R Portfolio Final Project
Here is a compilation of the different R code maps I worked on in Fall 2024.
GRAPI by Tennessee House District ARTICLE DESCRIBING
## Loading required package: tidyverse## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr 1.1.4 ✔ readr 2.1.5
## ✔ forcats 1.0.0 ✔ stringr 1.5.1
## ✔ ggplot2 3.5.1 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.1
## ✔ purrr 1.0.2
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## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
## Loading required package: tidycensus
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## Loading required package: sf
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## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUE
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## Loading required package: mapview
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## Loading required package: gtExtras
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## Loading required package: gt
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## To install your API key for use in future sessions, run this function with `install = TRUE`.| Variables | ||
| name | label | concept |
|---|---|---|
| DP04_0136P | Percent!!GROSS RENT AS A PERCENTAGE OF HOUSEHOLD INCOME (GRAPI)!!Occupied units paying rent (excluding units where GRAPI cannot be computed) | Selected Housing Characteristics |
| DP04_0137P | Percent!!GROSS RENT AS A PERCENTAGE OF HOUSEHOLD INCOME (GRAPI)!!Occupied units paying rent (excluding units where GRAPI cannot be computed)!!Less than 15.0 percent | Selected Housing Characteristics |
| DP04_0138P | Percent!!GROSS RENT AS A PERCENTAGE OF HOUSEHOLD INCOME (GRAPI)!!Occupied units paying rent (excluding units where GRAPI cannot be computed)!!15.0 to 19.9 percent | Selected Housing Characteristics |
| DP04_0139P | Percent!!GROSS RENT AS A PERCENTAGE OF HOUSEHOLD INCOME (GRAPI)!!Occupied units paying rent (excluding units where GRAPI cannot be computed)!!20.0 to 24.9 percent | Selected Housing Characteristics |
| DP04_0140P | Percent!!GROSS RENT AS A PERCENTAGE OF HOUSEHOLD INCOME (GRAPI)!!Occupied units paying rent (excluding units where GRAPI cannot be computed)!!25.0 to 29.9 percent | Selected Housing Characteristics |
| DP04_0141P | Percent!!GROSS RENT AS A PERCENTAGE OF HOUSEHOLD INCOME (GRAPI)!!Occupied units paying rent (excluding units where GRAPI cannot be computed)!!30.0 to 34.9 percent | Selected Housing Characteristics |
| DP04_0142P | Percent!!GROSS RENT AS A PERCENTAGE OF HOUSEHOLD INCOME (GRAPI)!!Occupied units paying rent (excluding units where GRAPI cannot be computed)!!35.0 percent or more | Selected Housing Characteristics |
| DP04_0143P | Percent!!GROSS RENT AS A PERCENTAGE OF HOUSEHOLD INCOME (GRAPI)!!Occupied units paying rent (excluding units where GRAPI cannot be computed)!!Not computed | Selected Housing Characteristics |
## Getting data from the 2018-2022 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
## Using the ACS Data Profile## | | | 0% | |== | 3% | |==== | 6% | |======= | 10% | |========= | 13% | |=========== | 16% | |============= | 19% | |================ | 22% | |================== | 25% | |==================== | 29% | |====================== | 32% | |======================== | 35% | |=========================== | 38% | |============================= | 41% | |=============================== | 44% | |================================= | 48% | |=================================== | 50% | |===================================== | 53% | |======================================= | 56% | |========================================== | 59% | |============================================ | 63% | |============================================== | 66% | |================================================ | 69% | |=================================================== | 72% | |===================================================== | 75% | |======================================================= | 79% | |========================================================= | 82% | |=========================================================== | 85% | |============================================================== | 88% | |================================================================ | 91% | |================================================================== | 94% | |==================================================================== | 98% | |======================================================================| 100%| Estimate by district | ||||
| District | Estimate | Estimate_MOE | From | To |
|---|---|---|---|---|
| State House District 13 (2022), Tennessee | 42.9 | 7.7 | 35.2 | 50.6 |
| State House District 37 (2022), Tennessee | 40.3 | 5.5 | 34.8 | 45.8 |
| State House District 34 (2022), Tennessee | 35.9 | 4.8 | 31.1 | 40.7 |
| State House District 49 (2022), Tennessee | 35.7 | 5.6 | 30.1 | 41.3 |
| State House District 48 (2022), Tennessee | 34.4 | 5.9 | 28.5 | 40.3 |
# Installing and loading 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("gtExtras"))
install.packages("gtExtras")
library(tidyverse)
library(tidycensus)
library(sf)
library(mapview)
library(gtExtras)
# Transmitting API key
census_api_key("9924ba1b40725ddbadf37b730bce3faf0d1da566")
# Fetching ACS codebooks
DetailedTables <- load_variables(2022, "acs5", cache = TRUE)
SubjectTables <- load_variables(2022, "acs5/subject", cache = TRUE)
ProfileTables <- load_variables(2022, "acs5/profile", cache = TRUE)
Codebook <- DetailedTables %>%
select(name, label, concept)
Codebook <- bind_rows(Codebook,SubjectTables)
Codebook <- bind_rows(Codebook,ProfileTables)
Codebook <- Codebook %>%
distinct(label, .keep_all = TRUE)
rm(DetailedTables,
SubjectTables,
ProfileTables)
# Filtering the codebook
MyVars <- Codebook %>%
filter(grepl("GRAPI", label) &
grepl("Percent!!", label))
# Making a table of the filtered variables
MyVarsTable <- gt(MyVars) %>%
tab_header("Variables") %>%
cols_align(align = "left") %>%
gt_theme_538
# Displaying the table
MyVarsTable
# Defining the variable to retrieve
VariableList =
c(Estimate_ = "DP04_0142P")
# Fetching data
AllData <- get_acs(
geography = "state legislative district (lower chamber)",
state = "TN",
variables = VariableList,
year = 2022,
survey = "acs5",
output = "wide",
geometry = TRUE
)
# Mutating, selecting and sorting the data
AllData <- AllData %>%
mutate(
District = NAME,
Estimate = Estimate_E,
Estimate_MOE = Estimate_M,
From = round(Estimate - Estimate_MOE, 2),
To = round(Estimate + Estimate_MOE, 2)
) %>%
select(District, Estimate, Estimate_MOE, From, To, geometry) %>%
arrange(desc(Estimate))
# Filtering for Rutherford County districts
MyData <- AllData %>%
filter(
District == "State House District 13 (2022), Tennessee" |
District == "State House District 37 (2022), Tennessee" |
District == "State House District 49 (2022), Tennessee" |
District == "State House District 48 (2022), Tennessee" |
District == "State House District 34 (2022), Tennessee"
)
# Producing a map
MapData <- st_as_sf(MyData)
MyMap <- mapview(MapData,
zcol = "Estimate",
layer.name = "Estimate",
popup = TRUE)
#Displaying the map
MyMap
# Producing a table
TableData <- st_drop_geometry(MapData)
MyTable <- gt(TableData) %>%
tab_header("Estimate by district") %>%
cols_align(align = "left") %>%
gt_theme_538
# Displaying the table
MyTable