inst/rmd/apr2020/code.R
In joebrew/maragra: An analysis of malaria at the Maragra sugar company, Mozambique
# Packages
library(sp)
library(tidyverse)
library(knitr)
library(Hmisc)
library(brew)
library(maragra)
library(knitr)
library(RColorBrewer)
# library(lme4)
library(lfe)
library(restorepoint)
library(regtools)
classify_protection <- function(days_since){
ifelse(days_since < 21 | days_since > (365+21),0,
(((365+21) - days_since)/(365))
)
}
if('prepared_data.RData' %in% dir()){
load('prepared_data.RData')
} else {
# Read in data
ab <- maragra::ab
ab_panel <- maragra::ab_panel
bairros <- maragra::bairros
bes <- maragra::bes
census <- maragra::census
clinic <- maragra::clinic
clinic_agg <- maragra::clinic_agg
mc <- maragra::mc
workers <- maragra::workers
if(!'weather.RData' %in% dir()){
weather <- maragra::weather %>%
mutate(precipitation = ifelse(is.na(precipitation),
0,
precipitation))
# Get lags for weather
starts <- c(15, 30, 60, 90)
ends <- c(30, 60, 90, 120)
for(k in 1:length(starts)){
for(j in 1:length(ends)){
this_start <- starts[k]
this_end <- ends[j]
if(this_start < this_end){
precipitation_column_name <- paste0('precipitation_lag_', this_start, '_', this_end)
weather[,precipitation_column_name] <- NA
temp_column_name <- paste0('temp_lag_', this_start, '_', this_end)
}
}
}
for(i in 1:nrow(weather)){
starts <- c(15, 30, 60, 90)
ends <- c(30, 60, 90, 120)
for(k in 1:length(starts)){
for(j in 1:length(ends)){
this_start <- starts[k]
this_end <- ends[j]
if(this_start < this_end){
this_date <- weather$date[i]
end_date <- this_date - this_start
start_date <- this_date - this_end
if(start_date >= min(weather$date,na.rm = TRUE) &
end_date <= max(weather$date, na.rm = TRUE)){
sub_data <- weather %>%
filter(date >= start_date,
date <= end_date)
precipitation_column_name <- paste0('precipitation_lag_', this_start, '_', this_end)
weather[i,precipitation_column_name] <- mean(sub_data$precipitation, na.rm = TRUE)
temp_column_name <- paste0('temp_lag_', this_start, '_', this_end)
weather[i,temp_column_name] <- mean(sub_data$temp, na.rm = TRUE)
}
}
}
}
}
save(weather, file = 'weather.RData')
} else {
load('weather.RData')
}
# Create a dictionary to classify protection
protection_dict <-
data_frame(days_since = seq(min(irs$days_since, na.rm = TRUE),
max(irs$days_since, na.rm = TRUE),
by = 1)) %>%
mutate(protection = classify_protection(days_since))
model_data <-
ab_panel %>%
dplyr::select(oracle_number, date, absent, unidade) %>%
left_join(irs %>%
dplyr::select(date, unidade, chemical, days_since),
by = c('unidade', 'date')) %>%
# mutate(protection = ifelse(days_since < 21 |
# days_since > (183+21) |
# is.na(days_since),
# 0, 1)) %>%
left_join(protection_dict) %>%
mutate(protection = ifelse(is.na(protection), 0, protection)) %>%
dplyr::select(-days_since) %>%
group_by(oracle_number) %>%
mutate(ever_sprayed = length(which(protection > 0)) > 0) %>%
# Bring in some information on workers
left_join(workers %>%
dplyr::select(oracle_number,
permanent_or_temporary,
department,
sex,
# date_of_birth,
perm_id,
census_name_match_score) %>%
# void the permids of anyone with a match score of greater than 0.25
mutate(perm_id = ifelse(census_name_match_score > 0.25,
NA,
perm_id))) %>%
# Bring in some info from the census
left_join(census %>%
filter(!duplicated(perm_id)) %>%
dplyr::select(perm_id,
maragra_bairro,
maragra_fabrica,
education,
floor_material)) %>%
# Bring in some data for weather
left_join(weather)
########## !!!!!!!!!!!!!!! REMOVING 2016 for temp workers
model_data <- model_data %>%
filter(permanent_or_temporary == 'Permanent' |
(permanent_or_temporary == 'Temporary' & date <= '2015-12-31'))
# Since ad and factory are same, keep same
model_data$field <- ifelse(model_data$department == 'Field',
