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script.R
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script.R
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#FINAL SCRIPT#
########################################
# Libraries & Dependencies & Environments
#########################################
library(stringr)
library(data.table)
library(RSocrata)
library(anytime)
#library(sp)
#library(rgdal)
library(sf)
library(dplyr)
library(RCurl)
dat1=Sys.Date()-1
options(scipen=999)
#mode function
Mode <- function(x) {
ux <- unique(x)
ux[which.max(tabulate(match(x, ux)))]
}
#environment variable
source("envs.R")
###########################################################
# Dataset 1: DOB ECB VIOLATIONS - Class 1 Hazardous
################################################################
## dataset was filtered for infraction code1 - Class 1 hazardous
#getting infraction codes that are Class 1 in a comma separated list for api call
codes=read.csv('infraction_codes.csv')
head(codes)
codes$cleanCodes=substring(codes$Infraction.Code, 2)
# run code below in r script to get correct output, not working in jupyter
paste0(codes$cleanCodes, collapse = '\',\'')
# these are the infraction codes associated with Class 1 violations that are Immediately Hazardous
# https://www1.nyc.gov/site/buildings/safety/ecb-violations.page
#Codes below are being used to create api link '179','144','1B7','1G5','1G4','127','134','156','158','1C7','1C9','1C8','106','101','1C6','113','121','157','160','182','114','125','161','122','107','108','124','103','187','1F9','1G2','172','199','1E6','112','1F8','1G1','105','1E8','1E9','1E5','153','188','1A9','189','102','154','126','132','133','136','137','138','139','140','155','163','104','1E3','1EF','151','1F7','1B1','191','1G3','147','148','1G9','141','143','1C5','190','1B8','1B2','1B3','1B4','1B5','162','1C1','1C2','152','1F1','109','115','118','120','1G6','1F2','192','181','194','183','1B9','1C3','1C4','111','110','1F3','1F4','1F5','1F6','117','176','116','175','159','1E7','131','123','119','193','174','185','130','149','150','129','146','142','177','1A1','1A2','1A3','1A4','1A5','1A6','1A7','1A8','1D1','1D2','1D3','1D4','1D5','1D6','1D7','1D8','1D9','1E1','1E2','1G7','1G8','180','135','170','164','168','165','166','169','171','167','178','1B6'
#example of socrata
#https://data.cityofchicago.org/resource/6zsd-86xi.json?$where=primary_type in('THEFT', 'ROBBERY', 'INTIMIDATION')
#link works in url
# https://data.cityofnewyork.us/resource/6bgk-3dad.csv?$limit=900000&$where=infraction_code1 in('179','144','1B7','1G5','1G4','127','134','156','158','1C7','1C9','1C8','106','101','1C6','113','121','157','160','182','114','125','161','122','107','108','124','103','187','1F9','1G2','172','199','1E6','112','1F8','1G1','105','1E8','1E9','1E5','153','188','1A9','189','102','154','126','132','133','136','137','138','139','140','155','163','104','1E3','1EF','151','1F7','1B1','191','1G3','147','148','1G9','141','143','1C5','190','1B8','1B2','1B3','1B4','1B5','162','1C1','1C2','152','1F1','109','115','118','120','1G6','1F2','192','181','194','183','1B9','1C3','1C4','111','110','1F3','1F4','1F5','1F6','117','176','116','175','159','1E7','131','123','119','193','174','185','130','149','150','129','146','142','177','1A1','1A2','1A3','1A4','1A5','1A6','1A7','1A8','1D1','1D2','1D3','1D4','1D5','1D6','1D7','1D8','1D9','1E1','1E2','1G7','1G8','180','135','170','164','168','165','166','169','171','167','178','1B6')
##########api read in
#modified to be read by r
p1='https://data.cityofnewyork.us/resource/6bgk-3dad.csv?$limit=900000&$where=infraction_code1 in('
p1.5='\''
