National Crime Victimization Survey (NCVS)

License: GPL v3

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The primary information source on victims of nonfatal personal crimes and household property crimes (especially those not reported to the police), and also victim experience within the justice system.

  • Three tables, the first one row per household per interview, the second one per person-interview, the third one per incident reported across each sampled household’s seven-interview, three-year period.

  • A complex survey designed to generalize to civilian, non-institutional americans aged 12 and older.

  • Released annually since its 1992 rename and redesign, related surveys dating to the early 1970s.

  • Sponsored by the Bureau of Justics Statistics and administered by the US Census Bureau.


Please skim before you begin:

  1. National Crime Victimization Survey, 2016: Technical Documentation

  2. A New Measure of Prevalence for the National Crime Victimization Survey

  3. A haiku regarding this microdata:

# saint peter's sports bar
# evil deed instant replay
# sinful thought jukebox

Download, Import, Preparation

Define a function to extract values stored in parentheses:

ncvs_numeric_to_factor <- 
    function( this_column ) as.numeric( gsub( "^\\(([0-9]+)\\) (.*)" , "\\1" , this_column ) )

Define a function to merge aggregated information onto main data.frame objects:

left_join_zero_missings <-
    function( left_df , right_df ){

        final_df <-
            merge(
                left_df ,
                right_df ,
                all.x = TRUE
            )
            
        stopifnot( nrow( final_df ) == nrow( left_df ) )

        for( this_column in setdiff( names( right_df ) , names( left_df ) ) ){
            final_df[ is.na( final_df[ , this_column ] ) , this_column ] <- 0
        }
        
        gc()

        final_df
    }
  1. Register for the National Archive of Criminal Justice Data at https://www.icpsr.umich.edu/web/NACJD/series/95

  2. Choose National Crime Victimization Survey, Concatenated File, [United States], 1992-2022 (ICPSR 38604)

  3. Download the R version of the September 18, 2023 file.

Import the three main files:

ncvs_household_df_name <-
    load( file.path( path.expand( "~" ) , "ICPSR_38604/DS0001/38604-0001-Data.rda" ) )
    
ncvs_person_df_name <-
    load( file.path( path.expand( "~" ) , "ICPSR_38604/DS0002/38604-0002-Data.rda" ) )

ncvs_incident_df_name <-
    load( file.path( path.expand( "~" ) , "ICPSR_38604/DS0003/38604-0003-Data.rda" ) )

ncvs_household_df <- get( ncvs_household_df_name )
ncvs_person_df <- get( ncvs_person_df_name )
ncvs_incident_df <- get( ncvs_incident_df_name )

rm( list = ncvs_household_df_name ) ; gc()
rm( list = ncvs_person_df_name ) ; gc()
rm( list = ncvs_incident_df_name ) ; gc()

names( ncvs_household_df ) <- tolower( names( ncvs_household_df ) )
names( ncvs_person_df ) <- tolower( names( ncvs_person_df ) )
names( ncvs_incident_df ) <- tolower( names( ncvs_incident_df ) )

Determine which variables from each table to retain:

household_variables_to_keep <-
    c( 'year' , 'yearq' , 'idhh' , 'wgthhcy' , 'v2002' , 'sc214a' , 
    'v2026' , 'v2126a' , 'v2126b' , 'v2015' , 'v2017' , 'v2117' , 
    'v2118' , 'v2125' , 'v2071' , 'v2072' , 'v2127b' , 'v2129' )

person_variables_to_keep <-
    c( 'year' , 'yearq' , 'v3018' , 'v3014' , 'sc214a' , 'v3023' , 
    'v3023a' , 'v3024' , 'v3024a' , 'v2117' , 'v2118' , 'v3002' , 
    'idhh' , 'idper' , 'wgtpercy' , 'v3015' , 'v3033' , 'v2026' )

incident_variables_to_keep <-
    c( 'year' , 'yearq' , 'v2117' , 'v2118' , 'v4022' , 
    paste0( 'v401' , 6:9 ) , 'v4399' , 'v4529' , 'v4049' , paste0( 'v405' , 0:8 ) , 
    'v4060' , 'v4062' , paste0( 'v41' , 11:22 ) , 'v4064' , paste0( 'v41' , 27:37 ) , 
    'v4467' , 'v4234' , 'v4245' , 'v4243' , 'v4241' , 'v4256' , 'v4258' , 'v4278' , 
    'v4262' , paste0( 'v42' , 59:61 ) , 'v4269' , 'v4270' , 'v4268' , 'v4267' , 
    'v4271' , 'v4266' , 'v4265' , 'wgtviccy' , 'idhh' , 'idper' , 'v4002' , 'v4288' , 
    'v4290' , 'v4400' , 'v4437' , 'v4422' , 'v4024' )