'Field worker',
'Not field worker')
model_data <- model_data %>%
mutate(group = paste0(permanent_or_temporary, ' ',
tolower(field)))
# Add a month column
model_data$month_number <- add_zero(format(model_data$date, '%m'), 2)
# Get geographic info for externality analysis
# Get latitude / longitude into model_data
model_data <- model_data %>%
left_join(census %>%
filter(!duplicated(unidade)) %>%
dplyr::select(unidade,
longitude_aura,
latitude_aura),
by = 'unidade')
model_data$p <- model_data$protection
# Centers of each bairro
bairro_loc <- apply(coordinates(maragra::bairros_maragra_bairro), 2, mean)
# Define function for weighting indirect protection
weighter <- function(x){
# out <- (1 / (x+0.01))
# out <- 1/x
out <- ifelse(x > 1, 0, 1-x)
return(out)
}
# Radial weighter
radial_weighter <- function(x){
out <- ifelse(x > 0.4, 0, 1)
return(out)
}
# Define function for getting distance between houses
overall_spatial <- model_data %>%
group_by(lat = latitude_aura,
lng = longitude_aura) %>%
tally %>%
dplyr::distinct(lat, lng, .keep_all = TRUE) %>%
dplyr::select(-n) %>%
filter(!is.na(lat),
!is.na(lng)) %>%
bind_rows(data.frame(lat = bairro_loc[2],
lng = bairro_loc[1])) %>%
mutate(x = lng,
y = lat)
overall_spatial$overall_id <- 1:nrow(overall_spatial)
coordinates(overall_spatial) <- ~x+y
proj4string(overall_spatial) <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
overall_distances <- spDists(x = overall_spatial, longlat = TRUE)
get_protection <- function(lat, lng, protection, guess_missing = TRUE, radial = FALSE){
# Build a dataframe
df <- data_frame(lat, lng, protection)
# Get id
df$id <- 1:nrow(df)
if(guess_missing){
# Define function for guessing
guess <- function(x){
x <- x %>%
mutate(lng = ifelse(is.na(lng),
bairro_loc[1],
lng)) %>%
mutate(lat = ifelse(is.na(lat),
bairro_loc[2],
lat))
return(x)
}
# if no geography (due to no census matching, just use average geo)
df <- guess(df)
}
# save for later
original_df <- df
# Get unique lat/lng pairs
df <- df %>%
group_by(lat, lng) %>%
summarise(protection = mean(protection, na.rm = TRUE))
# Join to overall spatial to get distances
df <- left_join(df, overall_spatial@data, by = c('lat', 'lng'))
keeps <- df$overall_id
distances <- overall_distances[keeps, keeps]
# # Get a spatial version
# (no longer necessary with above approach)
# df_spatial <- df %>% filter(!is.na(lng),
# !is.na(lat))
#
# coordinates(df_spatial) <- ~lng+lat
# proj4string(df_spatial) <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
#
# # Get distances
# distances <- spDists(x = df_spatial, longlat = TRUE)
# Weight distances and return weighted protection
out <- apply(distances, 1, function(x){
val <- df$protection * weighter(x)
# Infinite means distance was 0/self; remove
val[is.infinite(val)] <- NA
# Keep only those within a kilometer
val <- val[x <= 1]
mean(val, na.rm = TRUE)
})
radial_out <- apply(distances, 1, function(x){
val <- df$protection * radial_weighter(x)
# Infinite means distance was 0/self; remove
val[is.infinite(val)] <- NA
mean(val, na.rm = TRUE)
})
right <- data.frame(lat = df$lat[!is.na(df$lat)],
lng = df$lng[!is.na(df$lng)],
herd = out,
herd_radial = radial_out)
# Join to original df
original_df <- original_df %>%
left_join(right,
by = c('lat', 'lng'))
# Return just the protection column
if(radial){
return(original_df$herd_radial)
} else {
return(original_df$herd)
}
}
# For each day, get real, and hypothetical protection scores
dates <- sort(unique(model_data$date))
out_list <- list()
for(i in 1:length(dates)){
this_date <- dates[i]
message(this_date)
this_data <- model_data %>%
filter(date == this_date)
# Get protection score
protection_scores <- get_protection(lat = this_data$latitude_aura,
lng = this_data$longitude_aura,
