Survey of Health, Ageing and Retirement in Europe (SHARE)

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The Survey of Health, Ageing and Retirement in Europe interviews senior citizens across the continent for their entire life. Allows for findings like, “Among Belgians who were 50-74 years old in 2004, X% lived in nursing homes by 2010.”

  • Many tables, most with one row per sampled respondent for the period.

  • A complex sample longitudinal survey designed to generalize to the civilian, non-institutionalized population of participating European countries aged 50 or older.

  • Released every two or three years since 2004.

  • Coordinated at the Max Planck Institute and funded by consortium.

Simplified Download and Importation

The R lodown package easily downloads and imports all available SHARE microdata by simply specifying "share" with an output_dir = parameter in the lodown() function. Depending on your internet connection and computer processing speed, you might prefer to run this step overnight.

library(lodown)
lodown( "share" , output_dir = file.path( path.expand( "~" ) , "SHARE" ) , 
    your_username = "username" , 
    your_password = "password" )

lodown also provides a catalog of available microdata extracts with the get_catalog() function. After requesting the SHARE catalog, you could pass a subsetted catalog through the lodown() function in order to download and import specific extracts (rather than all available extracts).

library(lodown)
# examine all available SHARE microdata files
share_cat <-
    get_catalog( "share" ,
        output_dir = file.path( path.expand( "~" ) , "SHARE" ) , 
        your_username = "username" , 
        your_password = "password" )

# wave 1, wave 6, and longitudinal weights only
share_cat <- subset( share_cat , grepl( "ave 1|ave 6|ongitudinal" , output_folder ) )
# download the microdata to your local computer
lodown( "share" , share_cat , 
    your_username = "username" , 
    your_password = "password" )

Analysis Examples with the survey library

Construct a complex sample survey design:

options( survey.lonely.psu = "adjust" )

library(survey)

available_files <-
    list.files( 
        file.path( path.expand( "~" ) , "SHARE" ) , 
        recursive = TRUE , 
        full.names = TRUE 
    )

# wave six demographics file
share_dn6_df <-
    readRDS( grep( "6\\.0\\.0(.*)sharew6(.*)dn\\.rds" , available_files , value = TRUE ) )

share_dn6_df <-
    share_dn6_df[ c( "mergeid" , "country" , "dn042_" , "dn004_" ) ]
    
# wave six physical health file
share_ph1_df <-
    readRDS( grep( "sharew1(.*)ph\\.rds" , available_files , value = TRUE ) )

share_ph1_df$weight_in_2004 <-
        ifelse( share_ph1_df$ph012_ < 0 , NA , share_ph1_df$ph012_ )
        
share_ph1_df <-
    share_ph1_df[ c( "mergeid" , "weight_in_2004" , "ph005_" ) ]
    
# wave six physical health file
share_ph6_df <-
    readRDS( grep( "6\\.0\\.0(.*)sharew6(.*)ph\\.rds" , available_files , value = TRUE ) )

share_ph6_df$weight_in_2015 <-
        ifelse( share_ph6_df$ph012_ < 0 , NA , share_ph6_df$ph012_ )
        
share_ph6_df <-
    share_ph6_df[ c( "mergeid" , "weight_in_2015" , "ph003_" ) ]
    

# longitudinal weights file
share_longwt_df <-
    readRDS( grep( "longitudinal_weights_w1\\-(.*)\\.rds" , available_files , value = TRUE ) )

# france only longitudinal weights
france_df <- subset( share_longwt_df , country == 17 & ( cliw_a > 0 ) )

nrow_check <- nrow( france_df )

# merge on each of the tables
france_df <- merge( france_df , share_dn6_df )
france_df <- merge( france_df , share_ph1_df )
france_df <- merge( france_df , share_ph6_df )

# confirm no change in records
stopifnot( nrow( france_df ) == nrow_check )

share_design <- 
    svydesign( 
        ~ psu + ssu , 
        strata = ~ stratum1 + stratum2 , 
        data = france_df , 
        weights = ~ cliw_a , 
        nest = TRUE 
    )

Variable Recoding

Add new columns to the data set:

share_design <- 
    update( 
        share_design , 
        
        one = 1 ,
        
        sexe = factor( dn042_ , levels = 1:2 , labels = c( 'masculin' , 'feminin' ) ) ,
        
        health_in_general_2015 =
            factor( ph003_ , levels = 1:5 , labels =
                c( "excellente" , "tres bonne" , "bonne" , "acceptable" , "mediocre" )
            ) ,
            
        fortemente_limite_2004 = ifelse( ph005_ %in% 1:3 , as.numeric( ph005_ == 1 ) , NA )

    )

