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05_functions-stack.R
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05_functions-stack.R
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# functions for 05_stack-cumulative.R
#' name standardization
std_name <- function(tbl, is_panel = FALSE) {
cces_year <- as.integer(unique(tbl$year))
if (is_panel) cces_year <- paste0(cces_year, "_", "panel")
# pettigrew ------
if (identical(cces_year, 2006:2011)) {
tbl <- tbl %>%
rename(
tookpost = survey_complete,
zipcode = zip_pre,
county_fips = county_fips_pre,
starttime = start_pre,
reg_self = registered_pre,
economy_retro = economy_retrospective,
newsint = news_interest,
marstat = marriage_status,
religpew = religion,
pew_religimp = relig_importance,
pew_bornagain = born_again,
religpew_protestant = relig_protestant,
pew_churatd = church_attendence,
voted_pres_08 = vote_pres_08,
voted_rep = vote_house,
voted_sen = vote_sen,
voted_gov = vote_gov,
intent_pres_08 = vote_intent_pres_08,
intent_pres_12 = vote_intent_pres_12,
intent_rep = vote_intent_house,
intent_sen = vote_intent_senate,
intent_gov = vote_intent_gov,
immstat = immigration_status,
unionhh = union_household,
ownhome = home_owner,
child18 = children,
milstat_5 = military_none,
healthins_6 = health_ins_none,
vv_regstatus = reg_validation, # check for vv_st as well, cc06 has matchState
vv_turnout_gvm = gen_validated,
vv_turnout_pvm = prim_validated
) %>%
mutate(
voted_trn = coalesce(as_factor(turnout_10), as_factor(turnout_08), as_factor(turnout_06)),
voted_trn = replace(voted_trn, !year %in% c(2006, 2008, 2010), NA),
tookpost = replace(tookpost, year %% 2 == 1, NA), # % NA for odd years
voted_rep = replace(voted_rep, year %% 2 == 1, NA), # % NA for odd years
voted_sen = replace(voted_sen, year %% 2 == 1, NA)) %>% # % NA for odd years
# fix county misalignment
mutate(county_fips = (county_fips < 1000) * as.numeric(state_pre) * 1000 + county_fips) %>%
mutate(county_fips = as.character(county_fips))
}
# 2006 ------
if (identical(cces_year, 2006L)) {
tbl <- tbl %>%
rename(
approval_pres = gwbapp,
approval_rep = congmanapp,
approval_sen1 = sen1app,
approval_sen2 = sen2app,
approval_gov = govapp,
economy_retro = natleconyear,
family_income_old = income,
zipcode = inputzip,
county_fips = profile_fips,
intent_trn = vote2006,
voted_trn = v4004,
intent_rep = profile_housevote_coded,
intent_sen = profile_senvote_coded,
intent_gov = profile_govvote_coded,
vv_turnout_gvm = g2006
) %>%
mutate(marstat = coalesce(profile_marstat, marstat)) %>%
labelled::add_value_labels(race = c("Other" = 7)) %>%
labelled::add_value_labels(pid7 = c("Not Very Strong Democrat" = 2,
"Not Very Strong Republican" = 6))
}
# 2008 -------
if (identical(cces_year, 2008L)) {
