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analyze_indices_cluster.R
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# load, plot and analyze indices
.libPaths("/home/rmk7/other_R_libs2")
require(cmdstanr)
cmdstanr::set_cmdstan_path("/home/rmk7/cmdstan")
require(idealstan)
require(ggplot2)
require(tidyverse)
require(ggthemes)
require(posterior)
require(stringr)
require(posterior)
require(lubridate)
require(forcats)
plot_countries <- c("United States of America","Brazil","China","United Arab Emirates",
"South Africa","Turkey","Singapore","South Korea",
"Australia")
source('apply_draws.R')
source('summarize_draws_mc.R')
# whether to load and summarize data (will take some time)
sum_data <- T
if(sum_data) {
# Business ----------------------------------------------------------------
biz_mod <- readRDS("/scratch/rmk7/coronanet/activity_fit_rwbiz_random_walk_run_4.rds")
all_mods <- lapply(list.files(path = "/scratch/rmk7/coronanet/",
pattern="biz\\_random\\_walk\\_run\\_4.*rds",
full.names=T),readRDS)
all_mods_mat <- lapply(all_mods, function(c) c@stan_samples$draws())
biz_draws <- do.call(bind_draws, c(all_mods_mat,list(along="chain")))
biz_sum <- summarize_draws_mc(biz_draws,"median","quantile2",posterior::rhat,cores=20)
all_mods_mat <- lapply(all_mods, function(c) as_draws_array(c@time_varying))
rm(all_mods)
biz_time <- do.call(bind_draws, c(all_mods_mat,list(along="chain"))) %>%
subset_draws(variable="tp1",regex=T)
biz_time <- apply_draws(biz_time,FUN=function(c) as.numeric(scale(c)),MARGIN=c(1:2))
sum_time <- summarize_draws_mc(biz_time,"median","quantile2",posterior::rhat,cores=20)
rm(all_mods_mat)
gc()
get_all_discrim <- filter(biz_sum,grepl(x=variable,pattern="reg\\_full"))
get_all_discrim$id <- levels(biz_mod@score_data@score_matrix$item_id)
get_all_discrim$id_rec <- fct_recode(get_all_discrim$id,
"Other Regulations" = "biz_cond_other",
"Contact Tracing" = "biz_cont_trace",
"Essential Businesses" = "biz_essential",
"Health Certification" = "biz_health_cert",
"Health Questionnaire" = "biz_health_q",
"Hours Restricted" = "biz_hours",
"Hygiene Requirements" = "biz_hygiene",
"Masks" = "biz_mask",
"Meetings Restricted" = "biz_meeting",
"Non-essential Businesses" = "biz_nonessential",
"Number of Customers" = "biz_num_cust",
"All Sectors" = "biz_restrict_all",
"Commercial" = "biz_restrict_comm",
"Construction" = "biz_restrict_construct",
"Agriculture" = "biz_restrict_farm",
"Finance" = "biz_restrict_finance",
"Grocery" = "biz_restrict_grocery",
"Grooming" = "biz_restrict_groom",
"Health Care" = "biz_restrict_health",
"Hotels" = "biz_restrict_hotel",
"ICT" = "biz_restrict_info",
"Insurance" = "biz_restrict_insurance",
"Mining" = "biz_restrict_mining",
"No Sector" = "biz_restrict_na",
"Other Sector" = "biz_restrict_other",
"Pharmacy" = "biz_restrict_pharmacy",
"Publishing" = "biz_restrict_publish",
"Restaurants" = "biz_restrict_rest",
"Retail" = "biz_restrict_retail",
"Shops" = "biz_restrict_shop",
"Delivery"="biz_delivery",
"Takeout"="biz_takeaway",
"Telecom" = "biz_restrict_telecom",
"Transport" = "biz_restrict_transport",
"Warehouses" = "biz_restrict_warehouse",
"Water" = "biz_restrict_water",
"Social Distancing" = "biz_social_distance",
"Store Size" = "biz_store_size",
"Temperature Checks" = "biz_temp",
"Work at Home" = "biz_work_home",
"Oxford Closing Workplaces" = "ox_workplace_close")
biz_rhat <- sum_time %>%
#filter(grepl(x=variable,pattern="sigma\\_reg|L\\_tp1")) %>%
ggplot(aes(x=rhat)) +
geom_histogram() +
geom_vline(xintercept=1.1,linetype=2,colour="blue") +
theme_tufte() +
labs(x="",y="") +
ggtitle("Business Restrictions")
saveRDS(biz_rhat,"coronanet/biz_rhat.rds")
biz <- get_all_discrim %>%
ggplot(aes(y=median,x=reorder(id_rec,median))) +
geom_pointrange(aes(ymin=q5,ymax=q95)) +
theme_tufte() +
coord_flip() +
labs(x="",y="Level of Discrimination") +
ggtitle("Business")
biz
saveRDS(biz,"coronanet/biz_discrim_object.rds")
ggsave("plots/biz_discrim.pdf")
biz_time_data_scaled <- biz_time %>% as_draws_df() %>%
gather(key="variable",value="estimate",-.chain,-.iteration,-.draw) %>%
group_by(.iteration) %>%
mutate(estimate=plogis(estimate)*100,