Limit columns in each data.frame to those specified above:

ncvs_household_df <- ncvs_household_df[ , household_variables_to_keep ]

ncvs_person_df <- ncvs_person_df[ , person_variables_to_keep ]

ncvs_incident_df <- ncvs_incident_df[ , incident_variables_to_keep ]

gc()

In this example, limit the 1993-2022 data.frame to only the first & last years for quicker processing:

ncvs_household_df <- ncvs_household_df[ ncvs_household_df[ , 'year' ] %in% c( 1994 , 2022 ) , ]

ncvs_person_df <- ncvs_person_df[ ncvs_person_df[ , 'year' ] %in% c( 1994 , 2022 ) , ]

ncvs_incident_df <- ncvs_incident_df[ ncvs_incident_df[ , 'year' ] %in% c( 1994 , 2022 ) , ]

gc()

Recode identifiers to character class:

ncvs_household_df[ , 'idhh' ] <- as.character( ncvs_household_df[ , 'idhh' ] )

ncvs_person_df[ c( 'idhh' , 'idper' ) ] <-
    sapply( ncvs_person_df[ c( 'idhh' , 'idper' ) ] , as.character )

ncvs_incident_df[ c( 'idhh' , 'idper' ) ] <-
    sapply( ncvs_incident_df[ c( 'idhh' , 'idper' ) ] , as.character )

Recode factor variables to numeric values:

ncvs_household_df[ sapply( ncvs_household_df , class ) == 'factor' ] <-
    sapply( 
        ncvs_household_df[ sapply( ncvs_household_df , class ) == 'factor' ] , 
        ncvs_numeric_to_factor , 
        simplify = FALSE 
    )

ncvs_person_df[ sapply( ncvs_person_df , class ) == 'factor' ] <-
    sapply( 
        ncvs_person_df[ sapply( ncvs_person_df , class ) == 'factor' ] , 
        ncvs_numeric_to_factor ,
        simplify = FALSE
    )

ncvs_incident_df[ sapply( ncvs_incident_df , class ) == 'factor' ] <-
    sapply( 
        ncvs_incident_df[ sapply( ncvs_incident_df , class ) == 'factor' ] , 
        ncvs_numeric_to_factor ,
        simplify = FALSE
    )

Add a column of ones to each data.frame:

ncvs_household_df[ , 'one' ] <- 1
    
ncvs_person_df[ , 'one' ] <- 1

ncvs_incident_df[ , 'one' ] <- 1

Add a year group variable to each data.frame:

ncvs_household_df[ , 'yr_grp' ] <-
    findInterval( ncvs_household_df[ , 'year' ] , c( 1992 , 1997 , 2006 , 2016 ) )
    
ncvs_person_df[ , 'yr_grp' ] <-
    findInterval( ncvs_person_df[ , 'year' ] , c( 1992 , 1997 , 2006 , 2016 ) )

ncvs_incident_df[ , 'yr_grp' ] <-
    findInterval( ncvs_incident_df[ , 'year' ] , c( 1992 , 1997 , 2006 , 2016 ) )

Add a flag indicating whether each incident occurred inside the country:

ncvs_incident_df[ , 'exclude_outus' ] <-
    ncvs_incident_df[ , 'v4022' ] %in% 1

Add a half-year indicator to the incident data.frame:

ncvs_incident_df <-
    transform(
        ncvs_incident_df ,
        half_year =
            ifelse( substr( yearq , 6 , 6 ) %in% c( '1' , '2' ) , 1 ,
            ifelse( substr( yearq , 6 , 6 ) %in% c( '3' , '4' ) , 2 ,
                NA ) )
    )
    
stopifnot( all( ncvs_incident_df[ , 'half_year' ] %in% 1:2 ) )