p2=paste0(codes$cleanCodes, collapse = '\',\'')
p2.5='\''
p3=')'
paste(p1,p1.5, p2, p2.5, p3, collapse="")
###check data
dobecb=read.socrata(paste(p1,p1.5, p2, p2.5, p3, collapse=""))
str(dobecb)
dim(dobecb)
names(dobecb)
#saving an original just in case
dobecb1=dobecb
### getting bbl key column
#leading zeros are needed
dobecb$block=str_pad(dobecb$block, 5, pad = "0")
dobecb$lot=str_pad(dobecb$lot, 4, pad = "0")
dobecb$bbl=gsub(" ", "",paste(dobecb$boro, dobecb$block, dobecb$lot))
### getting issue date into date format
dobecb$IssueDate=as.Date(anytime(as.character(dobecb$issue_date)))
### removing entries older than 2018
### columns in final dataset will be: 'Violations Issued Last Year' and 'Violations Issued this Year so Far'
cy=format(as.Date(Sys.Date(), format="%d/%m/%Y"),"%Y")
py=as.character(as.numeric(format(as.Date(Sys.Date(), format="%d/%m/%Y"),"%Y"))-1)
#py=paste(py,'01','01', sep="-")
#db=dobecb[which(dobecb$IssueDate>=py),]
## create Year column for when new violations are issued in the present year (this is for the 'Violations Issued this Year so Far' column)
dobecb$Year=format(as.Date(dobecb$IssueDate, format="%d/%m/%Y"),"%Y")
### removing unnecessary columns, keep: $bbl, $IssueDate, $infraction_code1, $violation_type, $violation_description, $respondent_name
db=dobecb[,c("bbl", "IssueDate", "infraction_code1", "violation_type", "violation_description", "respondent_name")]
head(db,1)
## create Classification column, all are Class 1 (Immediately Hazardous)
db$Classification=rep('Class 1', nrow(db))
# remove strange values from bbl column
db=db[-which(db$bbl=="5NANA"),]
db=db[-which(db$bbl=="4NANA"),]
db=db[-which(db$bbl=="3NANA"),]
db=db[-which(db$bbl=="2NANA"),]
db=db[-which(db$bbl=="1NANA"),]
#subsetting year
#total number of violations for a bbl
lastyr=data.table(table(db[which(db$Year==as.character(as.numeric(format(as.Date(Sys.Date(), format="%d/%m/%Y"),"%Y"))-1)),5]))
thisyr=data.table(table(db[which(db$Year==as.character(as.numeric(format(as.Date(Sys.Date(), format="%d/%m/%Y"),"%Y")))),5]))
names(lastyr)[1:2]<- c('BBL','Violations_Last_Year')
names(thisyr)[1:2]<- c('BBL','Violations_This_Year')
#getting all the infraction codes, violation types, and descriptions for a unique bbl
db=db[which(db$Year>=py),]
db1=data.table(db)
setkey(db1, key='bbl')
infcode=db1[,infraction_code1, by=.(bbl)]
viot=db1[,violation_type, by=.(bbl)]
viod=db1[,violation_description, by=.(bbl)]
resn=db1[,respondent_name, by=.(bbl)]
#######################
u=unique(db1$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=infcode[which(infcode$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
infcode2=data.table(cbind(u,u2))
names(infcode2)<-c('bbl', 'Infraction_Codes')
###################################
u=unique(db1$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=viot[which(viot$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
viot2=data.table(cbind(u,u2))
names(viot2)<-c('bbl', 'Violation_Types')
########################
u=unique(db1$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=viod[which(viod$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
viod2=data.table(cbind(u,u2))
names(viod2)<-c('bbl', 'Violation_Descriptions')
#############################
u=unique(db1$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=resn[which(resn$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
resn2=data.table(cbind(u,u2))
names(resn2)<-c('bbl', 'Respondent_Names')
v=data.frame(cbind(infcode2, viot2$Violation_Types, viod2$Violation_Descriptions, resn2$Respondent_Names))
#joining
#library(dplyr)
v2=left_join(v,lastyr,by=c("bbl"="BBL"))
