library(tidyverse) # Add the tidyverse package to my current library.
library(haven) # Handle labelled data.
library(texreg)# Output regression results
library(splitstackshape) #transform wide data (with stacked variables) to long data
library(plm) #linear models for panel data
1.Import data
wave1 <- read_dta("anchor1_50percent_Eng.dta")
wave2 <- read_dta("anchor2_50percent_Eng.dta")
wave3 <- read_dta("anchor3_50percent_Eng.dta")
wave4 <- read_dta("anchor4_50percent_Eng.dta")
wave5 <- read_dta("anchor5_50percent_Eng.dta")
wave6 <- read_dta("anchor6_50percent_Eng.dta")
2.Clean data
clean_fun <- function(df) { df %>%
transmute(
id,
age,
wave,
sex=as_factor(sex_gen), #make sex_gen as a factor
relstat=as_factor(relstat), #make relstat as a factor
relstat=case_when(relstat== "-7 Incomplete data" ~ as.character(NA), #specify when is missing
TRUE ~ as.character(relstat))%>% as_factor(), #make relstat as a factor again
health=case_when(hlt1<0 ~ as.numeric(NA), #specify when hlt1 is missing
TRUE ~ as.numeric(hlt1)),
sat=case_when(sat6<0 ~ as.numeric(NA), #specify when sat6 is missing
TRUE ~ as.numeric(sat6)),
partner=case_when(relstat %in% c("1 Never married single","6 Divorced/separated single","9 Widowed single") ~ "No",
# when relstat has any of the situations, I assign "No"
relstat %in% c("2 Never married LAT","3 Never married COHAB",
"4 Married COHAB","5 Married noncohabiting",
"7 Divorced/separated LAT","8 Divorced/separated COHAB",
"10 Widowed LAT", "11 Widowed COHAB") ~ 'Yes') %>%
as_factor() %>%
fct_relevel("No", "Yes")
# when relstat has any of the situations, I assign "Yes"
)%>% drop_na() }
wave1a <- clean_fun(wave1)
wave2a <- clean_fun(wave2)
wave3a <- clean_fun(wave3)
wave4a <- clean_fun(wave4)
wave5a <- clean_fun(wave5)
wave6a <- clean_fun(wave6)
3. Sample selection and renaming
wave1b <- wave1a %>% filter(partner=="No")%>%
rename(wave_1=wave, age_1=age, sex_1=sex, relstat_1=relstat, health_1=health, sat_1=sat, partner_1=partner) #rename variables
wave2b <- wave2a %>%
rename(wave_2=wave, age_2=age, sex_2=sex, relstat_2=relstat, health_2=health, sat_2=sat, partner_2=partner)
wave3b <- wave3a %>%
rename(wave_3=wave, age_3=age, sex_3=sex, relstat_3=relstat, health_3=health, sat_3=sat, partner_3=partner)
wave4b <- wave4a %>%
rename(wave_4=wave, age_4=age, sex_4=sex, relstat_4=relstat, health_4=health, sat_4=sat, partner_4=partner)
wave5b <- wave5a %>%
rename(wave_5=wave, age_5=age, sex_5=sex, relstat_5=relstat, health_5=health, sat_5=sat, partner_5=partner)
wave6b <- wave6a %>%
rename(wave_6=wave, age_6=age, sex_6=sex, relstat_6=relstat, health_6=health, sat_6=sat, partner_6=partner)
4. create a full dataset containing six waves
sixwaves_wide <- left_join(wave1b, wave2b, by = "id") %>% # left join wave1b and wave2b
left_join(wave3b, by = "id") %>% # left join with wave3b
left_join(wave4b, by = "id") %>% # left join with wave4b
left_join(wave5b, by = "id") %>% # left join with wave5b
left_join(wave6b, by = "id") # left join with wave6b
#by using left_join I keep those single in wave1 and follow them
### Check the participation over time
sixwaves_wide$check <- paste(sixwaves_wide$wave_1, sixwaves_wide$wave_2, sixwaves_wide$wave_3,
sixwaves_wide$wave_4, sixwaves_wide$wave_5, sixwaves_wide$wave_6, sep='-')
table(sixwaves_wide$check)
##
## 1-2-3-4-5-6 1-2-3-4-5-NA 1-2-3-4-NA-6 1-2-3-4-NA-NA
## 1045 142 48 154
## 1-2-3-NA-5-6 1-2-3-NA-5-NA 1-2-3-NA-NA-NA 1-2-NA-4-5-6
## 45 16 202 27
## 1-2-NA-4-5-NA 1-2-NA-4-NA-6 1-2-NA-4-NA-NA 1-2-NA-NA-NA-NA
## 10 1 14 267
## 1-NA-3-4-5-6 1-NA-3-4-5-NA 1-NA-3-4-NA-6 1-NA-3-4-NA-NA
## 30 10 6 12
## 1-NA-3-NA-5-6 1-NA-3-NA-5-NA 1-NA-3-NA-NA-NA 1-NA-NA-4-5-6
## 4 1 30 9
## 1-NA-NA-NA-5-NA 1-NA-NA-NA-NA-NA
## 1 517
### Generate a panel dataset of six waves
sixwaves_long1<- merged.stack(sixwaves_wide, #dataset for transfrom
var.stubs = c("age", "wave", "sex","relstat", "health", "sat", "partner"),
#var.stubs is to specify the prefixes of the variable groups
sep = "_") %>%
#sep is to specify the character that separates the "variable name" from the "times" in the source
drop_na(wave)
#drop the observations which did not join the wave
sixwaves_long1 <- sixwaves_long1 %>%
group_by(id) %>%
mutate(
wave=as.numeric(wave), #once we define sixwaves_long1 as a panel structure, wave becomes a factor; so transfer back to numeric
getpartner=case_when( partner!=dplyr::lag(partner, 1) & partner=="Yes" & dplyr::lag(partner, 1)=="No" ~ 1,
TRUE ~ 0), #identify the event of getting a partner
breakpartner=case_when( partner!=dplyr::lag(partner, 1) & partner=="No" & dplyr::lag(partner, 1)=="Yes" ~ 1,
TRUE ~ 0), #identify the event of breaking up
times_partner=cumsum(getpartner), # identify how many times a person gets a partner in a cumulative way
times_departer=cumsum(breakpartner), # identify how many times a person breaks up in a cumulative way
)
sixwaves_long2 <- sixwaves_long1 %>%
filter(times_departer==0) #drop observations once an individuals experience at least 1 time of break up
sixwaves_long3 <- sixwaves_long2 %>%
group_by(id) %>%
mutate(
wave=as.numeric(wave),#once we define sixwaves_long2a as a panel structure, wave becomes a factor; so transfer back to numeric
partnerwave=case_when(getpartner==1 ~ wave,
TRUE ~ 99 ), #identify at which wave the person get a partner; for the rest, make it 99.
anchorwave=min(partnerwave) #anchor the time of the event
)