Final Exam
Alison Wich
2024-04-22
Part 1:
The map represents the percentage of households occupied in Davidson County as well as surrounding counties. The highest percentage of rentals occupied is Davidson County with a percentage of 45.8%. The lowest percent of rentals occupied is in Cheatham County with a percentage of 18.8%.
if (!require("tidyverse")) install.packages("tidyverse")## Loading required package: tidyverse## Warning: package 'tidyverse' was built under R version 4.3.3## ── 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 1.3.1
## ✔ 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 3.11.2, GDAL 3.7.2, PROJ 9.3.0; sf_use_s2() is TRUEif (!require("mapview")) install.packages("mapview")## Loading required package: mapview## Warning: package 'mapview' was built under R version 4.3.3library(tidyverse)
library(tidycensus)
library(sf)
library(mapview)
census_api_key("df0faecd0926862cb1236c2095af91dbdec0b3da")## To install your API key for use in future sessions, run this function with `install = TRUE`.DetailedTables <- load_variables(2022, "acs5", cache = TRUE)
SubjectTables <- load_variables(2022, "acs5/subject", cache = TRUE)
ProfileTables <- load_variables(2022, "acs5/profile", cache = TRUE)
ChosenVars <- filter(ProfileTables,name == "DP04_0047P"|
name == "DP02_0001")
print(ChosenVars$name)## [1] "DP02_0001" "DP04_0047P"print(ChosenVars$label)## [1] "Estimate!!HOUSEHOLDS BY TYPE!!Total households"
## [2] "Percent!!HOUSING TENURE!!Occupied housing units!!Renter-occupied"print(ChosenVars$concept)## [1] "Selected Social Characteristics in the United States"
## [2] "Selected Housing Characteristics"VariableList =
c(Rentals_ = "DP04_0047P",
Households_ = "DP02_0001")
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%mydata <-
separate_wider_delim(mydata,
NAME,
delim = ", ",
names = c("County", "State"))
filtereddata <- mydata %>%
filter(County == "Davidson County"|
County == "Rutherford County"|
County == "Williamson County"|
County == "Cheatham County"|
County == "Robertson County"|
County == "Sumner County"|
County == "Wilson County")
ggplot(filtereddata, aes(x = Rentals_E, y = reorder(County, Rentals_E))) +
geom_errorbarh(aes(xmin = Rentals_E - Rentals_M, xmax = Rentals_E + Rentals_M)) +
geom_point(size = 3, color = "darkblue") +
theme_minimal(base_size = 12.5) +
labs(title = "Pct. households with rentals",
subtitle = "Nashville-area counties. Brackets show error margins.",
x = "2018-2022 ACS estimate",
y = "")
mapdata <- filtereddata %>%
rename(Rentals = Rentals_E,
Households = Households_E)
mapdata <- st_as_sf(mapdata)
mapviewOptions(basemaps.color.shuffle = FALSE)
mapview(mapdata, zcol = "Rentals",
layer.name = "Pct. with rentals",
popup = TRUE)CSVdata <- st_drop_geometry(mapdata)
write.csv(CSVdata, "mydata.csv", row.names = FALSE)Part 2:
Each chart shows a representation of the differences between photo, text, and video. The comparison between video and text had a significant difference because it was under 0.05. On the other hand, video and photo stayed close to comparison. Lastly, text and photo had a difference but it wasn’t as significant. After analyzing the results, the videographer does help improve social media engagement.
```{r}if (!require(“tidyverse”))} install.packages(“tidyverse”) library(tidyverse)
mydata <- read.csv(“https://raw.githubusercontent.com/drkblake/Data/main/SocialData.csv”)
mydata\(DV <- mydata\)Impressions mydata\(IV <- mydata\)Type
averages <- group_by(mydata, IV) %>% summarise(mean = mean(DV, na.rm = TRUE)) ggplot(mydata, aes(x = DV)) + geom_histogram() + facet_grid(IV ~ .) + geom_histogram(color = “black”, fill = “#1f78b4”) + geom_vline(data = averages, aes(xintercept = mean, ))
group_by(mydata, IV) %>% summarise( count = n(), mean = mean(DV, na.rm = TRUE), sd = sd(DV, na.rm = TRUE), min = min(DV, na.rm = TRUE), max = max(DV, na.rm = TRUE))
options(scipen = 999) oneway.test(mydata\(DV ~ mydata\)IV, var.equal = FALSE)
anova_1 <- aov(mydata\(DV ~ mydata\)IV) TukeyHSD(anova_1)
### Part 3:
The search terms I chose were election, POTUS, Harris, and vote. I found these terms to be trending and to have a distinct connection due to the upcoming election in November. The overall sum of all terms was 1,218 out of the 5,508 posts on X.
```r
if (!require("tidyverse")) install.packages("tidyverse")
if (!require("tidytext")) install.packages("tidytext")## Loading required package: tidytext## Warning: package 'tidytext' was built under R version 4.3.3library(tidyverse)
library(tidytext)
mydata <- read.csv("https://raw.githubusercontent.com/drkblake/Data/main/WhiteHouse.csv")
tidy_text <- mydata %>%
unnest_tokens(word,Full.Text) %>%
count(word, sort = TRUE)
data("stop_words")
tidy_text <- tidy_text %>%
anti_join(stop_words)## Joining with `by = join_by(word)`my_stopwords <- tibble(word = c("https",
"t.co",
"rt"))
tidy_text <- tidy_text %>%
anti_join(my_stopwords)## Joining with `by = join_by(word)`searchterms <- "Election|POTUS|Harris|Vote"
mydata$Biden <- ifelse(grepl(searchterms,
mydata$Full.Text,
ignore.case = TRUE),1,0)
sum(mydata$Biden)## [1] 1218