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cv_zip_versions.R
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library(tidyverse)
library(lubridate)
library(rvest)
library(magrittr)
library(STRAD)
# #initial data pull####
# #get all the commits - just manually filling in the strings initially
# commits_zip1 <-read_html("https://github.com/nychealth/coronavirus-data/commits/master/data-by-modzcta.csv")
#
# commits_zip2 <- read_html("https://github.com/nychealth/coronavirus-data/commits/master?after=ce3f3a6952914b8222b3f495ccd69a7f3b881e45+34&branch=master&path%5B%5D=data-by-modzcta.csv")
#
# commits_zip3 <- read_html("https://github.com/nychealth/coronavirus-data/commits/master?after=ce3f3a6952914b8222b3f495ccd69a7f3b881e45+69&branch=master&path%5B%5D=data-by-modzcta.csv")
#
# #look for nodes with dates, except 6/24 upload doesn't have a date
# data_upload_strings_zip <- commits_zip1 %>% html_children() %>% extract2(2) %>% html_nodes("a") %>% extract(str_detect(.,"[:digit:]{1,2}/[:digit:]{1,2}")&str_detect(.,"coronavirus-data")) %>% as.character() %>%
# c(commits_zip2 %>% html_children() %>% extract2(2) %>% html_nodes("a") %>% extract(str_detect(.,"[:digit:]{1,2}/[:digit:]{1,2}|Add files via upload")&str_detect(.,"coronavirus-data")) %>% as.character()) %>%
# c(commits_zip3 %>% html_children() %>% extract2(2) %>% html_nodes("a") %>% extract(str_detect(.,"[:digit:]{1,2}/[:digit:]{1,2}")&str_detect(.,"coronavirus-data")) %>% as.character())
#
#
#
# commit_df_zip <- data_upload_strings_zip %>% map_dfr(~tibble(date_string=str_extract(.x,"[:digit:]{1,2}/[:digit:]{1,2}"),commit_string=str_extract(.x,"nychealth/coronavirus-data/commit/[:alnum:]{40}") %>% str_remove("nychealth/coronavirus-data/commit/"))) %>%
# mutate(date=mdy(paste0(str_extract(date_string,"[:digit:]{1,2}/[:digit:]{1,2}"),"/20")))
#
# commit_df_zip %<>% mutate(date=case_when(commit_string=="be46b4e48653c29fab890473672cd7d7e0c7e59c"~ymd("2020-06-24"),T~date))
#
#
# c_v_zip_stacked <- commit_df_zip %>% mutate(data=map(commit_string,~read_csv(paste0("https://raw.githubusercontent.com/nychealth/coronavirus-data/",.x,"/data-by-modzcta.csv"))))
#
# c_v_zip_stacked %<>% unnest(cols = c(data))
#
# c_v_zip_stacked %<>% mutate(MODIFIED_ZCTA=as.character(MODIFIED_ZCTA))
#
# saveRDS(c_v_zip_stacked,"c_v_zip_stacked.Rds")
#add new####
c_v_zip_stacked <- readRDS("c_v_zip_stacked.Rds")
c_v_zip_stacked %>% count(date) %>% tail()
commits_zip <-read_html("https://github.com/nychealth/coronavirus-data/commits/master/data-by-modzcta.csv")
data_upload_strings_zip <- commits_zip %>% html_children() %>% extract2(2) %>% html_nodes("a") %>% extract(str_detect(.,"[:digit:]{1,2}/[:digit:]{1,2}")&str_detect(.,"coronavirus-data")) %>% as.character()
commit_df_zip_new <- data_upload_strings_zip %>% map_dfr(~tibble(date_string=str_extract(.x,"[:digit:]{1,2}/[:digit:]{1,2}"),commit_string=str_extract(.x,"nychealth/coronavirus-data/commit/[:alnum:]{40}") %>% str_remove("nychealth/coronavirus-data/commit/"))) %>%
mutate(date=mdy(paste0(str_extract(date_string,"[:digit:]{1,2}/[:digit:]{1,2}"),"/20"))) %>%
filter(!date %in% c_v_zip_stacked$date)
