data-raw/eqls_data_wrangling.R
In ddauber/r4np: R for Non-Programmers Companion
# European Quality of Life Survey (EQLS)
# Date: 2024-02-27
# LOAD PACKAGES -----------------------------------------------------------
library(tidyverse)
library(purrr)
# IMPORT DATA -------------------------------------------------------------
eqls_raw <- read_csv("data-raw/eqls_2007and2011.csv")
iso_country_codes <- read_csv("data-raw/iso_country_codes.csv")
glimpse(eqls_raw)
# FIX COUNTRY NAMES -------------------------------------------------------
result <- eqls_raw |> count(ISO3166_Country)
eqls_raw %>%
dplyr::count(ISO3166_Country) %>%
print(n = nrow(.))
eqls_iso_country <-
eqls_raw |>
mutate(iso_code = str_extract(ISO3166_Country, "([^:]+)$"))
# Check whether we have done it correctly - randomly select 50 rows
eqls_iso_country |> select(ISO3166_Country, iso_code) |> slice_sample(n = 50) |> print(n = Inf)
eqls_country_clean <- left_join(eqls_iso_country, iso_country_codes, by = "iso_code")
#check the country is correct
eqls_country_clean |> select(ISO3166_Country, iso_code, `Country Name`) |> slice_sample(n = 50) |> print(n = Inf)
#rename column `Country Name`
eqls_country_clean <-
eqls_country_clean |>
mutate(country_name = `Country Name`)
glimpse(eqls_country_clean)
# SELECT INTERESTING DATA -------------------------------------------------
eqls_select <-
eqls_country_clean |>
select(UniqueID, # participant ID
Wave, # Wave of data collection
country_name, # country name
iso_code, # iso code of country
Y11_Q31, # marital status
Y11_Q32, # number of children
Y11_Q49, # rural/urban living
Y11_Agecategory, # Age in 5 categories
Y11_ISCEDsimple, # highest education completed
Y11_HH2a, # gender
Y11_EmploymentStatus, # Employment - 7 groups
Y11_Q24, # Can most people be trusted?
Y11_Q21a, # How frequently attend religious services?
Y11_Q21b, # How frequently use the Internet other than for work?
Y11_Q21c, # How frequently take part in sports or exercise?
Y11_Q21d, # How frequently participate in social activities?
Y11_Q22a, # How often worked unpaid for community services last 12 months?
Y11_Q22b, # How often worked unpaid for education/cultural etc organisation last 12 months?
Y11_Q22c, # How often worked unpaid for social movements/charities last 12 months?
Y11_Q22d, # How often worked unpaid for political parties or trade unions last 12 months?
Y11_Q22e, # How often worked unpaid for other voluntary org last 12 months?
Y11_Volunteering, # Volunteering frequency
Y11_Q29e, # Feel left out of society?
Y11_Q29f, # Can't find the way because life has become so complicated?
Y11_Q29g, # The value of what I do is not recognised by others?
Y11_Q29h, # People look down on me because of my job situation or income?
Y11_Q29i, # Feel close to people in the area where I live
Y11_SocExIndex, # Social Exclusion Index
Y11_Q45a, # How often felt cheerful and in good spirits last 2 weeks?
Y11_Q45b, # How often felt calm and relaxed last 2 weeks?
Y11_Q45c, # How often felt active and vigorous last 2 weeks?
Y11_Q45d, # How often woke up feeling fresh and rested last 2 weeks?
Y11_Q45e, # How often felt your daily life has been filled with things that interest you last 2 weeks?
Y11_Q46a, # How often felt particularly tense last 2 weeks?
Y11_Q46b, # How often felt lonely last 2 weeks?
Y11_Q46c, # How often felt downhearted and depressed last 2 weeks?
Y11_MWIndex, # WHO-5 mental wellbeing index
Y11_Q12a, # Come home from work too tired to do some of the household jobs
Y11_Q12b, # Difficult to fulfil family responsibilities because of the time at work
Y11_Q12c, # Difficult to concentrate at work because of family responsibilities
Y11_Strainbasedconflict, # Work-life balance conflict?
Y11_Q7, # How many hours do you work in 1st job?
Y11_Q7b, # How many hours per week worked in the additional job?
