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03a_dom_educ.R
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03a_dom_educ.R
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"
Deliberative Distortions
Table 3 and 4: Description of Unequal Influence: Hi/Low Educ
"
# Set basedir
setwd(githubdir)
setwd("distortions/")
# Set StringsAsFactors as FALSE
options(stringsAsFactors = FALSE)
# Load data
dpdat <- read.csv("data/polardata.csv")
att_indices <- read.csv("data/poll_indices.csv")
# Load library
if ("package:dplyr" %in% search()) detach(package:dplyr)
library(plyr)
library(goji)
# Table 3
# Output: Poll_name, No. of small Groups, No. of Indices, Frequency (disadv), Extent (disadv), Frequency (Group), Extent (Group)
# Towards better educated
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# No. of groups*No. of indices
res <- data.frame(pollname = 1:21, pollnum = NA, ngroups = NA, nindices = NA, freqdis = NA, extdis = NA, freqgrp = NA, extgrp = NA)
res_normed <- data.frame(pollname = 1:21, pollnum = NA, ngroups = NA, nindices = NA, extgrp = NA, extdis = NA, extgrp_normed = NA, extdis_normed = NA)
freqgrp_grp <- extgrp_grp <- freqdis_grp <- extdis_grp <- unique_id <- group_id <- poll_id <- poll_name <- c()
k <- 1
# Iterate over polls
for(i in unique(dpdat$pollid)) {
# For poll i: vector of variable names
t1vars <- as.character(att_indices$t1var[att_indices$poll_id == i])
t2vars <- as.character(att_indices$t2_t3var[att_indices$poll_id == i])
# Data from poll i from respondents for whom bettered is not missing
smdata <- dpdat[dpdat$pollid == i & !is.na(dpdat$bettered), ]
# Initializing result vectors
freqdis <- extdis <- freqgrp <- extgrp <- uniqueid <- groupid <- pollid <- pollname <- grpex_normed <- disgrpex_normed <- c()
# Loop over all variables
for(j in 1:length(t1vars)){
# Group mean at t1 and t2
grpindex <- ddply(smdata, .(pollgroup), summarise,
t1mean = mean(get(t1vars[j]), na.rm = T),
t2mean = mean(get(t2vars[j]), na.rm = T))
# Subgroup means at t1 and t2
subgrpindex <- ddply(smdata, .(pollgroup, bettered), summarise,
t1mean = nona(mean(get(t1vars[j]), na.rm = T)),
t2mean = nona(mean(get(t2vars[j]), na.rm = T)))
# Remove groups in which there is no person from one of the subgroups. For e.g., no low educated person
grpindex <- grpindex[match(subgrpindex$pollgroup[duplicated(subgrpindex$pollgroup)], grpindex$pollgroup), ]
subgrpindex <- subgrpindex[(subgrpindex$pollgroup %in% subgrpindex$pollgroup[duplicated(subgrpindex$pollgroup)]), ]
t1_grp <- grpindex$t1mean - subgrpindex$t1mean[subgrpindex$bettered == 0]
t2_grp <- grpindex$t2mean - subgrpindex$t1mean[subgrpindex$bettered == 0]
signed_grp <- NA
signed_grp[t1_grp < 0 & grpindex$t2mean > grpindex$t1mean] <- (t2_grp - t1_grp)[t1_grp < 0 & grpindex$t2mean > grpindex$t1mean]
signed_grp[t1_grp < 0 & grpindex$t2mean < grpindex$t1mean] <- (t2_grp - t1_grp)[t1_grp < 0 & grpindex$t2mean < grpindex$t1mean]
signed_grp[t1_grp > 0 & grpindex$t2mean > grpindex$t1mean] <- -(t2_grp - t1_grp)[t1_grp > 0 & grpindex$t2mean > grpindex$t1mean]
signed_grp[t1_grp > 0 & grpindex$t2mean < grpindex$t1mean] <- -(t2_grp - t1_grp)[t1_grp > 0 & grpindex$t2mean < grpindex$t1mean]
signed_grp[t1_grp == 0] <- 0