tbl <- tbl %>%
rename(
approval_pres = CC335bush,
approval_rep = CC335rep,
approval_sen1 = CC335sen1,
approval_sen2 = CC335sen2,
approval_gov = CC335gov,
economy_retro = CC302,
family_income_old = V246,
marstat = V214,
starttime = V300,
intent_trn = CC326,
voted_trn = CC403,
intent_rep = CC339,
intent_sen = CC335,
intent_gov = CC336,
intent_pres_08 = CC327,
voted_pres_08 = CC410,
pid3 = CC307,
pid7 = CC307a,
ideo5 = V243,
weight = V201,
educ = V213,
newsint = V244,
gender = V208,
birthyr = V207,
race = V211,
partyreg = CC402
) %>%
mutate(zipcode = as.character(as_factor(V202)))
}
# 2009 --------
if (identical(cces_year, 2009L)) {
tbl <- tbl %>%
rename(
approval_pres = cc09_43e,
approval_rep = cc09_43a,
approval_sen1 = cc09_43b,
approval_sen2 = cc09_43c,
approval_gov = cc09_43f,
economy_retro = cc09_20,
voted_pres_08 = cc09_31,
pid3 = cc423,
pid7 = cc424,
ideo5 = v261,
weight = v200,
educ = v213,
newsint = v244,
marstat = v214,
family_income_old = v246,
gender = v208,
age = v288,
birthyr = v207,
race = v211,
) %>%
mutate(zipcode = as.character(as_factor(v253)),
county_fips = as.character(as_factor(v269)))
}
# 2010 - 2011 ----
# only those necessary for custom fix
if (identical(cces_year, 2010L)) {
tbl <- tbl |>
rename(
gender = V208,
marstat = V214,
voted_pres_08 = CC317
)
}
if (identical(cces_year, 2011L)) {
tbl <- tbl |>
rename(
gender = V208,
marstat = V214,
voted_pres_08 = CC331
)
}
# 2012 ----------
if (identical(cces_year, 2012L)) {
tbl <- rename(
tbl,
weight = weight_vv,
weight_post = weight_vv_post,
approval_pres = CC308a,
approval_rep = CC315a,
approval_sen1 = CC315b,
approval_sen2 = CC315c,
economy_retro = CC302,
approval_gov = CC308d,
intent_pres_12 = CC354c,
intent_pres_12x = CC354b,
voted_pres_12 = CC410a,
voted_pres_08 = CC317,
voted_rep = CC412,
voted_sen = CC410b,
voted_gov = CC411,
voted_trn = CC401,
intent_sen = CC355b,
intent_senx = CC355,
intent_gov = CC356b,
intent_govx = CC356,
intent_rep = CC390b,
intent_repx = CC390,
intent_trn = CC354,
vv_turnout_gvm = e2012g,
vv_turnout_pvm = e2012congp,
vv_turnout_ppvm = e2012presp,
vv_regstatus = voter_status,
vv_party_gen = PartyRegist,
vv_party_prm = congprim_pty,
vv_party_pprm = presprim_pty
)
}
if (identical(cces_year, "2012_panel")) {
tbl <- tbl %>%
rename(
approval_pres = CC12_308a,
approval_rep = CC12_315a,
approval_sen1 = CC12_315b,
approval_sen2 = CC12_315c,
approval_gov = CC12_308d,
economy_retro = CC12_302,
intent_pres_12 = CC12_354c,
intent_pres_12x = CC12_354b,
voted_pres_12 = CC12_410a,
voted_pres_08 = CC12_317,
voted_rep = CC12_412,
voted_sen = CC12_410b,
voted_gov = CC12_411,
intent_sen = CC12_355b,
intent_senx = CC12_355,
intent_gov = CC12_356b,
intent_govx = CC12_356,
intent_rep = CC12_390b,