DV_Q7 # DV: Total number of working hours
)
# compute total working hrs across first, second and other additional work
# If values exist in both columns add the hours together into one score
eqls_work_hrs <-
eqls_select |>
rowwise() |>
mutate(work_hrs = if_else(is.na(Y11_Q7), Y11_Q7b,
if_else(is.na(Y11_Q7b), Y11_Q7,
Y11_Q7 + Y11_Q7b)
)
) |>
ungroup()
## Check outcome
eqls_work_hrs |>select(Y11_Q7, Y11_Q7b, work_hrs) |> slice_sample(n = 50) |> print(n = Inf)
eqls_work_hrs |> select(Y11_Q7, Y11_Q7b, work_hrs) |> naniar::vis_miss(warn_large_data = FALSE)
sum(is.na(eqls_work_hrs$Y11_Q7))
sum(is.na(eqls_work_hrs$work_hrs))
eqls_work_hrs |> count(is.na(Y11_Q7), is.na(Y11_Q7b))
# INSPECT DATA ------------------------------------------------------------
## Check for missing data
# naniar::vis_miss(eqls_work_hrs, warn_large_data = FALSE)
## Check for interesting relationships
eqls_work_hrs |>
select(Y11_SocExIndex,
Y11_Volunteering,
Y11_Strainbasedconflict,
Y11_MWIndex) |>
correlation::correlation()
eqls_work_hrs |>
select(Y11_SocExIndex,
Y11_Volunteering,
Y11_Strainbasedconflict,
Y11_MWIndex) |>
pivot_longer(names_to = 'variable',
cols = everything()) |>
group_by(variable) |>
na.omit() |>
summarise(mean = mean(value))
eqls_work_hrs |>
ggplot(aes(x = as_factor(Y11_Agecategory),
y = Y11_Strainbasedconflict,
fill = country_name)) +
geom_boxplot(outliers = FALSE) +
facet_wrap(~ country_name) +
coord_flip()
eqls_work_hrs |>
ggplot(aes(x = Y11_Q29e,
y = as_factor(Y11_HH2a),
fill = as_factor(Y11_HH2a))) +
geom_boxplot(outliers = TRUE) +
coord_flip() +
guides(fill = "none")
# Checking countries for multiple group comparison and Social Exclusion Index
eqls_work_hrs |>
# filter(country_name == "Sweden" |
# country_name == "Norway" |
# country_name == "Bulgaria" |
# country_name == "France"
# ) |>
ggplot(aes(x = as_factor(country_name),
y = Y11_SocExIndex,
fill = country_name)) +
geom_boxplot() +
coord_flip()
eqls_work_hrs |>
count(Y11_SocExIndex)
range(eqls_work_hrs$Y11_SocExIndex, na.rm = TRUE)
# RENAME VARIABLE NAMES ---------------------------------------------------
# select(UniqueID, # participant ID
# country_name, # country name
# iso_code, # iso code of country
# Y11_Q31, # marital status
# Y11_Q32, # number of children
# Y11_Q49, # rural/urban living
# Y11_Agecategory, # Age in 5 categories
# Y11_ISCEDsimple, # highest education completed
# Y11_HH2a, # gender
# Y11_EmploymentStatus, # Employment - 7 groups
# Y11_Q24, # Can most people be trusted?
# Y11_Q21a, # How frequently attend religious services?
# Y11_Q21b, # How frequently use the Internet other than for work?
# Y11_Q21c, # How frequently take part in sports or exercise?
# Y11_Q21d, # How frequently participate in social activities?
# Y11_Q22a, # How often worked unpaid for community services last 12 months?
# Y11_Q22b, # How often worked unpaid for education/cultural etc organisation last 12 months?
# Y11_Q22c, # How often worked unpaid for social movements/charities last 12 months?
# Y11_Q22d, # How often worked unpaid for political parties or trade unions last 12 months?
# Y11_Q22e, # How often worked unpaid for other voluntary org last 12 months?
# Y11_Volunteering, # Volunteering frequency
# Y11_Q29e, # Feel left out of society?
# Y11_Q29f, # Can't find the way because life has become so complicated?
# Y11_Q29g, # The value of what I do is not recognised by others?
# Y11_Q29h, # People look down on me because of my job situation or income?
# Y11_Q29i, # Feel close to people in the area where I live
# Y11_SocExIndex, # Social Exclusion Index
# Y11_Q45a, # How often felt cheerful and in good spirits last 2 weeks?
# Y11_Q45b, # How often felt calm and relaxed last 2 weeks?
# Y11_Q45c, # How often felt active and vigorous last 2 weeks?
# Y11_Q45d, # How often woke up feeling fresh and rested last 2 weeks?
# Y11_Q45e, # How often felt your daily life has been filled with things that interest you last 2 weeks?
# Y11_Q46a, # How often felt particularly tense last 2 weeks?
# Y11_Q46b, # How often felt lonely last 2 weeks?
# Y11_Q46c, # How often felt downhearted and depressed last 2 weeks?
# Y11_MWIndex, # WHO-5 mental wellbeing index
# Y11_Q12a, # Come home from work too tired to do some of the household jobs
# Y11_Q12b, # Difficult to fulfil family responsibilities because of the time at work
# Y11_Q12c, # Difficult to concentrate at work because of family responsibilities
# Y11_Strainbasedconflict, # Work-life balance conflict?
# Y11_Q7, # How many hours do you work in 1st job?
# Y11_Q7b, # How many hours per week worked in the additional job?
# DV_Q7 # DV: Total number of working hours
eqls_renamed <-
eqls_work_hrs |>
rename(
id = UniqueID,
wave = Wave, # wave 2007 or 2011
marital_status = Y11_Q31, # marital status
num_of_children = Y11_Q32, # number of children
rural_urban_living = Y11_Q49, # rural/urban living
age_category = Y11_Agecategory, # Age in 5 categories
education = Y11_ISCEDsimple, # highest education completed
gender = Y11_HH2a, # gender
employment = Y11_EmploymentStatus, # Employment - 7 groups
trust_people = Y11_Q24, # Can most people be trusted?