v3=left_join(v2,thisyr,by=c("bbl"="BBL"))
f_dobecb=v3
#filling nas with zero
f_dobecb[which(is.na(f_dobecb$Violations_This_Year)==TRUE),7]<-0
f_dobecb[which(is.na(f_dobecb$Violations_Last_Year)==TRUE),6]<-0
#to be used for cerating geojsons
names(f_dobecb)<-c("BBL", "Infraction_Codes", "Violation_Types", "Violation_Descriptions", "Respondent_Names", "Violations_Last_Year", "Violations_This_Year")
#save csv copy
write.csv(f_dobecb, 'dobecb_class1_vio.csv', row.names=FALSE)
###########################################################
# Dataset 2: 311 SERVICE REQUESTS HPD (heat hot water, unsanitary conditions)
################################################################
#reading in dataset via api
cd=format(Sys.time(), format="%Y-%m-%dT%H:%M:%S")
py=as.character(as.numeric(format(as.Date(Sys.Date(), format="%d/%m/%Y"),"%Y"))-1)
pd=paste(py,'01','01T00:00:00', sep="-")
#ct='HEAT/HOT WATER'
#https://data.cityofnewyork.us/resource/fhrw-4uyv.csv?$limit=9000000&$where=created_date>='2018-01-01T00:00:00' AND complaint_type in('HEAT/HOT WATER', 'UNSANITARY CONDITION')
#p=paste(c('https://data.cityofnewyork.us/resource/fhrw-4uyv.csv?$limit=9000000&$where=created_date between', cd, 'and', pd, 'and complaint_type=', ct, 'and bbl!=', '0000000000\''), collapse="'")
p=paste(c('https://data.cityofnewyork.us/resource/fhrw-4uyv.csv?$limit=9000000&$where=created_date>=', pd, 'and complaint_type in(', 'HEAT/HOT WATER\')' ), collapse="'")
hhw=read.socrata(p)
#saving copy of read in dataset
hhw1=hhw
##############################################
#cleaning hhw dataset
str(hhw)
max(hhw$created_date)
min(hhw$created_date)
min(hhw$bbl)
max(hhw$bbl)
#removing bbls with NA
hhw=hhw[-which(is.na(hhw$bbl)==TRUE),]
hhw=hhw[-which(hhw$bbl==0),]
#adding year column
hhw$Year=format(as.Date(hhw$created_date, format="%Y-%m-%dT%H:%M:%S"), "%Y")
unique(hhw$Year)
#total number of 311 hhw for each bbl each year
cy=format(as.Date(Sys.Date(), format="%d/%m/%Y"),"%Y")
#making bbl character
hhw$bbl=as.character(hhw$bbl)
lastyr=data.table(table(hhw[which(hhw$Year==py),4]))
thisyr=data.table(table(hhw[which(hhw$Year==cy),4]))
names(lastyr)[1:2]<- c('BBL','Requests_Last_Year')
names(thisyr)[1:2]<- c('BBL','Requests_This_Year')
#getting all the descriptors and resolution_descriptions for a unique bbl
#subsettting hhw for relevant columns
dbly=data.table(hhw[,c(4,12,16,36,46)])
setkey(dbly, key='bbl')
comp=dbly[,complaint_type, by=.(bbl)]
wd=dbly[,descriptor, by=.(bbl)]
rd=dbly[,resolution_description, by=.(bbl)]
###################
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=wd[which(wd$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
wd2=data.table(cbind(u,u2))
names(wd2)<-c('bbl', 'Descriptors')
################
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=rd[which(rd$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
rd2=data.table(cbind(u,u2))
names(rd2)<-c('bbl', 'Resolution_Descriptions')
##################
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=comp[which(comp$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
comp2=data.table(cbind(u,u2))
names(comp2)<-c('bbl', 'complaint_type')
### cbinding
v=data.frame(cbind(wd2,rd2$Resolution_Descriptions, comp2$complaint_type))
#joining
#library(dplyr)
v2=left_join(v,lastyr,by=c("bbl"="BBL"))
v3=left_join(v2,thisyr,by=c("bbl"="BBL"))
#make Na into 0
v3[which(is.na(v3$Requests_Last_Year)==TRUE),5]<-0
v3[which(is.na(v3$Requests_This_Year)==TRUE),6]<-0
names(v3)<-c("BBL", "Descriptors", "Resolution_Descriptions", "Complaint_Type", "Requests_Last_Year", "Requests_This_Year")
#to be used for creating geojsons
f_hhw=v3
#save csv copy
