analysis/01_data.R
In mikajoh/descr2: Rearch Compendium for the "DESCREP2" Project
## Mikael Poul Johannesson (mikajoh@gmail.com)
## 2018
## Start matter ------------------------------------------------------
library(here)
library(haven)
library(tidyverse)
## Some sanity tests for the finished data, should not be needed.
run_tests <- TRUE
if (run_tests) library(assertthat)
## Get data ----------------------------------------------------------
## EIPS Iceland.
## Md5sum: 492e6210db4b3fcb047819bdf0bd2955
## tools::md5sum(here("data", "EIPS_is.sav"))
is_raw <- read_sav(here("data", "EIPS_is.sav"))
## EIPS Netherlands.
## Md5sum: 2fcad34e4ddda3c8ff18d17f94878b8a
## tools::md5sum(here("data", "L_PanelCollaboration_wave4_4p_EN.sav"))
nl_raw <- read_sav(here("data", "L_PanelCollaboration_wave4_4p_EN.sav"))
## EIPS Norway.
## Md5sum: 74d22c43548599e69ea50b78f8630ce3
## tools::md5sum(here("data", "Norwegian Citizen Panel - wave 9 EN.sav"))
no_raw <- read_sav(here("data", "Norwegian Citizen Panel - wave 9 EN.sav"))
## EIPS Sweden.
## Md5sum: 2ebae375d080fb29e4d2ed3586144ce3
## tools::md5sum(here("data", "EIPS2017_Swedish_Citizen_Panel_20180112.dta"))
se_raw <- read_dta(here("data", "EIPS2017_Swedish_Citizen_Panel_20180112.dta"))
## EIPS France.
## Md5sum: d910c074786ce467fc4aa9293d1689f1
## tools::md5sum(here("data", "EIPS2017-France.sav"))
fr_raw <- read_sav(here("data", "EIPS2017-France.sav"))
## Prepare Iceland ---------------------------------------------------
is_01 <-
is_raw %>%
mutate(
rsp_id = as.numeric(paste0(100, row_number())),
rsp_country = "Iceland",
rsp_gender = case_when(
kyn == 1 ~ "Male",
kyn == 2 ~ "Female"),
rsp_gender = factor(rsp_gender),
rsp_age = as.numeric(aldur),
rsp_edu = case_when(
menntun == 1 ~ "Lower",
menntun == 2 ~ "Intermediate",
menntun == 3 ~ "Intermediate",
menntun == 4 ~ "Higher",
menntun == 5 ~ "Higher",
menntun == 6 ~ "Higher",
menntun == 7 ~ "Higher",
menntun == 8 ~ "Higher")
)
is_02 <-
is_01 %>%
gather(var, value, matches("EIPS2017G_[12]_\\w\\w\\w.*"), na.rm = TRUE) %>%
mutate(
cnd_n = as.numeric(gsub("EIPS2017G_([12])_.*", "\\1", var)),
var = gsub("EIPS2017G_[12]_", "", var)) %>%
spread(var, value) %>%
mutate(
cnd_age = case_when(
age == 1 ~ "28",
age == 2 ~ "45",
age == 3 ~ "71"),
cnd_edu = case_when(
edu == 0 ~ "High school",
edu == 1 ~ "University level",
edu == 2 ~ "University level"),
cnd_gender = case_when(
gender == 0 ~ "Male",
gender == 1 ~ "Female"),
cnd_religion = case_when(
religion == 1 ~ "No religion",
religion == 2 ~ "Christian",
religion == 3 ~ "Non-practicing Christian",
religion == 4 ~ "Muslim",
religion == 5 ~ "Non-praciticing Muslim"),
cnd_class = case_when(
class == 1 ~ "Working class",
class == 2 ~ "Middle class",
class == 3 ~ "Upper class"),
EIPS2017G_1_dv = ifelse(EIPS2017G_1_dv %in% 96:99, NA, EIPS2017G_1_dv),
EIPS2017G_2_dv = ifelse(EIPS2017G_2_dv %in% 96:99, NA, EIPS2017G_2_dv),
exp_post = ifelse(!is.na(EIPS2017G_1_dv), EIPS2017G_1_dv, EIPS2017G_2_dv),
cnd_post = case_when(
exp_post == cnd_n ~ 1,
exp_post != cnd_n ~ 0),
exp_treat = ifelse(!is.na(EIPS2017G_1_dv), "Closest candidate", "Prefered candidate"),
exp_treat = ifelse(is.na(EIPS2017G_1_dv) & is.na(EIPS2017G_2_dv), NA, exp_treat)
) %>%
select(matches("^rsp_"), matches("^exp_"), matches("^cnd_"))
is <- is_02
