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bay_creek_data/bay_creek_data.R

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# ---- load libraries
library(tidyverse)
library(sf)
library(ggmap)
library(scales)
library(ggrepel)
library(ggpattern)
library(RColorBrewer)
# ---- load data
block_data_2022 <- sf::read_sf("data/nip_bg_22/nip_bg_22.shp")
block_metadata_2022 <- read_csv("data/nip_bg_22/nip_metadata_22.csv")
extent_madison <- st_bbox(block_data_2022)
census_data_2022 <- sf::read_sf("data/nip_tr_22/nip_tr_22.shp")
census_metadata_2022 <- read_csv("data/nip_tr_22/nip_metadata_22.csv")
# ---- define areas of interest
block_interest <- read_csv("block_interest.csv", col_types = "cc")
block_interest_data <- block_data_2022 %>%
filter(geo_id %in% block_interest$geo_id) %>%
mutate(center_geom = st_centroid(geometry)) %>%
mutate(lon = st_coordinates(center_geom)[,1],
lat = st_coordinates(center_geom)[,2])
block_interest_data <- left_join(block_interest_data, block_interest, join_by(geo_id)) %>%
select(geo_id, name, lon, lat, baycreek) %>%
mutate(interest = ifelse(baycreek, "baycreek", TRUE))
extent_blocks <- st_bbox(block_interest_data)
block_data_2022 <- left_join(block_data_2022, block_interest_data %>%st_drop_geometry(), join_by(geo_id))
#census_interest <- read_csv("census_interest.csv", col_types = "cc")
# ---- data pivoting
races <- c("pc_wht", "pc_afrm", "pc_asn", "pc_othm", "pc_hisp")
races <- block_metadata_2022 %>% filter(variable %in% races) %>% select(variable, name)
wealth <- c("pc_unem", "pc_fmpv")
wealth <- block_metadata_2022 %>% filter(variable %in% wealth) %>% select(variable, name)
# ---- download basemap
zoom_level <- 13
buffer <- 0.01
extent <- extent_blocks
if(file.exists(paste0("data/basemap_cache/basemap_", zoom_level, ".RData"))){
load(file = paste0("data/basemap_cache/basemap_", zoom_level, ".RData"))
} else {
register_stadiamaps(substr(read_file("data/stadia_api_key.txt"), 1, 36),
write = FALSE)
basemap <- ggmap::get_stadiamap(bbox = c(left = as.double(extent[1]) - buffer,
bottom = as.double(extent[2]) - buffer,
right = as.double(extent[3]) + buffer,
top = as.double(extent[4])) + buffer,
zoom = zoom_level,
maptype = "alidade_smooth",
color = "bw",
force = TRUE)
save(basemap, file = paste0("data/basemap_cache/basemap_", zoom_level, ".RData"))
}
# ---- plot figures
# --- plot maps
ggmap(basemap) +
geom_sf(data = block_data_2022,
fill = NA,
color = "black",
inherit.aes = FALSE,
alpha = 0.6) +
geom_label_repel(data = block_interest_data,
aes(label = paste(name),
y = lat,
x = lon),
min.segment.length = 0.02) +
theme(axis.text.x=element_blank(),
axis.text.y=element_blank(),
axis.ticks=element_blank(),
axis.title.x=element_blank(),
axis.title.y=element_blank()) +
scale_fill_continuous(label = scales::label_dollar(),
type = "viridis") +
labs(title = "Median Income",
fill = NULL)
ggsave(file = "figures/block_map.png",
device = "png",
width = 11,
height = 8.5,
units = "in")
# ---- plot graphs
ggplot(data = block_data_2022 %>%
arrange(medhhinc)) +
geom_hline(data = block_data_2022 %>% filter(geo_id == "Madison"),
aes(yintercept = medhhinc),
linetype = "dashed") +
geom_label(data = block_data_2022 %>% filter(geo_id == "Madison"),
aes(y = medhhinc,
x = 20),
label = "Madison median") +
geom_col(aes(x = reorder(geo_id, medhhinc, sum),
y = medhhinc,
fill = baycreek),
color = "black",
size = 0.01,
position = position_dodge2(padding = 0)) +
geom_label_repel(aes(x = reorder(geo_id, medhhinc, sum),
y = medhhinc,
label = name),
min.segment.length = 0) +
scale_x_discrete(labels = NULL, breaks = NULL) +
scale_y_continuous(label = scales::label_dollar(), expand = expansion(mult = c(0,NA))) +
scale_fill_discrete(guide="none") +
theme(axis.text.x=element_blank(),
axis.title.x=element_blank()) +
labs(title = "Median Income by Block Group",
x = NULL,
y = NULL)
ggsave(file = "figures/median_income.png",
device = "png",
width = 11,
height = 8.5,
units = "in")
ggplot() +
geom_hline(data = block_data_2022 %>% filter(geo_id == "Madison"),
aes(yintercept = medhhinc),
linetype = "dashed") +
