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rm(list = ls(all = TRUE))
setwd("~/Downloads/hbg_replication")
# Load required packages
library(plyr)
library(car)
library(anesrake)
# Load relevant functions.
source("scripts/helper_functions.R")
## Load data.
# Load TPNW experimental data.
tpnw <- read.csv("data/tpnw_raw.csv", stringsAsFactors = FALSE, row.names = 1)
# Load original income question data.
orig_inc <- read.csv("data/tpnw_orig_income.csv", stringsAsFactors = FALSE,
row.names = 1)
# Load YouGov data (including covariates and awareness question).
aware <- read.csv("data/tpnw_aware_raw.csv", stringsAsFactors = FALSE,
row.names = 1)
### Clean TPNW data.
## Clean data.
# Remove first two (extraneous) rows.
tpnw <- tpnw[-c(1, 2),]
orig_inc <- orig_inc[-c(1, 2),]
# Remove respondents who did not consent.
tpnw <- tpnw[tpnw$consent == "1",]
orig_inc <- orig_inc[orig_inc$consent == "1",]
# Coalesce income variables.
orig_inc <- within(orig_inc, {
income <- as.numeric(income)
income <- ifelse(income < 1000, NA, income)
income <- ifelse(income < 15000, 1, income)
income <- ifelse(income >= 15000 & income < 25000, 2, income)
income <- ifelse(income >= 25000 & income < 50000, 3, income)
income <- ifelse(income >= 50000 & income < 75000, 4, income)
income <- ifelse(income >= 75000 & income < 100000, 5, income)
income <- ifelse(income >= 100000 & income < 150000, 6, income)
income <- ifelse(income >= 150000 & income < 200000, 7, income)
income <- ifelse(income >= 200000 & income < 250000, 8, income)
income <- ifelse(income >= 250000 & income < 500000, 9, income)
income <- ifelse(income >= 500000 & income < 1000000, 10, income)
income <- ifelse(income >= 1000000, 11, income)
})
orig_inc <- data.frame(pid = orig_inc$pid, income_old = orig_inc$income)
tpnw <- plyr::join(tpnw, orig_inc, by = "pid", type = "left")
tpnw <- within(tpnw, {
income <- coalesce(as.numeric(income), as.numeric(income_old))
})
# Note meta variables.
meta <- c("consent", "confirmation_code", "new_income_q")
# Note Qualtrics variables.
qualtrics_vars <- c("StartDate", "EndDate", "Status", "Progress",
"Duration..in.seconds.", "Finished", "RecordedDate",
"DistributionChannel", "UserLanguage")
# Note Dynata variables.
dynata_vars <- c("pid", "psid")
# Note non-numeric variables.
char_vars <- c(qualtrics_vars, dynata_vars,
c("ResponseId"), names(tpnw)[grep("text", tolower(names(tpnw)))])
char_cols <- which(names(tpnw) %in% char_vars)
# Numericize other variables
tpnw <- data.frame(apply(tpnw[, -char_cols], 2, as.numeric), tpnw[char_cols])
tpnw_atts <- which(names(tpnw) %in% c("danger", "peace", "safe", "use_unaccept",
"always_cheat", "cannot_elim", "slow_reduc"))
names(tpnw)[tpnw_atts] <- paste("tpnw_atts", names(tpnw)[tpnw_atts], sep = "_")
# Coalesce relevant variables.
tpnw <- within(tpnw, {
# Clean gender variable.
female <- ifelse(gender == 95, NA, gender)
# Transform birthyr variable to age.
age <- 2019 - birthyr
# Transform income variable.
income <- car::recode(income, "95 = NA")
# Combine pid and pid_forc variables.
pid3 <- ifelse(pid3 == 0, pid_forc, pid3)
# Recode ideology variable.
ideo <- car::recode(ideo, "3 = NA")
# Recode education variable.
educ <- car::recode(educ, "95 = NA")
