Last updated: 2020-12-09

Checks: 7 0

Knit directory: GeoPKO/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20200629)

The command set.seed(20200629) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 7a7271b

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 7a7271b. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  gallery/

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/viz.Rmd) and HTML (docs/viz.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 7a7271b Nguyen Ha 2020-12-09 merging gallery
html 7a7271b Nguyen Ha 2020-12-09 merging gallery
Rmd 6a20fd9 Lou van Roozendaal 2020-12-04 update global animated map
html a966566 Nguyen Ha 2020-12-03 Build site.
Rmd eafa002 Nguyen Ha 2020-12-03 adapting to new dataset version
html acee4ed Nguyen Ha 2020-11-25 Build site.
Rmd b6fea7a Nguyen Ha 2020-11-25 Revised viz, index, incorporated merge attempt 2.rmd
html 2011bda Nguyen Ha 2020-11-25 Build site.
Rmd f2f9e90 Nguyen Ha 2020-11-25 Before building with wflowr
html f2f9e90 Nguyen Ha 2020-11-25 Before building with wflowr
html 7447d5c Nguyen Ha 2020-08-13 Build site.
Rmd 03c6897 Nguyen Ha 2020-08-13 wflow_publish(all = TRUE)
html 34eb60e Nguyen Ha 2020-08-13 Build site.
Rmd 4343536 Nguyen Ha 2020-08-13 wflow_git_commit(all = TRUE)
html 9a57942 Nguyen Ha 2020-08-13 Build site.
Rmd 3c3a981 Nguyen Ha 2020-08-13 Updating new maps, new pages

Visualizing deployment locations

An advantage to the Geo-PKO dataset is that it records the numbers of troops by their specific deployment locations. Therefore, users can quickly visualize where active troops are in a mission, be it for a specific mission or a region. Below are some examples of visualization.

To start with, we can take a snapshot of the deployment of all the missions that were active in 2018 in Africa. We start by pulling the shapefiles for country outlines from the package rnaturalearth, and filter for countries in Africa.

library(rnaturalearth)
library(rnaturalearthdata)
library(sf)

world <- ne_countries(scale = "medium", returnclass = "sf")
library(dplyr)
AFR_sf <- world %>% filter(region_un == "Africa")

Did that work? We can plot the shapefiles to find out.

library(ggplot2)

ggplot(data=AFR_sf) + geom_sf()

Next, we need to subset the main dataset to include only entries (1) for the year of 2018 and (2) in the continent Africa, all with (3) our variables of interests. Geo-PKO reports deployment sizes according to the available maps published by the UN. Therefore, to obtain the numbers of troop deployment at the yearly level, we calculate the average number of troops per location over the months recorded. We should end up with something similar to the table below.

library(readr)
library(knitr)
library(kableExtra) 

GeoPKO <- readr::read_csv("data/Geo_PKO_v2.0.csv", col_types = cols(.default="c")) 

GeoPKO2018 <- GeoPKO %>% filter(year==2018) %>%  
  select(mission, year, location, latitude, longitude, no.troops, hq, country) %>%
  mutate_at(vars(latitude, longitude, no.troops), as.numeric) %>% 
  group_by(location, mission, latitude, longitude) %>% 
  mutate(YearlyAverage = round(mean(no.troops, na.rm=TRUE))) %>%
  arrange(desc(hq)) %>% slice(1)