Define violent crimes on the incident data.frame:

# rape and sexual assault
ncvs_incident_df[ , 'rsa' ] <- 
    ncvs_incident_df[ , 'v4529' ] %in% c( 1:4 , 15 , 16 , 18 , 19 )

# robbery
ncvs_incident_df[ , 'rob' ] <- 
    ncvs_incident_df[ , 'v4529' ] %in% 5:10

# assault
ncvs_incident_df[ , 'ast' ] <- 
    ncvs_incident_df[ , 'v4529' ] %in% c( 11:14 , 17 , 20 )
    
# simple assault
ncvs_incident_df[ , 'sast' ] <- 
    ncvs_incident_df[ , 'v4529' ] %in% c( 14 , 17 , 20 )

# aggravated assault
ncvs_incident_df[ , 'aast' ] <- 
    ncvs_incident_df[ , 'v4529' ] %in% 11:13

# violent crime
ncvs_incident_df[ , 'violent' ] <-
    apply( ncvs_incident_df[ c( 'rsa' , 'rob' , 'ast' ) ] , 1 , any )

# violent crime excluding simple assault
ncvs_incident_df[ , 'sviolent' ] <-
    apply( ncvs_incident_df[ , c( 'rsa' , 'rob' , 'aast' ) ] , 1 , any )

Define personal theft and then person-crime on the incident data.frame:

ncvs_incident_df[ , 'ptft' ] <- 
    ncvs_incident_df[ , 'v4529' ] %in% 21:23

ncvs_incident_df[ , 'personcrime' ] <-
    apply( ncvs_incident_df[ , c( 'violent' , 'ptft' ) ] , 1 , any )

Define property crimes on the incident data.frame:

ncvs_incident_df[ , 'hhburg' ] <-
    ncvs_incident_df[ , 'v4529' ] %in% 31:33

# completed theft with something taken
ncvs_incident_df[ , 'burg_ct' ] <-
        ( ncvs_incident_df[ , 'v4529' ] %in% 31:33 ) &
        ( ncvs_incident_df[ , 'v4288' ] %in% 1 )

# attempted theft
ncvs_incident_df[ , 'burg_at' ] <-
        ( ncvs_incident_df[ , 'v4529' ] %in% 31:33 ) &
        ( ncvs_incident_df[ , 'v4290' ] %in% 1 )

ncvs_incident_df[ , 'burg_ncat' ] <-
        ( ncvs_incident_df[ , 'v4529' ] %in% 31:33 ) &
        ( ncvs_incident_df[ , 'v4288' ] %in% 2 ) &
        ( ncvs_incident_df[ , 'v4290' ] %in% 2 )

ncvs_incident_df[ , 'burgcats2' ] <- 0
ncvs_incident_df[ ncvs_incident_df[ , 'burg_ncat' ] , 'burgcats2' ] <- 2
ncvs_incident_df[ ncvs_incident_df[ , 'burg_ct' ] | ncvs_incident_df[ , 'burg_at' ] , 'burgcats2' ] <- 1
    

ncvs_incident_df[ , 'burg' ] <- 
    ncvs_incident_df[ , 'burgcats2' ] %in% 1

# trespassing
ncvs_incident_df[ , 'tres' ] <- 
    ncvs_incident_df[ , 'burgcats2' ] %in% 2

# motor vehicle theft
ncvs_incident_df[ , 'mvtft' ] <-
    ncvs_incident_df[ , 'v4529' ] %in% 40:41

# household theft
ncvs_incident_df[ , 'hhtft' ] <-
    ncvs_incident_df[ , 'v4529' ] %in% 54:59

# property crime
ncvs_incident_df[ , 'property' ] <-
    apply( ncvs_incident_df[ c( 'hhburg' , 'mvtft' , 'hhtft' ) ] , 1 , any )

Define a series weight on the incident data.frame:

ncvs_incident_df[ , 'series' ] <- 2

ncvs_incident_df[ 

    ncvs_incident_df[ , 'v4017' ] %in% c( 1 , 8 ) |
    ncvs_incident_df[ , 'v4018' ] %in% c( 2 , 8 ) |
    ncvs_incident_df[ , 'v4019' ] %in% c( 1 , 8 )

