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01_clean_scrutiny_data.R
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01_clean_scrutiny_data.R
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# AUTHORS: Colin Cookman + Luke Sonnet
# NOTES: check INVESTIGATE lines for things to check against raw pdfs, TODO for unfinished sections
# ----------
library(tidyverse)
library(stringr)
candidate_import <- read.csv("data/scraped_scrutiny_forms.csv", stringsAsFactors = FALSE)
candidate_df <- candidate_import
# ----------
# Cut some duplicate or misfiled data
# ----------
# ECP included duplicate copies of the following KPK Provincial Assembly candidate filings in the KPK National Assembly folder - cutting from list
# PK-1-27, PK-23-11, PK-52-21, PK-6-25, PK-83-25
candidate_df <- subset(
candidate_df,
!(candidate_df$assembly == "National Assembly" & grepl("PK-", candidate_df$constituency_number))
)
# The PP-9 folder is also housing lots of duplicate data folders, including some from totally different constituencies -- all of these appear to
# exist in their proper homes, so removing these instances
temp <- filter(candidate_df, constituency_number == "PP-9")
temp <- filter(temp, is.na(temp$candidate_number))
candidate_df <- candidate_df[!(candidate_df$target %in% temp$target), ]
# NA-92 filings listed as being part of KPK are a misfiled duplicate folder and can be deleted
candidate_df <- candidate_df[!(grepl("NA-92", candidate_df$constituency_number) & grepl("KPK", candidate_df$province)), ]
# NA-41-1, NA-41-14, and NA-41-20 are duplicated in the NA-40 folder and can be deleted
candidate_df <- candidate_df[!(grepl("data/2018 Candidate Scrutiny Forms/KPK/National Assembly/NA-41/NA-40-0001_2110519937741", candidate_df$target)), ]
candidate_df <- candidate_df[!(grepl("data/2018 Candidate Scrutiny Forms/KPK/National Assembly/NA-41/NA-40-0014_2110353189415", candidate_df$target)), ]
candidate_df <- candidate_df[!(grepl("data/2018 Candidate Scrutiny Forms/KPK/National Assembly/NA-41/NA-40-0020_2110366405785", candidate_df$target)), ]
# ----------
# Clean candidate metadata
# ----------
# years as factors ------------------------------------------------------------
candidate_df$tax_year <- as.factor(candidate_df$tax_year)
# clean up constituency numbers -----------------------------------------------
# clean up stray parentheses in some constituency numbers due to multiple folders per constituency
candidate_df$constituency_number <- gsub("\\s{1}\\(\\d*\\)", "", candidate_df$constituency_number)
# clean up reserved seats lists -----------------------------------------------
candidate_df$assembly <- as.character(candidate_df$assembly)
candidate_df$constituency_number <- as.character(candidate_df$constituency_number)
candidate_df$constituency_number[candidate_df$assembly == "NAM"] <- "Minority List"
candidate_df$assembly <- gsub("NAM", "National Assembly", candidate_df$assembly)
candidate_df$constituency_number <- gsub("\\w*W$", "Womens List", candidate_df$constituency_number)
candidate_df$constituency_number <- gsub("\\w*M$", "Minority List", candidate_df$constituency_number)
# at least in my current local version, NASW is spread out across a couple folders, corrected here
candidate_df$constituency_number[grepl("NASW", candidate_df$constituency_number)] <- "Womens List"
# at least in available ECP data national assembly minority candidates don't have provincial affiliations
candidate_df$province <- gsub("Minority", "National", candidate_df$province)
