infer: Tidy Statistical Inference

library(dplyr)
library(forcats)
library(srvyr)
library(ggplot2)

# pull gss data
temp <- tempfile()
download.file("https://gss.norc.org/documents/stata/GSS_stata.zip", temp)

# if this next line errors with "No such file or directory", try
# incrementing the number after "_R"
gss_orig <- haven::read_dta(unz(temp, filename = "GSS7218_R2.DTA")) |>
  haven::as_factor()
unlink(temp)

# select relevant columns
gss_small <- gss_orig |>
  filter(!stringr::str_detect(sample, "blk oversamp")) |> # this is for weighting
  select(
    year,
    age,
    sex,
    college = degree,
    partyid,
    hompop,
    hours = hrs1,
    income,
    class,
    finrela,
    weight = wtssall
  ) |>
  mutate_if(
    is.factor,
    ~ fct_collapse(., NULL = c("IAP", "NA", "iap", "na"))
  ) |>
  mutate(
    age = age |>
      fct_recode("89" = "89 or older", NULL = "DK") |> # truncated at 89
      as.character() |>
      as.numeric(),
    hompop = hompop |>
      fct_collapse(NULL = c("DK")) |>
      as.character() |>
      as.numeric(),
    hours = hours |>
      fct_recode("89" = "89+ hrs", NULL = "DK") |> # truncated at 89
      as.character() |>
      as.numeric(),
    weight = weight |>
      as.character() |>
      as.numeric(),
    partyid = fct_collapse(
      partyid,
      dem = c("strong democrat", "not str democrat"),
      rep = c("strong republican", "not str republican"),
      ind = c("ind,near dem", "independent", "ind,near rep"),
      other = "other party"
    ),
    income = factor(income, ordered = TRUE),
    college = fct_collapse(
      college,
      degree = c("junior college", "bachelor", "graduate"),
      "no degree" = c("lt high school", "high school"),
      NULL = "dk" # no dks show up in the data, so drop this level
    )
  )

# sample 3k rows, first dropping NAs
set.seed(20200201)
gss <- gss_small |>
  drop_na() |>
  sample_n(500)

# check that the sample is similar unweighted to weighted
gss_wt <- srvyr::as_survey_design(gss, weights = weight)

unweighted <- gss |>
  group_by(year, sex, partyid) |>
  summarize(n = n()) |>
  ungroup() |>
  group_by(year, sex) |>
  mutate(prop = n / sum(n))

weighted <- gss_wt |>
  group_by(year, sex, partyid) |>
  summarize(prop = srvyr::survey_mean())

# save data into package
usethis::use_data(gss, overwrite = TRUE)

devtools::document()