aquadata.data.mapping/README.Rmd at main · WorldFishCenter/aquadata.data.mapping · GitHub

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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",
  out.width = "100%"
)
```

# aquadata.data.mapping

<!-- badges: start -->

[![DOI](https://zenodo.org/badge/584795495.svg)](https://zenodo.org/badge/latestdoi/584795495)

<!-- badges: end -->

![](man/figures/aquadata_lg_sticker.png){width="200"}

The goal of **aquadata.data.mapping** is to collect metadata within the context aquatic food systems in order to identify data gaps and generate new data in key geographic region: Bangladesh, Cambodia, Ghana, India, Myanmar, Nigeria, Solomon Islands, Timor-Leste and Zambia.

![](man/figures/metadata_treemap.png){width="800"}

**aquadata.data.mapping** uses metadata information to download data and use a Shiny interface to elaborate data summaries, evidence-based stories and scientific reports' summaries using AI tools, as OpenAI engines GPT-4 and GPT-3.5. It also provides an interface to upload text files to be processed by AI tools

![](man/figures/upload_interface.png){width="800"}

This repository is part of work package 1 "***AquaData***" of the initiative "***Resilient Aquatic Food Systems for Healthy People and Planet***" (<https://cgspace.cgiar.org/handle/10568/121141> ).

Further info:

<https://www.cgiar.org/initiative/15-resilient-aquatic-foods-for-healthy-people-and-planet/>

## To get started

Install and load the R package from the most recent version using the following commands on the R console:

```{r}
# install.packages("devtools")
# devtools::install_github("WorldFishCenter/aquadata.data.mapping")
library(aquadata.data.mapping)
```

## Explore the data

Once installed, you can access and explore the data directly from the package. `dataverse_metadata` include metadata information on the data of several CGIAR organizations.

```{r}
# Summary of dataverse_metadata
dplyr::glimpse(dataverse_metadata)

# CGIAR organizations
unique(dataverse_metadata$organization)
```

You can filter the data using the `filterby_word` function and show the first 10 rows:

```{r}
filtered_metadata <- filterby_word(word = "Bangladesh")
head(filtered_metadata)
```

## Download Data

Use `get_dataset` and `get_dataset_file` to download data.

## Generate stories using AI

Use AI tools (ChatGPT) to generate stories from downloaded data.

```{r}
# output <-
#  chatgpt_wrapper(
#    document_path = "inst/docs_dataverse/5636634.txt",
#    openaikey = OPENAI_TOKEN,
#    engine = "gpt-3.5-turbo",
#    temperature = 0.7,
#    refine_text = REFINE_TEXT
#  )
# cat(output$output_text)
```