# one candidate for PS-87 is buried in a subfolder and did not get properly assigned their candidate number, correcting
candidate_df$candidate_number[candidate_df$target == "data/2018 Candidate Scrutiny Forms/Sindh/Provincial Assembly/PS-87/PS-87/PS-87-0018_4250114748170"] <- "18"
# reclaim ECP candidate numbers for minority / women's candidates, just for listing purposes
candidate_df$candidate_number <- as.character(candidate_df$candidate_number)
candidate_df$candidate_number[candidate_df$constituency_number == "Womens List"] <-
str_match(str_split(candidate_df$target[candidate_df$constituency_number == "Womens List"], "/", simplify = TRUE)[,6], "(?<=\\-)(.*?)(?=\\_)")[,1]
candidate_df$candidate_number[candidate_df$constituency_number == "Minority List"] <-
str_match(str_split(candidate_df$target[candidate_df$constituency_number == "Minority List"], "/", simplify = TRUE)[,6], "(?<=\\-)(.*?)(?=\\_)")[,1]
candidate_df$candidate_number[candidate_df$constituency_number == "Minority List" & candidate_df$province == "National"] <-
str_match(str_split(candidate_df$target[candidate_df$constituency_number == "Minority List" & candidate_df$province == "National"], "/", simplify = TRUE)[,5], "(?<=\\-)(.*?)(?=\\_)")[,1]
# convert candidate numeric to numeric for constitency (remove prefix 0s for WL / ML)
candidate_df$candidate_number <- as.numeric(candidate_df$candidate_number)
# concatenate a constituency-candidate number code
candidate_df <- candidate_df %>% mutate(
candidate_code = paste(candidate_df$constituency_number,
as.numeric(as.character(candidate_df$candidate_number)), sep = "-")
)
# from this abbreviate the reserved list codes
candidate_df$candidate_code[candidate_df$province == "National" & candidate_df$constituency_number == "Minority List"] <-
gsub("Minority List-", "NA-ML-", candidate_df$candidate_code[candidate_df$province == "National" & candidate_df$constituency_number == "Minority List"])
candidate_df$candidate_code[candidate_df$province == "Balochistan" & candidate_df$constituency_number == "Minority List"] <-
gsub("Minority List", "PB-ML", candidate_df$candidate_code[candidate_df$province == "Balochistan" & candidate_df$constituency_number == "Minority List"])
candidate_df$candidate_code[candidate_df$province == "KPK" & candidate_df$constituency_number == "Minority List"] <-
gsub("Minority List", "PK-ML", candidate_df$candidate_code[candidate_df$province == "KPK" & candidate_df$constituency_number == "Minority List"])
candidate_df$candidate_code[candidate_df$province == "Punjab" & candidate_df$constituency_number == "Minority List"] <-
gsub("Minority List", "PP-ML", candidate_df$candidate_code[candidate_df$province == "Punjab" & candidate_df$constituency_number == "Minority List"])
candidate_df$candidate_code[candidate_df$province == "Sindh" & candidate_df$constituency_number == "Minority List"] <-
gsub("Minority List", "PS-ML", candidate_df$candidate_code[candidate_df$province == "Sindh" & candidate_df$constituency_number == "Minority List"])
candidate_df$candidate_code[candidate_df$province == "Balochistan" &
candidate_df$assembly == "National Assembly" &
candidate_df$constituency_number == "Womens List"] <-
gsub("Womens List", "NA-BW", candidate_df$candidate_code[candidate_df$province == "Balochistan" &
candidate_df$assembly == "National Assembly" &
candidate_df$constituency_number == "Womens List"]
)
candidate_df$candidate_code[candidate_df$province == "Balochistan" &
candidate_df$assembly == "Provincial Assembly" &
candidate_df$constituency_number == "Womens List"] <-
gsub("Womens List", "PB-WL", candidate_df$candidate_code[candidate_df$province == "Balochistan" &