country=levels(biz_mod@score_data@score_matrix$person_id)[as.numeric(str_extract(variable,"[0-9]+(?=\\])"))],
date_policy=unique(biz_mod@score_data@score_matrix$time_id)[as.numeric(str_extract(variable,"(?<=\\[)[0-9]+"))]) %>%
group_by(date_policy,country) %>%
summarize(med_est=quantile(estimate,.5),
high_est=quantile(estimate,.95),
low_est=quantile(estimate,.05),
sd_est=sd(estimate))
biz_time_data <- biz_time %>% as_draws_df() %>%
gather(key="variable",value="estimate",-.chain,-.iteration,-.draw) %>%
mutate(country=levels(biz_mod@score_data@score_matrix$person_id)[as.numeric(str_extract(variable,"[0-9]+(?=\\])"))],
date_policy=unique(biz_mod@score_data@score_matrix$time_id)[as.numeric(str_extract(variable,"(?<=\\[)[0-9]+"))]) %>%
group_by(date_policy,country) %>%
summarize(med_est=quantile(estimate,.5),
high_est=quantile(estimate,.95),
low_est=quantile(estimate,.05),
sd_est=sd(estimate))
# make it complete
expand_index <- group_by(biz_time_data,country) %>%
expand(date_policy=seq(ymd("2020-01-01"),max(biz_time_data$date_policy),
by="1 day"))
biz_time_data <- left_join(expand_index,biz_time_data,
by=c("country","date_policy")) %>%
fill(med_est,high_est,low_est,sd_est,.direction="downup")
biz_time_data_scaled <- left_join(expand_index,biz_time_data_scaled,
by=c("country","date_policy")) %>%
fill(med_est,high_est,low_est,sd_est,.direction="downup")
saveRDS(biz_time_data,"indices/biz_time_data.rds")
saveRDS(biz_time_data_scaled,"indices/biz_time_data_scaled.rds")
write_csv(biz_time_data,"indices/biz_time_data.csv")
write_csv(biz_time_data_scaled,"indices/biz_time_data_scaled.csv")
sample_plot_dates <- group_by(biz_time_data_scaled,country) %>%
sample_n(1)
biz_time <- biz_time_data_scaled %>%
ggplot(aes(y=med_est,x=date_policy)) +
geom_line(colour="#8DD3C7",aes(group=country)) +
geom_ribbon(aes(ymin=low_est,ymax=high_est,group=country),alpha=0.1) +
geom_text(aes(label=country),colour="#FFFFB3",fontface="bold",
data=sample_plot_dates,check_overlap = T,size=2) +
theme_tufte() +
labs(x="",y="") +
ggtitle("Business Restrictions") +
theme(axis.text.x=element_blank(),
axis.ticks.x = element_blank())
biz_time
saveRDS(biz_time,"coronanet/biz_mod_plot_object.rds")
ggsave("plots/biz_mod_plot.pdf")
biz_time_single <- biz_time_data_scaled %>%
filter(country %in% plot_countries) %>%
ggplot(aes(y=med_est,x=date_policy)) +
geom_line(colour="#8DD3C7",aes(group=country)) +
geom_ribbon(aes(ymin=low_est,ymax=high_est,group=country),alpha=0.1) +
geom_text(aes(label=country),colour="black",fontface="bold",
data=filter(sample_plot_dates,country %in% plot_countries),
check_overlap = T,size=2) +
theme_tufte() +
labs(x="",y="") +
ggtitle("Business Restrictions") +
theme(axis.text.x=element_blank(),
axis.ticks.x = element_blank())
biz_time_single
saveRDS(biz_time_single,"coronanet/biz_mod_plot_single_object.rds")
ggsave("plots/biz_mod_plot_single.pdf")
rm(biz_mod)
# Mask --------------------------------------------------------------------
mask_mod <- readRDS("/scratch/rmk7/coronanet/activity_fit_rwmask_random_walk_run_4.rds")
all_mods <- lapply(list.files(path = "/scratch/rmk7/coronanet/",
pattern="mask\\_random\\_walk\\_run\\_[0-4].*rds",
full.names=T),readRDS)
all_mods_mat <- lapply(all_mods, function(c) c@stan_samples$draws())
mask_draws <- do.call(bind_draws, c(all_mods_mat,list(along="chain")))
mask_sum <- summarize_draws_mc(mask_draws,"median","quantile2",posterior::rhat,cores=20)
all_mods_mat <- lapply(all_mods, function(c) as_draws_array(c@time_varying))
rm(all_mods)
mask_time <- do.call(bind_draws, c(all_mods_mat,list(along="chain"))) %>%
subset_draws(variable="tp1",regex=T)
mask_time <- apply_draws(mask_time,FUN=function(c) as.numeric(scale(c)),MARGIN=c(1:2))
sum_time <- summarize_draws_mc(mask_time,"median","quantile2",posterior::rhat,cores=20)
rm(all_mods_mat)
gc()
get_all_discrim <- filter(mask_sum,grepl(x=variable,pattern="reg\\_full"))
get_all_discrim$id <- levels(mask_mod@score_data@score_matrix$item_id)
get_all_discrim$id_rec <- fct_recode(get_all_discrim$id,
"Businesses" = "mask_business",
"Everywhere" = "mask_everywhere",
"Higher Ed" = "mask_higher_ed",
"Preschool" = "mask_preschool",
