General Summary: This assignment aims to visualize the changes in the number of prisoners of war (POW) for Ukraine-War 2022. The dataset keeps track of POW count from the dates 2022-02-25 to 2022-04-06. The original dataset used for this assignment comes from Kaggle. Please click the link below for further information regarding the dataset.

Data Source Link (Hold CTRL + Click The Link):
Click Here for Data Source

Import Packages, Import Dataset, & Data Editing

#Import Packages
library(tidyverse)
## -- Attaching packages --------------------------------------- tidyverse 1.3.1 --
## v ggplot2 3.3.5     v purrr   0.3.4
## v tibble  3.1.4     v dplyr   1.0.7
## v tidyr   1.1.4     v stringr 1.4.0
## v readr   2.1.0     v forcats 0.5.1
## -- Conflicts ------------------------------------------ tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()
library(zoo)
## 
## Attaching package: 'zoo'
## The following objects are masked from 'package:base':
## 
##     as.Date, as.Date.numeric
library(janitor)
## 
## Attaching package: 'janitor'
## The following objects are masked from 'package:stats':
## 
##     chisq.test, fisher.test
library(viridis)
## Warning: package 'viridis' was built under R version 4.1.3
## Loading required package: viridisLite
library(ggdark)
## Warning: package 'ggdark' was built under R version 4.1.3
library(knitr)

# Import Packages
df = read.csv("russia_losses_personnel.csv")

#clean variable names
df = clean_names(df)

# Convert dat variable to date format
df$date = as.Date(df$date, format = "%m/%d/%Y")

#Create POW Number Change
df$pow_change = c(0,diff(df$pow))

Graph 1: Ukraine-War POW Count from 2022-02-25 to 2022-04-06

# Cumulative Change
df%>%
  ggplot(aes(date,pow, color = pow))+geom_line(size = 1.1)+geom_point(size = 2)+
  labs(title = "Culminative Change in POW Over Time in the \n Ukraine 2022 War",
       x = "Dates", y = "Prisoners of War Count")+ 
  scale_color_viridis(option = "D")+dark_theme_gray()
## Inverted geom defaults of fill and color/colour.
## To change them back, use invert_geom_defaults().

Graph 2: Ukraine-War POW Count Change from 2022-02-25 to 2022-04-06

#Absolute Change
df%>%
  ggplot(aes(date,pow_change, color = pow_change))+geom_line(size = 1.1)+geom_point(size = 2)+
  labs(title = "Absolute Change in POW Over Time in the \n Ukraine 2022 War",
       x = "Dates", y = "Prisoners of War Count") +
  scale_color_viridis(option = "D")+dark_theme_gray()

Raw Dataset values

kable(df[,-c(3:4)])
date day pow pow_change
2022-02-25 2 0 0
2022-02-26 3 0 0
2022-02-27 4 0 0
2022-02-28 5 0 0
2022-03-01 6 200 200
2022-03-02 7 200 0
2022-03-03 8 200 0
2022-03-04 9 200 0
2022-03-05 10 216 16
2022-03-06 11 232 16
2022-03-07 12 259 27
2022-03-08 13 284 25
2022-03-09 14 360 76
2022-03-10 15 371 11
2022-03-11 16 389 18
2022-03-12 17 389 0
2022-03-13 18 389 0
2022-03-14 19 389 0
2022-03-15 20 389 0
2022-03-16 21 389 0
2022-03-17 22 405 16
2022-03-18 23 405 0
2022-03-19 24 405 0
2022-03-20 25 405 0
2022-03-21 26 405 0
2022-03-22 27 411 6
2022-03-23 28 412 1
2022-03-24 29 412 0
2022-03-25 30 412 0
2022-03-26 31 412 0
2022-03-27 32 421 9
2022-03-28 33 421 0
2022-03-29 34 430 9
2022-03-30 35 430 0
2022-03-31 36 459 29
2022-04-01 37 459 0
2022-04-02 38 460 1
2022-04-03 39 460 0
2022-04-04 40 460 0
2022-04-05 41 467 7
2022-04-06 42 467 0