write.csv(f_hhw, 'hhw.csv', row.names=FALSE)
###########################################################
# Dataset 3: HPD Housing Maintenance Code Violations - Class C
################################################################
##########api read in
#modified to be read by r
py=as.character(as.numeric(format(as.Date(Sys.Date(), format="%d/%m/%Y"),"%Y"))-1)
pd=paste(py,'01','01T00:00:00', sep="-")
p=paste(c('https://data.cityofnewyork.us/resource/b2iz-pps8.csv?$limit=9000000&$where=novissueddate>=', pd, 'AND class=', 'C\'' ), collapse="'")
hmc=read.socrata(p)
#check dataset
dim(hmc)
str(hmc)
names(hmc)
min(hmc$novissueddate)
max(hmc$novissueddate)
min(hmc$bbl)
##save a copy
hmc2=hmc
##remove NAs from bbl and 0s
hmc=hmc[-which(is.na(hmc$bbl)==TRUE),]
hmc=hmc[-which(hmc$bbl==0),]
# adding year column
hmc$Year=format(as.Date(hmc$novissueddate, format="%Y-%m-%dT%H:%M:%S"), "%Y")
unique(hmc$Year)
cy=format(as.Date(Sys.Date(), format="%d/%m/%Y"),"%Y")
lastyr=data.table(table(hmc[which(hmc$Year==py),3]))
thisyr=data.table(table(hmc[which(hmc$Year==cy),3]))
names(lastyr)[1:2]<- c('BBL','Violations_Last_Year')
names(thisyr)[1:2]<- c('BBL','Violations_This_Year')
#getting all the novdescriptions and novtypes for a unique bbl
#subsettting hhw
dbly=data.table(hmc[,c(3,26,29)])
setkey(dbly, key='bbl')
nd=dbly[,novdescription, by=.(bbl)]
nt=dbly[,novtype, by=.(bbl)]
#######loop
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=nd[which(nd$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
nd2=data.table(cbind(u,u2))
names(nd2)<-c('BBL', 'Descriptions')
#########loop
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=nt[which(nt$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
nt2=data.table(cbind(u,u2))
names(nt2)<-c('BBL', 'Violation_Types')
########cbind
v=data.frame(cbind(nd2,nt2$Violation_Types))
#joining year counts to column counts
library(dplyr)
v2=left_join(v,lastyr,by=c("BBL"="BBL"))
v3=left_join(v2,thisyr,by=c("BBL"="BBL"))
##make nas into 0
v3[which(is.na(v3$Violations_This_Year)==TRUE),5]<-0
v3[which(is.na(v3$Violations_Last_Year)==TRUE),4]<-0
##
#to be used to create geojsons
f_hmc=v3
names(f_hmc)<- c("BBL","Descriptions","Violation_Types","Violations_Last_Year","Violations_This_Year")
#save csv copy
write.csv(f_hmc, 'hmc.csv', row.names=FALSE)
###########################################################
# Dataset 4: Certificate of No Harrassment Building List
################################################################
#api read in
p='https://data.cityofnewyork.us/resource/fajq-hihd.csv?$limit=900000'
conh=read.socrata(p)
#check data
#save a copy
conh2=conh
#remove NAs in bbl column
conh=conh[-which(is.na(conh$bbl)==TRUE),]
conh$bbl=as.character(as.numeric(conh$bbl))
#doesnt need year column yet
#total number of bbls
t=data.table(table(conh$bbl))
names(t)<-c('BBL','Number_of_Buildings')
#t$BBL=as.character(as.numeric(t$BBL))
#getting all the bins for a unique bbl
#subsettting conh for relevant columns
dbly=data.table(conh[,c(1:2)])
setkey(dbly, key='bbl')
bn=dbly[,bin, by=.(bbl)]
###############loop
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=bn[which(bn$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
bn2=data.table(cbind(u,u2))
names(bn2)<-c('BBL', 'Bins')
###join
f_conh=data.table(cbind(t,bn2$Bins))
names(f_conh)<- c("BBL", "Number_of_Buildings", "Bins")
#save file
write.csv(f_conh, 'conh.csv', row.names=FALSE)
###########################################################
# Dataset 5: Subsidized Housing
################################################################
#dataset has no api
#update this csv when dataset is updated on furman center
