Merge branch 'main' of https://github.com/NewYorkCityCouncil/street_c…

…leanliness_sept23 into main

code/01_311_initial_request.Rmd → code/311_committee_requests/01_311_initial_request.Rmd

@@ -87,7 +87,7 @@ od_dl_sub <- unique(od_dl_sub)
 
 ```{r}
 # ---- Street cleanliness over time: plot
-ggplot(od_dl_sub[od_dl_sub$date_ym < "2023-09-01"], aes(x= date_ym, y=n_comp, color=complaint_type), group=complaint_type) + geom_line() + 
+ggplot(od_dl_sub, aes(x= date_ym, y=n_comp, color=complaint_type), group=complaint_type) + geom_line() + 
   theme_bw() + labs(x="Date", y="Number of Complaints", title="311 Data on Street Cleanliness Complaints Over Time", 
                     subtitle="Jan 2018 - July 2023", colour="Complaint type") + theme_nycc()
 # ggsave(filename = "clean_over_time.png", path = "visuals", width=10, height=7)
@@ -100,6 +100,7 @@ clean_over_time_df <- dcast(melt(as.data.table(od_dl_sub[od_dl_sub$date_ym < "20
                                  id.vars = c("complaint_type", "date_ym")), 
                             date_ym + variable ~ complaint_type, value.var = "value") %>%
   select(date_ym, `Dirty Conditions`, `Missed Collection`, `Sanitation Condition`)
+clean_over_time_df
 # write.csv(clean_over_time_df, "data/output/clean_over_time.csv")
 ```
 
@@ -108,6 +109,7 @@ clean_over_time_df <- dcast(melt(as.data.table(od_dl_sub[od_dl_sub$date_ym < "20
 clean_by_cd_df <- od_dl[complaint_type %in% c("Dirty Conditions", "Missed Collection", "Sanitation Condition")] %>%
   group_by(community_board) %>% 
   summarise(count=n())
+clean_by_cd_df
 # write.csv(clean_by_cd_df, "data/output/clean_by_comm.csv")
 ```
 
@@ -122,6 +124,7 @@ dsny_311_id <- dsny_311[dsny_311$complaint_type == "Illegal Dumping",]
 ```{r}
 # ---- Illegal dumping over time: table
 id_over_time_df <- dsny_311_id %>% group_by(date_ym) %>% summarise(n_comp=n())
+id_over_time_df
 # write.csv(id_over_time_df, "data/output/id_over_time.csv")
 ```
 
@@ -134,6 +137,7 @@ ggplot(id_over_time_df[id_over_time_df$date_ym < "2023-09-01",], aes(x= date_ym,
 ```{r}
 # ---- Illegal dumping by community district: table
 id_by_cd_df <- dsny_311_id %>% group_by(community_board) %>% summarise(n_comp=n())
+id_by_cd_df
 # write.csv(id_by_cd_df, "data/output/id_by_comm.csv")
 ```
 
@@ -147,6 +151,7 @@ dsny_311_dv <- dsny_311[dsny_311$complaint_type == "Derelict Vehicles",]
 ```{r}
 # ---- Derelict vehicles over time: table
 dv_over_time_df <- dsny_311_dv %>% group_by(date_ym) %>% summarise(n_comp=n())
+dv_over_time_df
 # write.csv(dv_over_time_df, "data/output/dv_over_time.csv")
 ```
 
@@ -159,6 +164,7 @@ ggplot(dv_over_time_df[dv_over_time_df$date_ym < "2023-09-01",], aes(x= date_ym,
 ```{r}
 # ---- Derelict vehicles by community district: table
 dv_by_cd_df <- dsny_311_dv %>% group_by(community_board) %>% summarise(n_comp=n())
+dv_by_cd_df
 # write.csv(dv_over_time_df, "data/output/dv_by_comm.csv")
 ```
 
@@ -204,7 +210,7 @@ cb_by_cd_df <- dep_311_cb %>% group_by(community_board) %>% summarise(n_comp=n()
 
 ```{r}
 dep_311_grease <- dep_311[grepl("grease", dep_311$descriptor, ignore.case = TRUE),]
-# dep_311_grease
+dep_311_grease
 # write.csv(dep_311_grease, "data/output/all_grease_data.csv")
 ```
 

code/02_311_interactive.R → code/311_committee_requests/02_311_interactive.R

@@ -2,16 +2,30 @@ library(tidyverse)
 library(htmltools)
 library(ggiraph)
 library(zoo)
+library(readr)
+library(data.table)
+library(councildown)
 
-# run 01_311_inital_request.Rmd first
+# accompanying interactive plots to 01_311_initial_request.Rmd
+# you don't have to run the first file if you just want the interactive plots
+
+dsny_311 <- fread("https://data.cityofnewyork.us/resource/fhrw-4uyv.csv?agency='DSNY'&$where=created_date>='2018-01-01'&$limit=999999999999")
+setDT(dsny_311)
+dsny_311[, created_date := as.Date(created_date)][, yr := year(created_date)][, mo := month(created_date)]
+dsny_311[, date_ym:=as.yearmon(created_date)]
+
+dep_311 <- fread("https://data.cityofnewyork.us/resource/fhrw-4uyv.csv?agency='DEP'&$where=created_date>='2018-01-01'&$limit=999999999999")
+setDT(dep_311)
+dep_311[, created_date := as.Date(created_date)][, yr := year(created_date)][, mo := month(created_date)]
+dep_311[, date_ym:=as.yearmon(created_date)]
 
 # ---- street cleanliness 
 od_dl <- dsny_311
 od_dl[complaint_type %in% c("Overflowing Litter Baskets", "Dirty Condition", "Dirty"), complaint_type := "Dirty Conditions"]
 od_dl[grepl("Litter", complaint_type, ignore.case = TRUE) | grepl("litter", descriptor, ignore.case = TRUE), complaint_type := "Litter"]
 od_dl_sub <- od_dl[complaint_type %in% c("Dirty Conditions", "Litter")]
 # > sum((od_dl_sub[od_dl_sub$date_ym > "2022-08-01" & od_dl_sub$complaint_type == "Dirty Conditions",])$n_comp)
-# [1] 47537 - from old version, littering adds ~10k
+# [1] 47537 - from old version, littering adds ~3k
 
 od_dl_sub[, n_comp := .N, by = c("complaint_type", "date_ym")] # count by complaint type, date
 od_dl_sub <- od_dl_sub[, .(complaint_type, date_ym, n_comp)] # subset to complaint type, date, n
@@ -114,6 +128,7 @@ id_interactive <- girafe(ggobj = id_plot,
                            ))
 # htmltools::save_html(id_interactive, "../visuals/id_complaints_citywide_line.html")
 
+dep_311_grease <- dep_311[grepl("grease", dep_311$descriptor, ignore.case = TRUE),]
 # yellow/brown grease
 grease_over_time_df <- dep_311_grease[dep_311_grease$date_ym >= "2022-08-01" & dep_311_grease$date_ym <= "2023-08-01",] %>% 
   group_by(date_ym) %>%