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00_scrape_scrutiny_forms.R
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00_scrape_scrutiny_forms.R
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# AUTHORS: Colin Cookman + Luke Sonnet
# CONTACT: ccookman at gmail dot com
# ----------
# Loading packages
# ----------
library(XML)
library(pdftools)
library(tidyverse)
library(readtext)
library(lubridate)
clear_logs <- TRUE
if (clear_logs) {
cat("", file = "data/err.log", append = FALSE)
cat("", file = "data/missing.log", append = FALSE)
cat("", file = "data/success.log", append = FALSE)
}
# ---------
# Build candidate directories
# ----------
# Use project instead of setting directory (CC: create R project at root of both raw data and analysis)
file_list <- list.dirs("data/2018 Candidate Scrutiny Forms")
# just select directories with candidate forms in them
candidate_dirs <- file_list[grepl("_", file_list)]
# -- need to check and confirm this isn't missing anything due to unexpected folder naming convention breaks
# exclusion and duplication checks
# excluded <- file_list[!file_list %in% candidate_dirs]
# duplicated <- temp %>% filter(duplicated(data.frame(candidate_dirs)))
# ----------
# Helper functions
# ----------
# reads pdf_text and saves any errors or warnings
read_pdf_text <- function(file, target) {
pdf_text <- tryCatch({
o <- capture.output(file_import <- pdf_text(file), type = "message")
if (any(grepl("PDF error", o))) {
cat(paste0(target, "\t", deparse(substitute(file)), " PDF error\n"),
file = "data/err.log",
append = TRUE)
}
return(read_lines(toString(file_import)))
},
error = function(e) {
cat(paste0(target, "\t", deparse(substitute(file)), " read error ", as.character(e), "\n"),
file = "data/err.log",
append = TRUE)
return(NULL)
})
}
# ----------
# Function to read individual candidate's data
# ----------
read_candidate_folder <- function(target) {
#target <- sub(".", "", target)
# CC: I think you were just removing the leading slash, maybe?
# But this just breaks my code as all it does it removes the first character from the target
# Get metadata about candidate -------------------------------------------------------------
cand_meta <- str_split(target, "/", simplify = TRUE)
meta_dat <- data.frame(
province = cand_meta[1, 3],
assembly = cand_meta[1, 4]
)
if (meta_dat$province == "Minority") {
meta_dat$constituency_number <- NA
meta_dat$candidate_number <- str_match(cand_meta[1, 5], "(?<=\\d\\-)(.*?)(?=\\_)")[1]
} else {
meta_dat$assembly <- cand_meta[1, 4]
meta_dat$constituency_number <- cand_meta[1, 5]
meta_dat$candidate_number <- str_match(cand_meta[1, 6], "(?<=\\d\\-)(.*?)(?=\\_)")[1]
}
# Get files from each admin. body
FBR <- list.files(
path = target, # added this instead of changing directory
pattern = "_FBR.pdf$",
full.names = TRUE)
NAB <- list.files(
path = target,
pattern = "_NAB.pdf$",
full.names = TRUE)
SBP <- list.files(
path = target,
pattern = "_SBP.pdf$",
full.names = TRUE)
# CC: Skip for now if empty while it syncs
if (length(FBR) == 0 | length(NAB) == 0) {
cat(paste0(target, "\tmissing a file\n"), file = "data/missing.log", append = TRUE)
return(data.frame())
}
# FBR form import -------------------------------------------------------------
FBR_text <- read_pdf_text(FBR, target)
# Means there was read error
if (!is.character(FBR_text)) return(data.frame())
FBR_dat <- data.frame(
tax_year = 2015:2017
)
CNIC_row <- grep("CNIC\\s*:", FBR_text)
FBR_dat$candidate_CNIC_FBR <- trimws(str_split(FBR_text[CNIC_row], ":", simplify = TRUE)[1,2])
Name_row <- grep("Name\\s*:", FBR_text)