sub=read.csv('SubsidizedHousingFurman.csv')
dim(sub)
str(sub)
names(sub)
min(sub$bbl)
#save a copy
sub2=sub
#checking dataset
unique(sub$agency_name)
unique(sub$regulatory_tool)
unique(sub$program_name)
length(unique(sub$project_name))
#change bbl class to character
sub$bbl=as.character(as.numeric(sub$bbl))
#getting expiration period 0-5, 5-10, 10-15, 15+
library(anytime)
end=anytime(as.character(sub$end_date))
endyr=format(as.Date(end, format="%d/%m/%Y"),"%Y")
currentyr=format(as.Date(Sys.time(), format="%d/%m/%Y"),"%Y")
yrsexpire=as.numeric(endyr)-as.numeric(currentyr)
yrsexpirecat=yrsexpire
summary(yrsexpire)
summary(endyr)
class(yrsexpirecat)
w1=which(yrsexpire>14)
w2=which(yrsexpire<0)
w3=which(yrsexpire>-1 & yrsexpire<5)
w4=which(yrsexpire>4 & yrsexpire<10)
w5=which(yrsexpire>9 & yrsexpire<15)
w6=which(is.na(yrsexpire)==TRUE)
yrsexpirecat[1:5]
yrsexpire[1:5]
yrsexpirecat[w1]<-"15 or more years"
yrsexpirecat[w2]<-"Already Expired"
yrsexpirecat[w3]<-"Less than 5 years"
yrsexpirecat[w4]<-"Between 5-9 years"
yrsexpirecat[w5]<-"Between 10-14 years"
yrsexpirecat[w6]<-"Permanentaly Affordable"
yrsexpire[1:5]
yrsexpirecat[1:5]
unique(yrsexpire)
unique(yrsexpirecat)
sub$yrsexpire=yrsexpire
sub$yrsexpirecat=yrsexpirecat
t=data.table(table(sub$bbl))
names(t)<-c('BBL','Number_of_Subsidies')
t$BBL=as.character(as.numeric(t$BBL))
#getting all the agency names, regulatory tools, program names, and project names for a unique bbl
#subsettting conh
dbly=data.table(sub[,c(1,5:8,33)])
setkey(dbly, key='bbl')
agn=dbly[,agency_name, by=.(bbl)]
rgt=dbly[,regulatory_tool, by=.(bbl)]
pgn=dbly[,program_name, by=.(bbl)]
pjn=dbly[,project_name, by=.(bbl)]
yec=dbly[,yrsexpirecat, by=.(bbl)]
####loop
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=agn[which(agn$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
agn2=data.table(cbind(u,u2))
names(agn2)<-c('BBL', 'agency_name')
###############
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=rgt[which(rgt$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
rgt2=data.table(cbind(u,u2))
names(rgt2)<-c('BBL', 'regulatory_tool')
#########
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=pgn[which(pgn$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
pgn2=data.table(cbind(u,u2))
names(pgn2)<-c('BBL', 'program_name')
##########
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=pjn[which(pjn$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
pjn2=data.table(cbind(u,u2))
names(pjn2)<-c('BBL', 'project_name')
##########
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=yec[which(yec$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
yec2=data.table(cbind(u,u2))
names(yec2)<-c('BBL', 'yrsexpirecat')
####join
library(dplyr)
v=data.frame(cbind(agn2,rgt2$regulatory_tool, pgn2$program_name, pjn2$project_name, yec2$yrsexpirecat))
#joining
#library(dplyr)
v2=left_join(v,t,by=c("BBL"="BBL"))
f_sub=v2
names(f_sub)<- c('BBL','Agency_Names', 'Regulatory_Tools', 'Program_Names', 'Project_Names', 'Expiration_Category', 'Number_of_Subsidies')
#make Na into none
f_sub[which(f_sub$Project_Names==""),5]<-"Not Provided"
#f_sub[which(f_sub$Number_of_Subsidies==""),7]<-0
#save file
write.csv(f_sub, 'sub.csv', row.names=FALSE)
###################################################
# Dataset 6: Speculation Watchlist
#################################################
#api read in
p='https://data.cityofnewyork.us/resource/dge8-thm3.csv?$limit=900000'
spw=read.socrata(p)
#check data
head(spw)
names(spw)
dim(spw)
length(unique(spw$bbl))
class(spw$bbl)
unique(spw$yearqtr)
#save a copy
spw2=spw
#remove NAs in bbl column