kable(GeoPKO2018, caption = "An extract of the 2018 dataframe") %>% kable_styling() %>% 
  scroll_box(width = "100%", height = "200px")
An extract of the 2018 dataframe
mission year location latitude longitude no.troops hq country YearlyAverage
UNISFA 2018 Abyei Camp 9.6270950 28.434498 1050 3 Sudan 1170
MONUSCO 2018 Adikivu -2.3257290 28.812325 1250 2 DRC 796
UNISFA 2018 Agany Toak 9.5274830 28.434313 35 0 Sudan 35
UNISFA 2018 Agok 9.3574860 28.582579 70 0 Sudan 91
MINUSMA 2018 Aguelhok 19.4613900 0.858890 335 0 Mali 335
MINURSO 2018 Agwanit 22.1934000 -13.140930 0 0 Western Sahara 0
UNAMID 2018 Al Da’ein 11.4618600 26.125830 370 2 Sudan 269
UNISFA 2018 Alal 9.7936640 28.339436 150 0 Sudan 150
MINUSMA 2018 Ansongo 15.6597000 0.502200 950 0 Mali 950
UNFICYP 2018 Athienou 35.0618300 33.541660 35 0 Cyprus 35
UNISFA 2018 Athony 9.4705590 28.464043 335 2 Sudan 335
UNMISS 2018 Aweil 8.7681570 27.400190 150 0 South Sudan 150
MINURSO 2018 Awsard 22.5516390 -14.331643 0 0 Western Sahara 0
MINUSMA 2018 Bamako 12.6500000 -8.000000 705 3 Mali 705
MINUSCA 2018 Bambari 5.7638800 20.670733 800 0 Central African Republic 1017
MINUSCA 2018 Bangassou 4.7379210 22.816399 650 0 Central African Republic 650
MINUSCA 2018 Bangui 4.3937970 18.559861 4085 3 Central African Republic 4308
UNISFA 2018 Banton 9.5063890 28.470323 335 0 Sudan 335
MONUSCO 2018 Baraka -4.1043250 29.089285 150 0 DRC 150
MINUSCA 2018 Batangafo 7.3009280 18.289260 0 0 Central African Republic 0
MONUSCO 2018 Bendera -5.0655560 28.915556 150 0 DRC 150
MONUSCO 2018 Beni 0.5000000 29.466667 800 1 DRC 633
MONUSCO 2018 Beni Boikene 0.5402770 29.489585 0 1 DRC 0
UNMISS 2018 Bentiu 9.2599050 29.800110 1450 2 South Sudan 1450
MINUSMA 2018 Ber 16.8349270 -2.531944 150 0 Mali 150
MINUSCA 2018 Berberati 4.2573560 15.787821 650 0 Central African Republic 650
MINURSO 2018 Bir Lahlou 26.3268627 -9.549474 0 0 Western Sahara 0
MINUSCA 2018 Birao 10.2932620 22.782273 650 0 Central African Republic 650
MONUSCO 2018 Bogoro 1.4102150 30.279670 150 0 DRC 150
UNMISS 2018 Bor 6.2059310 31.556326 1750 2 South Sudan 1750
MINUSCA 2018 Bossangoa 6.4975080 17.450452 650 0 Central African Republic 650
MINUSCA 2018 Bouar 5.9430660 15.598526 1100 2 Central African Republic 1050
MINUSCA 2018 Bria 6.5390230 21.991110 150 2 Central African Republic 417
MONUSCO 2018 Bukavu -2.5000000 28.866667 600 2 DRC 600
MONUSCO 2018 Bunia 1.5625000 30.248417 1700 2 DRC 1108
UNMISS 2018 Bunji 9.9466650 33.810268 150 0 South Sudan 150
UNAMID 2018 Buram 10.8566980 25.159272 150 0 Sudan 150
MONUSCO 2018 Butembo 0.1142830 29.301407 150 0 DRC 150
UNDOF 2018 Camp Faouar 33.2388940 35.910609 520 3 Syria 432
UNDOF 2018 Camp Ziouani 33.1089360 35.816438 1485 0 Syria 1423
MINUJUSTH 2018 Cap-Haïtien 19.7593800 -72.198150 NA 0 Haiti NaN
MINUSCA 2018 Carnot 4.9397900 15.877884 0 0 Central African Republic 0
UNDOF 2018 Damascus 33.5102000 36.291280 0 3 Syria 0
MINUSCA 2018 Dékoa 6.3188650 19.077846 0 0 Central African Republic 0
UNFICYP 2018 Dhenia 35.1667500 33.145990 0 0 Cyprus 0
UNFICYP 2018 Dherinia 35.0648400 33.960830 35 0 Cyprus 35
UNISFA 2018 Diffra 10.0392480 28.406966 335 2 Sudan 335
UNISFA 2018 Dokura 9.6789860 28.458475 300 2 Sudan 300
MINUSMA 2018 Douentza 15.0015500 -2.949780 1100 0 Mali 1160
UNISFA 2018 Dungoup 9.6291360 28.495775 150 0 Sudan 150
