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CHARLS_input.R
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# import ------------------------------------------------------------------
library(tidyverse)
library(haven)
charls <- read_dta('D:/projects/SES_charls/sourcedata/charls_job.dta') %>%
mutate(across(c(wave,
fd002),
factor)) %>%
select(1, 3, 109, 243, # gender, age
244, # education
41:68, # occupation
144, 150, # family income & retirement
80:90, # activities
128:134, # physical activity, drinking, smoking
247:249, # memory, executive function, general cognition
96:107, # mental state
112:127, # self-rated health and chronic diseases
169:172, 177, # BMI, CESD (0-30)
211:227 # blood biomarkers
) %>%
rename(interact = 串门,
majong = 打麻将,
help = 提供帮助,
sport = 跳舞,
organization = 参加社团组织活动,
voluntary = 志愿者活动或者慈善活动,
course = 上学或者参加培训课程,
care = 照顾病人或残疾人,
stock = 炒股,
internet = 上网,
others = 其他社交活动)
charls %>%
group_by(wave) %>%
summarise(sum_na = sum(is.na(charls$跳舞)))
# read w1-w5 raw data -----------------------------------------------------
charls_w1_demo <- read_dta('D:/projects/SES_charls/sourcedata/w1-2011/household_and_community_questionnaire_data/demographic_background.dta')
charls_w1_work <- read_dta('D:/projects/SES_charls/sourcedata/w1-2011/household_and_community_questionnaire_data/work_retirement_and_pension.dta')
charls_w1_income <- read_dta('D:/projects/SES_charls/sourcedata/w1-2011/household_and_community_questionnaire_data/individual_income.dta')
charls_w2_demo <- read_dta('D:/projects/SES_charls/sourcedata/w2-2013/CHARLS2013_Dataset/Demographic_Background.dta')
charls_w2_work <- read_dta('D:/projects/SES_charls/sourcedata/w2-2013/CHARLS2013_Dataset/Work_Retirement_and_Pension.dta')
charls_w2_income <- read_dta('D:/projects/SES_charls/sourcedata/w2-2013/CHARLS2013_Dataset/Individual_Income.dta')
charls_w3_demo <- read_dta('D:/projects/SES_charls/sourcedata/w3-2015/CHARLS2015r/Demographic_Background.dta')
charls_w3_work <- read_dta('D:/projects/SES_charls/sourcedata/w3-2015/CHARLS2015r/Work_Retirement_and_Pension.dta')
charls_w3_income <- read_dta('D:/projects/SES_charls/sourcedata/w3-2015/CHARLS2015r/Individual_Income.dta')
charls_w4_demo <- read_dta('D:/projects/SES_charls/sourcedata/w4-2018/CHARLS2018r/Demographic_Background.dta')
charls_w4_work <- read_dta('D:/projects/SES_charls/sourcedata/w4-2018/CHARLS2018r/Work_Retirement.dta')
charls_w4_income <- read_dta('D:/projects/SES_charls/sourcedata/w4-2018/CHARLS2018r/Individual_Income.dta')
charls_w5_demo <- read_dta('D:/projects/SES_charls/sourcedata/w5-2020/CHARLS2020r/Demographic_Background.dta')
charls_w5_work <- read_dta('D:/projects/SES_charls/sourcedata/w5-2020/CHARLS2020r/Work_Retirement.dta')
charls_w5_income <- read_dta('D:/projects/SES_charls/sourcedata/w5-2020/CHARLS2020r/Individual_Income.dta')
# merge w1 ----------------------------------------------------------------
charls_w1_ses <- charls_w1_demo %>%
select(ID, householdID, communityID,
ba002_1, ba002_2, ba002_3, # birth year/month/day
bd001, bd002, bd003, bd005, bd006, bd008, # level of education, ys of primary school, ys after primary school
