Analysis
Last updated: 2020-07-02
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Knit directory: GeoPKO/
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This document contains a series of steps that the project members have performed to explore the Geo-PKO dataset. More information on the dataset can be found on its homepage.
Load packages.
library(tidyverse)
library(readr)
library(ggthemes)
library(knitr)Import the dataset.
GeoPKO <- read_csv("data/geopko.csv")Now we can start with the actual fun stuff! Let's have a quick look at the first few rows of the dataset.
head(GeoPKO)# A tibble: 6 x 71
Source Mission year month location country latitude longitude No.troops RPF
<chr> <chr> <dbl> <dbl> <chr> <chr> <dbl> <dbl> <chr> <lgl>
1 Map n~ BINUB 2007 3 Bujumbu~ Burundi -3.38 29.4 0 NA
2 Map n~ BINUB 2009 8 Bujumbu~ Burundi -3.38 29.4 0 NA
3 Map n~ MINUCI 2003 8 Abidjan Ivory ~ 5.31 -4.01 0 NA
4 Map n~ MINUCI 2003 11 Abidjan Ivory ~ 5.31 -4.01 0 NA
5 Map n~ MINUCI 2004 1 Abidjan Ivory ~ 5.31 -4.01 0 NA
6 Map n~ MINURCA 1998 6 Bangui Centra~ 4.38 18.6 1385 NA
# ... with 61 more variables: RPF_No <lgl>, RES <dbl>, RES_No <dbl>, FP <dbl>,
# FP_No <dbl>, No.TCC <chr>, name.of.TCC1 <chr>, No.troops.per.TCC1 <chr>,
# name.of.TCC2 <chr>, No.troops.per.TCC2 <chr>, name.of.TCC3 <chr>,
# No.troops.per.TCC3 <dbl>, name.of.TCC4 <chr>, No.troops.per.TCC4 <dbl>,
# name.of.TCC5 <chr>, No.troops.per.TCC5 <dbl>, name.of.TCC6 <chr>,
# No.troops.per.TCC6 <dbl>, name.of.TCC7 <chr>, No.troops.per.TCC7 <dbl>,
# name.of.TCC8 <chr>, No.troops.per.TCC8 <dbl>, name.of.TCC9 <chr>,
# No.troops.per.TCC9 <dbl>, name.of.TCC10 <lgl>, No.troops.per.TCC10 <lgl>,
# name.of.TCC11 <lgl>, No.troops.per.TCC11 <lgl>, name.of.TCC12 <lgl>,
# No.troops.per.TCC12 <lgl>, name.of.TCC13 <lgl>, No.troops.per.TCC13 <lgl>,
# name.of.TCC14 <lgl>, No.troops.per.TCC14 <lgl>, UNPOL..dummy. <dbl>,
# UNMO..dummy. <dbl>, HQ <dbl>, LO <dbl>, comments <chr>, cow_code <dbl>,
# gwno <dbl>, name <chr>, TCC1 <dbl>, TCC2 <dbl>, TCC3 <dbl>, TCC4 <dbl>,
# TCC5 <dbl>, TCC6 <dbl>, TCC7 <dbl>, TCC8 <dbl>, TCC9 <dbl>, TCC10 <lgl>,
# TCC11 <lgl>, TCC12 <lgl>, TCC13 <lgl>, TCC14 <lgl>, ADM1_id <dbl>,
# ADM1_name <chr>, ADM2_id <dbl>, ADM2_name <chr>, PRIOID <dbl>The dataset covers UN peacekeeping missions in Africa between 1994 and 2018. We can use the dataset to extract the number of active missions during this period.
NoMission <- GeoPKO %>% select(year, Mission) %>% distinct(year, Mission) %>% count(year)
Plot1 <- ggplot(NoMission, aes(x=(as.numeric(year)), y=n)) + geom_point() + geom_line(size=0.5) +
scale_x_continuous("Year", breaks=seq(1994, 2018, 1))+theme_classic()+
scale_y_continuous("Number of missions", breaks=seq(0,10,1)) +
theme(panel.grid=element_blank(),
axis.text.x=element_text(angle=45, vjust=0.5))
Plot1
We want to have a quick snapshot of the deployment size in 2018, as well as the missions that were active in that year. We start by subsetting the main dataset to include entries for the year of 2018 and our variables of interests. GeoPKO 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.