intent_repx = CC12_390,
CC350 = CC12_350 # rename later
)
}
# 2013 ----------
if (identical(cces_year, 2013L)) {
tbl <- tbl %>%
rename(
approval_pres = CC312a,
approval_rep = CC13_313a,
approval_sen1 = CC13_313b,
approval_sen2 = CC13_313c,
approval_gov = CC312d,
economy_retro = CC13_302,
voted_pres_12 = CC13_315
)
}
# 2014 - 2015 -------
if (identical(cces_year, 2014L)) {
tbl <- tbl %>% rename(
approval_pres = CC14_308a,
approval_rep = CC14_315a,
approval_sen1 = CC14_315b,
approval_sen2 = CC14_313c,
approval_gov = CC14_308d,
economy_retro = CC14_302,
voted_rep = CC412,
voted_pres_12 = CC14_317,
voted_sen = CC410b,
voted_gov = CC411,
intent_sen = CC355,
intent_senx = CC355x,
intent_gov = CC356,
intent_govx = CC356x,
intent_rep = CC360,
intent_repx = CC360x,
intent_trn = CC354,
voted_trn = CC401,
vv_turnout_gvm = e2014gvm,
vv_turnout_pvm = e2014pvm,
vv_regstatus = voterstatus,
vv_party_gen = partyaffiliation,
vv_party_prm = e2014pep,
vv_st = state_cl
) %>%
mutate(
tookpost = labelled(tookpost, labels = c(`1` = 1, `0` = 0)) # for consistency
)
}
if (identical(cces_year, 2015L)) {
tbl <- rename(tbl, CC350 = CC15_350) %>%
rename(
approval_pres = CC15_312a,
approval_rep = CC15_313a,
approval_sen1 = CC15_313b,
approval_sen2 = CC15_313c,
approval_gov = CC15_312f,
economy_retro = CC15_302,
voted_pres_12 = CC15_315
)
}
# 2016 - 2017 ----
if (identical(cces_year, 2016L)) {
tbl <- tbl %>%
rename(
weight = commonweight_vv,
weight_post = commonweight_vv_post,
CC350 = CC16_360,
starttime = starttime_pre,
approval_pres = CC16_320a,
approval_rep = CC16_320f,
approval_sen1 = CC16_320g,
approval_sen2 = CC16_320h,
approval_gov = CC16_320d,
economy_retro = CC16_302,
intent_trn = CC16_364,
intent_pres_16 = CC16_364c,
intent_pres_16x = CC16_364b,
intent_rep = CC16_367,
intent_repx = CC16_367x, # house early vote (already voted)
intent_sen = CC16_365,
intent_senx = CC16_365x,
intent_gov = CC16_366,
intent_govx = CC16_366x,
voted_trn = CC16_401,
voted_pres_12 = CC16_326,
voted_pres_16 = CC16_410a,
voted_rep = CC16_412,
voted_sen = CC16_410b,
voted_gov = CC16_411,
vv_turnout_gvm = CL_E2016GVM,
vv_turnout_pvm = CL_E2016PVM,
vv_turnout_ppvm = CL_E2016PPVM,
vv_regstatus = CL_voterstatus,
vv_party_gen = CL_partyaffiliation,
vv_party_prm = CL_E2016PEP,
vv_party_pprm = CL_E2016PPEP,
vv_st = CL_state
)
}
if (identical(cces_year, 2017L)) {
tbl <- rename(tbl, CC350 = CC17_325) %>%
rename(
weight = weights_common,
approval_pres = CC17_322a,
approval_rep = CC17_323a,
approval_sen1 = CC17_323b,
approval_sen2 = CC17_323c,
approval_gov = CC17_322e,
economy_retro = CC17_301,
faminc = faminc_new,
voted_pres_16 = CC17_327) %>%
mutate(
countyfips = NA
) %>%
labelled::add_value_labels(marstat = c("Domestic Partnership" = 6, "Single" = 5))
