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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# MATSS
<!-- badges: start -->
[](https://travis-ci.org/weecology/MATSS)
[](https://raw.githubusercontent.com/weecology/MATSS/master/LICENSE)
[](https://codecov.io/github/weecology/MATSS?branch=master)
[](https://doi.org/10.5281/zenodo.3333008)
<!-- badges: end -->
## Overview
**`MATSS`** is a package for conducting Macroecological Analyses of Time Series Structure. We designed it to help researchers quickly get started in analyses of ecological time series, and to reinforce and spread good practices in computational analyses.
We provide functionality to:
- obtain time series data from ecological communities, processed into a common [data format](weecology.github.io/articles/data-formats.html)
- perform basic processing and summaries of those datasets; see [data processing](weecology.github.io/articles/data-processing.html)
- build an analysis pipeline for macroecological analyses, using the workflow framework of the `drake` package
- package the above data analytical work in a reproducible way in a [research compendium](https://weecology.github.io/MATSS/articles/MATSS.html#template-research-compendium)
## Contributing
For more information about contributing code, datasets, or analyses, please check out the [Contributing Guide](CONTRIBUTING.md).
## Installation
You can install **`MATSS`** from github with:
```{r, eval = FALSE}
# install.packages("remotes")
remotes::install_github("weecology/MATSS", build_opts = c("--no-resave-data", "--no-manual"))
```
**`MATSS`** also uses the **`rdataretriever`** package to download additional datasets. To get this package and its dependencies wokring, we recommend following the online [installation instructions](https://docs.ropensci.org/rdataretriever/).
## Datasets
```{r setup packages, include = FALSE}
library(MATSS)
library(drake)
```
```{r compute info on datasets, include = FALSE}
datasets <- build_datasets_plan(include_retriever_data = TRUE,
include_bbs_data = TRUE,
include_gpdd_data = TRUE,
include_biotime_data = TRUE)
count_ts <- function(data) {NCOL(data$abundance)}
analyses <- drake_plan(num_ts = target(count_ts(data),
transform = map(data = !!rlang::syms(datasets$target))),
total = target(sum(num_ts),
transform = combine(num_ts)))
plan <- bind_plans(datasets, analyses)
make(plan)
```
**`MATSS`** pulls data from a variety of sources, including:
* `r NROW(build_datasets_plan(include_retriever_data = TRUE))` individual datasets that we've added,
* the North American Breeding Bird Survey database (spanning `r NROW(build_bbs_datasets_plan())` separate datasets),
* the Global Population Dynamics Database (spanning `r NROW(build_gpdd_datasets_plan())` separate datasets),
* and the BioTime database (spanning `r NROW(build_biotime_datasets_plan())` separate datasets).
Combined, there are **`r readd(total)`** individual time series across all of these data sources.
## Getting Started
We recommend you take a look at our vignette on [Getting Started](https://weecology.github.io/MATSS/articles/MATSS.html) for more details about how to begin using **`MATSS`**.
If you have the package installed, you can also view the vignette from within R:
```{r, eval = FALSE}
vignette("MATSS")
```
## Example Use Cases
Here are some examples of analyses built on **`MATSS`**:
* [MATSS-LDATS](https://github.com/weecology/MATSS-LDATS) applies the [**`LDATS`**](https://github.com/weecology/LDATS) package to investigate changepoints in community dynamics across the datasets in **`MATSS`**
* [MATSS-Forecasting](https://github.com/weecology/MATSS-forecasting) investigates which properties are associated with the predictability of population time series across the datasets in **`MATSS`**
## Acknowledgments
We thank Erica Christensen and Joan Meiners for their contributions and input on early prototypes of this project. This project would not be possible without the support of Henry Senyondo and the [retriever](https://www.data-retriever.org/) team. Finally, we thank Will Landau and the [drake](https://ropensci.github.io/drake/) team for their input and responsiveness to feedback.
Package development is supported through various funding sources: including the [Gordon and Betty Moore Foundation’s Data-Driven Discovery Initiative](http://www.moore.org/programs/science/data-driven-discovery), [Grant GBMF4563](http://www.moore.org/grants/list/GBMF4563) to E. P. White (supporting also the time of J. Simonis and H. Ye), the [National Science Foundation](http://nsf.gov/), [Grant DEB-1622425](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1622425) to S. K. M. Ernest, and a [National Science Foundation Graduate Research Fellowship](https://www.nsfgrfp.org/)
(No. [DGE-1315138](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1315138)
and [DGE-1842473](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1842473)) to R. Diaz.