freq_rel_service_use = Y11_Q21a, # How frequently attend religious services?
freq_internet_personal = Y11_Q21b, # How frequently use the Internet other than for work?
freq_sports = Y11_Q21c, # How frequently take part in sports or exercise?
freq_social_activities = Y11_Q21d, # How frequently participate in social activities?
vol_community_service = Y11_Q22a, # How often worked unpaid for community services last 12 months?
vol_education_cult_org = Y11_Q22b, # How often worked unpaid for education/cultural etc organisation last 12 months?
vol_social_charities = Y11_Q22c, # How often worked unpaid for social movements/charities last 12 months?
vol_political_parties = Y11_Q22d, # How often worked unpaid for political parties or trade unions last 12 months?
vol_other_organisation = Y11_Q22e, # How often worked unpaid for other voluntary org last 12 months?
volunteering_frequency = Y11_Volunteering, # Volunteering frequency
sei_left_out_soc = Y11_Q29e, # Feel left out of society?
sei_life_is_complicated = Y11_Q29f, # Can't find the way because life has become so complicated?
sei_not_recognised = Y11_Q29g, # The value of what I do is not recognised by others?
sei_looked_down = Y11_Q29h, # People look down on me because of my job situation or income?
sei_feel_close_to_people = Y11_Q29i, # Feel close to people in the area where I live
sei = Y11_SocExIndex, # Social Exclusion Index
mwbi_felt_cheerful = Y11_Q45a, # How often felt cheerful and in good spirits last 2 weeks?
mwbi_felt_calm = Y11_Q45b, # How often felt calm and relaxed last 2 weeks?
mwbi_felt_active = Y11_Q45c, # How often felt active and vigorous last 2 weeks?
mwbi_felt_fresh = Y11_Q45d, # How often woke up feeling fresh and rested last 2 weeks?
mwbi_life_is_interesting = Y11_Q45e, # How often felt your daily life has been filled with things that interest you last 2 weeks?
mwbi_felt_tense = Y11_Q46a, # How often felt particularly tense last 2 weeks?
mwbi_felt_lonely = Y11_Q46b, # How often felt lonely last 2 weeks?
mwbi_felt_downhearted = Y11_Q46c, # How often felt downhearted and depressed last 2 weeks?
mwbi = Y11_MWIndex, # WHO-5 mental wellbeing index
wlbc_too_tired_household = Y11_Q12a, # Come home from work too tired to do some of the household jobs
wlbc_fam_responsibilities = Y11_Q12b, # Difficult to fulfil family responsibilities because of the time at work
wlbc_cannot_focus_on_work = Y11_Q12c, # Difficult to concentrate at work because of family responsibilities
wlbc = Y11_Strainbasedconflict, # Work-life balance conflict?
hrs_work_first_job = Y11_Q7, # How many hours do you work in 1st job?
hrs_work_additional_job = Y11_Q7b, # How many hours per week worked in the additional job?
total_hrs_working = DV_Q7 # DV: Total number of working hours)
)
# REPLACE NUMERIC FACTOR LEVELS WITH ACTUAL CATEGORY NAMES ----------------
## num_of_children ----
# 5 = 5 or more children
# Replace '5' with category label and convert to ordinal variable
eqls_clean_factors <-
eqls_renamed |>
mutate(num_of_children = if_else(as_factor(num_of_children) == "5", "5 or more children", as_factor(num_of_children)),
num_of_children = factor(num_of_children, ordered = TRUE))
# Check
eqls_clean_factors |> count(num_of_children)
## marital_status ----
# 1 = Married or living with partner
# 2 = Separated or divorced and not living with partner
# 3 = Widowed and not living with partner
# 4 = Never married and not living with partner
eqls_clean_factors <-
eqls_clean_factors |>
mutate(marital_status = case_when(
marital_status == 1 ~ "Married or living with partner",
marital_status == 2 ~ "Separated or divorced and not living with partner",
marital_status == 3 ~ "Widowed and not living with partner",
marital_status == 4 ~ "Never married and not living with partner"
),
marital_status = as_factor(marital_status)
)
# check
eqls_clean_factors |> count(marital_status)
## rural_urban_living ----
# 1 = The open countryside
# 2 = A village/small town
# 3 = A medium to large town
# 4 = A city or city suburb
eqls_clean_factors <-
eqls_clean_factors |>
mutate(rural_urban_living = case_when(
rural_urban_living == 1 ~ "The open countryside",
rural_urban_living == 2 ~ "A village/small town",
rural_urban_living == 3 ~ "A medium to large town",
rural_urban_living == 4 ~ "A city or city suburb"
),
rural_urban_living = as_factor(rural_urban_living)
)
# check
eqls_clean_factors |> count(rural_urban_living)