candidate_df$assembly == "Provincial Assembly" &
candidate_df$constituency_number == "Womens List"]
)
candidate_df$candidate_code[candidate_df$province == "KPK" &
candidate_df$assembly == "National Assembly" &
candidate_df$constituency_number == "Womens List"] <-
gsub("Womens List", "NA-KW", candidate_df$candidate_code[candidate_df$province == "KPK" &
candidate_df$assembly == "National Assembly" &
candidate_df$constituency_number == "Womens List"]
)
candidate_df$candidate_code[candidate_df$province == "KPK" &
candidate_df$assembly == "Provincial Assembly" &
candidate_df$constituency_number == "Womens List"] <-
gsub("Womens List", "PK-WL", candidate_df$candidate_code[candidate_df$province == "KPK" &
candidate_df$assembly == "Provincial Assembly" &
candidate_df$constituency_number == "Womens List"]
)
candidate_df$candidate_code[candidate_df$province == "Punjab" &
candidate_df$assembly == "National Assembly" &
candidate_df$constituency_number == "Womens List"] <-
gsub("Womens List", "NA-PW", candidate_df$candidate_code[candidate_df$province == "Punjab" &
candidate_df$assembly == "National Assembly" &
candidate_df$constituency_number == "Womens List"]
)
candidate_df$candidate_code[candidate_df$province == "Punjab" &
candidate_df$assembly == "Provincial Assembly" &
candidate_df$constituency_number == "Womens List"] <-
gsub("Womens List", "PP-WL", candidate_df$candidate_code[candidate_df$province == "Punjab" &
candidate_df$assembly == "Provincial Assembly" &
candidate_df$constituency_number == "Womens List"]
)
candidate_df$candidate_code[candidate_df$province == "Sindh" &
candidate_df$assembly == "National Assembly" &
candidate_df$constituency_number == "Womens List"] <-
gsub("Womens List", "NA-SW", candidate_df$candidate_code[candidate_df$province == "Sindh" &
candidate_df$assembly == "National Assembly" &
candidate_df$constituency_number == "Womens List"]
)
candidate_df$candidate_code[candidate_df$province == "Sindh" &
candidate_df$assembly == "Provincial Assembly" &
candidate_df$constituency_number == "Womens List"] <-
gsub("Womens List", "PS-WL", candidate_df$candidate_code[candidate_df$province == "Sindh" &
candidate_df$assembly == "Provincial Assembly" &
candidate_df$constituency_number == "Womens List"]
)
# pull ECP subfolder CNIC listings to compare with FBR/NAB/SBP reports --------
candidate_df$candidate_CNIC_ECP <- str_split(candidate_df$target, "_", simplify = TRUE)[,2]
# strip dashes from CNIC / MNIC numbers ---------------------------------------
# for reference SBP CNIC pattern is 12345-1234567-1, MNIC pattern 123-12-123456, latter may also have alphumerics
candidate_df$candidate_CNIC_SBP <- as.factor(gsub("-", "", candidate_df$candidate_CNIC_SBP))
candidate_df$candidate_MNIC_NAB <- as.factor(gsub("NULL", NA, candidate_df$candidate_MNIC_NAB))
candidate_df$candidate_MNIC_SBP <- as.factor(gsub("-", "", candidate_df$candidate_MNIC_SBP))
candidate_df$candidate_MNIC_SBP <- as.factor(gsub("NULL", NA, candidate_df$candidate_MNIC_SBP))
# setting NA values for missing data ------------------------------------------
# we want to keep '0' observations as distinct from NA/missing data, although conversion to numeric for any calculations will turn zeros into NAs
candidate_df$candidate_name_SBP <- gsub("NA", NA, candidate_df$candidate_name_SBP)
candidate_df$candidate_NTN_issue <- gsub("NA", NA, candidate_df$candidate_NTN_issue)
candidate_df$candidate_tax_remarks <- gsub("NA", NA, candidate_df$candidate_tax_remarks)
