Last updated: 2020-07-08

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: b3d8755

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 b3d8755. 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:  figure/index.Rmd/animatedgraph-1.gif

Unstaged changes:
    Modified:   animatedUNPKO.gif

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/index.Rmd) and HTML (docs/index.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 b3d8755 Nguyen Ha 2020-07-08 Adjusting graph output size
html 884b86f Nguyen Ha 2020-07-08 Build site.
Rmd e863725 Nguyen Ha 2020-07-08 building site with Tanushree’s additions
Rmd 8fe33fc GitHub 2020-07-08 Merge branch ‘master’ into hey
html 325ac4f Nguyen Ha 2020-07-08 Build site.
Rmd f5bc390 Nguyen Ha 2020-07-08 Updating the stupid animated graph hope it will render
html 6bcd405 Nguyen Ha 2020-07-08 Build site.
Rmd 176eca5 Nguyen Ha 2020-07-08 wflow_publish(“analysis/index.Rmd”)
Rmd ebb000d Nguyen Ha 2020-07-08 Animation added to index.rmd, waiting to be rendered
html ebb000d Nguyen Ha 2020-07-08 Animation added to index.rmd, waiting to be rendered
Rmd 5edc140 Tanushree Rao 2020-07-07 new map
html 819ccf2 Nguyen Ha 2020-07-03 Build site.
Rmd 9c0642e Nguyen Ha 2020-07-03 wflow_publish(“analysis/index.Rmd”)
Rmd 0d5cc47 Nguyen Ha 2020-07-03 Fixed conflict with main fol.
Rmd 4cf53c8 Nguyen Ha 2020-07-03 Fixing legend position; previous tables
Rmd 14ab237 GitHub 2020-07-03 Update index.Rmd
Rmd 557ebbf Lou van Roozendaal 2020-07-03 Lou_change_graph
html 3bde5f5 Nguyen Ha 2020-07-02 Build site.
Rmd 8b39fe5 Nguyen Ha 2020-07-02 wflow_publish(c(“index.Rmd”, “about.Rmd”))
html 8b39fe5 Nguyen Ha 2020-07-02 wflow_publish(c(“index.Rmd”, “about.Rmd”))
html 01d2b98 hatnguyen267 2020-06-29 Build site.
Rmd 0a3c9c9 hatnguyen267 2020-06-29 more to index.rmb
html 50d7b31 hatnguyen267 2020-06-29 Build site.
Rmd 4db5b4f hatnguyen267 2020-06-29 adding items to index.rmb
html 4d2d90f hatnguyen267 2020-06-29 Build site.
Rmd a7817f7 hatnguyen267 2020-06-29 wflow_publish(all = TRUE)
Rmd 8e7fc45 hatnguyen267 2020-06-29 Start workflowr project.

This document contains a series of steps that the project members have performed to extract meaningful information the Geo-PKO dataset. More details on the dataset, as well as the version used here, can be found on its homepage.

Setting up

Load packages.

library(tidyverse)
library(readr)
library(ggthemes)
library(knitr)
library(kableExtra)

Import the dataset.

GeoPKO <- read_csv("data/geopko.csv")

An overview

Let’s have a quick look at the first few rows of the dataset.