## age_category ----
# 1 = 18-24
# 2 = 25-34
# 3 = 35-49
# 4 = 50-64
# 5 = 65+
eqls_clean_factors <-
eqls_clean_factors |>
mutate(age_category = case_when(
age_category == 1 ~ "18-24",
age_category == 2 ~ "25-34",
age_category == 3 ~ "35-49",
age_category == 4 ~ "50-64",
age_category == 5 ~ "65+"
),
age_category = factor(age_category, ordered = TRUE)
)
eqls_clean_factors |> count(age_category)
## education ----
# 1 = No education completed (ISCED 0)
# 2 = Primary education (ISCED 1)
# 3 = Lower secondary education (ISCED 2)
# 4 = Upper secondary education (ISCED 3)
# 5 = Post-secondary including pre-vocational or vocational education but not tertiary (ISCED 4)
# 6 = Tertiary education – first level (ISCED 5)
# 7 = Tertiary education – advanced level (ISCED 6)
# 8 = Completed education abroad"
eqls_clean_factors <-
eqls_clean_factors |>
mutate(education = case_when(
education == 1 ~ "No education completed (ISCED 0)",
education == 2 ~ "Primary education (ISCED 1)",
education == 3 ~ "Lower secondary education (ISCED 2)",
education == 4 ~ "Upper secondary education (ISCED 3)",
education == 5 ~ "Post-secondary including pre-vocational or vocational education but not tertiary (ISCED 4)",
education == 6 ~ "Tertiary education – first level (ISCED 5)",
education == 7 ~ "Tertiary education – advanced level (ISCED 6)",
education == 8 ~ "Completed education abroad"
),
education = factor(education)
)
eqls_clean_factors |> count(education)
## gender ----
# 1 = Male
# 2 = Female
eqls_clean_factors <-
eqls_clean_factors |>
mutate(gender = case_when(
gender == 1 ~ "Male",
gender == 2 ~ "Female"
),
gender = as_factor(gender)
)
eqls_clean_factors |> count(gender)
## employment ----
# 1 = Employed (includes on leave)
# 2 = Unemployed
# 3 = Unable to work - disability/illness
# 4 = Retired
# 5 = Homemaker
# 6 = Student
# 7 = Other
eqls_clean_factors <-
eqls_clean_factors |>
mutate(employment = case_when(
employment == 1 ~ "Employed (includes on leave)",
employment == 2 ~ "Unemployed",
employment == 3 ~ "Unable to work - disability/illness",
employment == 4 ~ "Retired",
employment == 5 ~ "Homemaker",
employment == 6 ~ "Student",
employment == 7 ~ "Other"
),
employment = as_factor(employment)
)
## Wave recoding
# 2 = 2007
# 3 = 2011
eqls_clean_factors <-
eqls_clean_factors |>
mutate(wave = case_when(
wave == 2 ~ "2007",
wave == 3 ~ "2011"
),
wave = as_factor(wave)
)
eqls_clean_factors |> count(wave)
## Conversions to factors (clean-up)
eqls_clean_factors <-
eqls_clean_factors |>
mutate(country_name = as_factor(country_name),
iso_code = as_factor(iso_code)
)
# REMOVE VARIABLES NOT NEEDED ANYMORE -------------------------------------
eqls_clean_factors <-
eqls_clean_factors |>
select(-total_hrs_working)
# FIX: NON-ASCII strings --------------------------------------------------
## In response to the following warning:
##
## Warning: found non-ASCII strings
## 'Tertiary education <e2><80><93> advanced level (ISCED 6)' in object 'eqls'
## 'Tertiary education <e2><80><93> first level (ISCED 5)' in object 'eqls'
## Function to replace non-ASCII en dash with ASCII hyphen
replace_non_ascii <- function(x) {
str_replace_all(x, "\u2013", "-") # Replace en dash (U+2013) with hyphen
}
## Apply the replacement to the entire dataset
eqls_ascii_strings <-
eqls_clean_factors %>%
# mutate(across(everything(), ~ replace_non_ascii(.)))
mutate(education = as_factor(replace_non_ascii(education)))
# FINAL DATASETS ----------------------------------------------------------
eqls <- eqls_ascii_strings
## Dataset which only contains 2011 data
eqls_2011 <-
eqls |>
filter(wave == 2011)
## Dataset with country aggregates
eqls_country_2011 <-
eqls_2011 |>
group_by(country_name) |>
summarise(trust_people = mean(trust_people, na.rm = TRUE),
sei = mean(sei, na.rm = TRUE),
mwbi = mean(mwbi, na.rm = TRUE),
wlbc = mean(wlbc, na.rm = TRUE)) |>
na.omit()
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# European Quality of Life Survey (EQLS)
# Date: 2024-02-27
# LOAD PACKAGES -----------------------------------------------------------
library(tidyverse)
library(purrr)
# IMPORT DATA -------------------------------------------------------------
eqls_raw <- read_csv("data-raw/eqls_2007and2011.csv")
iso_country_codes <- read_csv("data-raw/iso_country_codes.csv")
glimpse(eqls_raw)
# FIX COUNTRY NAMES -------------------------------------------------------
result <- eqls_raw |> count(ISO3166_Country)
eqls_raw %>%
dplyr::count(ISO3166_Country) %>%
print(n = nrow(.))