c_v_zip_stacked_new <- commit_df_zip_new %>% mutate(data=map(commit_string,~read_csv(paste0("https://raw.githubusercontent.com/nychealth/coronavirus-data/",.x,"/data-by-modzcta.csv"))))
c_v_zip_stacked_new %<>% unnest(cols = c(data))
c_v_zip_stacked_new %<>% mutate(MODIFIED_ZCTA=as.character(MODIFIED_ZCTA))
c_v_zip_stacked_new %>% count(date)
#c_v_zip_stacked_new %<>% filter(!commit_string=="7138e6906c9ee1117e2dac06656f0e1c03190370")
#ad to old####
c_v_zip_stacked %<>% bind_rows(c_v_zip_stacked_new) %>% arrange(desc(date))
#standardize neighborhood names - use most recent
zips_neighborhoods <- c_v_zip_stacked %>%
group_by(MODIFIED_ZCTA) %>%
filter(date==max(date)) %>%
ungroup() %>%
select(MODIFIED_ZCTA,borough=BOROUGH_GROUP,neighborhood=NEIGHBORHOOD_NAME) %>%
left_join(STRAD::zcta_demographics_all_acs_2018 %>% select(ZIPCODE,median_income,perc_pov,median_income_bin,perc_pov_bin,pop_18),by=c(MODIFIED_ZCTA="ZIPCODE"))
c_v_zip_stacked %<>%
select(-neighborhood) %>%
left_join(zips_neighborhoods %>% select(MODIFIED_ZCTA,neighborhood))
saveRDS(c_v_zip_stacked,"c_v_zip_stacked.Rds")
#plot/analyze####
c_v_zip_stacked %>% filter(MODIFIED_ZCTA==10012) %>% select(date,neighborhood,COVID_CASE_COUNT,MODIFIED_ZCTA) %>% head()
#plot 1 zip
c_v_zip_stacked %>% filter(MODIFIED_ZCTA==10012) %>% select(date,NEIGHBORHOOD_NAME,COVID_CASE_COUNT,MODIFIED_ZCTA) %>% ggplot(aes(x=date,y=COVID_CASE_COUNT)) + geom_line()
c_v_zip_stacked %>% filter(grepl("Village",neighborhood)|grepl("SoHo",neighborhood)) %>% group_by(date) %>% summarize(COVID_CASE_COUNT=sum(COVID_CASE_COUNT)) %>% ggplot(aes(x=date,y=COVID_CASE_COUNT)) + geom_line()
#plot borough neighborhoods (names change in june)
c_v_zip_stacked %>%
filter(BOROUGH_GROUP=="Bronx",date>=ymd("2020-08-01")) %>%
group_by(BOROUGH_GROUP,neighborhood,date) %>%
summarize(case_count=sum(COVID_CASE_COUNT)) %>%
ggplot(aes(x=date,y=case_count,color=neighborhood)) +
geom_line() + scale_color_discrete(guide = F)
#new cases week over week
zip_weekly_neighborhoods <- c_v_zip_stacked %>%
select(-neighborhood) %>%
left_join(zips_neighborhoods) %>%
group_by(date,borough,neighborhood) %>%
summarize(COVID_CASE_COUNT=sum(COVID_CASE_COUNT),zips=n(),population=sum(POP_DENOMINATOR),
pop2=sum(pop_18),perc_pov=sum(perc_pov*pop_18)/sum(pop_18)) %>%
ungroup() %>%
mutate(case_rate=COVID_CASE_COUNT/population,
perc_pov_bin=cut(perc_pov,breaks = c(-1,.05,.1,.15,.2,.3,.4,.6),
labels = c('0-5%', '5-10%', '10-15%', '15-20%', '20-30%', '30-40%', '40+%'))) %>%
mutate(week=week(date)) %>%
filter(max(date)-date>=7) %>%
arrange(neighborhood,date) %>%
group_by(week,neighborhood) %>%
mutate(day_count=row_number()) %>%
filter(day_count==1) %>%
#filter(week %in% 32:33) %>%
group_by(neighborhood) %>%
mutate(increase=COVID_CASE_COUNT-lag(COVID_CASE_COUNT)) %>%
ungroup() %>%
filter(!is.na(increase)) %>%
arrange(desc(increase)) %>%
select(date,borough,neighborhood,COVID_CASE_COUNT,increase,population,perc_pov,perc_pov_bin,case_rate) %>%
mutate(increase_per_thous_pop=1000*increase/population) %>%
arrange(desc(increase_per_thous_pop))
zip_weekly_neighborhoods %>% filter(borough=="Bronx",date>=ymd("2020-06-04")) %>%
ggplot(aes(x=date,y=increase_per_thous_pop,group = neighborhood,color=perc_pov_bin)) +
