This repo scrapes this list 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.
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.
read_csv("data/daily/2022-03-26_oryx_data.csv")
#> Rows: 1798 Columns: 13
#> ── Column specification ────────────────────────────────────────────────────────
#> Delimiter: ","
#> chr (6): equipment_type, cntry_army, flag, system, status, image_link
#> dbl (6): total_equipment_type_oryx, total_system_oryx, total_destroyed_oryx...
#> dttm (1): timestamp
#>
#> ℹ Use `spec()` to retrieve the full column specification for this data.
#> ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
#> # A tibble: 1,798 × 13
#> equipment_type cntry_army flag system status image_link total_equipment…
#> <chr> <chr> <chr> <chr> <chr> <chr> <dbl>
#> 1 Tanks Russia https://… T-64BV destr… https://i… 7
#> 2 Tanks Russia https://… T-64BV destr… https://i… 7
#> 3 Tanks Russia https://… T-64BV destr… https://i… 7
#> 4 Tanks Russia https://… T-64BV destr… https://i… 7
#> 5 Tanks Russia https://… T-64BV damag… https://i… 7
#> 6 Tanks Russia https://… T-64BV captu… https://i… 7
#> 7 Tanks Russia https://… T-64BV captu… https://i… 7
#> 8 Tanks Russia https://… T-72A destr… https://i… 8
#> 9 Tanks Russia https://… T-72A destr… https://i… 8
#> 10 Tanks Russia https://… T-72A captu… https://i… 8
#> # … with 1,788 more rows, and 6 more variables: total_system_oryx <dbl>,
#> # total_destroyed_oryx <dbl>, total_abandoned_oryx <dbl>,
#> # total_captured_oryx <dbl>, total_damaged_oryx <dbl>, timestamp <dttm>The data for dates is kindly provided by @Narretz (click here) 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.