## Prepare Netherlands -----------------------------------------------
nl_01 <-
nl_raw %>%
mutate(
rsp_id = as.numeric(paste0(200, nomem_encr)),
rsp_country = "Netherlands",
rsp_gender = case_when(
geslacht == 1 ~ "Male",
geslacht == 2 ~ "Female"),
rsp_age = as.numeric(leeftijd),
rsp_popdensity = 6 - as.numeric(sted),
rsp_edu = case_when(
oplcat == 1 ~ "Lower",
oplcat %in% 2:4 ~ "Intermediate",
oplcat %in% 5:6 ~ "Higher"),
rsp_inc = as.numeric(nettocat),
rsp_inc_cat = case_when(
rsp_inc %in% 0:3 ~ "Low inc",
rsp_inc %in% 4:12 ~ "High inc"
)
) %>%
filter(rsp_age >= 18)
nl_02 <-
nl_01 %>%
gather(var, value, matches("EIPS2017G_[12]_.*"), na.rm = TRUE) %>%
mutate(
cnd_n = as.numeric(gsub("EIPS2017G_([12])_.*", "\\1", var)),
var = gsub("EIPS2017G_[12]_", "", var)
) %>%
spread(var, value) %>%
mutate(
cnd_age = case_when(
age == 1 ~ "28",
age == 2 ~ "45",
age == 3 ~ "71"),
cnd_edu = case_when(
edu == 1 ~ "High school",
edu == 2 ~ "University level"),
cnd_gender = case_when(
gender == 1 ~ "Male",
gender == 2 ~ "Female"),
cnd_religion = case_when(
religion == 1 ~ "No religion",
religion == 2 ~ "Christian",
religion == 3 ~ "Non-practicing Christian",
religion == 4 ~ "Muslim",
religion == 5 ~ "Non-praciticing Muslim"),
cnd_class = case_when(
class == 1 ~ "Working class",
class == 2 ~ "Middle class",
class == 3 ~ "Upper class"),
exp_post = ifelse(!is.na(EIPS2017G_1), EIPS2017G_1, EIPS2017G_2),
cnd_post = case_when(
exp_post == cnd_n ~ 1,
exp_post != cnd_n ~ 0),
exp_treat = ifelse(!is.na(EIPS2017G_1), "Closest candidate", "Prefered candidate"),
exp_treat = ifelse(is.na(EIPS2017G_1) & is.na(EIPS2017G_2), NA, exp_treat)
) %>%
select(matches("^rsp_"), matches("^exp_"), matches("^cnd_"))
nl <- nl_02
## Prepare Norway ----------------------------------------------------
no_01 <-
no_raw %>%
mutate(
rsp_id = as.numeric(paste0(300, responseid)),
rsp_country = "Norway",
rsp_gender = case_when(
R9P1 == 1 ~ "Male",
R9P1 == 2 ~ "Female"),
rsp_age_10a = case_when(
R9P5_1 == 1 ~ "18-25",
R9P5_1 == 2 ~ "26-35",
R9P5_1 == 3 ~ "36-45",
R9P5_1 == 4 ~ "46-55",
R9P5_1 == 5 ~ "56-65",
R9P5_1 == 6 ~ "66-75",
R9P5_1 == 7 ~ "75 <"),
rsp_edu = case_when(
R9P4_1 == 1 ~ "Lower",
R9P4_1 == 2 ~ "Intermediate",
R9P4_1 == 3 ~ "Higher")
)
no_02 <-
no_01 %>%
gather(var, value, matches("eips2017g_[12]_.*"), na.rm = TRUE) %>%
mutate(
cnd_n = as.numeric(gsub("eips2017g_([12])_.*", "\\1", var)),
var = gsub("eips2017g_[12]_", "", var)
) %>%
spread(var, value) %>%
mutate(
cnd_age = case_when(
age == 1 ~ "28",
age == 2 ~ "45",
age == 3 ~ "71"),
cnd_edu = case_when(
edu == 0 ~ "High school",
edu == 1 ~ "University level"),
cnd_gender = case_when(
gender == 0 ~ "Male",
gender == 1 ~ "Female"),
cnd_religion = case_when(
religion == 1 ~ "No religion",
religion == 2 ~ "Christian",
religion == 3 ~ "Non-practicing Christian",
religion == 4 ~ "Muslim",
religion == 5 ~ "Non-praciticing Muslim"),
cnd_class = case_when(
class == 1 ~ "Working class",
class == 2 ~ "Middle class",
class == 3 ~ "Upper class"),
eips2017g_1 = ifelse(eips2017g_1 %in% 97:98, NA, eips2017g_1),
eips2017g_2 = ifelse(eips2017g_2 %in% 97:98, NA, eips2017g_2),
exp_post = ifelse(!is.na(eips2017g_1), eips2017g_1, eips2017g_2),
cnd_post = case_when(
exp_post == cnd_n ~ 1,
exp_post != cnd_n ~ 0),
