group_birds_by_location {gaiah}

Group bird isotope data by locations

Description

This takes as input a data frame of feather isotope data that also has the isoscape predictions attached to it, just like the data frame returned by extract_isopredictions. The data frame must have a column that gives the general location by which you will group birds for the rescaling function. The isoscape predictions by default should be in columns named iso_pred for the actual prediction, and iso_sd for the standard deviation, as produced by extract_isopredictions, but those are user configurable, as well.

Usage

group_birds_by_location(
  D,
  feather_isotope_col,
  location_col,
  iso_pred_col = "iso_pred",
  iso_sd_col = "iso_sd"
)

Arguments

D

the data frame of feather isotope data with the isoscape predictions extracted for each location, as well, and a column giving general grouping locations for the birds.

feather_isotope_col

the string name of the column holding the feather isotope data.

location_col

the string name of the column holding the locations to be used for grouping.

iso_pred_col

name of the column holding the predicted values from the isoscape. Default is iso_pred.

iso_sd_col

name of the column holding the standard deviations of the predicted values from the isoscape. Default is iso_sd_col.

Details

This function returns a data frame with columns for the mean and SD of feather/bird values, (meanH and sdH) and the mean predicted isotope value and the mean sd of the predicted isotope values (meaniso and sdiso) for all the samples within each location. It also returns the Location column itself and a column cnt that gives the number of bird/tissue samples from each location.

This function throws an error if any of the locations has only 1 sample. If that is the case, you may consider merging that sample with another location (or dropping it?).

Examples

# first run the example for extract_isopredictions to get the variable "x"
example("extract_isopredictions")

# If this were run it gives an error because there is only 1 bird at the
# location "Charlevoix"
## Not run: 
group_birds_by_location(x, feather_isotope_col = "Isotope.Value", location_col = "Location")

## End(Not run)


# remove that one bird at Charlevoix and re-run
y <- x %>%
  dplyr::filter(Location != "Charlevoix")

# then group birds by location
gbl <- group_birds_by_location(D = y,
                               feather_isotope_col = "Isotope.Value",
                               location_col = "Location")