The 'ausplotsR' package functions help as pre-processing the AusPlots data extracted with the 'get_ausplots' function into data frames useful for data analysis. For example, the 'species_table' function generates a 'species occurrence by site' matrix (contained in a data frame) from AusPlots raw data (i.e. from point intercept hits in the 'veg.PI' data frame created by the 'get_ausplots' function).

However, often we want to enrich the data in these derived data frames with additional information. Typically we need to add some information about the sites were the plot data was collected from 'site.info' data frame (automatically created by the 'get_ausplots function'. For example, we would like add to the 'species occurrence by site' data frame information about the IBRA7 bioregion, longitude and latitude of the plot sites. To do so we need to merge the 'species_table' and 'site.info' data frames, using all the variables in the former data frame and the three required variables in the latter data frame in the merge.

The 'merge' function in the 'base' package (i.e. it is provided with initial R installation) merges two data frames by common columns or row names (or other versions of database join operations). We need to provide (at least) the following arguments to the 'merge' function:

Data frames (or objects) to be coerced into one (arguments 'x' and 'y').
Columns used for merging: By default the data frames are merged on the columns with names that they both have (argument 'by'), but separate specifications of the columns can be given by the arguments 'by.x' and 'by.y'.
Columns to merge: We can provide the names of the columns we want to include in the merge between '[]', in a similar fashion to when we access columns in a data frame. To include all the columns in a data frame the 'names' function in the 'base' package can be used. The arguments 'all', 'all.x', 'all.y' can also be used for, in SQL terminology, a 'full outer joint', a 'left outer join', and a 'right outer join' respectively (i.e. returning unmatched rows form both tables, the left table, and the right table respectively).

EXAMPLE

An example merging all the data in a 'species_table' data frame ('AP.data.SppbySites.PcC.PFC') and the variables 'bioregion_name', 'longitude', and 'latitude' in the 'site.info' data frame (in the 'AP.data' list) is presented below. The `AP.data' list of data frames contains information for all the currently available AusPlots sites and was previously created in the 'Obtaining AusPlots data: 'get_ausplots' function' Step-by-Step Guide (we use the list created in Example 4). The 'AP.data.SppbySites.PcC.PFC' contains Species by Site data, scored using 'Percent Cover' as scoring metric and .Prejected Foliage Cover as cover type, which was created in the 'Species-Level data: 'species_table' function' Step-by-Step Guide. Before proceeding with the merging, we create a 'site_unique' variable in the 'species_table' data frame to have a common column for merging. The 'site_unique' variable contains a unique id for each site. The required data to create the this variables is present as 'rownames' in the 'species_table' data frame.

We end the example by creating a new column ('Tot_PcC') containing the Total Percentance Cover from the original values in the 'species_table' data frame and plotting a buble plot representing the Total Percentage Cover in space. To create this plot we use three of the variables we added to the original 'species_table' data frame, 'Tot_PcC', 'longitude', and 'latitude' (the latter two were added in the data frames merge). We use funtions in the 'ggplot2' data visualisation package to generate this plot. More sofisticated graphs of AusPlots data, including graphs of site metrics over a map of Australia, are constructed and displayed in the 'TERN's DSDP 'ECOSYSTEM SURVEILLANCE (AusPlots) TUTORIAL: UNDERSTANDING AND USING THE ‘ausplotsR’ PACKAGE AND AusPlots DATA' tutorial.

Boxes with grey background contain code snippets, and boxes with white background containt code (text) outputs.

# ==================
# Meging data frames
# ==================

# Add the Unique ID ('site_unique') to the Species Occurrence Data Frame
# ----------------------------------------------------------------------
AP.data.SppbySites.PcC.PFC$site_unique = rownames(AP.data.SppbySites.PcC.PFC)
dim(AP.data.SppbySites.PcC.PFC)

## [1]  653 3666

# Merge Data Frames by Unique ID ('site_unique')
# ----------------------------------------------
# Include all Variables in Species Occurrence Data Frame. To do so use 'names' function,
# and 3 variables in the AP.data$site.info Data Frame (bioregion_name.f, longitude, and latitude)
AP.data.SppbySites.PcC.PFC = merge(AP.data.SppbySites.PcC.PFC, AP.data$site.info, 
                                   by="site_unique")[,c(names(AP.data.SppbySites.PcC.PFC), 
                                                     "bioregion_name", "longitude", "latitude")]
dim(AP.data.SppbySites.PcC.PFC)

## [1]  653 3669

tail(names(AP.data.SppbySites.PcC.PFC))

## [1] "Zygophyllum.simile" "Zygophyllum.sp."    "site_unique"       
## [4] "bioregion_name"     "longitude"          "latitude"

# Plot
# ----

# Simple version
#plot(AP.data.SppbySites.PcC.PFC$longitude, AP.data.SppbySites.PcC.PFC$latitude, 
#     xlab="Longitude (degrees)", ylab="Latitude (degrees)")

# Compute variable containing the Total Percent Cover per Site ('Tot_PcC')
# '-4' 'cos we have added: 'site_unique', 'bioregion_name', 'logitude', 'latitude'.
AP.data.SppbySites.PcC.PFC$Tot_PcC = rowSums(AP.data.SppbySites.PcC.PFC[,1:(dim(AP.data.SppbySites.PcC.PFC)[2]-4)])
tail(names(AP.data.SppbySites.PcC.PFC))

## [1] "Zygophyllum.sp." "site_unique"     "bioregion_name"  "longitude"      
## [5] "latitude"        "Tot_PcC"

# Plot using functions in 'ggplot2' package
library(ggplot2)
ggplot(AP.data.SppbySites.PcC.PFC, aes(x=longitude, y=latitude)) + geom_point(aes(size=Tot_PcC), alpha=0.5)

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