Assignment 2 - JMoya
Judith Moya
2024-10-01
#install.packages("tidycensus")
library(tidycensus)
library(sf)## Linking to GEOS 3.12.1, GDAL 3.8.4, PROJ 9.3.1; sf_use_s2() is TRUElibrary(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, union#----------install the census API key-----------#
#https://api.census.gov/data/key_signup.html
census_api_key("b09b67e4355d371d222c288897dceff62a272ccd",install=) ## To install your API key for use in future sessions, run this function with `install = TRUE`.census_var <- load_variables(2010, 'acs5', cache = TRUE)
ageMale <- get_decennial(geography = "state",
variables = "P013002",
year = 2010)## Getting data from the 2010 decennial Census## Using Census Summary File 1tx <- get_decennial(geography = "zcta", variables = 'P013002', year = 2010,
state = "TX", geometry = TRUE)## Getting data from the 2010 decennial Census## Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.## Using Census Summary File 1## | | | 0% | |= | 1% | |= | 2% | |== | 2% | |== | 3% | |=== | 4% | |=== | 5% | |==== | 5% | |==== | 6% | |===== | 7% | |===== | 8% | |====== | 8% | |====== | 9% | |======== | 11% | |======== | 12% | |========= | 13% | |========== | 14% | |=========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============= | 18% | |============= | 19% | |============== | 19% | |============== | 20% | |=============== | 21% | |=============== | 22% | |================ | 23% | |================= | 24% | |================= | 25% | |================== | 26% | |=================== | 27% | |=================== | 28% | |==================== | 28% | |==================== | 29% | |===================== | 29% | |===================== | 30% | |===================== | 31% | |====================== | 31% | |====================== | 32% | |======================= | 33% | |======================== | 34% | |======================== | 35% | |========================= | 36% | |========================== | 36% | |========================== | 37% | |========================== | 38% | |=========================== | 38% | |=========================== | 39% | |============================ | 40% | |============================ | 41% | |============================= | 41% | |============================= | 42% | |============================== | 42% | |============================== | 43% | |=============================== | 44% | |=============================== | 45% | |================================ | 46% | |================================= | 47% | |================================= | 48% | |================================== | 49% | |=================================== | 50% | |=================================== | 51% | |==================================== | 51% | |==================================== | 52% | |===================================== | 53% | |===================================== | 54% | |====================================== | 54% | |====================================== | 55% | |======================================= | 55% | |======================================= | 56% | |======================================== | 57% | |======================================== | 58% | |========================================= | 58% | |========================================= | 59% | |========================================== | 60% | |=========================================== | 61% | |============================================== | 65% | |================================================ | 69% | |=================================================== | 73% | |====================================================== | 76% | |======================================================== | 80% | |=========================================================== | 84% | |============================================================= | 88% | |================================================================ | 92% | |=================================================================== | 95% | |=================================================================== | 96% | |==================================================================== | 97% | |===================================================================== | 98% | |===================================================================== | 99% | |======================================================================| 99% | |======================================================================| 100%options(repos = c(CRAN = "https://cloud.r-project.org/"))
install.packages("writexl")## Installing package into 'C:/Users/dejmo/AppData/Local/R/win-library/4.4'
## (as 'lib' is unspecified)## package 'writexl' successfully unpacked and MD5 sums checked## Warning: cannot remove prior installation of package 'writexl'## Warning in file.copy(savedcopy, lib, recursive = TRUE): problem copying
## C:\Users\dejmo\AppData\Local\R\win-library\4.4\00LOCK\writexl\libs\x64\writexl.dll
## to C:\Users\dejmo\AppData\Local\R\win-library\4.4\writexl\libs\x64\writexl.dll:
## Permission denied## Warning: restored 'writexl'##
## The downloaded binary packages are in
## C:\Users\dejmo\AppData\Local\Temp\RtmpI3vpb3\downloaded_packageslibrary(writexl)
write_xlsx(x = tx, path = "C:/Users/dejmo/OneDrive/Documents/URP Cert/URP 5363- Planning Methods I/Assignment 2/tx.xlsx", col_names = TRUE)
#----------Use the census API key to get the ACS data-----------#
#https://api.census.gov/data/2020/acs/acs5/variables.html
var <- c("B19013_001E","B03002_012E") #household income, hispanic pop
Bexar_mhi <- get_acs(geography = "county", variables = "B19013_001E",
