adding readme and code

README.md

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+# permit_parking_violations

permit_parking_violations.R

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+library(sf)
+library(leaflet)
+library(tidyverse)
+library(stringr)
+library(ggplot2)
+library(DescTools)
+library(chron)
+library(data.table)
+
+
+#Read in Illegal Parking dataset
+
+parking <- read.csv("~/Documents/parking_permit_violations/illegal_parking.csv", stringsAsFactors = FALSE)
+parking <- parking[,-1]
+
+as.data.frame(table(parking$Descriptor))
+as.data.frame(table(parking[- grep("bike", parking$Descriptor),][Descriptor]))
+
+
+
+#create geometry for parking data
+
+parking <- parking[!is.na(parking$Latitude),] %>% st_as_sf(coords = c("Longitude", "Latitude"), crs = "+proj=longlat +datum=WGS84")
+
+#subset data to only include permit parking and placard complaints
+permits <- parking[grep("Permit", parking$Descriptor),]
+  #parking[parking$Descriptor  "Permit", ]
+permits$Created.Date <- as.chron(permits$Created.Date, format = "%m/%d/%Y %I:%M:%S %p")
+permits$Closed.Date <- as.chron(permits$Closed.Date, format = "%m/%d/%Y %I:%M:%S %p")
+
+permits_subset <- with(permits, permits[Created.Date < "08/06/2018", ])
+
+
+placards <- read.csv('~/Documents/parking_permit_violations/placards.csv', stringsAsFactors = FALSE)
+placards <- placards[!is.na(placards$Latitude),] %>% st_as_sf(coords = c("Longitude", "Latitude"), crs = "+proj=longlat +datum=WGS84")
+placards$Created.Date <- as.chron(placards$Created.Date, format = "%m/%d/%Y %I:%M:%S %p")
+placards$res_desc <- rep('Ambiguous',nrow(placards))
+
+permits$res_desc <- gsub('string', 'Other', permits$res_desc)
+permits$res_desc <- gsub('other', 'Other', permits$res_desc)
+permits$res_desc <- gsub('no_action_taken', 'No Action Taken', permits$res_desc)
+permits$res_desc <- gsub('fixed', 'Fixed', permits$res_desc)
+permits$res_desc <- gsub('ambiguous', 'Ambiguous', permits$res_desc)
+permits$res_desc <- gsub('wrong_agency', 'Wrong Agency', permits$res_desc)
+permits$res_desc <- gsub('did_not_observe', 'Did Not Observe', permits$res_desc)
+permits$res_desc <- gsub('ongoing', 'Ongoing', permits$res_desc)
+
+
+
+
+
+
+binded <- rbind(permits, placards)
+binded$complaint <- ifelse(binded$Complaint.Type == 'City Vehicle Placard Complaint', 'Placard Complaint', 'Permit Parking Complaint')
+binded$opened <- as.Date(binded$Created.Date)
+aggbind <- aggregate(x = binded$complaint,
+                     FUN = count,
+                     by = list(Group.date = binded$opened))
+setDT(binded)
+agg <- binded[,.N,by=c('complaint', 'opened')]
+
+
+
+
+tmp <- subset(permits, res_desc == "ambiguous")
+
+pal <- colorFactor(c('red','orange','yellow','blue', 'green','violet','maroon','black', 'brown'),
+                   domain = c("Did Not Observe", "No Action Taken", 'Ongoing', 'violations_issued', 'Fixed', 'Ambiguous', 'Wrong Agency', 'duplicate', 'Other'))
+groups = as.character(unique(permits$res_desc))
+
+
+map = leaflet(permits) %>%
+  addProviderTiles("CartoDB.Positron") %>% 
+  addLegend("topleft", values = ~res_desc, pal = pal,
+            #labels = c("Did Not Observe", "No Action Taken", 'Ongoing', 'Violations Issued', 'Fixed', 'Ambiguous', 'Wrong Agency', 'Duplicate', 'Other'),
+            title = "Resolution Categorization",
+            opacity = .7)
+for(g in groups){
+  d = permits[permits$res_desc == g, ]
+  map = map %>% addCircleMarkers(data = d,color = ~pal(res_desc),group = g,
+                                 fill = TRUE, fillOpacity = .45, stroke = FALSE, radius = 1.5)
+}
+map %>% addLayersControl(overlayGroups = groups, options = layersControlOptions(collapsed = FALSE))
+
+
+ggplot(agg, aes(x = opened, y = N)) + 
+  geom_line(aes(color = complaint), size = 1) +
+  scale_color_manual(values = c("#00AFBB", "#E7B800")) +
+  theme_minimal()
+
+
+
+# determining average close time
+
+permits$res_time_hours <- difftime(permits$Closed.Date,permits$Created.Date , units = c("days"))
+
+  permits$Closed.Date-permits$Closed.Date
+
+
+
+difftime(permits$Created.Date ,permits$Closed.Date , units = c("days"))
+
+
+#see number of complaints in 2018 by first creating a year column in the dataset
+permits$year <- as.numeric(format(permits$Created.Date,'%Y'))
+nrow(permits[permits$year==2018])
+tmp <- permits %>% filter(year==2018)
+nrow(tmp)
+
+# see top 10 resolution descriptions
+sort(table(permits$Resolution.Description), decreasing = TRUE)[1:10]
+
+
+tmp2 <- as.data.frame(table(tmp$Resolution.Description))
+
+write.csv(tmp2, file = 'placards_2018_resolutions.csv')    
+
+
+     \