MONUSCO 2018 Dungu 3.6166670 28.566667 950 2 DRC 733
MINUSMA 2018 Dyabali 14.6937000 -6.019700 1100 0 Mali 1100
UNAMID 2018 El Fasher 13.6333333 25.350000 935 3 Sudan 935
UNAMID 2018 El Geneina 13.4500000 22.450000 635 2 Sudan 721
UNAMID 2018 El Sireaf 13.8873030 23.517703 150 0 Sudan 150
UNFICYP 2018 Famagusta 35.1248900 33.941350 150 2 Cyprus 150
UNISFA 2018 Farouk 10.1109360 28.420397 150 0 Sudan 150
MINUJUSTH 2018 Fort-Liberté 19.6627300 -71.837980 0 0 Haiti 0
MINUSMA 2018 Gao 16.2716700 -0.044720 5635 2 Mali 5605
UNISFA 2018 Gok-machar 9.2154120 26.859909 0 2 Sudan 0
UNISFA 2018 Goli 9.8485970 28.477926 220 0 Sudan 220
UNAMID 2018 Golo 13.1322800 24.280820 150 0 Sudan 188
MONUSCO 2018 Goma -1.6833330 29.233333 1550 2 DRC 1008
MINUJUSTH 2018 Gonaïves 19.4475500 -72.689280 NA 0 Haiti NaN
MINUSMA 2018 Gossi 15.8229940 -1.300646 150 0 Mali 150
MINUSMA 2018 Goundam 16.4145300 -3.670750 300 0 Mali 300
UNAMID 2018 Graida 11.3394850 25.139697 300 1 Sudan 225
UNISFA 2018 Highway 9.5526470 28.467694 150 0 Sudan 270
MONUSCO 2018 Himbe -1.4326730 28.888605 300 0 DRC 300
MONUSCO 2018 Himbi -1.6367610 29.247930 300 0 DRC 300
MINUJUSTH 2018 Hinche 19.1500000 -72.016670 0 0 Haiti 0
MINUJUSTH 2018 Jacmel 18.2342700 -72.535390 0 0 Haiti 0
MINUJUSTH 2018 Jérémie 18.6500000 -74.116670 NA 0 Haiti NaN
UNMISS 2018 Juba 4.8459720 31.601203 3350 3 South Sudan 2830
UNAMID 2018 Kabkabiya 13.6474600 24.086729 185 1 Sudan 214
UNISFA 2018 Kadugli 11.0105960 29.713688 0 2 Sudan 0
MINUSCA 2018 Kaga Bandoro 6.9959470 19.184232 1100 2 Central African Republic 1100
MONUSCO 2018 Kalemie -5.9033440 29.192303 650 0 DRC 700
UNAMID 2018 Kalma 12.0155960 24.998158 150 0 Sudan 150
MONUSCO 2018 Kamango 0.6177730 29.901965 150 0 DRC 150
MONUSCO 2018 Kamanyola -2.7404250 29.004775 150 0 DRC 150
MONUSCO 2018 Kampala 0.3155556 32.565556 0 0 Uganda 0
MONUSCO 2018 Kananga -5.9000000 22.416667 300 0 DRC 175
MONUSCO 2018 Kanyabayonga -0.7111110 29.173056 150 0 DRC 150
UNAMID 2018 Kas 12.5000000 24.283333 150 0 Sudan 150
UNFICYP 2018 Kato Pyrgos 35.1782400 32.684920 0 0 Cyprus 0
MONUSCO 2018 Kavumu -2.3027780 28.816111 625 0 DRC 569
UNAMID 2018 Khor Abeche 12.6602080 25.337603 300 1 Sudan 262
MINUSMA 2018 Kidal 18.4411100 1.407780 3135 2 Mali 3163
MONUSCO 2018 Kigali -1.9546000 30.061000 0 0 Rwanda 0
MONUSCO 2018 Kinshasa -4.3297220 15.315000 1400 3 DRC 1200
MONUSCO 2018 Kisangani 0.5000000 25.200000 150 2 DRC 150
MONUSCO 2018 Kiwanja -1.1400000 29.439445 650 0 DRC 483
MONUSCO 2018 Komanda 1.3623420 29.776738 150 0 DRC 150
MONUSCO 2018 Kongolo -5.3833330 27.000000 35 0 DRC 35
UNAMID 2018 Korma 13.8333333 24.733333 185 0 Sudan 185
UNMIK 2018 Kosovska Mitrovica 42.8833300 20.866670 0 2 Kosovo 0
UNMISS 2018 Kuacjok 8.3040400 27.993269 150 0 South Sudan 150
UNAMID 2018 Kutum 14.2000000 24.666667 300 0 Sudan 300
MINURSO 2018 Laayoune 27.1350000 -13.162500 35 3 Western Sahara 35
UNAMID 2018 Labado 12.0908920 25.410895 300 0 Sudan 300
UNMISS 2018 Leer 8.3017900 30.141790 150 0 South Sudan 150
UNFICYP 2018 Leonarisso 35.4689200 34.138860 0 0 Cyprus 0
MINUJUSTH 2018 Les Cayes 18.1933100 -73.746010 0 0 Haiti 0
UNFICYP 2018 Linou 35.0785600 32.904700 0 0 Cyprus 0
MONUSCO 2018 Lubumbashi -11.6666670 27.466667 150 2 DRC 150
UNISFA 2018 Madingthon 9.6013440 28.431379 70 0 Sudan 70
MINURSO 2018 Mahbas 27.4242670 -9.065980 0 0 Western Sahara 0
