rioja (version 1.0-6)

Merge: Merges two or more data frames on the basis of common column names.

Description

Merges two or more data frames on the basis of common column names.

Usage

Merge(..., join="outer", fill=0, split=FALSE, verbose=TRUE)

Value

If split is set to FALSE the function returns a single data frame with the number of rows equal to the combined rows of the input data and columns sorted alphabetically according to the join type. Otherwise returns a named list of the merged data frames.

Arguments

...

two or more data frames to merge.

join

type of join to perform. Should be an unambiguous abbreviation of either "outer", "inner", or "leftouter". An outer join produces a data frame that contains all the unique column names of the input data, ie, the union of all input column names. An inner join produces a data frame containing only column names that are common across the input data, ie. the intersection of the input column names. A left outer join produces a data frame containing all column names of the first data frame only: column names that occur in subsequent data frames are omitted.

fill

value to use to fill non-matched columns. Defaults to zero which is appropriate for species abundance data.

split

logical to return a single data frame (TRUE) or a named list containing separate (original) data frames with a common set of merged columns (FALSE). Defaults to TRUE (a single data frame).

verbose

logical to suppress warning messages.

Author

Steve Juggins

Details

Merge is a utilty function for combining separate datasets of biological count data that have only a subset of taxa (column names) in common. The outer join is appropriate for merging prior to a joint ordination or for merging a training set and core data prior to environmental reconstruction using the modern analogue technique (MAT). A left outer join should be used to prepare data for an ordination of a training set and subsequent projection of a second onto the ordination axes. The function is capitalised to distinguish it from merge in the base R.

See Also

merge.

Examples

data(RLGH)
data(SWAP)
# Merge RLGH core data with SWAP training set
# Extract species data from datasets
SWAPsp <- SWAP$spec
RLGHsp <- RLGH$spec
# full outer join for joint ordination of both datasets
comb <- Merge(SWAPsp, RLGHsp)

if (FALSE) {
# superimpose core trajectory on ordination plot
library(vegan) # decorana
ord <- decorana(comb, iweigh=1)
par(mfrow=c(1,2))
plot(ord, display="sites")
sc <- scores(ord, display="sites")
sc <- sc[(nrow(SWAPsp)+1):nrow(comb), ]
lines(sc, col="red")
title("Joint DCA ordination of surface and core")

# Do the same but this time project core passively
# Note we cannot use data from the outer join since decorana
# will delete taxa only present in the core - the resulting
# ordination model will then not match the taxa in the core
comb2 <- Merge(SWAPsp, RLGHsp, join="leftouter", split=TRUE)
ord2 <- decorana(comb2$SWAPsp, iweigh=1)
sc2 <- predict(ord2, comb2$RLGHsp, type="sites")
plot(ord2, display="sites")
lines(sc2, col="red")
title("DCA with core added \"passively\"")
}