Open ernestguevarra opened 8 months ago
when merged with the sudan1
map object, the object will look like this:
Simple feature collection with 18 features and 12 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 21.81328 ymin: 8.668605 xmax: 38.59369 ymax: 23.14288
Geodetic CRS: WGS 84
First 10 features:
stateID state_name oedema muw suw mst sst mam_whz sam_whz mam_muac sam_muac
1 16 Central Darfur 0.0087516513 0.1983819 0.12826563 0.1983556 0.2038369 0.10987316 0.05656496 0.06177993 0.011855068
2 17 East Darfur 0.0069271758 0.2178256 0.15885517 0.2069717 0.1986532 0.12746281 0.07418577 0.09048767 0.020767662
3 7 Al-Gadarif 0.0022424171 0.2061527 0.10810148 0.2339727 0.2242554 0.08142015 0.05733283 0.03745592 0.010093640
4 6 Kassala 0.0002602472 0.2221930 0.09374178 0.2517947 0.2206860 0.07835870 0.03136995 0.07636888 0.018862981
5 3 Khartoum 0.0010373444 0.1696429 0.08705357 0.1747507 0.1072076 0.10763569 0.04415823 0.03942731 0.008810573
6 9 Blue Nile 0.0022805017 0.1753637 0.07177498 0.2095820 0.1738959 0.06866784 0.02707475 0.05349556 0.011587486
7 11 North Kourdofan 0.0002986858 0.2395356 0.09624198 0.2393822 0.1652510 0.09930502 0.02996139 0.05526875 0.010021257
8 1 Northern 0.0046557216 0.1918433 0.07405420 0.1853842 0.1074691 0.11738892 0.05458036 0.05161627 0.010427529
9 5 Red Sea 0.0027404086 0.2578551 0.19683199 0.2208931 0.2604997 0.13057410 0.10066756 0.18822023 0.079897567
10 2 River Nile 0.0003934684 0.2280518 0.13273411 0.2157027 0.1779077 0.12834225 0.06780749 0.08605887 0.024529845
maternal_gam geom
1 0.06017384 MULTIPOLYGON (((23.77629 12...
2 0.07748009 MULTIPOLYGON (((27.25195 11...
3 0.05677441 MULTIPOLYGON (((35.85431 14...
4 0.14146258 MULTIPOLYGON (((37.12488 17...
5 0.03263337 MULTIPOLYGON (((31.69787 16...
6 0.11310541 MULTIPOLYGON (((34.1051 9.5...
7 0.08452118 MULTIPOLYGON (((31.02074 12...
8 0.03433607 MULTIPOLYGON (((24.97221 20...
9 0.26306306 MULTIPOLYGON (((38.28729 18...
10 0.05968992 MULTIPOLYGON (((32.40482 21...
the locality level underweight prevalence data.frame object will look something like this (showing first 10 rows):
state_id state_name locality_id locality_name oedema muw suw mst sst mam_whz sam_whz mam_muac sam_muac maternal_gam
1 1 Northern 1 Dongola 0 0.189 0.0882 0.169 0.106 0.127 0.101 0.0403 0.00671 0.0203
2 1 Northern 2 El Golid 0.00668 0.165 0.0816 0.160 0.0914 0.119 0.0567 0.0432 0.0126 0.0309
3 1 Northern 3 Merwoe 0.00260 0.198 0.0815 0.231 0.126 0.0889 0.0389 0.112 0.016 0.0436
4 1 Northern 4 El Daba 0.0189 0.202 0.0578 0.171 0.0788 0.124 0.0458 0.0357 0.00630 0.0410
5 1 Northern 5 Halfa 0 0.186 0.0368 0.183 0.105 0.101 0.0277 0.0710 0.0128 0.0392
6 1 Northern 6 Delgo 0.00161 0.229 0.103 0.233 0.119 0.135 0.0525 0.0523 0.0101 0.0371
7 1 Northern 7 El Burgaig 0.00540 0.174 0.0624 0.164 0.127 0.115 0.0403 0.0277 0.0111 0.0393
8 2 River Nile 8 El Matama 0.00155 0.242 0.108 0.199 0.150 0.129 0.0613 0.149 0.0376 0.0374
