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Copy pathcaselaw_processing.R
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caselaw_processing.R
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library(tidyverse)
library(progress)
library(ggmap)
library(geojsonio)
library(rvest)
sample_data_dir <- "datasets/Maharashtra/"
all_files <-
Sys.glob(glue::glue(("{sample_data_dir}/*/*/*.json")))
pb <- progress::progress_bar$new(total = length(all_files))
case_fir_details <- list()
case_history_list <- list()
for (i in 1:length(all_files)) {
x <- jsonlite::read_json(all_files[[i]])
cino <- ifelse(is.null(x$cino),"",x$cino)
## List of variables from Vivek
fil_year <- ifelse(is.null(x$fil_year),"",as.character(x$fil_year))
disp_nature <- ifelse(is.null(x$disp_nature),"",as.character(x$disp_nature))
jcode <- ifelse(is.null(x$jcode),"",as.character(x$jcode))
date_of_decision <- ifelse(is.null(x$date_of_decision),"",as.character(x$date_of_decision))
reg_year <- ifelse(is.null(x$reg_year),"",as.character(x$reg_year))
transfer_est_name <- ifelse(is.null(x$transfer_est_name),"",as.character(x$transfer_est_name))
lower_court_dec_dt <- ifelse(is.null(x$lower_court_dec_dt),"",as.character(x$lower_court_dec_dt))
fir_year <- ifelse(is.null(x$fir_year),"",as.character(x$fir_year))
pet_adv <- ifelse(is.null(x$pet_adv),"",as.character(x$pet_adv))
type_name<- ifelse(is.null(x$type_name),"",as.character(x$type_name))
res_name <- ifelse(is.null(x$res_name),"",as.character(x$res_name))
petNameAdd <- ifelse(is.null(x$petNameAdd),"",as.character(x$petNameAdd))
subordinateCourtInfoStr <- ifelse(is.null(x$subordinateCourtInfoStr),"",as.character(x$subordinateCourtInfoStr))
lower_court_code <- ifelse(is.null(x$lower_court_code),"",as.character(x$lower_court_code))
res_adv <- ifelse(is.null(x$res_adv),"",as.character(x$res_adv))
petparty_name <- ifelse(is.null(x$petparty_name),"",as.character(x$petparty_name))
purpose_next <- ifelse(is.null(x$purpose_next),"",as.character(x$purpose_next))
est_code <- ifelse(is.null(x$est_code),"",as.character(x$est_code))
state_name <- ifelse(is.null(x$state_name),"",as.character(x$state_name))
date_last_list <- ifelse(is.null(x$date_last_list),"",as.character(x$date_last_list))
court_no <- ifelse(is.null(x$court_no),"",as.character(x$court_no))
reg_no <- ifelse(is.null(x$reg_no),"",as.character(x$reg_no))
dt_regis <- ifelse(is.null(x$dt_regis),"",as.character(x$dt_regis))
date_of_filing <- ifelse(is.null(x$date_of_filing),"",as.character(x$date_of_filing))
purpose_name <- ifelse(is.null(x$purpose_name),"",as.character(x$purpose_name))
fir_no <- ifelse(is.null(x$fir_no),"",as.character(x$fir_no))
police_st_code <- ifelse(is.null(x$police_st_code),"",as.character(x$police_st_code))
district_code <- ifelse(is.null(x$district_code),"",as.character(x$district_code))
courtno <- ifelse(is.null(x$courtno),"",as.character(x$courtno))
case_no <- ifelse(is.null(x$case_no),"",as.character(x$case_no))
desgname <- ifelse(is.null(x$desgname),"",as.character(x$desgname))
pet_name <- ifelse(is.null(x$pet_name),"",as.character(x$pet_name))
resparty_name <- ifelse(is.null(x$resparty_name),"",as.character(x$resparty_name))
fir_details <- ifelse(is.null(x$fir_details),"",as.character(x$fir_details))
court_name <- ifelse(is.null(x$court_name),"",as.character(x$court_name))
district_name<- ifelse(is.null(x$district_name),"",as.character(x$district_name))
case_fir_details[[i]] <-
data.frame(
cino,
disp_nature,
fil_year,
jcode,
date_of_decision,
reg_year,
transfer_est_name,
lower_court_dec_dt,
fir_year,
pet_adv,
type_name,
res_name,
petNameAdd,
subordinateCourtInfoStr,
lower_court_code,
res_adv,
petparty_name,
purpose_next,
est_code,
state_name,
date_last_list,
court_no,
reg_no,
dt_regis,
