(unlist(stri_split_regex(.x, "[[:space:]]{3,}"))) # find the columns      if (stri_detect_fixed(.x, "%")) { # lines with a `%` in them distinguish charges from payments        rec <- set_names(rec, c("date", "description", "daily_cash_pct", "daily_cash_amt", "amt")) # ones with charges have cash back columns      } else {        rec <- set_names(rec, c("date", "description", "amt")) # ones w/o % do not      }    }) %>%    mutate(      date = lubridate::mdy(date), # make dates dates      amt = stri_replace_first_fixed(amt, "$", "") %>% parse_number(), # dollars to numbers      daily_cash_pct = parse_number(daily_cash_pct)/100, # % to numbers      daily_cash_amt = parse_number(daily_cash_amt) # dollars to numbners    )}list.files("~/Downloads", pattern = "Apple Card Statement", full.names = TRUE) %>%   map_df(read_apple_card_statement)

You can send the PDF statements from the Apple Card app to your Mac via Air Drop and it will put them into ~/Downloads. I recommend putting them somewhere else since you’ve likely given all sorts of applications access to ~/Downloads when prompted to on Catalina (yay security theatre). Wherever you put them, you can read them individually with read_apple_card_statment() or you can then just list_files() and bind all the individual statements together:

list.files("~/WhereYouPutAppleCardStatements", pattern = "Apple Card Statement", full.names = TRUE) %>%   map_df(read_apple_card_statement)

FIN

Be very wary of what you put your trust into online. Just because a site is benign one day does not mean it won’t be malicious (deliberately or otherwise) the next. Also, lobby Apple to provide data in more useful formats, especially since it provides applications like Numbers for free with their operating system.