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README.rmd
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---
title: "README2"
author: "Darius Görgen"
date: "8/22/2020"
output: md_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(kableExtra)
```
<!-- badges: start -->
[](https://www.tidyverse.org/lifecycle/#experimental)
<!-- badges: end -->
## wapoR - Acces to the FAO GISManager API from R
This package includes functionalities to search for datasets in different
collections of the FAO GISManager API, query the metadata of included products
and finally download GTiff files of the desired spatio-temporal extent to your local machine.
This package is at an experimental state and currently only supports the download
of raster files.
To install the package run:
```{r install, eval = FALSE}
remotes::install_github("goergen95/wapoR")
```
Once installed, you can load the library and start looking at the available
collections.
```{r collections}
library(wapoR)
collections = wapor_collections()
collections
```
In its current state, the package mainly supports products from the WAPOR collections.
For some other collection it is also possible to query and download raster datasets.
However, for some of the collections errors will be returned if queried.
Currently, you might run into errors for these collections:
- C2ATLAS
- DLMF
- NASA
- NATURAL_EARTH
To query the available products within the collections we can run `wapor_products("<collection_name>").
```{r products}
products = wapor_products("WAPOR_2")
length(products)
str(products[1:3], max.level = 2)
```
In the WAPOR_2 collection we have a total number of `r length(products)`.
As you can see from the structured output, each element in the list object represents
one product, e.g. `r products[[1]]$product$code`. In the lower level you find
tow distinct objects, one called product the other one called meta.
In the product object you will get some general information about the specific product.
```{r info}
kable(products[[1]]$product)
```
In the meta object you will find more specific information such as the spatio-temporal
extent of the dataset as well as some information on the methodology and data types.
```{r meta}
str(products[[1]]$meta)
```
With these information combined, we can start downloading some data.
Let's say we are interested in the Gross Biomass Water Productivity (GPWP) during
the first season of the year 2015 in Uganda. Let's prepare a call to download this
data.
```{r download, eval = FALSE}
library(sf)
library(rnaturalearth)
ugn = ne_countries(country = "Uganda", returnclass = "sf")
collection = "WAPOR_2"
product = "L2_GBWP_S" # product code
begin = as.Date("2015-01-01") # begin date is inclusive
end = as.Date("2016-01-01") # end date is exclusive
dimensions = list(SEASON = c("S1", "S2")) # GBWP only has dimension SEASON - S1 and S2
key = "<your_personal_API_Key" # can be obtained in the profile section of the WAPOR website
# at https://wapor.apps.fao.org
# run the query command - see ?wapor_queryRaster() for more information
wapor_queryRaster(collection = collection,
product = product,
dimensions = dimensions,
aoi = ugn,
begin = begin,
end = end,
APIkey = APIkey,
outdir = ".",
cutline = FALSE,
tiled = TRUE,
compressed = TRUE,
overviews = TRUE)
```