American Time Use Survey (ATUS)

License: GPL v3

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Sampled individuals write down everything they do for a single twenty-four hour period, in ten minute intervals. Time use data allows for the study of uncompensated work like cooking, chores, childcare.

  • Many tables with structures described in the user guide, linkable to the Current Population Survey.

  • A complex survey generalizing to person-hours among civilian non-institutional americans aged 15+.

  • Released annually since 2003.

  • Administered by the Bureau of Labor Statistics.


Please skim before you begin:

  1. American Time Use Survey User’s Guide

  2. Wikipedia Entry

  3. A haiku regarding this microdata:

# don't judge me bruno
# eat one hour, sleep the rest
# it's my lazy day

Download, Import, Preparation

Define a function to download, unzip, and import each comma-separated value dat file:

library(httr)

atus_csv_import <-
    function( this_url ){
        
        this_tf <- tempfile()
        
        this_dl <- GET( this_url , user_agent( "email@address.com") )
        
        writeBin( content( this_dl ) , this_tf )

        unzipped_files <- unzip( this_tf , exdir = tempdir() )
        
        this_dat <- grep( '\\.dat$' , unzipped_files , value = TRUE )
        
        this_df <- read.csv( this_dat )
        
        file.remove( c( this_tf , unzipped_files ) )
        
        names( this_df ) <- tolower( names( this_df ) )
        
        this_df
    }

Download and import the activity, respondent, roster, and weights tables:

act_df <- atus_csv_import( "https://www.bls.gov/tus/datafiles/atusact-2021.zip" )

resp_df <- atus_csv_import( "https://www.bls.gov/tus/datafiles/atusresp-2021.zip" )

rost_df <- atus_csv_import( "https://www.bls.gov/tus/datafiles/atusrost-2021.zip" )

wgts_df <- atus_csv_import( "https://www.bls.gov/tus/datafiles/atuswgts-2021.zip" )

Specify which variables to keep in each of the data.frame objects:

act_df <- act_df[ c( 'tucaseid' , 'tutier1code' , 'tutier2code' , 'tuactdur24' ) ]

resp_df <- resp_df[ c( 'tucaseid' , 'tufinlwgt' , 'tulineno' ) ]

rost_df <- rost_df[ , c( 'tucaseid' , 'tulineno' , 'teage' , 'tesex' ) ]

Distribute travel-related activities (tutier1code == 18 from the lexicon) based on their second tier code:

act_df[ act_df[ , 'tutier1code' ] == 18 & act_df[ , 'tutier2code' ] == 99 , 'tutier1code' ] <- 50

act_df[ act_df[ , 'tutier1code' ] == 18 , 'tutier1code' ] <-
    act_df[ act_df[ , 'tutier1code' ] == 18 , 'tutier2code' ]

Sum up all durations at the (respondent x major activity category)-level:

act_long_df <- aggregate( tuactdur24 ~ tucaseid + tutier1code , data = act_df , sum )

act_wide_df <-
    reshape( act_long_df , idvar = 'tucaseid' , timevar = 'tutier1code' , direction = 'wide' )

# for individuals not engaging in an activity category, replace missings with zero minutes
act_wide_df[ is.na( act_wide_df ) ] <- 0

# for all columns except the respondent identifier, convert minutes to hours
act_wide_df[ , -1 ] <- act_wide_df[ , -1 ] / 60

Merge the respondent and summed activity tables, then the roster table, and finally the replicate weights:

resp_act_df <- merge( resp_df , act_wide_df )

stopifnot( nrow( resp_act_df ) == nrow( resp_df ) )

resp_act_rost_df <- merge( resp_act_df , rost_df )

stopifnot( nrow( resp_act_rost_df ) == nrow( resp_df ) )

atus_df <- merge( resp_act_rost_df , wgts_df )

stopifnot( nrow( atus_df ) == nrow( resp_df ) )

# remove dots from column names
names( atus_df ) <- gsub( "\\." , "_" , names( atus_df ) )

atus_df[ , 'one' ] <- 1

Save locally  

Save the object at any point:

