R/load_data.R
In danielgils/cobeneficioswwf: Evaluación de Cobeneficios WWF
Defines functions
load_data
#' Load data for model and setting
#'
#' Loads and processes data from file. Local data for the setting and global data for the model.
#' Writes objects to the global environment.
#'
#' @param setup_call_summary_filename name of file to write to
#' @param speeds named list of mode speeds
#'
#'
#' @export
load_data <- function(setup_call_summary_filename,speeds = list(
bus = 15,
bus_driver = 15,
minibus = 15,
minibus_driver = 15,
car = 21,
taxi = 21,
pedestrian = 4.8,
walk_to_pt = 4.8,
cycle = 14.5,
motorcycle = 25,
truck = 21,
van = 15,
subway = 28,
rail = 35,
auto_rickshaw = 22,
shared_auto = 22,
cycle_rickshaw = 10
)){
# Loading global data of Dose-Responses curves
global_path <- paste0(file.path(find.package('cobeneficioswwf',
lib.loc = .libPaths()),
'data/global/'), '/')
# Importing mortality causes and it's relation to AP and PA
DISEASE_INVENTORY <<- read.csv(paste0(global_path, "dose_response/disease_outcomes_lookup.csv"))
# Importing dose-response for causes related to air pollution
DR_AP <<- read.csv(paste0(global_path, "dose_response/drap/dose_response.csv"))
DR_AP_LIST <<- readRDS(paste0(global_path,"dose_response/drap/dr_ap_list.Rds"))
# Importing dose-response for causes related to physical activity
list_of_files <- list.files(path = paste0(global_path,"dose_response/drpa/extdata/"), recursive = TRUE, pattern = "\\.csv$", full.names = TRUE)
for (i in 1:length(list_of_files)) { # Loop through all files
# Save each file in the global environment
# Really nice explanation of how R saves and searches stuff: http://blog.obeautifulcode.com/R/How-R-Searches-And-Finds-Stuff/
assign(stringr::str_sub(basename(list_of_files[[i]]), end = -5),
readr::read_csv(list_of_files[[i]], col_types = readr::cols()),
pos = 1)
} #End loop
cat(paste0('\n Dose--response read from ',
global_path,
'dose_response/drpa/extdata/ \n\n'),
file = setup_call_summary_filename, append = T)
# Importing local datasets
# These datasets are all local, saved in local folder.
local_path <- PATH_TO_LOCAL_DATA
#####
# Trip dataset
## we need columns: trip_id, trip_mode, stage_mode, stage_duration,
## trip_distance, stage_distance
## trips can be composed of multiple stages
## all trip columns are used for scenario generation alone
## stage columns are used for downstream calculation
## if either trip or stage labels are missing, we copy over from the other.
filename <- paste0(local_path, "trips_", CITY, ".csv")
trip_set <- read_csv(filename, col_types = cols())
cat(paste0('\n Trips read from ', filename,' \n\n'), file = setup_call_summary_filename, append = T)
# Create new participant_id
trip_set$participant_id <- as.numeric(as.factor(trip_set$participant_id))
## Copy over trip or stage information as required
mode_cols <- c('trip_mode','stage_mode')
if (sum(mode_cols %in% colnames(trip_set)) == 0)
stop(paste0('Please include a column labelled "trip_mode" or "stage_mode" in ', filename))
if ('trip_mode' %in% colnames(trip_set) && !'stage_mode' %in% colnames(trip_set))
trip_set$stage_mode <- trip_set$trip_mode
if ('stage_mode' %in% colnames(trip_set) && !'trip_mode' %in% colnames(trip_set))
trip_set$trip_mode <- trip_set$stage_mode
if ('trip_duration' %in% colnames(trip_set) && !'stage_duration' %in% colnames(trip_set))
trip_set$stage_duration <- trip_set$trip_duration
if ('trip_distance' %in% colnames(trip_set) && !'stage_distance' %in% colnames(trip_set))
trip_set$stage_distance <- trip_set$trip_distance
if ('stage_distance' %in% colnames(trip_set) && !'trip_distance' %in% colnames(trip_set))
trip_set$trip_distance <- trip_set$stage_distance
# Transform any mode for standardized ones
# This shouldn't be needed because of the function "standardize_modes" in file
# other_functions.R when cleaning trip datasets
## use specified words for key modes
walk_words <- c('walk','walked','pedestrian')
cycle_words <- c('bike','cycle','cycling')
mc_words <- c('motorcycle','mcycle','mc','mtw')
subway_words <- c('metro','underground')
