Notes & Details

Author

Steffi LaZerte

Published

July 4, 2024

This section contains notes and details about the analysis, from design choices to coding explanations. This is most relevant for those who may be working with the code, as opposed to those interpreting the outputs.

Quarto

Typst

In one situation (Range maps), we create a pdf copy of just the range maps by specifing multiple formats in the YAML and using Quarto conditional content.

We specify that in addition to html we also want to use typst, a new typesetting system which can be used instead of LaTeX for creating PDFs. This creates a PDF of just the plots which can be easily distributed via email (while we are waiting on eBird permission to include the range maps semi-openly).

Pipes |>

Here I use pipes, namely the R base pipe |> which allows you to ‘pipe’ the output from the first line as the input to the second line (details).

One handy way of reading code with pipes is to think “and then…” every time you see it.

For example, the following code could be read as:

To make the sp_ebird object (here a data frame) first…

  • select columns from ebirdst_runs, and then…
  • filter the data, and then…
  • join the data to the sp data frame, and then…
  • arrange the data by the english column
sp_ebird <- select(ebirdst_runs, "species_code", "scientific_name") |>
  filter(species_code != "yebsap-example") |> # Remove eBird example species
  right_join(sp, by = c("scientific_name" = "scientific")) |>
  arrange(english)

map() and family

The functions map(), imap(), walk(), etc. are from the purrr package and are ways in which to loop over a list. Using map() is very similar to using lapply() or for() loops (details).

In this workflow, map() is often use to perform a task on each Motus database or project. This is why it often iterates over projects (the list of project ids) or dbs (the list of database connections).

For example, we use dbs <- map(projects, \(x) tagme(x, dir = "Data/01_Raw", update = FALSE)) in the Setup to read each data base connection and put it in the dbs list.

Similarly, in Download Data, we use walk(dbs, metadata) to ‘walk’ through the list of databases and add the full metadata to each one.

Occasionally we also use the furrr package and its future_map() function(s) to run map() and family in parallel to maximize the speed of a computation.

Spatial

st_make_valid()

In manipulation of spatial data sets, they occasionally become invalid and return errors on future manipulations. st_make_valid() is a function which fixes these errors and will occasionally be found in the middle of a pipe for this reason.

Databases

Avoiding anti_join()

  • This is pretty slow for SQLite databases
  • So we’ll use a left join plus filter() instead (if joining on Databases) (but remember that may have to filter to keep missing values if appropriate)
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