eqls_iso_country <-
eqls_raw |>
mutate(iso_code = str_extract(ISO3166_Country, "([^:]+)$"))
# Check whether we have done it correctly - randomly select 50 rows
eqls_iso_country |> select(ISO3166_Country, iso_code) |> slice_sample(n = 50) |> print(n = Inf)
eqls_country_clean <- left_join(eqls_iso_country, iso_country_codes, by = "iso_code")
#check the country is correct
eqls_country_clean |> select(ISO3166_Country, iso_code, `Country Name`) |> slice_sample(n = 50) |> print(n = Inf)
#rename column `Country Name`
eqls_country_clean <-
eqls_country_clean |>
mutate(country_name = `Country Name`)
glimpse(eqls_country_clean)
# SELECT INTERESTING DATA -------------------------------------------------
eqls_select <-
eqls_country_clean |>
select(UniqueID, # participant ID
Wave, # Wave of data collection
country_name, # country name
iso_code, # iso code of country
Y11_Q31, # marital status
Y11_Q32, # number of children
Y11_Q49, # rural/urban living
Y11_Agecategory, # Age in 5 categories
Y11_ISCEDsimple, # highest education completed
Y11_HH2a, # gender
Y11_EmploymentStatus, # Employment - 7 groups
Y11_Q24, # Can most people be trusted?
Y11_Q21a, # How frequently attend religious services?
Y11_Q21b, # How frequently use the Internet other than for work?
Y11_Q21c, # How frequently take part in sports or exercise?
Y11_Q21d, # How frequently participate in social activities?
Y11_Q22a, # How often worked unpaid for community services last 12 months?
Y11_Q22b, # How often worked unpaid for education/cultural etc organisation last 12 months?
Y11_Q22c, # How often worked unpaid for social movements/charities last 12 months?
Y11_Q22d, # How often worked unpaid for political parties or trade unions last 12 months?
Y11_Q22e, # How often worked unpaid for other voluntary org last 12 months?
Y11_Volunteering, # Volunteering frequency
Y11_Q29e, # Feel left out of society?
Y11_Q29f, # Can't find the way because life has become so complicated?
Y11_Q29g, # The value of what I do is not recognised by others?
Y11_Q29h, # People look down on me because of my job situation or income?
Y11_Q29i, # Feel close to people in the area where I live
Y11_SocExIndex, # Social Exclusion Index
Y11_Q45a, # How often felt cheerful and in good spirits last 2 weeks?
Y11_Q45b, # How often felt calm and relaxed last 2 weeks?
Y11_Q45c, # How often felt active and vigorous last 2 weeks?
Y11_Q45d, # How often woke up feeling fresh and rested last 2 weeks?
Y11_Q45e, # How often felt your daily life has been filled with things that interest you last 2 weeks?
Y11_Q46a, # How often felt particularly tense last 2 weeks?
Y11_Q46b, # How often felt lonely last 2 weeks?
Y11_Q46c, # How often felt downhearted and depressed last 2 weeks?
Y11_MWIndex, # WHO-5 mental wellbeing index
Y11_Q12a, # Come home from work too tired to do some of the household jobs
Y11_Q12b, # Difficult to fulfil family responsibilities because of the time at work
Y11_Q12c, # Difficult to concentrate at work because of family responsibilities
Y11_Strainbasedconflict, # Work-life balance conflict?
Y11_Q7, # How many hours do you work in 1st job?
Y11_Q7b, # How many hours per week worked in the additional job?
