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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. Note that the current version of this report is produced using version 1.2.

Setting up

Load packages.

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

We start by importing the dataset. To get a sense of how read_csv would parse the dataset, run spec_csv() on the dataset beforehand.

specs <- spec_csv("data/geopko.csv")
Parsed with column specification:
cols(
  .default = col_double(),
  Source = col_character(),
  Mission = col_character(),
  location = col_character(),
  country = col_character(),
  No.troops = col_character(),
  RPF = col_logical(),
  RPF_No = col_logical(),
  No.TCC = col_character(),
  name.of.TCC1 = col_character(),
  No.troops.per.TCC1 = col_character(),
  name.of.TCC2 = col_character(),
  No.troops.per.TCC2 = col_character(),
  name.of.TCC3 = col_character(),
  name.of.TCC4 = col_character(),
  name.of.TCC5 = col_character(),
  name.of.TCC6 = col_character(),
  name.of.TCC7 = col_character(),
  name.of.TCC8 = col_character(),
  name.of.TCC9 = col_character(),
  name.of.TCC10 = col_logical()
  # ... with 18 more columns
)
See spec(...) for full column specifications.

This shows that R might arbitrarily parse our columns as logical, which may mess up the data. Here, we are going to dodge this issue by telling R to parse all columns as character.

GeoPKO <- read_csv("data/geopko.csv",  
                   col_types = cols(.default="c"))

An overview

Let’s have a quick look at the dataset.