# Recode state variable.
state <- recode(state, "1 = 'Alabama';
2 = 'Alaska';
4 = 'Arizona';
5 = 'Arkansas';
6 = 'California';
8 = 'Colorado';
9 = 'Connecticut';
10 = 'Delaware';
11 = 'Washington DC';
12 = 'Florida';
13 = 'Georgia';
15 = 'Hawaii';
16 = 'Idaho';
17 = 'Illinois';
18 = 'Indiana';
19 = 'Iowa';
20 = 'Kansas';
21 = 'Kentucky';
22 = 'Louisiana';
23 = 'Maine';
24 = 'Maryland';
25 = 'Massachusetts';
26 = 'Michigan';
27 = 'Minnesota';
28 = 'Mississippi';
29 = 'Missouri';
30 = 'Montana';
31 = 'Nebraska';
32 = 'Nevada';
33 = 'New Hampshire';
34 = 'New Jersey';
35 = 'New Mexico';
36 = 'New York';
37 = 'North Carolina';
38 = 'North Dakota';
39 = 'Ohio';
40 = 'Oklahoma';
41 = 'Oregon';
42 = 'Pennsylvania';
44 = 'Rhode Island';
45 = 'South Carolina';
46 = 'South Dakota';
47 = 'Tennessee';
48 = 'Texas';
49 = 'Utah';
50 = 'Vermont';
51 = 'Virginia';
53 = 'Washington';
54 = 'West Virginia';
55 = 'Wisconsin';
56 = 'Wyoming'")
# Create regional indicators.
northeast <- state %in% c("Connecticut", "Maine", "Massachusetts",
"New Hampshire", "Rhode Island", "Vermont",
"New Jersey", "New York", "Pennsylvania")
midwest <- state %in% c("Illinois", "Indiana", "Michigan", "Ohio",
"Wisconsin", "Iowa", "Kansas", "Minnesota",
"Missouri", "Nebraska", "North Dakota",
"South Dakota")
south <- state %in% c("Delaware", "Florida", "Georgia", "Maryland",
"North Carolina", "South Carolina", "Virginia",
"Washington DC", "West Virginia", "Alabama",
"Kentucky", "Mississippi", "Tennessee", "Arkansas",
"Louisiana", "Oklahoma", "Texas")
west <- state %in% c("Arizona", "Colorado", "Idaho", "Montana", "Nevada",
"New Mexico", "Utah", "Wyoming", "Alaska",
"California", "Hawaii", "Oregon", "Washington")
# Recode join_tpnw outcome.
join_tpnw <- car::recode(join_tpnw, "2 = 0")
# Create indicator variables for each treatment arm.
control <- treatment == 0
group_cue <- treatment == 1
security_cue <- treatment == 2
norms_cue <- treatment == 3
institutions_cue <- treatment == 4
# Recode attitudinal outcomes.
tpnw_atts_danger <- recode(tpnw_atts_danger, "-2 = 2; -1 = 1; 1 = -1; 2 = -2")
tpnw_atts_use_unaccept <- recode(tpnw_atts_use_unaccept, "-2 = 2; -1 = 1;
1 = -1; 2 = -2")
tpnw_atts_always_cheat <- recode(tpnw_atts_always_cheat, "-2 = 2; -1 = 1;
1 = -1; 2 = -2")
tpnw_atts_cannot_elim <- recode(tpnw_atts_cannot_elim, "-2 = 2; -1 = 1;
1 = -1; 2 = -2")
})
# Use mean imputation for missingness.
# Redefine char_cols object.
char_cols <- which(names(tpnw) %in% c(char_vars, meta, "state", "pid_forc",
"income_old", "gender"))
# Define out_vars object.
out_vars <- which(names(tpnw) %in% c("join_tpnw", "n_nukes", "n_tests") |
startsWith(names(tpnw), "tpnw_atts") |
startsWith(names(tpnw), "physical_eff") |
startsWith(names(tpnw), "testing_matrix"))
# Mean impute.
tpnw[,-c(char_cols, out_vars)] <-
data.frame(apply(tpnw[, -c(char_cols, out_vars)], 2, function (x) {
replace(x, is.na(x), mean(x, na.rm = TRUE))
}))
