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)

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.

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.

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.

Examples

Run this code

# NOT RUN {
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)

# }
# NOT RUN {
# 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\"")
# }