exp_treat = ifelse(!is.na(eips2017g_1), "Closest candidate", "Prefered candidate"),
exp_treat = ifelse(is.na(eips2017g_1) & is.na(eips2017g_2), NA, exp_treat)
) %>%
select(matches("^rsp_"), matches("^exp_"), matches("^cnd_"))
no <- no_02
## Prepare Sweden ----------------------------------------------------
se_01 <-
se_raw %>%
mutate(
rsp_id = as.numeric(paste0(400, id)),
rsp_country = "Sweden",
rsp_gender = case_when(
sex == 2 ~ "Male",
sex == 1 ~ "Female"),
rsp_age = as.numeric(age),
rsp_edu = case_when(
edu3 == 1 ~ "Lower",
edu3 == 2 ~ "Intermediate",
edu3 == 3 ~ "Higher")
)
se_02 <-
se_01 %>%
select(-edu, -age) %>%
gather(var, value, matches("EIPS2017G_[12]_\\w\\w\\w.*"), na.rm = TRUE) %>%
mutate(
cnd_n = as.numeric(gsub("EIPS2017G_([12])_.*", "\\1", var)),
var = gsub("EIPS2017G_[12]_", "", var)) %>%
spread(var, value) %>%
mutate(
cnd_age = case_when(
age == 1 ~ "28",
age == 2 ~ "45",
age == 3 ~ "71"),
cnd_edu = case_when(
edu == 1 ~ "High school",
edu == 2 ~ "University level"),
cnd_gender = case_when(
gender == 0 ~ "Male",
gender == 1 ~ "Female"),
cnd_religion = case_when(
religion == 1 ~ "No religion",
religion == 2 ~ "Christian",
religion == 3 ~ "Non-practicing Christian",
religion == 4 ~ "Muslim",
religion == 5 ~ "Non-praciticing Muslim"),
cnd_class = case_when(
class == 1 ~ "Working class",
class == 2 ~ "Middle class",
class == 3 ~ "Upper class"),
EIPS2017G_1_dv = ifelse(EIPS2017G_1_dv %in% 96:99, NA, EIPS2017G_1_dv),
EIPS2017G_2_dv = ifelse(EIPS2017G_2_dv %in% 96:99, NA, EIPS2017G_2_dv),
exp_post = ifelse(!is.na(EIPS2017G_1_dv), EIPS2017G_1_dv, EIPS2017G_2_dv),
cnd_post = case_when(
exp_post == cnd_n ~ 1,
exp_post != cnd_n ~ 0),
exp_treat = ifelse(!is.na(EIPS2017G_1_dv), "Closest candidate", "Prefered candidate"),
exp_treat = ifelse(is.na(EIPS2017G_1_dv) & is.na(EIPS2017G_2_dv), NA, exp_treat)
) %>%
select(matches("^rsp_"), matches("^exp_"), matches("^cnd_"))
se <- se_02
## Prepare France ----------------------------------------------------
fr_01 <-
fr_raw %>%
mutate(
rsp_id = as.numeric(paste0(500, UID_pe03)),
rsp_country = "France",
rsp_gender = case_when(
ea17_A1 == 1 ~ "Male",
ea17_A1 == 2 ~ "Female"),
rsp_age_5 = case_when(
ea17_A2A_rec == 4 ~ "18-24",
ea17_A2A_rec == 5 ~ "25-29",
ea17_A2A_rec == 6 ~ "30-34",
ea17_A2A_rec == 7 ~ "35-39",
ea17_A2A_rec == 8 ~ "40-44",
ea17_A2A_rec == 9 ~ "45-59",
ea17_A2A_rec == 10 ~ "50-54",
ea17_A2A_rec == 11 ~ "55-59",
ea17_A2A_rec == 12 ~ "60-64",
ea17_A2A_rec == 13 ~ "65-69",
ea17_A2A_rec == 14 ~ "70 <"),
rsp_age_10b = case_when(
CAL_AGE10 == 1 ~ "18-22",
CAL_AGE10 == 2 ~ "23-34",
CAL_AGE10 == 3 ~ "35-44",
CAL_AGE10 == 4 ~ "45-54",
CAL_AGE10 == 5 ~ "55-64",
CAL_AGE10 == 6 ~ "65-75",
CAL_AGE10 == 7 ~ "76-79"),
rsp_edu = case_when(
CAL_DIPLOME == 1 ~ "Lower",
CAL_DIPLOME == 2 ~ "Intermediate",
CAL_DIPLOME %in% 3:4 ~ "Higher")
)
fr_02 <-
fr_01 %>%
gather(
var, value,
matches("pe03_EIPS2017G_C[12]_\\w\\w\\w.*"),
na.rm = TRUE
) %>%
mutate(
cnd_n = as.numeric(gsub("pe03_EIPS2017G_C([12])_.*", "\\1", var)),
var = gsub("pe03_EIPS2017G_C[12]_", "", var),
var = tolower(var)) %>%
spread(var, value) %>%
mutate(
cnd_age = age,
cnd_edu = case_when(
edu == "N'a pas fait d'études supérieures" ~ "High school",
edu == "A fait des études supérieures" ~ "University level"),
cnd_gender = case_when(