geom_line() +
scale_color_discrete(guide=F)
zip_weekly_neighborhoods %>% filter(grepl("West Village",neighborhood)|grepl("East Village",neighborhood)|grepl("SoHo",neighborhood)) %>%
ggplot(aes(x=date,y=increase_per_thous_pop,color=neighborhood)) + geom_line()
zip_weekly_neighborhoods %>%
ggplot(aes(x=date,y=increase_per_thous_pop,group=neighborhood,color=perc_pov_bin)) + geom_line() +facet_grid(cols = vars(perc_pov_bin),rows = vars(borough),scales = "free") + scale_color_discrete(guide=F)
zip_weekly_neighborhoods %>%
filter(date>ymd("2020-06-03")) %>%
ggplot(aes(x=date,y=increase_per_thous_pop,group=neighborhood,color=perc_pov_bin)) +
geom_line() +
facet_grid(cols = vars(perc_pov_bin),rows = vars(borough)) +
scale_color_discrete(guide=F) +
ylim(0,1.2)
zip_weekly_neighborhoods %>%
filter(date>ymd("2020-06-03")) %>%
ggplot(aes(x=date,y=case_rate,group=neighborhood,color=perc_pov_bin)) +
geom_line() +
facet_grid(cols = vars(perc_pov_bin),rows = vars(borough)) +
scale_color_discrete(guide=F)
zip_ch_from_aug_1 <- c_v_zip_stacked %>%
filter(date %in% c(ymd("2020-08-01"),max(date))) %>%
group_by(MODIFIED_ZCTA,neighborhood,borough=BOROUGH_GROUP) %>%
arrange(MODIFIED_ZCTA,date) %>%
summarize(begin_cases=first(COVID_CASE_COUNT),end_cases=last(COVID_CASE_COUNT)) %>%
mutate(perc_ch=end_cases/begin_cases-1,
perc_ch=ifelse(perc_ch<0,NA,perc_ch),
ch=end_cases-begin_cases,
label=paste0(MODIFIED_ZCTA,"<br>",
neighborhood,"<br>",
begin_cases,"/",end_cases,"<br>",
ch,"<br>",
format_perc(perc_ch,1))) %>%
ungroup()
STRAD::zcta_nyc_metro_geom %>%
filter(nyc) %>%
left_join(zip_ch_from_aug_1,by=c(ZIPCODE="MODIFIED_ZCTA")) %>%
ggplot(aes(fill=perc_ch)) +
geom_sf()
fill_palette <- colorBin(palette = RColorBrewer::brewer.pal(5,"YlOrRd"),domain = zip_ch_from_aug_1$perc_ch)
fill_palette_ch <- colorBin(palette = RColorBrewer::brewer.pal(5,"YlOrRd"),domain = zip_ch_from_aug_1$ch)
leaflet(data = STRAD::zcta_nyc_metro_geom %>%
filter(nyc) %>%
left_join(zip_ch_from_aug_1,by=c(ZIPCODE="MODIFIED_ZCTA")) %>%
st_transform(4326)) %>%
setView(lng = as.numeric(lm_limits$xmin), lat = as.numeric(lm_limits$ymax),zoom = 12) %>%
addProviderTiles(provider = "CartoDB") %>%
addPolygons(
fillColor = ~fill_palette_ch(ch),
#fillColor = ~fill_palette(perc_ch),
popup = ~label,
opacity = .8,
color = "grey",
weight = 1)
#alt version - city's four week files####
#data released on thursday, updated through previous saturday
# so far: 10/1 file, updated through 9/26; 10/8 file, updated through 10/3
#initial pull####
# # #get all the commits
# commits_recent_zip <-read_html("https://github.com/nychealth/coronavirus-data/commits/master/recent")
#
# data_upload_strings_recent_zip <- commits_recent_zip %>% html_children() %>% extract2(2) %>% html_nodes("a") %>% extract(str_detect(.,"data upload|data update")) %>% as.character()
#
# commit_df_recent_zip <- data_upload_strings_recent_zip %>%
# map_dfr(~tibble(date_string=str_extract(.x,"[:digit:]{1,2}/[:digit:]{1,2}"),commit_string=str_extract(.x,"nychealth/coronavirus-data/commit/[:alnum:]{40}") %>% str_remove("nychealth/coronavirus-data/commit/"))) %>%
# mutate(date=mdy(paste0(str_extract(date_string,"[:digit:]{1,2}/[:digit:]{1,2}"),"/20"))) %>%
# filter(date!