---
title: "Download time series from multiple stations/variables"
author: "Stijn Van Hoey"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Download time series from multiple stations/variables}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

## Introduction

In many studies, the interest of the user is to download a batch of time series following on a selection criterion. Examples are:

* downloading air pressure data for the last day for all available measurement stations. 
* downloading all measured variables at a frequency of 15 minutes for a given measurement station.

In this vignette, this type of batch downloads is explained, using the available functions of the `wateRinfo` package in combination with already existing [tidyverse](https://www.tidyverse.org/) functionalities.

```{r load_libraries, message = FALSE, warning = FALSE}
library(dplyr)
library(ggplot2)
```

## Download all stations for a given variable

Consider the scenario: "downloading air pressure data for the last day for all available measurement stations". We can achieve this by downloading all the stations information providing air_pressure data (`get_stations()`) and for each of the `ts_id` values in the resulting data.frame, applying the `get_timeseries_tsid()` function:

```{r station_of_var, eval = FALSE}
# extract the available stations for a predefined variable
variable_of_interest <- "air_pressure"
stations <- get_stations(variable_of_interest)

# Download the data for a given period for each of the stations
air_pressure <- stations %>%
    group_by(ts_id) %>%
    do(get_timeseries_tsid(.$ts_id, period = "P1D", to = "2017-01-02")) %>%
    ungroup() %>%
    left_join(stations, by = "ts_id")
```

```{r load_saved_data, echo = FALSE}
air_pressure <- wateRinfo::air_pressure
```

As this results in a tidy data set, we can use the power of ggplot to plot the data of the individual measurement stations:

```{r plot_pressure, fig.width = 7, fig.height = 6}
# create a plot of the individual datasets
air_pressure %>% 
    ggplot(aes(x = Timestamp, y = Value)) + 
    geom_point() + xlab("1 Jan 2017") + 
    facet_wrap(c("station_name", "stationparameter_name")) + 
    scale_x_datetime(date_labels = "%H:%M",
                     date_breaks = "6 hours")
```

## Download set of variables from a station

Consider the scenario: "downloading all soil_moisture (in dutch: 'bodemvocht') variables at a frequency of 15 minutes for the measurement station Liedekerke". We can achieve this by downloading all the variables information of the Liedekerke station(`get_variables()`) using the station code of the waterinfo.be interface (`ME07_006`), filtering on the `P.15` time series and for each of the `ts_id` values, applying the `get_timeseries_tsid()` function:

```{r var_of_station, eval = FALSE}
liedekerke_stat <- "ME07_006"
variables <- get_variables(liedekerke_stat)

variables_to_download <- variables %>% 
    filter(parametertype_name == "Bodemvocht") %>%
    filter(ts_name == "P.15")

liedekerke <- variables_to_download %>%
    group_by(ts_id) %>%
    do(get_timeseries_tsid(.$ts_id, period = "P1M", from = "2017-01-01")) %>%
    ungroup() %>%
    left_join(variables, by = "ts_id")
```

```{r load_saved_data_liedekerke, echo = FALSE}
liedekerke <- wateRinfo::liedekerke
```

As this results in a tidy data set, we can use the power of ggplot to plot the data of the individual measurement stations:

```{r liedekerke_viz, fig.width = 7, fig.height = 6}
liedekerke %>% 
    ggplot(aes(x = Timestamp, y = Value)) + 
    geom_line() + xlab("") + ylab("bodemvocht") + 
    facet_wrap(c("ts_name", "stationparameter_name"), scales = "free") +
    scale_x_datetime(date_labels = "%d-%m\n%Y",
                     date_breaks = "10 days")    
```
