# custom dataset

## 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). 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:

    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:

    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)

    nc_name <- file.path(tempdir(), "CHELSA_TraCE21k_bio01.nc")
    terra::writeCDF(bio01,
      filename = nc_name, varname = "bio01",
      compression = 9, overwrite = TRUE
    )
    #> Warning in new_CppObject_xp(fields$.module, fields$.pointer, ...): GDAL Message
    #> 1: dimension #0 (time) is not a Time or Vertical dimension.

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

    custom_series <- region_series(
      bio_variables = "bio01",
      dataset = "custom",
      path_to_nc = nc_name
    )
    #> Warning in new_CppObject_xp(fields$.module, fields$.pointer, ...): GDAL Message
    #> 1: dimension #0 (time) is not a Time or Vertical dimension.

    #> Warning in new_CppObject_xp(fields$.module, fields$.pointer, ...): GDAL Message
    #> 1: dimension #0 (time) is not a Time or Vertical dimension.
    custom_series
    #> class       : SpatRasterDataset
    #> subdatasets : 1
    #> dimensions  : 174, 360 (nrow, ncol)
    #> nlyr        : 3
    #> resolution  : 1, 1  (x, y)
    #> extent      : -180.0001, 179.9999, -90.00014, 83.99986  (xmin, xmax, ymin, ymax)
    #> coord. ref. : lon/lat WGS 84 (EPSG:4326)
    #> source(s)   : CHELSA_TraCE21k_bio01.nc
    #> names       : bio01

As expected, there is only one variable (“bio01”) and 3 time steps (nlyr). We can get the times of those time steps with:

    get_time_bp_steps(dataset = "custom", path_to_nc = nc_name)
    #> Warning in new_CppObject_xp(fields$.module, fields$.pointer, ...): GDAL Message
    #> 1: dimension #0 (time) is not a Time or Vertical dimension.
    #> [1]    0 -100 -200

And we can slice the series and plot a given time point:

    climate_100 <- slice_region_series(custom_series, time_bp = -100)
    terra::plot(climate_100)
    #> Warning in x@pnt$readStart(): GDAL Message 1: dimension #0 (time) is not a Time
    #> or Vertical dimension.

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 “./data-raw/data_files/dataset_list_included.csv”.

    #>   variable ncvar dataset monthly                 file_name download_path
    #> 1    bio01  BIO1 Example   FALSE example_climate_v1.3.0.nc
    #> 2    bio10 BIO10 Example   FALSE example_climate_v1.3.0.nc
    #>   download_function file_name_orig download_path_orig version
    #> 1                                                       1.3.0
    #> 2                                                       1.3.0
    #>                             long_name      abbreviated_name time_frame
    #> 1             annual mean temperature           ann. mean T       year
    #> 2 mean temperature of warmest quarter mean T of warmest qtr       year
    #>             units  units_exp dataset_list_v
    #> 1 degrees Celsius *degree*C*          1.3.9
    #> 2 degrees Celsius *degree*C*

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.

`donwload_function`: for datasets which can be easily converted by the user into a valid netcdf, it is possibly to leave `download_path` empty, and to create an internal function that downloads and converts the files. For an example, see the WorldClim datasets.

`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/make_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.

5. Make a Pull Request on GitHub.