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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
library(tidyverse)
```
# Equipment losses in Russia-Ukraine War 2022
This repo scrapes [this list](https://www.oryxspioenkop.com/2022/02/attack-on-europe-documenting-equipment.html) by Oryxspioenkop (daily) to document and visualize equipment losses in the Russia-Ukraine war.
Oryxspioenkop says about this dataset:
> This list only includes destroyed vehicles and equipment of which photo or videographic evidence is available. Therefore, the amount of equipment destroyed is significantly higher than recorded here. Small arms, munitions, civilian vehicles, trailers and derelict equipment (including aircraft) are not included in this list. All possible effort has gone into discerning the status of equipment between captured or abandoned. Many of the entries listed as 'abandoned' will likely end up captured or destroyed. Similarly, some of the captured equipment might be destroyed if it can't be recovered. ATGMs and MANPADS are included in the list but not included in the ultimate count. The Soviet flag is used when the equipment in question was produced prior to 1991.
Note: since this relies on publicly shared data there may also be a bias where losses for Ukraine and Russia are underreported or overreported, respectively. While it may be true that Russia is losing much more equipment than Ukraine, it might be faulty to assume so based on this data alone.
## Main dataset
The main dataset is `data/oryx_data.rds` but there are also daily `.csv` to be found in the `data/daily` subfolder. Simply retrieve the data from the latest available day or from any other timestamp that you would like to analyze.
```{r}
read_csv("data/daily/2022-03-26_oryx_data.csv")
```
## Timestamping Equipment Losses
### OCR
The data for dates is kindly provided by @[Narretz](https://twitter.com/Narretz) (click [here](https://invasion.pages.dev/)) who runs the Oryx source images through OCR in Azure Vision API. Since there could be errors in reporting, I only ever aggregate the data by week (not showing daily counts as that might be misleading). The graphs always *exclude* the latest week, as that one is not finished yet and might give a biased impression.
## Visualizations
### Overall losses by type I
### Overall losses by type II
### Tank losses by status
### Armor losses by status
### Artillery losses by status
### Aircraft losses by status
### Overall losses over time
### Overall losses over time by (some) vehicle types
### Artillery losses over time
### Overall tank losses over time