### Clean YouGov data.
## Indicate all non-numeric variables.
# Indicate YouGov metadata variables (e.g., start/end time, respondent ID) that
# may contain characters.
yougov_vars <- c("starttime", "endtime")
# Numericize all numeric variables
aware <- data.frame(apply(aware[, -which(names(aware) %in% yougov_vars)], 2,
as.numeric), aware[which(names(aware) %in% yougov_vars)])
# Coalesce relevant variables.
aware <- within(aware, {
# Clean gender variable to an indicator of female gender (renamed below).
gender <- recode(gender, "8 = NA") - 1
# Transform birthyr variable to age (renamed below).
birthyr <- 2020 - birthyr
# Recode pid3 variable.
pid3 <- recode(pid3, "1 = -1; 2 = 1; 3 = 0; c(5, 8, 9) = NA")
# Recode pid7
pid7 <- recode(pid7, "1 = -3; 2 = -2; 3 = -1; 4 = 0; 5 = 1; 6 = 2; 7 = 3;
c(8, 98) = NA")
# Code pid variable from pid7.
party <- recode(pid7, "c(-3, -2, -1) = -1; c(1, 2, 3) = 1")
# Recode ideology variable.
ideo5 <- recode(ideo5, "c(6, 8, 9) = NA") - 3
# Recode education variable.
educ <- recode(educ, "c(8, 9) = NA")
# Recode state variable.
state <- recode(inputstate, "1 = 'Alabama';
2 = 'Alaska';
4 = 'Arizona';
5 = 'Arkansas';
6 = 'California';
8 = 'Colorado';
9 = 'Connecticut';
10 = 'Delaware';
11 = 'Washington DC';
12 = 'Florida';
13 = 'Georgia';
15 = 'Hawaii';
16 = 'Idaho';
17 = 'Illinois';
18 = 'Indiana';
19 = 'Iowa';
20 = 'Kansas';
21 = 'Kentucky';
22 = 'Louisiana';
23 = 'Maine';
24 = 'Maryland';
25 = 'Massachusetts';
26 = 'Michigan';
27 = 'Minnesota';
28 = 'Mississippi';
29 = 'Missouri';
30 = 'Montana';
31 = 'Nebraska';
32 = 'Nevada';
33 = 'New Hampshire';
34 = 'New Jersey';
35 = 'New Mexico';
36 = 'New York';
37 = 'North Carolina';
38 = 'North Dakota';
39 = 'Ohio';
40 = 'Oklahoma';
41 = 'Oregon';
42 = 'Pennsylvania';
44 = 'Rhode Island';
45 = 'South Carolina';
46 = 'South Dakota';
47 = 'Tennessee';
48 = 'Texas';
49 = 'Utah';
50 = 'Vermont';
51 = 'Virginia';
53 = 'Washington';
54 = 'West Virginia';
55 = 'Wisconsin';
56 = 'Wyoming'")
# Define US Census geographic regions.
northeast <- inputstate %in% c(9, 23, 25, 33, 44, 50, 34, 36, 42)
midwest <- inputstate %in% c(18, 17, 26, 39, 55, 19, 20, 27, 29, 31, 38, 46)
south <- inputstate %in% c(10, 11, 12, 13, 24, 37, 45, 51,
54, 1, 21, 28, 47, 5, 22, 40, 48)
west <- inputstate %in% c(4, 8, 16, 35, 30, 49, 32, 56, 2, 6, 15, 41, 53)
# Recode employment.
employ <- recode(employ, "c(9, 98, 99) = NA")
# Recode outcome.
awareness <- recode(awareness, "8 = NA")
# Normalize weights.
weight <- weight / sum(weight)
})
# Rename demographic questions.
aware <- rename(aware, c("gender" = "female", "birthyr" = "age",
"faminc_new" = "income", "ideo5" = "ideo"))
