### Initialize workspace. 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")