be001, be002, # marital status, living with someone
) %>%
full_join(., select(charls_w1_work,
c(
ID, householdID, communityID,
fc004, fc008, # farming for others (months), household(1 = yes)
fd002, fd008, fd010, # employed, government, ownership type of bussiness
fd012_gb, fd012_isco, fd013,
fh005_gb, fh005_isco # non-farm self employed work
)
),
by = c('ID' = 'ID')) %>%
select(!c(householdID.x, communityID.x, householdID.y, communityID.y)) %>%
full_join(., select(charls_w1_income,
c(ID, householdID, communityID,
ga002, ga002_1, # money last year or per month
ga004_1_1_:ga004_1_11_, # all kinds of income per year
ga004_2_1_:ga004_2_11_ # all kinds of income per month
)
),
by = c('ID' = 'ID')) %>%
mutate(
eduy = case_when(
# If both bd005 and bd006 are not NA, calculate eduyear as bd006 - bd005
!is.na(bd005) & !is.na(bd006) &
(bd006 - bd005 >= 0) & (bd006 - bd005 <= 22) ~ bd006 - bd005,
# Fallback logic if bd005 or bd006 is NA
bd001 == 1 ~ 0, # No formal education
bd001 == 2 ~ ifelse(is.na(bd002), 0, bd002), # Did not finish primary school
bd001 == 3 ~ ifelse(is.na(bd002), 0, bd002), # Sishu/home school
bd001 == 4 ~ 6 + ifelse(is.na(bd003), 0, bd003), # Elementary school
bd001 == 5 ~ 8 + ifelse(is.na(bd003), 0, bd003), # Middle school
bd001 == 6 ~ 11 + ifelse(is.na(bd003), 0, bd003), # High school
bd001 == 7 ~ 12 + ifelse(is.na(bd003), 0, bd003), # Vocational school
bd001 == 8 ~ 13 + ifelse(is.na(bd003), 0, bd003), # Two-/Three-Year College/Associate degree
bd001 == 9 ~ 15 + ifelse(is.na(bd003), 0, bd003), # Four-Year College/Bachelor’s degree
bd001 == 10 ~ 18 + ifelse(is.na(bd003), 0, bd003), # Master's degree
bd001 == 11 ~ 21 + ifelse(is.na(bd003), 0, bd003), # PhD
# If bd001 is NA
is.na(bd001) ~ NA_real_ # bd001 is NA
),
inny = if_else(
rowSums(is.na(select(., ga002, ga004_1_1_:ga004_1_11_))) == ncol(select(., ga002, ga004_1_1_:ga004_1_11_)),
NA_real_, # If all values are NA, set income to NA
rowSums(select(., ga002, ga004_1_1_:ga004_1_11_), na.rm = TRUE) # Otherwise, calculate the sum
),
innm = if_else(
rowSums(is.na(select(., ga002_1, ga004_2_1_:ga004_2_11_))) == ncol(select(., ga002_1, ga004_2_1_:ga004_2_11_)),
NA_real_, # If all values are NA, set income to NA
rowSums(select(., ga002_1, ga004_2_1_:ga004_2_11_), na.rm = TRUE) # Otherwise, calculate the sum
)
) %>%
mutate(ID = paste0(substring(ID, 1, 9), '0', substring(ID, 10, 11))) # recode the ID to be harmonized with w2-w5 as 12 digits
# occupation code need to be solved and merged into charls_w1_ses
charls_w1_occ <- charls_w1_ses %>%
select(ID, fc004, fc008, fd012_gb, fh005_gb) %>%
mutate(occ_gb = as.numeric(as.character(fd012_gb)),
selfocc_gb = as.numeric(as.character(fh005_gb))) %>%
mutate(
# (1) Determine employment type
employed_status = case_when(
!is.na(occ_gb) & is.na(selfocc_gb) ~ "employed", # Only employed
is.na(occ_gb) & !is.na(selfocc_gb) ~ "self-employed", # Only self-employed
!is.na(occ_gb) & !is.na(selfocc_gb) ~ "both", # Both employed and self-employed
(fc004 > 0 | fc008 == 1) ~ "farmer",
is.na(occ_gb) & is.na(selfocc_gb) ~ NA_character_ # Both are NA
) %>%
factor(levels = c('employed', 'self-employed', 'both', 'farmer')),
# (2) Combine occupation codes
occupation = case_when(