gender == "Homme" ~ "Male",
gender == "Femme" ~ "Female"),
cnd_religion = case_when(
religion == "Sans religion" ~ "No religion",
religion == "Chrétien" ~ "Christian",
religion == "Chrétien non-pratiquant" ~ "Non-practicing Christian",
religion == "Musulman" ~ "Muslim",
religion == "Musulman non-pratiquant" ~ "Non-praciticing Muslim"),
cnd_class = case_when(
class == "Classe ouvrière" ~ "Working class",
class == "Classe moyenne" ~ "Middle class",
class == "Classe supérieure" ~ "Upper class"),
exp_post = case_when(
pe03_EIPS2017G_1_DV == 1 ~ 1,
pe03_EIPS2017G_2_DV == 1 ~ 1,
pe03_EIPS2017G_1_DV == 2 ~ 2,
pe03_EIPS2017G_2_DV == 2 ~ 2),
cnd_post = case_when(
exp_post == cnd_n ~ 1,
exp_post != cnd_n ~ 0),
exp_treat = case_when(
pe03_EIPS2017G_1_DV == 66 ~ "Prefered candidate",
pe03_EIPS2017G_2_DV == 66 ~ "Closest candidate")
) %>%
select(matches("^rsp_"), matches("^exp_"), matches("^cnd_"))
fr <- fr_02
## Combine EIPS ------------------------------------------------------
eips_raw <- bind_rows(is, nl, no, se, fr) %>%
filter(!is.na(cnd_post)) %>%
mutate(
rsp_edu_2 = case_when(
rsp_edu == "Higher" ~ "Higher",
rsp_edu %in% c("Intermediate", "Lower") ~ "Lower"),
rsp_age_cat_3 = case_when(
rsp_age_10a %in% c("18-25", "26-35") ~ "18-35",
rsp_age_10b %in% c("18-22", "23-34") ~ "18-35",
rsp_age %in% 18:35 ~ "18-35",
rsp_age_10a %in% c("36-45", "46-55") ~ "36-55",
rsp_age_10b %in% c("35-44", "45-54") ~ "36-55",
rsp_age %in% 36:55 ~ "36-55",
rsp_age_10a %in% c("56-65", "66-75", "75 <") ~ "55<",
rsp_age_10b %in% c("55-64", "65-75", "76-79") ~ "55<",
rsp_age >= 56 ~ "55<"),
rsp_age_cat_4 = case_when(
rsp_age_10a %in% c("18-25", "26-35") ~ "18-35",
rsp_age_10b %in% c("18-22", "23-34") ~ "18-35",
rsp_age %in% 18:35 ~ "18-35",
rsp_age_10a %in% c("36-45", "46-55") ~ "36-55",
rsp_age_10b %in% c("35-44", "45-54") ~ "36-55",
rsp_age %in% 36:55 ~ "36-55",
rsp_age_10a %in% c("56-65") ~ "56-65",
rsp_age_10b %in% c("55-64") ~ "56-65",
rsp_age %in% 56:65 ~ "56-65",
rsp_age_10a %in% c("66-75", "75 <") ~ "65<",
rsp_age_10b %in% c("65-75", "76-79") ~ "65<",
rsp_age >= 66 ~ "65<")
)
## Run sanity checks -------------------------------------------------
if (run_tests) {
assert_that(
are_equal(
length(eips_raw$cnd_post == 1),
length(eips_raw$cnd_post == 2)
),
msg = "There are not an equal nr of 0/1 choice observations."
)
n_levels <- tribble(
~var, ~lvls,
"rsp_gender", 2,
"rsp_edu", 3,
"rsp_edu_2", 2,
"cnd_age", 3,
"cnd_edu", 2,
"cnd_gender", 2,
"cnd_religion", 5,
"cnd_class", 3,
"exp_treat", 2
)
for (i in 1:nrow(n_levels)) {
.lvls <- length(levels(factor(eips_raw[[n_levels$var[i]]])))
assert_that(
are_equal(.lvls, n_levels$lvls[i]),
msg = paste0(
"There are ", .lvls, " levels in `", n_levels$var[i], "`, ",
"but there should be ", n_levels$lvls[i], ". "
)
)
}
assert_that(
all(table(eips_raw$rsp_id) == 2),
msg = "There are more or less than 2 obs per `rsp_id`."
)
}
## Write data to file ------------------------------------------------
write.csv(
x = eips_raw,
file = here("data", "eips.csv"),
row.names = FALSE
)
## Update the vault.
## Remeber to set the path to the private key by:
## Sys.setenv(USER_KEY = "path/to/private/key")
update_secret(
name = "eips_raw",
value = eips_raw,
vault = here("vault")
)
mikajoh/descr2 documentation built on May 8, 2019, 3:14 a.m.