rail_words <- c('train')
for (i in 1:length(mode_cols)) { # Loop for each column
## lower case mode names
trip_set[[mode_cols[i]]] <- tolower(trip_set[[mode_cols[i]]])
## replaces spaces with _
trip_set[[mode_cols[i]]] <- sapply(trip_set[[mode_cols[i]]],
function(x) gsub(' ','_',as.character(x)))
trip_set[[mode_cols[i]]][trip_set[[mode_cols[i]]] == 'private_car'] <- 'car'
trip_set[[mode_cols[i]]][trip_set[[mode_cols[i]]] %in% walk_words] <- 'pedestrian'
trip_set[[mode_cols[i]]][trip_set[[mode_cols[i]]] %in% cycle_words] <- 'cycle'
trip_set[[mode_cols[i]]][trip_set[[mode_cols[i]]] %in% mc_words] <- 'motorcycle'
trip_set[[mode_cols[i]]][trip_set[[mode_cols[i]]] %in% subway_words] <- 'subway'
trip_set[[mode_cols[i]]][trip_set[[mode_cols[i]]] %in% rail_words] <- 'rail'
}
# Remove rows that doesn't have age or sex
trip_set <- subset(trip_set,!is.na(age))
trip_set <- subset(trip_set,!is.na(sex))
trip_set$sex <- tolower(trip_set$sex)
trip_set$trip_id[is.na(trip_set$stage_mode)] <- 0
TRIP_SET <<- trip_set
#####
# GBD and population datasets
# In GBD "cause" column should match what is in DISEASE_INVENTORY (above)
# Import GBD dataset
filename <- paste0(local_path, "/gbd_", CITY, ".csv")
GBD_DATA <- read_csv(filename, col_types = cols())
cat(paste0('\n GBD read from ', filename, ' \n\n'), file = setup_call_summary_filename, append = T)
# Import population dataset
filename <- paste0(local_path,"/population_",CITY,".csv")
demographic <- read_csv(filename,col_types = cols())
# Remove rows with NA
demographic <- demographic[!apply(demographic, 1, anyNA),]
demographic$sex <- tolower(demographic$sex)
cat(paste0('\n Population read from ', filename, ' \n\n'), file = setup_call_summary_filename, append = T)
# Obtain max and min age from trip and population datasets as well as AGE_RANGE parameter
age_category <- demographic$age
max_age <- max(as.numeric(sapply(age_category, function(x) strsplit(x, '-')[[1]][2])))
max_age <- min(max_age, max(trip_set$age), AGE_RANGE[2])
min_age <- min(as.numeric(sapply(age_category, function(x) strsplit(x, '-')[[1]][1])))
min_age <- max(min_age, min(trip_set$age), AGE_RANGE[1])
# Filter demographic dataset to only age ranges considered and save it in global environment
DEMOGRAPHIC <<- demographic[as.numeric(sapply(age_category, function(x) strsplit(x,'-')[[1]][1])) <= max_age &
as.numeric(sapply(age_category, function(x) strsplit(x, '-')[[1]][2])) >= min_age,]
# Get age-category details from population data
AGE_CATEGORY <<- unique(DEMOGRAPHIC$age)
AGE_LOWER_BOUNDS <<- as.numeric(sapply(AGE_CATEGORY, function(x) strsplit(x, '-')[[1]][1]))
MAX_AGE <<- max(as.numeric(sapply(AGE_CATEGORY, function(x) strsplit(x, '-')[[1]][2])))
# Filter GBD datasets to only causes we're interested in and only age ranges considered
disease_names <- c(as.character(DISEASE_INVENTORY$GBD_name), 'Road injuries')
GBD_DATA <- subset(GBD_DATA,cause_name %in% disease_names)
GBD_DATA$min_age <- as.numeric(sapply(GBD_DATA$age_name, function(x) str_split(x, ' to ')[[1]][1]))
GBD_DATA$max_age <- as.numeric(sapply(GBD_DATA$age_name, function(x) str_split(x, ' to ')[[1]][2]))
GBD_DATA <- subset(GBD_DATA,max_age >= AGE_LOWER_BOUNDS[1])
GBD_DATA <- subset(GBD_DATA,min_age <= MAX_AGE)
names(GBD_DATA)[c(1,3,4,5)] <- c('measure','sex','age','cause')
GBD_DATA$sex <- tolower(GBD_DATA$sex)
# Create a dataframe of burden of disease with info from GBD and population datasets
# Expand.grid creates all possible combinations of these variables
burden_of_disease <- expand.grid(measure = unique(GBD_DATA$measure),
sex = unique(DEMOGRAPHIC$sex),
age = unique(DEMOGRAPHIC$age),
cause = disease_names,
stringsAsFactors = F)
# Add population considering age range and sex from demographic
burden_of_disease <- dplyr::left_join(burden_of_disease, DEMOGRAPHIC, by = c('age', 'sex'))
burden_of_disease$min_age <- as.numeric(sapply(burden_of_disease$age, function(x) str_split(x, '-')[[1]][1]))
burden_of_disease$max_age <- as.numeric(sapply(burden_of_disease$age, function(x) str_split(x, '-')[[1]][2]))
