Filters a contact_survey object using an expression. The expression is
evaluated against whichever table(s) contain the referenced columns
(participants, contacts, or both). When participants are filtered, contacts
are automatically pruned to matching part_ids.
Examples
data(polymod)
polymod[country == "United Kingdom"]
#> $participants
#> Key: <hh_id>
#> hh_id part_id part_gender part_occupation part_occupation_detail
#> <char> <int> <char> <int> <int>
#> 1: Mo08HH4517 4517 M 5 4
#> 2: Mo08HH4518 4518 F 5 NA
#> 3: Mo08HH4519 4519 F 5 2
#> 4: Mo08HH4520 4520 F 5 4
#> 5: Mo08HH4521 4521 M 5 3
#> ---
#> 1008: Mo08HH5518 5518 M NA NA
#> 1009: Mo08HH5519 5519 F NA NA
#> 1010: Mo08HH5520 5520 F NA NA
#> 1011: Mo08HH5521 5521 F 4 NA
#> 1012: Mo08HH5522 5522 M NA NA
#> part_education part_education_length participant_school_year
#> <int> <int> <int>
#> 1: 4 13 NA
#> 2: 4 13 NA
#> 3: 4 13 NA
#> 4: 4 13 NA
#> 5: 4 13 NA
#> ---
#> 1008: 4 13 NA
#> 1009: 4 13 NA
#> 1010: 4 13 NA
#> 1011: 5 16 NA
#> 1012: 4 13 NA
#> participant_nationality child_care child_care_detail child_relationship
#> <char> <char> <int> <int>
#> 1: UK Y NA 1
#> 2: UK Y NA 1
#> 3: UK Y NA 1
#> 4: UK Y NA 1
#> 5: UK Y NA 2
#> ---
#> 1008: OT NA NA
#> 1009: OT NA NA
#> 1010: OT NA NA
#> 1011: UK NA NA
#> 1012: OT NA NA
#> child_nationality problems diary_how diary_missed_unsp diary_missed_skin
#> <char> <char> <int> <int> <int>
#> 1: UK NA 1 1
#> 2: UK NA 3 1
#> 3: UK NA 1 1
#> 4: UK NA 1 1
#> 5: UK NA 1 1
#> ---
#> 1008: NA 1 1
#> 1009: NA 1 1
#> 1010: NA 1 1
#> 1011: NA 1 1
#> 1012: NA 1 1
#> diary_missed_noskin sday_id type day month year dayofweek hh_age_1
#> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1: 1 20060420 3 20 4 2006 4 5
#> 2: 3 20060420 3 20 4 2006 4 0
#> 3: 1 20060420 3 20 4 2006 4 6
#> 4: 1 20060422 3 22 4 2006 6 3
#> 5: 1 20060422 3 22 4 2006 6 1
#> ---
#> 1008: 1 20060512 1 12 5 2006 5 43
#> 1009: 1 20060512 1 12 5 2006 5 57
#> 1010: 1 20060512 1 12 5 2006 5 19
#> 1011: 1 20060512 1 12 5 2006 5 0
#> 1012: 1 20060512 1 12 5 2006 5 35
#> hh_age_2 hh_age_3 hh_age_4 hh_age_5 hh_age_6 hh_age_7 hh_age_8 hh_age_9
#> <int> <int> <int> <int> <int> <int> <int> <int>
#> 1: 7 29 31 NA NA NA NA NA
#> 2: 5 30 30 NA NA NA NA NA
#> 3: 28 31 NA NA NA NA NA NA
#> 4: 33 NA NA NA NA NA NA NA
#> 5: 2 25 30 NA NA NA NA NA
#> ---
#> 1008: 50 NA NA NA NA NA NA NA
#> 1009: 64 NA NA NA NA NA NA NA
#> 1010: 27 52 55 NA NA NA NA NA
#> 1011: 3 34 40 NA NA NA NA NA
#> 1012: 39 NA NA NA NA NA NA NA
#> hh_age_10 hh_age_11 hh_age_12 hh_age_13 hh_age_14 hh_age_15 hh_age_16
