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53 lines
1.9 KiB
53 lines
1.9 KiB
## Creates single file containing educational attainment variables at the
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## 2010-2014 through 2013-2017 American Community Surveys for tracts
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## in the DC Area
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## tot25o = Total population 25 and older
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## lh = Less than high school
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## hs = High school or GED
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## sc = Some college
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## aa = Associate's degree
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## ba = Bachelor's degree
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## gr = Graduate degree
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## Set up environment
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library(tidyverse)
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## Identify variable abbreviations
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attain <- c('tot25o', 'lh', 'hs', 'sc', 'aa', 'ba', 'gr')
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pattain <- paste0('p',attain[-1])
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attainvars <- c(attain, pattain)
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geovars <- c('GISJOIN', 'STATE', 'COUNTY')
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## Set Functions
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source('dcarea_functions.R')
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## Create dataset that contains only DC-area tracts and constructed
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## educational attainment variables
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select.attainvars <- function(dta) {
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dta <- select.dcarea(dta)
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dta$GISJOIN <- as.character(dta$GISJOIN)
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return(dta[, c(geovars, attainvars)])
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}
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## Load datasets containing educational attainment variables from files based on
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## 2010 tracts
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load('../US/tracts/2010/tabular/educ-attainment/dataset/tracts-2010TIGER-educ-attainment.Rdata')
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## Create list of data frames for each year and restrict to created
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## educational attainmnet variables
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dtas <- list(trt14_ed, trt15_ed, trt16_ed, trt17_ed)
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attaindtas <- lapply(dtas, select.attainvars)
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## Create single wide data frame with year appended to variable name
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namelist <- lapply(14:17,
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function(x) c(geovars, paste0(attainvars, x)))
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attaindta <- mapply(setNames, attaindtas, namelist, SIMPLIFY = FALSE) %>%
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reduce(left_join, by='GISJOIN')
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attaindta$STATE <- attaindta$STATE.x
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attaindta$COUNTY <- attaindta$COUNTY.x
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attaindta <- attaindta[, -(grep('^STATE.+|^COUNTY.+', names(attaindta), perl=TRUE))]
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## Write file containing wide dataset to CSV format
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write.csv(attaindta, 'tracts/2010/tabular/educ-attainment/dataset/tracts-2010TIGER-educ-attainment.csv')
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