A function to apply palaeoverse functionality across subsets (groups) of
data, delineated using one or more variables. Functions which receive a
data.frame as input (e.g. nrow, ncol, lengths, unique) may also be
used.
Arguments
- occdf
dataframe. A dataframe of fossil occurrences or taxa, as relevant to the desired function. This dataframe must contain the grouping variables and the necessary variables for the function you wish to call (see function-specific documentation for required columns).- group
character. A vector of column names, specifying the desired subgroups (e.g. "collection_no", "stage_bin"). Supplying more than one grouping variable will produce an output containing subgroups for each unique combination of values.- fun
function. The function you wish to apply tooccdf. See details for compatible functions.- ...
Additional arguments available in the called function. These arguments may be required for function arguments without default values, or if you wish to overwrite the default argument value (see examples).
Value
A data.frame of the outputs from the selected function, with
appended column(s) indicating the user-defined groups. If a single vector
is returned via the called function, it will be transformed to a
data.frame with the column name equal to the input function.
Details
group_apply applies functions to subgroups of data within a
supplied dataset, enabling the separate analysis of occurrences or taxa from
different time intervals, spatial regions, or trait values. The function
serves as a wrapper around palaeoverse functions. Other functions which
can be applied to a data.frame (e.g. nrow, ncol, lengths,
unique) may also be used.
All palaeoverse functions which require a dataframe input can be used in
conjunction with the group_apply function. However, this is unnecessary
for many functions (e.g. bin_time) as groups do not need to
be partitioned before binning. This list provides
users with palaeoverse functions that might be interesting to apply across
group(s):
tax_unique: return the number of unique taxa per grouping variable.tax_range_time: return the temporal range of taxa per grouping variable.tax_range_space: return the geographic range of taxa per grouping variable.tax_check: return potential spelling variations of the same taxon per grouping variable. Note:verboseneeds to be set to FALSE.
Examples
# Examples
# Get tetrapods data
occdf <- tetrapods[1:100, ]
# Remove NA data
occdf <- subset(occdf, !is.na(genus))
# Count number of occurrences from each country
ex1 <- group_apply(occdf = occdf, group = "cc", fun = nrow)
# Unique genera per collection with group_apply and input arguments
ex2 <- group_apply(occdf = occdf,
group = c("collection_no"),
fun = tax_unique,
genus = "genus",
family = "family",
order = "order",
class = "class",
resolution = "genus")
# Use multiple variables (number of occurrences per collection and formation)
ex3 <- group_apply(occdf = occdf,
group = c("collection_no", "formation"),
fun = nrow)
# Compute counts of occurrences per latitudinal bin
# Set up lat bins
bins <- lat_bins_degrees()
# bin occurrences
occdf <- bin_lat(occdf = occdf, bins = bins)
# Calculate number of occurrences per bin
ex4 <- group_apply(occdf = occdf, group = "lat_bin", fun = nrow)