str(GeoPKO)
tibble [12,190 x 71] (S3: spec_tbl_df/tbl_df/tbl/data.frame)
 $ Source             : chr [1:12190] "Map no. 4309" "Map no. 4309 Rev. 1" "Map no. 4203 Rev. 2" "Map no. 4203 Rev. 3" ...
 $ Mission            : chr [1:12190] "BINUB" "BINUB" "MINUCI" "MINUCI" ...
 $ year               : chr [1:12190] "2007" "2009" "2003" "2003" ...
 $ month              : chr [1:12190] "3" "8" "8" "11" ...
 $ location           : chr [1:12190] "Bujumbura" "Bujumbura" "Abidjan" "Abidjan" ...
 $ country            : chr [1:12190] "Burundi" "Burundi" "Ivory Coast" "Ivory Coast" ...
 $ latitude           : chr [1:12190] "-3.3822" "-3.3822" "5.309657" "5.309657" ...
 $ longitude          : chr [1:12190] "29.3644" "29.3644" "-4.012656" "-4.012656" ...
 $ No.troops          : chr [1:12190] "0" "0" "0" "0" ...
 $ RPF                : chr [1:12190] NA NA NA NA ...
 $ RPF_No             : chr [1:12190] NA NA NA NA ...
 $ RES                : chr [1:12190] "0" "0" "0" "0" ...
 $ RES_No             : chr [1:12190] "0" "0" "0" "0" ...
 $ FP                 : chr [1:12190] "0" "0" "0" "0" ...
 $ FP_No              : chr [1:12190] "0" "0" "0" "0" ...
 $ No.TCC             : chr [1:12190] "0" "0" "0" "0" ...
 $ name.of.TCC1       : chr [1:12190] NA NA NA NA ...
 $ No.troops.per.TCC1 : chr [1:12190] NA NA NA NA ...
 $ name.of.TCC2       : chr [1:12190] NA NA NA NA ...
 $ No.troops.per.TCC2 : chr [1:12190] NA NA NA NA ...
 $ name.of.TCC3       : chr [1:12190] NA NA NA NA ...
 $ No.troops.per.TCC3 : chr [1:12190] NA NA NA NA ...
 $ name.of.TCC4       : chr [1:12190] NA NA NA NA ...
 $ No.troops.per.TCC4 : chr [1:12190] NA NA NA NA ...
 $ name.of.TCC5       : chr [1:12190] NA NA NA NA ...
 $ No.troops.per.TCC5 : chr [1:12190] NA NA NA NA ...
 $ name.of.TCC6       : chr [1:12190] NA NA NA NA ...
 $ No.troops.per.TCC6 : chr [1:12190] NA NA NA NA ...
 $ name.of.TCC7       : chr [1:12190] NA NA NA NA ...
 $ No.troops.per.TCC7 : chr [1:12190] NA NA NA NA ...
 $ name.of.TCC8       : chr [1:12190] NA NA NA NA ...
 $ No.troops.per.TCC8 : chr [1:12190] NA NA NA NA ...
 $ name.of.TCC9       : chr [1:12190] NA NA NA NA ...
 $ No.troops.per.TCC9 : chr [1:12190] NA NA NA NA ...
 $ name.of.TCC10      : chr [1:12190] NA NA NA NA ...
 $ No.troops.per.TCC10: chr [1:12190] NA NA NA NA ...
 $ name.of.TCC11      : chr [1:12190] NA NA NA NA ...
 $ No.troops.per.TCC11: chr [1:12190] NA NA NA NA ...
 $ name.of.TCC12      : chr [1:12190] NA NA NA NA ...
 $ No.troops.per.TCC12: chr [1:12190] NA NA NA NA ...
 $ name.of.TCC13      : chr [1:12190] NA NA NA NA ...
 $ No.troops.per.TCC13: chr [1:12190] NA NA NA NA ...
 $ name.of.TCC14      : chr [1:12190] NA NA NA NA ...
 $ No.troops.per.TCC14: chr [1:12190] NA NA NA NA ...
 $ UNPOL..dummy.      : chr [1:12190] "0" "0" "0" "0" ...
 $ UNMO..dummy.       : chr [1:12190] "0" "0" "0" "0" ...
 $ HQ                 : chr [1:12190] "3" "3" "3" "3" ...
 $ LO                 : chr [1:12190] "0" "0" "0" "0" ...
 $ comments           : chr [1:12190] NA NA "MINUCI HQ; Also ECOWAS main HQ; France HQ" "MINUCI HQ; Also ECOWAS main HQ; France HQ; Fanci HQ" ...
 $ cow_code           : chr [1:12190] "516" "516" "437" "437" ...
 $ gwno               : chr [1:12190] "516" "516" "437" "437" ...
 $ name               : chr [1:12190] "Burundi" "Burundi" "Cote D’Ivoire" "Cote D’Ivoire" ...
 $ TCC1               : chr [1:12190] NA NA NA NA ...
 $ TCC2               : chr [1:12190] NA NA NA NA ...
 $ TCC3               : chr [1:12190] NA NA NA NA ...
 $ TCC4               : chr [1:12190] NA NA NA NA ...
 $ TCC5               : chr [1:12190] NA NA NA NA ...
 $ TCC6               : chr [1:12190] NA NA NA NA ...
 $ TCC7               : chr [1:12190] NA NA NA NA ...
 $ TCC8               : chr [1:12190] NA NA NA NA ...
 $ TCC9               : chr [1:12190] NA NA NA NA ...
 $ TCC10              : chr [1:12190] NA NA NA NA ...
 $ TCC11              : chr [1:12190] NA NA NA NA ...
 $ TCC12              : chr [1:12190] NA NA NA NA ...
 $ TCC13              : chr [1:12190] NA NA NA NA ...
 $ TCC14              : chr [1:12190] NA NA NA NA ...
 $ ADM1_id            : chr [1:12190] "3348" "3348" "3411" "3411" ...
 $ ADM1_name          : chr [1:12190] "Bujumbura Mairie" "Bujumbura Mairie" "Lagunes" "Lagunes" ...
 $ ADM2_id            : chr [1:12190] "20147" "20147" "40389" "40389" ...
 $ ADM2_name          : chr [1:12190] "Roherero" "Roherero" "Abidjan" "Abidjan" ...
 $ PRIOID             : chr [1:12190] "124979" "124979" "137152" "137152" ...
 - attr(*, "spec")=
  .. cols(
  ..   .default = col_character(),
  ..   Source = col_character(),
  ..   Mission = col_character(),
  ..   year = col_character(),
  ..   month = col_character(),
  ..   location = col_character(),
  ..   country = col_character(),
  ..   latitude = col_character(),
  ..   longitude = col_character(),
  ..   No.troops = col_character(),
  ..   RPF = col_character(),
  ..   RPF_No = col_character(),
  ..   RES = col_character(),
  ..   RES_No = col_character(),
  ..   FP = col_character(),
  ..   FP_No = col_character(),
  ..   No.TCC = col_character(),
  ..   name.of.TCC1 = col_character(),
  ..   No.troops.per.TCC1 = col_character(),
  ..   name.of.TCC2 = col_character(),
  ..   No.troops.per.TCC2 = col_character(),
  ..   name.of.TCC3 = col_character(),
  ..   No.troops.per.TCC3 = col_character(),
  ..   name.of.TCC4 = col_character(),
  ..   No.troops.per.TCC4 = col_character(),
  ..   name.of.TCC5 = col_character(),
  ..   No.troops.per.TCC5 = col_character(),
  ..   name.of.TCC6 = col_character(),
  ..   No.troops.per.TCC6 = col_character(),
  ..   name.of.TCC7 = col_character(),
  ..   No.troops.per.TCC7 = col_character(),
  ..   name.of.TCC8 = col_character(),
  ..   No.troops.per.TCC8 = col_character(),
  ..   name.of.TCC9 = col_character(),
  ..   No.troops.per.TCC9 = col_character(),
  ..   name.of.TCC10 = col_character(),
  ..   No.troops.per.TCC10 = col_character(),
  ..   name.of.TCC11 = col_character(),
  ..   No.troops.per.TCC11 = col_character(),
  ..   name.of.TCC12 = col_character(),
  ..   No.troops.per.TCC12 = col_character(),
  ..   name.of.TCC13 = col_character(),
  ..   No.troops.per.TCC13 = col_character(),
  ..   name.of.TCC14 = col_character(),
  ..   No.troops.per.TCC14 = col_character(),
  ..   UNPOL..dummy. = col_character(),
  ..   UNMO..dummy. = col_character(),
  ..   HQ = col_character(),
  ..   LO = col_character(),
  ..   comments = col_character(),
  ..   cow_code = col_character(),
  ..   gwno = col_character(),
  ..   name = col_character(),
  ..   TCC1 = col_character(),
  ..   TCC2 = col_character(),
  ..   TCC3 = col_character(),
  ..   TCC4 = col_character(),
  ..   TCC5 = col_character(),
  ..   TCC6 = col_character(),
  ..   TCC7 = col_character(),
  ..   TCC8 = col_character(),
  ..   TCC9 = col_character(),
  ..   TCC10 = col_character(),
  ..   TCC11 = col_character(),
  ..   TCC12 = col_character(),
  ..   TCC13 = col_character(),
  ..   TCC14 = col_character(),
  ..   ADM1_id = col_character(),
  ..   ADM1_name = col_character(),
  ..   ADM2_id = col_character(),
  ..   ADM2_name = col_character(),
  ..   PRIOID = col_character()
  .. )
kable(GeoPKO[1:5,]) %>% kable_styling() %>%
  scroll_box(width = "100%", height = "200px") #displaying the first five rows
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 -3.3822 29.3644 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 -3.3822 29.3644 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