## when we sum ages, we assume that all age boundaries used coincide with the GBD age boundaries.
# Compute rate of Ylls and deaths for country's population
burden_of_disease$rate <- apply(burden_of_disease, 1,
function(x){
# Filter only rows needed in GBD for each row in burden_of_disease
# subtab is usually a single row, but if there's
# overlap in age ranges, then there will be more
# than one row
subtab <- subset(GBD_DATA,
measure == as.character(x[1]) &
sex == as.character(x[2]) &
cause == as.character(x[4]) &
min_age >= as.numeric(x[6]) &
max_age <= as.numeric(x[7]));
# Compute the rate between Ylls or deaths and country's population
# sum is used in case there are more than 1 row
sum(subtab$val)/sum(subtab$population)
}
)
# To compute the burden at city level, we applied the rate computed at country level
# to city's population
burden_of_disease$burden <- burden_of_disease$population * burden_of_disease$rate
burden_of_disease$burden[is.na(burden_of_disease$burden)] <- 0
DISEASE_BURDEN <<- burden_of_disease # Saved in global env
# Compute the average number of YLLs due to road injuries
gbd_injuries <- DISEASE_BURDEN[which(DISEASE_BURDEN$cause == "Road injuries"),]
gbd_injuries$sex_age <- paste0(gbd_injuries$sex, "_", gbd_injuries$age)
## calculating the ratio of YLL to deaths for each age and sex group (average YLLs)
gbd_injuries <- arrange(gbd_injuries, measure)
gbd_inj_yll <- gbd_injuries[which(gbd_injuries$measure == "YLLs (Years of Life Lost)"),]
gbd_inj_dth <- gbd_injuries[which(gbd_injuries$measure == "Deaths"),]
gbd_inj_yll$yll_dth_ratio <- gbd_inj_yll$burden/gbd_inj_dth$burden
GBD_INJ_YLL <<- gbd_inj_yll
#####
## Physical activity dataset
filename <- paste0(local_path, "/pa_", CITY, ".csv")
pa_set <- read_csv(filename,col_types = cols())
pa_set$sex <- tolower(pa_set$sex)
PA_SET <<- pa_set
cat(paste0('\n Physical activity survey read from ', filename, ' \n\n'), file = setup_call_summary_filename, append = T)
#####
## Injuries dataset
## injury data
filename <- paste0(local_path,"/injuries_", CITY,".csv")
injuries <- read_csv(filename,col_types = cols())
cat(paste0('\n Injuries read from ', filename, ' \n\n'), file = setup_call_summary_filename, append = T)
# Categorize victim's age
if ('cas_age' %in% colnames(injuries))
injuries <- assign_age_groups(injuries,age_label = 'cas_age')
# Lowercase everything
injuries$cas_mode <- tolower(injuries$cas_mode)
injuries$strike_mode <- tolower(injuries$strike_mode)
if ('cas_gender' %in% colnames(injuries))
injuries$cas_gender <- tolower(injuries$cas_gender)
#injuries$strike_mode[is.na(injuries$strike_mode)] <- 'listed_na'
# Replace nov words to nov, although this shouldn't be used because this should've
# been done in the cleaning
nov_words <- c('no.other.fixed.or.stationary.object', 'no other vehicle', 'none')
injuries$strike_mode[injuries$strike_mode %in% nov_words] <- 'nov'
## add weight column if missing
if (!'weight' %in% colnames(injuries))
injuries$weight <- 1
# Call function to set contigency tables for WHW and NOV for each combination
# of year, cas_mode, strike_mode, age_cat and cas_gender
set_injury_contingency(injuries)
}
danielgils/cobeneficioswwf documentation built on Dec. 31, 2020, 11:10 p.m.
