View source: R/extract_coords_combine.R
extract_coords_combine | R Documentation |
Combines the split output files from functions extract_dynamic_coords()
and
extract_buffered_coords()
into single data frame containing all occurrence records and
explanatory variables.
extract_coords_combine(
varnames,
local.directory,
set_class = FALSE,
col_classes
)
varnames |
a character string, the unique names for each explanatory variable. |
local.directory |
a character string or vector, the path to local directory or directories to read extracted explanatory data frames from. |
set_class |
a logical indicating whether to set the classes of each column in the data before merging to avoid error. See details for more information. |
col_classes |
optional; a named vector specifying the classes for columns within occ.data. |
When functions extract_dynamic_coords()
and extract_buffered_coords()
have been used
to extract dynamic explanatory variables for occurrence records, the output for individual
records and each variable will be split into separate “csv” files.
This function reads in these files and combines data into a single data frame containing each occurrence records and associated explanatory data from each variable.
To prevent error, the “csv” files must be uniquely named within the folder(s) and include an
exact character match for the varnames provided. All “csv” files matching the varnames should
have the same number and names of columns. This is the default output
ofextract_dynamic_coords()
and extract_buffered_coords()
.
Returns a data frame containing all occurrence records with associated explanatory variable data.
When co-ordinate data have been extracted using the split
method in extract_dynamic_coords()
and extract_buffered_coords()
, sometimes the classes of columns in the separate exported data
frames can vary. For instance, one split row may contain an NA value in one column, whilst
another split contains a numerical value. When extract_coords_combine()
attempts to read these
in and bind them together, an error can occur due to the class mismatch (logical compared to
numeric).
There are two arguments that can help to resolve this error:
If set_class = TRUE
and col_classes
is given, then the column classes for each split data
frame will be set as the classes in col_classes
.
If set_class = TRUE
and col_classes
is not given, then the column classes for each split
data frame will be set by the classes of columns in the first read in csv. Be aware that this
approach may be inaccurate and lead to parsing warnings, as it depends on the first split
containing the correct classes (e.g. numeric not a logical in the example above).
data(sample_filt_data)
dynamicSDM::extract_dynamic_coords(
occ.data = sample_filt_data,
datasetname = "UCSB-CHG/CHIRPS/DAILY",
bandname = "precipitation",
spatial.res.metres = 10000,
GEE.math.fun = "sum",
temporal.direction = "prior",
temporal.res = 56,
save.method = "split",
varname = "eightweekprec",
save.directory = tempdir()
)
dynamicSDM::extract_dynamic_coords(
occ.data = sample_filt_data,
datasetname = "UCSB-CHG/CHIRPS/DAILY",
bandname = "precipitation",
spatial.res.metres = 10000,
GEE.math.fun = "sum",
temporal.direction = "prior",
temporal.res = 364,
save.method = "combined",
varname = "annualweekprec",
save.directory = tempdir()
)
extract_coords_combine(varnames = c("eightweekprec", "annualweekprec"),
local.directory = tempdir(),
set_class = TRUE,
col_classes = c(sapply(sample_filt_data,class),
eightweekprec = "numeric",
annualweekprec="numeric"))
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