DV_Q7 # DV: Total number of working hours
)
# compute total working hrs across first, second and other additional work
# If values exist in both columns add the hours together into one score
eqls_work_hrs <-
eqls_select |>
rowwise() |>
mutate(work_hrs = if_else(is.na(Y11_Q7), Y11_Q7b,
if_else(is.na(Y11_Q7b), Y11_Q7,
Y11_Q7 + Y11_Q7b)
)
) |>
ungroup()
## Check outcome
eqls_work_hrs |>select(Y11_Q7, Y11_Q7b, work_hrs) |> slice_sample(n = 50) |> print(n = Inf)
eqls_work_hrs |> select(Y11_Q7, Y11_Q7b, work_hrs) |> naniar::vis_miss(warn_large_data = FALSE)
sum(is.na(eqls_work_hrs$Y11_Q7))
sum(is.na(eqls_work_hrs$work_hrs))
eqls_work_hrs |> count(is.na(Y11_Q7), is.na(Y11_Q7b))
# INSPECT DATA ------------------------------------------------------------
## Check for missing data
# naniar::vis_miss(eqls_work_hrs, warn_large_data = FALSE)
## Check for interesting relationships
eqls_work_hrs |>
select(Y11_SocExIndex,
Y11_Volunteering,
Y11_Strainbasedconflict,
Y11_MWIndex) |>
correlation::correlation()
eqls_work_hrs |>
select(Y11_SocExIndex,
Y11_Volunteering,
Y11_Strainbasedconflict,
Y11_MWIndex) |>
pivot_longer(names_to = 'variable',
cols = everything()) |>
group_by(variable) |>
na.omit() |>
summarise(mean = mean(value))
eqls_work_hrs |>
ggplot(aes(x = as_factor(Y11_Agecategory),
y = Y11_Strainbasedconflict,
fill = country_name)) +
geom_boxplot(outliers = FALSE) +
facet_wrap(~ country_name) +
coord_flip()
eqls_work_hrs |>
ggplot(aes(x = Y11_Q29e,
y = as_factor(Y11_HH2a),
fill = as_factor(Y11_HH2a))) +
geom_boxplot(outliers = TRUE) +
coord_flip() +
guides(fill = "none")
# Checking countries for multiple group comparison and Social Exclusion Index
eqls_work_hrs |>
# filter(country_name == "Sweden" |
# country_name == "Norway" |
# country_name == "Bulgaria" |
# country_name == "France"
# ) |>
ggplot(aes(x = as_factor(country_name),
y = Y11_SocExIndex,
fill = country_name)) +
geom_boxplot() +
coord_flip()
eqls_work_hrs |>
count(Y11_SocExIndex)
range(eqls_work_hrs$Y11_SocExIndex, na.rm = TRUE)
# RENAME VARIABLE NAMES ---------------------------------------------------
# select(UniqueID, # participant ID
# country_name, # country name
# iso_code, # iso code of country
# Y11_Q31, # marital status
# Y11_Q32, # number of children
# Y11_Q49, # rural/urban living
# Y11_Agecategory, # Age in 5 categories
# Y11_ISCEDsimple, # highest education completed
# Y11_HH2a, # gender
# Y11_EmploymentStatus, # Employment - 7 groups
# Y11_Q24, # Can most people be trusted?
# Y11_Q21a, # How frequently attend religious services?
# Y11_Q21b, # How frequently use the Internet other than for work?
# Y11_Q21c, # How frequently take part in sports or exercise?
# Y11_Q21d, # How frequently participate in social activities?
# Y11_Q22a, # How often worked unpaid for community services last 12 months?
# Y11_Q22b, # How often worked unpaid for education/cultural etc organisation last 12 months?
# Y11_Q22c, # How often worked unpaid for social movements/charities last 12 months?
# Y11_Q22d, # How often worked unpaid for political parties or trade unions last 12 months?
# Y11_Q22e, # How often worked unpaid for other voluntary org last 12 months?
# Y11_Volunteering, # Volunteering frequency
# Y11_Q29e, # Feel left out of society?
# Y11_Q29f, # Can't find the way because life has become so complicated?
# Y11_Q29g, # The value of what I do is not recognised by others?
# Y11_Q29h, # People look down on me because of my job situation or income?
# Y11_Q29i, # Feel close to people in the area where I live
# Y11_SocExIndex, # Social Exclusion Index
# Y11_Q45a, # How often felt cheerful and in good spirits last 2 weeks?
# Y11_Q45b, # How often felt calm and relaxed last 2 weeks?
# Y11_Q45c, # How often felt active and vigorous last 2 weeks?
# Y11_Q45d, # How often woke up feeling fresh and rested last 2 weeks?
# Y11_Q45e, # How often felt your daily life has been filled with things that interest you last 2 weeks?
# Y11_Q46a, # How often felt particularly tense last 2 weeks?
# Y11_Q46b, # How often felt lonely last 2 weeks?
# Y11_Q46c, # How often felt downhearted and depressed last 2 weeks?
# Y11_MWIndex, # WHO-5 mental wellbeing index
# Y11_Q12a, # Come home from work too tired to do some of the household jobs
# Y11_Q12b, # Difficult to fulfil family responsibilities because of the time at work
# Y11_Q12c, # Difficult to concentrate at work because of family responsibilities
# Y11_Strainbasedconflict, # Work-life balance conflict?
# Y11_Q7, # How many hours do you work in 1st job?
# Y11_Q7b, # How many hours per week worked in the additional job?
# DV_Q7 # DV: Total number of working hours
eqls_renamed <-
eqls_work_hrs |>
rename(
id = UniqueID,
wave = Wave, # wave 2007 or 2011
marital_status = Y11_Q31, # marital status
num_of_children = Y11_Q32, # number of children
rural_urban_living = Y11_Q49, # rural/urban living
age_category = Y11_Agecategory, # Age in 5 categories
education = Y11_ISCEDsimple, # highest education completed
gender = Y11_HH2a, # gender
employment = Y11_EmploymentStatus, # Employment - 7 groups
trust_people = Y11_Q24, # Can most people be trusted?
