Final
Tara Cartwright
2024-04-26
#Part one Mapping
# Summary: With the Tennessee map, we are shown that estimately there is a higher proportions of renter-occcupied household amongst davidson county
# While outside of davidson there is a lower proportions.
# Installing/loading required packages
if (!require("tidyverse")) install.packages("tidyverse")## 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 3.4.4 ✔ tibble 3.2.1
## ✔ lubridate 1.9.3 ✔ tidyr 1.3.0
## ✔ purrr 1.0.2
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errorsif (!require("tidycensus")) install.packages("tidycensus")## Loading required package: tidycensusif (!require("sf")) install.packages("sf")## Loading required package: sf
## Linking to GEOS 3.11.0, GDAL 3.5.3, PROJ 9.1.0; sf_use_s2() is TRUEif (!require("mapview")) install.packages("mapview")## Loading required package: mapviewif (!require("ggplot2")) install.packages("ggplot2")
if (!require("tidytext")) install.packages("tidytext")## Loading required package: tidytextlibrary(tidyverse)
library(tidycensus)
library(sf)
library(mapview)
library(ggplot2)
library(tidytext)
# API key
census_api_key("916a8fd205d9f1b56f9af332e03df179fdcbb79a")## To install your API key for use in future sessions, run this function with `install = TRUE`.# 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)
All_ACS_Variables <- bind_rows(DetailedTables, ProfileTables)
All_ACS_Variables <- bind_rows(All_ACS_Variables, SubjectTables)
rm (DetailedTables, SubjectTables, ProfileTables)
#Specifiy estimated variable
VariableList =
c(Estimate_ = "DP04_0047P")
#fetched data
mydata <- get_acs(
geography = "county",
state = "TN",
variables = VariableList,
year = 2022,
survey = "acs5",
output = "wide",
geometry = TRUE)## 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##
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|======================================================================| 100%# Reformatting data
mydata <-
separate_wider_delim(mydata,
NAME,
delim = ", ",
names = c("County", "State"))
# Filtering data
mydata <- mydata %>%
filter(County == "Cheatham County"|
County == "Davidson County"|
County == "Robertson County"|
County == "Rutherford County"|
County == "Hickman County"|
County == "Sumner County"|
County == "Williamson County"|
County == "Wilson County")
# Mapping data
mapdata <- mydata %>%
rename(Estimate = Estimate_E, Estimate_MOE = Estimate_M)
mapdata <- st_as_sf(mapdata)
mapviewOptions(basemaps.color.shuffle = FALSE)
mapview(mapdata, zcol = "Estimate",
layer.name = "Estimate",
popup = TRUE)# 2. Statistics
# With the given data from our dataset, it is concluded that by heading
#our data by arranging Impressions (views) in descending sort, we can see out our top ten programs, 7 are Videos
mydata2 <- read.csv("https://raw.githubusercontent.com/drkblake/Data/main/SocialData.csv")
mydata3 <- arrange(mydata2, desc(Impressions))
head(mydata3,10)## ID Type Impressions
## 1 67 Video 1952
## 2 104 Video 1934
## 3 21 Video 1889
## 4 53 Video 1856
## 5 8 Photo 1810
## 6 89 Video 1807
## 7 58 Video 1753
## 8 78 Text 1746
## 9 73 Video 1724
## 10 124 Photo 1714ggplot(mydata3, aes(x=Type, fill=Impressions )) +
geom_bar()+
labs(x = "Type of Programs",
y = "Adverage Impresion")## Warning: The following aesthetics were dropped during statistical transformation: fill
## ℹ This can happen when ggplot fails to infer the correct grouping structure in
## the data.
## ℹ Did you forget to specify a `group` aesthetic or to convert a numerical
## variable into a factor?
# 3 Text analysis
mydata4 <- read.csv("https://raw.githubusercontent.com/drkblake/Data/main/WhiteHouse.csv")
head(mydata4)## Date
## 1 2024-04-20 19:00:00.0
## 2 2024-04-20 15:00:00.0
## 3 2024-04-20 13:05:00.0
## 4 2024-04-19 21:30:01.0
## 5 2024-04-19 19:00:01.0
## 6 2024-04-19 18:08:31.0
## Url Author
## 1 http://twitter.com/WhiteHouse/statuses/1781759741460349255 WhiteHouse
## 2 http://twitter.com/WhiteHouse/statuses/1781699342178332678 WhiteHouse
## 3 http://twitter.com/WhiteHouse/statuses/1781670403099811998 WhiteHouse
## 4 http://twitter.com/WhiteHouse/statuses/1781435108047688124 WhiteHouse
## 5 http://twitter.com/WhiteHouse/statuses/1781397356098838539 WhiteHouse
## 6 http://twitter.com/WhiteHouse/statuses/1781384395871453642 WhiteHouse
## Full.Text
## 1 Our Administration announced a new rule this month clarifying when someone selling guns must get a license and run background checks. This will help keep guns out of the hands of domestic abusers and felons.
## 2 It’s time for Congress to act. https://t.co/KEDm5EQg13
## 3 25 years ago today, 12 students and one teacher were killed at Columbine after two students obtained firearms without background checks. Tom Mauser continues to fight to end gun violence so no other parent knows the pain of losing a child to gun violence. https://t.co/EBb48NbnAb
## 4 President Biden knows that Wall Street didn’t build America – the middle class built America. And unions built the middle class. https://t.co/eZXAMOqdR4
## 5 Raised by a single mom, Tiffany went to college to pursue a future her mom didn’t have. She’s been paying her student loans back since 1994. Now, because of the Biden-Harris Administration’s actions, her loans were forgiven and she has the freedom to achieve her dreams. https://t.co/VjmjgT7r3i
## 6 Tune in for a briefing with Press Secretary Karine Jean-Pierre. https://t.co/dIpmUWPnk3