I was trying to find the price per square feet and used dplyr but I got an output with NA
history <- dbReadTable(con,"NYC_HISTORICAL")
building <- dbReadTable(con,"BUILDING_CLASS")
neighboorhood <- dbReadTable(con,"NEIGHBORHOOD")
combine1 <-
history %>%
left_join (y=building, by=c("BUILDING_CLASS_FINAL_ROLL"="X.BUILDING_CODE_ID"))
filter <-
combine1 %>%
filter(TYPE=="RESIDENTIAL")
combine2<-
history %>%
left_join(y=neighboorhood, by= "NEIGHBORHOOD_ID" )
filter3<-
combine2%>%
filter(NEIGHBORHOOD_NAME=="ASTORIA")%>%
group_by(SALE_DATE)
table5<-
filter3 %>%
select(ZIP_CODE,SALE_PRICE,GROSS_SQUARE_FEET) %>%
group_by (ZIP_CODE)
gross1<-
table5%>%
filter(GROSS_SQUARE_FEET>0)
gross1
gross<-summarise(table5, mean(SALE_PRICE/GROSS_SQUARE_FEET))
gross
1 " 0" NaN
2 "10128" Inf
3 "11001" NaN
4 "11006" Inf
5 "11101" NaN
6 "11102" NA
7 "11103" NA
8 "11104" 73.8
9 "11105" NaN
10 "11106" NA
11 "11361" 257.
12 "11363" NaN
13 "11370" NaN
14 "11375" Inf
15 "11377" Inf
16 "11422" Inf
That's my output, can anyone help me why am I getting NA or NAN, and if you explain what it means?
Thank you so much for your help!