employed_status == "employed" ~ as.character(occ_gb), # Copy `occupation` for employed
employed_status == "self-employed" ~ as.character(selfocc_gb), # Copy `self_occupation` for self-employed
employed_status == "both" ~ as.character(occ_gb), # Set to occ_gb if both are present
is.na(employed_status) ~ NA_character_
)
) %>%
mutate(occupation = as.numeric(as.character(occupation)))
charls_w1_ses <- charls_w1_ses %>%
left_join(., charls_w1_occ, by = c('ID' = 'ID'))
# charls_w1_ses shoul be merged with charls activities and cognitive performances for modelling
# merge w2 ----------------------------------------------------------------
charls_w2_ses <- charls_w2_demo %>%
# filter(bd001_w2_1 == 1 & bd001_w2_4 != 12)
# # 1017 bd001_w2_1 == 2 but dc001_w2_4 = 12; 2457 bd001_w2_1 == 1 but dc001_w2_4 = !12
select(ID, householdID, communityID,
ba002_1, ba002_2, ba002_3, # birth year/month/day
bd001_w2_1, bd001_w2_4, # check if it is the same with wave 1
bd002, bd002_w2_1, bd003, bd005, bd006, bd008,
# level of education, ys of primary school, ys after primary school, only for thoes bd001_w2_1 == 2
be001 # marital status, living with someone: be001 = 7
) %>%
full_join(., select(charls_w2_work,
c(
ID,
fc001, fc004, # farming
fd002 # a lot of missings
)
),
by = c('ID' = 'ID')) %>%
full_join(., select(charls_w2_income,
c(ID, householdID, communityID,
ga002_1, ga002_2, # money last year or per month
ga004_1_1_:ga004_1_9_, # all kinds of income per year
ga004_2_1_:ga004_2_9_ # all kinds of income per month
)
),
by = c('ID' = 'ID')) %>%
left_join(., charls_w1_occ %>% select(ID,
employed_status_w1 = employed_status,
occupation_w1 = occupation),
by = c('ID' = 'ID')) %>%
select(!c(householdID.x, communityID.x,
householdID.y, communityID.y)) %>%
# Education: Left join Wave 1 education year by ID
left_join(.,
charls_w1_ses %>% select(ID, eduy_w1 = eduy),
by = c('ID' = 'ID')
) %>%
mutate(
eduy = case_when(
# Scenario 1: bd001_w2_1 == 1
bd001_w2_1 == 1 ~ eduy_w1,
# Scenario 2: bd001_w2_1 == 2 & bd001_w2_4 == 12 or bd001_w2_4 is NA
bd001_w2_1 == 2 & (bd001_w2_4 == 12 | is.na(bd001_w2_4)) ~ eduy_w1,
# Scenario 3: bd001_w2_1 == 2 & bd001_w2_4 != 12, apply fallback logic
bd001_w2_1 == 2 & bd001_w2_4 != 12 ~ case_when(
# Calculate education years as bd006 - bd005 if valid
!is.na(bd005) & !is.na(bd006) &
(bd006 - bd005 >= 0) & (bd006 - bd005 <= 22) ~ bd006 - bd005,
# Fallback to education level and additional years logic
bd001_w2_4 == 1 ~ 0, # No formal education
bd001_w2_4 == 2 ~ ifelse(is.na(bd002), 0, bd002), # Did not finish primary school
bd001_w2_4 == 3 ~ ifelse(is.na(bd002_w2_1), 0, bd002_w2_1), # Sishu/home school
bd001_w2_4 == 4 ~ 6 + ifelse(is.na(bd003), 0, bd003), # Elementary school
bd001_w2_4 == 5 ~ 8 + ifelse(is.na(bd003), 0, bd003), # Middle school
bd001_w2_4 == 6 ~ 11 + ifelse(is.na(bd003), 0, bd003), # High school
bd001_w2_4 == 7 ~ 12 + ifelse(is.na(bd003), 0, bd003), # Vocational school
bd001_w2_4 == 8 ~ 13 + ifelse(is.na(bd003), 0, bd003), # Associate degree
bd001_w2_4 == 9 ~ 15 + ifelse(is.na(bd003), 0, bd003), # Bachelor's degree
bd001_w2_4 == 10 ~ 18 + ifelse(is.na(bd003), 0, bd003), # Master's degree
