[[6]{.chapter-number}  [Data Processing]{.chapter-title}]{#sec-data-processing .quarto-section-identifier}

6 Data Processing

This manual is still under development and may be subject to change.

This warning will be removed once the manual is finalized.

We also developed the worldclim_to_ascii in the orbis R package (Vartanian, 2026) to do the job of converting the WorldClim GeoTIFF files to Esri ASCII Grid format. Like worldclim_download(), this function is very straightforward to use.

Here is a parameter overview:

worldclim_to_ascii(
  file,
  shape = NULL,
  box = NULL,
  shift_longitude = TRUE,
  dx = -45,
  extreme_outlier_fix = TRUE,
  overwrite = TRUE,
  na_flag = -99,
  dir = dirname(file[1])
)

worldclim_to_ascii allow you not only convert the GeoTIFF files to Esri ASCII format, but also crop the data to a specific region of interest. This is done using the shape or box parameters.

The first parameter, shape, expects a spatial object (e.g., an sf object) that defines the area to crop. The second parameter, box, allows you to specify a bounding box using numeric coordinates.

To obtain shapes of countries and their administrative boundaries, we recommend using the geodata R package (Hijmans et al., 2024), which provides access to and downloads shapes from the GADM database (Hijmans, n.d.).

# install.packages("geodata")
library(geodata)

The gadm function identifies countries based on the ISO 3166-1 alpha-3 standard. Click here to see its documentation.

country_shape <- country |> gadm("FRA")

For example, if you want to crop WorldClim data to the boundaries of France, you could use the following code:

worldclim_to_ascii(
  file = "path/to/the/files/you/download/with/worldclim_download",
  shape = country_shape
)

The function default values already takes care of possible distortions caused by geographic projection or rotating issues (e.g., shapes that cross the International Date Line). It also removes unique outliers from the data. All these features can be adjusted using the function parameters.

After processing the data, you just need to point at its directory using the model interface, or by setting its path in the global variable data-path, to start using LogoClim.

See the function documentation for more details.