protection = this_data$protection,
guess_missing = TRUE)
radial_protection_scores <- get_protection(lat = this_data$latitude_aura,
lng = this_data$longitude_aura,
protection = this_data$protection,
guess_missing = TRUE, radial = TRUE)
this_data$herd_protection <- protection_scores
this_data$radial_herd_protection <- radial_protection_scores
out_list[[i]] <- this_data
}
model_data <- bind_rows(out_list)
# Plots of maps
# Libraries
library(tidyverse)
library(raster)
library(tidyr)
library(ggplot2)
library(RColorBrewer)
library(broom)
library(ggthemes)
# Get each countries shapefile
countries <- c('Mozambique')
iso3s <- c('MOZ')
for(i in 1:length(countries)){
message('Fetching data for ', countries[i])
x <- getData(name = 'GADM', level = 2, country = iso3s[i])
assign(tolower(countries[i]),
x,
envir = .GlobalEnv)
}
# Define which districts are "special" -------------
specials <- 'Manhiça'
# Mozambique
mozambique@data$special <- FALSE
mozambique@data$special[mozambique@data$NAME_2 %in% specials] <- TRUE
# Combine all data into a "long" / "tidy" format
make_long <- function(x, region = "NAME_2"){
shp_df <- broom::tidy(x, region = region)
return(shp_df)
}
mozambique_long <- make_long(mozambique) %>% mutate(country = 'Mozambique')
combined <- mozambique_long
combined$special <- combined$id %in% specials
# combined$special[!combined$special] <- NA
# Get a map of africa to use as a background
# from the cism package!
africa <- cism::africa
africa_long <- make_long(africa, region = 'COUNTRY')
africa_long$special <- africa_long$id %in% countries
save.image(file = 'prepared_data.RData')
}
model_data$year <- as.numeric(format(model_data$date, '%Y'))
# Add 5th worker type
model_data <- model_data %>%
# Get grade
left_join(ab_panel %>% dplyr::distinct(grade, oracle_number, date)) %>%
mutate(year = as.numeric(format(date, '%Y'))) %>%
left_join(wages %>% dplyr::distinct(grade, department, year, daily_usd))
model_data$group5 <- ifelse(is.na(model_data$daily_usd) & model_data$group != 'Temporary not field worker', 'No wage data', model_data$group)
# As of now, model_data and final_data are the same
# We want to restrict model_data downward a bit (to not include issues that are bad for modeling)
# while keeping final data (since it's useful for simulations)
final_data <- model_data
# Remove christmas
model_data <- model_data %>%
mutate(day_number = as.numeric(format(date, '%d')))
remove_christmas <- TRUE
if(remove_christmas){
remove_these <- which(
(model_data$month_number == '12' &
model_data$day_number >= 15) |
model_data$month_number == '01' &
model_data$day_number <= 15
)
}
# Remove 2013 temp workers from model data (since some problems)
model_data <- model_data %>%
filter(!(permanent_or_temporary == 'Temporary' & year == 2013))
final_data <- final_data %>%
filter(!(permanent_or_temporary == 'Temporary' & year == 2013))
# # only the ever sprayed
model_data <- model_data %>%
filter(ever_sprayed)
final_data <- final_data %>% filter(ever_sprayed)
# Prepare simulations
if('simulations.RData' %in% dir()){
load('simulations.RData')
} else {
time_optimize_df <-
data.frame(date = seq(as.Date('2020-01-01'),
as.Date('2020-12-31'),
by = 1)) %>%
mutate(month_day = format(date, '%m-%d'))
time_optimize_df$day_number <- (1:nrow(time_optimize_df))+20
time_optimize_df$the_protection <- classify_protection(time_optimize_df$day_number)
time_optimize_df <- time_optimize_df %>% dplyr::select(month_day, the_protection)
strategies <- c('Zero', 'Max', 'Time-optimized')
strategy_list <- list()
for(s in 1:length(strategies)){
dates <- sort(unique(final_data$date))
out_list <- list()
this_strategy <- strategies[s]
for(i in 1:length(dates)){
this_date <- dates[i]
message(this_strategy, '...', this_date)
this_data <- final_data %>%
