contactdata

The goal of contactdata is to provide access to social contact data for 152 countries. This data comes from

Kiesha Prem, Alex R. Cook, Mark Jit, Projecting social contact matrices in 152 countries using contact surveys and demographic data, PLoS Comp. Biol. (2017), https://doi.org/10.1371/journal.pcbi.1005697.

(please cite it in your publications, alongside this package).

contactdata offers an easier access to this data, makes it readily compatible with tidyverse packages, such as ggplot2, via the contact_countries() function, and provides an easy way to harmonise country nomenclature by using the countrycode package as authoritative name source.

Installation

You can install the development version of contactdata from GitHub with:

# install.packages("remotes")
remotes::install_github("Bisaloo/contactdata")

Example

The most basic function allows you to get matrix data for a specific country:

library(contactdata)
contact_matrix("France")
#>           00_05     05_10     10_15     15_20     20_25     25_30     30_35
#> 00_05 3.8049208 1.1064463 0.4119145 0.2553693 0.3417530 0.7532100 1.2090488
#> 05_10 1.0620240 5.0325631 1.0108210 0.2729936 0.1637277 0.4151037 0.9207158
#> 10_15 0.2383228 1.5384390 6.9859632 0.8459108 0.2870553 0.2443827 0.4032533
#> 15_20 0.1242916 0.3084664 2.3013378 7.8316731 1.3599526 0.6511973 0.5309225
#> 20_25 0.1997494 0.1682122 0.2555462 2.1654802 3.9337904 1.7076407 1.1677744
#> 25_30 0.5331867 0.2486300 0.1435870 0.7309595 1.9436259 3.4581082 1.7769048
#> 30_35 0.7222459 0.8480973 0.5299038 0.4228065 1.0050076 1.7204472 2.9192993
#> 35_40 0.7037954 1.0721441 0.8366342 0.6832771 0.7646973 1.4071985 1.7597999
#> 40_45 0.3095332 0.6566442 0.9827524 1.1093668 0.9235031 1.2518923 1.5914371
#> 45_50 0.4058094 0.4671793 0.6117632 1.5367229 0.9454052 0.9938984 1.2592405
#> 50_55 0.2517178 0.5947777 0.8288772 1.2016733 1.0188128 1.2906068 1.1792561
#> 55_60 0.5424359 0.6711899 0.5736708 0.7492978 0.6135626 0.9317515 0.9175372
#> 60_65 0.3961294 0.3629084 0.2566179 0.3614150 0.3387789 0.5080470 0.6393848
#> 65_70 0.1980189 0.3134375 0.2568708 0.1574851 0.2121279 0.3071351 0.4501078
#> 70_75 0.1052495 0.2944891 0.3077932 0.3553412 0.1558914 0.2445032 0.2530902
#> 75_80 0.2435584 0.3171220 0.4522256 0.3598813 0.1542999 0.1882229 0.3050821
#>           35_40     40_45     45_50     50_55     55_60      60_65      65_70
#> 00_05 1.0419960 0.4955829 0.3104594 0.3070544 0.2722105 0.18633754 0.12804520
#> 05_10 1.1384301 0.8451808 0.3314044 0.2073083 0.1854381 0.17691472 0.11164899
#> 10_15 0.8269250 1.0806856 0.5704767 0.2657533 0.1466135 0.10152928 0.08817052
#> 15_20 0.7621321 1.0248384 1.0489743 0.4785021 0.1639009 0.07173157 0.05282605
#> 20_25 1.1331411 0.9700452 1.2702652 0.8133999 0.3272061 0.08382776 0.04576510
#> 25_30 1.4707826 1.2626267 0.9969784 0.9610877 0.3574627 0.11253397 0.05165248
#> 30_35 1.8937794 1.4574822 1.1351731 0.8100567 0.4053920 0.18410812 0.08541583
#> 35_40 3.2108263 2.0977977 1.3443554 0.9195016 0.3641096 0.25969059 0.15240004
#> 40_45 1.8766171 2.9541130 1.6818322 1.1128901 0.2732150 0.18353558 0.11084809
#> 45_50 1.4654930 1.5731579 2.1964738 1.0863042 0.3406847 0.15129942 0.08636681
#> 50_55 1.2145400 1.5942381 1.7430635 1.9184501 0.6491669 0.26726729 0.11850585
#> 55_60 0.7479099 0.7927704 0.6808703 0.9229155 1.4619218 0.51294863 0.21825949
#> 60_65 0.6940436 0.5234400 0.4162769 0.4414520 0.6750623 1.40597284 0.42649139
#> 65_70 0.4543158 0.4253709 0.3023152 0.3242779 0.4404185 0.51803705 1.00304348
#> 70_75 0.4770518 0.5149822 0.4340009 0.3392186 0.3381533 0.65997723 0.61920693
#> 75_80 0.3824259 0.4514450 0.4428716 0.5388023 0.3440813 0.27980028 0.36715407
#>            70_75      75_80
#> 00_05 0.08780229 0.05386407
#> 05_10 0.05825985 0.05311071
#> 10_15 0.06970996 0.06966683
#> 15_20 0.03453706 0.02613884
#> 20_25 0.05447291 0.05237283
#> 25_30 0.03057661 0.02361016
#> 30_35 0.04700946 0.05253904
#> 35_40 0.09805799 0.04637476
#> 40_45 0.08697388 0.04801201
#> 45_50 0.08310938 0.08187873
#> 50_55 0.08806304 0.09026068
#> 55_60 0.11994582 0.09467785
#> 60_65 0.26297656 0.12855394
#> 65_70 0.27590642 0.13898285
#> 70_75 0.97407644 0.32576427
#> 75_80 0.36754549 0.64291815

You can also get several countries at once with the contact_df_countries() function, as detailed in the vignette.