spw=spw[which(is.na(spw$bbl)==FALSE),]
spw$bbl=as.character(as.numeric(spw$bbl))
#make columns into character for join to work
#spw$crfn=as.character(as.numeric(spw$crfn))
#needs a yr column
spw$Year=substr(spw$yearqtr,1,4)
#create netoperating income column
spw$netoperincome=spw$cap_rate*spw$price
#boro cap_rate as percentage
spw$borough_cap_rate=spw$borough_cap_rate*100
#set cap_rate as percentage
spw$cap_rate=spw$cap_rate*100
#total number of bbls
t=data.table(table(spw$bbl))
names(t)<-c('BBL','Speculation_Potential')
#t$BBL=as.character(as.numeric(t$BBL))
#getting all the crfn, boro cap rates, cap rates, buyer/sellers names, NTAs, zips, prices, yearqtrs, and deed dates for a unique bbl
#in this case they are all unique, but in case in future they aren't
#subsettting spw for relevant columns
dbly=data.table(spw[,c(1,6:7, 11:12, 18:19, 25:27, 29:30)])
cy=format(as.Date(Sys.Date(), format="%d/%m/%Y"),"%Y")
lastyr=data.table(table(spw[which(spw$Year==py),1]))
thisyr=data.table(table(spw[which(spw$Year==cy),1]))
names(lastyr)[1:2]<- c('BBL','Speculations_Last_Year')
names(thisyr)[1:2]<- c('BBL','Speculations_This_Year')
setkey(dbly, key='bbl')
bcr=dbly[,borough_cap_rate, by=.(bbl)]
cr=dbly[,cap_rate, by=.(bbl)]
#crfn=dbly[,crfn, by=.(bbl)]
#nta=dbly[,nta, by=.(bbl)]
#deed=dbly[,deed, by=.(bbl)]
pr=dbly[,price, by=.(bbl)]
yqtr=dbly[,yearqtr, by=.(bbl)]
slr= dbly[,grantee, by=.(bbl)]
byr= dbly[,grantor, by=.(bbl)]
################
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=bcr[which(bcr$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
bcr2=data.table(cbind(u,u2))
names(bcr2)<-c('BBL', 'borough_cap_rate')
################
################
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=cr[which(cr$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
cr2=data.table(cbind(u,u2))
names(cr2)<-c('BBL', 'cap_rate')
################
################
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=pr[which(pr$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
pr2=data.table(cbind(u,u2))
names(pr2)<-c('BBL', 'price')
################
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=yqtr[which(yqtr$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
yqtr2=data.table(cbind(u,u2))
names(yqtr2)<-c('BBL', 'qtryr')
################
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=slr[which(slr$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
slr2=data.table(cbind(u,u2))
names(slr2)<-c('BBL', 'Sellers')
################
u=unique(dbly$bbl)
r=list()
for (i in 1:length(u)){
r[[i]]=byr[which(byr$bbl==u[i]),2]
}
r[1:5]
u2=c()
for (i in 1:length(r))
{
u2[i]=paste(sort(unique(unlist(r[[i]]))), collapse=", ")
}
u2[3]
byr2=data.table(cbind(u,u2))
names(byr2)<-c('BBL', 'Buyers')
v=data.frame(cbind(yqtr2,bcr2$borough_cap_rate,cr2$cap_rate, pr2$price,slr2$Sellers,byr2$Buyers))
#joining
library(dplyr)
v2=left_join(v,lastyr,by=c("BBL"="BBL"))
#none so far
#v3=left_join(v2,thisyr,by=c("BBL"="BBL"))
#f_spw=v3
f_spw=v2
names(f_spw)<- c("BBL","Quarter","bcaprate","cap_rate", "Price", "Sellers", "Buyers", 'Spec_LY')
write.csv(f_spw, 'spw.csv', row.names=FALSE)
################################################
# Dataset 7: Rent Stabilized Buildings list
#############################################
#scraped script to be inserted here
#dataset has no api
#update this csv when new years list is posted
stab=read.csv('stabilizedbldgs.csv')
dim(stab)
str(stab)
names(stab)
#checking dataset
unique(stab$Brough)
class(stab$Brough)
#save a copy
stab2=stab
#checking dataset
unique(stab$Brough)
as.character(stab$Lot)
as.character(stab$Block)
#make borough column into character