date_of_filing,
purpose_name,
fir_no,
police_st_code,
district_code,
courtno,
case_no,
desgname,
pet_name,
resparty_name,
fir_details,
court_name,
district_name,stringsAsFactors = FALSE, check.names = FALSE
)
## Processing hearing details from cases
case_history <- ifelse(is.null(x$historyOfCaseHearing),"",x$historyOfCaseHearing)
if(case_history != ""){
case_history_table <- case_history %>% read_html() %>% html_table()
case_history_table <- case_history_table[[1]]
case_history_table$cino <- cino
} else {
case_history_table <- data.frame("cino"=cino,"Judge"="", "Business On Date" = "", "Hearing Date" = "",check.names=FALSE)
}
case_history_list[[i]] <- case_history_table
# View progress
pb$tick()
}
# Transforming case meta variables
case_fir_df <- dplyr::bind_rows(case_fir_details)
case_fir_df$police_station <- stringr::str_replace_all(case_fir_df$fir_details, pattern = "\\^", replacement = "")
case_fir_df$police_station <- stringr::str_replace_all(case_fir_df$police_station, pattern = "[:digit:]+", replacement = "")
#Transforming case history variables
case_history_df <- dplyr::bind_rows(case_history_list)
## Find unique police station across the database and assign unique ID's
case_fir_df$police_station <- stringr::str_trim(stringr::str_to_title(case_fir_df$police_station))
police_station_id <- unique(case_fir_df$police_station)
police_station_id <- police_station_id[police_station_id != ""]
police_station_id <- data.frame(police_station = police_station_id)
police_station_id$police_station_id <- 1:nrow(police_station_id)
case_fir_df <- dplyr::left_join(case_fir_df, police_station_id, by="police_station")
## Using google geocoding API to get police station coordinates
get_coordinates <- function(police_station_name) {
police_station_name <- paste0(police_station_name,", Maharashtra, India")
police_station_coords <- ggmap::geocode(police_station_name, output = "latlon") %>% data.frame()
return(police_station_coords)
}
## Generate a master dataset of police stations
police_station_coord_list <- lapply(police_station_id$police_station, get_coordinates)
police_station_coord_df <- dplyr::bind_rows(police_station_coord_list)
police_station_coord_df$police_station_id <- 1:nrow(police_station_coord_df)
police_station_id <- dplyr::left_join(police_station_id, police_station_coord_df, by='police_station_id')
## Convert coordinates to geojson for creating Maps on Superset
get_police_station_geo_json <- function(station_id){
geography_df <- police_station_id[police_station_id$police_station_id == station_id,]
geojson_col <- suppressWarnings(geojsonio::geojson_json(input = geography_df, lat = 'lat', lon = 'lon'))
return(as.character(geojson_col))
}
all_geojson <- lapply(police_station_id$police_station_id, get_police_station_geo_json) %>% unlist()
police_station_id$geojson <- all_geojson
# Joining the master dataset of police stations back with the meta dataset
case_fir_df <- dplyr::left_join(case_fir_df, police_station_id[,c(-1)], by='police_station_id')
# Exporting datasets - Individual datasets for Case Meta and Case History
data.table::fwrite(case_fir_df, "datasets/Maharashtra/processed/mh_pocso_01.csv")
data.table::fwrite(case_history_df, "datasets/Maharashtra/processed/mh_pocso_case_history_02.csv")
data.table::fwrite(police_station_id, "datasets/Maharashtra/processed/police_station_geo_details.csv")
# Exporting data for Mapbox -----------------------------------------------
case_fir_mapbox <- case_fir_df %>% group_by(district_name, police_station, police_station_id, lon, lat) %>% summarise(total_cases = length(district_name))
case_fir_mapbox <- case_fir_mapbox[case_fir_mapbox$police_station != "",]
data.table::fwrite(case_fir_mapbox, "datasets/Maharashtra/processed/case_fir_mapbox.csv")