UNMISS 2018 Malakal 9.5334240 31.660485 2050 2 South Sudan 1930
MONUSCO 2018 Manono -7.3000000 27.416667 150 0 DRC 150
UNISFA 2018 Marial Achak 9.4793280 28.624917 150 0 Sudan 150
UNIFIL 2018 Maritime Task Force 33.1216998 35.134000 NA 0 Lebanon NaN
UNAMID 2018 Masteri 13.1166667 22.150000 150 0 Sudan 150
MONUSCO 2018 Mavivi 0.5861170 29.484516 335 1 DRC 902
MONUSCO 2018 Mayimoya 0.7651220 29.569722 150 0 DRC 150
MONUSCO 2018 Mbuji-Mayi -6.1500000 23.600000 150 0 DRC 150
MINURSO 2018 Mehaires 26.1472000 -11.069200 0 0 Western Sahara 0
UNMISS 2018 Melut 10.4417950 32.200806 150 0 South Sudan 150
MINUSMA 2018 Ménaka 15.9182000 2.402200 450 0 Mali 450
UNAMID 2018 Menawashi 12.6666700 24.983330 150 0 Sudan 150
MINURSO 2018 Mijek 23.4735871 -12.759754 0 0 Western Sahara 0
MINUJUSTH 2018 Miragoâne 18.4459900 -73.089570 NA 0 Haiti NaN
MONUSCO 2018 Moba -7.0666670 29.766667 35 0 DRC 35
MONUSCO 2018 Monigi Camp -1.6349200 29.248316 300 0 DRC 300
UNMIL 2018 Monrovia 6.3105560 -10.804722 150 0 Liberia 150
MINUSMA 2018 Mopti/Sevare 14.5274200 -4.093440 950 0 Mali 950
UNAMID 2018 Mournei 12.9500000 22.866667 150 0 Sudan 150
UNDOF 2018 Mt Hermon Base 33.3597330 35.820466 150 0 Syria 173
UNAMID 2018 Mukhjar 11.9571480 23.269138 300 0 Sudan 300
MONUSCO 2018 Mushake -1.5281580 28.979520 150 0 DRC 150
UNIFIL 2018 Naqoura 33.1117970 35.125602 2200 3 Lebanon 2200
UNMISS 2018 Nasser 8.6000000 33.066667 150 0 South Sudan 150
MINUSCA 2018 Ndélé 8.4110470 20.647584 0 0 Central African Republic 0
MONUSCO 2018 Ndromo -1.3595120 28.733120 650 0 DRC 650
UNAMID 2018 Nertiti 12.9632500 24.047643 950 0 Sudan 950
UNFICYP 2018 Nicosia 35.1694400 33.360810 150 2 Cyprus 150
UNISFA 2018 Noong 9.7033280 28.452392 300 0 Sudan 300
UNAMID 2018 Nyala 12.0500000 24.883333 970 2 Sudan 869
MINUSCA 2018 Obo 5.3956700 26.491755 0 0 Central African Republic 0
MINURSO 2018 Oum Dreyga 24.1666670 -13.250000 0 0 Western Sahara 0
MINUSCA 2018 Paoua 7.2473780 16.434391 0 0 Central African Republic 0
UNMISS 2018 Pariang 9.9154200 29.981090 150 0 South Sudan 150
UNMISS 2018 Pibor 6.7985290 33.130445 150 0 South Sudan 150
MINUJUSTH 2018 Port-au-Prince 18.5434900 -72.338810 NA 0 Haiti NaN
MINUJUSTH 2018 Port-de-Paix 19.9398400 -72.830370 0 0 Haiti 0
UNIFIL 2018 Position 1-0A 33.1083330 35.209167 300 0 Lebanon 300
UNIFIL 2018 Position 1-26 33.1695210 35.185870 150 1 Lebanon 150
UNIFIL 2018 Position 2-1 33.2576090 35.312757 450 1 Lebanon 450
UNIFIL 2018 Position 2-3 33.1521074 35.197265 1250 2 Lebanon 1250
UNIFIL 2018 Position 2-31 33.1559650 35.196764 150 0 Lebanon 150
UNIFIL 2018 Position 2-45 33.1291670 35.391389 600 1 Lebanon 600
UNIFIL 2018 Position 2-5 33.2776120 35.249281 600 1 Lebanon 600
UNIFIL 2018 Position 4-2 33.3688890 35.623056 150 1 Lebanon 150
UNIFIL 2018 Position 4-28 33.2783330 35.636111 150 0 Lebanon 150
UNIFIL 2018 Position 4-3 33.3770720 35.640359 300 0 Lebanon 300
UNIFIL 2018 Position 4-30 33.3265710 35.641810 150 0 Lebanon 150
UNIFIL 2018 Position 4-31 33.3115033 35.705589 150 0 Lebanon 150
UNIFIL 2018 Position 4-7C 33.3294440 35.736667 150 0 Lebanon 150
UNIFIL 2018 Position 5-10 33.1838310 35.231681 150 0 Lebanon 150
UNIFIL 2018 Position 5-20 33.0898060 35.370954 150 0 Lebanon 150
UNIFIL 2018 Position 5-42 33.1052786 35.317299 150 0 Lebanon 150
UNIFIL 2018 Position 5-66 33.1282430 35.350073 150 1 Lebanon 150
