Social Contact Matrices for 177 Countries.
contactdata 
The goal of contactdata is to provide access to social contact data for 177 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.
and
Kiesha Prem, Kevin van Zandvoort, Petra Klepac, Rosalind M. Eggo, Nicholas G. Davies, CMMID COVID-19 Working Group, Alex R. Cook, Mark Jit, Projecting contact matrices in 177 geographical regions: An update and comparison with empirical data for the COVID-19 era, PLoS Comp. Biol. (2021), https://doi.org/10.1371/journal.pcbi.1009098.
(please cite them in your publications, alongside this package).
Note that this package does not make any geopolitical statement and only provides the data as it has been published.<
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 this package from CRAN:
install.packages("contactdata")
or the development version from GitHub, via my r-universe:
install.packages("contactdata", repos = "https://bisaloo.r-universe.dev")
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.
Because it is very likely that users of this package will also need data about the population in each age group, it is also bundled in this package for convenience. Please see ?age_df_countries for more information.