Aggregate along theme dimension — theme

Aggregation along the theme dimension uses causal mappings to aggregate from lower to higher levels. This function sets up the data with specific prep for the way the theme dimension works, and then wraps general_aggregate(). Many of the arguments here are just passed through to general aggregate.

Usage

theme_aggregate(
  dat,
  from_theme,
  to_theme,
  groupers,
  aggCols,
  funlist,
  causal_edges,
  geonames = NULL,
  failmissing = TRUE,
  auto_ewr_PU = FALSE,
  ...
)

Arguments

dat: sf or tibble or dataframe of values to aggregate, with necessary grouping information for non-theme axis (e.g. scenario, location). Not necessarily the same as data in general_aggregate()- this function makes necessary adjustments to the data before calling general_aggregate()
from_theme: character, column name of the theme level the data is currently in
to_theme: character, column name of the theme level to aggregate to
groupers: as in general_aggregate(), with the note that these should be all grouping columns except the theme column specified by to_theme and any spatial information. These are both automatically added to groupers before passing to general_aggregate().
aggCols: an expression for the columns to aggregate (the data columns). See selectcreator for formats
funlist: a list of functions and their arguments used to aggregate the data. See functionlister for creation in many cases. The situation with a bare anonymous function, e.g. ~mean(., na.rm = T) is not supported because we need a name. Use a named list if using anonymous functions, e.g. list(mean = ~mean(., na.rm = T)). If using functions with a data-variable argument, e.g. weighted.mean with a column of weights, we now (as of dplyr 1.1) have some workarounds. One option is if specified as a function argument, the function can just go in as a bare name or anonymous. If specified elsewhere, it can be wrapped in rlang::quo(), e.g. agglist <- rlang::quo(list(mean = mean, wm = ~weighted.mean(., weight_column_name, na.rm = T))). If it isn't, there is now an internal workaround to add that on that seems to be stable but may cause unforeseen issues. This workaround also allows building custom aggregation functions (not-anonymous) with the data-variable argument either exposed or hardcoded (see SpatialWeightedMean()). The error checks for names do not work for quosures, so make sure you name the list if using rlang::quo().
causal_edges: the causal links between theme levels. Needs to include from_theme and to_theme, but can also include others. Creates the theme grouping
geonames: additional columns of information to persist through the aggregation. Cannot change with the main groupers. Developed to persist information about polygons through subsequent theme aggregations, but could be more general
failmissing: logical, default TRUE: fail if the requested grouping or aggregation columns not exist. If FALSE, proceed with those that do exist and silently drop those that don't. Similar to tidyselect::all_of() vs tidyselect::any_of() in tidyselect
auto_ewr_PU: logical, default FALSE. If TRUE, automatically infers whether this is an EWR dataset and has not yet been spatially aggregated. If so, applies grouping by 'planning_unit_name' and SWSDLName' since EWRs are defined in those units. The preferred solution is to include it in groupers here (for on-off), or in multi_aggregate() to use group_until. If none of those solutions happen, though, it aborts to prevent incorrect pooling over rows in different planning units and sdl units.
...: passed to general_aggregate()

Value

a dataframe in the same format as read-in (sf or tibble), aggregated to the to_theme level and retaining desired geographic information if included