filter(date == this_date)
# Define scores
if(this_strategy == 'Zero'){
this_data$protection <- 0
the_protection <- rep(0, length(this_data$protection))
}
if(this_strategy == 'Max'){
this_data$protection <- 1
the_protection <- rep(1, length(this_data$protection))
}
if(this_strategy == 'Time-optimized'){
this_data$month_day <- format(this_data$date, '%m-%d')
x <- this_data %>%
left_join(time_optimize_df)
the_protection <- x$the_protection
this_data$protection <- the_protection
}
# Get protection score
protection_scores <- get_protection(lat = this_data$latitude_aura,
lng = this_data$longitude_aura,
protection = the_protection,
guess_missing = TRUE)
this_data$herd_protection <- protection_scores
out_list[[i]] <- this_data
}
out <- bind_rows(out_list)
out$strategy <- this_strategy
strategy_list[[s]] <- out
}
simulations <- bind_rows(strategy_list)
simulations <- simulations %>%
bind_rows(final_data %>% mutate(strategy = 'Real'))
save(simulations, file = 'simulations.RData')
}
# Add precipitation
model_data$rain_var <- model_data$precipitation_lag_15_90
final_data$rain_var <- final_data$precipitation_lag_15_90
simulations$rain_var <- simulations$precipitation_lag_15_90
# Make the group var
model_data$group4 <- model_data$group
final_data$group4 <- final_data$group
simulations$group4 <- simulations$group
three_transform <- function(x){
ifelse(grepl('temporary', tolower(x)), 'Temporary worker', x)
}
model_data$group3 <- three_transform(model_data$group)
final_data$group3 <- three_transform(final_data$group)
simulations$group3 <- three_transform(simulations$group)
model_data$group <- model_data$group3
final_data$group <- final_data$group3
simulations$group <- simulations$group3
# final_model <- felm(log(absent+1) ~ protection_var + (rain_var) | oracle_number | 0 | 0,
# data = final_data)
final_model <- felm(log(absent+1) ~ protection +
herd_protection +
rain_var | oracle_number | 0 | 0,
data = model_data)
summary(model_data)
final_model_lm <- lm(log(absent+1) ~ protection +
herd_protection + (rain_var),
data = model_data)
final_model_radial <- felm(log(absent+1) ~ protection +
radial_herd_protection +
rain_var | oracle_number | 0 | 0,
data = model_data)
summary(final_model_radial)
final_model_radial_lm <- lm(log(absent+1) ~ protection +
radial_herd_protection + (rain_var),
data = model_data)
# Model for each worker group
worker_groups <- sort(unique(model_data$group))
model_data_list <- model_list <- model_list_lm <-
radial_model_data_list <- radial_model_list <- radial_model_list_lm <- list()
for (i in 1:length(worker_groups)){
sub_data <- model_data %>% filter(group == worker_groups[i])
this_model <- felm(log(absent+1) ~ protection +
herd_protection +
rain_var | oracle_number | 0 | 0,
data = sub_data)
this_model_radial <- felm(log(absent+1) ~ protection +
radial_herd_protection +
rain_var | oracle_number | 0 | 0,
data = sub_data)
this_model_lm <- lm(log(absent+1) ~ protection +
herd_protection + (rain_var),
data = sub_data)
this_model_lm_radial <- lm(log(absent+1) ~ protection +
radial_herd_protection + (rain_var),
data = sub_data)
formatted <- broom::tidy(this_model)
formatted$group5 <- worker_groups[i]
formatted_radial <- broom::tidy(this_model_radial)
formatted_radial$group5 <- worker_groups[i]
model_list[[i]] <- this_model
model_list_lm[[i]] <- this_model_lm
model_data_list[[i]] <- formatted
radial_model_list[[i]] <- this_model_radial
radial_model_list_lm[[i]] <- this_model_lm_radial
radial_model_data_list[[i]] <- formatted_radial
}
names(model_list) <- names(model_data_list) <- names(model_list_lm) <-
names(radial_model_list) <- names(radial_model_data_list) <- names(radial_model_list_lm) <-
worker_groups
joebrew/maragra documentation built on Aug. 11, 2020, 8:39 p.m.
