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Update documentation to midwest variables #4274

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Dec 2, 2020
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Update documentation to midwest variables #4274

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ee3e94c
Add periods to midwest variables for consistency
erictleung Nov 25, 2020
f429522
Add reference for midwest data
erictleung Nov 26, 2020
80beae6
Add missing midwest variable descriptions
erictleung Nov 26, 2020
bedfed0
Fix variable comment for population to density
erictleung Nov 27, 2020
95d14f5
Add user note for using midwest dataset
erictleung Nov 29, 2020
763d499
Use canonical form of CRAN acs package link
erictleung Nov 29, 2020
132edcd
Document
actions-user Dec 1, 2020
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39 changes: 22 additions & 17 deletions R/data.R
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Expand Up @@ -48,16 +48,21 @@

#' Midwest demographics
#'
#' Demographic information of midwest counties
#' Demographic information of midwest counties from 2000 US census
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Reference
https://github.com/hadley/ggplot2-book/blob/a55525c4fc9e4a2f2a951639047a4175ee0fa8a6/statistical-summaries.Rmd#L40

#'
#' Note: this dataset is included for illustrative purposes. The original
#' descriptions were not documented and the current descriptions here are based
#' on speculation. For more accurate and up-to-date US census data, see the
#' [`acs` package](https://cran.r-project.org/package=acs).
#'
#' @format A data frame with 437 rows and 28 variables:
#' \describe{
#' \item{PID}{}
#' \item{county}{}
#' \item{state}{}
#' \item{area}{}
#' \item{poptotal}{Total population}
#' \item{popdensity}{Population density}
#' \item{PID}{Unique county identifier.}
#' \item{county}{County name.}
#' \item{state}{State to which county belongs to.}
#' \item{area}{Area of county (units unknown).}
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@erictleung erictleung Nov 27, 2020 •
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for area, I tried looking up the land area units for this and nothing came out as something reasonable. So I've just made this units unknown.

The other variables here (PID, county, and state), I'm just spelling out more on what those variables are to be thorough.

#' \item{poptotal}{Total population.}
#' \item{popdensity}{Population density (person/unit area).}
#' \item{popwhite}{Number of whites.}
#' \item{popblack}{Number of blacks.}
#' \item{popamerindian}{Number of American Indians.}
Expand All @@ -69,17 +74,17 @@
#' \item{percasian}{Percent Asian.}
#' \item{percother}{Percent other races.}
#' \item{popadults}{Number of adults.}
#' \item{perchsd}{}
#' \item{perchsd}{Percent with high school diploma.}
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Not very reliable, but I found someone's homework that lists perchsd as "the percentage of people with a high schooldiploma in each county".

I couldn't verify this, but given the surrounding other variables on education status (percollege and percprof), I figured this to be a high school diploma.

#' \item{percollege}{Percent college educated.}
#' \item{percprof}{Percent profession.}
#' \item{poppovertyknown}{}
#' \item{percpovertyknown}{}
#' \item{percbelowpoverty}{}
#' \item{percchildbelowpovert}{}
#' \item{percadultpoverty}{}
#' \item{percelderlypoverty}{}
#' \item{inmetro}{In a metro area.}
#' \item{category}{}
#' \item{percprof}{Percent with professional degree.}
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@erictleung erictleung Nov 27, 2020 •
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For percprof, I got the same hint from that one homework assignment. And with the context of the other variables (perchsd and percollege), this would make more sense that this is a professional degree rather than merely "profession". The percentages of professional degrees in the data set are much lower than the percollege variable, which is a good sanity check.

#' \item{poppovertyknown}{Population with known poverty status.}
#' \item{percpovertyknown}{Percent of population with known poverty status.}
#' \item{percbelowpoverty}{Percent of people below poverty line.}
#' \item{percchildbelowpovert}{Percent of children below poverty line.}
#' \item{percadultpoverty}{Percent of adults below poverty line.}
#' \item{percelderlypoverty}{Percent of elderly below poverty line.}
Comment on lines +80 to +85
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@erictleung erictleung Nov 27, 2020 •
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poppovertyknown is always less than population total poptotal. So I personally inferred that this meant how many of these individuals do we actually know their poverty status for.

This is validated with some manual calculations to check if our manual calculation of percent poverty is equal to the one in the data set.

library(ggplot2)
library(dplyr)

data("midwest")

# Find number of counties our manual calculation fails
midwest %>%
  select(poptotal, poppovertyknown, percpovertyknown) %>%
  mutate(manual_percpoverty = poppovertyknown / poptotal * 100) %>%
  mutate(not_equal_per = !all.equal(percpovertyknown, manual_percpoverty)) %>%
  pull(not_equal_per) %>%
  sum()
#> [1] 0

Created on 2020-11-27 by the reprex package (v0.3.0)

The remaining variables (percbelowpoverty, percchildbelowpovert, percadultpoverty, and percelderlypoverty) were my own personal inferences on the pieces of information based on the variable name itself. The ages for poverty (child, adult, elderly) are also seen in poverty reports (PDF) as "Under 18 years", "18 to 64 years", and "65 years and over".

#' \item{inmetro}{County considered in a metro area.}
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@erictleung erictleung Nov 27, 2020 •
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For inmetro, this is merely giving more detail on what is in a metro area, which is the county/

#' \item{category}{Miscellaneous.}
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@erictleung erictleung Nov 27, 2020 •
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For category, I could not find out what this was but I didn't want to leave this blank, so I just gave it this name. Happy to remove this if it is pointless.

#' }
#'
"midwest"
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40 changes: 23 additions & 17 deletions man/midwest.Rd
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