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## Mikael Poul Johannesson (mikajoh@gmail.com)
## 2018
## Start matter ------------------------------------------------------
library(here)
library(haven)
library(tidyverse)
## Some sanity tests for the finished data, should not be needed.
run_tests <- TRUE
if (run_tests) library(assertthat)
## Get data ----------------------------------------------------------
## EIPS Iceland.
## Md5sum: 492e6210db4b3fcb047819bdf0bd2955
## tools::md5sum(here("data", "EIPS_is.sav"))
is_raw <- read_sav(here("data", "EIPS_is.sav"))
## EIPS Netherlands.
## Md5sum: 2fcad34e4ddda3c8ff18d17f94878b8a
## tools::md5sum(here("data", "L_PanelCollaboration_wave4_4p_EN.sav"))
nl_raw <- read_sav(here("data", "L_PanelCollaboration_wave4_4p_EN.sav"))
## EIPS Norway.
## Md5sum: 74d22c43548599e69ea50b78f8630ce3
## tools::md5sum(here("data", "Norwegian Citizen Panel - wave 9 EN.sav"))
no_raw <- read_sav(here("data", "Norwegian Citizen Panel - wave 9 EN.sav"))
## EIPS Sweden.
## Md5sum: 2ebae375d080fb29e4d2ed3586144ce3
## tools::md5sum(here("data", "EIPS2017_Swedish_Citizen_Panel_20180112.dta"))
se_raw <- read_dta(here("data", "EIPS2017_Swedish_Citizen_Panel_20180112.dta"))
## EIPS France.
## Md5sum: d910c074786ce467fc4aa9293d1689f1
## tools::md5sum(here("data", "EIPS2017-France.sav"))
fr_raw <- read_sav(here("data", "EIPS2017-France.sav"))
## Prepare Iceland ---------------------------------------------------
is_01 <-
is_raw %>%
mutate(
rsp_id = as.numeric(paste0(100, row_number())),
rsp_country = "Iceland",
rsp_gender = case_when(
kyn == 1 ~ "Male",
kyn == 2 ~ "Female"),
rsp_gender = factor(rsp_gender),
rsp_age = as.numeric(aldur),
rsp_edu = case_when(
menntun == 1 ~ "Lower",
menntun == 2 ~ "Intermediate",
menntun == 3 ~ "Intermediate",
menntun == 4 ~ "Higher",
menntun == 5 ~ "Higher",
menntun == 6 ~ "Higher",
menntun == 7 ~ "Higher",
menntun == 8 ~ "Higher")
)
is_02 <-
is_01 %>%
gather(var, value, matches("EIPS2017G_[12]_\\w\\w\\w.*"), na.rm = TRUE) %>%
mutate(
cnd_n = as.numeric(gsub("EIPS2017G_([12])_.*", "\\1", var)),
var = gsub("EIPS2017G_[12]_", "", var)) %>%
spread(var, value) %>%
mutate(
cnd_age = case_when(
age == 1 ~ "28",
age == 2 ~ "45",
age == 3 ~ "71"),
cnd_edu = case_when(
edu == 0 ~ "High school",
edu == 1 ~ "University level",
edu == 2 ~ "University level"),
cnd_gender = case_when(
gender == 0 ~ "Male",
gender == 1 ~ "Female"),
cnd_religion = case_when(
religion == 1 ~ "No religion",
religion == 2 ~ "Christian",
religion == 3 ~ "Non-practicing Christian",
religion == 4 ~ "Muslim",
religion == 5 ~ "Non-praciticing Muslim"),
cnd_class = case_when(
class == 1 ~ "Working class",
class == 2 ~ "Middle class",
class == 3 ~ "Upper class"),
EIPS2017G_1_dv = ifelse(EIPS2017G_1_dv %in% 96:99, NA, EIPS2017G_1_dv),
EIPS2017G_2_dv = ifelse(EIPS2017G_2_dv %in% 96:99, NA, EIPS2017G_2_dv),
exp_post = ifelse(!is.na(EIPS2017G_1_dv), EIPS2017G_1_dv, EIPS2017G_2_dv),
cnd_post = case_when(
exp_post == cnd_n ~ 1,
exp_post != cnd_n ~ 0),
exp_treat = ifelse(!is.na(EIPS2017G_1_dv), "Closest candidate", "Prefered candidate"),
exp_treat = ifelse(is.na(EIPS2017G_1_dv) & is.na(EIPS2017G_2_dv), NA, exp_treat)
) %>%
select(matches("^rsp_"), matches("^exp_"), matches("^cnd_"))
is <- is_02
