Assignment 2
Omar
2024-10-01
library(tidycensus)
library(ggplot2)
library(writexl)
census_api_key("0d539976d5203a96fa55bbf4421110d4b3db3648", overwrite=TRUE)## To install your API key for use in future sessions, run this function with `install = TRUE`.census_var <- load_variables(2010, 'acs5', cache = TRUE)
#Q1
median_age_of_males_TX <- get_decennial(geography = "tract", variables = 'P013002', year = 2010,
state = "TX", geometry = TRUE)## Getting data from the 2010 decennial Census## Downloading feature geometry from the Census website. 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write_xlsx(median_age_of_males_TX, path = "C:/Users/عمر/Desktop/Fall 2024/Rstudio/Week 6/median_age_of_males_TX.xlsx", col_names = TRUE)
#Q3
median_household_income <- get_acs(geography = "tract", variables = "B19013_001E",
state = "TX", county = "Bexar", year=2018, geometry = TRUE)## Getting data from the 2014-2018 5-year ACS## Downloading feature geometry from the Census website. 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hispanic_population<- get_acs(geography = "county", variables = "B03002_012E",
state = "TX", year=2018, geometry = TRUE)## Getting data from the 2014-2018 5-year ACS
## Downloading feature geometry from the Census website. 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|======================================================================| 99% | |======================================================================| 100%#Q5
names(hispanic_population)[4] <- "HispanicPop"
#Q6
ggplot(hispanic_population, aes(y=HispanicPop)) +
geom_boxplot()
#Q7
poverty_TX <- get_acs(geography = "tract", variables = "B17001_002E",
state = "TX", county = "Bexar", year=2018, geometry = TRUE)## Getting data from the 2014-2018 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.#Q8
var <- c(poptotal='B03002_001E',
hispanic='B03002_012E',
white='B03002_003E',
black='B03002_004E',
asian='B03002_006E',
poptotal2='B17017_001E',
poverty='B17017_002E')
race_bexar_county <- get_acs(geography = "tract",
variables = c(hispanic = "B03002_012E",
white = "B03002_003E",
black = "B03002_004E"),
state = "TX",
county = "Bexar",
year = 2018)## Getting data from the 2014-2018 5-year ACS#Q9
st <-"TX"
ct <-"Bexar"
cbg <- get_acs(geography = "tract", variables = var, count=ct,
state = st,output="wide", year = 2018, geometry = TRUE)## Getting data from the 2014-2018 5-year ACS
## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.cbg$black_pct <- cbg$black / cbg$poptotal
cbg$white_pct <- cbg$white / cbg$poptotal
cbg$hispanic_pct <- cbg$hispanic / cbg$poptotal
cbg$poverty_pct <- cbg$poverty / cbg$poptotal2
cbg$Poor <- ifelse(cbg$poverty_pct > 0.3, "Poor", "Nonpoor")
cbg$Race <- "Other"
cbg$Race[cbg$white_pct > 0.5] <- "White"
cbg$Race[cbg$black_pct > 0.5] <- "Black"
cbg$Race[cbg$hispanic_pct > 0.5] <- "Hispanic"
cbg$race_poverty <- paste0(cbg$Poor, " and ", cbg$Race)
#Q10
ggplot(cbg, aes(x = race_poverty, fill = race_poverty)) +
geom_bar() 