kable(GeoPKO[1:5,]) %>% kable_styling() %>%
  scroll_box(width = "100%", height = "200px")
Source Mission year month location country latitude longitude No.troops RPF RPF_No RES RES_No FP FP_No No.TCC name.of.TCC1 No.troops.per.TCC1 name.of.TCC2 No.troops.per.TCC2 name.of.TCC3 No.troops.per.TCC3 name.of.TCC4 No.troops.per.TCC4 name.of.TCC5 No.troops.per.TCC5 name.of.TCC6 No.troops.per.TCC6 name.of.TCC7 No.troops.per.TCC7 name.of.TCC8 No.troops.per.TCC8 name.of.TCC9 No.troops.per.TCC9 name.of.TCC10 No.troops.per.TCC10 name.of.TCC11 No.troops.per.TCC11 name.of.TCC12 No.troops.per.TCC12 name.of.TCC13 No.troops.per.TCC13 name.of.TCC14 No.troops.per.TCC14 UNPOL..dummy. UNMO..dummy. HQ LO comments cow_code gwno name TCC1 TCC2 TCC3 TCC4 TCC5 TCC6 TCC7 TCC8 TCC9 TCC10 TCC11 TCC12 TCC13 TCC14 ADM1_id ADM1_name ADM2_id ADM2_name PRIOID
Map no. 4309 BINUB 2007 3 Bujumbura Burundi -3.382200 29.364400 0 NA NA 0 0 0 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0 0 3 0 NA 516 516 Burundi NA NA NA NA NA NA NA NA NA NA NA NA NA NA 3348 Bujumbura Mairie 20147 Roherero 124979
Map no. 4309 Rev. 1 BINUB 2009 8 Bujumbura Burundi -3.382200 29.364400 0 NA NA 0 0 0 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0 0 3 0 NA 516 516 Burundi NA NA NA NA NA NA NA NA NA NA NA NA NA NA 3348 Bujumbura Mairie 20147 Roherero 124979
Map no. 4203 Rev. 2 MINUCI 2003 8 Abidjan Ivory Coast 5.309657 -4.012656 0 NA NA 0 0 0 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0 0 3 0 MINUCI HQ; Also ECOWAS main HQ; France HQ 437 437 Cote D’Ivoire NA NA NA NA NA NA NA NA NA NA NA NA NA NA 3411 Lagunes 40389 Abidjan 137152
Map no. 4203 Rev. 3 MINUCI 2003 11 Abidjan Ivory Coast 5.309657 -4.012656 0 NA NA 0 0 0 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0 0 3 0 MINUCI HQ; Also ECOWAS main HQ; France HQ; Fanci HQ 437 437 Cote D’Ivoire NA NA NA NA NA NA NA NA NA NA NA NA NA NA 3411 Lagunes 40389 Abidjan 137152
Map no. 4203 Rev. 4 MINUCI 2004 1 Abidjan Ivory Coast 5.309657 -4.012656 0 NA NA 0 0 0 0 0 NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA 0 0 3 0 MINUCI HQ; ECOWAS main HQ; France HQ; Fanci HQ 437 437 Cote D’Ivoire NA NA NA NA NA NA NA NA NA NA NA NA NA NA 3411 Lagunes 40389 Abidjan 137152

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

Visualizing deployment locations

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 coercion
map2018df <- GeoPKO %>% filter(year==2018) %>% 
  select(Mission, year, location, latitude, longitude, No.troops, HQ, country)
map2018df1 <- map2018df %>% group_by(location, Mission) %>% 
  mutate(ave = mean(No.troops, na.rm=TRUE)) %>% distinct()
kable(map2018df1[90:95,], caption = "A preview of this dataframe") %>% kable_styling()
A preview of this dataframe
Mission year location latitude longitude No.troops HQ country ave
MONUSCO 2018 Butembo 0.114283 29.30141 150 0 DRC 150
MONUSCO 2018 Kinshasa -4.329722 15.31500 1250 3 DRC 1190
MONUSCO 2018 Tshikapa -6.423230 20.79399 150 0 DRC 150
MONUSCO 2018 Kalemie -5.903344 29.19230 800 0 DRC 680
MONUSCO 2018 Dungu 3.616667 28.56667 300 0 DRC 820
MONUSCO 2018 Bunia 1.562500 30.24842 900 2 DRC 1150

Next, we obtain the geometric shapes from rnaturalearth, and filter for countries in Africa.

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

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

p2 <-  ggplot(data=Africa) + geom_sf() + 
  geom_point(data = map2018df1, aes(x=longitude, y=latitude, size= ave, color= ave), 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 = map2018df1 %>% filter(HQ==3), aes (x=longitude, y=latitude), color = "red", shape = 4, size=7)+
  geom_label_repel(data = map2018df1 %>% filter(HQ==3), aes(x=longitude, y=latitude, label=Mission)) +
  labs (title ="UN Peacekeeping Deployment in Africa - 2018 (approx.)", 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()
  )
p2

Here is the same map, but this time the points show both troop size and country.

