Chapter 1 Background

　R language (R Core Team 2016) 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 (Oksanen et al. 2019), ape (Paradis and Schliep 2018) and picante (Kembel et al. 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 and Holmes 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 et al. 2018), rANOMALY (Theil and Rifa 2021), tidyMicro (Carpenter et al. 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 and Holmes 2015), MicrobiomeExplorer (Reeder et al. 2021), animalcules (Zhao et al. 2021) and Namco (Dietrich et al. 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 (C. Liu et al. 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.

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