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Defines functions load_data
#' Load data for model and setting
#'
#' Loads and processes data from file. Local data for the setting and global data for the model.
#' Writes objects to the global environment.
#'
#' @param setup_call_summary_filename name of file to write to
#' @param speeds named list of mode speeds
#'
#'
#' @export
load_data <- function(setup_call_summary_filename,speeds = list(
bus = 15,
bus_driver = 15,
minibus = 15,
minibus_driver = 15,
car = 21,
taxi = 21,
pedestrian = 4.8,
walk_to_pt = 4.8,
cycle = 14.5,
motorcycle = 25,
truck = 21,
van = 15,
subway = 28,
rail = 35,
auto_rickshaw = 22,
shared_auto = 22,
cycle_rickshaw = 10
)){
# Loading global data of Dose-Responses curves
global_path <- paste0(file.path(find.package('cobeneficioswwf',
lib.loc = .libPaths()),
'data/global/'), '/')
# Importing mortality causes and it's relation to AP and PA
DISEASE_INVENTORY <<- read.csv(paste0(global_path, "dose_response/disease_outcomes_lookup.csv"))
# Importing dose-response for causes related to air pollution
DR_AP <<- read.csv(paste0(global_path, "dose_response/drap/dose_response.csv"))
DR_AP_LIST <<- readRDS(paste0(global_path,"dose_response/drap/dr_ap_list.Rds"))
# Importing dose-response for causes related to physical activity
list_of_files <- list.files(path = paste0(global_path,"dose_response/drpa/extdata/"), recursive = TRUE, pattern = "\\.csv$", full.names = TRUE)
for (i in 1:length(list_of_files)) { # Loop through all files
# Save each file in the global environment
# Really nice explanation of how R saves and searches stuff: http://blog.obeautifulcode.com/R/How-R-Searches-And-Finds-Stuff/
assign(stringr::str_sub(basename(list_of_files[[i]]), end = -5),
readr::read_csv(list_of_files[[i]], col_types = readr::cols()),
pos = 1)
} #End loop
cat(paste0('\n Dose--response read from ',
global_path,
'dose_response/drpa/extdata/ \n\n'),
file = setup_call_summary_filename, append = T)
# Importing local datasets
# These datasets are all local, saved in local folder.
local_path <- PATH_TO_LOCAL_DATA
#####
# Trip dataset
## we need columns: trip_id, trip_mode, stage_mode, stage_duration,
## trip_distance, stage_distance
## trips can be composed of multiple stages
## all trip columns are used for scenario generation alone
## stage columns are used for downstream calculation
## if either trip or stage labels are missing, we copy over from the other.
filename <- paste0(local_path, "trips_", CITY, ".csv")
trip_set <- read_csv(filename, col_types = cols())
cat(paste0('\n Trips read from ', filename,' \n\n'), file = setup_call_summary_filename, append = T)
# Create new participant_id
trip_set$participant_id <- as.numeric(as.factor(trip_set$participant_id))
## Copy over trip or stage information as required
mode_cols <- c('trip_mode','stage_mode')
if (sum(mode_cols %in% colnames(trip_set)) == 0)
stop(paste0('Please include a column labelled "trip_mode" or "stage_mode" in ', filename))
if ('trip_mode' %in% colnames(trip_set) && !'stage_mode' %in% colnames(trip_set))
trip_set$stage_mode <- trip_set$trip_mode
if ('stage_mode' %in% colnames(trip_set) && !'trip_mode' %in% colnames(trip_set))
trip_set$trip_mode <- trip_set$stage_mode
if ('trip_duration' %in% colnames(trip_set) && !'stage_duration' %in% colnames(trip_set))
trip_set$stage_duration <- trip_set$trip_duration
if ('trip_distance' %in% colnames(trip_set) && !'stage_distance' %in% colnames(trip_set))
trip_set$stage_distance <- trip_set$trip_distance
if ('stage_distance' %in% colnames(trip_set) && !'trip_distance' %in% colnames(trip_set))
trip_set$trip_distance <- trip_set$stage_distance
# Transform any mode for standardized ones
# This shouldn't be needed because of the function "standardize_modes" in file
# other_functions.R when cleaning trip datasets
## use specified words for key modes
walk_words <- c('walk','walked','pedestrian')
cycle_words <- c('bike','cycle','cycling')
mc_words <- c('motorcycle','mcycle','mc','mtw')
subway_words <- c('metro','underground')