    , 'series' ] <- 1
     
ncvs_incident_df[ , 'serieswgt' ] <- 1

ncvs_incident_df[ !( ncvs_incident_df[ , 'v4016' ] %in% 997:998 ) , 'n10v4016' ] <-
    pmin( ncvs_incident_df[ !( ncvs_incident_df[ , 'v4016' ] %in% 997:998 ) , 'v4016' ] , 10 )
     
ncvs_incident_df[ ncvs_incident_df[ , 'series' ] == 2 , 'serieswgt' ] <-
    ncvs_incident_df[ ncvs_incident_df[ , 'series' ] == 2 , 'n10v4016' ]

ncvs_incident_df[ ncvs_incident_df[ , 'series' ] == 2 & is.na( ncvs_incident_df[ , 'n10v4016' ] ) , 'serieswgt' ] <- 6

Aggregate property-crimes to the household-interview level:

summed_hh_crimes <-
    aggregate(
        cbind(
            property * serieswgt ,
            hhburg * serieswgt ,
            mvtft * serieswgt ,
            burg * serieswgt ,
            tres * serieswgt
        ) ~ yearq + idhh + v4002 + wgtviccy ,
        
        data = subset( ncvs_incident_df , !exclude_outus & property ) ,
        
        sum
    )

names( summed_hh_crimes ) <-
    c( 'yearq' , 'idhh' , 'v2002' , 'wgtviccy' , 'property' , 'hhburg' , 
    'mvtft' , 'burg' , 'tres' )

Merge aggregated property-crimes on to the household-interview data.frame:

ncvs_household_df <- left_join_zero_missings( ncvs_household_df , summed_hh_crimes )

rm( summed_hh_crimes ) ; gc()

Aggregate person-crimes to the person-interview level:

summed_person_crimes <-
    aggregate(
        cbind(
            violent * serieswgt ,
            sviolent * serieswgt ,
            rsa * serieswgt ,
            rob * serieswgt ,
            aast * serieswgt ,
            sast * serieswgt ,
            ptft * serieswgt
        ) ~ yearq + idhh + v4002 + idper + wgtviccy ,
        
        data = subset( ncvs_incident_df , !exclude_outus & personcrime ) ,
        
        sum
    )
    

names( summed_person_crimes ) <-
    c( 'yearq' , 'idhh' , 'v3002' , 'idper' , 'wgtviccy' , 'violent' , 
    'sviolent' , 'rsa' , 'rob' , 'aast' , 'sast' , 'ptft' )

Merge aggregated property-crimes on to the person-interview data.frame:

ncvs_person_df <- left_join_zero_missings( ncvs_person_df , summed_person_crimes )

rm( summed_person_crimes ) ; gc()

Starting here, the weight calculation prepares an adjustment for all violence combined with the variables violent and violent_year. To calculate the prevalence rate of a subset of person-crimes, starting at this point, replace these two values with variables like rob and rob_year.

Aggregate violent crimes to the person-year level:

summed_person_year_violent_crimes <-
    aggregate(
        violent * serieswgt ~ idhh + idper + year ,
        data = subset( ncvs_incident_df , !exclude_outus & violent ) ,
        sum
    )

names( summed_person_year_violent_crimes )[ ncol( summed_person_year_violent_crimes ) ] <- 
    'violent_year'

Merge aggregated person-year violent crime series weights on to the person-interview data.frame:

ncvs_person_df <- left_join_zero_missings( ncvs_person_df , summed_person_year_violent_crimes )

rm( summed_person_year_violent_crimes ) ; gc()

Aggregate violent crimes to the person-half-year level, then reshape into a wide data.frame:

summed_person_half_year_violent_crimes <-
    aggregate(
        wgtviccy ~ idhh + idper + year + half_year ,
        data = subset( ncvs_incident_df , !exclude_outus & violent ) ,
        mean
    )

first_half_violent_crimes <-
    subset( summed_person_half_year_violent_crimes , half_year == 1 )
    
second_half_violent_crimes <-
    subset( summed_person_half_year_violent_crimes , half_year == 2 )

first_half_violent_crimes[ , 'half_year' ] <-
    second_half_violent_crimes[ , 'half_year' ] <- NULL
    
names( first_half_violent_crimes )[ ncol( first_half_violent_crimes ) ] <- 'vwgt1'
names( second_half_violent_crimes )[ ncol( second_half_violent_crimes ) ] <- 'vwgt2'

wide_person_half_year_violent_crimes <-
    merge(
        first_half_violent_crimes ,
        second_half_violent_crimes ,
        all = TRUE
    )