=ymd(20200818))
#
# recent_zip_history <- commit_df_recent_zip %>% mutate(data=map(commit_string,~read_csv(paste0("https://raw.githubusercontent.com/nychealth/coronavirus-data/",.x,"/recent/recent-4-week-by-modzcta.csv"))))
#
# weekly_zcta_weeks <- tibble(week_end=min(recent_zip_history$date)-5-weeks(0:3),
# week_start=week_end-6) %>%
# bind_rows(tibble(week_end = recent_zip_history$date-5,
# week_start=week_end-6)) %>%
# distinct() %>%
# arrange(week_start) %>%
# mutate(week_num_abs=row_number()) %>%
# select(week_num_abs,week_start,week_end)
#
#
# make_recent_zcta_tall <- function(recent_zcta_df,upload_date){
# weeks_ends <- (upload_date-5)-weeks(0:4)
# recent_zcta_df %>%
# select(-contains("4WEEK"),-PCT_CHANGE_2WEEK) %>%
# pivot_longer(cols = -c(MODIFIED_ZCTA,NEIGHBORHOOD_NAME)) %>%
# mutate(week_num_rel=str_extract(name,"WEEK[:digit:]{1}") %>% str_remove("WEEK") %>% as.numeric(),
# week_end=weeks_ends[week_num_rel],
# measure=str_remove(name,"_WEEK[:digit:]{1}") %>% str_remove("COVID_CASE_") %>% str_to_lower(),
# iteration=recent_modzcta_iteration) %>%
# left_join(weekly_zcta_weeks,by="week_end")
#
# }
#
# recent_modzcta_tall <- recent_zip_history %>%
# mutate(data_tall=map2(data,date,~make_recent_zcta_tall(.x,.y))) %>% select(commit_string,data_tall) %>% unnest(data_tall) %>%
# left_join(commit_df_recent_zip %>% select(commit_string,upload_date=date))
#
# saveRDS(recent_modzcta_tall,"recent_modzcta_tall.Rds")
# saveRDS(weekly_zcta_weeks,"weekly_zcta_weeks.Rds")
#add a week####
weekly_zcta_weeks <- readRDS("weekly_zcta_weeks.Rds")
recent_modzcta_tall <- readRDS("recent_modzcta_tall.Rds")
commits_recent_zip <-read_html("https://github.com/nychealth/coronavirus-data/commits/master/recent")
data_upload_strings_recent_zip <- commits_recent_zip %>% html_children() %>% extract2(2) %>% html_nodes("a") %>% extract(str_detect(.,"data upload|data update")) %>% as.character()
commit_df_recent_zip_new <- data_upload_strings_recent_zip %>%
map_dfr(~tibble(date_string=str_extract(.x,"[:digit:]{1,2}/[:digit:]{1,2}"),commit_string=str_extract(.x,"nychealth/coronavirus-data/commit/[:alnum:]{40}") %>% str_remove("nychealth/coronavirus-data/commit/"))) %>%
mutate(date=mdy(paste0(str_extract(date_string,"[:digit:]{1,2}/[:digit:]{1,2}"),"/20"))) %>%
filter(date!=ymd(20200818),!commit_string %in% recent_modzcta_tall$commit_string)
weekly_zcta_weeks %<>%
bind_rows(tibble(week_end = commit_df_recent_zip_new$date-5,
week_start=week_end-6,
week_num_abs=max(weekly_zcta_weeks$week_num_abs)+1))
make_recent_zcta_tall <- function(recent_zcta_df,upload_date){
weeks_ends <- (upload_date-5)-weeks(0:4)
recent_zcta_df %>%
select(-contains("4WEEK"),-PCT_CHANGE_2WEEK) %>%
pivot_longer(cols = -c(MODIFIED_ZCTA,NEIGHBORHOOD_NAME)) %>%
mutate(week_num_rel=str_extract(name,"WEEK[:digit:]{1}") %>% str_remove("WEEK") %>% as.numeric(),
week_end=weeks_ends[week_num_rel],
measure=str_remove(name,"_WEEK[:digit:]{1}") %>% str_remove("COVID_CASE_") %>% str_to_lower()) %>%
left_join(weekly_zcta_weeks,by="week_end")
}
recent_modzcta_tall_new <- commit_df_recent_zip_new %>%