p3 <- ggplot(data=Africa) + geom_sf() +
    geom_point(data=map2018df1, 
                aes(x=longitude, y=latitude, size=ave, color=country), alpha=.4)+
    geom_point(data=map2018df1 %>% filter(HQ==3), 
                aes(x=longitude, y=latitude), color="black", shape=16, size=2)+
    geom_label_repel(data=map2018df1 %>% filter(HQ==3), 
                aes(x=longitude, y=latitude, label=Mission))+
    labs(title="UN Peacekeeping Deployment in Africa - 2018")+
    scale_size(range = c(1, 12))+
    labs(size="Average number of troops \n(continuous scale)",col="Country")+
    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(),
    legend.position="right",
    legend.box="horizontal"
  )
p3

How has this changed over the period covered by the dataset? For that, we attempted to make an animated graph. The first step is to prepare a dataframe, much similar to what has been done above for 2018. First we would calculate the average number of troops that is deployed to a location per mission per year.

gif_df <- GeoPKO %>% select(Mission, year, location, latitude, longitude, No.troops, HQ) %>%
  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)

Next, we add animation to the above map using the package gganimate.

library(gganimate)

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

ggplot(data=Africa) + geom_sf() + 
  geom_point(data = gif_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(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())+
  transition_states(states=year, transition_length = 3, state_length=3)+
  labs(title="UN Peacekeeping in Africa: {closest_state}", 
       color="Average Deployment Size")+
  enter_fade()

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

sessionInfo()
R version 3.5.2 (2018-12-20)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 18362)

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] gganimate_1.0.6         viridis_0.5.1           viridisLite_0.3.0      
 [4] ggrepel_0.8.2           sf_0.9-4                rnaturalearthdata_0.1.0
 [7] rnaturalearth_0.1.0     kableExtra_1.1.0        knitr_1.29.3           
[10] ggthemes_4.2.0          forcats_0.5.0           stringr_1.4.0          
[13] dplyr_0.8.3             purrr_0.3.4             readr_1.3.1            
[16] tidyr_1.0.0             tibble_3.0.1            ggplot2_3.3.1          
[19] tidyverse_1.3.0         workflowr_1.6.2        

loaded via a namespace (and not attached):
 [1] nlme_3.1-137       fs_1.4.1           lubridate_1.7.8    webshot_0.5.2     
 [5] progress_1.2.2     httr_1.4.1         rprojroot_1.3-2    tools_3.5.2       
 [9] backports_1.1.7    rgdal_1.4-8        R6_2.4.1           KernSmooth_2.23-15
[13] rgeos_0.5-2        DBI_1.1.0          colorspace_1.4-1   withr_2.2.0       
[17] sp_1.4-2           tidyselect_0.2.5   gridExtra_2.3      prettyunits_1.1.1 
[21] compiler_3.5.2     git2r_0.27.1       cli_2.0.2          rvest_0.3.5       
[25] xml2_1.3.2         labeling_0.3       scales_1.1.1       classInt_0.4-3    
[29] digest_0.6.25      rmarkdown_1.18     pkgconfig_2.0.3    htmltools_0.4.0   
[33] dbplyr_1.4.2       highr_0.8          rlang_0.4.6        readxl_1.3.1      
[37] rstudioapi_0.11    farver_2.0.3       generics_0.0.2     jsonlite_1.6.1    
[41] magrittr_1.5       Rcpp_1.0.4.6       munsell_0.5.0      fansi_0.4.1       
[45] lifecycle_0.2.0    stringi_1.4.6      whisker_0.4        yaml_2.2.1        
[49] plyr_1.8.6         grid_3.5.2         promises_1.1.0     crayon_1.3.4      
[53] lattice_0.20-38    haven_2.2.0        hms_0.5.3          pillar_1.4.4      
[57] reprex_0.3.0       glue_1.4.1         evaluate_0.14      gifski_0.8.6      
[61] modelr_0.1.5       vctrs_0.3.1        tweenr_1.0.1       httpuv_1.5.2      
[65] cellranger_1.1.0   gtable_0.3.0       assertthat_0.2.1   xfun_0.15         
[69] broom_0.5.6        e1071_1.7-3        later_1.0.0        class_7.3-14      
[73] units_0.6-6        ellipsis_0.3.0