The goal of algaeClassify is to facilitate the analysis of taxonomic and functional trait data for phytoplankton.
You can install the released version of algaeClassify from CRAN with:
The development version can be installed from github with:
library(devtools) install_github("vppatil/GEISHA_phytoplankton/package builds/algaeClassify",ref="working")
This is a basic example which shows you how to use algaeClassify to 1) identify anomalies in a time-series of phytoplankton species 2) verify/correct species names using algaebase 3) calculate aggregate abundance at a higher taxonomic level (genus) 4) re-plot species accumulation curves to see if the taxonomic standardization and aggregation to higher taxonomy have resolved the anomalies.
library(algaeClassify) data(lakegeneva) #load a demonstration dataset #view species accumulation curve over duration of dataset to check for anomalies accum(lakegeneva,phyto_name='genus',column='biovol_um3_ml',n=100,datename='date_dd_mm_yy',dateformat='%d-%m-%y') #clean up binomial names and extract genus and species to new columns lakegeneva<-genus_species_extract(lakegeneva,phyto.name='phyto_name') #compare names against accepted taxonomy in algaebase, and extract higher taxonomy lakegeneva.algaebase<-spp_list_algaebase(lakegeneva,long=TRUE,write=FALSE) #merge taxonomic information into the original database lakegeneva<-merge(lakegeneva,lakegeneva.algaebase) #aggregate abundance data to genus level lakegeneva.genus<-phyto_ts_aggregate(lakegeneva,SummaryType='abundance',AbundanceVar='biovol_um3_ml', GroupingVar1='genus') #plot accumulation curve again, but at genus level accum(lakegeneva.genus,phyto_name='genus',column='biovol_um3_ml',n=100,datename='date_dd_mm_yy',dateformat='%Y-%m-%d')
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