t1_disgrp <- subgrpindex$t1mean[subgrpindex$bettered == 1] - subgrpindex$t1mean[subgrpindex$bettered == 0]
t2_disgrp <- subgrpindex$t2mean[subgrpindex$bettered == 1] - subgrpindex$t1mean[subgrpindex$bettered == 0]
signed_disgrp <- NA
signed_disgrp[t1_disgrp < 0 & subgrpindex$t2mean[subgrpindex$bettered == 1] > subgrpindex$t1mean[subgrpindex$bettered == 0]] <- (t2_disgrp - t1_disgrp)[t1_disgrp < 0 & subgrpindex$t2mean[subgrpindex$bettered == 1] > subgrpindex$t1mean[subgrpindex$bettered == 0]]
signed_disgrp[t1_disgrp < 0 & subgrpindex$t2mean[subgrpindex$bettered == 1] < subgrpindex$t1mean[subgrpindex$bettered == 0]] <- (t2_disgrp - t1_disgrp)[t1_disgrp < 0 & subgrpindex$t2mean[subgrpindex$bettered == 1] < subgrpindex$t1mean[subgrpindex$bettered == 0]]
signed_disgrp[t1_disgrp > 0 & subgrpindex$t2mean[subgrpindex$bettered == 1] > subgrpindex$t1mean[subgrpindex$bettered == 0]] <- -(t2_disgrp - t1_disgrp)[t1_disgrp > 0 & subgrpindex$t2mean[subgrpindex$bettered == 1] > subgrpindex$t1mean[subgrpindex$bettered == 0]]
signed_disgrp[t1_disgrp > 0 & subgrpindex$t2mean[subgrpindex$bettered == 1] < subgrpindex$t1mean[subgrpindex$bettered == 0]] <- -(t2_disgrp - t1_disgrp)[t1_disgrp > 0 & subgrpindex$t2mean[subgrpindex$bettered == 1] < subgrpindex$t1mean[subgrpindex$bettered == 0]]
signed_disgrp[t1_disgrp == 0] <- 0
freqgrp = append(freqgrp, signed_grp > 0)
extgrp = append(extgrp, signed_grp)
freqdis = append(freqdis, signed_disgrp > 0)
extdis = append(extdis, signed_disgrp)
grpex_normed <- append(grpex_normed, ifelse(grpindex$t2mean > grpindex$t1mean, signed_grp/(1 - grpindex$t1mean), ifelse(grpindex$t2mean < grpindex$t1mean, signed_grp/grpindex$t1mean, 0)))
disgrpex_normed <- append(disgrpex_normed, ifelse(subgrpindex$t2mean[subgrpindex$bettered == 1] > subgrpindex$t1mean[subgrpindex$bettered == 1],
signed_disgrp/(1 - subgrpindex$t1mean[subgrpindex$bettered == 1]),
ifelse(subgrpindex$t2mean[subgrpindex$bettered == 1] < subgrpindex$t1mean[subgrpindex$bettered == 1], signed_disgrp/subgrpindex$t1mean[subgrpindex$bettered == 1], 0)))
uniqueid <- append(uniqueid, paste0(grpindex$pollgroup, t1vars[j]))
pollid <- append(pollid, rep(att_indices$dpnum[att_indices$poll_id == i][1], length(grpindex$pollgroup)))
pollname <- append(pollname, rep(att_indices$poll_name[att_indices$poll_id == i][1], length(grpindex$pollgroup)))
groupid <- append(groupid, grpindex$pollgroup)
}
# Preserve grp issue pairs
freqgrp_grp <- append(freqgrp_grp, freqgrp)
extgrp_grp <- append(extgrp_grp, extgrp)
freqdis_grp <- append(freqdis_grp, freqdis)
extdis_grp <- append(extdis_grp, extdis)
unique_id <- append(unique_id, uniqueid)
poll_id <- append(poll_id, pollid)
poll_name <- append(poll_name, pollname)
group_id <- append(group_id, groupid)
# For each poll, out data
res[k, ] <- c(unique(as.character(smdata$pollname)), att_indices$dpnum[att_indices$poll_id == i][1], length(unique(smdata$pollgroup)), mean(smdata$numindices), mean(freqdis, na.rm = T), mean(extdis, na.rm = T), mean(freqgrp, na.rm = T), mean(extgrp, na.rm = T))
# Normed/For each poll, out data
res_normed[k, ] <- c(unique(as.character(smdata$pollname)), att_indices$dpnum[att_indices$poll_id == i][1], length(unique(smdata$pollgroup)), mean(smdata$numindices), mean(extgrp, na.rm = T), mean(extdis, na.rm = T), mean(grpex_normed, na.rm = T), mean(disgrpex_normed, na.rm = T))
