# Hands-on 3: Load data by startR

## Goal
Use startR to load the data used in CSTools [RainFARM vignette](https://earth.bsc.es/gitlab/external/cstools/-/blob/master/vignettes/RainFARM_vignette.Rmd). Learn how to adjust data while loading data.

## 0. Load required packages

```r
library(startR)
library(CSTools)
```

## 1. Load data from data repository (esarchive/)

**Data description**:  
This sample data set contains a small cutout of gridded seasonal precipitation 
forecast data from the Copernicus Climate Change ECMWF-System 5 forecast system.
Specifically, for the 'prlr' (precipitation) variable, for the first 6 forecast 
ensemble members, daily values, for all 31 days in March following the forecast 
starting dates in November of years 2010 to 2012, for a small 4x4 pixel cutout in 
a region in the North-Western Italian Alps (44N-47N, 6E-9E). The data resolution is 1 degree.

Use the above information to define the variable, start dates, longitude and latitude.

```r
  repos <- "/esarchive/exp/ecmwf/system5c3s/daily_mean/$var$_s0-24h/$var$_$sdate$.nc"
  var <- ___<fill>___
  sdate <- paste0(___<fill>___, '1101')
  lon.min <- ___<fill>___
  lon.max <- ___<fill>___
  lat.min <- ___<fill>___
  lat.max <- ___<fill>___
```

Calculate the forecast time step. Desired days: the whole March (leap year ignored)

```r
  library(lubridate)
  ## Check which year is leap year
  leap_year(2011:2013)
  ## Find the first time step we want
  ftime_s <- as.integer(ymd("2011-03-01", tz = "UTC") - ymd("2010-11-01", tz = "UTC")) + 1
  ftime <- ftime_s:(ftime_s + 30)
  print(ftime)
```

Use Start() to load the data.

```r
  data <- Start(dat = repos,
                var = var,
                sdate = sdate,
                ensemble = indices(___<fill>___),
                time = ftime,
                latitude = values(list(lat.min, lat.max)),
                latitude_reorder = Sort(),
                longitude = values(list(lon.min, lon.max)),
                longitude_reorder = CircularSort(0, 360),
                synonims = list(latitude = c('lat', 'latitude'),
                                longitude = c('lon', 'longitude')),
                return_vars = list(time = 'sdate',
                                   longitude = NULL, latitude = NULL),
                retrieve = TRUE)
```
   

## 2. Compare with CSTools lonlat_prec

Load CSTools data `lonlat_prec`.

```r
  stored_data <- CSTools::lonlat_prec
```

### Dimensions

```r
  dim(data)
  dim(stored_data$data)
```

### Dates

```r
  data_time_attr <- attr(data, 'Variables')$common$time
  dim(data_time_attr)
  dim(stored_data$Dates$start)
  str(stored_data$Dates$start)
```

### Latitude and Longitude

```r
  data_lat_attr <- attr(data, 'Variables')$common$latitude
  as.vector(data_lat_attr)
  as.vector(stored_data$lat)

  data_lon_attr <- attr(data, 'Variables')$common$longitude
  as.vector(data_lon_attr)
  as.vector(stored_data$lon)
```

## 3. Adjust Start() call to get more consistent result with lonlat_prec

You should already see that the output of Start() `data` does have the same values 
as `lonlat_prec`, but there are some differences. For example, the "member" and "time" 
dimensions have different names; latitude vector order is different.

1. We can use parameter `synonims` to change the dimension name freely.
In Start(), change `ensemble` to `member` and `time` to `ftime`, and define their synonims.

2. Sort() in startR is a wrapper function of base function sort(). You can find it in Start()
call above, `latitude_reorder = Sort()`. Type `?sort` in R console to find how to change 
latitude to descending order.