"Primary School" = "mask_primary_school",
"In Public" = "mask_public",
"Secondary School" = "mask_sec_school",
"Mass Transport" = "mask_transport",
"Unspecified Conditions" = "mask_unspec",
"Oxford Mask" = "ox_mask"
)
mask_rhat <- sum_time %>%
#filter(grepl(x=variable,pattern="sigma\\_reg|L\\_tp1")) %>%
ggplot(aes(x=rhat)) +
geom_histogram() +
geom_vline(xintercept=1.1,linetype=2,colour="blue") +
theme_tufte() +
labs(x="",y="") +
ggtitle("Masks")
saveRDS(mask_rhat,"coronanet/mask_rhat.rds")
mask <- get_all_discrim %>%
ggplot(aes(y=median,x=reorder(id_rec,median))) +
geom_pointrange(aes(ymin=q5,ymax=q95)) +
theme_tufte() +
coord_flip() +
labs(x="",y="Level of Discrimination") +
ggtitle("Masks")
mask
saveRDS(mask,"coronanet/mask_discrim_object.rds")
ggsave("plots/mask_discrim.pdf")
mask_time_data_scaled <- mask_time %>% as_draws_df() %>%
gather(key="variable",value="estimate",-.chain,-.iteration,-.draw) %>%
group_by(.iteration) %>%
mutate(estimate=plogis(estimate)*100,
country=levels(mask_mod@score_data@score_matrix$person_id)[as.numeric(str_extract(variable,"[0-9]+(?=\\])"))],
date_policy=unique(mask_mod@score_data@score_matrix$time_id)[as.numeric(str_extract(variable,"(?<=\\[)[0-9]+"))]) %>%
group_by(date_policy,country) %>%
summarize(med_est=quantile(estimate,.5),
high_est=quantile(estimate,.95),
low_est=quantile(estimate,.05),
sd_est=sd(estimate))
mask_time_data <- mask_time %>% as_draws_df() %>%
gather(key="variable",value="estimate",-.chain,-.iteration,-.draw) %>%
mutate(country=levels(mask_mod@score_data@score_matrix$person_id)[as.numeric(str_extract(variable,"[0-9]+(?=\\])"))],
date_policy=unique(mask_mod@score_data@score_matrix$time_id)[as.numeric(str_extract(variable,"(?<=\\[)[0-9]+"))]) %>%
group_by(date_policy,country) %>%
summarize(med_est=quantile(estimate,.5),
high_est=quantile(estimate,.95),
low_est=quantile(estimate,.05),
sd_est=sd(estimate))
expand_index <- group_by(mask_time_data,country) %>%
expand(date_policy=seq(ymd("2020-01-01"),max(mask_time_data$date_policy),
by="1 day"))
mask_time_data <- left_join(expand_index,mask_time_data,
by=c("country","date_policy")) %>%
fill(med_est,high_est,low_est,sd_est,.direction="downup")
mask_time_data_scaled <- left_join(expand_index,mask_time_data_scaled,
by=c("country","date_policy")) %>%
fill(med_est,high_est,low_est,sd_est,.direction="downup")
saveRDS(mask_time_data,"indices/mask_time_data.rds")
saveRDS(mask_time_data_scaled,"indices/mask_time_data_scaled.rds")
write_csv(mask_time_data,"indices/mask_time_data.csv")
write_csv(mask_time_data_scaled,"indices/mask_time_data_scaled.csv")
sample_plot_dates <- group_by(mask_time_data_scaled,country) %>%
sample_n(1)
mask_time <- mask_time_data_scaled %>%
ggplot(aes(y=med_est,x=date_policy)) +
geom_line(colour="#8DD3C7",aes(group=country)) +
geom_ribbon(aes(ymin=low_est,ymax=high_est,group=country),alpha=0.1) +
geom_text(aes(label=country),colour="#FFFFB3",fontface="bold",
data=sample_plot_dates,check_overlap = T,size=2) +
theme_tufte() +
labs(x="",y="Index Score") +
ggtitle("Mask") +
theme(axis.text.x=element_blank(),
axis.ticks.x = element_blank())
mask_time
saveRDS(mask_time,"coronanet/mask_plot_object.rds")
ggsave("plots/mask_mod_plot.pdf")
mask_time_single <- mask_time_data_scaled %>%
filter(country %in% plot_countries) %>%
ggplot(aes(y=med_est,x=date_policy)) +
geom_line(colour="#8DD3C7",aes(group=country)) +
geom_ribbon(aes(ymin=low_est,ymax=high_est,group=country),alpha=0.1) +
geom_text(aes(label=country),colour="black",fontface="bold",
data=filter(sample_plot_dates,country %in% plot_countries),
check_overlap = T,size=2) +
theme_tufte() +
labs(x="",y="Index Score") +
ggtitle("Mask") +
theme(axis.text.x=element_blank(),
axis.ticks.x = element_blank())
mask_time_single
saveRDS(mask_time_single,"coronanet/mask_plot_single_object.rds")
ggsave("plots/mask_mod_plot_single.pdf")
rm(mask_mod)
# Health monitoring -------------------------------------------------------
hm2_mod <- readRDS("/scratch/rmk7/coronanet/activity_fit_rwhm2_random_walk_run_4.rds")
all_mods <- lapply(list.files(path = "/scratch/rmk7/coronanet/",
pattern="hm2\\_random\\_walk\\_run\\_4.*rds",
