options(scipen=999)
library(tidyverse)
library(ggplot2)
library(ggthemes)
library(socviz)
library(maps)
library(mapproj)
library(viridis)
library(tidyverse)
library(tidycensus)
library(leaflet)
library(stringr)Lab 7
# US Cenus data api key
census_api_key("2a77906af0fc956d3f4b78ea7795c1d7cfbcf860", install = TRUE, overwrite = TRUE)[1] "2a77906af0fc956d3f4b78ea7795c1d7cfbcf860"ok_pop <-
get_acs(geography = "county",
variables = "B01003_001",
state = "OK",
geometry = TRUE)
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|======================================================================| 100%head(as_tibble(ok_pop))# A tibble: 6 × 6
GEOID NAME variable estimate moe geometry
<chr> <chr> <chr> <dbl> <dbl> <MULTIPOLYGON [°]>
1 40095 Marshall County, Okla… B01003_… 15494 NA (((-96.96963 34.07102, -…
2 40013 Bryan County, Oklahoma B01003_… 46528 NA (((-96.62975 33.85087, -…
3 40023 Choctaw County, Oklah… B01003_… 14285 NA (((-95.99154 34.1568, -9…
4 40027 Cleveland County, Okl… B01003_… 295060 NA (((-97.67146 35.3469, -9…
5 40147 Washington County, Ok… B01003_… 52579 NA (((-96.00114 36.43976, -…
6 40075 Kiowa County, Oklahoma B01003_… 8446 NA (((-99.40928 35.11693, -…Choropleth Graph of Oklahoma (Using Leaflet)
MapPalette <- colorQuantile(palette = rev("RdYlBu"), domain = ok_pop$estimate, n = 20)
library(sf)
ok_pop %>%
st_transform(crs = "+proj=longlat +datum=WGS84") %>%
leaflet(width = "100%", height = 500) %>%
addProviderTiles(provider = "Esri.WorldPhysical") %>%
addPolygons(popup = ~NAME,
stroke = FALSE,
smoothFactor = 0,
fillOpacity = 0.9,
color = ~ MapPalette(estimate)) %>%
addLegend("bottomright",
pal = MapPalette,
values = ~ estimate,
title = "Population Percentiles",
opacity = 1) Insights: I chose Oklahoma because it is the state I grew up in. The color palette uses red and blue to distinguish between the different population percentiles. I chose this because I felt it was easier to interpret the information for each individual county.
Plot Subway Locations in Oklahoma
library(readxl)
subway <- read_excel("subway.xlsm")
#View(subway)
sbar <- subway %>% filter(state=="OK")
sbar %>% leaflet(width = "100%") %>%
addTiles() %>%
addMarkers(lat = ~latitude,
lng = ~longitude,
popup = sbar$name)Combining Cloropeth and Locations
MapPalette <- colorQuantile(palette = "RdYlBu", domain = ok_pop$estimate, n = 20)
ok_pop %>%
st_transform(crs = "+proj=longlat +datum=WGS84") %>%
leaflet(width = "100%", height = 500) %>%
addProviderTiles(provider = "OpenStreetMap.DE") %>%
addPolygons(popup = ~NAME,
stroke = FALSE,
smoothFactor = 0,
fillOpacity = 0.6,
color = ~ MapPalette(estimate)) %>%
addLegend("bottomright",
pal = MapPalette,
values = ~ estimate,
title = "Population Percentiles",
opacity = 1) %>%
addCircleMarkers(data = sbar,
lat = sbar$latitude,
lng = sbar$longitude,
popup = sbar$name,
weight = 1,
radius=4,
color = "darkblue",
opacity = 1)Insights: This graph overlays the subway locations in Oklahoma by county. There is a high concentration in Oklahoma City and Tulsa, with the other locations being established alongside major highways. The subway locations are marked by a dark blue color, which I felt matched the aesthetic of the graph.