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visualizations.R
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222 lines (173 loc) · 5.96 KB
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library(tidyverse)
library(patchwork)
library(rgdal)
library(broom)
acs_geo <- read_rds("data/acs_geo.rds")
p1 <- ggplot(acs_geo) +
geom_sf(aes(fill = black_perc)) +
coord_sf() +
scale_fill_viridis_c() +
theme_classic() +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
axis.line = element_blank()) +
labs(fill = "% Black")
p2 <- ggplot(acs_geo) +
geom_sf(aes(fill = hisp_perc)) +
coord_sf() +
scale_fill_viridis_c() +
theme_classic() +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
axis.line = element_blank()) +
labs(fill = "% Hispanic/Latino")
p3 <- ggplot(acs_neighborhoods) +
geom_sf(aes(fill = white_perc)) +
coord_sf() +
scale_fill_viridis_c() +
theme_classic() +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
axis.line = element_blank()) +
labs(fill = "% White")
p4 <- ggplot(acs_neighborhoods) +
geom_sf(aes(fill = asian_perc)) +
coord_sf() +
scale_fill_viridis_c() +
theme_classic() +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
axis.line = element_blank()) +
labs(fill = "% Asian")
(p1+p2) / (p3+p4)
p5<- ggplot(acs_neighborhoods) +
geom_sf(aes(fill = unemp_perc)) +
coord_sf() +
scale_fill_viridis_c() +
theme_classic() +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
axis.line = element_blank()) +
labs(fill = "% Unemployed")
p6 <- ggplot(acs_neighborhoods) +
geom_sf(aes(fill = MEDINC)) +
coord_sf() +
scale_fill_viridis_c() +
theme_classic() +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
axis.line = element_blank()) +
labs(fill = "Median Income")
p7 <- ggplot(acs_neighborhoods) +
geom_sf(aes(fill = avg_hh_size)) +
coord_sf() +
scale_fill_viridis_c() +
theme_classic() +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
axis.line = element_blank()) +
labs(fill = "Average Household Size")
p8 <- ggplot(acs_neighborhoods) +
geom_sf(aes(fill = bach_perc)) +
coord_sf() +
scale_fill_viridis_c() +
theme_classic() +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
axis.line = element_blank()) +
labs(fill = "% with a College Degree")
(p5+p6) / (p7+p8)
p9 <- ggplot(acs_geo, aes(geometry = geometry)) +
geom_sf(aes(fill = station_pop_ratio)) +
coord_sf() +
scale_fill_viridis_c() +
theme_classic() +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
axis.line = element_blank()) +
labs(fill = "# of Divvy Stations \n to Population Ratio")
p10 <- ggplot(acs_geo, aes(geometry = geometry)) +
geom_sf(aes(fill = bike_area)) +
coord_sf() +
scale_fill_viridis_c() +
theme_classic() +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
axis.line = element_blank()) +
labs(fill = "# of Divvy Station's \n to Area Ratio")
p11 <- ggplot(acs_geo, aes(geometry = geometry)) +
geom_sf(aes(fill = not_eng_perc)) +
coord_sf() +
scale_fill_viridis_c() +
theme_classic() +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
axis.line = element_blank()) +
labs(fill = "% That Speak Language \n Other than English at Home")
p12 <- ggplot(acs_geo, aes(geometry = geometry)) +
geom_sf(aes(fill = walk_bike_perc)) +
coord_sf() +
scale_fill_viridis_c() +
theme_classic() +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
axis.line = element_blank()) +
labs(fill = "% That Walk or \n Bike to Work")
(p9 + p10) / (p11 + p12)
#########################################
#Getting road and bike path data ready
bikeRouteData <- readOGR("data/Bike Routes/geo_export_5acdd40a-defd-4e38-bc51-9d35252ce617.shp")
bikeRouteDataFort <- tidy(bikeRouteData)
roadsData <- readOGR("data/tl_2019_17_prisecroads/tl_2019_17_prisecroads.shp")
roadsDataFort <- tidy(roadsData)
roadsDataChi <- roadsDataFort %>%
filter(long > -87.82, lat > 41.64, lat < 42.03)
###############################################
#Visualizations from presentation
#Index map
ggplot() +
geom_sf(data = acs_geo,aes( fill = index, geometry = geometry)) +
coord_sf() +
scale_fill_viridis_c(labels = c("Low", "", "", "","High")) +
annotate("text", x = -87.87, y = 41.79, label = "Marginalization \n and Socioeconomic \n Hardship Index", size = 4) +
theme_classic() +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
axis.line = element_blank(),
legend.title = element_blank(),
aspect.ratio = 1,
legend.position = c(.23,.2))
#Index map with bike routs
ggplot() +
geom_sf(data = acs_geo,aes( fill = index, geometry = geometry)) +
coord_sf() +
scale_fill_viridis_c(labels = c("Low", "", "", "","High")) +
geom_path(data = bikeRouteDataFort,
aes(x = long, y = lat, group = group),
color = "white",
size = .702) +
annotate("text", x = -87.87, y = 41.79, label = "Marginalization \n and Socioeconomic \n Hardship Index", size = 4) +
theme_classic() +
theme(axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_blank(),
axis.line = element_blank(),
legend.title = element_blank(),
aspect.ratio = 1,
legend.position = c(.23,.2))
comm_dens_geo <- read_rds("data/comm_dens_geo")
#Divvy connectivity
ggplot() +
geom_sf(data = comm_dens_geo, aes(fill = avg_in_2_mi_radius, geometry = geometry)) +
coord_sf() +
scale_fill_viridis_c() +
annotate("text", x = -87.87, y = 41.78, label = "Avg # of Stations \n in a 2 mi Radius", size = 4) +
theme_classic() +
theme(axis.ticks = element_blank(),
axis.title = element_blank(),
axis.text = element_blank(),
axis.line = element_blank(),
legend.title = element_blank(),
aspect.ratio = 1,
legend.position = c(.23,.2))