agroclimatico

Conjunto de funciones para calcular índices y estadísticos climáticos hidrológicos a partir de datos tidy. Incluye una función para graficar resultados georeferenciados y e información cartográfica.

https://github.com/ropensci/agroclimatico

Science Score: 26.0%

This score indicates how likely this project is to be science-related based on various indicators:

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CITATION.cff file
✓
codemeta.json file
Found codemeta.json file
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.zenodo.json file
Found .zenodo.json file
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DOI references
○
Academic publication links
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Committers with academic emails
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (10.4%) to scientific vocabulary

Keywords

agricultura meteorologia

Last synced: 10 months ago · JSON representation

Repository

Basic Info

Host: GitHub
Owner: ropensci
License: other
Language: C++
Default Branch: main
Homepage: https://docs.ropensci.org/agroclimatico
Size: 26.6 MB

Statistics

Stars: 18
Watchers: 3
Forks: 1
Open Issues: 4
Releases: 1

Topics

agricultura meteorologia

Created over 5 years ago · Last pushed about 1 year ago

Metadata Files

Readme Changelog Contributing License Codemeta

README.Rmd

---
output: github_document
---



```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.path = "man/figures/README-",
  out.width = "100%"
)
```

# agroclimatico 



[![R-CMD-check](https://github.com/ropensci/agroclimatico/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/ropensci/agroclimatico/actions/workflows/R-CMD-check.yaml)
[![Codecov test coverage](https://codecov.io/gh/ropensci/agroclimatico/branch/master/graph/badge.svg)](https://codecov.io/gh/ropensci/agroclimatico?branch=master)
[![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/599_status.svg)](https://github.com/ropensci/software-review/issues/599)


El paquete {agroclimatico} incluye una serie de funciones para calcular índices y estadísticos climáticos e hidrológicos a partir de datos [*tidy*](https://es.r4ds.hadley.nz/12-tidy.html). Por ejemplo `umbrales()` permite contar la cantidad de observaciones que cumplen una determinada condición y `dias_promedio()` devuelve el primer y último día del año promedio de ocurrencia de un evento. 

Otras funciones como `spi_indice()` funcionan como wrappers de funciones de otros paquetes y buscan ser compatibles con el manejo de datos tidy usando por ejemplo los verbos de dplyr. 


Finalmente el paquete incluye una función de graficado de datos georeferenciados `mapear()` con el estilo y logo propios de [Instituto Nacional de Tecnología Agropecuaria](https://www.argentina.gob.ar/inta) (INTA). Como complementos el paquete también provee mapas de Argentina a nivel nacional, provincial y por departamentos que se pueden usar en el contexto de ggplot para graficar variables meteorológicas e índices agroclimáticos. 

## Instalación 

Para instalar la versión actual del paquete podés usar: 

```r
install.packages("agroclimatico", repos = "https://ropensci.r-universe.dev")
```
O usando `pak`:

``` r
# install.packages("pak")
pak::pkg_install("ropensci/agroclimatico")
```


## Ejemplos

A continuación se muestra el uso de algunas funciones. Podés encontrar más ejemplos y usos en la viñeta, con 

```r 
vignette("estadisticas-e-indices-climaticos", "agroclimatico")
```

Si se quieren utilizar los datos con formato NH (archivos con columnas de ancho fijo usado por el INTA para distribuir los datos de las estaciones meteorológicas de su red) se puede utilizar la función `leer_nh()` y opcionalmente acceder a sus metadatos con `metadatos_nh()`.

```{r message=FALSE, warning=FALSE}
library(agroclimatico)
library(dplyr)

archivo <- system.file("extdata", "NH0358.DAT", package = "agroclimatico")

datos <- leer_nh(archivo)
```

### Días promedio

Si por ejemplo se quiere obtener el día de la primera y última helada en promedio, asumiendo que la ocurrencia de helada corresponde a temperatura mínima menor a 0°C, se puede utilizar la función `dias_promedio()` en el contexto de `reframe()`.

```{r}
datos %>% 
  filter(t_min <= 0) %>% 
  reframe(dias_promedio(fecha))
```


