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Change","sub_category":"Climate Data Processing and Analysis","monthly_downloads":0,"total_dependent_repos":0,"total_dependent_packages":0,"readme":"---\noutput: github_document\n---\n\n\u003c!-- README.md is generated from README.Rmd. Please edit that file --\u003e\n\n```{r, include = FALSE}\nknitr::opts_chunk$set(\n  collapse = TRUE,\n  comment = \"#\u003e\",\n  fig.path = \"man/figures/README-\",\n  out.width = \"100%\"\n)\n```\n\n# agroclimatico \u003cimg src='man/figures/logo.png' align=\"right\" height=\"139\" /\u003e\n\n\n\u003c!-- badges: start --\u003e\n[![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)\n[![Codecov test coverage](https://codecov.io/gh/ropensci/agroclimatico/branch/master/graph/badge.svg)](https://codecov.io/gh/ropensci/agroclimatico?branch=master)\n[![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/599_status.svg)](https://github.com/ropensci/software-review/issues/599)\n\u003c!-- badges: end --\u003e\n\nEl 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. \n\nOtras 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. \n\n\nFinalmente 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. \n\n## Instalación \n\nPara instalar la versión actual del paquete podés usar: \n\n```r\ninstall.packages(\"agroclimatico\", repos = \"https://ropensci.r-universe.dev\")\n```\nO usando `pak`:\n\n``` r\n# install.packages(\"pak\")\npak::pkg_install(\"ropensci/agroclimatico\")\n```\n\n\n## Ejemplos\n\nA continuación se muestra el uso de algunas funciones. Podés encontrar más ejemplos y usos en la viñeta, con \n\n```r \nvignette(\"estadisticas-e-indices-climaticos\", \"agroclimatico\")\n```\n\nSi 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()`.\n\n```{r message=FALSE, warning=FALSE}\nlibrary(agroclimatico)\nlibrary(dplyr)\n\narchivo \u003c- system.file(\"extdata\", \"NH0358.DAT\", package = \"agroclimatico\")\n\ndatos \u003c- leer_nh(archivo)\n```\n\n### Días promedio\n\nSi 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()`.\n\n```{r}\ndatos %\u003e% \n  filter(t_min \u003c= 0) %\u003e% \n  reframe(dias_promedio(fecha))\n```\n\n\n### Mapear \n\nLa 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.\n\n```{r message=FALSE, warning=FALSE}\nabril \u003c- datos_nh_mensual %\u003e%\n  filter(mes == unique(mes)[4]) #datos del cuarto mes en la base, abril.\n\nabril %\u003e% \n  mapear(precipitacion_mensual, lon, lat, cordillera = TRUE,\n              escala = escala_pp_mensual,\n              titulo = \"Precipitación en abril de 2019\", \n              fuente = \"Fuente: INTA\",\n              variable = \"pp\")\n```\n\n\n\n\n## Cómo contribuir\n\nPara contribuir con este paquete podés leer la siguiente [guía para contribuir](https://github.com/ropensci/agroclimatico/blob/master/.github/CONTRIBUTING.md). 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