---
title: "custom dataset"
#output: rmarkdown::pdf_document
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{custom dataset}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Formatting a custom dataset for `pastclim`

This guide is aimed at formatting data in such a way that they can be used with `pastclim`. `pastclim` is designed to extract data from `netcdf` files, a format commonly used for storing climate reconstructions. `netcdf` files have a number of advantages, as they can store compressed information, as well as allowing access to only the data required (e.g. extracting only the time steps or location of interest without reading all the data in memory). The expected format for `pastclim` requires that all time steps of a given variable be stored within a single netcdf file. How variables are combined (or not) is then flexible: you can have a separate file for each variable, collate everything within a single file, or create multiple files each including a number of variables. The time variable should be in years since 1950 (i.e. with negative integers indicating the past). There are a number of command line tools as well as R libraries (e.g. `cdo`, `gdal`, `terra`) that can help creating and editing netcdf files. 

## An example: the Trace21k-CHELSEA

Here we provide a simple example of how to format such a dataset in R. We will use a version of the Trace21k dataset, downscaled to 30 arcsecs using the CHELSEA algorithm(available from [this website](https://chelsa-climate.org/chelsa-trace21k/)). The data are stored as geoTIFF files, one file per time step per variable. First, we need to collate all the files for a given variable (we will use *bio01* as an example) within a single `netcdf` file. As the original files are large, we will illustrate here how do to that for only a few time steps which were aggregated to 3x3 degrees to keep files sizes small.

We start by translating each geoTIFF into a `netcdf` file. The files have the prefix *CHELSA_TraCE21k_bio01_-**xxx**_V1.0.small.tif*, where **xxx** is the number of the time step.
We will only use 3 time step for illustrative purposes.

We store all the files in a single directory, and create a `spatRaster` from a list of the files
in that directory:

```{r}
tiffs_path <- system.file("extdata/CHELSA_bio01",package="pastclim")
list_of_tiffs <- file.path(tiffs_path,dir(tiffs_path))
bio01 <- terra::rast(list_of_tiffs)
```

*NOTE: `terra` has changed the way it handles time when reading from netcdf. The
dev version of `terra` can more easily format netcdf files correctly, but this
vignette presents a number of workarounds needed for the version on CRAN*

Now we need to set the time axis of the raster (in this case, reconstructions are every 100 years), and generate some user friendly names to layers in the raster:

```{r}
library(pastclim)
time_bp(bio01)<-c(0,-100,-200)
names(bio01)<-paste("bio01",terra::time(bio01),sep="_")
```

Now we save the data as a *nc* file (we will use the temporary directory)
```{r}
nc_name <- file.path(tempdir(),"CHELSA_TraCE21k_bio01.nc")
terra::writeCDF(bio01, filename = nc_name, varname = "bio01", overwrite=TRUE)
```

We can now read in our custom netcdf file with `pastclim`.

```{r echo=FALSE, results='hide'}
data_path <- file.path(tempdir(),"pastclim_data")
# clear it in case it exists already
unlink(data_path, recursive = TRUE) 
# set data path
set_data_path(path_to_nc = data_path,
              ask = FALSE,
              write_config = FALSE,
              copy_example = TRUE)
```

```{r}
custom_series <- region_series(bio_variables = "bio01",
                                dataset = "custom",
                                path_to_nc = nc_name
)
custom_series
```
As expected, there is only one variable ("bio01") and
3 time steps (nlyr). We can get the times of those time steps with:
```{r}
get_time_steps(dataset="custom", path_to_nc = nc_name)
```

And we can slice the series and plot a given time point:
```{r}
climate_100<-slice_region_series(custom_series, time_bp = -100)
terra::plot(climate_100)
```

Note that these reconstructions include the ocean and the ice sheets, and it would
be much better to remove them as they are not needed for most ecological/archaeological studies (and
it allows for smaller files).

# Making the data available to others

Once you have created suitably formatted netcdf files that can be used as custom datasets in 
`pastclim`, you can add those data officially to the package, and thus make them available
to others. Here are the necessary steps:

1) Put your files in a freely available repository.

2) Update the table used by `pastclim` to
store information about available datasets. This table is found in
"./inst/extdata/variable_table.csv".

```{r}
head(read.csv(system.file("extdata/dataset_list_included.csv",
                     package="pastclim")), n=2)
```

This includes the following fields:

`variable`: the variable name used by `pastclim`

`ncvar`: the variable name within the *nc* file (it can be the same
as `variable`)

`dataset`: the name of the dataset.

`monthly`: boolean on whether the variable is monthly.

`file_name`: the name of the file for that variable.

`download_path`: the URL to download the file.

`file_name_orig`: the name of the original file(s) used to create
the *nc* dataset.

`download_path_orig`: the path of those original files.

`version`: the version of the dataset that you created

`long_name`: the long name for the variable

`abbreviated_name`: an abbreviated version of `long_name` (used for plot labels)

`time_frame`: either `year` or the appropriate `month`

`units`: units for the variable, to be displayed in a plain text table

`units_exp`: units formatted to be included in `expression` when creating plot labels

3) Once you have added lines detailing the variables in your dataset, run the script "./raw-data/dataset_list_included.R" to store
that information into the appropriate dataset in `pastclim`.

4) Provide information on the new dataset in the file "./R/dataset_docs", using `roxygen2` syntax. Make sure that you provide an appropriate reference for the original data, as it is important that users can refer back to the original source.

4) Make a Pull Request on GitHub.