R Package Documentation
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# Packages
library(sp)
library(tidyverse)
library(knitr)
library(Hmisc)
library(brew)
library(maragra)
library(knitr)
library(RColorBrewer)
# library(lme4)
library(lfe)
library(restorepoint)
library(regtools)
classify_protection <- function(days_since){
ifelse(days_since < 21 | days_since > (365+21),0,
(((365+21) - days_since)/(365))
)
}
if('prepared_data.RData' %in% dir()){
load('prepared_data.RData')
} else {
# Read in data
ab <- maragra::ab
ab_panel <- maragra::ab_panel
bairros <- maragra::bairros
bes <- maragra::bes
census <- maragra::census
clinic <- maragra::clinic
clinic_agg <- maragra::clinic_agg
mc <- maragra::mc
workers <- maragra::workers
if(!'weather.RData' %in% dir()){
weather <- maragra::weather %>%
mutate(precipitation = ifelse(is.na(precipitation),
0,
precipitation))
# Get lags for weather
starts <- c(15, 30, 60, 90)
ends <- c(30, 60, 90, 120)
for(k in 1:length(starts)){
for(j in 1:length(ends)){
this_start <- starts[k]
this_end <- ends[j]
if(this_start < this_end){
precipitation_column_name <- paste0('precipitation_lag_', this_start, '_', this_end)
weather[,precipitation_column_name] <- NA
temp_column_name <- paste0('temp_lag_', this_start, '_', this_end)
}
}
}
for(i in 1:nrow(weather)){
starts <- c(15, 30, 60, 90)
ends <- c(30, 60, 90, 120)
for(k in 1:length(starts)){
for(j in 1:length(ends)){
this_start <- starts[k]
this_end <- ends[j]
if(this_start < this_end){
this_date <- weather$date[i]
end_date <- this_date - this_start
start_date <- this_date - this_end
if(start_date >= min(weather$date,na.rm = TRUE) &
end_date <= max(weather$date, na.rm = TRUE)){
sub_data <- weather %>%
filter(date >= start_date,
date <= end_date)
precipitation_column_name <- paste0('precipitation_lag_', this_start, '_', this_end)
weather[i,precipitation_column_name] <- mean(sub_data$precipitation, na.rm = TRUE)
temp_column_name <- paste0('temp_lag_', this_start, '_', this_end)
weather[i,temp_column_name] <- mean(sub_data$temp, na.rm = TRUE)
}
}
}
}
}
save(weather, file = 'weather.RData')
} else {
load('weather.RData')
}
# Create a dictionary to classify protection
protection_dict <-
data_frame(days_since = seq(min(irs$days_since, na.rm = TRUE),
max(irs$days_since, na.rm = TRUE),
by = 1)) %>%
mutate(protection = classify_protection(days_since))
model_data <-
ab_panel %>%
dplyr::select(oracle_number, date, absent, unidade) %>%
left_join(irs %>%
dplyr::select(date, unidade, chemical, days_since),
by = c('unidade', 'date')) %>%
# mutate(protection = ifelse(days_since < 21 |
# days_since > (183+21) |
# is.na(days_since),
# 0, 1)) %>%
left_join(protection_dict) %>%
mutate(protection = ifelse(is.na(protection), 0, protection)) %>%
dplyr::select(-days_since) %>%
group_by(oracle_number) %>%
mutate(ever_sprayed = length(which(protection > 0)) > 0) %>%
# Bring in some information on workers
left_join(workers %>%
dplyr::select(oracle_number,
permanent_or_temporary,
department,
sex,
# date_of_birth,
perm_id,
census_name_match_score) %>%
# void the permids of anyone with a match score of greater than 0.25
mutate(perm_id = ifelse(census_name_match_score > 0.25,
NA,
perm_id))) %>%
# Bring in some info from the census
left_join(census %>%
filter(!duplicated(perm_id)) %>%
dplyr::select(perm_id,
maragra_bairro,
maragra_fabrica,
education,
floor_material)) %>%
# Bring in some data for weather
left_join(weather)
########## !!!!!!!!!!!!!!! REMOVING 2016 for temp workers
model_data <- model_data %>%
filter(permanent_or_temporary == 'Permanent' |
(permanent_or_temporary == 'Temporary' & date <= '2015-12-31'))
# Since ad and factory are same, keep same
model_data$field <- ifelse(model_data$department == 'Field',
'Field worker',
