Format POLYMOD data and filter contacts to certain settings
get_polymod_contact_data.RdProvides contact and participant POLYMOD data from selected countries. It impute missing contact ages via one of three methods:
imputing contact ages from a random uniform distribution from the range of ages. 2) using the average of the ages, 3) removal of those participants. The contact settings are then classified as "home", "school", "work" and "others", where "others" include locations such as leisure, transport or other places. The participants with missing contact ages or settings are removed, and the number of contacts per participant and contact age from ages 0-100 are obtained for various countries and settings.
Arguments
- setting
Which setting to extract data from. Default is all settings. Options are: "all", "home", "work", "school", and "other".
- countries
countries to extract data from. Default is all countries from this list: "Belgium", "Finland", "Germany", "Italy", "Luxembourg", "Netherlands", "Poland", and "United Kingdom".
- ages
Which ages to return. Default is ages 0 to 100.
- contact_age_imputation
How to handle age when it is missing. Choose one of three methods: 1) "sample", which imputes contact ages from a random uniform distribution from the range of ages. 2) "mean", use the average of the ages, 3) "remove_participant" removal of those participants. Default is "sample".
Value
A data.frame with columns: "setting" (all, work, home, etc. as specified in "setting" argument); "age_from" - the age of the participant; "age_to" - the age of the person the participant had contact with; "contacts" the number of contacts that person had; "participants" the number of participants in that row.
Examples
get_polymod_contact_data()
#> # A tibble: 8,787 × 5
#> setting age_from age_to contacts participants
#> <chr> <int> <dbl> <int> <int>
#> 1 all 0 0 31 92
#> 2 all 0 1 13 92
#> 3 all 0 2 24 92
#> 4 all 0 3 23 92
#> 5 all 0 4 14 92
#> 6 all 0 5 13 92
#> 7 all 0 6 12 92
#> 8 all 0 7 11 92
#> 9 all 0 8 7 92
#> 10 all 0 9 8 92
#> # … with 8,777 more rows
get_polymod_contact_data(setting = "home")
#> # A tibble: 8,787 × 5
#> setting age_from age_to contacts participants
#> <chr> <int> <dbl> <int> <int>
#> 1 home 0 0 10 92
#> 2 home 0 1 7 92
#> 3 home 0 2 11 92
#> 4 home 0 3 15 92
#> 5 home 0 4 11 92
#> 6 home 0 5 6 92
#> 7 home 0 6 9 92
#> 8 home 0 7 9 92
#> 9 home 0 8 6 92
#> 10 home 0 9 6 92
#> # … with 8,777 more rows
get_polymod_contact_data(countries = "Belgium")
#> # A tibble: 8,282 × 5
#> setting age_from age_to contacts participants
#> <chr> <int> <dbl> <int> <int>
#> 1 all 0 0 8 5
#> 2 all 0 1 3 5
#> 3 all 0 2 6 5
#> 4 all 0 3 2 5
#> 5 all 0 4 1 5
#> 6 all 0 5 1 5
#> 7 all 0 6 0 5
#> 8 all 0 7 3 5
#> 9 all 0 8 0 5
#> 10 all 0 9 0 5
#> # … with 8,272 more rows
get_polymod_contact_data(countries = c("Belgium", "Italy"))
#> # A tibble: 8,383 × 5
#> setting age_from age_to contacts participants
#> <chr> <int> <dbl> <int> <int>
#> 1 all 0 0 9 11
#> 2 all 0 1 4 11
#> 3 all 0 2 6 11
#> 4 all 0 3 5 11
#> 5 all 0 4 2 11
#> 6 all 0 5 1 11
#> 7 all 0 6 0 11
#> 8 all 0 7 4 11
#> 9 all 0 8 1 11
#> 10 all 0 9 0 11
#> # … with 8,373 more rows
get_polymod_contact_data(ages = 0:50)
#> # A tibble: 7,184 × 5
#> setting age_from age_to contacts participants
#> <chr> <int> <dbl> <int> <int>
#> 1 all 0 0 31 92
#> 2 all 0 1 11 92
#> 3 all 0 2 26 92
#> 4 all 0 3 24 92
#> 5 all 0 4 14 92
#> 6 all 0 5 12 92
#> 7 all 0 6 11 92
#> 8 all 0 7 12 92
#> 9 all 0 8 7 92
#> 10 all 0 9 8 92
#> # … with 7,174 more rows
get_polymod_contact_data(contact_age_imputation = "sample")
#> # A tibble: 8,787 × 5
#> setting age_from age_to contacts participants
#> <chr> <int> <dbl> <int> <int>
#> 1 all 0 0 31 92
#> 2 all 0 1 13 92
#> 3 all 0 2 25 92
#> 4 all 0 3 22 92
#> 5 all 0 4 15 92
#> 6 all 0 5 11 92
#> 7 all 0 6 13 92
#> 8 all 0 7 11 92
#> 9 all 0 8 7 92
#> 10 all 0 9 8 92
#> # … with 8,777 more rows
get_polymod_contact_data(contact_age_imputation = "mean")
#> # A tibble: 8,787 × 5
#> setting age_from age_to contacts participants
#> <chr> <int> <dbl> <int> <int>
#> 1 all 0 0 31 93
#> 2 all 0 1 11 93
#> 3 all 0 2 27 93
#> 4 all 0 3 23 93
#> 5 all 0 4 16 93
#> 6 all 0 5 12 93
#> 7 all 0 6 11 93
#> 8 all 0 7 12 93
#> 9 all 0 8 7 93
#> 10 all 0 9 8 93
#> # … with 8,777 more rows
get_polymod_contact_data(contact_age_imputation = "remove_participant")
#> # A tibble: 8,686 × 5
#> setting age_from age_to contacts participants
#> <chr> <int> <dbl> <int> <int>
#> 1 all 0 0 1 36
#> 2 all 0 1 6 36
#> 3 all 0 2 3 36
#> 4 all 0 3 8 36
#> 5 all 0 4 4 36
#> 6 all 0 5 2 36
#> 7 all 0 6 5 36
#> 8 all 0 7 3 36
#> 9 all 0 8 3 36
#> 10 all 0 9 7 36
#> # … with 8,676 more rows