## Prepare Netherlands -----------------------------------------------
nl_01 <-
nl_raw %>%
mutate(
rsp_id = as.numeric(paste0(200, nomem_encr)),
rsp_country = "Netherlands",
rsp_gender = case_when(
geslacht == 1 ~ "Male",
geslacht == 2 ~ "Female"),
rsp_age = as.numeric(leeftijd),
rsp_popdensity = 6 - as.numeric(sted),
rsp_edu = case_when(
oplcat == 1 ~ "Lower",
oplcat %in% 2:4 ~ "Intermediate",
oplcat %in% 5:6 ~ "Higher"),
rsp_inc = as.numeric(nettocat),
rsp_inc_cat = case_when(
rsp_inc %in% 0:3 ~ "Low inc",
rsp_inc %in% 4:12 ~ "High inc"
)
) %>%
filter(rsp_age >= 18)
nl_02 <-
nl_01 %>%
gather(var, value, matches("EIPS2017G_[12]_.*"), na.rm = TRUE) %>%
mutate(
cnd_n = as.numeric(gsub("EIPS2017G_([12])_.*", "\\1", var)),
var = gsub("EIPS2017G_[12]_", "", var)
) %>%
spread(var, value) %>%
mutate(
cnd_age = case_when(
age == 1 ~ "28",
age == 2 ~ "45",
age == 3 ~ "71"),
cnd_edu = case_when(
edu == 1 ~ "High school",
edu == 2 ~ "University level"),
cnd_gender = case_when(
gender == 1 ~ "Male",
gender == 2 ~ "Female"),
cnd_religion = case_when(
religion == 1 ~ "No religion",
religion == 2 ~ "Christian",
religion == 3 ~ "Non-practicing Christian",
religion == 4 ~ "Muslim",
religion == 5 ~ "Non-praciticing Muslim"),
cnd_class = case_when(
class == 1 ~ "Working class",
class == 2 ~ "Middle class",
class == 3 ~ "Upper class"),
exp_post = ifelse(!is.na(EIPS2017G_1), EIPS2017G_1, EIPS2017G_2),
cnd_post = case_when(
exp_post == cnd_n ~ 1,
exp_post != cnd_n ~ 0),
exp_treat = ifelse(!is.na(EIPS2017G_1), "Closest candidate", "Prefered candidate"),
exp_treat = ifelse(is.na(EIPS2017G_1) & is.na(EIPS2017G_2), NA, exp_treat)
) %>%
select(matches("^rsp_"), matches("^exp_"), matches("^cnd_"))
nl <- nl_02
## Prepare Norway ----------------------------------------------------
no_01 <-
no_raw %>%
mutate(
rsp_id = as.numeric(paste0(300, responseid)),
rsp_country = "Norway",
rsp_gender = case_when(
R9P1 == 1 ~ "Male",
R9P1 == 2 ~ "Female"),
rsp_age_10a = case_when(
R9P5_1 == 1 ~ "18-25",
R9P5_1 == 2 ~ "26-35",
R9P5_1 == 3 ~ "36-45",
R9P5_1 == 4 ~ "46-55",
R9P5_1 == 5 ~ "56-65",
R9P5_1 == 6 ~ "66-75",
R9P5_1 == 7 ~ "75 <"),
rsp_edu = case_when(
R9P4_1 == 1 ~ "Lower",
R9P4_1 == 2 ~ "Intermediate",
R9P4_1 == 3 ~ "Higher")
)
no_02 <-
no_01 %>%
gather(var, value, matches("eips2017g_[12]_.*"), na.rm = TRUE) %>%
mutate(
cnd_n = as.numeric(gsub("eips2017g_([12])_.*", "\\1", var)),
var = gsub("eips2017g_[12]_", "", var)
) %>%
spread(var, value) %>%
mutate(
cnd_age = case_when(
age == 1 ~ "28",
age == 2 ~ "45",
age == 3 ~ "71"),
cnd_edu = case_when(
edu == 0 ~ "High school",
edu == 1 ~ "University level"),
cnd_gender = case_when(
gender == 0 ~ "Male",
gender == 1 ~ "Female"),
cnd_religion = case_when(
religion == 1 ~ "No religion",
religion == 2 ~ "Christian",
religion == 3 ~ "Non-practicing Christian",
religion == 4 ~ "Muslim",
religion == 5 ~ "Non-praciticing Muslim"),
cnd_class = case_when(
class == 1 ~ "Working class",
class == 2 ~ "Middle class",
class == 3 ~ "Upper class"),
eips2017g_1 = ifelse(eips2017g_1 %in% 97:98, NA, eips2017g_1),
eips2017g_2 = ifelse(eips2017g_2 %in% 97:98, NA, eips2017g_2),
exp_post = ifelse(!is.na(eips2017g_1), eips2017g_1, eips2017g_2),
cnd_post = case_when(
exp_post == cnd_n ~ 1,
exp_post != cnd_n ~ 0),
