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rev_pitch.R
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rev_pitch.R
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library(rgdal)
library(leaflet)
library(ggplot2)
library(dplyr)
champaign <- read.csv('data/all_cityworks_records.csv', stringsAsFactors = F)
## data preprocessing
champaign$DESCRIPTION <- as.factor(champaign$DESCRIPTION)
champaign$DATETIMECLOSED <- as.Date(champaign$DATETIMECLOSED, "%m/%d/%Y")
champaign$DATETIMEINIT <- as.Date(champaign$DATETIMEINIT, "%m/%d/%Y")
champaign$STATUS <- as.factor(champaign$STATUS)
champaign$PROBADDRESS <- as.factor(toupper(champaign$PROBADDRESS))
## data cleansing
# removing rows with empty values
champaign <- champaign[!(champaign$SRX=="" | champaign$SRY==""), ]
# removing commas from lat/long
champaign$SRX <- gsub(',',"",champaign$SRX)
champaign$SRY <- gsub(',',"",champaign$SRY)
# subsetting data into OPEN & CLOSED STATUSES
champaign_open = subset(champaign, STATUS=='OPEN' | STATUS=='Open')
champaign_closed = subset(champaign, STATUS=='CLOSED')
champaign_closed$TIMESPAN <- as.numeric(champaign_closed$DATETIMECLOSED -
champaign_closed$DATETIMEINIT)
# removing rows where DATETIMECLOSED < DATETIMEINIT
champaign_closed <- champaign_closed[!((champaign_closed$TIMESPAN)<0 |
is.na(champaign_closed$DATETIMECLOSED)),]
champaign_closed_sorted <- champaign_closed[order(-champaign_closed$TIMESPAN),]
champaign_closed_nad83 <- data.frame(long=as.double(champaign_closed$SRX),
lat=as.double(champaign_closed$SRY))
champaign_open_nad83 <- data.frame(long=as.double(champaign_open$SRX),
lat=as.double(champaign_open$SRY))
champaign_closed_sorted_nad83 <- data.frame(long=as.double(champaign_closed_sorted$SRX),
lat=as.double(champaign_closed_sorted$SRY))
# setting properties of the Spatial df
# EPSG:3435 for given .prj file
coordinates(champaign_closed_nad83) <- c('long','lat')
proj4string(champaign_closed_nad83)=CRS("+init=EPSG:3435")
coordinates(champaign_open_nad83) <- c('long','lat')
proj4string(champaign_open_nad83)=CRS("+init=EPSG:3435")
coordinates(champaign_closed_sorted_nad83) <- c('long','lat')
proj4string(champaign_closed_sorted_nad83)=CRS("+init=EPSG:3435")
#transforming from NAD83 Coordinate Ref. System to WGS84 (standard)
champaign_closed_wgs84 <- spTransform(champaign_closed_nad83, CRS("+init=epsg:4326"))
champaign_open_wgs84 <- spTransform(champaign_open_nad83, CRS("+init=epsg:4326"))
champaign_closed_sorted_wgs84 <- spTransform(champaign_closed_sorted_nad83,
CRS("+init=epsg:4326"))
icons_closed <- awesomeIcons(
icon = 'ios-close',
iconColor = 'black',
library = 'ion',
markerColor = "green"
)
icons_open <- awesomeIcons(
icon = 'ios-close',
iconColor = 'black',
library = 'ion',
markerColor = "red"
)
m_closed <- leaflet(champaign_closed_wgs84) %>% addTiles() %>%
addAwesomeMarkers(lng=champaign_closed_wgs84$long, lat=champaign_closed_wgs84$lat,
icon=icons_closed,
label = champaign_closed$DESCRIPTION,
popup = paste("Address:", champaign_closed$PROBADDRESS,
"<br><br>",
"Problem: ", champaign_closed$DESCRIPTION,
"<br><br>",
"Init date: ", champaign_closed$DATETIMEINIT,
"<br><br>",
"Days taken to resolve: ",
champaign_closed$TIMESPAN),
clusterOptions = markerClusterOptions())
m_open <- leaflet(champaign_open_wgs84) %>% addTiles() %>%
addAwesomeMarkers(lng=champaign_open_wgs84$long, lat=champaign_open_wgs84$lat,
icon=icons_open, label = champaign_open$DESCRIPTION,
popup = paste("Address:", champaign_open$PROBADDRESS,
"<br><br>",
"Problem: ", champaign_open$DESCRIPTION,
"<br><br>",
"Init date: ", champaign_open$DATETIMEINIT),
clusterOptions = markerClusterOptions())
# 20 requsts that took the most time to solve
m_most_time <- leaflet(champaign_closed_sorted_wgs84[1:20,]) %>% addTiles() %>%
addAwesomeMarkers(lng=champaign_closed_sorted_wgs84$long[1:20],
lat=champaign_closed_sorted_wgs84$lat[1:20],
icon=icons_closed,
label = champaign_closed_sorted$DESCRIPTION[1:20],
popup = paste("Address: ", champaign_closed_sorted$PROBADDRESS[1:20],
"<br><br>",
"Problem: ", champaign_closed_sorted$DESCRIPTION[1:20],
"<br><br>",
"Init date: ", champaign_closed_sorted$DATETIMEINIT[1:20],
"<br><br>",
"Days taken: ",
champaign_closed_sorted$TIMESPAN[1:20]))
# plot for Top 10 service requests
avg_days_per_issue <- setNames(aggregate(champaign_closed$TIMESPAN,
list(champaign_closed$DESCRIPTION), mean),
c('DESCRIPTION', 'TIMESPAN'))
avg_days_per_issue <- avg_days_per_issue[order(-avg_days_per_issue$TIMESPAN),]
top_service_requests <- ggplot(avg_days_per_issue[1:10,]) +
aes(DESCRIPTION, TIMESPAN) +
geom_col(fill='#FF6666') + coord_flip() +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab(c('Service request')) + ylab(c('Avg days to resolve')) +
ggtitle('Top 10 requests that took the most time to solve')
# plot for Top 10 service addresses
avg_days_per_place <- setNames(aggregate(champaign_closed$TIMESPAN,
list(champaign_closed$PROBADDRESS), mean),
c('PROBADDRESS', 'TIMESPAN'))
avg_days_per_place <- avg_days_per_place[order(-avg_days_per_place$TIMESPAN),]
top_service_places <- ggplot(avg_days_per_place[1:10,]) +
aes(PROBADDRESS, TIMESPAN) +
geom_col(fill='#FF6666') +
theme(axis.text.x = element_text(angle = 90, hjust = 1)) +
xlab(c('Address')) + ylab(c('Avg days to resolve'))
binpal <- colorBin("Reds", champaign_closed_sorted$TIMESPAN, 4, pretty = FALSE,
reverse = TRUE)
w <- leaflet(champaign_closed_sorted_wgs84[1:20,]) %>% addTiles() %>%
addCircles(lng = champaign_closed_sorted_wgs84$long[1:20],
lat = champaign_closed_sorted_wgs84$lat[1:20], weight = 0.5,
radius = sqrt(champaign_closed_sorted$TIMESPAN)*20 ,
color = binpal(champaign_closed_sorted$TIMESPAN),
popup = paste("Days to solve problem:",
champaign_closed_sorted$TIMESPAN[1:20], sep = ' '))