boro=as.character(stab$Brough)
boro[which(boro=='BRONX')]<-'2'
boro[which(boro=='RONX')]<-'2'
boro[which(boro=='BROOKLYN')]<-'3'
boro[which(boro=='ROOKLYN')]<-'3'
boro[which(boro=='NEW YORK')]<-'1'
boro[which(boro=='EW YORK')]<-'1'
boro[which(boro=='STATEN ISLAND')]<-'5'
boro[which(nchar(boro)>1)]<-'4'
stab$boro=boro
stab$Block=str_pad(stab$Block, 5, pad = "0")
stab$Lot=str_pad(stab$Lot, 4, pad = "0")
stab$BBL=gsub(" ", "",paste(stab$boro, stab$Block, stab$Lot))
t=data.table(table(stab$BBL))
names(t)<-c('BBL','Rent_Stabilized_Bldgs')
t$BBL=as.character(as.numeric(t$BBL))
#join with subsidy file
####join
full_subrent=full_join(f_sub,t,by=c("BBL"="BBL"))
#make Na into none
full_subrent[which(is.na(full_subrent$Rent_Stabilized_Bldgs)==TRUE),8]<-0
full_subrent[which(is.na(full_subrent$Number_of_Subsidies)==TRUE),7]<-0
#save file
write.csv(full_subrent, 'sub_rent.csv', row.names=FALSE)
#####################################
# Read in need shapefiles
#########################################
# read in pluto shapefiles
###sf
bx <-st_read('Bronx/Bronx_Pluto_18.shp',layer="Bronx_Pluto_18")
st_transform(bx, "+proj=longlat +datum=WGS84")
bk<-st_read('Brooklyn/Brooklyn_Pluto_18.shp',layer="Brooklyn_Pluto_18")
st_transform(bk, "+proj=longlat +datum=WGS84")
mn<- st_read('Manhattan/Manhattan_Pluto_18.shp',layer="Manhattan_Pluto_18")
st_transform(mn, "+proj=longlat +datum=WGS84")
qn<-st_read('Queens/Queens_Pluto_18.shp',layer="Queens_Pluto_18")
st_transform(qn, "+proj=longlat +datum=WGS84")
si<-st_read('StatenIsland/StatenIsland_Pluto_18.shp',layer="StatenIsland_Pluto_18")
st_transform(si, "+proj=longlat +datum=WGS84")
#join boroughs in nyc
nyc=rbind(mn,bx,bk,qn,si)
#change class to character
nyc$BBL=as.character(as.numeric(nyc$BBL))
nyc$LandUse=as.character(as.numeric(nyc$LandUse))
nyc$OwnerName=as.character(nyc$OwnerName)
#subset nyc
nyc=nyc[,c(1,4,8:9,17,30,33,45:48,62,72)]
#land use conversions
nyc[which(nyc$LandUse=="1"),6]<-"1-2 Family"
nyc[which(nyc$LandUse=="2"),6]<-"Multifamily Walkup"
nyc[which(nyc$LandUse=="3"),6]<-"Multifamily Elevator"
nyc[which(nyc$LandUse=="4"),6]<-"Mixed Residential/Commercial"
nyc[which(nyc$LandUse=="5"),6]<-"Commercial/Office"
nyc[which(nyc$LandUse=="6"),6]<-"Industrial"
nyc[which(nyc$LandUse=="7"),6]<-"Transportation/Utility"
nyc[which(nyc$LandUse=="8"),6]<-"Public Facilities"
nyc[which(nyc$LandUse=="9"),6]<-"Open Space"
nyc[which(nyc$LandUse=="10"),6]<-"Parking"
nyc[which(nyc$LandUse=="11"),6]<-"Vacant"
nyc[which(is.na(nyc$LandUse)==TRUE),6]<-"Not Provided"
#data.table
#nyc=as.data.table(nyc)
#getting nyc as a data.table only
df_nyc <- nyc
st_geometry(df_nyc) <- NULL
#read in hexbin
#sf
hex <- st_read('hex_small/Small_1500.shp')
hex=st_transform(hex,"+proj=longlat +datum=WGS84")
#read in zipcode
zip_sf <- st_read('ZIP_CODE/ZIP_CODE_040114.shp')%>%
st_transform("+proj=longlat +datum=WGS84")
zip_sf$ZIPCODE=as.numeric(as.character(zip_sf$ZIPCODE))
#read in community board
cd_sf <- st_read('Community_Districts/geo_export_a3cf7f07-83ef-460c-bcd8-bee5ef2f297d.shp')%>%
st_transform("+proj=longlat +datum=WGS84")
#read in council district
cc_sf <- st_read('City_Council_Districts/geo_export_2cee7667-624f-4e9d-a7bf-06e9901cf2ee.shp') %>%
st_transform("+proj=longlat +datum=WGS84")
#read in senate district
senate <- st_read('State_Senate_Districts/geo_export_7b5d585c-56e1-46ec-b1c2-ac8f3398a70e.shp') %>%
st_transform("+proj=longlat +datum=WGS84")
#read in assembly district
assembly <-st_read('State_Assembly_Districts/geo_export_743f1700-3b1c-469b-8489-808b1c49e6d3.shp') %>%
st_transform("+proj=longlat +datum=WGS84")
######################################