'Not field worker')
model_data <- model_data %>%
mutate(group = paste0(permanent_or_temporary, ' ',
tolower(field)))
# Add a month column
model_data$month_number <- add_zero(format(model_data$date, '%m'), 2)
# Get geographic info for externality analysis
# Get latitude / longitude into model_data
model_data <- model_data %>%
left_join(census %>%
filter(!duplicated(unidade)) %>%
dplyr::select(unidade,
longitude_aura,
latitude_aura),
by = 'unidade')
model_data$p <- model_data$protection
# Centers of each bairro
bairro_loc <- apply(coordinates(maragra::bairros_maragra_bairro), 2, mean)
# Define function for weighting indirect protection
weighter <- function(x){
# out <- (1 / (x+0.01))
# out <- 1/x
out <- ifelse(x > 1, 0, 1-x)
return(out)
}
# Radial weighter
radial_weighter <- function(x){
out <- ifelse(x > 0.4, 0, 1)
return(out)
}
# Define function for getting distance between houses
overall_spatial <- model_data %>%
group_by(lat = latitude_aura,
lng = longitude_aura) %>%
tally %>%
dplyr::distinct(lat, lng, .keep_all = TRUE) %>%
dplyr::select(-n) %>%
filter(!is.na(lat),
!is.na(lng)) %>%
bind_rows(data.frame(lat = bairro_loc[2],
lng = bairro_loc[1])) %>%
mutate(x = lng,
y = lat)
overall_spatial$overall_id <- 1:nrow(overall_spatial)
coordinates(overall_spatial) <- ~x+y
proj4string(overall_spatial) <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
overall_distances <- spDists(x = overall_spatial, longlat = TRUE)
get_protection <- function(lat, lng, protection, guess_missing = TRUE, radial = FALSE){
# Build a dataframe
df <- data_frame(lat, lng, protection)
# Get id
df$id <- 1:nrow(df)
if(guess_missing){
# Define function for guessing
guess <- function(x){
x <- x %>%
mutate(lng = ifelse(is.na(lng),
bairro_loc[1],
lng)) %>%
mutate(lat = ifelse(is.na(lat),
bairro_loc[2],
lat))
return(x)
}
# if no geography (due to no census matching, just use average geo)
df <- guess(df)
}
# save for later
original_df <- df
# Get unique lat/lng pairs
df <- df %>%
group_by(lat, lng) %>%
summarise(protection = mean(protection, na.rm = TRUE))
# Join to overall spatial to get distances
df <- left_join(df, overall_spatial@data, by = c('lat', 'lng'))
keeps <- df$overall_id
distances <- overall_distances[keeps, keeps]
# # Get a spatial version
# (no longer necessary with above approach)
# df_spatial <- df %>% filter(!is.na(lng),
# !is.na(lat))
#
# coordinates(df_spatial) <- ~lng+lat
# proj4string(df_spatial) <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs")
#
# # Get distances
# distances <- spDists(x = df_spatial, longlat = TRUE)
# Weight distances and return weighted protection
out <- apply(distances, 1, function(x){
val <- df$protection * weighter(x)
# Infinite means distance was 0/self; remove
val[is.infinite(val)] <- NA
# Keep only those within a kilometer
val <- val[x <= 1]
mean(val, na.rm = TRUE)
})
radial_out <- apply(distances, 1, function(x){
val <- df$protection * radial_weighter(x)
# Infinite means distance was 0/self; remove
val[is.infinite(val)] <- NA
mean(val, na.rm = TRUE)
})
right <- data.frame(lat = df$lat[!is.na(df$lat)],
lng = df$lng[!is.na(df$lng)],
herd = out,
herd_radial = radial_out)
# Join to original df
original_df <- original_df %>%
left_join(right,
by = c('lat', 'lng'))
# Return just the protection column
if(radial){
return(original_df$herd_radial)
} else {
return(original_df$herd)
}
}
# For each day, get real, and hypothetical protection scores
dates <- sort(unique(model_data$date))
out_list <- list()
for(i in 1:length(dates)){
this_date <- dates[i]
message(this_date)
this_data <- model_data %>%
filter(date == this_date)
# Get protection score
protection_scores <- get_protection(lat = this_data$latitude_aura,
lng = this_data$longitude_aura,
