Veil of Darkness

Dummy text

library(lubridate)

## 
## Attaching package: 'lubridate'

## The following object is masked from 'package:base':
## 
##     date

library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:lubridate':
## 
##     intersect, setdiff, union

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

library(maptools)

## Loading required package: sp

## Checking rgeos availability: TRUE

library(stringr)
library(tidyr)
library(ggalt)

## Loading required package: ggplot2

library(ggplot2)
stops_sub <- read.csv("stops_sub.csv")

town_list <- c("East Haven", 
                "Waterbury",
                  "Windsor",
               "Groton City",
               "Branford",
               "Darien",
               "Norwalk",
               "Stonington",
               "New Haven")


for (i in 1:length(town_list)) {
  
  stops_sub2 <- subset(stops_sub, Department.Name==town_list[i])
  stops_sub2$RealTime2 <- ymd_hms(stops_sub2$RealTime2, tz="America/New_York")
  #stops_sub2 <- subset(stops, Department.Name=="Bloomfield")
  
  stops_sub3 <- stops_sub2
  stops_sub3$RealTime2 <- NULL
  stops_sub3$sunrise <- NULL
  stops_sub3$solarnoon <- NULL
  stops_sub3$sunset <- NULL
  
  light_dark_df1 <- stops_sub3 %>%
    group_by(ethnicity, light_dark) %>%
    summarise(count=n()) %>%
    spread(light_dark, count) %>%
    mutate(dark_percent=(round(dark/(dark+light)*100,2)),light_percent=(round(light/(dark+light)*100,2)))
  
  light_dark_df2 <- stops_sub3 %>%
    group_by(ethnicity, light_dark) %>%
    summarise(count=n()) %>%
    spread(ethnicity, count) %>%
    mutate(minority_percent=(round(Minority/(Minority+White)*100,2)),white_percent=(round(White/(Minority+White)*100,2)))
  
  dark_percent <- light_dark_df2[1,4]
  
  light_percent <- light_dark_df2[2,4]
  
  department_name <- town_list[i]
  
  p <- ggplot()
  p <- p + geom_point(data=stops_sub2, aes(x=RealTime2, y=hours_since, colour=ethnicity))
  p <- p + geom_rect(aes(xmin = as.POSIXct(strptime("2016-05-08 17:15", format = "%Y-%m-%d %H:%M")), xmax = as.POSIXct(strptime("2016-05-08 21:15", format = "%Y-%m-%d %H:%M")), ymin = 0, ymax = 4), alpha = 0.1, fill = "grey") 
  p <- p + geom_hline(yintercept = 0)
  p <- p + theme_minimal(base_family="Arial Narrow")
  p <- p + theme(plot.title=element_text(face="bold"))
  p <- p + labs(x="Clock time", y="Hours since darkness", 
                title=paste0(department_name, " traffic stops around sunset"),
                subtitle=paste0("During daylight hours, ", light_percent, "% of stops involved minority drivers; after dark, this figure changed to ", dark_percent, "%. \nThe large diagonal gap is a result of the shift from Eastern Daylight Time to Eastern Standard Time."),
                caption="Traffic Stop Data Report, CCSU Institute for Municipal and Regional Policy")
  print(p)

  light_dark_df3 <- stops_sub3 %>%
  group_by(ethnicity, light_dark) %>%
  summarise(count=n())

  
g <-ggplot(light_dark_df3, aes(x=ethnicity, y=count, fill=light_dark)) +
  geom_bar(stat="identity", position=position_dodge(), colour="black") +
  coord_flip() + 
  labs(x="Traffic stops", y="Ethnicity", 
                      title=paste0(department_name, " traffic stops: Daylight versus night"),
                      subtitle=paste0("During daylight hours, ", light_percent, "% of stops involved minority drivers; after dark, this figure changed to ", dark_percent, "%."),
                      caption="Traffic Stop Data Report, CCSU Institute for Municipal and Regional Policy")

print(g)
  
}