candidate_df$candidate_tax_paid <- as.factor(gsub("NA", NA, candidate_df$candidate_tax_paid))
# ----------
# Clean up tax data
# ----------
# convert '-' instances to NA values
candidate_df$candidate_tax_receipts <- as.factor(gsub("-", NA, candidate_df$candidate_tax_receipts))
candidate_df$candidate_tax_receipts <- as.factor(gsub("NA", NA, candidate_df$candidate_tax_receipts))
candidate_df$candidate_tax_income <- as.factor(gsub("-", NA, candidate_df$candidate_tax_income))
candidate_df$candidate_tax_income <- as.factor(gsub("NA", NA, candidate_df$candidate_tax_income))
# setting all unregistered tax filers w/o NTNs to tax type "unregistered" -------
candidate_df$candidate_tax_type <- gsub("missing", "Unregistered", candidate_df$candidate_tax_type)
# note -- following NTNs are NA values (not specifically listed as unregistered but no data)
# 2018 Candidate Scrutiny Forms/KPK/Provincial Assembly/PK-38/PK-38-0017_1310144915515
# 2018 Candidate Scrutiny Forms/Punjab/National Assembly/NA-70/NA-70-0012_3420282747187
# 2018 Candidate Scrutiny Forms/Punjab/Provincial Assembly/PP-174/PP-174-0007_3510290157271
# standardize RTO names -------------------------------------------------------
candidate_df$candidate_RTO <- as.character(candidate_df$candidate_RTO)
candidate_df$candidate_RTO <- gsub("Regional Tax Office, Islamabad", "RTO ISLAMABAD", candidate_df$candidate_RTO)
candidate_df$candidate_RTO <- gsub("RTO Islamabad", "RTO ISLAMABAD", candidate_df$candidate_RTO)
candidate_df$candidate_RTO <- gsub("RTO Faisalabad", "RTO FAISALABAD", candidate_df$candidate_RTO)
candidate_df$candidate_RTO <- gsub("RTO Multan", "RTO MULTAN", candidate_df$candidate_RTO)
candidate_df$candidate_RTO <- gsub("RTO, Multan", "RTO MULTAN", candidate_df$candidate_RTO)
candidate_df$candidate_RTO <- gsub("LTU-II KARACHI", "LTU KARACHI II", candidate_df$candidate_RTO)
candidate_df$candidate_RTO <- gsub("RTO-II KARACHI", "RTO KARACHI II", candidate_df$candidate_RTO)
candidate_df$candidate_RTO <- gsub("RTO-III KARACHI", "RTO KARACHI III", candidate_df$candidate_RTO)
candidate_df$candidate_RTO <- gsub("RTO-II LAHORE", "RTO LAHORE II", candidate_df$candidate_RTO)
candidate_df$candidate_RTO <- gsub("RTO \\(Corporate\\) Lahore", "RTO (CORPORATE) LAHORE", candidate_df$candidate_RTO)
# assuming these two are abbreviations?
candidate_df$candidate_RTO <- gsub("RTO FSD", "RTO FAISALABAD", candidate_df$candidate_RTO)
candidate_df$candidate_RTO <- gsub("RTO, Mn", "RTO MULTAN", candidate_df$candidate_RTO)
# clean up tax remarks -------------------------------------------
tax_vars <- c("candidate_tax_income", "candidate_tax_receipts", "candidate_tax_paid")
candidate_df <- candidate_df %>%
mutate_at(tax_vars,
funs(gsub("\\,|\\s+", "", .))) %>%
mutate_at(tax_vars,
funs(num = as.numeric(.)))
na_if_no_match <- function(.x) {
if (length(.x) == 0) NA else sum(as.numeric(.x))
}
for (tax_var in tax_vars) {
na_num <- is.na(candidate_df[[paste0(tax_var, "_num")]])
candidate_df[[tax_var]][na_num] <-
map_dbl(
str_extract_all(candidate_df[[tax_var]][na_num],
"\\d+(\\.\\d{2})?"),
na_if_no_match
)
}
# ----------
# Clean up NAB comments
# ----------
# table(candidate_df$candidate_NAB_status)
candidate_df <- candidate_df %>%
mutate(
candidate_NAB_guilty = ifelse(
grepl(
"It is certified that no information about( any)? conviction or plea bargain",
candidate_NAB_status
),
"No Conviction or Plea Bargain",
"Conviction or Plea Bargain"
),
candidate_NAB_conviction = ifelse(
grepl(
"It is certified that information about conviction",
candidate_NAB_status
),
"Conviction",
"No conviction"
),