}
# 2018 - 2019 ------
if (identical(cces_year, 2018L)) {
tbl <- tbl %>%
rename(
weight = commonweight,
rvweight = vvweight,
rvweight_post = vvweight_post,
weight_post = commonpostweight,
approval_pres = CC18_308a,
approval_rep = CC18_311a,
approval_sen1 = CC18_311b,
approval_sen2 = CC18_311c,
approval_gov = CC18_308d,
economy_retro = CC18_301,
faminc = faminc_new,
intent_trn = CC18_350,
intent_rep = CC18_353,
intent_repx = CC18_353x,
intent_sen = CC18_351,
intent_senx = CC18_351x,
intent_gov = CC18_352,
intent_govx = CC18_352x,
voted_trn = CC18_401,
voted_pres_16 = CC18_317,
voted_rep = CC18_412,
voted_sen = CC18_410b,
voted_gov = CC18_411,
vv_turnout_gvm = CL_2018gvm,
vv_turnout_pvm = CL_2018pvm,
vv_regstatus = CL_voter_status,
vv_party_gen = CL_party,
vv_party_prm = CL_2018pep,
vv_st = CL_state
) %>%
# party straight ticket
mutate(voted_rep = replace(voted_rep, CC18_409 == 1, 1),
voted_rep = replace(voted_rep, CC18_409 == 2, 2),
# we found that only in US House, sometimes party 2 was not a Republican.
voted_rep = replace(voted_rep, CC18_409 == 2 & HouseCand2Party_post != "Republican", NA),
voted_sen = replace(voted_sen, CC18_409 == 1, 1),
voted_sen = replace(voted_sen, CC18_409 == 2, 2),
voted_gov = replace(voted_gov, CC18_409 == 1, 1),
voted_gov = replace(voted_gov, CC18_409 == 2, 2),
) %>%
mutate_at(vars(matches("^vv")), ~replace_na(as.character(as_factor(.x)), "")) %>%
labelled::add_value_labels(marstat = c("Domestic Partnership" = 6, "Single" = 5))
}
if (identical(cces_year, 2019L)) {
tbl <- tbl %>%
rename(
weight = commonweight,
approval_pres = CC19_308a,
approval_rep = CC19_311a,
approval_sen1 = CC19_311b,
approval_sen2 = CC19_311c,
approval_gov = CC19_308e,
economy_retro = CC19_301,
faminc = faminc_new,
voted_pres_16 = presvote16post
) %>%
labelled::add_value_labels(marstat = c("Domestic Partnership" = 6, "Single" = 5))
}
# 2020 - 2021 ----
if (identical(cces_year, 2020L)) {
tbl <- tbl %>%
# called "Two or more races" in 2020
mutate(race = sjlabelled::replace_labels(
race, labels = c("Mixed" = 6))) %>%
# rename
rename(
weight = commonweight,
rvweight = vvweight,
rvweight_post = vvweight_post,
weight_post = commonpostweight,
approval_pres = CC20_320a,
approval_rep = CC20_320f,
approval_sen1 = CC20_320g,
approval_sen2 = CC20_320h,
approval_gov = CC20_320d,
economy_retro = CC20_302,
faminc = faminc_new,
intent_trn = CC20_363,
intent_pres_20 = CC20_364b,
intent_pres_20x = CC20_364a, # double check if this is actually voted
intent_rep = CC20_367,
intent_repx = CC20_367_voted,
intent_sen = CC20_365,
intent_senx = CC20_365_voted,
intent_gov = CC20_366,
intent_govx = CC20_366_voted,
voted_trn = CC20_401,
voted_pres_16 = presvote16post,
voted_pres_20 = CC20_410,
voted_rep = CC20_412,
voted_sen = CC20_411,
voted_gov = CC20_413,
vv_turnout_gvm = CL_2020gvm,
vv_turnout_pvm = CL_2020pvm,
vv_turnout_ppvm = CL_2020ppvm,
vv_regstatus = CL_voter_status,
vv_party_gen = CL_party,
vv_party_prm = CL_2020pep,
vv_party_pprm = CL_2020ppep,
vv_st = CL_state
) %>% # combine early vote?
mutate_at(vars(matches("^vv")), ~replace_na(as.character(as_factor(.x)), "")) %>%
labelled::add_value_labels(marstat = c("Domestic Partnership" = 6, "Single" = 5))
}
if (identical(cces_year, 2021L)) {
tbl <- tbl %>%
# called "Two or more races" in 2020-2021
mutate(race = sjlabelled::replace_labels(
race, labels = c("Mixed" = 6))) %>%
rename(
weight = commonweight,
approval_pres = CC21_315a,
approval_rep = CC21_315f,
approval_sen1 = CC21_315g,
approval_sen2 = CC21_315h,
approval_gov = CC21_315d,
economy_retro = CC21_301,
faminc = faminc_new,
voted_pres_16 = presvote16post,
voted_pres_20 = presvote20post
) %>%
labelled::add_value_labels(marstat = c("Domestic Partnership" = 6, "Single" = 5))
}
# 2022 - 2023 ----
if (identical(cces_year, 2022L)) {
tbl <- tbl %>%
# called "Two or more races" in 2020
mutate(race = sjlabelled::replace_labels(