full.names=T),readRDS)
all_mods_mat <- lapply(all_mods, function(c) c@stan_samples$draws())
hm2_draws <- do.call(bind_draws, c(all_mods_mat,list(along="chain")))
hm2_sum <- summarize_draws_mc(hm2_draws,"median","quantile2",posterior::rhat,cores=20)
all_mods_mat <- lapply(all_mods, function(c) as_draws_array(c@time_varying))
rm(all_mods)
hm2_time <- do.call(bind_draws, c(all_mods_mat,list(along="chain"))) %>%
subset_draws(variable="tp1",regex=T)
hm2_time <- apply_draws(hm2_time,FUN=function(c) as.numeric(scale(c)),MARGIN=c(1:2))
sum_time <- summarize_draws_mc(hm2_time,"median","quantile2",posterior::rhat,cores=20)
rm(all_mods_mat)
gc()
get_all_discrim <- filter(hm2_sum,grepl(x=variable,pattern="reg\\_full"))
get_all_discrim$id <- levels(hm2_mod@score_data@score_matrix$item_id)
get_all_discrim$id_rec <- fct_recode(get_all_discrim$id,
"Certification" = "hm_cert",
"Home Visits" = "hm_home_visit",
"Buses" = "hm_loc_buses",
"Nursing" = "hm_loc_nursing",
"Other Locations" = "hm_loc_other",
"Subway" = "hm_loc_subway",
"Trains" = "hm_loc_trains",
"Other Monitoring" = "hm_other_mon",
"Questionnaires" = "hm_q",
"Other In person" = "hm_snap_other",
"Temperature" = "hm_snap_temp",
"Human Contact Tracing" = "hm_stra_contact_human",
"Mobile Contact Tracing" = "hm_stra_contact_phone",
"Other Tracing" = "hm_stra_other",
"Wearable Tracking" = "hm_stra_wearable",
"Bluetooth Tracking" = "hm_tech_bluetooth",
"GPS Tracking" = "hm_tech_gps",
"QR Codes"="hm_tech_qr",
"Other Tracking" = "hm_tech_other",
"Phone Calls" = "hm_telephone",
"Individuals Pay" = "ht_cost_all_pay",
"Free Tests" = "ht_cost_free_all",
"Other Cost Policies" = "ht_cost_other",
"Door to Door Tests" = "ht_door2door",
"Drive-in Tests" = "ht_drivein",
"Entire Population" = "ht_entire_pop",
"Fixed Locations" = "ht_fixed",
"Testing in Clinics" = "ht_loc_clinic",
"Testing in Hospitals" = "ht_loc_hospital",
"Other Test Locations" = "ht_loc_other",
"Testing in Pharmacies" = "ht_loc_pharmacy",
"Mobile Testing" = "ht_mobile",
"Other Testing" = "ht_other",
"Other Testing Portals" = "ht_portal_other",
"Antibody Tests" = "ht_type_antibody",
"Antigen Tests" = "ht_type_antigen",
"Other Tests" = "ht_type_other",
"PCR Tests" = "ht_type_pcr",
"Oxford Tests" = "ox_test"
)
hm2_rhat <- sum_time %>%
ggplot(aes(x=rhat)) +
geom_histogram() +
geom_vline(xintercept=1.1,linetype=2,colour="blue") +
theme_tufte() +
labs(x="",y="") +
ggtitle("Health Monitoring")
saveRDS(hm2_rhat,"coronanet/hm2_rhat.rds")
hm2 <- get_all_discrim %>%
ggplot(aes(y=median,x=reorder(id_rec,median))) +
geom_pointrange(aes(ymin=q5,ymax=q95)) +
theme_tufte() +
coord_flip() +
labs(x="",y="Level of Discrimination") +
ggtitle("Health Monitoring")
hm2
saveRDS(hm2,"coronanet/hm2_discrim_object.rds")
ggsave("plots/hm2_discrim.pdf")
hm2_time_data_scaled <- hm2_time %>% as_draws_df() %>%
gather(key="variable",value="estimate",-.chain,-.iteration,-.draw) %>%
group_by(.iteration) %>%
mutate(estimate=plogis(estimate)*100,
country=levels(hm2_mod@score_data@score_matrix$person_id)[as.numeric(str_extract(variable,"[0-9]+(?=\\])"))],
date_policy=unique(hm2_mod@score_data@score_matrix$time_id)[as.numeric(str_extract(variable,"(?<=\\[)[0-9]+"))]) %>%
group_by(date_policy,country) %>%
summarize(med_est=quantile(estimate,.5),
high_est=quantile(estimate,.95),
low_est=quantile(estimate,.05),
sd_est=sd(estimate))
hm2_time_data <- hm2_time %>% as_draws_df() %>%
gather(key="variable",value="estimate",-.chain,-.iteration,-.draw) %>%
mutate(country=levels(hm2_mod@score_data@score_matrix$person_id)[as.numeric(str_extract(variable,"[0-9]+(?=\\])"))],
date_policy=unique(hm2_mod@score_data@score_matrix$time_id)[as.numeric(str_extract(variable,"(?<=\\[)[0-9]+"))]) %>%
group_by(date_policy,country) %>%
summarize(med_est=quantile(estimate,.5),
high_est=quantile(estimate,.95),
low_est=quantile(estimate,.05),
sd_est=sd(estimate))
expand_index <- group_by(hm2_time_data,country) %>%
expand(date_policy=seq(ymd("2020-01-01"),max(hm2_time_data$date_policy),
by="1 day"))
hm2_time_data <- left_join(expand_index,hm2_time_data,
by=c("country","date_policy")) %>%
fill(med_est,high_est,low_est,sd_est,.direction="downup")