UNIFIL 2018 Position 6-43 33.1910620 35.392703 150 0 Lebanon 150
UNIFIL 2018 Position 7-1 33.2713890 35.491667 300 1 Lebanon 300
UNIFIL 2018 Position 7-2 33.3760410 35.616939 900 2 Lebanon 900
UNIFIL 2018 Position 7-3 33.3810280 35.613174 450 0 Lebanon 450
UNIFIL 2018 Position 8-30 33.1836190 35.505105 150 1 Lebanon 150
UNIFIL 2018 Position 8-31 33.2045920 35.485374 150 0 Lebanon 150
UNIFIL 2018 Position 8-32 33.1966805 35.540474 150 0 Lebanon 150
UNIFIL 2018 Position 8-33 33.2091250 35.534695 150 0 Lebanon 150
UNIFIL 2018 Position 8-34 33.1628270 35.524991 150 0 Lebanon 150
UNIFIL 2018 Position 8-36 33.1299700 35.492847 150 0 Lebanon 150
UNDOF 2018 Position 80 32.9621450 35.883407 35 0 Syria 112
UNIFIL 2018 Position 9-1 33.2638580 35.410404 450 1 Lebanon 450
UNIFIL 2018 Position 9-10 33.2696800 35.416412 150 0 Lebanon 150
UNIFIL 2018 Position 9-2 33.3029797 35.449903 150 0 Lebanon 150
UNIFIL 2018 Position 9-63 33.2456370 35.539750 150 0 Lebanon 150
UNIFIL 2018 Position 9-66 33.3021770 35.564996 150 0 Lebanon 150
UNIFIL 2018 Position HIN 33.1049040 35.277992 150 0 Lebanon 150
UNMIK 2018 Pristina 42.6727200 21.166880 0 3 Kosovo 0
UNFICYP 2018 Pyla 35.0046900 33.693810 35 0 Cyprus 35
UNMISS 2018 Renk 11.7431000 32.804900 150 0 South Sudan 150
UNISFA 2018 Rumajak/Dokura 9.6789860 28.458475 300 2 Sudan 300
UNMISS 2018 Rumbek 6.8019960 29.691227 650 0 South Sudan 650
MONUSCO 2018 Rwindi -0.7847220 29.290556 650 0 DRC 483
MONUSCO 2018 Sake -1.5736110 29.045000 2750 0 DRC 2487
MONUSCO 2018 Sange -3.0915510 29.114144 650 0 DRC 650
UNAMID 2018 Saraf Omra 13.4500000 23.266667 150 0 Sudan 150
MONUSCO 2018 Semuliki Bridge 0.7392390 29.789729 255 0 DRC 255
MONUSCO 2018 Shabunda -2.6915400 27.346250 150 0 DRC 150
UNAMID 2018 Shaeria 12.4426150 25.586193 NA 0 Sudan NaN
UNAMID 2018 Shangil Tobay 13.0166670 25.250000 150 0 Sudan 150
MINUSCA 2018 Sibut 5.7314540 19.087667 650 0 Central African Republic 650
UNFICYP 2018 Skouriotissa 35.0917400 32.884140 150 2 Cyprus 150
MINURSO 2018 Smara 26.7384100 -11.671940 0 0 Western Sahara 0
UNAMID 2018 Sortony 13.4505400 24.397706 185 0 Sudan 185
UNFICYP 2018 Strovilia 35.0940970 33.899095 0 0 Cyprus 0
UNAMID 2018 Tawila 13.5000000 24.900000 185 1 Sudan 185
UNISFA 2018 Tejalei 9.6736060 28.593325 300 0 Sudan 300
MINUSMA 2018 Tessalit 20.2012600 1.011860 1320 0 Mali 1320
MINURSO 2018 Tifariti 26.0927780 -10.608889 0 0 Western Sahara 0
MINURSO 2018 Tindouf 27.6711100 -8.147430 0 0 Algeria 0
UNISFA 2018 Todach 9.7347390 28.472629 520 0 Sudan 520
MINUSMA 2018 Tombouctou (Timbuktu) 16.7734800 -3.007420 2370 2 Mali 2370
UNMISS 2018 Torit 4.4133330 32.567778 300 0 South Sudan 210
MONUSCO 2018 Tshikapa -6.4232300 20.793987 150 0 DRC 150
UNISFA 2018 Um Khariet 9.7781030 28.637597 150 0 Sudan 150
UNAMID 2018 Umm Barru 15.0500000 23.716667 NA 0 Sudan NaN
UNFICYP 2018 UNPA 35.1585509 33.270104 255 3 Cyprus 255
MONUSCO 2018 Uvira -3.3730550 29.144967 650 0 DRC 539
MONUSCO 2018 Walungu -2.6283333 28.665833 650 0 DRC 483
UNMISS 2018 Wau 7.7028610 27.995300 950 2 South Sudan 980
UNFICYP 2018 Xeros 35.1395200 32.834310 150 0 Cyprus 150
MINUSCA 2018 Yaloke 5.3150660 17.098075 0 0 Central African Republic 0
UNMISS 2018 Yambio 4.5721310 28.395488 150 0 South Sudan 270
UNMISS 2018 Yei 4.0950350 30.677920 150 0 South Sudan 150
UNAMID 2018 Zalingei 12.9095990 23.474061 335 2 Sudan 309