race, labels = c("Mixed" = 6))) %>%
# rename
rename(
weight = commonweight,
weight_post = commonpostweight,
# rvweight = vvweight,
# rvweight_post = vvweight_post,
# gender = gender4,
approval_pres = CC22_320a,
approval_rep = CC22_320f,
approval_sen1 = CC22_320g,
approval_sen2 = CC22_320h,
approval_gov = CC22_320d,
economy_retro = CC22_302,
faminc = faminc_new,
intent_trn = CC22_363,
intent_sen = CC22_365,
intent_senx = CC22_365_voted,
intent_gov = CC22_366,
intent_govx = CC22_366_voted,
intent_rep = CC22_367,
intent_repx = CC22_367_voted,
voted_trn = CC22_401,
voted_pres_16 = presvote16post,
voted_pres_20 = presvote20post,
voted_sen = CC22_411,
voted_rep = CC22_412,
voted_gov = CC22_413,
vv_turnout_gvm = TS_g2022,
vv_turnout_pvm = TS_p2022,
vv_regstatus = TS_voterstatus,
vv_party_gen = TS_partyreg,
vv_party_prm = TS_p2022_party,
vv_st = TS_state
) %>%
mutate_at(vars(matches("^vv")), ~replace_na(as.character(as_factor(.x)), "")) %>%
labelled::add_value_labels(marstat = c("Domestic Partnership" = 6, "Single" = 5))
}
if (identical(cces_year, 2023L)) {
tbl <- tbl %>%
# called "Two or more races" in 2020-2021
mutate(race = sjlabelled::replace_labels(
race, labels = c("Mixed" = 6))) %>%
rename(
weight = commonweight,
approval_pres = CC23_312a,
approval_rep = CC23_312f,
approval_sen1 = CC23_312g,
approval_sen2 = CC23_312h,
approval_gov = CC23_312d,
economy_retro = CC23_301,
faminc = faminc_new,
voted_pres_20 = presvote20post
) %>%
labelled::add_value_labels(marstat = c("Domestic Partnership" = 6, "Single" = 5))
}
# more standardization for post 2012 ------
if (cces_year[1] %in% c(2012:2023) | cces_year[1] == "2012_panel") {
tbl <- tbl %>%
rename(
reg_self = votereg,
family_income = faminc,
zipcode = lookupzip,
county_fips = countyfips
) %>%
mutate(
age = year - birthyr
)
}
if (cces_year[1] != 2022) {
tbl <- tbl %>%
labelled::add_value_labels(marstat = c("Domestic Partnership" = 6, "Single" = 5))
}
# gender ----
if (cces_year[1] %in% c(2021:2023)) {
tbl <- tbl |>
mutate(
gender = labelled(zap_labels(gender4), c("Male" = 1, "Female" = 2)),
gender = replace(gender, gender %in% 3:4, NA_real_)
)
} else {
tbl <- tbl |>
mutate(sex = gender)
}
# hispanic origin ----
## 20-22: "_hisp_", CC20_asian_1
## 16: "Hispanic_origin", Asian_Origin
## 15, 17-19: variants of CC15_354ax
if (cces_year[1] >= 2015) {
# regex to find the variables
hisp_regex <- "(_hisp|Hispanic_origin|CC15_354ax|CC17_353a|CC18_354a|CC19_353a)_[0-9]"
qlb_regex <- "(Country ancestry|Latin Heritage|Hispanic Origin|Hispanic_origin) - "
hisp_key <-
tbl |>
select(matches(hisp_regex)) |>
map_chr(\(x) attr(x, "label")) |> enframe() |>
transmute(
name,
lab = str_squish(str_remove_all(value, regex(qlb_regex, ignore_case = TRUE))))
# do not concatenate these
rm_values <- c("No country in particular",
"No Country in Particular",
"Not Latino, Hispanic",
"I am not of Latino, Hispanic or Spanish heritage",
"I am not of Latino, Hispanic or Spanish Heritage",
"Other")
# reshape, recode, and concatenate
hisp_origin <- tbl |>
select(case_id, matches(hisp_regex)) |>
pivot_longer(-case_id) |>
left_join(hisp_key) |>
mutate(lab = replace(lab, lab %in% rm_values, NA_character_)) |>
filter(!is.na(value) & value != 9 & value == 1 & !is.na(lab)) |>
arrange(name) |>
summarize(hisp_origin = str_c(lab, collapse = "!!"), .by = case_id)
tbl <- tbl |>
left_join(hisp_origin, by = "case_id")
}
return(tbl)
}
# inner extraction function for find_stack
#' @param tbl data
#' @param var NSE variable name to look for
#' @param var_name in characters
#' @param chr_var_name name of the variablethat will hold the labels (in chars)
#' @param num_var_name name of the variable that will hold the levels (in integers)
#'
#' @return
#' A dataset, for each year, with standardized names, separating labels and values (if factor).