freq_rel_service_use = Y11_Q21a, # How frequently attend religious services?
freq_internet_personal = Y11_Q21b, # How frequently use the Internet other than for work?
freq_sports = Y11_Q21c, # How frequently take part in sports or exercise?
freq_social_activities = Y11_Q21d, # How frequently participate in social activities?
vol_community_service = Y11_Q22a, # How often worked unpaid for community services last 12 months?
vol_education_cult_org = Y11_Q22b, # How often worked unpaid for education/cultural etc organisation last 12 months?
vol_social_charities = Y11_Q22c, # How often worked unpaid for social movements/charities last 12 months?
vol_political_parties = Y11_Q22d, # How often worked unpaid for political parties or trade unions last 12 months?
vol_other_organisation = Y11_Q22e, # How often worked unpaid for other voluntary org last 12 months?
volunteering_frequency = Y11_Volunteering, # Volunteering frequency
sei_left_out_soc = Y11_Q29e, # Feel left out of society?
sei_life_is_complicated = Y11_Q29f, # Can't find the way because life has become so complicated?
sei_not_recognised = Y11_Q29g, # The value of what I do is not recognised by others?
sei_looked_down = Y11_Q29h, # People look down on me because of my job situation or income?
sei_feel_close_to_people = Y11_Q29i, # Feel close to people in the area where I live
sei = Y11_SocExIndex, # Social Exclusion Index
mwbi_felt_cheerful = Y11_Q45a, # How often felt cheerful and in good spirits last 2 weeks?
mwbi_felt_calm = Y11_Q45b, # How often felt calm and relaxed last 2 weeks?
mwbi_felt_active = Y11_Q45c, # How often felt active and vigorous last 2 weeks?
mwbi_felt_fresh = Y11_Q45d, # How often woke up feeling fresh and rested last 2 weeks?
mwbi_life_is_interesting = Y11_Q45e, # How often felt your daily life has been filled with things that interest you last 2 weeks?
mwbi_felt_tense = Y11_Q46a, # How often felt particularly tense last 2 weeks?
mwbi_felt_lonely = Y11_Q46b, # How often felt lonely last 2 weeks?
mwbi_felt_downhearted = Y11_Q46c, # How often felt downhearted and depressed last 2 weeks?
mwbi = Y11_MWIndex, # WHO-5 mental wellbeing index
wlbc_too_tired_household = Y11_Q12a, # Come home from work too tired to do some of the household jobs
wlbc_fam_responsibilities = Y11_Q12b, # Difficult to fulfil family responsibilities because of the time at work
wlbc_cannot_focus_on_work = Y11_Q12c, # Difficult to concentrate at work because of family responsibilities
wlbc = Y11_Strainbasedconflict, # Work-life balance conflict?
hrs_work_first_job = Y11_Q7, # How many hours do you work in 1st job?
hrs_work_additional_job = Y11_Q7b, # How many hours per week worked in the additional job?
total_hrs_working = DV_Q7 # DV: Total number of working hours)
)
# REPLACE NUMERIC FACTOR LEVELS WITH ACTUAL CATEGORY NAMES ----------------
## num_of_children ----
# 5 = 5 or more children
# Replace '5' with category label and convert to ordinal variable
eqls_clean_factors <-
eqls_renamed |>
mutate(num_of_children = if_else(as_factor(num_of_children) == "5", "5 or more children", as_factor(num_of_children)),
num_of_children = factor(num_of_children, ordered = TRUE))
# Check
eqls_clean_factors |> count(num_of_children)
## marital_status ----
# 1 = Married or living with partner
# 2 = Separated or divorced and not living with partner
# 3 = Widowed and not living with partner
# 4 = Never married and not living with partner
eqls_clean_factors <-
eqls_clean_factors |>
mutate(marital_status = case_when(
marital_status == 1 ~ "Married or living with partner",
marital_status == 2 ~ "Separated or divorced and not living with partner",
marital_status == 3 ~ "Widowed and not living with partner",
marital_status == 4 ~ "Never married and not living with partner"
),
marital_status = as_factor(marital_status)
)
# check
eqls_clean_factors |> count(marital_status)
## rural_urban_living ----
# 1 = The open countryside
# 2 = A village/small town
# 3 = A medium to large town
# 4 = A city or city suburb
eqls_clean_factors <-
eqls_clean_factors |>
mutate(rural_urban_living = case_when(
rural_urban_living == 1 ~ "The open countryside",
rural_urban_living == 2 ~ "A village/small town",
