Exploring the Geo-PKO dataset
Last updated: 2020-08-17
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Knit directory: GeoPKO/
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| Rmd | 9ec337b | Nguyen Ha | 2020-07-10 | Update |
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| Rmd | b3d8755 | Nguyen Ha | 2020-07-08 | Adjusting graph output size |
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| Rmd | e863725 | Nguyen Ha | 2020-07-08 | building site with Tanushree’s additions |
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| html | 325ac4f | Nguyen Ha | 2020-07-08 | Build site. |
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| 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”)) |
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| html | 01d2b98 | hatnguyen267 | 2020-06-29 | Build site. |
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| 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. 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")| 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