Merge both violent crime weights on to the person-interview data.frame:

ncvs_person_df <- left_join_zero_missings( ncvs_person_df , wide_person_half_year_violent_crimes )

rm( wide_person_half_year_violent_crimes ) ; gc()

Find the maximum incident victim weight among three half-year periods:

max_half_v_crimes <-
    aggregate(
        wgtviccy ~ idhh + idper + year + half_year + v4002 ,
        data = subset( ncvs_incident_df , !exclude_outus & violent ) ,
        max
    )

max_half_v_crimes <-
    max_half_v_crimes[ 
        do.call( 
            order , 
            max_half_v_crimes[ c( 'idhh' , 'idper' , 'year' , 'half_year' ) ] ) , 
    ]

max_half_v_crimes[ , 'byvar' ] <-
    apply( 
        max_half_v_crimes[ c( 'idhh' , 'idper' , 'year' , 'half_year' ) ] , 
        1 , 
        paste , 
        collapse = ' ' 
    )
    
max_half_v_crimes[ 1 , 'id' ] <- 1

for( i in seq( 2 , nrow( max_half_v_crimes ) ) ){

    if( max_half_v_crimes[ i , 'byvar' ] == max_half_v_crimes[ i - 1 , 'byvar' ] ){
    
        max_half_v_crimes[ i , 'id' ] <- max_half_v_crimes[ i - 1 , 'id' ] + 1
        
    } else {
    
        max_half_v_crimes[ i , 'id' ] <- 1
        
    }
}

max_half_v_crimes[ , 'label' ] <- 
    paste0( 
        '_' , 
        max_half_v_crimes[ , 'half_year' ] , 
        '_' , 
        max_half_v_crimes[ , 'id' ] 
    )

max_half_v_crimes[ , 'byvar' ] <- NULL

stopifnot( all( max_half_v_crimes[ , 'label' ] %in% c( '_1_1' , '_2_1' , '_1_2' ) ) )

h_1_1_df <-
    max_half_v_crimes[ 
        max_half_v_crimes[ , 'label' ] == '_1_1' , 
        c( 'idhh' , 'idper' , 'year' , 'wgtviccy' )
    ]
    
names( h_1_1_df )[ ncol( h_1_1_df ) ] <- 'wgtviccy_1_1'
    
h_2_1_df <-
    max_half_v_crimes[ 
        max_half_v_crimes[ , 'label' ] == '_2_1' , 
        c( 'idhh' , 'idper' , 'year' , 'wgtviccy' )
    ]
    
names( h_2_1_df )[ ncol( h_2_1_df ) ] <- 'wgtviccy_2_1'
    
h_1_2_df <-
    max_half_v_crimes[ 
        max_half_v_crimes[ , 'label' ] == '_1_2' , 
        c( 'idhh' , 'idper' , 'year' , 'wgtviccy' )
    ]
    
names( h_1_2_df )[ ncol( h_1_2_df ) ] <- 'wgtviccy_1_2'

three_half_df <-
    Reduce( function( ... ) merge( ... , all = TRUE ) , list( h_1_1_df , h_2_1_df , h_1_2_df ) )
    
rm( h_1_1_df , h_2_1_df , h_1_2_df ) ; gc()

Merge these three half-year period weights on to the person-interview data.frame:

ncvs_person_df <- left_join_zero_missings( ncvs_person_df , three_half_df )

rm( three_half_df ) ; gc()

Aggregate interview counts to the person-year level:

summed_person_year_interviews <-
    aggregate(
        one ~ idhh + idper + year ,
        data = subset( ncvs_person_df , wgtpercy > 0 ) ,
        sum
    )

names( summed_person_year_interviews )[ ncol( summed_person_year_interviews ) ] <- 
    'interview_count'

