BioTIMEx

Description

This research compendium regroups scripts used to download, re-structure and aggregate data sets to constitute a large meta-analysis of communities in experimental setups sampled several times. The code found here was originally versionned using git and stored on github <>, and was eventually submitted to Zenodo <>. This code accompanies the article: XXXXX.

Reproducibility and R environment

To ensure that the working environment (R version and package version) are documented and isolated, the package renv (https://rstudio.github.io/renv/index.html) was used. By running renv::restore(), renv will install all missing packages at once. This function will use the renv.lock file to download the same versions of packages that we used.

Methods

Data sets were originally searched for among LTER data sets and suitable open access data stored on EPI were selected (https://portal.edirepository.org/nis/home.jsp).

Suitable data sets were individually downloaded from R. Scripts managing these downloads are grouped inside R/data download/. These scripts follow EDI process of data checking and formatting. You can run all these scripts at once by running this command here or from R/1.0_downloading_raw_data.r:

if(!dir.exists('data/raw data/'))   dir.create('data/raw data/')
listF <- list.files('./R/data download', pattern = ".R|.r", full.names = TRUE)
lapply(listF, function(fullPath) source(fullPath, encoding = 'UTF-8', echo = FALSE, local = TRUE))

All downloaded data sets are saved in separate folders named following the convention author_year.

In a second step, each data set is re-structured or wrangled to fit a common format before analysis. The scripts turning the original heterogeneously structured data sets into comparable tables are in the ./R/data wrangling/ folder. You can run all these scripts at once by running this command here or from R/2.0-wrangling_raw_data.r:

if(!dir.exists('data/wrangled data/'))   dir.create('data/wrangled data/')
listF <- list.files('R/data wrangling', pattern = ".R|.r", full.names = TRUE)
lapply(listF, function(fullPath) source(fullPath, encoding = 'UTF-8', echo = FALSE, local = TRUE))

Finally, all restructured tables are aggregated together in a final table by the .R/3.0_merging_long-format_tables.r script. The structure of the end-product table is a long format with each row recording the composition of a community in one place at a given time. Format is described in ./data/template long format.txt and variables are defined.

Analyses

Further analyses were carried at on R too by Shane Blowes and collaborators.