#' Sets Not Asked to NA
extract_yr <- function(tbl, var, var_name, chr_var_name, num_var_name, is_factor = TRUE) {
if (is_factor) {
if (var_name %in% colnames(tbl)) { # factor
select(tbl, year, case_id, !! var) %>%
mutate(
!! chr_var_name := as.character(as_factor(.data[[var_name]])),
!! num_var_name := as.integer(.data[[var_name]])
) %>%
select(-!! var)
} else {# if var does not exist
select(tbl, year, case_id) %>%
mutate(
!! chr_var_name := NA,
!! num_var_name := NA
)
}
} else {# if not factor
if (var_name %in% colnames(tbl)) {
select(tbl, year, case_id, !! var) %>%
mutate(!! var_name := zap_labels(!! var))
} else {
select(tbl, year, case_id) %>%
mutate(!! var_name := NA)
}
}
}
# takes all datasets available, and given a var, pulls it out of each and stacks
#'
#' @param dflist a list of cces datasets. column names need to be standardized.
#' @param var a NSE variable to find and stack
#' @param type a string, "factor", "numeric", "integer", or "dattetime". If numeric or datetime
#' that type is returned. if factor the label and values are returned separately as
#' different columns, unless labelled = TRUE
#' @param make_labelled to bind the two _char and _num columns to a label. ONLY
#' do this if the numbers and labels match 1:1 across all years
find_stack <- function(dflist = list(), var, type = "factor", make_labelled = FALSE, new_reorder = TRUE) {
var <- enquo(var)
var_name <- quo_name(var)
chr_var_name <- paste0(var_name, "_char")
num_var_name <- paste0(var_name, "_num")
if (type == "factor") {
list_yr <- foreach(yr = 1:length(dflist), .combine = "bind_rows") %do% {
extract_yr(dflist[[yr]], enquo(var), var_name, chr_var_name, num_var_name)
}
if (grepl("^vv_", chr_var_name)) { # vv were not displayed questions so can be sorted by frequency, and no numeric left
list_yr <- mutate(list_yr,
!!chr_var_name := std_vvv(.data[[chr_var_name]], varname = chr_var_name, yrvec = .data[["year"]]))
list_yr_factor <- list_yr %>%
clean_values(chr_var_name = chr_var_name, num_var_name = num_var_name) %>%
mutate(!!var_name := fct_infreq(.data[[chr_var_name]])) %>%
select(year, case_id, !!var_name)
return(list_yr_factor)
}
# otherwise just clean up values as text
list_yr <- clean_values(list_yr, chr_var_name = chr_var_name, num_var_name = num_var_name)
}
if (type != "factor") {
list_yr <- foreach(yr = 1:length(dflist), .combine = "bind_rows") %do% {
extract_yr(dflist[[yr]], enquo(var), var_name, chr_var_name, num_var_name, is_factor = FALSE)
}
# change to specified type
if (type == "numeric") list_yr <- mutate(list_yr, !! var_name := as.numeric(.data[[var_name]]))
if (type == "integer") list_yr <- mutate(list_yr, !! var_name := as.integer(.data[[var_name]]))
if (type == "character") list_yr <- mutate(list_yr, !! var_name := as.character(as_factor(!! var)))
if (type == "datetime") list_yr <- mutate(list_yr, !! var_name := as.POSIXct(.data[[var_name]]))
list_yr <- list_yr %>%
mutate(!! var_name := replace(.data[[var_name]], is.nan(.data[[var_name]]), NA))
}
# specific recoding
if (grepl("approval_(rep|sen|pres|gov)", chr_var_name)) {
list_yr <- recode_apv(list_yr, chr_var_name)
}
# fit to labels or factors
# coerce to labelled? do this if same across year
if (type == "factor" & make_labelled) list_yr <- set_to_label(list_yr, num_var_name, var_name)
# if not labelled, consider reordering rather than keeping them separate
if (type == "factor" & new_reorder & !make_labelled) {
list_yr <- list_yr %>%
mutate(!!var_name := fct_reorder2(.data[[chr_var_name]],
.x = .data[[num_var_name]],