Whew, this list was kind of long, but this was because the GeoPKO includes 12,190 rows and 73 columns.

What missions are included?

unique(GeoPKO$Mission)
 [1] "BINUB"      "MINUCI"     "MINURCA"    "MINURCAT"   "MINURSO"   
 [6] "MINUSCA"    "MINUSMA"    "MONUA"      "MONUC"      "MONUSCO"   
[11] "ONUB"       "ONUMOZ"     "UNAMID"     "UNAMIR"     "UNAMSIL"   
[16] "UNAVEM II"  "UNAVEM III" "UNIOSIL"    "UNIPSIL"    "UNISFA"    
[21] "UNMIL"      "UNMIS"      "UNMISS"     "UNOCI"      "UNOMIL"    
[26] "UNOMSIL"    "UNOSOM II" 

In total there are 27 missions in 23 different countries included in the version 1.2 of the dataset.

str(TotalCount <- with(GeoPKO, table(Mission, country)))
 'table' int [1:27, 1:23] 0 0 0 0 75 0 0 0 0 0 ...
 - attr(*, "dimnames")=List of 2
  ..$ Mission: chr [1:27] "BINUB" "MINUCI" "MINURCA" "MINURCAT" ...
  ..$ country: chr [1:23] "Algeria" "Angola" "Burundi" "Central African Republic" ...

The following table shows missions (bottom row) and countries (top row). It can so be derived that 40 missions are active in only 1 country. Only 1 mission is active in 9 countries at the same time.

table(TotalCount. <- rowSums(TotalCount>0))

 1  2  3  4  9 
21  2  2  1  1 

To see what missions are active in more than three countries we can do the following:

rownames(TotalCount)[TotalCount.>3]
[1] "MONUC" "UNMIS"

The dataset covers the period of 1994-2018. Some missions are still ongoing to this day, while others begun before 1994. From when till when are these missions covered?