## Impute missing values.
# Specify non-covariate numerical variables (other is exempted since over 10% of
# responses are missing; state is exempted since the variable is categorical).
non_covars <- names(aware)[names(aware) %in% c("caseid", "starttime", "endtime",
"awareness", "state", "weight")]
# Use mean imputation for missingness in covariates.
aware[, -which(names(aware) %in% non_covars)] <-
data.frame(apply(aware[, -which(names(aware) %in%
non_covars)], 2, function (x) {
replace(x, is.na(x), mean(x, na.rm = TRUE))
}))
### Produce weights for TPNW experimental data using anesrake.
## Create unique identifier variable for assigning weights.
tpnw$caseid <- 1:nrow(tpnw)
## Recode relevant covariates for reweighting: coarsen age; recode female; and
## recode geographic covariates.
# Coarsen age into a categorical variable for age groups.
tpnw$age_wtng <- cut(tpnw$age, c(0, 25, 35, 45, 55, 65, 99))
levels(tpnw$age_wtng) <- c("age1824", "age2534", "age3544",
"age4554", "age5564", "age6599")
# Recode female as a factor to account for NA values.
tpnw$female_wtng <- as.factor(tpnw$female)
levels(tpnw$female_wtng) <- c("male", "na", "female")
# Recode northeast as a factor.
tpnw$northeast_wtng <- as.factor(tpnw$northeast)
levels(tpnw$northeast_wtng) <- c("other", "northeast")
# Recode midwest as a factor.
tpnw$midwest_wtng <- as.factor(tpnw$midwest)
levels(tpnw$midwest_wtng) <- c("other", "midwest")
# Recode south as a factor.
tpnw$south_wtng <- as.factor(tpnw$south)
levels(tpnw$south_wtng) <- c("other", "south")
# Recode west as a factor.
tpnw$west_wtng <- as.factor(tpnw$west)
levels(tpnw$west_wtng) <- c("other", "west")
## Specify population targets for balancing (from US Census 2018 data).
# Specify gender proportion targets and assign names to comport with factors.
femaletarg <- c(.508, 0, .492)
names(femaletarg) <- c("female", "na", "male")
# Specify age-group proportion targets and assign names to comport with factors.
agetarg <- c(29363, 44854, 40659, 41537, 41700, 51080)/249193
names(agetarg) <- c("age1824", "age2534", "age3544",
"age4554", "age5564", "age6599")
# Specify northeast proportion targets and assign names to comport with factors.
northeasttarg <- c(1 - .173, .173)
names(northeasttarg) <- c("other", "northeast")
# Specify midwest proportion targets and assign names to comport with factors.
midwesttarg <- c(1 - .209, .209)
names(midwesttarg) <- c("other", "midwest")
# Specify south proportion targets and assign names to comport with factors.
southtarg <- c(1 - .380, .380)
names(southtarg) <- c("other", "south")
# Specify west proportion targets and assign names to comport with factors.
westtarg <- c(1 - .238, .238)
names(westtarg) <- c("other", "west")
# Create a list of all targets, with names to comport with relevant variables.
targets <- list(femaletarg, agetarg, northeasttarg,
midwesttarg, southtarg, westtarg)
names(targets) <- c("female_wtng", "age_wtng", "northeast_wtng",
"midwest_wtng", "south_wtng", "west_wtng")
# Produce anesrake weights.
anesrake_out <- anesrake(targets, tpnw, caseid = tpnw$caseid,
verbose = TRUE)
# Append anesrake weights to TPNW experimental data.
tpnw$anesrake_weight <- anesrake_out$weightvec
# Remove variables used for weighting.
tpnw <- tpnw[-grep("wtng$", names(tpnw))]
## Write data.
# Write full experimental dataset.
write.csv(tpnw, "data/tpnw_data.csv")
# write full YouGov dataset.
write.csv(aware, "data/tpnw_aware.csv")
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