Unweighted Counts

Count the unweighted number of records in the survey sample, overall and by groups:

sum( weights( share_design , "sampling" ) != 0 )

svyby( ~ one , ~ sexe , share_design , unwtd.count )

Weighted Counts

Count the weighted size of the generalizable population, overall and by groups:

svytotal( ~ one , share_design )

svyby( ~ one , ~ sexe , share_design , svytotal )

Descriptive Statistics

Calculate the mean (average) of a linear variable, overall and by groups:

svymean( ~ weight_in_2015 , share_design , na.rm = TRUE )

svyby( ~ weight_in_2015 , ~ sexe , share_design , svymean , na.rm = TRUE )

Calculate the distribution of a categorical variable, overall and by groups:

svymean( ~ health_in_general_2015 , share_design , na.rm = TRUE )

svyby( ~ health_in_general_2015 , ~ sexe , share_design , svymean , na.rm = TRUE )

Calculate the sum of a linear variable, overall and by groups:

svytotal( ~ weight_in_2015 , share_design , na.rm = TRUE )

svyby( ~ weight_in_2015 , ~ sexe , share_design , svytotal , na.rm = TRUE )

Calculate the weighted sum of a categorical variable, overall and by groups:

svytotal( ~ health_in_general_2015 , share_design , na.rm = TRUE )

svyby( ~ health_in_general_2015 , ~ sexe , share_design , svytotal , na.rm = TRUE )

Calculate the median (50th percentile) of a linear variable, overall and by groups:

svyquantile( ~ weight_in_2015 , share_design , 0.5 , na.rm = TRUE )

svyby( 
    ~ weight_in_2015 , 
    ~ sexe , 
    share_design , 
    svyquantile , 
    0.5 ,
    ci = TRUE ,
    keep.var = TRUE ,
    na.rm = TRUE
)

Estimate a ratio:

svyratio( 
    numerator = ~ weight_in_2015 , 
    denominator = ~ weight_in_2004 , 
    share_design ,
    na.rm = TRUE
)

Subsetting

Restrict the survey design to persons born in france:

sub_share_design <- subset( share_design , dn004_ == 1 )

Calculate the mean (average) of this subset:

svymean( ~ weight_in_2015 , sub_share_design , na.rm = TRUE )

Measures of Uncertainty

Extract the coefficient, standard error, confidence interval, and coefficient of variation from any descriptive statistics function result, overall and by groups:

this_result <- svymean( ~ weight_in_2015 , share_design , na.rm = TRUE )

coef( this_result )
SE( this_result )
confint( this_result )
cv( this_result )

grouped_result <-
    svyby( 
        ~ weight_in_2015 , 
        ~ sexe , 
        share_design , 
        svymean ,
        na.rm = TRUE 
    )
    
coef( grouped_result )
SE( grouped_result )
confint( grouped_result )
cv( grouped_result )

Calculate the degrees of freedom of any survey design object:

degf( share_design )

Calculate the complex sample survey-adjusted variance of any statistic:

svyvar( ~ weight_in_2015 , share_design , na.rm = TRUE )

Include the complex sample design effect in the result for a specific statistic:

# SRS without replacement
svymean( ~ weight_in_2015 , share_design , na.rm = TRUE , deff = TRUE )

# SRS with replacement
svymean( ~ weight_in_2015 , share_design , na.rm = TRUE , deff = "replace" )

Compute confidence intervals for proportions using methods that may be more accurate near 0 and 1. See ?svyciprop for alternatives:

svyciprop( ~ fortemente_limite_2004 , share_design ,
    method = "likelihood" , na.rm = TRUE )

Regression Models and Tests of Association

Perform a design-based t-test:

svyttest( weight_in_2015 ~ fortemente_limite_2004 , share_design )

Perform a chi-squared test of association for survey data:

svychisq( 
    ~ fortemente_limite_2004 + health_in_general_2015 , 
    share_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        weight_in_2015 ~ fortemente_limite_2004 + health_in_general_2015 , 
        share_design 
    )

summary( glm_result )

Analysis Examples with srvyr

The R srvyr library calculates summary statistics from survey data, such as the mean, total or quantile using dplyr-like syntax. srvyr allows for the use of many verbs, such as summarize, group_by, and mutate, the convenience of pipe-able functions, the tidyverse style of non-standard evaluation and more consistent return types than the survey package. This vignette details the available features. As a starting point for SHARE users, this code replicates previously-presented examples:

library(srvyr)
share_srvyr_design <- as_survey( share_design )

Calculate the mean (average) of a linear variable, overall and by groups:

share_srvyr_design %>%
    summarize( mean = survey_mean( weight_in_2015 , na.rm = TRUE ) )

share_srvyr_design %>%
    group_by( sexe ) %>%
    summarize( mean = survey_mean( weight_in_2015 , na.rm = TRUE ) )

Replication Example