### Mapear 

La función `mapear()` grafica una variable dada en puntos discretos e irregulares (en general observaciones o datos derivados de estaciones meteorológicas) utilizando kriging. Se puede definir el título y epígrafe y opcionamente se puede incluir la cordillera para enmascarar la variable a graficar.

```{r message=FALSE, warning=FALSE}
abril <- datos_nh_mensual %>%
  filter(mes == unique(mes)[4]) #datos del cuarto mes en la base, abril.

abril %>% 
  mapear(precipitacion_mensual, lon, lat, cordillera = TRUE,
              escala = escala_pp_mensual,
              titulo = "Precipitación en abril de 2019", 
              fuente = "Fuente: INTA",
              variable = "pp")
```




## Cómo contribuir

Para contribuir con este paquete podés leer la siguiente [guía para contribuir](https://github.com/ropensci/agroclimatico/blob/master/.github/CONTRIBUTING.md). Te pedimos también que revises nuestro Código de Conducta.

Owner

Name: rOpenSci
Login: ropensci
Kind: organization
Email: info@ropensci.org
Location: Berkeley, CA

Website: https://ropensci.org/
Twitter: rOpenSci
Repositories: 307
Profile: https://github.com/ropensci

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GitHub Events

Total

Create event: 2
Release event: 1
Issues event: 7
Watch event: 2
Delete event: 1
Issue comment event: 8
Push event: 14
Pull request event: 2

Last Year

Create event: 2
Release event: 1
Issues event: 7
Watch event: 2
Delete event: 1
Issue comment event: 8
Push event: 14
Pull request event: 2

Committers

Last synced: 10 months ago

All Time

Total Commits: 341
Total Committers: 7
Avg Commits per committer: 48.714
Development Distribution Score (DDS): 0.478

Past Year

Commits: 23
Committers: 2
Avg Commits per committer: 11.5
Development Distribution Score (DDS): 0.043

Top Committers

Name	Email	Commits
Pao Corrales	p**s@g**m	178
Elio Campitelli	e**i@g**m	152
Francisco Rodriguez-Sanchez	f**c@g**m	6
Yanina	y**i@g**m	2
Patrice Lecharpentier	p**r@i**r	1
NatiGattinoni	6****i	1
Jeroen Ooms	j**s@g**m	1

Committer Domains (Top 20 + Academic)

inrae.fr: 1

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Last synced: 10 months ago

All Time

Total issues: 51
Total pull requests: 7
Average time to close issues: 5 months
Average time to close pull requests: 20 days
Total issue authors: 7
Total pull request authors: 4
Average comments per issue: 2.9
Average comments per pull request: 0.43
Merged pull requests: 7
Bot issues: 1
Bot pull requests: 0

Past Year

Issues: 4
Pull requests: 2
Average time to close issues: about 1 month
Average time to close pull requests: 27 days
Issue authors: 4
Pull request authors: 1
Average comments per issue: 0.5
Average comments per pull request: 1.0
Merged pull requests: 2
Bot issues: 1
Bot pull requests: 0

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paocorrales (35)
NatiGattinoni (6)
eliocamp (5)
yabellini (2)
adamhsparks (1)
plecharpent (1)
github-actions[bot] (1)

Pull Request Authors

paocorrales (3)
plecharpent (2)
Pakillo (1)
jeroen (1)

Top Labels

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rOpenSci review (18) ready! (11) enhancement (2) help wanted (1) ro-hackathon-2025 (1)

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Dependencies

DESCRIPTION cran

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vdiffr * suggests

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ecosyste.ms

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Open Source Science

agroclimatico

Science Score: 26.0%

Keywords

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Basic Info

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Metadata Files

README.Rmd

Owner

CodeMeta (codemeta.json)

GitHub Events

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