This repository contains data files and R scripts for the book Compositional Data Analysis in Practice (Michael Greenacre, Chapman & Hall / CRC Press, 2018):
https://www.crcpress.com/Compositional-Data-Analysis-in-Practice/Greenacre/p/book/9781138316430
as well as some other files related to articles on compositional data analysis.
The easyCODA R package accompanies the book and is available on CRAN, presently version 0.34.
The package is still under development and the latest version can always be found on R-Forge, installing as follows from R:
install.packages("easyCODA", repos="http://R-Forge.R-project.org")
The package does include the data sets, but the data files are given here as well in Excel or character format.
ERRATA in "Compositional Data Analysis in Practice":
CDAiP_typos.pdf: For the readers of my book, there is a provisional list of errors that I and others have found since its publication.
CDAiP_typos.rtf: rich text format of the above
DATA SETS for Compositional Data Analysis in Practice:
Vegetables.txt: Vegetables data set, data object 'veg' in easyCODA
TimeBudget.txt: Time budget data set, data object 'time' in easyCODA
RomanCups.xls: Archaeometric data on Roman glass cups, data object 'cups' in easyCODA
FishMorphology.txt: Fish morphometric data, with three additional variables, data object 'fish' in easyCODA
R SCRIPTS for Compositional Data Analysis in Practice:
easyCODA_script.R: file of all the R commands in Appendix C, also with some slight corrections.
PLEASE REPORT ANY BUGS OR DIFFICULTIES WITH THE PACKAGE TO Michael Greenacre at [email protected]
ARTICLE: "A comparison of amalgamation logratio balances and isometric logratio balances in compositional data analysis", by Michael Greenacre, Eric Grunsky and John Bacon-Shone, Computers and Geosciences (2020)
CAGEOscript.R: script related Greenacre, Grunsky & Bacon-Shone (2020)
ARTICLE: "Amalgamations are valid in compositional data analysis, can be used in agglomerative clustering, and their logratios have an inverse transformation", by Michael Greenacre, Applied Computing and Geosciences (2020)
SLRscript.R: script related to article
ARTICLE: "The selection and analysis of fatty acid ratios: A new approach for the univariate and multivariate analysis of fatty acid trophic markers in marine pelagic organisms", by Martin Graeve and Michael Greenacre, Limnology & Oceanography Methods (2020)
amphipod_ratios.R: script related to the article by Graeve & Greenacre (2020)
amphipods.csv: data set for R script above
copepod_ratios.R: script related to the article "The selection and analysis of fatty acid ratios: A new approach for the univariate and multivariate analysis of fatty acid trophic markers in marine pelagic organisms", by Martin Graeve and Michael Greenacre, Limnology & Oceanography Methods (2020)
copepods.csv: data set for R script above
ARTICLE: "Compositional Data Analysis", by Michael Greenacre, Annual Reviews in Statistics and its Application (2021)
ANNUALREVIEWSscript.R: R script for article by Greenacre (2021)
copepods_TL.csv: data set for R script above (same data set as for Graeve & Greenacre (2020), with added variable Total Lipids (TL))
Baxter_OTU_table.txt: microbiome data set for R script above. From Baxter et al. (2016)
Baxter_metadata.txt: metadata that goes with the OTU table. From Baxter et al. (2016)
ARTICLE: "Making the most of expert knowledge to analyse archaeologicaldata: a case study on Parthian and Sasanian glazed pottery", by Jonathan Wood and Michael Greenacre, Archaeological and Anthropological Sciences (2021)
Supplementary material for the article by Wood & Greenacre (2021). Two zip files:
Wood&Greenacre_CSV: data files
Wood&Greenacre_CODE&FUNCTIONS: R code and additional R functions
ARTICLE: "Compositional data analysis of microbiome and any-omics datasets: a validation of the additive logratio transformation", by Michael Greenacre, Marina Martinez-Alvaro and Agustin Blasco, Frontiers in Microbiology (2021)
Frontiers_ALR.R: script for Greenacre et al. (2021)
Frontiers_ALR_supplementary.R: script for supplementary material of Greenacre et al. (2021)
FINDALR.R: function FINDALR to identify optimal ALR reference, without or with weights
Baxter_OTU_table.txt: microbiome data set (Baxter et al., 2016), used in supplementary material of Greenacre et al. (2021)
Baxter_metadata.txt: metadata of above data set (Baxter et al., 2016), used in supplementary material of Greenacre et al. (2021)
Deng_vaginal_microbiome.txt: Vaginal microbiome data (Deng et al., 2018), cited by Wu et al. (2021)
ARTICLE: "Three approaches to supervised learning for compositional data with pairwise logratios", by Germà Coenders and Michael Greenacre (2022)
Coenders&Greenacre_CODE.R: R code for analysis of Crohn data (Crohn data available in R package selbal, as shown in code)
STEPR.R: function for stepwise selection of logratios for GLM models (this function is in the pre-release of easyCODA on RForge)
ARTICLE: "Aitchison's Compositional Data Analysis 40 Years On: A Reappraisal", by Michael Greenacre, Eric Grunsky, John Bacon-Shone, Ionas Erb and Thomas Quinn (2022). There are two versions: the original Version 1, and the new revised Version 2
tellus_Appendix.R: script for reproducing the analysis of the Tellus geochemical data set in Appendix of Version 1
tellus_CoDA_script.R: script for reproducing the analysis of the Tellus geochemical data set in Version 2 (essentially the same as before)
tellus.xrf.a.cation.txt: Tellus cation data set
singlecell_CoDA_script.R: script for reproducing the analysis of the single cell genetic data set in Version 2
SingleCell.RData: R workspace containing all the data files for the single cell application in Section 6 of Version 2