Gorre Practical R

Code:

# ============================================================
# 0. INSTALL AND LOAD REQUIRED PACKAGES
# ============================================================

if (!require("tidyverse"))
  install.packages("tidyverse")
if (!require("tidycensus"))
  install.packages("tidycensus")
if (!require("sf"))
  install.packages("sf")
if (!require("mapview"))
  install.packages("mapview")
if (!require("leaflet"))
  install.packages("leaflet")
if (!require("leaflet.extras2"))
  install.packages("leaflet.extras2")
if (!require("gt"))
  install.packages("gt")
if (!require("gtExtras"))
  install.packages("gtExtras")
if (!require("plotly"))
  install.packages("plotly")

library(tidyverse)
library(tidycensus)
library(sf)
library(mapview)
library(leaflet)
library(leafpop)
library(gt)
library(gtExtras)
library(plotly)

# ============================================================
# 1. CENSUS API KEY. 

# NOTE: Replace PasteYourAPIKeyBetweenTheseQuoteMarks with your
# actual API key. If you don't the script won't work.

# ============================================================

census_api_key("f0e62cb9d2363c8c30463f42b91cfbabad1f4fb3")

# ============================================================
# 2. LOAD ACS CODEBOOKS
# ============================================================

DetailedTables <- load_variables(2023, "acs5", cache = TRUE)
SubjectTables  <- load_variables(2023, "acs5/subject", cache = TRUE)
ProfileTables  <- load_variables(2023, "acs5/profile", cache = TRUE)

# ============================================================
# 3. DEFINE VARIABLES OF INTEREST
# ============================================================

VariableList =
  c(
    Count_ = "DP02_0059",
    Percent_ = "DP02_0068P"
  )

# ============================================================
# 4. FETCH COUNTY SUBDIVISION DATA (TENNESSEE)
# ============================================================

mydata <- get_acs(
  geography = "county subdivision",
  state = "TN",
  variables = VariableList,
  year = 2023,
  survey = "acs5",
  output = "wide",
  geometry = TRUE
)

# ============================================================
# 5. CLEAN AND REFORMAT GEOGRAPHIC NAMES
# ============================================================

mydata <- mydata %>%
  separate_wider_delim(
    NAME,
    delim  = ", ",
    names  = c("Division", "County", "State")
  ) %>%
  mutate(County = str_remove(County, " County"))

# ============================================================
# 6. FILTER FOR A SINGLE COUNTY
# ============================================================

filtereddata <- mydata %>%
  filter(County %in% c("Rutherford"))

# ============================================================
# 7. MAP DATA FOR SINGLE COUNTY SUBDIVISIONS
# ============================================================

mapdata <- filtereddata %>%
  rename(
    Percent = Percent_E,
    PercentEM = Percent_M,
    Count = Count_E,
    CountEM = Count_M
  ) %>%
  st_as_sf()

mapviewOptions(basemaps.color.shuffle = FALSE)

DivisionMap <- mapview(
  mapdata,
  zcol = "Percent",
  layer.name = "Percent",
  popup = popupTable(
    mapdata,
    feature.id  = FALSE,
    row.numbers = FALSE,
    zcol = c("State", "County", "Division", "Percent",
             "PercentEM", "Count", "CountEM")
  )
)

DivisionMap

# ============================================================
# 8. INTERACTIVE PLOTLY GRAPH OF ESTIMATES WITH ERROR BARS
# ============================================================

mygraph <- plot_ly(
  data = filtereddata,
  x = ~Percent_E,
  y = ~reorder(Division, Percent_E),
  type = 'scatter',
  mode = 'markers',
  error_x = list(
    type = "data",
    array = ~Percent_M,
    visible = TRUE
  ),
  marker = list(color = "#099d91", size = 10)
) %>%
  layout(
    title = "Estimates by Area (Interactive)",
    xaxis = list(title = "2019–2023 ACS Estimate"),
    yaxis = list(title = "", automargin = TRUE)
  )

mygraph