k = k + 1
}
# Reorder
res <- res[order(as.numeric(res$pollnum)), ]
res_normed <- res_normed[order(as.numeric(res_normed$pollnum)), ]
# Add the means
res[(nrow(res) + 1), ] <- c("Mean", colMeans(sapply(res[, 2:8], as.numeric)))
res[(nrow(res) + 1), ] <- c("Weighted Mean (By Indices and Groups)", sapply(lapply(res[, 2:8], as.numeric), weighted.mean, w = as.numeric(res$ngroups)*as.numeric(res$nindices)))
# Write Results
write.csv(res, file = "tabs/04_table_4b_toward_highed.csv", row.names = F)
# Normed
write.csv(res_normed, file = "tabs/04_table_4b_toward_highed_normed.csv", row.names = F)
# To allow for correlating with D, produce getall
getall <- data.frame(unique_id = unique_id, poll_id = poll_id, poll_name = poll_name, group_id = group_id, freqgrp_grp = freqgrp_grp, extgrp_grp = extgrp_grp, freqdis_grp = freqdis_grp, extdis_grp =extdis_grp)
write.csv(getall, file = "tabs/03_dom_ed_by_group_issue.csv", row.names = F)
# Toward Lower Ed
# ~~~~~~~~~~~~~~~~~~~~~~~
# No. of groups*No. of indices
res <- data.frame(pollname = 1:21, pollnum = NA, ngroups = NA, nindices = NA, freqdis = NA, extdis = NA, freqgrp=NA, extgrp = NA)
res_normed <- data.frame(pollname = 1:21, pollnum = NA, ngroups = NA, nindices = NA, extgrp = NA, extdis = NA, extgrp_normed = NA, extdis_normed = NA)
freqgrp_grp <- extgrp_grp <- freqdis_grp <- extdis_grp <- unique_id <- group_id <- poll_id <- c()
k <- 1
for(i in unique(dpdat$pollid))
{
# For poll i: vector of variable names
t1vars <- as.character(att_indices$t1var[att_indices$poll_id == i])
t2vars <- as.character(att_indices$t2_t3var[att_indices$poll_id == i])
# Data from poll i from respondents for whom bettered is not missing
smdata <- dpdat[dpdat$pollid == i & !is.na(dpdat$bettered), ]
# Initializing result vectors
freqdis <- extdis <- freqgrp <- extgrp <- uniqueid <- groupid <- pollid <- grpex_normed <- disgrpex_normed <- c()
# Loop over all variables
for(j in 1:length(t1vars)){
# Group mean at t1 and t2
grpindex <- ddply(smdata, .(pollgroup), summarise,
t1mean = mean(get(t1vars[j]), na.rm = T),
t2mean = mean(get(t2vars[j]), na.rm = T))
# Subgroup means at t1 and t2
subgrpindex <- ddply(smdata, .(pollgroup, bettered), summarise,
t1mean = nona(mean(get(t1vars[j]), na.rm = T)),
t2mean = nona(mean(get(t2vars[j]), na.rm = T)))
# Remove groups in which there is no person from one of the subgroups. For e.g., no low educated person
grpindex <- grpindex[match(subgrpindex$pollgroup[duplicated(subgrpindex$pollgroup)], grpindex$pollgroup), ]
subgrpindex <- subgrpindex[(subgrpindex$pollgroup %in% subgrpindex$pollgroup[duplicated(subgrpindex$pollgroup)]), ]
# Is the group t2 mean closer to the t1 better educated mean than the t1
# abs(t2mean - t1mean(high_ed)) < abs(t1mean - t1mean(high_ed))
# If true: Group has moved towards t1 low ed.
#freqgrp = append(freqgrp, abs(grpindex$t2mean - subgrpindex$t1mean[subgrpindex$bettered == 1]) < abs(grpindex$t1mean - subgrpindex$t1mean[subgrpindex$bettered == 1]))
#extgrp = append(extgrp, abs(grpindex$t2mean - subgrpindex$t1mean[subgrpindex$bettered == 1]) - abs(grpindex$t1mean - subgrpindex$t1mean[subgrpindex$bettered == 1]))
