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Analytical_sample_prep.R
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Analytical_sample_prep.R
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# Author: Donata Stonkute
#########################
# This analysis uses data or information from the Harmonized Survey of Health,
# Ageing and Retirement in Europe dataset and Codebook, Version F as of February
# 2023 developed by the Gateway to Global Aging Data. The development of the
# Harmonized Survey of Health, Ageing and Retirement in Europe was funded by the National Institute on Aging
# (R01 AG030153, RC2 AG036619, 1R03AG043052). For more information, please refer to www.g2aging.org
#########################
Sys.setenv(LANG = "en")
library(tidyverse)
library(data.table)
library(labelled)
# Set working directory
# setwd("your_directory")
# Read the Stata file
## This is the data file retrieved as per instructions at https://g2aging.org/?section=page&pageid=22
mydt_wide <- read_dta("H_SHARE_e2.dta")
# I create this for easier identification of column numbers for variables of interest
col.names <- data.frame(colnames(mydt_wide))
# save variables that are not time-varying
constant_vars <- (mydt_wide[,grep("[1,2,3,4,5,6,7,8]", names(mydt_wide),
value=TRUE, invert=TRUE)])
constant_vars <- constant_vars[,c(1,2,3,4,5,12,14,19)]
# Age ---------------------------------------------------------------------
# This function helps to identify which columns contain a character string of interest
which(grepl("agey", col.names$colnames.mydt_wide.))
# We retrieve those next to the unique merge ID
mydt_age <- mydt_wide[,c(3, 276:282)]
# And convert sub-data from wide to long format
mydt_age <- mydt_age %>%
pivot_longer(
cols = starts_with("r"),
names_to = "wave_v1",
names_prefix = "r",
values_to = "age",
values_drop_na = FALSE
) %>%
mutate(wave =
substr(wave_v1,1,1)) %>%
select(mergeid, wave, age)
# Next, we add the converted sub-data to the constant variables
mydt_long <- merge(constant_vars, mydt_age, by="mergeid", all=TRUE)
remove(constant_vars)
remove(mydt_age)
table(mydt_long$wave, mydt_long$country)
# Now we will repeat the same steps for all variables of interest
# ADL ---------------------------------------------------------------------
which(grepl("r1adla", col.names$colnames.mydt_wide.))
mydt_adl <- mydt_wide[,c(3, 883:889)]
mydt_adl <- mydt_adl %>%
pivot_longer(
cols = starts_with("r"),
names_to = "wave_v1",
names_prefix = "r",
values_to = "adl",
values_drop_na = FALSE
) %>%
mutate(wave =
substr(wave_v1,1,1)) %>%
select(mergeid, wave, adl)
## merging with the rest
mydt_long <- merge(mydt_long, mydt_adl, by=c("mergeid", "wave"), all=TRUE)
## clearing from unnecessary sub-datasets
remove(mydt_adl)
table(mydt_long$wave, mydt_long$country)
# GALI ---------------------------------------------------------------------
which(grepl("hlthlma", col.names$colnames.mydt_wide.)) # whether health limits, categorical
mydt_gali <- mydt_wide[,c(3, 481:487)]
mydt_gali <- mydt_gali %>%
pivot_longer(
cols = starts_with("r"),
names_to = "wave_v1",
names_prefix = "r",
values_to = "hlthlma",
values_drop_na = FALSE
) %>%
mutate(wave =
substr(wave_v1,1,1)) %>%
select(mergeid, wave, hlthlma)
mydt_long <- merge(mydt_long, mydt_gali, by=c("mergeid", "wave"), all=TRUE)
remove(mydt_gali)
table(mydt_long$wave, mydt_long$country)
# Interview status -----------------------------------------------------
which(grepl("iwstat", col.names$colnames.mydt_wide.))
mydt_iwstat <- mydt_wide[,c(3, 92:99)]
mydt_iwstat <- mydt_iwstat %>%
pivot_longer(
cols = starts_with("r"),
names_to = "wave_v1",
names_prefix = "r",
values_to = "iwstat",
values_drop_na = FALSE
) %>%
mutate(wave =
substr(wave_v1,1,1)) %>%
select(mergeid, wave, iwstat)
mydt_long <- merge(mydt_long, mydt_iwstat, by=c("mergeid","wave"), all=TRUE)
remove(mydt_iwstat)
table(mydt_long$wave, mydt_long$country)
# Person-level analysis weights -------------------------------------------
which(grepl("wtresp", col.names$colnames.mydt_wide.))
