index.Rmd

--- 
title: "Tutorial for R microeco package (v0.14.1)"
author: "Chi Liu, Felipe R. P. Mansoldo, Umer Zeeshan Ijaz, Chenhao Li, Yang Cao, Jarrod J. Scott, Yaoming Cui, Alane B. Vermelho, Minjie Yao, Xiangzhen Li"
date: "`r Sys.Date()`"
site: bookdown::bookdown_site
output: bookdown::gitbook
documentclass: book
bibliography: [book.bib, packages.bib]
biblio-style: apalike
link-citations: yes
github-repo: rstudio/bookdown-demo
description: "The tutorial for R microeco, file2meco, meconetcomp and mecodev packages"
---


# Background

　R language [@R-base] and its packages ecosystem are wonderful tools for data analysis.
In community ecology, a series of packages are available for statistical analysis, 
such as vegan [@Jari_vegan_2019], ape [@Paradis_ape_2018] and picante [@Picante_Kembel_2010].
However, with the development of the high-throughput sequencing techniques,
the increasing data amount and complexity of studies make the data mining in microbiome a challenge.
There have been some R packages created specifically for the statistics and visualization of microbiome data, 
such as phyloseq [@Mcmurdie_phyloseq_2013],
microbiome (https://github.com/microbiome/microbiome), microbiomeSeq (http://www.github.com/umerijaz/microbiomeSeq), 
ampvis2 (https://github.com/KasperSkytte/ampvis2), MicrobiomeR(https://github.com/vallenderlab/MicrobiomeR),
theseus [@Price_theseus_2018], rANOMALY [@Theil_rANOMALY_2021], 
tidyMicro [@Carpenter_tidyMicro_2021], microbial (https://github.com/guokai8/microbial), 
amplicon (https://github.com/microbiota/amplicon),
MicrobiotaProcess (https://github.com/YuLab-SMU/MicrobiotaProcess) 
and so on.
In addition, some web tools associated with R language are also useful for microbiome data analysis, 
such as Shiny-phyloseq [@McMurdie_Shiny_2015], MicrobiomeExplorer [@Reeder_MicrobiomeExplorer_2021], 
animalcules [@Zhao_animalcules_2021] and Namco [@Dietrich_Namco_2022].
Even so, researchers still lack a flexible, comprehensive and modularized R package to analyze and manage the data fast and easily.
Based on this background, we created the R microeco package [@Liu_microeco_2021] (https://github.com/ChiLiubio/microeco).
Besides, we also developed the file2meco package (https://github.com/ChiLiubio/file2meco) for the data input from some famous tools easily.