This function creates a table with environmental variables from an specific subset of subcatchments.
Usage
get_predict_table(
variable,
statistics = "ALL",
tile_id,
input_var_path,
subcatch_id,
out_file_path,
n_cores = NULL,
read = TRUE,
quiet = TRUE,
tempdir = NULL,
overwrite = FALSE
)Arguments
- variable
character vector of variable names. Possible values are: all variables in the Env90m dataset, which can bew viewed by calling 'download_
_tables()'. For more details, see '?download_env90m_tables'. - statistics
character vector of statistics names. Possible values are "sd", "mean", "range" or "ALL". Default "ALL".
- tile_id
character. The IDs of the tiles of interest.
- input_var_path
path to directory that contains table with environmental variables for entire tiles. Tables may be in subdirectories of the provided directory.
- subcatch_id
path to a text file with subcatchment ids, or numeric vector containing subcatchment ids.
- out_file_path
character. The path to the output file to be created.
- n_cores
numeric. Number of cores used for parallelization.
- read
logical. If TRUE, the table with environmental variables gets read into R. If FALSE, the table is only stored on disk. Default is TRUE.
- quiet
logical. If FALSE, the standard output will be printed. Default is TRUE.
- tempdir
String. Path to the directory where to store/look for the file size table. If not passed, defaults to the output of
base::tempdir().- overwrite
logical. If TRUE, the output file will be overwritten if it. already exists. Useful for repeated testing. Default is FALSE.
Value
The function returns a table with
sub-catchment ID (subcID)
a column for each descriptive statistic of each variable (eg. bio1_mean: mean of the variable bio1)
Examples
# Download test data into the temporary R folder
# or define a different directory
my_directory <- tempdir()
download_test_data(my_directory) # TODO make test data available for download!
# Define variable and tile:
var <- c("bio1")
tile_id <- c("h18v02")
# Point to input data
in_path <- paste0(my_directory, '/hydrography90m_test_data')
subc_ids <- paste0(my_directory, '/hydrography90m_test_data/subc_IDs.txt')
output <- paste0(my_directory, '/hydrography90m_test_data/predictTB.csv')
# Run the function with 2 cores and calculate all statistics:
get_predict_table(variable = var,
statistics = c("ALL"),
tile_id = tile_id,
input_var_path = in_path,
subcatch_id = subc_ids,
out_file_path = output,
read = FALSE, quiet = FALSE,
n_cores = 2)
# Now you can see the result in /tmp/.../hydrography90m_test_data/predictTB.csv