Merge interview_count on to the person-interview data.frame:

ncvs_person_df <- left_join_zero_missings( ncvs_person_df , summed_person_year_interviews )

rm( summed_person_year_interviews ) ; gc()

Apply Interview/Incident Groups:

ncvs_person_df <-
    transform(
        ncvs_person_df ,
        interview_incident_groups =
        
            ifelse( violent_year == 0 , 
                1 ,
        
            ifelse( 
                interview_count == 1 & 
                ( ( as.numeric( vwgt1 > 0 ) + as.numeric( vwgt2 > 0 ) ) == 1 ) & 
                wgtviccy > 0 , 
                2 ,
            
            ifelse( 
                interview_count == 2 & 
                ( ( as.numeric( vwgt1 > 0 ) + as.numeric( vwgt2 > 0 ) ) == 1 ) , 
                3 ,
            
            ifelse( 
                interview_count == 2 & 
                ( vwgt1 > 0 ) & ( vwgt2 > 0 ) & ( wgtviccy > 0 ) , 
                4 ,
            
            ifelse( 
                interview_count == 3 & 
                ( ( 
                    as.numeric( wgtviccy_1_1 > 0 ) + 
                    as.numeric( wgtviccy_2_1 > 0 ) + 
                    as.numeric( wgtviccy_1_2 > 0 ) 
                ) == 1 ) , 
                5 ,
            
            ifelse( 
                interview_count == 3 & 
                ( wgtviccy_1_1 > 0 ) & ( wgtviccy_2_1 > 0 ) & ( wgtviccy_1_2 > 0 ) , 
                6 ,
            
            ifelse( 
                interview_count == 3 & 
                ( wgtviccy_1_1 > 0 ) & ( wgtviccy_2_1 > 0 ) & 
                substr( yearq , 6 , 6 ) %in% 1:2 , 
                7 ,
                
            ifelse( 
                interview_count == 3 & 
                ( wgtviccy_1_1 > 0 ) & ( wgtviccy_2_1 > 0 ) & 
                substr( yearq , 6 , 6 ) %in% 3:4 , 
                8 , 
                
                9 
            ) ) ) ) ) ) ) )
    )
            

# confirm all records in group 9 have both a wgtviccy == 0 & wgtpercy == 0
stopifnot( nrow( subset( ncvs_person_df , interview_incident_groups == 9 & wgtviccy > 0 ) ) == 0 )
stopifnot( nrow( subset( ncvs_person_df , interview_incident_groups == 9 & wgtpercy > 0 ) ) == 0 )

ncvs_person_df <-
    transform(
        ncvs_person_df ,
        
        prev_wgt0 =
            ifelse( interview_incident_groups == 1 , wgtpercy ,
            ifelse( interview_incident_groups == 2 , wgtviccy / 2 ,
            ifelse( interview_incident_groups == 3 , pmax( vwgt1 , vwgt2 , na.rm = TRUE ) / 2 ,
            ifelse( interview_incident_groups == 4 , wgtviccy / 2 ,
            ifelse( interview_incident_groups == 5 , 
                pmax( wgtviccy_1_1 , wgtviccy_1_2 , wgtviccy_2_1 , na.rm = TRUE ) / 2 ,
            ifelse( interview_incident_groups == 6 , wgtviccy / 2 ,
            ifelse( interview_incident_groups == 7 , wgtviccy_1_1 / 2 ,
            ifelse( interview_incident_groups == 8 , wgtviccy_2_1 / 2 ,
            ifelse( interview_incident_groups == 9 , 0 ,
                NA ) ) ) ) ) ) ) ) )
    )

# matches table 8
# https://www.ojp.gov/pdffiles1/bjs/grants/308745.pdf#page=44

Aggregate wgtviccy and prev_wgt0 sums to the year level, then merge:

summed_year_weights <-
    aggregate(
        cbind( wgtviccy , prev_wgt0 ) ~ year ,
        data = subset( ncvs_person_df , violent_year == 1 ) ,
        sum
    )

names( summed_year_weights ) <- c( 'year' , 'vwgt_1v' , 'prev_1v' )

ncvs_person_df <- left_join_zero_missings( ncvs_person_df , summed_year_weights )

rm( summed_year_weights ) ; gc()

