Apply a function to data for the observations for each taxon. This is similar to using obs() with lapply() or sapply().

obj$obs_apply(data, func, simplify = FALSE, value = NULL,
  subset = NULL, recursive = TRUE, ...)
obs_apply(obj, data, func, simplify = FALSE, value = NULL,
  subset = NULL, recursive = TRUE, ...)



The taxmap() object containing taxon information to be queried.


Either the name of something in obj$data that has taxon information or a an external object with taxon information. For tables, there must be a column named "taxon_id" and lists/vectors must be named by taxon ID.


(function) The function to apply.


(logical) If TRUE, convert lists to vectors.


What data to give to the function. This is usually the name of column in a table in obj$data. Any result of all_names(obj) can be used, but it usually only makes sense to use columns in the dataset specified by the data option. By default, the indexes of observation in data are returned.


Taxon IDs, TRUE/FALSE vector, or taxon indexes to use. Default: All taxa in obj will be used. Any variable name that appears in all_names() can be used as if it was a vector on its own.


(logical or numeric) If FALSE, only return the observation assigned to the specified input taxa, not subtaxa. If TRUE, return all the observations of every subtaxa, etc. Positive numbers indicate the number of ranks below the each taxon to get observations for 0 is equivalent to FALSE. Negative numbers are equivalent to TRUE.


Extra arguments are passed to the function.


# Find the average number of legs in each taxon obs_apply(ex_taxmap, "info", mean, value = "n_legs", simplify = TRUE)
#> [1] 3.5 0.0 4.0 2.0
# One way to implement `n_obs` and find the number of observations per taxon obs_apply(ex_taxmap, "info", length, simplify = TRUE)
#> [1] 4 2 1