rail_words <- c('train')
for (i in 1:length(mode_cols)) { # Loop for each column
## lower case mode names
trip_set[[mode_cols[i]]] <- tolower(trip_set[[mode_cols[i]]])
## replaces spaces with _
trip_set[[mode_cols[i]]] <- sapply(trip_set[[mode_cols[i]]],
function(x) gsub(' ','_',as.character(x)))
trip_set[[mode_cols[i]]][trip_set[[mode_cols[i]]] == 'private_car'] <- 'car'
trip_set[[mode_cols[i]]][trip_set[[mode_cols[i]]] %in% walk_words] <- 'pedestrian'
trip_set[[mode_cols[i]]][trip_set[[mode_cols[i]]] %in% cycle_words] <- 'cycle'
trip_set[[mode_cols[i]]][trip_set[[mode_cols[i]]] %in% mc_words] <- 'motorcycle'
trip_set[[mode_cols[i]]][trip_set[[mode_cols[i]]] %in% subway_words] <- 'subway'
trip_set[[mode_cols[i]]][trip_set[[mode_cols[i]]] %in% rail_words] <- 'rail'
}
# Remove rows that doesn't have age or sex
trip_set <- subset(trip_set,!is.na(age))
trip_set <- subset(trip_set,!is.na(sex))
trip_set$sex <- tolower(trip_set$sex)
trip_set$trip_id[is.na(trip_set$stage_mode)] <- 0
TRIP_SET <<- trip_set
#####
# GBD and population datasets
# In GBD "cause" column should match what is in DISEASE_INVENTORY (above)
# Import GBD dataset
filename <- paste0(local_path, "/gbd_", CITY, ".csv")
GBD_DATA <- read_csv(filename, col_types = cols())
cat(paste0('\n GBD read from ', filename, ' \n\n'), file = setup_call_summary_filename, append = T)
# Import population dataset
filename <- paste0(local_path,"/population_",CITY,".csv")
demographic <- read_csv(filename,col_types = cols())
# Remove rows with NA
demographic <- demographic[!apply(demographic, 1, anyNA),]
demographic$sex <- tolower(demographic$sex)
cat(paste0('\n Population read from ', filename, ' \n\n'), file = setup_call_summary_filename, append = T)
# Obtain max and min age from trip and population datasets as well as AGE_RANGE parameter
age_category <- demographic$age
max_age <- max(as.numeric(sapply(age_category, function(x) strsplit(x, '-')[[1]][2])))
max_age <- min(max_age, max(trip_set$age), AGE_RANGE[2])
min_age <- min(as.numeric(sapply(age_category, function(x) strsplit(x, '-')[[1]][1])))
min_age <- max(min_age, min(trip_set$age), AGE_RANGE[1])
# Filter demographic dataset to only age ranges considered and save it in global environment
DEMOGRAPHIC <<- demographic[as.numeric(sapply(age_category, function(x) strsplit(x,'-')[[1]][1])) <= max_age &
as.numeric(sapply(age_category, function(x) strsplit(x, '-')[[1]][2])) >= min_age,]
# Get age-category details from population data
AGE_CATEGORY <<- unique(DEMOGRAPHIC$age)
AGE_LOWER_BOUNDS <<- as.numeric(sapply(AGE_CATEGORY, function(x) strsplit(x, '-')[[1]][1]))
MAX_AGE <<- max(as.numeric(sapply(AGE_CATEGORY, function(x) strsplit(x, '-')[[1]][2])))
# Filter GBD datasets to only causes we're interested in and only age ranges considered
disease_names <- c(as.character(DISEASE_INVENTORY$GBD_name), 'Road injuries')
GBD_DATA <- subset(GBD_DATA,cause_name %in% disease_names)
GBD_DATA$min_age <- as.numeric(sapply(GBD_DATA$age_name, function(x) str_split(x, ' to ')[[1]][1]))
GBD_DATA$max_age <- as.numeric(sapply(GBD_DATA$age_name, function(x) str_split(x, ' to ')[[1]][2]))
GBD_DATA <- subset(GBD_DATA,max_age >= AGE_LOWER_BOUNDS[1])
GBD_DATA <- subset(GBD_DATA,min_age <= MAX_AGE)
names(GBD_DATA)[c(1,3,4,5)] <- c('measure','sex','age','cause')
GBD_DATA$sex <- tolower(GBD_DATA$sex)
# Create a dataframe of burden of disease with info from GBD and population datasets
# Expand.grid creates all possible combinations of these variables
burden_of_disease <- expand.grid(measure = unique(GBD_DATA$measure),
sex = unique(DEMOGRAPHIC$sex),
age = unique(DEMOGRAPHIC$age),
cause = disease_names,
stringsAsFactors = F)
# Add population considering age range and sex from demographic
burden_of_disease <- dplyr::left_join(burden_of_disease, DEMOGRAPHIC, by = c('age', 'sex'))
burden_of_disease$min_age <- as.numeric(sapply(burden_of_disease$age, function(x) str_split(x, '-')[[1]][1]))
burden_of_disease$max_age <- as.numeric(sapply(burden_of_disease$age, function(x) str_split(x, '-')[[1]][2]))