# atus_fn <- file.path( path.expand( "~" ) , "ATUS" , "this_file.rds" )
# saveRDS( atus_df , file = atus_fn , compress = FALSE )

Load the same object:

# atus_df <- readRDS( atus_fn )

Survey Design Definition

Construct a complex sample survey design:

library(survey)

atus_design <- 
    svrepdesign(
        weights = ~ tufinlwgt ,
        repweights = "finlwgt[0-9]" , 
        type = "Fay" , 
        rho = ( 1 - 1 / sqrt( 4 ) ) ,
        mse = TRUE ,
        data = atus_df
    )

Variable Recoding

Add new columns to the data set:

# caring for and helping household members is top level 03 from the lexicon
# https://www.bls.gov/tus/lexicons/lexiconnoex2021.pdf

atus_design <-
    update(
        atus_design ,
        
        any_care = as.numeric( tuactdur24_3 > 0 ) ,
        
        tesex = factor( tesex , levels = 1:2 , labels = c( 'male' , 'female' ) ) ,
        
        age_category = 
            factor( 
                1 + findInterval( teage , c( 18 , 35 , 65 ) ) , 
                labels = c( "under 18" , "18 - 34" , "35 - 64" , "65 or older" ) 
            )
    )

Analysis Examples with the survey library  

Unweighted Counts

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

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

svyby( ~ one , ~ age_category , atus_design , unwtd.count )

Weighted Counts

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

svytotal( ~ one , atus_design )

svyby( ~ one , ~ age_category , atus_design , svytotal )

Descriptive Statistics

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

svymean( ~ tuactdur24_1 , atus_design )

svyby( ~ tuactdur24_1 , ~ age_category , atus_design , svymean )

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

svymean( ~ tesex , atus_design )

svyby( ~ tesex , ~ age_category , atus_design , svymean )

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

svytotal( ~ tuactdur24_1 , atus_design )

svyby( ~ tuactdur24_1 , ~ age_category , atus_design , svytotal )

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

svytotal( ~ tesex , atus_design )

svyby( ~ tesex , ~ age_category , atus_design , svytotal )

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

svyquantile( ~ tuactdur24_1 , atus_design , 0.5 )

svyby( 
    ~ tuactdur24_1 , 
    ~ age_category , 
    atus_design , 
    svyquantile , 
    0.5 ,
    ci = TRUE 
)

Estimate a ratio:

svyratio( 
    numerator = ~ tuactdur24_5 , 
    denominator = ~ tuactdur24_12 , 
    atus_design 
)

Subsetting

Restrict the survey design to any time volunteering:

sub_atus_design <- subset( atus_design , tuactdur24_15 > 0 )

Calculate the mean (average) of this subset:

svymean( ~ tuactdur24_1 , sub_atus_design )

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( ~ tuactdur24_1 , atus_design )

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

grouped_result <-
    svyby( 
        ~ tuactdur24_1 , 
        ~ age_category , 
        atus_design , 
        svymean 
    )
    
coef( grouped_result )
SE( grouped_result )
confint( grouped_result )
cv( grouped_result )

Calculate the degrees of freedom of any survey design object:

degf( atus_design )

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

svyvar( ~ tuactdur24_1 , atus_design )

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

# SRS without replacement
svymean( ~ tuactdur24_1 , atus_design , deff = TRUE )

# SRS with replacement
svymean( ~ tuactdur24_1 , atus_design , deff = "replace" )

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

svyciprop( ~ any_care , atus_design ,
    method = "likelihood" )

Regression Models and Tests of Association

Perform a design-based t-test:

svyttest( tuactdur24_1 ~ any_care , atus_design )

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

svychisq( 
    ~ any_care + tesex , 
    atus_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        tuactdur24_1 ~ any_care + tesex , 
        atus_design 
    )

summary( glm_result )