Calibrate so that the weight sums to wgtviccy for persons with exactly one victimization:

ncvs_person_df <-
    transform(
        ncvs_person_df ,

        prev_wgt1 = 
            ifelse( violent_year == 0 , prev_wgt0 ,
            ifelse( violent_year > 0 & wgtpercy > 0 , 
                prev_wgt0 * ( vwgt_1v / prev_1v ) , 0 ) )
    )

Aggregate wgtviccy and prev_wgt0 sums to the year level, then merge:

summed_year_crimes <-
    aggregate(
        cbind( 
            wgtpercy , 
            ifelse( violent_year > 0 , prev_wgt1 , 0 ) , 
            ifelse( violent_year == 0 , prev_wgt1 , 0 )
        ) ~ year ,
        data = ncvs_person_df ,
        sum
    )

names( summed_year_crimes ) <- c( 'year' , 'total_persons' , 'prev_with_crime' , 'prev_no_crime' )

ncvs_person_df <- left_join_zero_missings( ncvs_person_df , summed_year_crimes )

rm( summed_year_crimes ) ; gc()

Calibrate so that the weight sums to wgtpercy for all persons:

ncvs_person_df <-
    transform(
        ncvs_person_df ,

        prev_wgt =
            ifelse( 
                violent_year == 0 ,
                prev_wgt1 * ( ( total_persons - prev_with_crime ) / prev_no_crime ) ,
                prev_wgt1 
            )
    )

Save locally  

Save the object at any point:

# ncvs_fn <- file.path( path.expand( "~" ) , "NCVS" , "this_file.rds" )
# saveRDS( ncvs_df , file = ncvs_fn , compress = FALSE )

Load the same object:

# ncvs_df <- readRDS( ncvs_fn )

Survey Design Definition

Construct a complex sample survey design:

library(survey)
    
options('survey.lonely.psu' = 'adjust')

# replace missing clusters
ncvs_person_df[ is.na( ncvs_person_df[ , 'v2118' ] ) , 'v2118' ] <- -1
ncvs_person_df[ is.na( ncvs_person_df[ , 'v2117' ] ) , 'v2117' ] <- -1

# subset this dataset to only 2022
ncvs_df <- subset( ncvs_person_df , year == max( year ) )

ncvs_design <-
    svydesign( 
        ~ v2118 ,
        strata = ~ interaction( yr_grp , v2117 ) ,
        data = ncvs_df ,
        weights = ~ prev_wgt ,
        nest = TRUE
    )

Variable Recoding

Add new columns to the data set:

ncvs_design <- 
    update( 
        ncvs_design , 

        one = 1 ,

        victim = as.numeric( violent_year > 0 ) ,

        sex = factor( v3018 , levels = 1:2 , labels = c( 'male' , 'female' ) ) ,
            
        linear_age = ifelse( v3014 == 99 , NA , v3014 ) ,
        
        times_moved_in_prior_five_years =
            ifelse( v3033 == 99 , NA , v3033 ) ,
        
        current_marital_status =
            factor( 
                v3015 , 
                levels = c( 1:5 , 8 ) , 
                labels = 
                    c( 'married' , 'widowed' , 'divorced' , 'separated' , 'single' , 'residue' )
            ) ,
        
        household_income_starting_2015q1 =
            factor(
                findInterval( sc214a , c( 1 , 9 , 13 , 16 , 18 ) ) ,
                levels = 1:5 ,
                labels = 
                    c( 'less than $25,000' , '$25,000 - $49,999' , '$50,000 - $99,999' , 
                    '$100,000 - $199,999' , '$200,000 or more' )
            ) ,
        
        household_income_75k = 
            ifelse( v2026 == 98 , NA , as.numeric( v2026 %in% 14:18 ) )
    )

Analysis Examples with the survey library  

Unweighted Counts

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

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

svyby( ~ one , ~ sex , ncvs_design , unwtd.count )

Weighted Counts

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

svytotal( ~ one , ncvs_design )

svyby( ~ one , ~ sex , ncvs_design , svytotal )

Descriptive Statistics

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

svymean( ~ victim , ncvs_design )

svyby( ~ victim , ~ sex , ncvs_design , svymean )

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

svymean( ~ current_marital_status , ncvs_design )

svyby( ~ current_marital_status , ~ sex , ncvs_design , svymean )