hm2_time_data_scaled <- left_join(expand_index,hm2_time_data_scaled,
by=c("country","date_policy")) %>%
fill(med_est,high_est,low_est,sd_est,.direction="downup")
saveRDS(hm2_time_data,"indices/hm2_time_data.rds")
saveRDS(hm2_time_data_scaled,"indices/hm2_time_data_scaled.rds")
write_csv(hm2_time_data,"indices/hm2_time_data.csv")
write_csv(hm2_time_data_scaled,"indices/hm2_time_data_scaled.csv")
sample_plot_dates <- group_by(hm2_time_data_scaled,country) %>%
sample_n(1)
hm2_time <- hm2_time_data_scaled %>%
ggplot(aes(y=med_est,x=date_policy)) +
geom_line(colour="#8DD3C7",aes(group=country)) +
geom_ribbon(aes(ymin=low_est,ymax=high_est,group=country),alpha=0.1) +
geom_text(aes(label=country),colour="#FFFFB3",fontface="bold",
data=sample_plot_dates,check_overlap = T,size=2) +
theme_tufte() +
labs(x="",y="Index Score") +
ggtitle("Health Monitoring") +
theme(axis.text.x=element_blank(),
axis.ticks.x = element_blank())
hm2_time
saveRDS(hm2_time,"coronanet/hm2_plot_object.rds")
ggsave("plots/hm2_mod_plot.pdf")
hm2_time_single <- hm2_time_data_scaled %>%
filter(country %in% plot_countries) %>%
ggplot(aes(y=med_est,x=date_policy)) +
geom_line(colour="#8DD3C7",aes(group=country)) +
geom_ribbon(aes(ymin=low_est,ymax=high_est,group=country),alpha=0.1) +
geom_text(aes(label=country),colour="black",fontface="bold",
data=filter(sample_plot_dates,country %in% plot_countries),
check_overlap = T,size=2) +
theme_tufte() +
labs(x="",y="Index Score") +
ggtitle("Health Monitoring") +
theme(axis.text.x=element_blank(),
axis.ticks.x = element_blank())
hm2_time_single
saveRDS(hm2_time_single,"coronanet/hm2_plot_single_object.rds")
ggsave("plots/hm2_mod_plot_single.pdf")
rm(hm2_mod)
# social distance ---------------------------------------------------------
sd_mod <- readRDS("/scratch/rmk7/coronanet/activity_fit_rwsd_random_walk_run_4.rds")
all_mods <- lapply(list.files(path = "/scratch/rmk7/coronanet/",
pattern="sd\\_random\\_walk\\_run\\_4.*rds",
full.names=T),readRDS)
all_mods_mat <- lapply(all_mods, function(c) c@stan_samples$draws())
sd_draws <- do.call(bind_draws, c(all_mods_mat,list(along="chain")))
sd_sum <- summarize_draws_mc(sd_draws,"median","quantile2",posterior::rhat,cores=20)
all_mods_mat <- lapply(all_mods, function(c) as_draws_array(c@time_varying))
rm(all_mods)
sd_time <- do.call(bind_draws, c(all_mods_mat,list(along="chain"))) %>%
subset_draws(variable="tp1",regex=T)
sd_time <- apply_draws(sd_time,FUN=function(c) as.numeric(scale(c)),MARGIN=c(1:2))
sum_time <- summarize_draws_mc(sd_time,"median","quantile2",posterior::rhat,cores=20)
rm(all_mods_mat)
gc()
get_all_discrim <- filter(sd_sum,grepl(x=variable,pattern="reg\\_full"))
get_all_discrim$id <- levels(sd_mod@score_data@score_matrix$item_id)
get_all_discrim$id_rec <- fct_recode(get_all_discrim$id,
"Allow Annual Events" = "allow_ann_event",
"Distancing in Buses" = "buses",
"Cancel Annual Events" = "cancel_annual_event",
"Curfew" = "curfew_length",
"Other" = "distance_other",
"No Audiences" = "event_no_audience",
"All Internal Restrictions" = "int_restrict_all",
"Restrict Internal Border" = "int_restrict_border",
"Restrict Buses" = "int_restrict_buses",
"Restrict Crusies" = "int_restrict_cruises",
"Restrict Ferries" = "int_restrict_ferries",
"Restrict Internal Flights" = "int_restrict_flights",
"Other Internal Restriction" = "int_restrict_NA",
"Internal Ports" = "int_restrict_ports",
"Restrict Trains" = "int_restrict_trains",
"Number at Mass Gathering" = "number_mass",
"Other Transportation" = "other_transport",
"Oxford Internal Restrictions" = "ox_internal",
"Oxford Mass Gathering" = "ox_mass_gathering",
"Oxford Public Events" = "ox_pub_events",
"Oxford Public Transport" = "ox_public_transport",
"Oxford Stay Home" = "ox_stay_home",
"Postpone Annual Event" = "postpone_ann_event",
"Postpone Recreational Event" = "postpone_rec_event",
"Prison Population" = "prison_pop",
"Stay at Home" = "social_distance",
"Distancing in Subways" = "subways")
sd_rhat <- sum_time %>%
ggplot(aes(x=rhat)) +
geom_histogram() +
geom_vline(xintercept=1.1,linetype=2,colour="blue") +
theme_tufte() +
labs(x="",y="") +
ggtitle("Social Distancing")