candidate_NAB_plea = ifelse(
grepl(
"It is certified that information about [pP]lea [bB]argain",
candidate_NAB_status
),
"Plea bargain",
"No plea bargain"
),
candidate_NAB_remarks = str_extract(
candidate_NAB_status,
"(?<=Remarks\\: ).*?(?=\\sShakeel)"
),
# Following isn't perfect
candidate_NAB_accused = ifelse(
grepl("is( an)? accused|pending", candidate_NAB_remarks),
"Accused or pending",
"Not accused or pending"
)
)
# with(candidate_df, table(candidate_NAB_conviction, candidate_NAB_guilty, candidate_NAB_plea))
# table(candidate_df$candidate_NAB_remarks)
# with(candidate_df, table(candidate_NAB_accused, candidate_NAB_conviction))
# with(candidate_df, table(candidate_NAB_accused, candidate_NAB_guilty))
# candidate_df$candidate_NAB_status[candidate_df$candidate_NAB_guilty == "Conviction or Plea Bargain" & candidate_df$candidate_NAB_conviction == "No conviction"]
# candidate_df$candidate_NAB_remarks[candidate_df$candidate_NAB_guilty == "Conviction or Plea Bargain" & candidate_df$candidate_NAB_accused == "Not accused"]
# unique(candidate_df$candidate_NAB_remarks[candidate_df$candidate_NAB_guilty == "No Conviction or Plea Bargain" & candidate_df$candidate_NAB_accused == "Not accused"])
# candidate_df$candidate_NAB_remarks[candidate_df$candidate_NAB_guilty == "Conviction or Plea Bargain" & candidate_df$candidate_NAB_accused == "Not accused"]
# unique(candidate_df$candidate_NAB_status[candidate_df$candidate_NAB_guilty == "Conviction or Plea Bargain"])
# ----------
# SBP cleaning
# ----------
# table(table(candidate_df$candidate_loan_info))
# table(candidate_df$candidate_loan_info)[table(candidate_df$candidate_loan_info) > 20000] # nada
# table(candidate_df$candidate_loan_info)[table(candidate_df$candidate_loan_info) == 8616] # nada
# table(candidate_df$candidate_loan_info)[table(candidate_df$candidate_loan_info) > 3 & table(candidate_df$candidate_loan_info) < 8000] # nada
# table(table(candidate_df$candidate_loan_info))
temp_loan <-
gsub(
"\\(Rs\\. in Million\\)\\s+CNIC\\/Name\\s+Relation( with)?\\s+(FI )?Name( of Company\\s+FI Name)?\\s+Overdue\\s+Writeoff (Candidate|with)?",
"",
candidate_df$candidate_loan_info
) %>%
gsub(
"Further\\, candidate/spouse/dependent is also director\\/owner of following companies having overdue \\/write off amounting to Rs 2 million and above for last one year",
"",
.
) %>%
str_split("\\:\\-") %>%
map_dfr(~ data.frame(candidate_personal_loan = trimws(.x[1]), candidate_business_loan = trimws(.x[2])))
candidate_df <- bind_cols(candidate_df, temp_loan)
# TODO actually extract loan data
# ----------
# Data checks and comparisons
# ----------
# check if FBR-NAB names match ------------------------------------------------
candidate_df <- candidate_df %>% mutate(
urdu_name_match = ifelse(as.character(candidate_name_FBR) != as.character(candidate_name_NAB), "Urdu Name Mismatch", "Name Match")
)
# many apparent name mismatches - on quick visual scan source appears to mostly be typos or nonstandardized name spelling
# check if ECP-FBR-NAB-SBP CNICs match --------------------------------------------
# There are pdf encoding issues with FBR CNIC strings - string reading / copying from original pdfs is not correct when compared to visual read
candidate_df <- candidate_df %>% mutate(
CNIC_ECP_FBR_match = ifelse(as.character(candidate_CNIC_ECP) != as.character(candidate_CNIC_FBR), "CNIC Mismatch", "CNIC Match"),
CNIC_ECP_NAB_match = ifelse(as.character(candidate_CNIC_ECP) != as.character(candidate_CNIC_NAB), "CNIC Mismatch", "CNIC Match"),