mydt_tresp <- mydt_wide [,c(3, 144:150)]
mydt_tresp <- mydt_tresp %>%
pivot_longer(
cols = starts_with("r"),
names_to = "wave_v1",
names_prefix = "r",
values_to = "wtresp",
values_drop_na = FALSE
) %>%
mutate(wave =
substr(wave_v1,1,1)) %>%
select(mergeid, wave, wtresp)
mydt_long <- merge(mydt_long, mydt_tresp, by=c("mergeid","wave"), all=TRUE)
remove(mydt_tresp)
table(mydt_long$wave, mydt_long$country)
## Sorting
dt_full <- mydt_long[order(mydt_long$mergeid, mydt_long$wave),]
# Cosmetic work -------------------------------------------
# Recode country codes to country names
dt_full$country <- recode(
dt_full$country,
"11" = "Austria",
"12" = "Germany",
"13" = "Sweden",
"14" = "Netherlands",
"15" = "Spain",
"16" = "Italy",
"17" = "France",
"18" = "Denmark",
"19" = "Greece",
"20" = "Switzerland",
"23" = "Belgium",
"25" = "Israel",
"28" = "Czechia",
"29" = "Poland",
"30" = "Ireland",
"31" = "Luxembourg",
"32" = "Hungary",
"33" = "Portugal",
"34" = "Slovenia",
"35" = "Estonia",
"47" = "Croatia",
"48" = "Lithuania",
"51" = "Bulgaria",
"53" = "Cyprus",
"55" = "Finland",
"57" = "Latvia",
"59" = "Malta",
"61" = "Romania",
"63" = "Slovakia"
)
# Interview status --------------------------------------------------------
# excluding not yet identified (=0), alive but did not respond (=4),
# died before last week (=6), not known (=9)
dt_present <- dt_full %>%
filter(iwstat %in% c(1, 5))
# Alive -------------------------------------------------------------------
# Filter respondents with interview status 1 (alive)
dt_alive <- dt_present %>%
filter(iwstat == 1)
# Age eligibility -------------------------------------------------------
# Filter out respondents aged less than 50
dt_complete <- dt_alive %>%
filter(age >= 50)
# Adding death indicator for survival analysis ----------------------------
# Set 'dead' column to 0 for all records
data.table::setDT(dt_complete)[, dead := 0]
# Update 'dead' column to 1 for respondents with non-NA 'radyear' values
dt_complete[.(dt_complete[!is.na(radyear), unique(mergeid)]),
on = .(mergeid),
mult = "last", dead := 1]
# Baseline indicator ------------------------------------------------------
# Set 'basel' column to 0 for all records
data.table::setDT(dt_complete)[, basel := 0]
# Update 'basel' column to 1 for respondents with unique 'mergeid' values
dt_complete[.(dt_complete[, unique(mergeid)]),
on = .(mergeid),
mult = "first", basel := 1]
# Data frame for analysis, renaming variables -----------------------------
# Rename and transform variables in dt_complete using dplyr
dt_complete <- dt_complete %>%
mutate(
gender = ifelse(ragender == 1, "man", "woman"),
edu = case_when(
raeducl == 1 ~ "low",
raeducl == 2 ~ "medium",
raeducl == 3 ~ "high"
),
gali = hlthlma,
adl_bi = ifelse(adl == 0, yes = 0, no = 1)
) %>%
select(mergeid, country, wave, gender, edu, age, adl_bi, gali, radyear, dead, basel)
# Analytical sample formatting --------------------------------------------
# Transform variables and create additional columns in dt_complete using dplyr
dt <- dt_complete %>%
mutate(
low = ifelse(edu == "low", 1, 0),
medium = ifelse(edu == "medium", 1, 0),
high = ifelse(edu == "high", 1, 0),
edu = factor(edu, levels = c("low", "medium", "high")),
year = case_when(
wave == 1 ~ 2004,
wave == 2 ~ 2007,
wave == 3 ~ 2009,
wave == 4 ~ 2011,
wave == 5 ~ 2013,
wave == 6 ~ 2015,