protection = this_data$protection,
guess_missing = TRUE)
radial_protection_scores <- get_protection(lat = this_data$latitude_aura,
lng = this_data$longitude_aura,
protection = this_data$protection,
guess_missing = TRUE, radial = TRUE)
this_data$herd_protection <- protection_scores
this_data$radial_herd_protection <- radial_protection_scores
out_list[[i]] <- this_data
}
model_data <- bind_rows(out_list)
# Plots of maps
# Libraries
library(tidyverse)
library(raster)
library(tidyr)
library(ggplot2)
library(RColorBrewer)
library(broom)
library(ggthemes)
# Get each countries shapefile
countries <- c('Mozambique')
iso3s <- c('MOZ')
for(i in 1:length(countries)){
message('Fetching data for ', countries[i])
x <- getData(name = 'GADM', level = 2, country = iso3s[i])
assign(tolower(countries[i]),
x,
envir = .GlobalEnv)
}
# Define which districts are "special" -------------
specials <- 'Manhiça'
# Mozambique
mozambique@data$special <- FALSE
mozambique@data$special[mozambique@data$NAME_2 %in% specials] <- TRUE
# Combine all data into a "long" / "tidy" format
make_long <- function(x, region = "NAME_2"){
shp_df <- broom::tidy(x, region = region)
return(shp_df)
}
mozambique_long <- make_long(mozambique) %>% mutate(country = 'Mozambique')
combined <- mozambique_long
combined$special <- combined$id %in% specials
# combined$special[!combined$special] <- NA
# Get a map of africa to use as a background
# from the cism package!
africa <- cism::africa
africa_long <- make_long(africa, region = 'COUNTRY')
africa_long$special <- africa_long$id %in% countries
save.image(file = 'prepared_data.RData')
}
model_data$year <- as.numeric(format(model_data$date, '%Y'))
# Add 5th worker type
model_data <- model_data %>%
# Get grade
left_join(ab_panel %>% dplyr::distinct(grade, oracle_number, date)) %>%
mutate(year = as.numeric(format(date, '%Y'))) %>%
left_join(wages %>% dplyr::distinct(grade, department, year, daily_usd))
model_data$group5 <- ifelse(is.na(model_data$daily_usd) & model_data$group != 'Temporary not field worker', 'No wage data', model_data$group)
# As of now, model_data and final_data are the same
# We want to restrict model_data downward a bit (to not include issues that are bad for modeling)
# while keeping final data (since it's useful for simulations)
final_data <- model_data
# Remove christmas
model_data <- model_data %>%
mutate(day_number = as.numeric(format(date, '%d')))
remove_christmas <- TRUE
if(remove_christmas){
remove_these <- which(
(model_data$month_number == '12' &
model_data$day_number >= 15) |
model_data$month_number == '01' &
model_data$day_number <= 15
)
}
# Remove 2013 temp workers from model data (since some problems)
model_data <- model_data %>%
filter(!(permanent_or_temporary == 'Temporary' & year == 2013))
final_data <- final_data %>%
filter(!(permanent_or_temporary == 'Temporary' & year == 2013))
# # only the ever sprayed
model_data <- model_data %>%
filter(ever_sprayed)
final_data <- final_data %>% filter(ever_sprayed)
# Prepare simulations
if('simulations.RData' %in% dir()){
load('simulations.RData')
} else {
time_optimize_df <-
data.frame(date = seq(as.Date('2020-01-01'),
as.Date('2020-12-31'),
by = 1)) %>%
mutate(month_day = format(date, '%m-%d'))
time_optimize_df$day_number <- (1:nrow(time_optimize_df))+20
time_optimize_df$the_protection <- classify_protection(time_optimize_df$day_number)
time_optimize_df <- time_optimize_df %>% dplyr::select(month_day, the_protection)
strategies <- c('Zero', 'Max', 'Time-optimized')
strategy_list <- list()
for(s in 1:length(strategies)){
dates <- sort(unique(final_data$date))
out_list <- list()
this_strategy <- strategies[s]
for(i in 1:length(dates)){
this_date <- dates[i]
message(this_strategy, '...', this_date)
this_data <- final_data %>%