FBR_dat$candidate_name_FBR <- trimws(str_split(FBR_text[Name_row], ":", simplify = TRUE)[1,2])
NTN_row <- grep("NTN\\s*:", FBR_text)
FBR_dat$candidate_NTN <- trimws(str_split(FBR_text[NTN_row], ":", simplify = TRUE)[1,2])
NTN_date_row <- grep("NTN Issuance\\s*:", FBR_text)
FBR_dat$candidate_NTN_issue <- dmy(trimws(str_split(FBR_text[NTN_date_row], ":", simplify = TRUE)[1,2]))
RTO_row <- grep("Tax Office\\s*:", FBR_text)
FBR_dat$candidate_RTO <- trimws(str_split(FBR_text[RTO_row], ":", simplify = TRUE)[1,2])
# Always load with NAs to start
FBR_dat$candidate_tax_income <-
FBR_dat$candidate_tax_receipts <-
FBR_dat$candidate_tax_paid <-
FBR_dat$candidate_tax_type <-
FBR_dat$candidate_tax_remarks <- rep(NA, 3)
Tax_info_start <- grep("Tax Regime", FBR_text)
Tax_info_end <- grep("Receipts under FTR", FBR_text)
Tax_info <- FBR_text[(Tax_info_start+1):(Tax_info_end-1)] # CC: trimming a little closer
# remove the year and numbering, also remove "remarks" row if it exists, we can deal with later?
Tax_remarks <- any(grepl("Remarks", Tax_info))
Tax_info <- gsub("([123]\\s+201[567]|Remarks)", "", Tax_info)
#Leading_ws <- map_dbl(Tax_info, ~ str_locate(.x, "\\w")[1,1] - 1)
Tax_info <- trimws(Tax_info)
#Leading_ws <- Leading_ws[Tax_info != ""]
Tax_info <- Tax_info[Tax_info != ""]
# NB: this means that "missing" FRB forms now are just empty vectors
if (length(Tax_info) == 0) {
FBR_dat$candidate_tax_type <- "missing"
} else {
# CC: Use purrr to get list of years so that its easy to loop over and apply functions to them
years <- map(Tax_info, str_split, "\\s{2,}", simplify = TRUE)
if (length(years) > 3) {
# If they have tax remarks, assume they are all rows beyond the first three for now
if (Tax_remarks) {
FBR_dat$candidate_tax_remarks <- paste(Tax_info[4:length(Tax_info)], collapse = "\n")
years <- years[1:3]
} else {
# When there are too many rows, the first row to have 3 cols should take everything below
# that isn't a 0 or "Non-Filer" until you get to another row with three years
# Only do the following if we can find two other rows beyond the first which are clearly the top row for that year
if (sum(map_lgl(years[-1], ~ ncol(.x) == 3 |
.x[1] == "Non-Filer" |
(length(.x) == 1 & .x[1] == "0")))
== 2) {
for (i in length(years):2) {
if (ncol(years[[i]]) != 3 & years[[i]][1] != "Non-Filer") {
years[[i-1]][1, 1] <- paste0(years[[i-1]][1, 1], "\n", years[[i]][1, 1])
years[[i]] <- NULL
}
}
cat(paste0(target, "\t", "too many rows in tax, but all three rows are identifiable\n"), file = "data/warn.log", append = TRUE)
} else if (sum(map_lgl(years, ~ grepl("AOP Share", .x)[1])) == 3) {
# Fix one candidate with "AOP SHARE"
for (i in c(1, 3, 5)) {
years[[i]][1, 1] <- paste0(years[[i]][1, 1], "\n", years[[i+1]][1, 1])
}
years[c(2, 4, 6)] <- NULL
cat(paste0(target, "\t", "too many rows in tax, but AOP share\n"), file = "data/warn.log", append = TRUE)
} else if (cand_meta[1, 6] == "NA-261-0007_5340382191767") {
years[[1]][1, 1] <- paste0(years[[1]][1, 1], "\n", years[[2]][1, 1])
years[[4]][1, 1] <- paste0(years[[4]][1, 1], "\n", years[[5]][1, 1])
years[c(2, 5)] <- NULL
cat(paste0(target, "\t", "too many rows in tax, manual fix\n"), file = "data/warn.log", append = TRUE)
} else {
cat(paste0(target, "\t", "too many rows in tax\n"), file = "data/err.log", append = TRUE)
return(data.frame())
}
}
} else if (length(years) < 3) {
# All examples showed a missing second year, adding NA, could check trailing WS to be
# more programmatic
years <- list(years[[1]], matrix(rep(NA, 3), 1), years[[2]])
cat(paste0(target, "\t", "adding row of NA for presumed missing second year\n"), file = "data/warn.log", append = TRUE)