wave == 7 ~ 2017,
wave == 8 ~ 2019
)
)
## COVID-19 period deaths ----------------------------------------------------
# Right censor deaths after 2019 (=going back in time)
dtt <- dt %>%
mutate(
covid_dd = ifelse(radyear %in% c(2020, 2021), yes = 1, no = 0),
dead = ifelse(covid_dd == 1, yes = 0, no = dead),
radyear = ifelse(covid_dd == 1, yes = NA, no = radyear)
)
# Add deaths --------------------------------------------------------------
# Arrange dtt by mergeid and descending wave, and add deaths to the dataset
dd <- dtt %>% arrange(mergeid, desc(wave)) %>%
filter(!is.na(radyear)) %>%
group_by(mergeid) %>%
slice_head() %>%
mutate(wave = wave + 1, age = age + 2, gali = 99) %>%
ungroup()
dat_dd <- rbind(dtt, dd)
# Clean death records
duomenys <- dat_dd %>%
arrange(mergeid, wave) %>%
mutate(dead = ifelse(gali == 99, 1, 0))
dtw <- duomenys %>%
group_by(mergeid) %>%
mutate(wave_lead = lead(wave),
wave_diff = wave_lead - wave) %>%
ungroup()
## Non-consecutive waves ---------------------------------------------------
dtf <- dtw # Create a temporary dataframe for non-consecutive waves
# Create a 'grp' column indicating if difference between waves is more than 1 (non-consecutive)
setDT(dtf)[, grp := cumsum(c(0, diff(wave)) > 1), by = mergeid]
dtf[, ID := .GRP, by = .(mergeid, grp)]
dtf[, time := .N, by = ID]
# Remove both non-consecutive observations and those that participated only in one wave
dat <- as.data.frame(dtf) %>%
filter(time > 1)
# Filter out last wave and wave 9 for analysis
dati <- dat %>%
mutate(lastw = ifelse(wave > 7 & dead == 0, 1, 0)) %>%
filter(lastw == 0, wave != 9)
dati <- dati[, -c(15:22)] # unnecessary columns
# Omit missing values -------------------------------------------------
dt_full <- dati %>%
filter(
!is.na(gender) &
!is.na(edu) &
!is.na(age) &
!is.na(adl_bi) &
!is.na(gali)
)
## Country selection -------------------------------------------------------
final_dta <- dt_full %>%
filter(
country %in% c(
"Austria",
"Belgium",
"Czechia",
"Denmark",
"Estonia",
"Spain",
"France",
"Italy",
"Sweden",
"Slovenia"
)
)
sample_info <- as.data.frame(table(final_dta$country))
colnames(sample_info) <- c("Country", "Observations")
# write.csv(final_dta, "file directory, name.csv", row.names = FALSE)
# Saving individual country files -----------------------------------------
# setwd("data folder")
austria <- final_dta %>% filter(country=="Austria")
belgium <- final_dta %>% filter(country=="Belgium")
czechia <- final_dta %>% filter(country=="Czechia")
denmark <- final_dta %>% filter(country=="Denmark")
estonia <- final_dta %>% filter(country=="Estonia")
france <- final_dta %>% filter(country=="France")
italy <- final_dta %>% filter(country=="Italy")
slovenia <- final_dta %>% filter(country=="Slovenia")
spain <- final_dta %>% filter(country=="Spain")
sweden <- final_dta %>% filter(country=="Sweden")
write.csv(austria, "Austria.csv", row.names=FALSE)
write.csv(belgium, "Belgium.csv", row.names=FALSE)
write.csv(czechia, "Czechia.csv", row.names=FALSE)
write.csv(denmark, "Denmark.csv", row.names=FALSE)
write.csv(estonia, "Estonia.csv", row.names=FALSE)
write.csv(france, "France.csv", row.names=FALSE)
write.csv(italy, "Italy.csv", row.names=FALSE)
write.csv(slovenia, "Slovenia.csv", row.names=FALSE)
write.csv(spain, "Spain.csv", row.names=FALSE)
write.csv(sweden, "Sweden.csv", row.names=FALSE)