# Is the t2 low_ed att. mean closer to t1 high_ed. att mean than
# abs(t2mean(low_ed) - t1mean(high_ed)) < abs(t1mean(low_ed) - t1mean(high_ed))
# If true: High ed. have moved towards t1 low ed.
#freqdis = append(freqdis, abs(subgrpindex$t2mean[subgrpindex$bettered == 0] - subgrpindex$t1mean[subgrpindex$bettered == 1]) < abs(subgrpindex$t1mean[subgrpindex$bettered == 0] - subgrpindex$t1mean[subgrpindex$bettered == 1]))
#extdis = append(extdis, abs(subgrpindex$t2mean[subgrpindex$bettered == 0] - subgrpindex$t1mean[subgrpindex$bettered == 1]) - abs(subgrpindex$t1mean[subgrpindex$bettered == 0] - subgrpindex$t1mean[subgrpindex$bettered == 1]))
#t1_grp <- abs(grpindex$t1mean - subgrpindex$t1mean[subgrpindex$bettered == 1])
#t2_grp <- abs(grpindex$t2mean - subgrpindex$t1mean[subgrpindex$bettered == 1])
#t1_disgrp <- abs(subgrpindex$t1mean[subgrpindex$bettered == 0] - subgrpindex$t1mean[subgrpindex$bettered == 1])
#t2_disgrp <- abs(subgrpindex$t2mean[subgrpindex$bettered == 0] - subgrpindex$t1mean[subgrpindex$bettered == 1])
#freqgrp = append(freqgrp, t2_grp < t1_grp)
#extgrp = append(extgrp, t2_grp - t1_grp)
#freqdis = append(freqdis, t2_disgrp < t1_disgrp)
#extdis = append(extdis, t2_disgrp - t1_disgrp)
t1_grp <- grpindex$t1mean - subgrpindex$t1mean[subgrpindex$bettered == 1]
t2_grp <- grpindex$t2mean - subgrpindex$t1mean[subgrpindex$bettered == 1]
signed_grp <- NA
signed_grp[t1_grp < 0 & grpindex$t2mean > grpindex$t1mean] <- (t2_grp - t1_grp)[t1_grp < 0 & grpindex$t2mean > grpindex$t1mean]
signed_grp[t1_grp < 0 & grpindex$t2mean < grpindex$t1mean] <- (t2_grp - t1_grp)[t1_grp < 0 & grpindex$t2mean < grpindex$t1mean]
signed_grp[t1_grp > 0 & grpindex$t2mean > grpindex$t1mean] <- -(t2_grp - t1_grp)[t1_grp > 0 & grpindex$t2mean > grpindex$t1mean]
signed_grp[t1_grp > 0 & grpindex$t2mean < grpindex$t1mean] <- -(t2_grp - t1_grp)[t1_grp > 0 & grpindex$t2mean < grpindex$t1mean]
signed_grp[t1_grp == 0] <- 0
t1_disgrp <- subgrpindex$t1mean[subgrpindex$bettered == 0] - subgrpindex$t1mean[subgrpindex$bettered == 1]
t2_disgrp <- subgrpindex$t2mean[subgrpindex$bettered == 0] - subgrpindex$t1mean[subgrpindex$bettered == 1]
signed_disgrp <- NA
signed_disgrp[t1_disgrp < 0 & subgrpindex$t2mean[subgrpindex$bettered == 0] > subgrpindex$t1mean[subgrpindex$bettered == 1]] <- (t2_disgrp - t1_disgrp)[t1_disgrp < 0 & subgrpindex$t2mean[subgrpindex$bettered == 0] > subgrpindex$t1mean[subgrpindex$bettered == 1]]
signed_disgrp[t1_disgrp < 0 & subgrpindex$t2mean[subgrpindex$bettered == 0] < subgrpindex$t1mean[subgrpindex$bettered == 1]] <- (t2_disgrp - t1_disgrp)[t1_disgrp < 0 & subgrpindex$t2mean[subgrpindex$bettered == 0] < subgrpindex$t1mean[subgrpindex$bettered == 1]]
signed_disgrp[t1_disgrp > 0 & subgrpindex$t2mean[subgrpindex$bettered == 0] > subgrpindex$t1mean[subgrpindex$bettered == 1]] <- -(t2_disgrp - t1_disgrp)[t1_disgrp > 0 & subgrpindex$t2mean[subgrpindex$bettered == 0] > subgrpindex$t1mean[subgrpindex$bettered == 1]]