saveRDS(sd_rhat,"coronanet/sd_rhat.rds")
sd <- get_all_discrim %>%
ggplot(aes(y=median,x=reorder(id_rec,median))) +
geom_pointrange(aes(ymin=q5,ymax=q95)) +
theme_tufte() +
coord_flip() +
labs(x="",y="Level of Discrimination") +
ggtitle("Social Distancing")
sd
saveRDS(sd,"coronanet/sd_discrim_object.rds")
ggsave("plots/sd_discrim.pdf")
sd_time_data_scaled <- sd_time %>% as_draws_df() %>%
gather(key="variable",value="estimate",-.chain,-.iteration,-.draw) %>%
group_by(.iteration) %>%
mutate(estimate=plogis(estimate)*100,
country=levels(sd_mod@score_data@score_matrix$person_id)[as.numeric(str_extract(variable,"[0-9]+(?=\\])"))],
date_policy=unique(sd_mod@score_data@score_matrix$time_id)[as.numeric(str_extract(variable,"(?<=\\[)[0-9]+"))]) %>%
group_by(date_policy,country) %>%
summarize(med_est=quantile(estimate,.5),
high_est=quantile(estimate,.95),
low_est=quantile(estimate,.05),
sd_est=sd(estimate))
sd_time_data <- sd_time %>% as_draws_df() %>%
gather(key="variable",value="estimate",-.chain,-.iteration,-.draw) %>%
mutate(country=levels(sd_mod@score_data@score_matrix$person_id)[as.numeric(str_extract(variable,"[0-9]+(?=\\])"))],
date_policy=unique(sd_mod@score_data@score_matrix$time_id)[as.numeric(str_extract(variable,"(?<=\\[)[0-9]+"))]) %>%
group_by(date_policy,country) %>%
summarize(med_est=quantile(estimate,.5),
high_est=quantile(estimate,.95),
low_est=quantile(estimate,.05),
sd_est=sd(estimate))
expand_index <- group_by(sd_time_data,country) %>%
expand(date_policy=seq(ymd("2020-01-01"),max(sd_time_data$date_policy),
by="1 day"))
sd_time_data <- left_join(expand_index,sd_time_data,
by=c("country","date_policy")) %>%
fill(med_est,high_est,low_est,sd_est,.direction="downup")
sd_time_data_scaled <- left_join(expand_index,sd_time_data_scaled,
by=c("country","date_policy")) %>%
fill(med_est,high_est,low_est,sd_est,.direction="downup")
saveRDS(sd_time_data,"indices/sd_time_data.rds")
saveRDS(sd_time_data_scaled,"indices/sd_time_data_scaled.rds")
write_csv(sd_time_data,"indices/sd_time_data.csv")
write_csv(sd_time_data_scaled,"indices/sd_time_data_scaled.csv")
sample_plot_dates <- group_by(sd_time_data_scaled,country) %>%
sample_n(1)
sd_time <- sd_time_data_scaled %>%
ggplot(aes(y=med_est,x=date_policy)) +
geom_line(colour="#8DD3C7",aes(group=country)) +
geom_ribbon(aes(ymin=low_est,ymax=high_est,group=country),alpha=0.1) +
geom_text(aes(label=country),colour="#FFFFB3",fontface="bold",
data=sample_plot_dates,check_overlap = T,size=2) +
theme_tufte() +
labs(x="",y="") +
ggtitle("Social Distancing")
sd_time
saveRDS(sd_time,"coronanet/sd_plot_object.rds")
ggsave("plots/sd_mod_plot.pdf")
sd_time_single <- sd_time_data_scaled %>%
filter(country %in% plot_countries) %>%
ggplot(aes(y=med_est,x=date_policy)) +
geom_line(colour="#8DD3C7",aes(group=country)) +
geom_ribbon(aes(ymin=low_est,ymax=high_est,group=country),alpha=0.1) +
geom_text(aes(label=country),colour="black",fontface="bold",
data=filter(sample_plot_dates,country %in% plot_countries),
check_overlap = T,size=2) +
theme_tufte() +
labs(x="",y="") +
ggtitle("Social Distancing")
sd_time_single
saveRDS(sd_time_single,"coronanet/sd_plot_single_object.rds")
ggsave("plots/sd_mod_plot_single.pdf")
rm(sd_mod)
# schools -----------------------------------------------------------------
school_mod <- readRDS("/scratch/rmk7/coronanet/activity_fit_rwschool_random_walk_run_4.rds")
all_mods <- lapply(list.files(path = "/scratch/rmk7/coronanet/",
pattern="school\\_random\\_walk\\_run\\_4.*rds",
full.names=T),readRDS)
all_mods_mat <- lapply(all_mods, function(c) c@stan_samples$draws())
school_draws <- do.call(bind_draws, c(all_mods_mat,list(along="chain")))
school_sum <- summarize_draws_mc(school_draws,"median","quantile2",posterior::rhat,cores=20)
all_mods_mat <- lapply(all_mods, function(c) as_draws_array(c@time_varying))
rm(all_mods)
school_time <- do.call(bind_draws, c(all_mods_mat,list(along="chain"))) %>%
subset_draws(variable="tp1",regex=T)
# standardize by iteration
school_time <- apply_draws(school_time,FUN=function(c) as.numeric(scale(c)),MARGIN=c(1:2))
sum_time <- summarize_draws_mc(school_time,"median","quantile2",posterior::rhat,cores=20)