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

svytotal( ~ victim , ncvs_design )

svyby( ~ victim , ~ sex , ncvs_design , svytotal )

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

svytotal( ~ current_marital_status , ncvs_design )

svyby( ~ current_marital_status , ~ sex , ncvs_design , svytotal )

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

svyquantile( ~ victim , ncvs_design , 0.5 )

svyby( 
    ~ victim , 
    ~ sex , 
    ncvs_design , 
    svyquantile , 
    0.5 ,
    ci = TRUE 
)

Estimate a ratio:

svyratio( 
    numerator = ~ times_moved_in_prior_five_years , 
    denominator = ~ linear_age , 
    ncvs_design ,
    na.rm = TRUE
)

Subsetting

Restrict the survey design to elderly americans:

sub_ncvs_design <- subset( ncvs_design , linear_age >= 65 )

Calculate the mean (average) of this subset:

svymean( ~ victim , sub_ncvs_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( ~ victim , ncvs_design )

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

grouped_result <-
    svyby( 
        ~ victim , 
        ~ sex , 
        ncvs_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( ncvs_design )

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

svyvar( ~ victim , ncvs_design )

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

# SRS without replacement
svymean( ~ victim , ncvs_design , deff = TRUE )

# SRS with replacement
svymean( ~ victim , ncvs_design , deff = "replace" )

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

svyciprop( ~ household_income_75k , ncvs_design ,
    method = "likelihood" )

Regression Models and Tests of Association

Perform a design-based t-test:

svyttest( victim ~ household_income_75k , ncvs_design )

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

svychisq( 
    ~ household_income_75k + current_marital_status , 
    ncvs_design 
)

Perform a survey-weighted generalized linear model:

glm_result <- 
    svyglm( 
        victim ~ household_income_75k + current_marital_status , 
        ncvs_design 
    )

summary( glm_result )

Replication Example

This example matches the 1994 and 2022 victimization rates and SEs in Appendix Table 1:

new_prevalence_design <-
    svydesign( 
        ~ v2118 ,
        strata = ~ interaction( yr_grp , v2117 ) ,
        data = ncvs_person_df ,
        weights = ~ prev_wgt ,
        nest = TRUE
    )

new_prevalence_results <-
    svyby( 
        ~ as.numeric( violent_year > 0 ) , 
        ~ year , 
        new_prevalence_design , 
        svymean
    )

# match new method (wgt_ovam) 1994 and 2022 estimates
stopifnot( 
    round( coef( new_prevalence_results )[ c( 1 , nrow( new_prevalence_results ) ) ] , 4 ) == 
    c( 0.0442 , 0.0151 )
)

# match new method (wgt_ovam) 1994 and 2022 standard errors
stopifnot( 
    round( SE( new_prevalence_results )[ c( 1 , nrow( new_prevalence_results ) ) ] , 5 ) == 
    c( 0.0010 , 0.00054 )
)

old_prevalence_design <-
    svydesign( 
        ~ v2118 ,
        strata = ~ interaction( yr_grp , v2117 ) ,
        data = ncvs_person_df ,
        weights = ~ wgtpercy ,
        nest = TRUE
    )

old_prevalence_results <-
    svyby(
        ~ as.numeric( violent_year > 0 ) ,
        ~ year ,
        old_prevalence_design ,
        svymean
    )

# match old method (wgtpercy) 1994 and 2022 estimates
stopifnot( 
    round( coef( old_prevalence_results )[ c( 1 , nrow( old_prevalence_results ) ) ] , 4 ) == 
    c( 0.0328 , 0.0124 )
)

# match old method (wgtpercy) 1994 and 2022 standard errors
stopifnot( 
    round( SE( old_prevalence_results )[ c( 1 , nrow( old_prevalence_results ) ) ] , 5 ) == 
    c( 0.00075 , 0.00042 )
)

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 NCVS users, this code replicates previously-presented examples:

library(srvyr)
ncvs_srvyr_design <- as_survey( ncvs_design )

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

ncvs_srvyr_design %>%
    summarize( mean = survey_mean( victim ) )

ncvs_srvyr_design %>%
    group_by( sex ) %>%
    summarize( mean = survey_mean( victim ) )