}
# check for filers for each year and bind the results together!
for (i in seq_along(years)) {
year <- years[[i]]
# Catch "Non-Filer"
if (any(grepl("Non-Filer", year))) {
tax_row <- c("Non-Filer", rep(NA, 3))
} else if (ncol(year) < 3) {
# Some rows have too few data columns
# Some of these are just 0s, so we can safely just fill those as 0
if (all(year == "0")) {
tax_row <- c("Filer", rep("0", 3))
} else {
if (ncol(year) == 2) {
# If the length is 2, count sequences of characters
space_seqs <- rle(strsplit(Tax_info[i], "")[[1]])
# If the sequence of spaces is longer than 30, than its cols 1 + 3, with col 2 missing
if (any(space_seqs$lengths[space_seqs$values == " "] > 40)) {
tax_row <- c("Filer", year[1], NA, year[2])
cat(paste0(target, "\t", "guessing some col 2 missing\n"), file = "data/warn.log", append = TRUE)
} else {
# If space in between is X length, then its 1 and 3, if its
# not, then it could be either 1 and 2 or 2 and 3, because of the
# trimws above
# two have 1 and 2 and no 3, and one just has a really long third column
if (any(space_seqs$lengths[space_seqs$values == " "] == 34)) {
tax_row <- c("Filer", year[1], NA, year[2])
cat(paste0(target, "\t", "guessing some col 2 missing\n"), file = "data/warn.log", append = TRUE)
} else {
tax_row <- c("Filer", year[1], year[2], NA)
cat(paste0(target, "\t", "guessing col 3 missing\n"), file = "data/warn.log", append = TRUE)
}
}
} else {
# So far all examples are only first cell filled in, if so, can do simple rule where all go to first position, otherwise have to count leading spaces and infer
tax_row <- c("Filer", year[1], NA, NA)
cat(paste0(target, "\t", "only 1 col\n"), file = "data/warn.log", append = TRUE)
}
}
} else {
tax_row <- c("Filer", year[1, ])
}
FBR_dat[i, c("candidate_tax_type", "candidate_tax_income", "candidate_tax_receipts", "candidate_tax_paid")] <- tax_row
}
}
# NAB form import -------------------------------------------------------------
# Skip "description" error files for now
NAB_text <- read_pdf_text(NAB, target)
if (!is.character(NAB_text)) return(data.frame())
CNIC_row <- grep("CNIC", NAB_text)
CNIC_MNIC <- trimws(str_split(NAB_text[CNIC_row], ":", simplify = TRUE)[1,2])
NAB_dat <- data.frame(
candidate_CNIC_NAB = trimws(str_split(CNIC_MNIC, "/", simplify = TRUE)[1,1]),
# in theory this should automatically match with the CNIC listed on the FBR form but in at least one case I've found, it does not, so better check
candidate_MNIC_NAB = trimws(str_split(CNIC_MNIC, "/", simplify = TRUE)[1,2])
)
Name_row <- grep("Name", NAB_text)
NAB_dat$candidate_name_NAB <- trimws(str_split(NAB_text[Name_row], ":", simplify = TRUE)[1,2])
NAB_status_row <- as.integer(CNIC_row + 1):length(NAB_text)
candidate_NAB_status <- trimws(NAB_text[NAB_status_row])
NAB_dat$candidate_NAB_status <- paste(candidate_NAB_status, collapse=" ")
# just taking the raw text for now until I can find an example where there may be differing results here (so far all samples I've checked were cleared)
# SBP form import -------------------------------------------------------------
SBP_text <- read_pdf_text(SBP, target)
if (!is.character(SBP_text)) {