GeoPKO$No.troops <- as.numeric(GeoPKO$No.troops)Warning: NAs introduced by coercionmap2018df <- GeoPKO %>% filter(year==2018) %>%
select(Mission, month, location, latitude, longitude, No.troops, HQ)
map2018df1 <- map2018df %>% group_by(location, latitude, longitude, Mission, HQ) %>%
summarize(ave = mean(No.troops, na.rm=TRUE)) %>% ungroup()
kable(map2018df1[1:5,], caption = "A preview of this dataframe")| location | latitude | longitude | Mission | HQ | ave |
|---|---|---|---|---|---|
| Abyei Camp | 9.627095 | 28.43450 | UNISFA | 3 | 1170.00 |
| Adikivu | -2.325729 | 28.81233 | MONUSCO | 0 | 568.75 |
| Adikivu | -2.325729 | 28.81233 | MONUSCO | 2 | 1250.00 |
| Agany Toak | 9.527483 | 28.43431 | UNISFA | 0 | 35.00 |
| Agok | 9.357486 | 28.58258 | UNISFA | 0 | 91.00 |
Next, we obtain the geometric shapes from rnaturalearth, and filter for countries in Africa.
library(rnaturalearth)Warning: package 'rnaturalearth' was built under R version 3.5.3library(rnaturalearthdata)Warning: package 'rnaturalearthdata' was built under R version 3.5.3library(sf)Warning: package 'sf' was built under R version 3.5.3Linking to GEOS 3.6.1, GDAL 2.2.3, PROJ 4.9.3world <- ne_countries(scale = "medium", returnclass = "sf")
Africa <- world %>% filter(region_un == "Africa")Finally, we plot our data onto the map.
library(ggrepel)Warning: package 'ggrepel' was built under R version 3.5.3p2 <- ggplot(data=Africa) + geom_sf() +
geom_point(data=map2018df1, aes(x=longitude, y=latitude, size=ave, color=ave), alpha=.7)+
scale_color_continuous()+
geom_point(data=map2018df1 %>% filter(HQ==3), aes(x=longitude, y=latitude),
color="red", shape=4, size=10)+
geom_label_repel(data=map2018df1 %>% filter(HQ==3), aes(x=longitude, y=latitude, label=Mission))+
labs(title="UN Peacekeeping Deployment in Africa - 2018 (approx.)")+
theme(panel.grid=element_blank(),
axis.title=element_blank(),
axis.ticks=element_blank(),
axis.text=element_blank(),
panel.background=element_blank())
p2Warning: Removed 2 rows containing missing values (geom_point).
sessionInfo()R version 3.5.0 (2018-04-23)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18363)
Matrix products: default
locale:
[1] LC_COLLATE=English_United States.1252
[2] LC_CTYPE=English_United States.1252
[3] LC_MONETARY=English_United States.1252
[4] LC_NUMERIC=C
[5] LC_TIME=English_United States.1252
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] ggrepel_0.8.1 sf_0.8-1 rnaturalearthdata_0.1.0
[4] rnaturalearth_0.1.0 knitr_1.28 ggthemes_4.2.0
[7] forcats_0.5.0 stringr_1.4.0 dplyr_0.8.4
[10] purrr_0.3.3 readr_1.3.1 tidyr_1.0.2
[13] tibble_2.1.3 ggplot2_3.2.1 tidyverse_1.3.0
loaded via a namespace (and not attached):
[1] Rcpp_1.0.4.6 lubridate_1.7.4 lattice_0.20-35 class_7.3-14
[5] assertthat_0.2.1 rprojroot_1.3-2 digest_0.6.25 utf8_1.1.4
[9] R6_2.4.1 cellranger_1.1.0 backports_1.1.5 reprex_0.3.0
[13] e1071_1.7-3 evaluate_0.14 httr_1.4.1 highr_0.8
[17] pillar_1.4.3 rlang_0.4.5 lazyeval_0.2.2 readxl_1.3.1
[21] rstudioapi_0.11 whisker_0.4 rmarkdown_2.3 labeling_0.3
[25] munsell_0.5.0 broom_0.5.5 compiler_3.5.0 httpuv_1.5.2
[29] modelr_0.1.6 xfun_0.12 pkgconfig_2.0.3 rgeos_0.5-2
[33] htmltools_0.4.0 tidyselect_1.0.0 workflowr_1.6.2 fansi_0.4.1
[37] crayon_1.3.4 dbplyr_1.4.2 withr_2.1.2 later_1.0.0
[41] grid_3.5.0 nlme_3.1-137 jsonlite_1.6.1 gtable_0.3.0
[45] lifecycle_0.1.0 DBI_1.1.0 git2r_0.26.1 magrittr_1.5
[49] units_0.6-5 scales_1.1.0 KernSmooth_2.23-15 cli_2.0.2
[53] stringi_1.4.6 farver_2.0.3 fs_1.3.1 promises_1.1.0
[57] sp_1.4-1 xml2_1.2.2 generics_0.0.2 vctrs_0.2.3
[61] tools_3.5.0 glue_1.4.0 hms_0.5.3 yaml_2.2.1
[65] colorspace_1.4-1 classInt_0.4-2 rvest_0.3.5 haven_2.2.0