README.Rmd

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output: github_document
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r, echo = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "README-"
)

```

[![Lifecycle: experimental](https://img.shields.io/badge/lifecycle-experimental-orange.svg)](https://www.tidyverse.org/lifecycle/#experimental) 

## Padrino  <a href='https://padrinoDB.github.io/Padrino'><img src='metadata/pdb_logo.png' align="right" height="139" /></a>


This repo hosts the code used to generate and upload data to the `Padrino` data base, as well as the flat tables that comprise both raw and clean versions of it. Users will find a clean interface between the database and `R` in the [`Rpadrino`](https://github.com/padrinoDB/Rpadrino) package. 

Currently, the data base is a set of 10 text files that consist of ASCII representation of model formulae, the coefficients associated with said models, and the upper/lower bounds of the state variables. Additionally, there are metadata to aid IPM selection, and further information on continuous and discrete environmental variation whenever it is present. 

Since the database itself isn't especially useful without an engine that translates IPM formulae into parse-able code, I strongly suggest using the `Rpadrino` interface to download it, select models, and generate kernels! However, if you're interested in the structure, see the `padrino-database/clean` folder for the tables. 

## Useful Numbers

Current number of unique species, unique publications, and unique `ipm_id`s that are in PADRINO, and have been quality checked for accuracy. Quality checked means that for deterministic models, the asymptotic per-capita growth rate ($\lambda$) is within $\pm 0.03$ of the published point estimate value. For stochastic models, we check for approximately the same stochastic population growth rate ($\lambda_s$), but do not try to replicate the analysis, as this usually requires too many computing resources to be feasible.  

```{r echo = FALSE, message = FALSE}

library(Rpadrino)

pdb <- pdb_download(save = FALSE)

full_tab <-  data.frame(
  Number_of_Species      = length(unique(pdb$Metadata$species_accepted)), 
  Number_of_Publications = length(unique(pdb$Metadata$apa_citation)),
  Number_of_IPM_ids      = nrow(pdb$Metadata)
)

names(full_tab) <- gsub("_", " ", names(full_tab))


knitr::kable(
  full_tab,
  col.names = c("# of Species", "# of Publications", "# of IPM id's")
)

```

## For developers


For now, all commits will go to the `main` branch as this is still so early in development that keeping a separate `devel` branch is pointless. This will change immediately before/after the first major release.

Development of the package `Rpadrino` is taking place over [here](https://github.com/padrinoDB/Rpadrino).

## For Compadrinos

The digitization guide and other help files are located in `metadata/digitization` and on the project's [webpage](https://padrinoDB.github.io/Padrino/). The [pdbDigitUtils](https://github.com/padrinoDB/pdbDigitUtils) package can help detect some problems with freshly digitized models. The digitization guide has more information on how that package works.