bd001_w2_4 == 11 ~ 21 + ifelse(is.na(bd003), 0, bd003), # PhD
# Default to NA if no conditions are met
TRUE ~ NA_real_
),
# Scenario 4: bd001_w2_1 is NA, but other variables may have values
is.na(bd001_w2_1) ~ case_when(
!is.na(bd005) & !is.na(bd006) &
(bd006 - bd005 >= 0) & (bd006 - bd005 <= 22) ~ bd006 - bd005,
bd001_w2_4 == 1 ~ 0,
bd001_w2_4 == 2 ~ ifelse(is.na(bd002), 0, bd002),
bd001_w2_4 == 3 ~ ifelse(is.na(bd002_w2_1), 0, bd002_w2_1),
bd001_w2_4 == 4 ~ 6 + ifelse(is.na(bd003), 0, bd003),
bd001_w2_4 == 5 ~ 8 + ifelse(is.na(bd003), 0, bd003),
bd001_w2_4 == 6 ~ 11 + ifelse(is.na(bd003), 0, bd003),
bd001_w2_4 == 7 ~ 12 + ifelse(is.na(bd003), 0, bd003),
bd001_w2_4 == 8 ~ 13 + ifelse(is.na(bd003), 0, bd003),
bd001_w2_4 == 9 ~ 15 + ifelse(is.na(bd003), 0, bd003),
bd001_w2_4 == 10 ~ 18 + ifelse(is.na(bd003), 0, bd003),
bd001_w2_4 == 11 ~ 21 + ifelse(is.na(bd003), 0, bd003),
TRUE ~ NA_real_
),
# Scenario 5: All variables are NA
is.na(bd001_w2_1) & is.na(bd001_w2_4) &
is.na(bd005) & is.na(bd006) ~ eduy_w1,
# Default to NA
TRUE ~ NA_real_
),
# Income
inny = if_else(
rowSums(is.na(select(., ga002_1, ga004_1_1_:ga004_1_9_))) == ncol(select(., ga002_1, ga004_1_1_:ga004_1_9_)),
NA_real_, # If all values are NA, set income to NA
rowSums(select(., ga002_1, ga004_1_1_:ga004_1_9_), na.rm = TRUE) # Otherwise, calculate the sum
),
innm = if_else(
rowSums(is.na(select(., ga002_2, ga004_2_1_:ga004_2_9_))) == ncol(select(., ga002_2, ga004_2_1_:ga004_2_9_)),
NA_real_, # If all values are NA, set income to NA
rowSums(select(., ga002_2, ga004_2_1_:ga004_2_9_), na.rm = TRUE) # Otherwise, calculate the sum
),
# Occupation
employed_status = case_when(
is.na(employed_status_w1) & fc004 > 0 ~ "farmer",
!is.na(employed_status_w1) ~ as.character(employed_status_w1),
TRUE ~ NA_character_
) %>%
factor(levels = c('employed', 'self-employed', 'both', 'farmer')),
occupation = occupation_w1
)
# charls_w1_ses shoul be merged with charls activities and cognitive performances for modelling
# merge w3 ----------------------------------------------------------------
charls_w3_ses <- charls_w3_demo %>%
# filter(bd001_w2_1 == 1 & bd001_w2_4 != 12)
# # 1017 bd001_w2_1 == 2 but dc001_w2_4 = 12; 2457 bd001_w2_1 == 1 but dc001_w2_4 = !12
select(ID, householdID, communityID,
ba002_1, ba002_2, ba002_3, # birth year/month/day
bd001_w2_4, # check if it is the same with wave 1
bd002_w3, # ys after bd001_w2_4, only for thoes bd001_w2_4 != 12
# bd002_w2_1, bd003, bd005, bd006, bd008,
be001 # marital status, living with someone: be001 = 7
) %>%
# no fd012
full_join(., select(charls_w3_work,
c(
ID,
# fc001, # do farm employed
fd002 # Type of employer
)
),
by = c('ID' = 'ID')) %>%
full_join(., select(charls_w3_income,
c(ID, householdID, communityID,
ga002, # money last year
ga004_1_:ga004_9_ # all kinds of income per year
# ga004_1_1_:ga004_1_9_ # all kinds of income per month
# ga004_2_1_:ga004_2_9_ # all kinds of income per month
# The above are all NA
)), by = c('ID' = 'ID')) %>%
left_join(., charls_w2_ses %>% select(ID,
employed_status_w2 = employed_status,
occupation_w2 = occupation),
by = c('ID' = 'ID')) %>%
select(!c(householdID.x, communityID.x,