9 2 River Nile 9 Shendi 0 0.264 0.166 0.258 0.192 0.116 0.0657 0.0635 0.0143 0.101
10 2 River Nile 10 Abu Hamad 0 0.252 0.152 0.196 0.198 0.178 0.126 0.0589 0.0126 0.0523
when combined with the sudan2
map, it will look like this:
Simple feature collection with 188 features and 14 fields
Geometry type: MULTIPOLYGON
Dimension: XY
Bounding box: xmin: 21.81328 ymin: 8.668602 xmax: 38.59369 ymax: 23.14288
Geodetic CRS: WGS 84
First 10 features:
stateID localityID state_name locality_name oedema muw suw mst sst mam_whz sam_whz
1 16 171 Central Darfur Azum 0.001818182 0.1654412 0.09926471 0.1713748 0.1337100 0.12686567 0.04664179
2 16 164 Central Darfur Zalingi 0.022850925 0.1942729 0.13026390 0.2142453 0.2018089 0.09592188 0.05743825
3 16 170 Central Darfur Nertiti 0.005873715 0.2629179 0.18085106 0.2190923 0.3239437 0.10248447 0.06055901
4 16 166 Central Darfur Mukjar 0.000000000 0.1374172 0.07284768 0.1604730 0.1486486 0.11538462 0.04682274
5 16 168 Central Darfur North Jebel Mara 0.015094340 0.2713178 0.17441860 0.2906977 0.2596899 0.15953307 0.03112840
6 16 172 Central Darfur Um Dukhun 0.001574803 0.1545741 0.13406940 0.1706924 0.1900161 0.11736334 0.06913183
7 16 165 Central Darfur Wadi Salih 0.001239157 0.2086957 0.11801242 0.1866330 0.1715006 0.13207547 0.07295597
8 16 169 Central Darfur Bendasi 0.000000000 0.2039801 0.07462687 0.1813602 0.1335013 0.08040201 0.03768844
9 17 181 East Darfur Yassin 0.000000000 0.2191358 0.28703704 0.2370130 0.2548701 0.14072848 0.16556291
10 17 180 East Darfur Shia-ria 0.004098361 0.2603306 0.28512397 0.2034632 0.2489177 0.20131291 0.16411379
mam_muac sam_muac maternal_gam geom
1 0.05667276 0.003656307 0.06378132 MULTIPOLYGON (((23.08108 13...
2 0.06743421 0.012609649 0.06166419 MULTIPOLYGON (((23.77662 12...
3 0.06646526 0.018126888 0.04347826 MULTIPOLYGON (((24.04072 12...
4 0.02814570 0.011589404 0.04535147 MULTIPOLYGON (((23.68988 12...
5 0.09541985 0.011450382 0.03482587 MULTIPOLYGON (((24.41425 13...
6 0.05511811 0.006299213 0.05346535 MULTIPOLYGON (((23.35338 11...
7 0.07071960 0.021091811 0.06103286 MULTIPOLYGON (((23.7763 12....
8 0.05472637 0.007462687 0.12177122 MULTIPOLYGON (((23.2672 12....
9 0.10105581 0.031674208 0.09780439 MULTIPOLYGON (((25.60531 12...
10 0.08606557 0.028688525 0.10714286 MULTIPOLYGON (((25.48468 12...
Now, you will just have two main map objects for state and locality level results. And these will be the objects you will need to plot the maps. This will be a lot more efficient than having multiple mapping objects to work with.
The state data.frame with undernutrition prevalence for child and mother would look something like this:
oedema = severe wasting by oedema muw = moderate underweight suw = severe underweight mst = moderate stunting sst = severe stunting mam_whz = moderate wasting by weight for height z-score sam_whz = severe wasting by weight for height z-score mam_muac = moderate wasting by MUAC sam_muac = severe wasting by MUAC maternal_gam = maternal wasting