Loading Libraries
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
Loading required package: grid
Loading required package: Matrix
Loading required package: survival
Attaching package: 'survey'
The following object is masked from 'package:graphics':
dotchart
Attaching package: 'srvyr'
The following object is masked from 'package:stats':
filter
Loading in Data
Data was taken from KIDSCOUNT
<- read.csv ("raw_data/Supplemental Nutrition Assistance (SNAP, formerly Food Stamps) recipients (total).csv" )
<- load_variables (2023 , "acs5" , cache = TRUE )
Pulling data from ACS
Getting data from the 2019-2023 5-year ACS
Warning: • You have not set a Census API key. Users without a key are limited to 500
queries per day and may experience performance limitations.
ℹ For best results, get a Census API key at
http://api.census.gov/data/key_signup.html and then supply the key to the
`census_api_key()` function to use it throughout your tidycensus session.
This warning is displayed once per session.
Getting data from the 2019-2023 5-year ACS
Downloading feature geometry from the Census website. To cache shapefiles for use in future sessions, set `options(tigris_use_cache = TRUE)`.
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Selecting Function
<- function (data) {<- data |> #unemployment mutate (snap_married_no_workers = snap_no_workers_married/ total_work_snap_married) |> mutate (no_snap_married_no_workers = no_snap_no_workers_married/ total_work_no_snap_married) |> mutate (snap_male_no_workers = snap_no_workers_male/ total_work_snap_males) |> mutate (snap_female_no_workers = snap_no_workers_female/ total_work_snap_females) |> mutate (no_snap_male_no_workers = no_snap_no_workers_male/ total_no_snap_male) |> mutate (no_snap_female_no_workers = no_snap_no_workers_female/ total_no_snap_female) |> mutate (total_no_work_snap = (snap_no_workers_married + snap_no_workers_male + snap_no_workers_female)/ total_work_snap) |> mutate (total_no_work_no_snap = (no_snap_no_workers_married + no_snap_no_workers_male + no_snap_no_workers_female)/ total_work_no_snap) |> #poverty level mutate (no_snap_above_pov = total_above_pov_no_snap/ total_pov_pop_no_snap) |> mutate (snap_above_pov = total_above_pov_snap/ total_pov_pop_snap) |> #disability mutate (snap_dis_per = snap_dis/ total_snap_dis) |> mutate (no_snap_dis_per = no_snap_dis/ total_no_snap_dis) |> mutate (snap_per = snap_rec/ total_pop)|> mutate (no_snap_per = no_snap/ total_pop) |> #selects median income select (snap_married_no_workers, no_snap_married_no_workers, total_no_work_snap, total_no_work_no_snap, no_snap_above_pov, snap_above_pov, total_med_income, total_med_income_snap, total_med_income_no_snap, snap_male_no_workers, snap_female_no_workers, no_snap_male_no_workers, no_snap_female_no_workers, snap_dis_per, no_snap_dis_per, snap_per, no_snap_per, NAME)return (final_data)
#state <- select_data (snap_dataset_state)<- select_data (snap_dataset_counties) |> mutate (NAME = gsub (", Texas" , "" , NAME)) |> rename (County = NAME)
STATE
<- snap_data_calc_state |> select (snap_per, no_snap_per)<- snap_data_calc_state |> select (total_med_income, total_med_income_snap, total_med_income_no_snap)<- snap_data_calc_state |> select (snap_married_no_workers, no_snap_married_no_workers, snap_male_no_workers, snap_female_no_workers, no_snap_male_no_workers, no_snap_female_no_workers)<- snap_data_calc_state |> select (no_snap_above_pov, snap_above_pov)<- snap_data_calc_state |> select (snap_dis_per, no_snap_dis_per)
write.csv (file = "snap_recipients_state.csv" , snap_rec_state, row.names = FALSE )write.csv (file = "snap_med_income_state.csv" , med_income_state, row.names = FALSE )write.csv (file = "snap_workers_state.csv" , snap_workers_state, row.names = FALSE )write.csv (file = "snap_poverty_state.csv" , poverty_state, row.names = FALSE )write.csv (file = "snap_dis_state.csv" , disability_state, row.names = FALSE )
# A tibble: 1 × 2
snap_per no_snap_per
<dbl> <dbl>
1 0.261 0.739
# A tibble: 1 × 2
no_snap_above_pov snap_above_pov
<dbl> <dbl>
1 0.906 0.568
# A tibble: 1 × 2
snap_dis_per no_snap_dis_per
<dbl> <dbl>
1 0.435 0.230
# A tibble: 1 × 3
total_med_income total_med_income_snap total_med_income_no_snap
<dbl> <dbl> <dbl>
1 76292 32969 83035
|> select (contains ("snap_" ))
# A tibble: 1 × 6
snap_married_no_workers no_snap_married_no_workers snap_male_no_workers
<dbl> <dbl> <dbl>
1 0.113 0.120 0.164
# ℹ 3 more variables: snap_female_no_workers <dbl>,
# no_snap_male_no_workers <dbl>, no_snap_female_no_workers <dbl>
|> select (contains ("no_snap_" ))
# A tibble: 1 × 3
no_snap_married_no_workers no_snap_male_no_workers no_snap_female_no_workers
<dbl> <dbl> <dbl>
1 0.120 0.0661 0.0972
COUNTIES
<- snap_data_calc_counties |> select (County, snap_per, no_snap_per) |> mutate (selection = "recipients" )<- snap_data_calc_counties |> select (County,total_med_income, total_med_income_snap, total_med_income_no_snap) |> mutate (selection = "median_income" )<- snap_data_calc_counties |> select (County,snap_married_no_workers, no_snap_married_no_workers, snap_male_no_workers, snap_female_no_workers, no_snap_male_no_workers, no_snap_female_no_workers) |> mutate (selection = "workers" )<- snap_data_calc_counties |> select (County,no_snap_above_pov, snap_above_pov) |> mutate (selection = "poverty" )<- snap_data_calc_counties |> select (County,snap_dis_per, no_snap_dis_per) |> mutate (selection = "disability" )
write.csv (file = "counties/SNAP_Recipients_Counties.csv" , snap_rec_counties, row.names = FALSE )write.csv (file = "counties/SNAP_Recipeints_Counties.csv" , med_income_state, row.names = FALSE )write.csv (file = "counties/SNAP_Recipeints_Counties.csv" , snap_workers_state, row.names = FALSE )write.csv (file = "counties/SNAP_Recipeints_Counties.csv" , poverty_state, row.names = FALSE )write.csv (file = "counties/SNAP_Disability_Counties.csv" , disability_state, row.names = FALSE )