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+
+## 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

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