geom_boxplot(data = block_data_2022,
aes(x = "Madison",
y = medhhinc),
outlier.shape = NA) +
geom_col(data = block_data_2022 %>% filter(interest %in% c(TRUE, "baycreek")) %>% arrange(medhhinc),
aes(x = reorder(name, medhhinc, sum),
y = medhhinc,
fill = baycreek)) +
scale_y_continuous(label = scales::label_dollar(), expand = expansion(mult = c(0,NA))) +
scale_fill_discrete(guide="none") +
theme(axis.text.x=element_text(angle = 30, vjust = 0.7),
axis.title.x=element_blank()) +
labs(title = "Median Income by Block",
x = NULL,
y = "Median Income")
ggsave(file = "figures/median_income_boxplot.png",
device = "png",
width = 11,
height = 8.5,
units = "in")
ggplot(data = block_data_2022 %>% filter(interest %in% c("baycreek", TRUE))) +
geom_hline(data = block_data_2022 %>% filter(geo_id == "Madison"),
aes(yintercept = medhhinc),
linetype = "dashed") +
geom_label(data = block_data_2022 %>% filter(geo_id == "Madison"),
aes(y = medhhinc,
x = 0.1,
label = "Madison Median")) +
geom_point(data = block_data_2022,
aes(x = pc_wht/100,
y = medhhinc),
size = 2,
alpha = 0.5,
color = "grey") +
geom_point(aes(x = pc_wht/100,
y = medhhinc,
color = interest),
size = 5) +
geom_label_repel(aes(x = pc_wht/100,
y = medhhinc,
label = name),
nudge_y = 5000,
min.segment.length = 0) +
scale_x_continuous(label = scales::label_percent(), expand = expansion(mult = c(0, 0)), limits = c(0, 1)) +
scale_y_continuous(label = scales::label_dollar(), expand = expansion(mult = c(0.1, 0.1))) +
scale_color_discrete(guide="none") +
labs(title = "Median income by racial makup of neighborhood",
x = "Percent of residents that are white",
y = "Median income")
ggsave(file = "figures/income_race.png",
device = "png",
width = 11,
height = 8.5,
units = "in")
ggplot(data = block_data_2022 %>%
filter(interest %in% c("baycreek", TRUE) | geo_id == "Madison") %>%
pivot_longer(cols = races$variable, names_to = "race", values_to = "percent")) +
geom_col(aes(x = ifelse(geo_id == "Madison", "Madison average", name),
y = percent/100,
fill = race),
color = "black") +
scale_y_continuous(label = scales::label_percent(), expand = expansion(mult = c(0, 0))) +
scale_fill_brewer(type = "qual", labels = deframe(races) %>% as.list()) +
theme(axis.text.x=element_text(angle = 30, vjust = 0.7),
axis.title.x=element_blank()) +
labs(title = "Racial makup of neighborhood",
x = NULL,
y = NULL,
fill = "Race")
ggsave(file = "figures/race_percent.png",
device = "png",
width = 11,
height = 8.5,
units = "in")
ggplot(data = block_data_2022 %>%
filter(interest %in% c("baycreek", TRUE) | geo_id == "Madison",
!geo_id %in% c("550250014012", "550250012004"))) +
geom_col(aes(x = ifelse(geo_id == "Madison", "Madison average", name),
y = pc_fmpv/100),
color = "black") +
scale_y_continuous(label = scales::label_percent(), expand = expansion(mult = c(0, 0.1))) +
theme(axis.text.x=element_text(angle = 30, vjust = 0.7),
axis.title.x=element_blank()) +
labs(title = "Families with income below the poverty lines",
x = NULL,
y = NULL,
fill = NULL)
ggsave(file = "figures/neighborhood_poverty.png",
device = "png",
width = 11,
height = 8.5,
units = "in")
ggplot(data = block_data_2022 %>%
st_drop_geometry() %>%
filter(geo_id != "Madison") %>%
mutate(baycreek = ifelse(interest == "baycreek", "Bay Creek", NA)) %>%
mutate(baycreek = ifelse(is.na(baycreek), "The rest of Madison", "Bay Creek")) %>%
group_by(baycreek) %>%
summarise(percentage = sum(tot_pop)/274622)) +
geom_bar(aes(x="", y=percentage, fill=baycreek),
stat="identity",
width=1) +
coord_polar("y", start=0) +
geom_label_repel(aes(x="",
y=percentage,
label = paste(baycreek, round(percentage * 100, 2), "%"),
fill = baycreek),
position = position_stack(vjust=0.5)) +
scale_y_continuous(label = NULL, breaks = NULL) +
scale_fill_brewer(type = "qual", palette = "Pastel1", guide = NULL) +
theme(axis.title.x=element_blank(),
axis.title.y=element_blank()) +
labs(title = NULL,
x = NULL,
y = NULL,
fill = NULL)
ggsave(file = "figures/baycreek_pie.png",
device = "png",
width = 11,
height = 8.5,
units = "in")
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