Hold up–the data frame still contains missions that did not take place in Africa. We need to filter this out somehow.

library(countrycode)

AFR_list <- codelist %>% filter(continent %in% "Africa")  %>% select(country.name.en) %>% pull()

GeoPKO2018_AFR <- GeoPKO2018 %>% 
  mutate(country=case_when(country=="DRC" ~ "Congo-Kinshasa",
                           TRUE~ as.character(country))) %>% 
  filter(country %in% AFR_list)

With that set, we can start plotting the deployment locations and their respective sizes for UN peacekeeping missions in Africa in 2018.

library(ggrepel) #to nudge labels nicely away from geom_point
library(viridis) #for pretty colors
library(ggthemes)

ggplot(data=AFR_sf) + geom_sf() + 
  geom_point(data = GeoPKO2018_AFR, aes(x=longitude, y=latitude, 
                                        size=YearlyAverage, color= YearlyAverage), alpha=.7)+
  scale_size_continuous(name="Average Troop Deployment", range=c(1,12), 
                        breaks=c(0, 100, 300, 500, 1000, 2000, 3000, 4000,5000)) +
  scale_color_viridis(option="cividis", 
                      breaks=c(0, 100, 300, 500, 1000, 2000, 3000, 4000,5000), 
                      name="Average Troop Deployment" ) +
  guides( colour = guide_legend()) +
  geom_point(data = GeoPKO2018_AFR %>% filter(hq==3), aes (x=longitude, y=latitude, shape="HQ"),
             fill = "red", size=2, color="red", alpha=.8)+
  scale_shape_manual(values=c(23), labels=c("HQ"="Mission HQ"), name="")+
  geom_label_repel(data = GeoPKO2018_AFR %>% filter(hq==3), aes(x=longitude, y=latitude, label=mission),
                   min.segment.length = 0, 
                   direction="both",
                   label.size = 0.5,
                   box.padding = 2,
                   size = 3, 
                   fill = alpha(c("white"),0.5),
                   shape=16, 
                   size=2) +
  labs(title ="UN Peacekeeping in Africa - 2018", color='Average Troop Deployment') +
  theme(
    text = element_text(color = "#22211d"),
    plot.background = element_rect(fill = "#f5f5f2", color = NA), 
    panel.background = element_rect(fill = "#f5f5f2", color = NA), 
    legend.background = element_rect(fill = "#f5f5f2", color = NA),
    plot.title = element_text(size= 14, hjust=0.01, color = "#4e4d47", margin = margin(b = -0.1, t = 0.8, l = 4, unit = "cm")),
    panel.grid=element_blank(),
    axis.title=element_blank(),
    axis.ticks=element_blank(),
    axis.text=element_blank(),
    legend.key=element_blank()
  )

Here is a similar visualization, but this time the color aesthetic for geom_point is mapped to shown country instead.