CNIC_ECP_SBP_match = ifelse(as.character(candidate_CNIC_ECP) != as.character(candidate_CNIC_SBP), "CNIC Mismatch", "CNIC Match"),
CNIC_FBR_NAB_match = ifelse(as.character(candidate_CNIC_FBR) != as.character(candidate_CNIC_NAB), "CNIC Mismatch", "CNIC Match"),
CNIC_FBR_SBP_match = ifelse(as.character(candidate_CNIC_FBR) != as.character(candidate_CNIC_SBP), "CNIC Mismatch", "CNIC Match"),
CNIC_NAB_SBP_match = ifelse(as.character(candidate_CNIC_NAB) != as.character(candidate_CNIC_SBP), "CNIC Mismatch", "CNIC Match")
)
# ECP and SBP CNICs match in all cases;
# Only two unique cases of ECP/SBP-NAB CNICs not matching
# (PP-32-1 and PP-32-2, a visual check of FBR + NAB forms shows FBR-SBP versions also match, NAB the outlier)
# 16787 out of 19538 FBR CNICs don't match with ECP - this issue appears to be due to encoding errors
# that are preventing an accurate parse of the number in the pdf
# check that NAB-SBP MNICs match ----------------------------------------------
candidate_df <- candidate_df %>% mutate(
MNIC_match = ifelse(as.character(candidate_MNIC_NAB) != as.character(candidate_MNIC_SBP), "MNIC Mismatch", "MNIC Match")
)
# PP-177-15 -- SBP MNIC missing last two digits of NAB MNIC
# PP-32-1 -- NAB form data does match with FBR or SBP by name, CNIC, or MNIC (but NAB metadata indicates this is for PP-32-1)
# PP-32-2 -- NAB form data does match with FBR or SBP by name, CNIC, or MNIC (but NAB metadata indicates this is for PP-32-2)
# PS-9-19 -- SBP MNIC missing last two digits of NAB MNIC
# PS-88-09 -- SBP MNIC missing last two digits of NAB MNIC
# PS-91-18 -- SBP MNIC missing last two digits of NAB MNIC
# establish candidate UIDs to identify multi-constituency contestants ------------
# just the first tax row for each candidacy filing
unique_filings <- candidate_df[!duplicated(candidate_df$candidate_code), 1:44]
# just the first instance of any individual candidate's filing in any constituency
unique_ECP_CNIC <- unique_filings[!duplicated(unique_filings$candidate_CNIC_ECP), 1:44]
# identify all cases amongst candidacy filings where there are matching CNICs
multiple_CNIC_filings <- unique_filings[(duplicated(unique_filings$candidate_CNIC_ECP) | duplicated(unique_filings$candidate_CNIC_ECP, fromLast = TRUE)), 1:44]
# identify candidates that contested multiple constituencies, or otherwise candidates whose names do not match despite sharing identical CNICs
multiple_CNIC_filings$candidate_name_FBR <- as.character(multiple_CNIC_filings$candidate_name_FBR)
multiple_CNIC_filings$candidate_name_NAB <- as.character(multiple_CNIC_filings$candidate_name_NAB)
multiple_CNIC_filings$candidate_CNIC_SBP <- as.character(multiple_CNIC_filings$candidate_CNIC_SBP)
multiple_CNIC_filings <- dplyr::arrange(multiple_CNIC_filings, desc(candidate_CNIC_ECP))
multiple_CNIC_filings <- multiple_CNIC_filings %>% group_by(candidate_CNIC_ECP) %>%
mutate(
multi_candidate_FBR = ifelse(candidate_name_FBR == lag(candidate_name_FBR), "Multi-Candidate", "Mismatch"),
multi_candidate_NAB = ifelse(candidate_name_NAB == lag(candidate_name_NAB), "Multi-Candidate", "Mismatch")
)
# multiple typos in FBR names are throwing mismatches but the only NAB name mismatch (candidate_code PS-35-3) is due to first name / last name reversal
# otherwise all repeat ECP CNICs in dataset are the same candidate (checked against NAB name) contesting in multiple races
# unnecessary to create new UIDs since CNICs are in fact unique
multi_candidate <- dplyr::select(multiple_CNIC_filings, candidate_CNIC_ECP)
multi_candidate <- unique(multi_candidate)
multi_candidate$multi_candidate <- "YES"
