icebreaker

The goal of icebreaker is to practice using geotargets to answer questions and use real data.

In this case, thanks to the gracrious assstance of @mdsumner, we explore how sea ice near Casey Station in Antarctica changes over time.

We used geotargets and dynamic branching to cleverly read in many rasters and not overload local memory.

As a first exploration, we produced this gif:

sea ice summaries

library(targets)
tar_source()
source("packages.R")
#> 
#> Attaching package: 'arrow'
#> The following object is masked from 'package:utils':
#> 
#>     timestamp
#> terra 1.8.70
tar_load(ice_summary)
tar_load(plotted_ice_summaries)
date_summary <- summary(ice_summary$time)
date_summary
#>                  Min.               1st Qu.                Median 
#> "2024-08-21 00:00:00" "2024-11-20 00:00:00" "2025-02-23 00:00:00" 
#>                  Mean               3rd Qu.                  Max. 
#> "2025-02-21 14:04:16" "2025-05-25 00:00:00" "2025-08-24 00:00:00"

We can get the aggregate of ice thickness by microwave - we can see the dates from 2024-08-21 to 2025-08-24.

$ \text{r} \text{ice\_summary} #> # \text{A} \text{tibble}: 365 \times 6 #> \text{time} \text{min} \text{q1} \text{median} \text{q3} \text{max} #> <\text{dttm}> <\text{dbl}> <\text{dbl}> <\text{dbl}> <\text{dbl}> <\text{dbl}> #> 1 2024-08-21 00:00:00 1 64 89 98 100 #> 2 2024-08-22 00:00:00 1 79 94 99 100 #> 3 2024-08-23 00:00:00 1 84 95 99 100 #> 4 2024-08-24 00:00:00 1 93 98 100 100 #> 5 2024-08-25 00:00:00 1 77 94 99 100 #> 6 2024-08-26 00:00:00 1 68 90 99 100 #> 7 2024-08-27 00:00:00 1 55 82 94 100 #> 8 2024-08-28 00:00:00 1 75 93 98 100 #> 9 2024-08-29 00:00:00 1 78 94 99 100 #> 10 2024-08-30 00:00:00 1 57 85 95 100 #> # ℹ 355 \text{more} \text{rows} $

We can also plot the median sea ice thickness

plotted_ice_summaries

And even have this as a POLAR CO-ORDINATE system!

plotted_ice_summaries + coord_polar()