p3 <- ggplot(data=AFR_sf) + geom_sf() +
  geom_point(data=GeoPKO2018_AFR, 
             aes(x=longitude, y=latitude, size=YearlyAverage, color=country), alpha=.4, shape=20)+
  geom_point(data=GeoPKO2018_AFR %>% filter(hq==3), 
             aes(x=longitude, y=latitude), 
             color="black", shape=16, size=2) +
  geom_label_repel(data=GeoPKO2018_AFR %>% filter(hq==3),
                   min.segment.length = 0.2,
                   label.size = 0.5,
                   box.padding = 2,
                   size = 3,
                   fill = alpha(c("white"),0.7),
                   aes(x=longitude, y=latitude, label=mission)) +
  labs(title="UN Peacekeeping Deployment and Mission HQs in Africa, 2018")+
  scale_size(range = c(2, 16))+
  labs(size="Average number of troops\n(continuous scale)",color="Country",shape="HQ")+
  theme(
    plot.background = element_rect(fill = "#f5f5f2", color = NA),
    legend.background = element_rect(fill = "#f5f5f2", color = NA),
    plot.title = element_text(size= 14, hjust=0.01, color = "#4e4d47", margin = margin(b = -0.1, t = 0.8, l = 4, unit = "cm")),
    panel.grid=element_blank(),
    axis.title=element_blank(),
    axis.ticks=element_blank(),
    axis.text=element_blank(),
    panel.background=element_blank(),
    legend.key = element_rect(fill = "#f5f5f2", color = NA),
    legend.key.size = unit(1, 'lines')
  )+
  guides(colour=guide_legend(ncol=2,override.aes = list(size=5)),
         size=guide_legend(ncol=2))
p3

How has this changed over the period covered by the dataset? An animated graph is great for this purpose. The first step is to prepare a dataframe, much similar to what has been done above for missions taking place in Africa in 2018. First we would calculate the average number of troops that is deployed to a location per mission per year, for every year between 1994 and 2018.

AFR_GIF <- GeoPKO %>%   
  mutate(country=case_when(country=="DRC" ~ "Congo-Kinshasa",
                           TRUE~ as.character(country))) %>%
  filter(country %in% AFR_list) %>% 
  select(mission, year, location, latitude, longitude, no.troops, hq) %>%
  mutate_at(vars(latitude, longitude), as.numeric) %>% 
  group_by_at(vars(-no.troops)) %>%
  summarise(ave.no.troops = round(mean(as.numeric(no.troops), na.rm=TRUE)))
`summarise()` regrouping output by 'mission', 'year', 'location', 'latitude', 'longitude' (override with `.groups` argument)

The animated graph is built on the above code for static graphics, using the cool package gganimate.

library(gganimate)

# Transforming the "year" variable into a discrete variable.
AFR_GIF$year <- as.factor(AFR_GIF$year)

ggplot(AFR_sf) + geom_sf() + 
  geom_point(data = AFR_GIF, aes(x=longitude, y=latitude, 
                                 size= ave.no.troops, color= ave.no.troops, group=year), alpha=.7)+
  scale_size_continuous(name="Average Troop Deployment", range=c(1,12), 
                        breaks=c(0, 100, 300, 500, 1000, 2000, 3000, 4000,5000)) +
  scale_color_viridis(option="cividis", 
                      breaks=c(0, 100, 300, 500, 1000, 2000, 3000, 4000,5000),
                      name="Average Troop Deployment" ) +
  guides(colour = guide_legend()) +
  theme(
    text = element_text(color = "#22211d"),
    plot.background = element_rect(fill = "#f5f5f2", color = NA), 
    panel.background = element_rect(fill = "#f5f5f2", color = NA), 
    legend.background = element_rect(fill = "#f5f5f2", color = NA),
    plot.title = element_text(size= 14, hjust=0.01, color = "#4e4d47", margin = margin(b = -0.1, t = 0.8, l = 4, unit = "cm")),
    panel.grid=element_blank(),
    axis.text=element_blank(),
    axis.ticks=element_blank(),
    axis.title=element_blank(),
    legend.key=element_blank(),
    plot.caption=element_text(hjust=1, face="italic"))+
  transition_states(states=year, transition_length = 3, state_length=3)+
  labs(title="UN Peacekeeping in Armed Conflicts in Africa: {closest_state}",
       caption="Source: The Geo-PKO dataset v2.0")+
  ease_aes()

#run the following command to save the plot
#anim_save("animatedUNPKO.gif", p4)

With the release of version 2.0, the Geo-PKO dataset has extended its coverage to missions taking place globally between 1994 and 2019. The above examples are but a few ways through which users can explore and exploit this rich dataset.

