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52 lines
1.7 KiB
52 lines
1.7 KiB
## Creates single file containing median 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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## Set up environment
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library(tidyverse)
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## Identify marital status variable names
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statuses <- c('mar', 'nvm', 'wid', 'div')
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pstatuses <- paste0('p', statuses)
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statusvars <- c(statuses, pstatuses)
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## Identify geographic identifiers to keep
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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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## Define function to create dataset that contains only DC-area tracts and
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## constructed marital status variables
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select.statusvars <- 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, statusvars)])
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}
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## Load datasets containing marital status variables using data from
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## files based on 2010 tracts
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load('../US/tracts/2010/tabular/marital-status/dataset/tracts-2010TIGER-marital-status.Rdata')
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## Create list of data frames for each year and restrict to created
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## marital status variables
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dtas <- list(trt14_ms, trt15_ms, trt16_ms, trt17_ms)
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statusdtas <- lapply(dtas, select.statusvars)
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## Create single wide data frame with year appended to variable name
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namelist <- lapply(14:17, function(x) c(geovars, paste0(statusvars, x)))
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statusdta <- mapply(setNames, statusdtas, namelist, SIMPLIFY = FALSE) %>%
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reduce(left_join, by='GISJOIN')
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statusdta$STATE <- statusdta$STATE.x
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statusdta$COUNTY <- statusdta$COUNTY.x
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statusdta <- statusdta[, -(grep('^STATE.+|^COUNTY.+', names(statusdta), perl=TRUE))]
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## Write file containing wide dataset to CSV format
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write.csv(statusdta,
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'tracts/2010/tabular/marital-status/dataset/tracts-2010TIGER-marital-status.csv')
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