Assignment 2
Elliott Coleman
2024-10-1
#1 Using your census API key, capture the median age of males (P013002) for all census tracts from the 2010 decennial census database in Texas.
library(tidycensus)
library(ggplot2)
census_api_key("be13863f38ebdc792d7bf1fdbd7f8089b2a708f2",install="TRUE", overwrite = TRUE)## Your original .Renviron will be backed up and stored in your R HOME directory if needed.## Your API key has been stored in your .Renviron and can be accessed by Sys.getenv("CENSUS_API_KEY").
## To use now, restart R or run `readRenviron("~/.Renviron")`## [1] "be13863f38ebdc792d7bf1fdbd7f8089b2a708f2"Texas_medage <- get_decennial(geography = "tract", variables = "P013002", year = 2010, state = "TX")## Getting data from the 2010 decennial Census## Using Census Summary File 1#2 Export the data collected in step 2 to a .xlsx file.
library(writexl)
write_xlsx(x = Texas_medage, path = "C:/Users/Elliott/Documents/URP_Methods1/Texas_medage.xlsx", col_names = TRUE)#3 Using your census API key, capture the median household income (B19013_001E) for all census tracts in Bexar County, TX, from the 2018 ACS database in Texas.
Bexar_medinc<- get_acs(geography = "tract", variables = "B19013_001E",year = 2018,
state = "TX",output="wide", geometry = FALSE)## Getting data from the 2014-2018 5-year ACS#4 Using your census API key, capture the Hispanic population (B03002_012E) for all counties in Texas from the 2018 ACS database.
HispPop<- get_acs(geography = "county", variables = "B03002_012E",year = 2018,
state = "TX",output="wide", geometry = FALSE)## Getting data from the 2014-2018 5-year ACS#5 Following step 5, rename the column of ‘estimate’ to ‘HispanicPop', and show all the column names of the dataframe.
colnames(HispPop)[3] ="HispanicPop"
colnames(HispPop)## [1] "GEOID" "NAME" "HispanicPop" "B03002_012M"#6 Make a boxplot to show the distribution of Hispanic population across TX counties.
ggplot(HispPop, aes(y=HispanicPop)) +
geom_boxplot()
#7 Using your census API key, capture the population in poverty, for all census tracts in Bexar County, TX, from the 2018 ACS database in Texas.
Bexar_pov <- get_acs(geography = "tract", variables = "B17017_002E",
state = "TX", county = "Bexar", 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)`.## | | | 0% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |== | 4% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |======= | 11% | |======== | 11% | |======== | 12% | |========= | 12% | |========= | 13% | |========== | 14% | |========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |============== | 21% | |=============== | 21% | |=============== | 22% | |================ | 22% | |================ | 23% | |================ | 24% | |================= | 24% | |================= | 25% | |================== | 25% | |================== | 26% | |=================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | |====================== | 31% | |====================== | 32% | |======================= | 32% | |======================= | 33% | |======================== | 34% | |======================== | 35% | |========================= | 35% | |========================= | 36% | |========================== | 37% | |========================== | 38% | |=========================== | 38% | |=========================== | 39% | |============================ | 39% | |============================ | 40% | |============================= | 41% | |============================= | 42% | |============================== | 42% | |============================== | 43% | |=============================== | 44% | |=============================== | 45% | |================================ | 45% | |================================ | 46% | |================================= | 47% | |================================== | 48% | |================================== | 49% | |=================================== | 50% | |==================================== | 51% | |==================================== | 52% | |===================================== | 52% | |===================================== | 53% | |====================================== | 54% | |====================================== | 55% | |======================================= | 55% | |======================================= | 56% | |======================================== | 57% | |======================================== | 58% | |========================================= | 58% | |========================================= | 59% | |========================================== | 60% | |========================================== | 61% | |=========================================== | 61% | |=========================================== | 62% | |============================================ | 63% | |============================================= | 64% | |============================================= | 65% | |============================================== | 65% | |============================================== | 66% | |=============================================== | 67% | |=============================================== | 68% | |================================================ | 68% | |================================================ | 69% | |======================================================================| 100%#8 Using your census API key, capture the Hispanic population, White population, and Black Population, for all census tracts in Bexar County, TX, from the 2018 ACS database in Texas.
var <- c(poptotal='B03002_001E',hispanic='B03002_012E',white='B03002_003E',black='B03002_004E',poptotal2='B17017_001E',poverty='B17017_002E')
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)`.## | | | 0% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |============================== | 43% | |================================== | 49% | |=================================== | 50% | |=================================== | 51% | |==================================== | 51% | |==================================== | 52% | |===================================== | 52% | |===================================== | 53% | |====================================== | 54% | |====================================== | 55% | |======================================= | 55% | |======================================= | 56% | |======================================== | 57% | |========================================= | 58% | |========================================= | 59% | |========================================== | 59% | |========================================== | 60% | |=========================================== | 61% | |=========================================== | 62% | |============================================ | 62% | |============================================ | 63% | |============================================= | 64% | |============================================= | 65% | |============================================== | 65% | |============================================== | 66% | |=============================================== | 67% | |=============================================== | 68% | |================================================ | 68% | |================================================ | 69% | |================================================= | 70% | |================================================= | 71% | |================================================== | 71% | |================================================== | 72% | |=================================================== | 73% | |==================================================== | 74% | |==================================================== | 75% | |===================================================== | 75% | |===================================================== | 76% | |====================================================== | 77% | |====================================================== | 78% | |======================================================= | 78% | |======================================================= | 79% | |======================================================== | 79% | |======================================================== | 80% | |========================================================= | 81% | |========================================================= | 82% | |========================================================== | 82% | |========================================================== | 83% | |=========================================================== | 84% | |=========================================================== | 85% | |============================================================ | 85% | |============================================================ | 86% | |============================================================= | 87% | |============================================================= | 88% | |============================================================== | 88% | |============================================================== | 89% | |=============================================================== | 90% | |================================================================ | 91% | |================================================================ | 92% | |================================================================= | 92% | |================================================================= | 93% | |================================================================= | 94% | |================================================================== | 94% | |================================================================== | 95% | |=================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |==================================================================== | 98% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%#9 Create a new column that combines poverty and race/ethnicity categories.
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$Race <- "Other" # Default value
cbg$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$Poor, " ", cbg$Race)#10 Following step 9, make a bar plot to show the number of different categories.
ggplot(cbg, aes(y=race_poverty, fill=race_poverty )) +
geom_bar()