candidate_df <- left_join(candidate_df, multi_candidate, by = "candidate_CNIC_ECP")
# ----------
# Final output
# ----------
# reorder data -------------------------------------------------------------
candidate_df <- dplyr::arrange(candidate_df, candidate_code)
# search for possible missing candidate forms in available sequence -----------
missing_cands <- candidate_df %>%
group_by(province, assembly, constituency_number) %>%
summarize(missing_candidate_number = list(setdiff(seq_len(max(candidate_number)), candidate_number))) %>%
unnest() %>%
mutate(candidate_code = paste0(constituency_number, "-", missing_candidate_number))
write.csv(missing_cands, file = "data/filing_sequence_gaps.csv", row.names = FALSE)
# summary counts --------------------------------------------------------------
constituency_filing_count <- unique_filings %>%
group_by(province, assembly, constituency_number) %>%
summarize(count = n())
table(constituency_filing_count$assembly)
write.csv(constituency_filing_count, file = "data/constituency_filing_count.csv", row.names = FALSE)
aggregate_filing_count <- constituency_filing_count %>%
group_by(province, assembly) %>%
summarize(
direct_seats = sum(count[constituency_number != "Womens List" & constituency_number != "Minority List"]),
womens_seats = sum(count[constituency_number == "Womens List"]),
minority_seats = sum(count[constituency_number == "Minority List"])
)
# Add 2016 Parliamentary data
parl_df <- read.csv("data/cleaned_parliamentary_2016.csv",
stringsAsFactors = FALSE)
# merge with wide data (no double merges, note same n of rows in dfw and mdf)
merged_df <- merge(
candidate_df,
parl_df,
by.x = "candidate_CNIC_ECP",
by.y = "CNIC",
all.x = TRUE
)
if (nrow(merged_df) != nrow(candidate_df)) {
stop("error in merge")
}
merged_df$parl_incumbent_tax_2016[is.na(merged_df$parl_incumbent_tax_2016)] <- 0
nrow(parl_df)
sum(merged_df$parl_incumbent_tax_2016)
# reorder columns --------------------
merged_df <- dplyr::select(merged_df,
# constituency meta
candidate_code, province, assembly, constituency_number, candidate_number,
# candidate meta
candidate_CNIC_ECP, # dropping candidate_CNIC_FBR, candidate_CNIC_NAB, and candidate_CNIC_SBP
multi_candidate,
candidate_NTN, candidate_NTN_issue, candidate_RTO,
candidate_MNIC_NAB, candidate_MNIC_SBP,
# tax data
tax_year,
candidate_tax_type,
candidate_tax_paid, candidate_tax_paid_num,
candidate_tax_receipts, candidate_tax_receipts_num,
candidate_tax_income, candidate_tax_income_num,
candidate_tax_remarks,
# NAB data
candidate_NAB_guilty, candidate_NAB_conviction, candidate_NAB_plea, candidate_NAB_accused,
candidate_NAB_remarks,
# SBP data
candidate_personal_loan, candidate_business_loan,
# Parliamentary incumbent data
parl_inc_tax_2016, parl_inc_name, parl_inc_constituency, parl_inc_chamber, parl_inc_province,
parl_inc_type, parl_tax_paid_2016, parl_aop_tax_paid_2016,
# additional metadata
candidate_name_FBR, candidate_name_NAB, candidate_name_SBP,
urdu_name_match,
MNIC_match,
target
)
# re-write csv for final output -----------------------------------------------
write.csv(merged_df, file = "pk_candidate_scrutiny_data_2018.csv", row.names = FALSE)
# Create wide version
names(merged_df)
candidate_df_wide <- merged_df %>%
gather(tax_variable, tax_value, -(candidate_NAB_guilty:target), -(candidate_code:tax_year)) %>%
unite(tax_var, tax_variable, tax_year) %>%
spread(tax_var, tax_value)
head(candidate_df_wide)
nrow(candidate_df_wide)
nrow(candidate_df) / 3
write.csv(candidate_df_wide, file = "pk_candidate_scrutiny_data_2018_wide.csv", row.names = FALSE)