householdID.y, communityID.y)) %>%
# Education: Left join Wave 1 education year by ID
left_join(.,
charls_w2_ses %>% select(ID, eduy_w2 = eduy),
by = c('ID' = 'ID')
) %>%
mutate(
eduy = case_when(
bd001_w2_4 == 12 ~ eduy_w2,
bd001_w2_4 != 12 & is.na(bd002_w3) ~ eduy_w2,
is.na(bd001_w2_4) ~ eduy_w2,
bd001_w2_4 != 12 & !is.na(bd002_w3) ~ case_when(
# Fallback to education level and additional years logic
bd001_w2_4 == 1 ~ 0, # No formal education
bd001_w2_4 == 2 ~ ifelse(is.na(bd002_w3), 0, bd002_w3), # Did not finish primary school
bd001_w2_4 == 3 ~ ifelse(is.na(bd002_w3), 0, bd002_w3), # Sishu/home school
bd001_w2_4 == 4 ~ 6 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Elementary school
bd001_w2_4 == 5 ~ 8 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Middle school
bd001_w2_4 == 6 ~ 11 + ifelse(is.na(bd002_w3), 0, bd002_w3), # High school
bd001_w2_4 == 7 ~ 12 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Vocational school
bd001_w2_4 == 8 ~ 13 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Associate degree
bd001_w2_4 == 9 ~ 15 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Bachelor's degree
bd001_w2_4 == 10 ~ 18 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Master's degree
bd001_w2_4 == 11 ~ 21 + ifelse(is.na(bd002_w3), 0, bd002_w3), # PhD
# Default to NA if no conditions are met
TRUE ~ NA_real_
)),
# Income
inny = if_else(
rowSums(is.na(select(., ga002, ga004_1_:ga004_9_))) == ncol(select(., ga002, ga004_1_:ga004_9_)),
NA_real_, # If all values are NA, set income to NA
rowSums(select(., ga002, ga004_1_:ga004_9_), na.rm = TRUE) # Otherwise, calculate the sum
),
# Occupation
employed_status = employed_status_w2,
occupation = occupation_w2
)
# merge w4 ----------------------------------------------------------------
charls_w4_ses <- charls_w4_demo %>%
# filter(bd001_w2_1 == 1 & bd001_w2_4 != 12)
# # 1017 bd001_w2_1 == 2 but dc001_w2_4 = 12; 2457 bd001_w2_1 == 1 but dc001_w2_4 = !12
select(ID, householdID, communityID,
ba000_w2_3, # Gender
ba002_1, ba002_2, ba002_3, # actual birth year/month/day
bd001_w2_4, # education level
bd002_w3, # ys after bd001_w2_4, only for thoes bd001_w2_4 != 12
# bd002_w2_1, bd003, bd005, bd006, bd008,
be001, be002 # marital status
) %>%
# no fd012
full_join(., select(charls_w4_work,
c(
ID,
# fc001, # do farm employed
fd002 # 4843 total
)
),
by = c('ID' = 'ID')) %>%
full_join(., select(charls_w4_income,
c(ID, householdID, communityID,
ga002, # money last year
ga003_w4_1:ga003_w4_9 # all kinds of income per year
# ga004_2_1_:ga004_2_9_ # all kinds of income per month
)), by = c('ID' = 'ID')) %>%
left_join(., charls_w2_ses %>% select(ID,
employed_status_w2 = employed_status,
occupation_w2 = occupation),
by = c('ID' = 'ID')) %>%
select(!c(householdID.x, communityID.x,
householdID.y, communityID.y)) %>%
# Education: Left join Wave 1 education year by ID
left_join(.,
charls_w3_ses %>% select(ID, eduy_w2 = eduy),
by = c('ID' = 'ID')
) %>%
mutate(
eduy = case_when(
# Fallback to education level and additional years logic
bd001_w2_4 == 1 ~ 0, # No formal education
bd001_w2_4 == 3 ~ ifelse(is.na(bd002_w3), 0, bd002_w3), # Did not finish primary school, as 3y
