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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}
library(knitr)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
cache = TRUE
)
```
# `easyclimate` <a href='https://verughub.github.io/easyclimate/'><img src="man/figures/easyclimate_logo_small.png" align="right" height="139"/></a>
# Easy access to high-resolution daily climate data for Europe
<!-- badges: start -->
`r badger::badge_cran_release()`
<a href="https://diffify.com/R/easyclimate" target="_blank"><img src="https://diffify.com/diffify-badge.svg" alt="The diffify page for the R package easyclimate" style="width: 100px; max-width: 100%;"></a>
[](https://www.repostatus.org/#active) [](https://github.com/VeruGHub/easyclimate/actions/workflows/R-CMD-check.yaml)
[](https://hits.dwyl.com/VeruGHub/easyclimate)
[](https://hits.dwyl.com/VeruGHub/easyclimate)
[](https://cran.r-project.org/package=easyclimate)
<!-- badges: end -->
Get high-resolution (1 km) daily climate data (precipitation, minimum and maximum temperatures) for Europe from the European climatic database hosted at [University of Natural Resources and Life Sciences, Vienna, Austria](https://boku.ac.at/en/wabo/waldbau/wir-ueber-uns/daten). Data are currently available from 1950 to 2022.
This climatic dataset was originally built by [A. Moreno & H. Hasenauer](https://doi.org/10.1002/joc.4436) and further developed by W. Rammer, C. Pucher & M. Neumann (see [this document](https://github.com/VeruGHub/easyclimate/blob/master/inst/Description_Evaluation_Validation_Downscaled_Climate_Data_v2.pdf) for more details on the development and characteristics of the climatic dataset, and [this document](https://doi.org/10.6084/m9.figshare.22962671.v1) for the updates of the last version - v4).
In this R package we implemented [Cloud-Optimised Geotiffs](http://cogeo.org/) so that we can obtain daily climate data for thousands of sites/days within seconds/minutes, without having to download huge rasters. But if you need to obtain data for large areas, please download the rasters from the FTP server (<ftp://palantir.boku.ac.at/Public/ClimateData/>) and extract the values locally rather than using this package, so as not to saturate the file server. For that, you may use a FTP client such as [FileZilla](https://filezilla-project.org/).
For a detailed description of {easyclimate}, please read [this paper](https://doi.org/10.1016/j.envsoft.2023.105627) (open access version [here](https://doi.org/10.32942/osf.io/mc8uj)) or visit the package [website](https://verughub.github.io/easyclimate/).
## Installation
Stable version from CRAN:
``` r
install.packages("easyclimate")
```
Development version from GitHub:
``` r
# install.packages("remotes")
remotes::install_github("VeruGHub/easyclimate")
```
## Examples
### Obtain a data frame of climatic values
To obtain a data frame of daily climatic values for point coordinates:
```{r example, message = FALSE}
library(easyclimate)
coords <- data.frame(lon = -5.36, lat = 37.40)
prec <- get_daily_climate(coords,
period = "2001-01-01:2001-01-03",
climatic_var = "Prcp",
version = 4) # default
```
```{r echo=FALSE}
kable(prec)
```
<br>
### Obtain a raster of climatic values
To obtain a (multi-layer) raster of daily climatic values for an area:
```{r message=FALSE, fig.width = 8, fig.height = 3}
library(terra)
## Download the polygon contour of a region
sobrarbe <- mapSpain::esp_get_comarca(comarca = "Sobrarbe")
## Coordinates must be in lonlat
sobrarbe <- project(vect(sobrarbe), "EPSG:4326")
## Download Tmax values for that region between 1st and 3rd May 2020
sobrarbetemp <- get_daily_climate(
coords = sobrarbe,
climatic_var = "Tmax",
period = "2020-05-01:2020-05-03",
output = "raster"
)
```
The output (`sobrarbetemp`) is a SpatRaster with 3 layers (for each of 3 days):
```{r}
sobrarbetemp
```
Let's make a map. First using terra:
```{r map_terra, eval=FALSE}
plot(sobrarbetemp, col = rev(RColorBrewer::brewer.pal(9, "RdYlBu")),
smooth = TRUE, nc = 3)
```
Now using ggplot2 and tidyterra:
```{r map_ggplot, message=FALSE, fig.width = 8, fig.height = 3}
library(ggplot2)
library(tidyterra)
ggplot() +
geom_spatraster(data = sobrarbetemp) +
facet_wrap(~lyr, ncol = 3) +
scale_fill_distiller(palette = "RdYlBu", na.value = "transparent") +
geom_spatvector(data = sobrarbe, fill = NA) +
labs(fill = "Maximum\ntemperature (ºC)") +
scale_x_continuous(breaks = c(-0.25, 0, 0.25)) +
scale_y_continuous(breaks = seq(42.2, 42.8, by = 0.2)) +
theme_minimal()
```
<br> Visit the articles of the [package website](https://verughub.github.io/easyclimate/) for more extended tutorials!
<br>
## CITATION
If you use easyclimate, please cite both the appropriate data source and the package as:
```{r echo=FALSE, results='asis', cache = FALSE}
print(citation("easyclimate"), style = "text")
```