filter(date == this_date)
# Define scores
if(this_strategy == 'Zero'){
this_data$protection <- 0
the_protection <- rep(0, length(this_data$protection))
}
if(this_strategy == 'Max'){
this_data$protection <- 1
the_protection <- rep(1, length(this_data$protection))
}
if(this_strategy == 'Time-optimized'){
this_data$month_day <- format(this_data$date, '%m-%d')
x <- this_data %>%
left_join(time_optimize_df)
the_protection <- x$the_protection
this_data$protection <- the_protection
}
# Get protection score
protection_scores <- get_protection(lat = this_data$latitude_aura,
lng = this_data$longitude_aura,
protection = the_protection,
guess_missing = TRUE)
this_data$herd_protection <- protection_scores
out_list[[i]] <- this_data
}
out <- bind_rows(out_list)
out$strategy <- this_strategy
strategy_list[[s]] <- out
}
simulations <- bind_rows(strategy_list)
simulations <- simulations %>%
bind_rows(final_data %>% mutate(strategy = 'Real'))
save(simulations, file = 'simulations.RData')
}
# Add precipitation
model_data$rain_var <- model_data$precipitation_lag_15_90
final_data$rain_var <- final_data$precipitation_lag_15_90
simulations$rain_var <- simulations$precipitation_lag_15_90
# Make the group var
model_data$group4 <- model_data$group
final_data$group4 <- final_data$group
simulations$group4 <- simulations$group
three_transform <- function(x){
ifelse(grepl('temporary', tolower(x)), 'Temporary worker', x)
}
model_data$group3 <- three_transform(model_data$group)
final_data$group3 <- three_transform(final_data$group)
simulations$group3 <- three_transform(simulations$group)
model_data$group <- model_data$group3
final_data$group <- final_data$group3
simulations$group <- simulations$group3
# final_model <- felm(log(absent+1) ~ protection_var + (rain_var) | oracle_number | 0 | 0,
# data = final_data)
final_model <- felm(log(absent+1) ~ protection +
herd_protection +
rain_var | oracle_number | 0 | 0,
data = model_data)
summary(model_data)
final_model_lm <- lm(log(absent+1) ~ protection +
herd_protection + (rain_var),
data = model_data)
final_model_radial <- felm(log(absent+1) ~ protection +
radial_herd_protection +
rain_var | oracle_number | 0 | 0,
data = model_data)
summary(final_model_radial)
final_model_radial_lm <- lm(log(absent+1) ~ protection +
radial_herd_protection + (rain_var),
data = model_data)
# Model for each worker group
worker_groups <- sort(unique(model_data$group))
model_data_list <- model_list <- model_list_lm <-
radial_model_data_list <- radial_model_list <- radial_model_list_lm <- list()
for (i in 1:length(worker_groups)){
sub_data <- model_data %>% filter(group == worker_groups[i])
this_model <- felm(log(absent+1) ~ protection +
herd_protection +
rain_var | oracle_number | 0 | 0,
data = sub_data)
this_model_radial <- felm(log(absent+1) ~ protection +
radial_herd_protection +
rain_var | oracle_number | 0 | 0,
data = sub_data)
this_model_lm <- lm(log(absent+1) ~ protection +
herd_protection + (rain_var),
data = sub_data)
this_model_lm_radial <- lm(log(absent+1) ~ protection +
radial_herd_protection + (rain_var),
data = sub_data)
formatted <- broom::tidy(this_model)
formatted$group5 <- worker_groups[i]
formatted_radial <- broom::tidy(this_model_radial)
formatted_radial$group5 <- worker_groups[i]
model_list[[i]] <- this_model
model_list_lm[[i]] <- this_model_lm
model_data_list[[i]] <- formatted
radial_model_list[[i]] <- this_model_radial
radial_model_list_lm[[i]] <- this_model_lm_radial
radial_model_data_list[[i]] <- formatted_radial
}
names(model_list) <- names(model_data_list) <- names(model_list_lm) <-
names(radial_model_list) <- names(radial_model_data_list) <- names(radial_model_list_lm) <-
worker_groups
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