state = "TX", geometry = TRUE, year=2018)## 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% | |== | 3% | |=== | 4% | |==== | 6% | |===== | 8% | |====== | 9% | |======= | 10% | |======== | 12% | |========= | 13% | |========== | 15% | |=========== | 16% | |============ | 17% | |============ | 18% | |============== | 20% | |=============== | 22% | |================ | 24% | |================== | 26% | |==================== | 28% | |===================== | 30% | |======================= | 32% | |======================== | 35% | |========================== | 37% | |=========================== | 39% | |============================= | 41% | |============================== | 43% | |=============================== | 44% | |================================ | 45% | |================================= | 47% | |================================== | 48% | |=================================== | 50% | |==================================== | 52% | |===================================== | 53% | |====================================== | 55% | |======================================= | 56% | |======================================== | 58% | |=========================================== | 61% | |============================================ | 63% | |============================================= | 64% | |============================================== | 66% | |=============================================== | 67% | |================================================ | 69% | |================================================= | 70% | |================================================== | 72% | |==================================================== | 74% | |===================================================== | 75% | |====================================================== | 78% | |======================================================= | 79% | |========================================================= | 81% | |========================================================== | 82% | |=========================================================== | 84% | |============================================================= | 87% | |============================================================== | 89% | |================================================================ | 91% | |================================================================= | 93% | |=================================================================== | 96% | |===================================================================== | 98% | |======================================================================| 100%texas_HisPop <- get_acs(geography = "county", variables = "B03002_012E",
state = "TX", geometry = TRUE, year = 2018)## 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)`.names(texas_HisPop)## [1] "GEOID" "NAME" "variable" "estimate" "moe" "geometry"names(texas_HisPop)[3] <- "HispanicPop"
library(ggplot2)
df1 <- texas_HisPop[, c(3)]
ggplot(texas_HisPop, aes(x= HispanicPop, y=))+
geom_boxplot()
############multiple variables########################
var <- c(poptotal='B03002_001E',
hispanic='B03002_012E',
white='B03002_003E',
black='B03002_004E',
asian='B03002_006E',
poptotal2='B17017_001E',
poverty='B17017_002E')
#'B03002_012E Estimate!!Total:!!Hispanic or Latino: HISPANIC OR LATINO ORIGIN BY RACE'
#'B03002_003E Estimate!!Total:!!Not Hispanic or Latino:!!White alone HISPANIC OR LATINO ORIGIN BY RACE'
#'B03002_004E Estimate!!Total:!!Not Hispanic or Latino:!!Black or African American alone HISPANIC OR LATINO ORIGIN BY RACE'
#'B03002_006E Estimate!!Total:!!Not Hispanic or Latino:!!Asian alone HISPANIC OR LATINO ORIGIN BY RACE'
#'B17001F_001 Estimate!!Total: POVERTY STATUS IN THE PAST 12 MONTHS BY HOUSEHOLD TYPE BY AGE OF HOUSEHOLDER
#'B17001F_002' Estimate!!Total:!!Income in the past 12 months below poverty level: POVERTY STATUS IN THE PAST 12 MONTHS BY HOUSEHOLD TYPE BY AGE OF HOUSEHOLDER
st <-"TX"
ct <-"Bexar"
cbg <- get_acs(geography = "block group", 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% | |== | 3% | |=== | 5% | |==== | 6% | |===== | 7% | |====== | 8% | |======= | 10% | |======== | 12% | |========= | 13% | |========== | 15% | |=========== | 16% | |============= | 18% | |=============== | 22% | |================== | 25% | |==================== | 29% | |======================= | 33% | |========================= | 36% | |============================ | 40% | |=============================== | 44% | |================================== | 48% | |===================================== | 52% | |======================================= | 55% | |========================================= | 58% | |========================================== | 60% | |============================================ | 63% | |============================================== | 66% | |================================================= | 70% | |==================================================== | 74% | |======================================================= | 78% | |========================================================= | 82% | |============================================================ | 86% | |============================================================== | 89% | |================================================================ | 92% | |================================================================== | 94% | |================================================================== | 95% | |==================================================================== | 97% | |======================================================================| 100%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
###define poverty neighborhood
cbg$Poor <- ifelse(cbg$poverty_pct > 0.3, "Poor", "Nonpoor")
###define minority neighborhood
cbg$Race[cbg$hispanic_pct > 0.5] <- "Hispanic"
cbg$Race[cbg$white_pct > 0.5] <- "White"
cbg$Race[cbg$black_pct > 0.5] <- "Black"
cbg$race_poverty <- paste0(cbg$Poor, cbg$Race)
ggplot(cbg, aes(y=race_poverty, fill=race_poverty )) +
geom_bar()
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