rm(all_mods_mat)
gc()
get_all_discrim <- filter(school_sum,grepl(x=variable,pattern="reg\\_full"))
get_all_discrim$id <- levels(school_mod@score_data@score_matrix$item_id)
get_all_discrim$id_rec <- fct_recode(get_all_discrim$id,
"Higher Ed" = "higher_ed",
"Oxford School Close" = "ox_school_close",
"Preschool" = "preschool",
"Primary School" = "primary_school",
"Sanitation" = "school_clean",
"Social Distancing" = "school_distance",
"Health Monitoring" = "school_health_monitoring",
"Health Questionnaire" = "school_health_q",
"Masks" = "school_mask",
"Maximum Number Students" = "school_num",
"Other" = "school_other",
"School Event"="school_event",
"School Hours"="school_hours",
"Provisions for Students" = "school_special_student",
"Provisions for Teachers" = "school_special_teacher",
"Temperature Checks" = "school_temp",
"Only Certain Students" = "school_type_pers",
"Secondary School" = "secondary_school")
school_rhat <- sum_time %>%
ggplot(aes(x=rhat)) +
geom_histogram() +
theme_tufte() +
geom_vline(xintercept=1.1,linetype=2,colour="blue") +
labs(x="",y="") +
ggtitle("School Restrictions")
saveRDS(school_rhat,"coronanet/school_rhat.rds")
school <- get_all_discrim %>%
ggplot(aes(y=median,x=reorder(id_rec,median))) +
geom_pointrange(aes(ymin=q5,ymax=q95)) +
theme_tufte() +
coord_flip() +
labs(x="",y="Level of Discrimination") +
ggtitle("Schools")
school
saveRDS(school,"coronanet/school_discrim_object.rds")
ggsave("plots/school_discrim.pdf")
school_time_data_scaled <- school_time %>% as_draws_df %>%
gather(key="variable",value="estimate",-.chain,-.iteration,-.draw) %>%
group_by(.iteration) %>%
mutate(estimate=plogis(estimate)*100,
country=levels(school_mod@score_data@score_matrix$person_id)[as.numeric(str_extract(variable,"[0-9]+(?=\\])"))],
date_policy=unique(school_mod@score_data@score_matrix$time_id)[as.numeric(str_extract(variable,"(?<=\\[)[0-9]+"))]) %>%
group_by(date_policy,country) %>%
summarize(med_est=quantile(estimate,.5),
high_est=quantile(estimate,.95),
low_est=quantile(estimate,.05),
sd_est=sd(estimate))
school_time_data <- school_time %>% as_draws_df %>%
gather(key="variable",value="estimate",-.chain,-.iteration,-.draw) %>%
mutate(country=levels(school_mod@score_data@score_matrix$person_id)[as.numeric(str_extract(variable,"[0-9]+(?=\\])"))],
date_policy=unique(school_mod@score_data@score_matrix$time_id)[as.numeric(str_extract(variable,"(?<=\\[)[0-9]+"))]) %>%
group_by(date_policy,country) %>%
summarize(med_est=quantile(estimate,.5),
high_est=quantile(estimate,.95),
low_est=quantile(estimate,.05),
sd_est=sd(estimate))
expand_index <- group_by(school_time_data,country) %>%
expand(date_policy=seq(ymd("2020-01-01"),max(school_time_data$date_policy),
by="1 day"))
school_time_data <- left_join(expand_index,school_time_data,
by=c("country","date_policy")) %>%
fill(med_est,high_est,low_est,sd_est,.direction="downup")
school_time_data_scaled <- left_join(expand_index,school_time_data_scaled,
by=c("country","date_policy")) %>%
fill(med_est,high_est,low_est,sd_est,.direction="downup")
saveRDS(school_time_data,"indices/school_time_data.rds")
saveRDS(school_time_data_scaled,"indices/school_time_data_scaled.rds")
write_csv(school_time_data,"indices/school_time_data.csv")
write_csv(school_time_data_scaled,"indices/school_time_data_scaled.csv")
sample_plot_dates <- group_by(school_time_data_scaled,country) %>%
sample_n(1)
school_time <- school_time_data_scaled %>%
ggplot(aes(y=med_est,x=date_policy)) +
geom_line(colour="#8DD3C7",aes(group=country)) +
geom_ribbon(aes(ymin=low_est,ymax=high_est,group=country),alpha=0.1) +
geom_text(aes(label=country),colour="#FFFFB3",fontface="bold",
data=sample_plot_dates,check_overlap = T,size=2) +
theme_tufte() +
labs(x="",y="") +
ggtitle("School Restrictions")
school_time
saveRDS(school_time,"coronanet/school_plot_object.rds")
ggsave("plots/school_mod_plot.pdf")
school_time_single <- school_time_data_scaled %>%
filter(country %in% plot_countries) %>%
ggplot(aes(y=med_est,x=date_policy)) +
geom_line(colour="#8DD3C7",aes(group=country)) +
geom_ribbon(aes(ymin=low_est,ymax=high_est,group=country),alpha=0.1) +