GeoPKO %>% select(Mission, year, month) %>% 
  mutate(date=zoo::as.yearmon(str_c(year, month, sep="-"))) %>% group_by(Mission) %>%
  summarize(start_date=min(date), end_date=max(date)) %>% arrange(start_date) -> mission.period


kable(mission.period, caption= "Missions arranged by the earliest start date",
      col.names=c("Mission", "Starting point", "End point")) %>% kable_styling() %>%
  scroll_box(width = "100%", height = "300px")
Missions arranged by the earliest start date
Mission Starting point End point
ONUMOZ Feb 1994 Oct 1994
UNOMIL Apr 1994 Sep 1997
UNAMIR May 1994 Nov 1995
UNOSOM II Jun 1994 Jun 1994
UNAVEM II Jul 1994 Jul 1994
MINURSO Mar 1995 Sep 2018
UNAVEM III Mar 1995 Apr 1997
MONUA Aug 1997 Feb 1999
MINURCA Jun 1998 Jan 2000
UNOMSIL Aug 1998 Sep 1999
MONUC Nov 1999 Apr 2010
UNAMSIL Dec 1999 Dec 2005
MINUCI Aug 2003 Jan 2004
UNMIL Dec 2003 Mar 2018
ONUB Mar 2004 Dec 2006
UNOCI May 2004 Jan 2017
UNMIS Jun 2005 Apr 2011
UNIOSIL Mar 2006 Apr 2008
BINUB Mar 2007 Aug 2009
UNAMID Apr 2008 Oct 2018
UNIPSIL Sep 2008 Sep 2008
MINURCAT Dec 2008 Nov 2010
MONUSCO Jul 2010 Dec 2018
UNISFA Oct 2011 Oct 2018
UNMISS Oct 2011 Nov 2018
MINUSMA Mar 2014 Dec 2018
MINUSCA Oct 2014 Oct 2018

One thing to note from the above table: the starting and end points are not necessarily the official start and end dates of the missions. Since data in GeoPKO is collected from deployment maps, these timestamps reflect the publication dates.

We can also extract the numbers of active missions during 1994-2018 and present the results with a simple line plot.

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

Number of troops

Placeholder for average troops by year calculation.

Other queries

Placeholder for other queries related to HQ, UNMO, UNPOL, etc.


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] lubridate_1.7.8  kableExtra_1.1.0 knitr_1.29.3     ggthemes_4.2.0  
 [5] forcats_0.5.0    stringr_1.4.0    dplyr_0.8.3      purrr_0.3.4     
 [9] readr_1.3.1      tidyr_1.0.0      tibble_3.0.1     ggplot2_3.3.2   
[13] tidyverse_1.3.0  workflowr_1.6.2 

loaded via a namespace (and not attached):
 [1] Rcpp_1.0.4.6      lattice_0.20-38   zoo_1.8-8         assertthat_0.2.1 
 [5] rprojroot_1.3-2   digest_0.6.25     R6_2.4.1          cellranger_1.1.0 
 [9] backports_1.1.7   reprex_0.3.0      evaluate_0.14     httr_1.4.1       
[13] highr_0.8         pillar_1.4.4      rlang_0.4.7       readxl_1.3.1     
[17] rstudioapi_0.11   whisker_0.4       rmarkdown_1.18    webshot_0.5.2    
[21] munsell_0.5.0     broom_0.5.6       compiler_3.5.2    httpuv_1.5.2     
[25] modelr_0.1.5      xfun_0.15         pkgconfig_2.0.3   htmltools_0.5.0  
[29] tidyselect_0.2.5  fansi_0.4.1       viridisLite_0.3.0 crayon_1.3.4     
[33] dbplyr_1.4.2      withr_2.2.0       later_1.0.0       grid_3.5.2       
[37] nlme_3.1-137      jsonlite_1.6.1    gtable_0.3.0      lifecycle_0.2.0  
[41] DBI_1.1.0         git2r_0.27.1      magrittr_1.5      scales_1.1.1     
[45] cli_2.0.2         stringi_1.4.6     farver_2.0.3      fs_1.4.1         
[49] promises_1.1.0    xml2_1.3.2        ellipsis_0.3.1    generics_0.0.2   
[53] vctrs_0.3.1       tools_3.5.2       glue_1.4.1        hms_0.5.3        
[57] yaml_2.2.1        colorspace_1.4-1  rvest_0.3.5       haven_2.2.0