mutate(data=map(commit_string,~read_csv(paste0("https://raw.githubusercontent.com/nychealth/coronavirus-data/",.x,"/recent/recent-4-week-by-modzcta.csv"))))
recent_modzcta_tall_new %<>%
mutate(data_tall=map2(data,date,~make_recent_zcta_tall(.x,.y))) %>%
select(commit_string,data_tall) %>% unnest(data_tall) %>%
left_join(commit_df_recent_zip %>% select(commit_string,upload_date=date))
recent_modzcta_tall_new
recent_modzcta_tall %<>% bind_rows(recent_modzcta_tall_new)
saveRDS(weekly_zcta_weeks,"weekly_zcta_weeks.Rds")
#analyze####
weekly_modzcta_latest <- recent_modzcta_tall %>% group_by(measure,MODIFIED_ZCTA,week_num_abs) %>% slice(which.max(upload_date)) %>% ungroup()
#these are the initially proposed shutdown zips, superseded by hotspots defined as polygons
zips_shutdown <- tibble(ZCTA= c(11691, 11219, 11223, 11230, 11204, 11210, 11229, 11415, 11367),
action = "full") %>%
bind_rows(tibble(ZCTA= c(11205, 11211, 11249, 11235, 11234, 11213, 11218, 11374, 11366, 11432 , 11365),
action = "limited"))
weekly_by_target <- weekly_modzcta_latest %>% left_join(zips_shutdown,by=c(MODIFIED_ZCTA="ZCTA")) %>%
mutate(shutdown=factor(replace_na(action,"none"))) %>%
group_by(shutdown,week_num_abs) %>%
filter(measure=="count") %>%
summarize(cases=sum(value)) %>%
spread(shutdown,cases)
weekly_by_target %>%
mutate_at(vars(full,limited,none),~.x/lag(.x)-1)
#cases
weekly_modzcta_latest %>% left_join(zips_shutdown,by=c(MODIFIED_ZCTA="ZCTA")) %>%
group_by(action,week_num_abs,week_start) %>%
filter(measure=="count") %>%
summarize(cases=sum(value)) %>%
mutate(shutdown=factor(replace_na(action,"none"))) %>%
ggplot(aes(x=week_start,y=cases,color=shutdown)) +
geom_line(size=3)
#rates
weekly_modzcta_latest %>% left_join(zips_shutdown,by=c(MODIFIED_ZCTA="ZCTA")) %>%
group_by(action,week_num_abs,week_start) %>%
filter(measure=="rate") %>%
summarize(mean_rate=mean(value)) %>%
mutate(shutdown=factor(replace_na(action,"none"))) %>%
ggplot(aes(x=week_start,y=mean_rate,color=shutdown)) +
geom_line(size=3)
weekly_modzcta_latest %>% left_join(zips_shutdown,by=c(MODIFIED_ZCTA="ZCTA")) %>%
group_by(action,week_num_abs,week_start) %>%
filter(measure=="rate",week_num_abs==max(week_num_abs)) %>%
mutate(shutdown=factor(replace_na(action,"none"))) %>%
ggplot(aes(x=value,group=shutdown,fill=shutdown)) +
geom_density(color="transparent",alpha = .5)
weekly_modzcta_latest %>% left_join(zips_shutdown,by=c(MODIFIED_ZCTA="ZCTA")) %>%
filter(week_num_abs==max(week_num_abs),measure=="rate") %>%
arrange(desc(value)) %>%
group_by(action) %>%
top_n(10,value) %>%
mutate(rank=row_number()) %>%
select(rank,value,action) %>%
spread(action,value)
library(mapboxapi)
library(leaflet)
leaflet() %>%
addMapboxTiles(username = "srhrnkn",style_id = "light-v9") %>%
setView(lng = addresses$Longitude[1],lat = addresses$Latitude[1],zoom = 13)
library(sf)
zcta_nyc_metro_geom %>%
filter(nyc) %>%
left_join(weekly_modzcta_latest %>% filter(measure=="rate") %>% mutate(ZIPCODE=as.character(MODIFIED_ZCTA))) %>%
ggplot(aes(fill=value)) +
geom_sf()
zcta_nyc_metro_geom %>%
filter(nyc) %>%
left_join(zips_shutdown %>% mutate(ZIPCODE=as.character(ZCTA))) %>%
mutate(shutdown=factor(replace_na(action,"none"))) %>%
ggplot(aes(fill=shutdown)) +
geom_sf()