andrewbtran

# this uses the R library Lubridate
# if you don't have that installed, uncomment and run the line below
# install.packages("lubridate")

library(lubridate)

# formats month-day-year format
# October 1, 1980 or 10-1-1980 or 10/1/1980, etc
# example - format_mdy("10/1/1980")

andrewbtran

# Filter out the tracts with NA in the total column

total_map <- subset(total_map, !is.na(total))

tm_ct <- ggplot() +
  geom_polygon(data = total_map, aes(x=long, y=lat, group=group, fill=total), color = "black", size=0.2) +
  coord_map() +
  scale_fill_distiller(type="seq", trans="reverse", palette = "Reds", breaks=pretty_breaks(n=10)) +
  theme_nothing(legend=TRUE) +
  labs(title="Where Hamden police conduct traffic stops", fill="")

andrewbtran

pm_ct <- ggplot() 
pm_ct <- pm_ct + geom_polygon(data = mapping_disparity, aes(x=long, y=lat, group=group, fill=min_disp/100), color="white", size=.25)
pm_ct <- pm_ct + geom_polygon(data = town_borders, aes(x=long, y=lat, group=group), fill=NA, color = "black", size=0.5)
pm_ct <- pm_ct + coord_map() 
pm_ct <- pm_ct + scale_fill_distiller(type="seq", trans="reverse", palette = "PuOr", label=percent, breaks=pretty_breaks(n=10), name="Gap") 
pm_ct <- pm_ct + theme_nothing(legend=TRUE) 
pm_ct <- pm_ct + labs(x=NULL, y=NULL, title="Hamden: Minority traffic stops versus population")
pm_ct <- pm_ct + theme(text = element_text(size=15))
pm_ct <- pm_ct + theme(plot.title=element_text(face="bold", hjust=.4))
pm_ct <- pm_ct + theme(plot.subtitle=element_text(face="italic", size=9, margin=margin(l=20)))

andrewbtran

mapping_disparity <- left_join(towntracts, joined_tracts)
mapping_disparity <- subset(mapping_disparity, !is.na(min_disp))

# A library for color scales

pm_ct <- ggplot() 
pm_ct <- pm_ct + geom_polygon(data = mapping_disparity, aes(x=long, y=lat, group=group, fill=min_disp/100), color="white", size=.25)
pm_ct <- pm_ct + geom_polygon(data = town_borders, aes(x=long, y=lat, group=group), fill=NA, color = "black", size=0.5)
pm_ct <- pm_ct + coord_map() 
pm_ct <- pm_ct + scale_fill_distiller(type="seq", trans="reverse", palette = "PuOr", label=percent, breaks=pretty_breaks(n=10), name="Gap") 

andrewbtran

# Determining the minority population by subtracting the white population from the total
race_tracts$minority_pop <- race_tracts$total_pop - race_tracts$white_pop

# Now figuring out the percent makeup of each census tract
race_tracts$white_pop_p <- round(race_tracts$white_pop/race_tracts$total_pop*100,2)
race_tracts$minority_pop_p <- round(race_tracts$minority_pop/race_tracts$total_pop*100,2)

kable(head(race_tracts,5))
# Joining the two datframes
joined_tracts <- left_join(joined_tracts, race_tracts)

andrewbtran

# If you do not yet have the censusapi package installed, uncomment the lines below and run them.

#install.packages("devtools")
#devtools::install_github("hrecht/censusapi")
library("censusapi")

# Loading my census key from an external script
source("keys.R")

# Replace census_key below with "your_own_key_whatever_it_is"

andrewbtran

coords <- subset(stops, ethnicity=="Minority")
coords <- coords[c("InterventionLocationLongitude", "InterventionLocationLatitude")]
coords <- coords[complete.cases(coords),]
sp <- SpatialPoints(coords)
proj4string(sp) <- "+proj=longlat +datum=WGS84 +no_defs +ellps=WGS84 +towgs84=0,0,0"
proj4string(sp)

m_tract <- over(sp, towntracts_only)

m_tract <- m_tract %>%

andrewbtran

townborders <- readOGR(dsn="shapes", layer="ctgeo")
townborders_only <- townborders
townborders<- fortify(townborders, region="NAME10")

# Subset the town borders to just Hamden since that's the department we're looking at
town_borders <- subset(townborders, id=="Hamden")

tm_ct <- ggplot() +
  geom_polygon(data = total_map, aes(x=long, y=lat, group=group, fill=total), color = "black", size=0.2) +
  geom_polygon(data = town_borders, aes(x=long, y=lat, group=group, fill=total), color = "black", fill=NA, size=0.5) +

andrewbtran

# Join the by_tract points to polygon dataframe to the original census tracts dataframe
total_map <- left_join(towntracts, by_tract)

require(ggmap)
require(scales)

tm_ct <- ggplot() +
  geom_polygon(data = total_map, aes(x=long, y=lat, group=group, fill=total), color = "black", size=0.2) +
  geom_polygon(data = total_map, aes(x=long, y=lat, group=group, fill=total), color = "black", size=0.2) +
  coord_map() +

andrewbtran

# Calculating points in a polygon

by_tract <- over(sp, towntracts_only)

# What just happened: Every point in the list now has a corresponding census tract 
kable(head(by_tract, 5))

# Use dplyr to gather up and count how many instances of census tracts there are

require(dplyr)