The following code shows how to make an animate plot with the global data by year, using the package “animate”.

library(tidyr)

#Create a new dataframe keeping all missions and regions
Global_df <- GeoPKO %>% 
  select(mission, year, location, latitude, longitude, no.troops) %>%
  mutate_at(vars(latitude, longitude, no.troops), as.numeric) %>% 
  group_by(mission, year, location) %>%
  mutate(ave.no.troops = as.integer(mean(no.troops, na.rm=TRUE))) %>% 
  select(-no.troops) %>% distinct() %>% drop_na(ave.no.troops) %>% filter(ave.no.troops>0)

#Make the year variable discrete
Global_df$year <- as.factor(Global_df$year)

#Make the plot
ggplot() +     
  borders("world",  xlim = c(-130, 140), ylim = c(-50, 50), colour = "gray85", fill = "gray80") + 
  theme_map()+
  geom_point(data = Global_df, aes(x=longitude, y=latitude, size= ave.no.troops, color= ave.no.troops, group=year), alpha=.7)+
  scale_size_continuous(name="Average Troop Deployment", range=c(1,12), breaks=c(10, 100,300, 500, 1000, 3000, 4500, 6000, 7000)) +
  scale_color_viridis(option="viridis", breaks=c(10, 100, 300, 500, 1000, 3000, 4500, 6000, 7000), name="Average Troop Deployment" ) +
  guides(colour = guide_legend()) + 
  theme(text = element_text(color = "#22211d"),
        legend.position =  c(1,0.2),
        plot.background = element_rect(fill = "#f5f5f2", color = NA), 
        panel.background = element_rect(fill = "#f5f5f2", color = NA), 
        legend.background = element_rect(fill = "#f5f5f2", color = NA),
        legend.key = element_rect(fill = "#f5f5f2", color = NA),
        plot.title = element_text(size= 14, hjust=0.6, color = "#4e4d47", 
                                  margin = margin(b = -0.1, t = 0.8, l = 4, unit = "cm")),
        panel.grid=element_blank(),
        axis.text=element_blank(),
        axis.ticks=element_blank(),
        axis.title=element_blank(),
        plot.margin = margin(0, 4.5, 0, -1, "cm")) +
  transition_states(states = year, transition_length = 3, state_length=3)+
  labs(title="UN Peacekeeping around the world: {closest_state}", 
       color="Average Deployment Size",
       caption="Source: The Geo-PKO dataset v2.0")+
  ease_aes()

#Animate the plot
#animate(animated_global, height = 400, width =800, fps = 4)

##To save the plot run the following line of code
#anim_save("Animated_GeoPKO2.0.gif")

sessionInfo()
R version 4.0.2 (2020-06-22)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19041)

Matrix products: default

locale:
[1] LC_COLLATE=English_Sweden.1252  LC_CTYPE=English_Sweden.1252   
[3] LC_MONETARY=English_Sweden.1252 LC_NUMERIC=C                   
[5] LC_TIME=English_Sweden.1252    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] tidyr_1.1.1             gganimate_1.0.6         ggthemes_4.2.0         
 [4] viridis_0.5.1           viridisLite_0.3.0       ggrepel_0.8.2          
 [7] countrycode_1.2.0       kableExtra_1.1.0        knitr_1.29             
[10] readr_1.3.1             ggplot2_3.3.2           dplyr_1.0.2            
[13] sf_0.9-5                rnaturalearthdata_0.1.0 rnaturalearth_0.1.0    
[16] workflowr_1.6.2        

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.5         lattice_0.20-41    prettyunits_1.1.1  class_7.3-17      
 [5] rprojroot_1.3-2    digest_0.6.25      plyr_1.8.6         R6_2.4.1          
 [9] backports_1.1.7    evaluate_0.14      e1071_1.7-3        httr_1.4.2        
[13] highr_0.8          pillar_1.4.6       progress_1.2.2     rlang_0.4.7       
[17] gifski_0.8.6       rstudioapi_0.11    whisker_0.4        rmarkdown_2.3     
[21] labeling_0.3       webshot_0.5.2      stringr_1.4.0      munsell_0.5.0     
[25] compiler_4.0.2     httpuv_1.5.4       xfun_0.16          pkgconfig_2.0.3   
[29] rgeos_0.5-3        htmltools_0.5.0    tidyselect_1.1.0   tibble_3.0.3      
[33] gridExtra_2.3      crayon_1.3.4       withr_2.2.0        later_1.1.0.1     
[37] grid_4.0.2         gtable_0.3.0       lifecycle_0.2.0    DBI_1.1.0         
[41] git2r_0.27.1       magrittr_1.5       units_0.6-7        scales_1.1.1      
[45] KernSmooth_2.23-17 stringi_1.4.6      farver_2.0.3       fs_1.5.0          
[49] promises_1.1.1     sp_1.4-2           xml2_1.3.2         ellipsis_0.3.1    
[53] generics_0.0.2     vctrs_0.3.2        tools_4.0.2        glue_1.4.1        
[57] tweenr_1.0.1       purrr_0.3.4        maps_3.3.0         hms_0.5.3         
[61] yaml_2.2.1         colorspace_1.4-1   classInt_0.4-3     rvest_0.3.6