/
03_table_2.R
82 lines (64 loc) · 2.74 KB
/
03_table_2.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
#
# Delib. in Kirkuk
# Table 2
#
# set directory
setwd(githubdir)
setwd("kirkuk/")
# Load libs
library(tidyr)
library(dplyr)
library(reshape2)
library(broom)
# Read in the data
source("scripts/01_recode.R")
# Table 2: Knowledge
# ----------------------
know <- paste0("know", 1:5)
know_all <- all_dat[, c(know, "know", "cond", "wave")] %>%
group_by(cond, wave) %>%
summarise_all(funs(mean(., na.rm = TRUE)))
# Get Condition/Wave concat
know_all$cond <- paste0(know_all$cond, know_all$wave)
# Transpose
know_all_t <- know_all %>%
gather(key = var_name, value = value, 2:8) %>%
spread_(key = names(know_all)[1], value = "value") %>%
filter(var_name != "wave")
know_all_t$diff_delib <- know_all_t$delib2 - know_all_t$delib1
know_all_t$diff_delib_info <- know_all_t$delib_info2 - know_all_t$delib_info1
# Pooled t1
know_t1_pooled <- all_dat[, c(know, "know", "wave")] %>%
group_by(wave) %>%
filter(wave == 1) %>%
summarise_all(funs(mean(., na.rm = TRUE))) %>%
melt(variable.name = "var_name",
value.name = "t1_pooled")
# Merge t1 pooled and other results
know_all <- know_t1_pooled %>%
left_join(know_all_t) %>%
filter(var_name != "wave")
# p-values (no missing issue as missing = 0)
# ---------------------------------------------
tee_1 <- paste0(c(know, "know"), "_t1")
tee_2 <- paste0(c(know, "know"),"_t2")
diff_delib <- wall_dat[wall_dat$cond_t1 == "delib", tee_2] - wall_dat[wall_dat$cond_t1 == "delib", tee_1]
diff_delib <- subset(diff_delib, select = tee_2)
res_delib <- do.call(rbind, lapply(diff_delib, function(x) tidy(t.test(x, mu = 0))))
names(res_delib) <- paste0(names(res_delib), "_d")
res_delib$var_name <- gsub("_t2", "", rownames(res_delib))
res_delib <- subset(res_delib, select = c("var_name", "estimate_d", "p.value_d"))
diff_delib_info <- wall_dat[wall_dat$cond_t1 == "delib_info", tee_2] - wall_dat[wall_dat$cond_t1 == "delib_info", tee_1]
diff_delib_info <- subset(diff_delib_info, select = tee_2)
res_delib_info <- do.call(rbind, lapply(diff_delib_info, function(x) tidy(t.test(x, mu = 0))))
names(res_delib_info) <- paste0(names(res_delib_info), "_di")
res_delib_info$var_name_di <- gsub("_t2", "", rownames(res_delib_info))
res_delib_info <- subset(res_delib_info, select = c("var_name_di", "estimate_di", "p.value_di"))
tab_2 <- know_all %>%
left_join(res_delib) %>%
left_join(res_delib_info, by = c("var_name" = "var_name_di"))
tab_2_col_order <- c("var_name", "t1_pooled", "control1",
"delib1", "delib2", "diff_delib", "p.value_d", "estimate_d",
"delib_info1", "delib_info2", "diff_delib_info", "p.value_di", "estimate_di")
tab_2 <- tab_2[, tab_2_col_order]
write.csv(tab_2, file = "tabs/02_table_2_know.csv", row.names = F)