signed_disgrp[t1_disgrp > 0 & subgrpindex$t2mean[subgrpindex$bettered == 0] < subgrpindex$t1mean[subgrpindex$bettered == 1]] <- -(t2_disgrp - t1_disgrp)[t1_disgrp > 0 & subgrpindex$t2mean[subgrpindex$bettered == 0] < subgrpindex$t1mean[subgrpindex$bettered == 1]]
signed_disgrp[t1_disgrp == 0] <- 0
freqgrp = append(freqgrp, signed_grp > 0)
extgrp = append(extgrp, signed_grp)
freqdis = append(freqdis, signed_disgrp > 0)
extdis = append(extdis, signed_disgrp)
grpex_normed <- append(grpex_normed, ifelse(grpindex$t2mean > grpindex$t1mean, signed_grp/(1 - grpindex$t1mean), ifelse(grpindex$t2mean < grpindex$t1mean, signed_grp/grpindex$t1mean, 0)))
disgrpex_normed <- append(disgrpex_normed, ifelse(subgrpindex$t2mean[subgrpindex$bettered == 0] > subgrpindex$t1mean[subgrpindex$bettered == 0],
signed_disgrp/(1 - subgrpindex$t1mean[subgrpindex$bettered == 0]),
ifelse(subgrpindex$t2mean[subgrpindex$bettered == 0] < subgrpindex$t1mean[subgrpindex$bettered == 0], signed_disgrp/subgrpindex$t1mean[subgrpindex$bettered == 0], 0)))
uniqueid <- append(uniqueid, paste0(grpindex$pollgroup, t1vars[j]))
pollid <- append(pollid, rep(att_indices$dpnum[att_indices$poll_id == i][1], length(grpindex$pollgroup)))
groupid <- append(groupid, grpindex$pollgroup)
}
# Preserve grp issue pairs
freqgrp_grp <- append(freqgrp_grp, freqgrp)
extgrp_grp <- append(extgrp_grp, extgrp)
freqdis_grp <- append(freqdis_grp, freqdis)
extdis_grp <- append(extdis_grp, extdis)
unique_id <- append(unique_id, uniqueid)
poll_id <- append(poll_id, pollid)
group_id <- append(group_id, groupid)
# For each poll, out data
res[k, ] <- c(unique(as.character(smdata$pollname)), att_indices$dpnum[att_indices$poll_id == i][1], length(unique(smdata$pollgroup)), mean(smdata$numindices), mean(freqdis, na.rm = T), mean(extdis, na.rm = T), mean(freqgrp, na.rm = T), mean(extgrp, na.rm = T))
# Normed/For each poll, out data
res_normed[k, ] <- c(unique(as.character(smdata$pollname)), att_indices$dpnum[att_indices$poll_id == i][1], length(unique(smdata$pollgroup)), mean(smdata$numindices), mean(extgrp, na.rm = T), mean(extdis, na.rm = T), mean(grpex_normed, na.rm = T), mean(disgrpex_normed, na.rm = T))
k <- k + 1
}
# Reorder
res <- res[order(as.numeric(res$pollnum)), ]
res_normed <- res_normed[order(as.numeric(res_normed$pollnum)), ]
# Add the means
res[(nrow(res) + 1), ] <- c("Mean", colMeans(sapply(res[, 2:8], as.numeric)))
res[(nrow(res) + 1), ] <- c("Weighted Mean (By Indices and Groups)", sapply(lapply(res[, 2:8], as.numeric), weighted.mean, w = as.numeric(res$ngroups)*as.numeric(res$nindices)))
# Write Results
write.csv(res, file = "tabs/04_table_4b_toward_lowed.csv", row.names = F)
# Normed
write.csv(res_normed, file = "tabs/04_table_4b_toward_lowed_normed.csv", row.names = F)
# To allow for correlating with D, produce getall
getall <- data.frame(unique_id = unique_id, poll_id = poll_id, group_id = group_id, freqgrp_grp = freqgrp_grp, extgrp_grp = extgrp_grp, freqdis_grp = freqdis_grp, extdis_grp = extdis_grp)
write.csv(getall, file = "tabs/03_dom_lowed_by_group_issue.csv", row.names = F)