exp_treat = ifelse(!is.na(eips2017g_1), "Closest candidate", "Prefered candidate"),
exp_treat = ifelse(is.na(eips2017g_1) & is.na(eips2017g_2), NA, exp_treat)
) %>%
select(matches("^rsp_"), matches("^exp_"), matches("^cnd_"))
no <- no_02
## Prepare Sweden ----------------------------------------------------
se_01 <-
se_raw %>%
mutate(
rsp_id = as.numeric(paste0(400, id)),
rsp_country = "Sweden",
rsp_gender = case_when(
sex == 2 ~ "Male",
sex == 1 ~ "Female"),
rsp_age = as.numeric(age),
rsp_edu = case_when(
edu3 == 1 ~ "Lower",
edu3 == 2 ~ "Intermediate",
edu3 == 3 ~ "Higher")
)
se_02 <-
se_01 %>%
select(-edu, -age) %>%
gather(var, value, matches("EIPS2017G_[12]_\\w\\w\\w.*"), na.rm = TRUE) %>%
mutate(
cnd_n = as.numeric(gsub("EIPS2017G_([12])_.*", "\\1", var)),
var = gsub("EIPS2017G_[12]_", "", var)) %>%
spread(var, value) %>%
mutate(
cnd_age = case_when(
age == 1 ~ "28",
age == 2 ~ "45",
age == 3 ~ "71"),
cnd_edu = case_when(
edu == 1 ~ "High school",
edu == 2 ~ "University level"),
cnd_gender = case_when(
gender == 0 ~ "Male",
gender == 1 ~ "Female"),
cnd_religion = case_when(
religion == 1 ~ "No religion",
religion == 2 ~ "Christian",
religion == 3 ~ "Non-practicing Christian",
religion == 4 ~ "Muslim",
religion == 5 ~ "Non-praciticing Muslim"),
cnd_class = case_when(
class == 1 ~ "Working class",
class == 2 ~ "Middle class",
class == 3 ~ "Upper class"),
EIPS2017G_1_dv = ifelse(EIPS2017G_1_dv %in% 96:99, NA, EIPS2017G_1_dv),
EIPS2017G_2_dv = ifelse(EIPS2017G_2_dv %in% 96:99, NA, EIPS2017G_2_dv),
exp_post = ifelse(!is.na(EIPS2017G_1_dv), EIPS2017G_1_dv, EIPS2017G_2_dv),
cnd_post = case_when(
exp_post == cnd_n ~ 1,
exp_post != cnd_n ~ 0),
exp_treat = ifelse(!is.na(EIPS2017G_1_dv), "Closest candidate", "Prefered candidate"),
exp_treat = ifelse(is.na(EIPS2017G_1_dv) & is.na(EIPS2017G_2_dv), NA, exp_treat)
) %>%
select(matches("^rsp_"), matches("^exp_"), matches("^cnd_"))
se <- se_02
## Prepare France ----------------------------------------------------
fr_01 <-
fr_raw %>%
mutate(
rsp_id = as.numeric(paste0(500, UID_pe03)),
rsp_country = "France",
rsp_gender = case_when(
ea17_A1 == 1 ~ "Male",
ea17_A1 == 2 ~ "Female"),
rsp_age_5 = case_when(
ea17_A2A_rec == 4 ~ "18-24",
ea17_A2A_rec == 5 ~ "25-29",
ea17_A2A_rec == 6 ~ "30-34",
ea17_A2A_rec == 7 ~ "35-39",
ea17_A2A_rec == 8 ~ "40-44",
ea17_A2A_rec == 9 ~ "45-59",
ea17_A2A_rec == 10 ~ "50-54",
ea17_A2A_rec == 11 ~ "55-59",
ea17_A2A_rec == 12 ~ "60-64",
ea17_A2A_rec == 13 ~ "65-69",
ea17_A2A_rec == 14 ~ "70 <"),
rsp_age_10b = case_when(
CAL_AGE10 == 1 ~ "18-22",
CAL_AGE10 == 2 ~ "23-34",
CAL_AGE10 == 3 ~ "35-44",
CAL_AGE10 == 4 ~ "45-54",
CAL_AGE10 == 5 ~ "55-64",
CAL_AGE10 == 6 ~ "65-75",
CAL_AGE10 == 7 ~ "76-79"),
rsp_edu = case_when(
CAL_DIPLOME == 1 ~ "Lower",
CAL_DIPLOME == 2 ~ "Intermediate",
CAL_DIPLOME %in% 3:4 ~ "Higher")
)
fr_02 <-
fr_01 %>%
gather(
var, value,
matches("pe03_EIPS2017G_C[12]_\\w\\w\\w.*"),
na.rm = TRUE
) %>%
mutate(
cnd_n = as.numeric(gsub("pe03_EIPS2017G_C([12])_.*", "\\1", var)),
var = gsub("pe03_EIPS2017G_C[12]_", "", var),
var = tolower(var)) %>%
spread(var, value) %>%
mutate(
cnd_age = age,
cnd_edu = case_when(
edu == "N'a pas fait d'études supérieures" ~ "High school",
edu == "A fait des études supérieures" ~ "University level"),
cnd_gender = case_when(
