---
title: "pacu: Precision Agriculture Computational Utilities - Weather data"
author: "Caio dos Santos & Fernando Miguez"
date: "`r Sys.Date()`"
output: 
  rmarkdown::html_vignette:
    toc: true
vignette: >
  %\VignetteIndexEntry{pacu: Precision Agriculture Computational Utilities - Weather data}
  %\VignetteEngine{knitr::rmarkdown}
  \usepackage[utf8]{inputenc}
---

# Specific vignettes

1. [Satellite data](pacu_sat.html)

2. [Weather data](pacu_weather.html)

3. [Yield monitor data](pacu_ym.html)

4. [Frequently asked questions](pacu_faq.html)


# Weather data

## Obtaining weather data

There are several packages and utilities that allow for downloading weather data. Here we use the **apsimx** package. This package has simple wrappers that 'get' weather from different sources:

* Iowa Environmental Mesonet
* NASA-POWER (via nasapower package)
* DayMet (via daymetr package)
* CHIRPS (via chirps package)

For more details about getting and working with weather data see the **apsimx** package.

## Using apsimx and pacu packages

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(apsimx)
library(pacu)
```


### Gathering and summarizing weather data

An alternative way of investigating the growing conditions
experienced by crops in a given year would be to summarize
the weather data and place it in a historical context. Let
us download some weather data first.

```{r download-weather, eval = FALSE}
weather.met <- pa_get_weather_sf(area.of.interest, '1990-01-01', '2020-12-31')
```

```{r, include=FALSE}
extd.dir <- system.file("extdata", package = "pacu")
weather.met <- read_apsim_met('example-weather.met', extd.dir, verbose = FALSE)
```

We can make simple plots for precipitation or temperature. A
filter is used to subset years 2017 to 2020 for easier
interpretation.

```{r simple-met-plot, fig.width=6}
## Precipitation (or rain)
plot(weather.met, met.var = "rain", cumulative = TRUE, 
     climatology = TRUE, years = 2017:2020)
## Temperature
plot(weather.met, cumulative = TRUE, 
     climatology = TRUE, years = 2017:2020)
```

There is a summary function for simple display of statistics

```{r summary-weather-met}
## Selecting just a few columns (1, 6, 7, 10) for simplicity
summary(weather.met, years = 2017:2020)[, c(1, 6, 7, 10)]
```

The apsimx package does not produce complex graphs for
weather data. This was included here to allow more detailed
interpretation of crop performance data for a given
location. In the pacu package we include functions which can
summarize data in a historical context.

```{r summarizing-weather-data, fig.width=6, fig.height=5}
pa_plot(weather.met,
        plot.type = 'climate_normals', 
        unit.system = 'int')
pa_plot(weather.met,
        plot.type = 'monthly_distributions', 
        unit.system = 'int', months = 5:10)


```