bd001_w2_4 == 3 ~ ifelse(is.na(bd002_w3), 0, bd002_w3), # Sishu/home school
bd001_w2_4 == 4 ~ 6 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Elementary school
bd001_w2_4 == 5 ~ 8 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Middle school
bd001_w2_4 == 6 ~ 11 + ifelse(is.na(bd002_w3), 0, bd002_w3), # High school
bd001_w2_4 == 7 ~ 12 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Vocational school
bd001_w2_4 == 8 ~ 13 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Associate degree
bd001_w2_4 == 9 ~ 15 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Bachelor's degree
bd001_w2_4 == 10 ~ 18 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Master's degree
bd001_w2_4 == 11 ~ 21 + ifelse(is.na(bd002_w3), 0, bd002_w3), # PhD
# Default to NA if no conditions are met
TRUE ~ NA_real_
),
# Income
inny = if_else(
rowSums(is.na(select(., ga002, ga003_w4_1:ga003_w4_9))) == ncol(select(., ga002, ga003_w4_1:ga003_w4_9)),
NA_real_, # If all values are NA, set income to NA
rowSums(select(., ga002, ga003_w4_1:ga003_w4_9), na.rm = TRUE) # Otherwise, calculate the sum
),
# Occupation
employed_status = employed_status_w2,
occupation = occupation_w2
)
# merge w5 ----------------------------------------------------------------
charls_w5_ses <- charls_w5_demo %>%
# filter(bd001_w2_1 == 1 & bd001_w2_4 != 12)
# # 1017 bd001_w2_1 == 2 but dc001_w2_4 = 12; 2457 bd001_w2_1 == 1 but dc001_w2_4 = !12
select(ID, householdID, communityID,
ba001, # Recorded Gender
ba003_1, ba003_2, ba003_3, # actual birth year/month/day
ba010, # education level
# bd002_w3, # ys after bd001_w2_4, only for thoes bd001_w2_4 != 12
# bd002_w2_1, bd003, bd005, bd006, bd008,
ba011, ba012 # marital status
) %>%
# work
full_join(., select(charls_w5_work,
c(
ID,
fa002_s1, fa002_s2 # do farm work self-employed or employed
# no employed exact job
)
),
by = c('ID' = 'ID')) %>%
full_join(., select(charls_w5_income,
c(ID, householdID, communityID,
ga002, # money last year, -1 for cannot answer
ga005_1:ga005_9 # all kinds of income per year
)), by = c('ID' = 'ID')) %>%
left_join(., charls_w2_ses %>% select(ID,
employed_status_w2 = employed_status,
occupation_w2 = occupation),
by = c('ID' = 'ID')) %>%
left_join(., charls_w4_ses %>%
select(ID, fd002),
by = c('ID' = 'ID')) %>%
select(!c(householdID.x, communityID.x,
householdID.y, communityID.y)) %>%
# Education: Left join Wave 1 education year by ID
left_join(.,
charls_w4_ses %>% select(ID, eduy),
by = c('ID' = 'ID') # wave 5 only record education level so use wave 4 education years
) %>%
mutate(
# eduy = case_when(
#
# # Fallback to education level and additional years logic
# bd001_w2_4 == 1 ~ 0, # No formal education
# bd001_w2_4 == 3 ~ ifelse(is.na(bd002_w3), 0, bd002_w3), # Did not finish primary school, as 3y
# bd001_w2_4 == 3 ~ ifelse(is.na(bd002_w3), 0, bd002_w3), # Sishu/home school
# bd001_w2_4 == 4 ~ 6 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Elementary school
# bd001_w2_4 == 5 ~ 8 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Middle school
# bd001_w2_4 == 6 ~ 11 + ifelse(is.na(bd002_w3), 0, bd002_w3), # High school
# bd001_w2_4 == 7 ~ 12 