.y = .data[["year"]],
.fun = median2,
.na_rm = FALSE,
.desc = FALSE)) %>%
select(year, case_id, !!var_name)
}
list_yr
}
#' Coerce-labelled
#'
#' @param df the slim table
#' @param numvarname the name of the variable (number)
set_to_label <- function(df, numvarname, varname) {
# change consistent vars in to a labelled factor
# make numbered vector
label_key <- select(df, -year, -case_id) %>%
distinct() %>%
select(matches("_char"), matches("_num")) %>%
arrange(!!sym(numvarname)) %>%
drop_na() |>
deframe()
df %>%
mutate(!! varname := labelled(as.integer(.data[[numvarname]]), label_key)) %>%
select(year, case_id, !! varname)
}
# order x by y
median2 <- function(x, y) {
median(x[order(y, na.last = FALSE)], na.rm = TRUE)
}
#' recode approval
#'
#'
recode_apv <- function(tbl, char_name) {
tbl %>%
mutate(!!char_name := recode(.data[[char_name]],
`Approve` = "Approve / Somewhat Approve",
`Somewhat Approve` = "Approve / Somewhat Approve",
`Disapprove` = "Disapprove / Somewhat Disapprove",
`Somewhat Disapprove` = "Disapprove / Somewhat Disapprove",
`Never Heard` = "Never Heard / Not Sure",
`Never Heard Of` = "Never Heard / Not Sure",
`Not Sure` = "Never Heard / Not Sure"))
}
#' clean up missing values, format to change NaN to NA
clean_values <- function(tbl, chr_var_name, num_var_name) {
if (grepl("approval|hispanic", chr_var_name)) {
skipped_num <- 8
notasked_num <- 9
} else {
skipped_num <- 98
notasked_num <- 99
}
tbl %>%
mutate( # replace NaN to NA
!! chr_var_name := na_if(.data[[chr_var_name]], "NaN"),
!! num_var_name := replace(.data[[num_var_name]], is.nan(.data[[num_var_name]]), NA)
) %>%
mutate( # change al lvalues to title case
!! chr_var_name := my_var_case(str_trim(.data[[chr_var_name]])),
!! chr_var_name := recode(.data[[chr_var_name]],
`Never Heard Of This Person` = "Never Heard",
`No Hs` = "No HS")
) %>%
mutate( # change not asked and skipped to NAs
!! chr_var_name := na_if(.data[[chr_var_name]], "Not Asked"),
!! chr_var_name := na_if(.data[[chr_var_name]], "Skipped"),
!! num_var_name := na_if(.data[[num_var_name]], notasked_num),
!! num_var_name := na_if(.data[[num_var_name]], skipped_num))
}
#' custom title case
my_var_case <- function(chr) {
str_to_title(chr) %>%
str_replace_all(" A ", " a ") %>%
str_replace_all(" An ", " an ") %>%
str_replace_all(" About ", " about ") %>%
str_replace_all(" By ", " by ") %>%
str_replace_all(" And ", " and ") %>%
str_replace_all(" For ", " for ") %>%
str_replace_all(" To ", " to ") %>%
str_replace_all(" Or ", " or ") %>%
str_replace_all(" Of ", " of ") %>%
str_replace_all(" In ", " in ") %>%
str_replace_all(" This ", " this ") %>%
str_replace_all(" The ", " the ") %>%
str_replace_all(" At ", " at ") %>%
str_replace_all(" Nor ", " nor ") %>%
str_replace_all(" Not ", " not ") %>%
str_replace_all("I'm not Sure", "Not Sure") %>%
str_replace_all("Most of the Time", "Most of the time") %>%
str_replace_all("Some of the Time", "Some of the time") %>%
str_replace_all("Only Now and Then", "Only now and then") %>%
str_replace_all("Mccain", "McCain") %>%
str_replace_all("Hardly at All", "Hardly at all")
}
#' standardize validated vote values
std_vvv <- function (vec, varname, yrvec) {
vtd <- "Voted"
nov <- "No Record of Voting"
# turnout
if (grepl("turnout", varname)) {
recoded <- recode(vec,
`Polling` = vtd,
`polling` = vtd,
`polling place` = vtd,
`Absentee` = vtd,
`absentee` = vtd,
`Early` = vtd,
`early` = vtd,
`earlyVote` = vtd,
`mail` = vtd,
`Mail` = vtd,
`provisional` = vtd,
`voted by unknown method` = vtd,