#> <int> <int> <int> <int> <int> <int> <lgcl>
#> 1: NA NA NA NA NA NA NA
#> 2: NA NA NA NA NA NA NA
#> 3: NA NA NA NA NA NA NA
#> 4: NA NA NA NA NA NA NA
#> 5: NA NA NA NA NA NA NA
#> ---
#> 1008: NA NA NA NA NA NA NA
#> 1009: NA NA NA NA NA NA NA
#> 1010: NA NA NA NA NA NA NA
#> 1011: NA NA NA NA NA NA NA
#> 1012: NA NA NA NA NA NA NA
#> hh_age_17 hh_age_18 hh_age_19 hh_age_20 class_size country hh_size
#> <lgcl> <lgcl> <lgcl> <lgcl> <int> <fctr> <int>
#> 1: NA NA NA NA NA United Kingdom 4
#> 2: NA NA NA NA NA United Kingdom 4
#> 3: NA NA NA NA NA United Kingdom 3
#> 4: NA NA NA NA NA United Kingdom 2
#> 5: NA NA NA NA NA United Kingdom 4
#> ---
#> 1008: NA NA NA NA NA United Kingdom 2
#> 1009: NA NA NA NA NA United Kingdom 2
#> 1010: NA NA NA NA NA United Kingdom 4
#> 1011: NA NA NA NA NA United Kingdom 4
#> 1012: NA NA NA NA NA United Kingdom 2
#> part_age_exact
#> <int>
#> 1: 5
#> 2: 5
#> 3: 6
#> 4: 3
#> 5: 2
#> ---
#> 1008: 50
#> 1009: 57
#> 1010: 52
#> 1011: 34
#> 1012: 39
#>
#> $contacts
#> cont_id part_id cnt_age_exact cnt_age_est_min cnt_age_est_max cnt_gender
#> <int> <int> <int> <int> <int> <char>
#> 1: 66023 4517 4 NA NA M
#> 2: 66024 4517 40 NA NA F
#> 3: 66025 4517 31 NA NA F
#> 4: 66026 4517 NA 50 55 F
#> 5: 66027 4517 29 NA NA M
#> ---
#> 11872: 77894 5522 NA 10 20 F
#> 11873: 77895 5522 35 NA NA F
#> 11874: 77896 5522 50 NA NA M
#> 11875: 77897 5522 NA 30 40 M
#> 11876: 77898 5522 NA 40 50 M
#> cnt_home cnt_work cnt_school cnt_transport cnt_leisure cnt_otherplace
#> <int> <int> <int> <int> <int> <int>
#> 1: 0 0 1 0 0 0
#> 2: 0 0 0 0 0 1
#> 3: 1 0 0 0 0 0
#> 4: 0 0 0 0 0 1
#> 5: 1 0 0 0 0 0
#> ---
#> 11872: 1 0 0 0 0 0
#> 11873: 1 0 0 0 0 0
#> 11874: 0 1 0 0 0 0
#> 11875: 0 1 0 0 0 0
#> 11876: 0 1 0 0 0 0
#> frequency_multi phys_contact duration_multi
#> <int> <int> <int>
#> 1: 1 1 4
#> 2: 2 2 2
#> 3: 1 1 5
#> 4: 2 2 2
#> 5: 1 1 4
#> ---
#> 11872: 1 2 1
#> 11873: 1 1 5
#> 11874: 2 1 3
#> 11875: 2 2 3
#> 11876: 1 1 4
#>
#> $reference
#> $reference$title
#> [1] "POLYMOD social contact data"
#>
#> $reference$bibtype
#> [1] "Misc"
#>
#> $reference$author
#> [1] "Joël Mossong" "Niel Hens"
#> [3] "Mark Jit" "Philippe Beutels"
#> [5] "Kari Auranen" "Rafael Mikolajczyk"
#> [7] "Marco Massari" "Stefania Salmaso"
#> [9] "Gianpaolo Scalia Tomba" "Jacco Wallinga"
#> [11] "Janneke Heijne" "Malgorzata Sadkowska-Todys"
#> [13] "Magdalena Rosinska" "W. John Edmunds"
#>
#> $reference$year
#> [1] 2017
#>
#> $reference$note
#> [1] "Version 1.1"
#>
#> $reference$doi
#> [1] "10.5281/zenodo.1157934"
#>
#>
#> attr(,"class")
#> [1] "contact_survey"