gender == "Homme" ~ "Male",
gender == "Femme" ~ "Female"),
cnd_religion = case_when(
religion == "Sans religion" ~ "No religion",
religion == "Chrétien" ~ "Christian",
religion == "Chrétien non-pratiquant" ~ "Non-practicing Christian",
religion == "Musulman" ~ "Muslim",
religion == "Musulman non-pratiquant" ~ "Non-praciticing Muslim"),
cnd_class = case_when(
class == "Classe ouvrière" ~ "Working class",
class == "Classe moyenne" ~ "Middle class",
class == "Classe supérieure" ~ "Upper class"),
exp_post = case_when(
pe03_EIPS2017G_1_DV == 1 ~ 1,
pe03_EIPS2017G_2_DV == 1 ~ 1,
pe03_EIPS2017G_1_DV == 2 ~ 2,
pe03_EIPS2017G_2_DV == 2 ~ 2),
cnd_post = case_when(
exp_post == cnd_n ~ 1,
exp_post != cnd_n ~ 0),
exp_treat = case_when(
pe03_EIPS2017G_1_DV == 66 ~ "Prefered candidate",
pe03_EIPS2017G_2_DV == 66 ~ "Closest candidate")
) %>%
select(matches("^rsp_"), matches("^exp_"), matches("^cnd_"))
fr <- fr_02
## Combine EIPS ------------------------------------------------------
eips_raw <- bind_rows(is, nl, no, se, fr) %>%
filter(!is.na(cnd_post)) %>%
mutate(
rsp_edu_2 = case_when(
rsp_edu == "Higher" ~ "Higher",
rsp_edu %in% c("Intermediate", "Lower") ~ "Lower"),
rsp_age_cat_3 = case_when(
rsp_age_10a %in% c("18-25", "26-35") ~ "18-35",
rsp_age_10b %in% c("18-22", "23-34") ~ "18-35",
rsp_age %in% 18:35 ~ "18-35",
rsp_age_10a %in% c("36-45", "46-55") ~ "36-55",
rsp_age_10b %in% c("35-44", "45-54") ~ "36-55",
rsp_age %in% 36:55 ~ "36-55",
rsp_age_10a %in% c("56-65", "66-75", "75 <") ~ "55<",
rsp_age_10b %in% c("55-64", "65-75", "76-79") ~ "55<",
rsp_age >= 56 ~ "55<"),
rsp_age_cat_4 = case_when(
rsp_age_10a %in% c("18-25", "26-35") ~ "18-35",
rsp_age_10b %in% c("18-22", "23-34") ~ "18-35",
rsp_age %in% 18:35 ~ "18-35",
rsp_age_10a %in% c("36-45", "46-55") ~ "36-55",
rsp_age_10b %in% c("35-44", "45-54") ~ "36-55",
rsp_age %in% 36:55 ~ "36-55",
rsp_age_10a %in% c("56-65") ~ "56-65",
rsp_age_10b %in% c("55-64") ~ "56-65",
rsp_age %in% 56:65 ~ "56-65",
rsp_age_10a %in% c("66-75", "75 <") ~ "65<",
rsp_age_10b %in% c("65-75", "76-79") ~ "65<",
rsp_age >= 66 ~ "65<")
)
## Run sanity checks -------------------------------------------------
if (run_tests) {
assert_that(
are_equal(
length(eips_raw$cnd_post == 1),
length(eips_raw$cnd_post == 2)
),
msg = "There are not an equal nr of 0/1 choice observations."
)
n_levels <- tribble(
~var, ~lvls,
"rsp_gender", 2,
"rsp_edu", 3,
"rsp_edu_2", 2,
"cnd_age", 3,
"cnd_edu", 2,
"cnd_gender", 2,
"cnd_religion", 5,
"cnd_class", 3,
"exp_treat", 2
)
for (i in 1:nrow(n_levels)) {
.lvls <- length(levels(factor(eips_raw[[n_levels$var[i]]])))
assert_that(
are_equal(.lvls, n_levels$lvls[i]),
msg = paste0(
"There are ", .lvls, " levels in `", n_levels$var[i], "`, ",
"but there should be ", n_levels$lvls[i], ". "
)
)
}
assert_that(
all(table(eips_raw$rsp_id) == 2),
msg = "There are more or less than 2 obs per `rsp_id`."
)
}
## Write data to file ------------------------------------------------
write.csv(
x = eips_raw,
file = here("data", "eips.csv"),
row.names = FALSE
)
## Update the vault.
## Remeber to set the path to the private key by:
## Sys.setenv(USER_KEY = "path/to/private/key")
update_secret(
name = "eips_raw",
value = eips_raw,
vault = here("vault")
)
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