Replication Example

This example matches the “Caring for and helping household members” row of Table A-1:

hours_per_day_civilian_population <- svymean( ~ tuactdur24_3 , atus_design )

stopifnot( round( coef( hours_per_day_civilian_population ) , 2 ) == 0.47 )

percent_engaged_per_day <- svymean( ~ any_care , atus_design )

stopifnot( round( coef( percent_engaged_per_day ) , 3 ) == 0.217 )

hours_per_day_among_engaged <- svymean( ~ tuactdur24_3 , subset( atus_design , any_care ) )

stopifnot( round( coef( hours_per_day_among_engaged ) , 2 ) == 2.17 )

This example matches the average hours and SE from Section 7.5 of the User’s Guide:

Download and import the activity, activity summary, respondent, and weights tables:

actsum07_df <- atus_csv_import( "https://www.bls.gov/tus/datafiles/atussum_2007.zip" )
resp07_df <- atus_csv_import( "https://www.bls.gov/tus/datafiles/atusresp_2007.zip" )
act07_df <- atus_csv_import( "https://www.bls.gov/tus/datafiles/atusact_2007.zip" )
wgts07_df <- atus_csv_import( "https://www.bls.gov/tus/datafiles/atuswgts_2007.zip" )

Option 1. Sum the two television fields from the activity summary file, removing zeroes:

television_per_person <-
    data.frame(
        tucaseid = actsum07_df[ , 'tucaseid' ] ,

        tuactdur24 = rowSums( actsum07_df[ , c( 't120303' , 't120304' ) ] )
    )

television_per_person <- 
    television_per_person[ television_per_person[ , 'tuactdur24' ] > 0 , ]

Option 2. Limit the activity file to television watching records according to the 2007 Lexicon:

television_activity <- 
    subset( 
        act07_df , 
        tutier1code == 12 &
        tutier2code == 3 &
        tutier3code %in% 3:4
    )

television_activity_summed <-
        aggregate(
            tuactdur24 ~ tucaseid ,
            data = television_activity ,
            sum
        )

Confirm both aggregation options yield the same results:

stopifnot(
    all( television_per_person[ , 'tucaseid' ] == television_activity_summed[ , 'tucaseid' ] )
)

stopifnot(
    all( television_per_person[ , 'tuactdur24' ] == television_activity_summed[ , 'tuactdur24' ] )
)

Merge the respondent and summed activity tables, then the replicate weights:

resp07_tpp_df <- 
    merge( 
        resp07_df[ , c( 'tucaseid' , 'tufinlwgt' ) ] , 
        television_per_person , 
        all.x = TRUE
    )

stopifnot( nrow( resp07_tpp_df ) == nrow( resp07_df ) )

# for individuals without television time, replace missings with zero minutes
resp07_tpp_df[ is.na( resp07_tpp_df[ , 'tuactdur24' ] ) , 'tuactdur24' ] <- 0

# convert minutes to hours
resp07_tpp_df[ , 'tuactdur24_hour' ] <- resp07_tpp_df[ , 'tuactdur24' ] / 60

atus07_df <- merge( resp07_tpp_df , wgts07_df )

stopifnot( nrow( atus07_df ) == nrow( resp07_df ) )

Construct a complex sample survey design:

atus07_design <- 
    svrepdesign(
        weights = ~ tufinlwgt ,
        repweights = "finlwgt[0-9]" ,
        type = "Fay" ,
        rho = ( 1 - 1 / sqrt( 4 ) ) ,
        data = atus07_df
    )

Match the statistic and SE of the number of hours daily that americans older than 14 watch tv:

result <- svymean( ~ tuactdur24_hour , atus07_design )

stopifnot( round( coef( result ) , 2 ) == 2.62 )
stopifnot( round( SE( result ) , 4 ) == 0.0293 )

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 ATUS users, this code replicates previously-presented examples:

library(srvyr)
atus_srvyr_design <- as_survey( atus_design )

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

atus_srvyr_design %>%
    summarize( mean = survey_mean( tuactdur24_1 ) )

atus_srvyr_design %>%
    group_by( age_category ) %>%
    summarize( mean = survey_mean( tuactdur24_1 ) )