geom_text(aes(label=country),colour="black",fontface="bold",
data=filter(sample_plot_dates,country %in% plot_countries),
check_overlap = T,size=2) +
theme_tufte() +
labs(x="",y="") +
ggtitle("School Restrictions")
school_time_single
saveRDS(school_time_single,"coronanet/school_plot_single_object.rds")
ggsave("plots/school_mod_plot_single.pdf")
rm(school_mod)
rm(school_time)
rm(school_time_data)
rm(school_time_data_scaled)
rm(school_sum)
rm(school)
rm(school_draws)
gc()
# health resources --------------------------------------------------------
hr_mod <- readRDS("/scratch/rmk7/coronanet/activity_fit_rwhr_random_walk_run_4.rds")
all_mods <- lapply(list.files(path = "/scratch/rmk7/coronanet/",
pattern="hr\\_random\\_walk\\_run\\_[0-4].*rds",
full.names=T),readRDS)
all_mods_mat <- lapply(all_mods, function(c) c@stan_samples$draws())
hr_draws <- do.call(bind_draws, c(all_mods_mat,list(along="chain")))
hr_sum <- summarize_draws_mc(hr_draws,"median","quantile2",posterior::rhat,cores=20)
all_mods_mat <- lapply(all_mods, function(c) as_draws_array(c@time_varying))
rm(all_mods)
hr_time <- do.call(bind_draws, c(all_mods_mat,list(along="chain"))) %>%
subset_draws(variable="tp1",regex=T)
hr_time <- apply_draws(hr_time,FUN=function(c) as.numeric(scale(c)),MARGIN=c(1:2))
sum_time <- summarize_draws_mc(hr_time,"median","quantile2",posterior::rhat,cores=20)
rm(all_mods_mat)
gc()
get_all_discrim <- filter(hr_sum,grepl(x=variable,pattern="reg\\_full"))
get_all_discrim$id <- levels(hr_mod@score_data@score_matrix$item_id)
get_all_discrim$id_rec <- fct_recode(get_all_discrim$id,
"Cold Storage" = "hr_cold_storage",
"Doctors" = "hr_doctors",
"Pharmaceuticals" = "hr_drugs",
"Dry Ice" = "hr_dry_ice",
"Other Facilities" = "hr_facilities",
"Hospitals" = "hr_hospitals",
"Health Insurance" = "hr_insurance",
"Mask Production" = "hr_masks",
"Nurses" = "hr_nurses",
"Other Infrastructure" = "hr_other_infra",
"Other Materials" = "hr_other_mat",
"Other Staff" = "hr_other_staff",
"PCR Test Production" = "hr_pcr",
"PPE" = "hr_ppe",
"Quarantine Funding" = "hr_quarantine",
"Sanitizer" = "hr_sanitizer",
"Syringe Production" = "hr_syringe",
"Targeted at Staff" = "hr_target_staff",
"Targeted at Supply" = "hr_target_supply",
"Test Kits" = "hr_test_kit",
"General Testing" = "hr_testing",
"Ventilators" = "hr_ventilator",
"Recruiting Volunteers" = "hr_volunteers",
"Oxford Health Investment" = "ox_health_invest")
hr_rhat <- sum_time %>%
ggplot(aes(x=rhat)) +
geom_histogram() +
geom_vline(xintercept=1.1,linetype=2,colour="blue") +
theme_tufte() +
labs(x="",y="") +
ggtitle("Health Resources")
saveRDS(hr_rhat,"coronanet/hr_rhat.rds")
hr <- get_all_discrim %>%
ggplot(aes(y=median,x=reorder(id_rec,median))) +
geom_pointrange(aes(ymin=q5,ymax=q95)) +
theme_tufte() +
coord_flip() +
labs(x="",y="Level of Discrimination") +
ggtitle("Health Resources")
hr
saveRDS(hr,"coronanet/hr_discrim_object.rds")
ggsave("plots/hr_discrim.pdf")
hr_time_data_scaled <- hr_time %>% as_draws_df() %>%
gather(key="variable",value="estimate",-.chain,-.iteration,-.draw) %>%
group_by(.iteration) %>%
mutate(estimate=plogis(estimate)*100,
country=levels(hr_mod@score_data@score_matrix$person_id)[as.numeric(str_extract(variable,"[0-9]+(?=\\])"))],
date_policy=unique(hr_mod@score_data@score_matrix$time_id)[as.numeric(str_extract(variable,"(?<=\\[)[0-9]+"))]) %>%
group_by(date_policy,country) %>%
summarize(med_est=quantile(estimate,.5),
high_est=quantile(estimate,.95),
low_est=quantile(estimate,.05),
sd_est=sd(estimate))
hr_time_data <- hr_time %>% as_draws_df() %>%
gather(key="variable",value="estimate",-.chain,-.iteration,-.draw) %>%
mutate(country=levels(hr_mod@score_data@score_matrix$person_id)[as.numeric(str_extract(variable,"[0-9]+(?=\\])"))],
date_policy=unique(hr_mod@score_data@score_matrix$time_id)[as.numeric(str_extract(variable,"(?<=\\[)[0-9]+"))]) %>%
group_by(date_policy,country) %>%
summarize(med_est=quantile(estimate,.5),
high_est=quantile(estimate,.95),
low_est=quantile(estimate,.05),
sd_est=sd(estimate))
expand_index <- group_by(hr_time_data,country) %>%
expand(date_policy=seq(ymd("2020-01-01"),max(hr_time_data$date_policy),
by="1 day"))