palaeoverse (version 1.4.0)

group_apply: Apply a function over grouping(s) of data

Description

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.

Usage

group_apply(occdf, group, fun, ...)

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.

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 to occdf. 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).

Developer(s)

Lewis A. Jones & William Gearty

Reviewer(s)

Kilian Eichenseer & Bethany Allen

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: verbose needs 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)