SBP_dat <- data.frame(
candidate_CNIC_SBP = NA,
candidate_MNIC_SBP = NA,
candidate_name_SBP = NA,
candidate_loan_info = NA
)
} else {
CNIC_row <- grep("CNIC of Candidate:", SBP_text)
MNIC_row <- grep("MNIC of Candidate:", SBP_text)
SBP_dat <- data.frame(
candidate_CNIC_SBP = trimws(str_split(SBP_text[CNIC_row], ":", simplify = TRUE)[1,2]),
candidate_MNIC_SBP = trimws(str_split(SBP_text[MNIC_row], ":", simplify = TRUE)[1,2])
)
Name_row <- grep("Name of Candidate:", SBP_text)
# candidate names missing in some SBP forms
if (length(Name_row) == 0) {
SBP_dat$candidate_name_SBP <- NA
} else {
SBP_dat$candidate_name_SBP <- trimws(str_split(SBP_text[Name_row], ":", simplify = TRUE)[1,2])
}
Loan_info_rows <- grep("below:", SBP_text)
Loan_info_rows <- as.integer(Loan_info_rows + 1):length(SBP_text)
candidate_loan_info <- trimws(SBP_text[Loan_info_rows])
SBP_dat$candidate_loan_info <- paste(candidate_loan_info, collapse = " ")
# again just taking raw text for now until can work out the possible outputs and patterns in this section
}
# Combine all form data -------------------------------------------------------
ret <- data.frame(
meta_dat,
FBR_dat,
NAB_dat,
SBP_dat,
# For debugging
target = target
)
# at some point will want to clean up the differing CNIC numbering conventions between SPB (includes dash) and FBR/NAB
cat(paste0(target, "\t", "success\n"), file = "data/success.log", append = TRUE)
# Reorders data, note `everything()` which will just get the rest
select(ret,
names(meta_dat), tax_year, contains("CNIC"), contains("MNIC"),
contains("name"), everything()
)
}
# ----------
# Run on all data
# ----------
# Final execution ----------
# Can use for loop like above, but growing data.frames recursively is _very_ slow
# R wants you to use lapply, but let's use purrr, which is the "tidy" way to do this
system.time({dat <- map_dfr(candidate_dirs, read_candidate_folder)})
# 894 seconds
write.csv(dat, file = "data/scraped_scrutiny_forms.csv", row.names = FALSE)
debugging <- FALSE
if (debugging) {
# For debugging ----------
# Lazy debugging
for (target in candidate_dirs) {
print(target)
dat <- read_candidate_folder(target)
}
# Invalid SBP PDF
target <- "data/2018 Candidate Scrutiny Forms/KPK/Provincial Assembly/PAKM/PAKM-0022_1720184712651"
# Forgot what bottom one's case was
target <- "data/2018 Candidate Scrutiny Forms/Balochistan/National Assembly/NA-265/NA-265-0019_5440005597555"
# Multiple rows of text in some boxes
target <- "data/2018 Candidate Scrutiny Forms/Balochistan/Provincial Assembly/PB-15/PB-15-0016_5440028686619"
target <- "data/2018 Candidate Scrutiny Forms/Punjab/National Assembly/NA-98/NA-98-0006_3810106314423" # first row
target <- "data/2018 Candidate Scrutiny Forms/Sindh/Provincial Assembly/PS-49/PS-49-0006_4230189906189" # third row
target <- "data/2018 Candidate Scrutiny Forms/Sindh/Provincial Assembly/PS-19/PS-19-0014_4510136091983" # first and second
target <- "data/2018 Candidate Scrutiny Forms/Punjab/Provincial Assembly/PP-15/PP-15-0011_3740552959443" # all three rows
target <- "data/2018 Candidate Scrutiny Forms/Punjab/Provincial Assembly/PP-104/PP-104-0015_3310690883097" # long
