| ## This is code to take the full dataset and produce the numbers in the | |
| ## flowchart for study 2 in the appendix | |
| library(tidyverse) | |
| library(haven) | |
| library(data.table) | |
| ## Can set your working directory here to the folder | |
| setwd("~/Dropbox/GKMR/PapersPresentations/2020Submission/Additions_PostReplication/code") | |
| ## Read in the pre and post data and the one used in the replication | |
| s02post <- read_csv("stud02_post.csv") %>% as.data.table() | |
| s02pre <- read_csv("stud02_pre.csv") %>% as.data.table() | |
| ## Change names to allow distinguishing pre- from post- variables | |
| setnames(s02pre, paste0("pre_", names(s02pre))) | |
| setnames(s02pre, "pre_Voter ID", "Voter_ID") #but keep the merging var the same | |
| ## Remove the one duplicated respondent | |
| s02pre <- s02pre[!(duplicated(Voter_ID))] | |
| ## Number invited to the wave 2 survey | |
| nrow(s02pre) | |
| ## [1] 3623 | |
| ## Combine | |
| setkey(s02pre, "Voter_ID") | |
| setkey(s02post, "Voter_ID") | |
| s02comb <- s02pre[s02post] | |
| ## Create the treatment condition variable | |
| ## First for those in control | |
| myvars <- str_subset(names(s02comb), "PositiveLegal") | |
| myvars | |
| ## [1] "FL_238_DO_PositiveLegal" "FL_268_DO_PositiveLegal" | |
| ## [3] "FL_265_DO_PositiveLegal" "FL_262_DO_PositiveLegal" | |
| s02comb[, condition := NA_integer_] | |
| s02comb[rowSums(s02comb[, ..myvars], na.rm = TRUE) == 1, condition := 0] | |
| ## Now for those in treatment | |
| myvars <- str_subset(names(s02comb), "PositiveIllegal") | |
| ## [1] "FL_238_DO_PositiveIllegal" "FL_268_DO_PositiveIllegal" | |
| ## [3] "FL_265_DO_PositiveIllegal" "FL_262_DO_PositiveIllegal" | |
| s02comb[rowSums(s02comb[, ..myvars], na.rm = TRUE) == 1, condition := 1] | |
| ## Number that didn't respond is difference between the pre and combined surveys | |
| nrow(s02pre) | |
| ## [1] 3623 | |
| nrow(s02comb) | |
| ## [1] 2632 | |
| nrow(s02pre) - nrow(s02comb) | |
| ## [1] 991 | |
| ## There are also people in this dataset who finished/started taking the survey | |
| ## after it closed; eliminate those here | |
| nrow(s02comb) | |
| ## [1] 2632 | |
| s02comb <- s02comb[as.POSIXct(EndDate) < as.POSIXct("2015-09-16 16:05:00 MDT")] | |
| nrow(s02comb) | |
| ## [1] 2399 | |
| 2632 - 2399 #233 removed for this reason | |
| ## How many in each condition now? | |
| table(s02comb$condition, useNA = "ifany") | |
| ## 0 1 <NA> | |
| ## 1065 1062 272 | |
| ## Remove the 272 respondents who were never assigned to a treatment condition | |
| s02comb <- s02comb[!is.na(condition)] | |
| nrow(s02comb) #new n-size is 2127 | |
| ## Who did not finish the survey? | |
| table(s02comb$condition, s02comb$Finished, useNA = "ifany") | |
| ## 0 1 | |
| ## 0 8 1057 | |
| ## 1 9 1053 | |
| ## 8 in control and 9 in treatment that didn't finish | |
| ## Remove those who did not finish the survey | |
| s02comb <- s02comb[Finished == 1] | |
| nrow(s02comb) #new n-size is 2110 | |
| ## What about non-whites/Latinos? | |
| ## In this raw dataset, this is asked in the question pre_Q58 | |
| table(s02comb$pre_Q58, useNA = "ifany") | |
| ## 1 2 3 4 5 6 7 8 <NA> | |
| ## 3 10 3 44 1977 14 14 40 5 | |
| ## Everyone labeled as a 5 is white, so look at 5 vs other | |
| table(s02comb$condition, as.integer(s02comb$pre_Q58 == 5), useNA = "ifany") | |
| ## 0 1 <NA> | |
| ## 0 74 978 5 | |
| ## 1 54 999 0 | |
| ## 74 in control and 54 in treatment that are non-white or Latino - for the NAs, | |
| ## keep those in (which in effect assumes that they are white) | |
| ## Remove those who are non-white or Latino | |
| s02comb <- s02comb[!(pre_Q58 %in% c(1:4, 6:8))] | |
| nrow(s02comb) #new n-size is 1982 | |
| table(s02comb$condition, useNA = "ifany") | |
| ## 0 1 | |
| ## 983 999 | |
| ## This gives us numbers that correspond to the final row of the flowchart | |
| ## in the appendix | |