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Vocational school
# bd001_w2_4 == 8 ~ 13 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Associate degree
# bd001_w2_4 == 9 ~ 15 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Bachelor's degree
# bd001_w2_4 == 10 ~ 18 + ifelse(is.na(bd002_w3), 0, bd002_w3), # Master's degree
# bd001_w2_4 == 11 ~ 21 + ifelse(is.na(bd002_w3), 0, bd002_w3), # PhD
#
# # Default to NA if no conditions are met
# TRUE ~ NA_real_
# ),
# Income
inny = if_else(
rowSums(is.na(select(., ga002, ga005_1:ga005_9))) == ncol(select(., ga002, ga005_1:ga005_9)),
NA_real_, # If all values are NA, set income to NA
rowSums(select(., ga002, ga005_1:ga005_9), na.rm = TRUE) # Otherwise, calculate the sum
),
# Occupation
employed_status = employed_status_w2,
occupation = occupation_w2
)
# for summary:
length(which(charls_w4_ses$bd001_w2_4 == 2 & charls_w4_ses$bd002_w3 != 0))
# clean w1-w5 ses ---------------------------------------------------------
charls_w1 <- charls_w1_ses %>%
select(ID,
eduy, inny, innm, fd002, employed_status, occupation) %>%
mutate(waves = 1)
charls_w2 <- charls_w2_ses %>%
select(ID,
eduy, inny, innm, fd002, employed_status, occupation
) %>%
mutate(waves = 2)
charls_w3 <- charls_w3_ses %>%
select(ID,
eduy, inny, fd002, employed_status, occupation) %>%
mutate(waves = 3)
charls_w4 <- charls_w4_ses %>%
select(ID,
eduy, inny, fd002, employed_status, occupation) %>%
mutate(waves = 4)
charls_w5 <- charls_w5_ses %>%
select(ID,
eduy, inny, fd002, employed_status, occupation) %>%
mutate(waves = 5)
# check innm for w1 and w2 and allign with other waves as inny
charls_w1 <- charls_w1 %>%
mutate(inny_new = case_when(
# If yearly income is NA and monthly income is not NA, calculate yearly income from monthly income
is.na(inny) & !is.na(innm) ~ innm * 12,
# If yearly income is not NA and monthly income is NA, use yearly income
!is.na(inny) & is.na(innm) ~ inny,
# If both yearly and monthly income are available
!is.na(inny) & !is.na(innm) ~ case_when(
inny == innm * 12 ~ inny, # If consistent, keep either
inny != innm * 12 ~ (inny + innm * 12)/2 # Otherwise, choose the larger value
),
# If both are NA, set result to NA
TRUE ~ NA_real_
)) %>%
select(ID, eduy, inny_new, fd002, employed_status, occupation, waves) %>%
rename(inny = inny_new)
charls_w2 <- charls_w2 %>%
mutate(inny_new = case_when(
# If yearly income is NA and monthly income is not NA, calculate yearly income from monthly income
is.na(inny) & !is.na(innm) ~ innm * 12,
# If yearly income is not NA and monthly income is NA, use yearly income
!is.na(inny) & is.na(innm) ~ inny,
# If both yearly and monthly income are available
!is.na(inny) & !is.na(innm) ~ case_when(
inny == innm * 12 ~ inny, # If consistent, keep either
inny != innm * 12 ~ (inny + innm * 12)/2 # Otherwise, choose the larger value
),
# If both are NA, set result to NA
TRUE ~ NA_real_
)) %>%
select(ID, eduy, inny_new, fd002, employed_status, occupation, waves) %>%
rename(inny = inny_new)
charls_ses <- rbind(charls_w1, charls_w2, charls_w3, charls_w4, charls_w5) %>%
mutate(waves = factor(waves)) %>%
select(ID, waves, eduy, employed_status, occupation, inny) %>%
arrange(ID, waves)