`unknown` = vtd,
`Unknown` = vtd,
`UnknownMethod` = vtd,
`validated record of voting in general election` = vtd,
`Virginia doesn't maintain vote history files` = "No Voter File",
`MatchedNoVote` = nov,
.default = nov
)
}
# regstatus
if (grepl("regstatus", varname)) {
recoded <- recode(vec,
inactiv = "Inactive",
multipl = "Multiple Appearances",
multipleAppearances = "Multiple Appearances",
multipleAppe = "Multiple Appearances",
unregis = "unregistered",
MovedUnregistered = "unregistered",
dropped = "Dropped",
NoVoterFileMatch = "No Record of Registration"
)
recoded <- replace(recoded, yrvec %in% c(2006, 2007, 2009, 2011), NA) # these should be missing
recoded <- replace(recoded, recoded == "", "No Record of Registration")
}
# regparty
if (grepl("party", varname)) {
dem <- "Democratic Party"
gop <- "Republican Party"
npa <- "No Party Affiliation"
recoded <- recode(vec,
`DEM` = dem,
`Democratic.Party` = dem,
`Democratic` = dem,
`REP` = gop,
`Republican.Party` = gop,
`Republican` = gop,
`NPA` = npa,
`No.Party.Affiliation` = npa,
`Constitution.Party` = "Constitution Party",
`CST` = "Constitution Party",
`Green.Party` = "Green Party",
`GRE` = "Green Party",
`Libertarian.Party` = "Libertarian Party",
`LIB` = "Libertarian Party",
`Independent.Party` = "Independent Party",
`IND` = "Independent Party",
`Reform.Party` = "Reform Party",
`REF` = "Reform Party",
`Socialist.Party` = "Socialist Party",
`SOC` = "Socialist Party",
`Declined.To.State` = "Declined to State",
`DTS` = "Declined to State",
`OTH` = "Other",
`UNK` = "Unknown"
)
recoded <- replace(recoded, recoded == "", "No Record of Party Registration")
}
recoded
}
#' Fix for voted08
clps_pres08 <- function(vec) {
vec %>%
as.character() %>%
str_trim() %>%
as_factor() %>%
fct_collapse(
`Barack Obama` = c("Barack Obama",
"Barack Obama (Democratic)"),
`John McCain` = c("John McCain (Republican)",
"John McCain"),
`Other / Someone Else` = c("Someone Else"),
`Did not Vote` = c("Did not Vote"),
`Not Sure / Don't Recall` = c("Don't Recall")
) %>%
fct_relevel("Barack Obama", "John McCain", "Other / Someone Else", "Did not Vote") %>%
fct_drop()
}
#' Quick fix for 2012 voted, where labels are too mixed to fix automatically
clps_pres12 <- function(vec) {
fct_collapse(
vec,
`Barack Obama` = c(
"Barack Obama",
"Barack Obama (Democratic)",
"Vote for Barack Obama"),
`Mitt Romney` = c(
"Mitt Romney",
"Mitt Romney (Republican)",
"Vote for Mitt Romney"),
`Other / Someone Else` = c(
"Someone Else",
"Vote for Someone Else",
"Other"),
`Did not Vote for this Office` = c(
"Did Not Vote",
"I Did Not Vote",
"Not Vote",
"Not Vote for this Office",
"I Did not Vote in this Race"),
`Not Sure / Don't Recall` = c("Not Sure", "Don't Recall")
) %>%
fct_lump(n = 6)
}
clps_pres16 <- function(vec) {
fct_collapse(
vec,
`Hilary Clinton` = c(
"Hillary Clinton",
"Hillary Clinton (Democrat)"),
`Donald Trump` = c(
"Donald Trump",
"Donald Trump (Republican)"),
`Other / Someone Else` = c(
"Gary Johnson (Libertarian)", "Gary Johnson",
"Evan Mcmullin (Independent)", "Evan Mcmullin",
"Jill Stein (Green)", "Jill Stein",
"Other", "Someone Else"),
`Did not Vote for this Office` = c(
"I Didn't Vote in this Election",
"Did not Vote for President",
"I Did not Cast a Vote for President"),
`Not Sure / Don't Recall` = c(
"I'm not Sure", "I Don't Recall")
) %>%
fct_relevel("Hilary Clinton", "Donald Trump") %>%
fct_lump(n = 5)
}
clps_pres20 <- function(vec) {
fct_collapse(
vec,
`Joe Biden` = c(
"Joe Biden",
"Joe Biden (Democrat)"),
`Donald Trump` = c(
"Donald Trump",