rural_urban_living == 3 ~ "A medium to large town",
rural_urban_living == 4 ~ "A city or city suburb"
),
rural_urban_living = as_factor(rural_urban_living)
)
# check
eqls_clean_factors |> count(rural_urban_living)
## age_category ----
# 1 = 18-24
# 2 = 25-34
# 3 = 35-49
# 4 = 50-64
# 5 = 65+
eqls_clean_factors <-
eqls_clean_factors |>
mutate(age_category = case_when(
age_category == 1 ~ "18-24",
age_category == 2 ~ "25-34",
age_category == 3 ~ "35-49",
age_category == 4 ~ "50-64",
age_category == 5 ~ "65+"
),
age_category = factor(age_category, ordered = TRUE)
)
eqls_clean_factors |> count(age_category)
## education ----
# 1 = No education completed (ISCED 0)
# 2 = Primary education (ISCED 1)
# 3 = Lower secondary education (ISCED 2)
# 4 = Upper secondary education (ISCED 3)
# 5 = Post-secondary including pre-vocational or vocational education but not tertiary (ISCED 4)
# 6 = Tertiary education – first level (ISCED 5)
# 7 = Tertiary education – advanced level (ISCED 6)
# 8 = Completed education abroad"
eqls_clean_factors <-
eqls_clean_factors |>
mutate(education = case_when(
education == 1 ~ "No education completed (ISCED 0)",
education == 2 ~ "Primary education (ISCED 1)",
education == 3 ~ "Lower secondary education (ISCED 2)",
education == 4 ~ "Upper secondary education (ISCED 3)",
education == 5 ~ "Post-secondary including pre-vocational or vocational education but not tertiary (ISCED 4)",
education == 6 ~ "Tertiary education – first level (ISCED 5)",
education == 7 ~ "Tertiary education – advanced level (ISCED 6)",
education == 8 ~ "Completed education abroad"
),
education = factor(education)
)
eqls_clean_factors |> count(education)
## gender ----
# 1 = Male
# 2 = Female
eqls_clean_factors <-
eqls_clean_factors |>
mutate(gender = case_when(
gender == 1 ~ "Male",
gender == 2 ~ "Female"
),
gender = as_factor(gender)
)
eqls_clean_factors |> count(gender)
## employment ----
# 1 = Employed (includes on leave)
# 2 = Unemployed
# 3 = Unable to work - disability/illness
# 4 = Retired
# 5 = Homemaker
# 6 = Student
# 7 = Other
eqls_clean_factors <-
eqls_clean_factors |>
mutate(employment = case_when(
employment == 1 ~ "Employed (includes on leave)",
employment == 2 ~ "Unemployed",
employment == 3 ~ "Unable to work - disability/illness",
employment == 4 ~ "Retired",
employment == 5 ~ "Homemaker",
employment == 6 ~ "Student",
employment == 7 ~ "Other"
),
employment = as_factor(employment)
)
## Wave recoding
# 2 = 2007
# 3 = 2011
eqls_clean_factors <-
eqls_clean_factors |>
mutate(wave = case_when(
wave == 2 ~ "2007",
wave == 3 ~ "2011"
),
wave = as_factor(wave)
)
eqls_clean_factors |> count(wave)
## Conversions to factors (clean-up)
eqls_clean_factors <-
eqls_clean_factors |>
mutate(country_name = as_factor(country_name),
iso_code = as_factor(iso_code)
)
# REMOVE VARIABLES NOT NEEDED ANYMORE -------------------------------------
eqls_clean_factors <-
eqls_clean_factors |>
select(-total_hrs_working)
# FIX: NON-ASCII strings --------------------------------------------------
## In response to the following warning:
##
## Warning: found non-ASCII strings
## 'Tertiary education <e2><80><93> advanced level (ISCED 6)' in object 'eqls'
## 'Tertiary education <e2><80><93> first level (ISCED 5)' in object 'eqls'
## Function to replace non-ASCII en dash with ASCII hyphen
replace_non_ascii <- function(x) {
str_replace_all(x, "\u2013", "-") # Replace en dash (U+2013) with hyphen
}
## Apply the replacement to the entire dataset
eqls_ascii_strings <-
eqls_clean_factors %>%
# mutate(across(everything(), ~ replace_non_ascii(.)))
mutate(education = as_factor(replace_non_ascii(education)))
# FINAL DATASETS ----------------------------------------------------------
eqls <- eqls_ascii_strings
## Dataset which only contains 2011 data
eqls_2011 <-
eqls |>
filter(wave == 2011)
## Dataset with country aggregates
eqls_country_2011 <-
eqls_2011 |>
group_by(country_name) |>
summarise(trust_people = mean(trust_people, na.rm = TRUE),
sei = mean(sei, na.rm = TRUE),
mwbi = mean(mwbi, na.rm = TRUE),
wlbc = mean(wlbc, na.rm = TRUE)) |>
na.omit()
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