September 08–12, 2025

On Wednesday, the Science Board will be given an update and we will discuss creating a formal FIMS Transition Plan. Thank you Jane for volunteering to attend the presentation to help take notes.
None of the PRs that I wanted to merge in last week happened so merging those is a goal again. Attend Code Club on Tuesday if you are interested in helping write R code to read in the json output.
By the end of the day on Wednesday there will be a prioritized list of issues that need completed before October 01, 2025 because they are needed for the documentation that will be sent to the CIE Reviewers.

FIMS ANNOUNCEMENTS

Please welcome 👋 Adrianne Wilson 👋 to the FIMS Team!
57 calendar days until the FIMS CIE Review, we now have a chair.

UPCOMING EVENTS

Tuesday, json in R

FIMS Code Club Time: 13:00–14:00 E; 10:00–11:00 P; 9:00–10:00 AK; 7:00–8:00 H Location: Virtual Online: Google Meet

Thursday, data and seasonality

FIMS Seaside Chat Time: 13:00–14:00 E; 10:00–11:00 P; 9:00–10:00 AK; 7:00–8:00 H Location: Virtual Online: Google Meet

PHOTO OF THE WEEK

Last week I showed the json output for dimensionality, this week I show the tidyverse code to create a tibble from that same information.

#' Covert the dimension information from a FIMS json output into a tibble
#'
#' Dimensions in the json output are stored as a list of length two, with the
#' header information containing the name of the dimension and the dimensions
#' containing integers specifying the length for each dimension. The result
#' helps interpret how the FIMS output is structured given it is dimension
#' folded into a single vector in the json output.
#'
#' @param data A list containing the header and dimensions information from a
#'   FIMS json output object.
#' @return
#' A tibble containing ordered rows for each combination of the dimensions.
#' @examples
#' dummy_dimensions <- list(
#'   header = list("nyears", "nages"),
#'   dimensions = list(30L, 12L)
#' )
#' dimensions_to_tibble(dummy_dimensions)
dimensions_to_tibble <- function(data) {
  # Create names that are informative for columns b/c nyears is not helpful
  better_names <- unlist(data[["header"]]) |>
    gsub(pattern = "^n(.+)s$", replacement = "\\1_ids")
  names(data[["dimensions"]]) <- better_names
  # Create the returned tibble by first sequencing from 1:n for each dimension
  data[["dimensions"]] |>
    purrr::map(seq) |>
    expand.grid() |>
    tibble::as_tibble() |>
    dplyr::arrange(!!! rlang::syms(better_names))
}