# Missing middle cells, etc...
target <- "data/2018 Candidate Scrutiny Forms/Balochistan/Provincial Assembly/PB-13/PB-13-0027_5340345710071"
target <- "data/2018 Candidate Scrutiny Forms/KPK/National Assembly/NA-32/NA-32-0002_4200072543509"
# Both of the above problems
target <- "data/2018 Candidate Scrutiny Forms/Balochistan/National Assembly/NA-261/NA-261-0007_5340382191767"
# Only has first cell, non-zero, non-"Non-filer"
target <- "data/2018 Candidate Scrutiny Forms/Punjab/National Assembly/NA-83/NA-83-0010_3410245994991"
target <- "data/2018 Candidate Scrutiny Forms/Sindh/National Assembly/NA-200/NA-200-0024_4320364410531"
target <- "data/2018 Candidate Scrutiny Forms/Sindh/National Assembly/NA-211/NA-211-0021_4530278910227"
target <- "data/2018 Candidate Scrutiny Forms/Sindh/National Assembly/NA-236/NA-236-0013_1550108773695"
target <- "data/2018 Candidate Scrutiny Forms/Sindh/Provincial Assembly/PS-12/PS-12-0020_4320173014423"
# "missing" example
target <- "data/2018 Candidate Scrutiny Forms/Balochistan/National Assembly/NABW/NABW-0031_5440021680144"
# "remarks" empty example
target <- "data/2018 Candidate Scrutiny Forms/Balochistan/Provincial Assembly/PABM/PABM-0010_5130121369039"
# Non-filer with 0s
target <- "data/2018 Candidate Scrutiny Forms/Balochistan/National Assembly/NA-272/NA-272-0023_5140169339383"
# Plain Non-filer
target <- "data/2018 Candidate Scrutiny Forms/Balochistan/Provincial Assembly/PB-9/PB-9-0019_5520276709583"
# too much data in tax form
target <- "data/2018 Candidate Scrutiny Forms/Balochistan/Provincial Assembly/PB-15/PB-15-0016_5440028686619"
# Example 0
target <- "data/2018 Candidate Scrutiny Forms/Balochistan/Provincial Assembly/PB-3/PB-3-0016_5620117350297"
# Has remarks that are meaningful
target <- "data/2018 Candidate Scrutiny Forms/KPK/Provincial Assembly/PK-54/PK-54-0009_1610271708743"
target <- "data/2018 Candidate Scrutiny Forms/KPK/Provincial Assembly/PK-86/PK-86-0026_6110171350283"
target <- "data/2018 Candidate Scrutiny Forms/KPK/Provincial Assembly/PK-91/PK-91-0019_1120104067713"
target <- "data/2018 Candidate Scrutiny Forms/Balochistan/Provincial Assembly/PB-5/PB-5-0010_5440074738019"
# Too long of col 3 for whitespace check to work
target <- "data/2018 Candidate Scrutiny Forms/Punjab/National Assembly/NA-138/NA-138-0002_3510237042907"
target <- "data/2018 Candidate Scrutiny Forms/Sindh/National Assembly/NASW (8)/NASW-0047_4210114071260"
target <- "data/2018 Candidate Scrutiny Forms/Sindh/Provincial Assembly/PS-49/PS-49-0006_4230189906189"
# Smarter debugging
debugonce(read_candidate_folder)
read_candidate_folder(target)
}