## when we sum ages, we assume that all age boundaries used coincide with the GBD age boundaries.
# Compute rate of Ylls and deaths for country's population
burden_of_disease$rate <- apply(burden_of_disease, 1,
function(x){
# Filter only rows needed in GBD for each row in burden_of_disease
# subtab is usually a single row, but if there's
# overlap in age ranges, then there will be more
# than one row
subtab <- subset(GBD_DATA,
measure == as.character(x[1]) &
sex == as.character(x[2]) &
cause == as.character(x[4]) &
min_age >= as.numeric(x[6]) &
max_age <= as.numeric(x[7]));
# Compute the rate between Ylls or deaths and country's population
# sum is used in case there are more than 1 row
sum(subtab$val)/sum(subtab$population)
}
)
# To compute the burden at city level, we applied the rate computed at country level
# to city's population
burden_of_disease$burden <- burden_of_disease$population * burden_of_disease$rate
burden_of_disease$burden[is.na(burden_of_disease$burden)] <- 0
DISEASE_BURDEN <<- burden_of_disease # Saved in global env
# Compute the average number of YLLs due to road injuries
gbd_injuries <- DISEASE_BURDEN[which(DISEASE_BURDEN$cause == "Road injuries"),]
gbd_injuries$sex_age <- paste0(gbd_injuries$sex, "_", gbd_injuries$age)
## calculating the ratio of YLL to deaths for each age and sex group (average YLLs)
gbd_injuries <- arrange(gbd_injuries, measure)
gbd_inj_yll <- gbd_injuries[which(gbd_injuries$measure == "YLLs (Years of Life Lost)"),]
gbd_inj_dth <- gbd_injuries[which(gbd_injuries$measure == "Deaths"),]
gbd_inj_yll$yll_dth_ratio <- gbd_inj_yll$burden/gbd_inj_dth$burden
GBD_INJ_YLL <<- gbd_inj_yll
#####
## Physical activity dataset
filename <- paste0(local_path, "/pa_", CITY, ".csv")
pa_set <- read_csv(filename,col_types = cols())
pa_set$sex <- tolower(pa_set$sex)
PA_SET <<- pa_set
cat(paste0('\n Physical activity survey read from ', filename, ' \n\n'), file = setup_call_summary_filename, append = T)
#####
## Injuries dataset
## injury data
filename <- paste0(local_path,"/injuries_", CITY,".csv")
injuries <- read_csv(filename,col_types = cols())
cat(paste0('\n Injuries read from ', filename, ' \n\n'), file = setup_call_summary_filename, append = T)
# Categorize victim's age
if ('cas_age' %in% colnames(injuries))
injuries <- assign_age_groups(injuries,age_label = 'cas_age')
# Lowercase everything
injuries$cas_mode <- tolower(injuries$cas_mode)
injuries$strike_mode <- tolower(injuries$strike_mode)
if ('cas_gender' %in% colnames(injuries))
injuries$cas_gender <- tolower(injuries$cas_gender)
#injuries$strike_mode[is.na(injuries$strike_mode)] <- 'listed_na'
# Replace nov words to nov, although this shouldn't be used because this should've
# been done in the cleaning
nov_words <- c('no.other.fixed.or.stationary.object', 'no other vehicle', 'none')
injuries$strike_mode[injuries$strike_mode %in% nov_words] <- 'nov'
## add weight column if missing
if (!'weight' %in% colnames(injuries))
injuries$weight <- 1
# Call function to set contigency tables for WHW and NOV for each combination
# of year, cas_mode, strike_mode, age_cat and cas_gender
set_injury_contingency(injuries)
}
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