This function reads in the environmental data that accompanies the genetic data read in with genDataRead.
Usage
envDataRead(
file.in = stop("'file.in' must be given!"),
dir.in = ".",
file.out = NULL,
dir.out = ".",
sep = ",",
cont = TRUE,
header = TRUE,
rownames = TRUE,
overwrite = NULL
)Arguments
- file.in
The name of the file with environmental data.
- dir.in
The path to the directory where the 'file.in' resides.
- file.out
The base name for the output files (see Details).
- dir.out
The path to the directory where the output files will be saved.
- sep
The separator character that separates values in each line of the file; "," by default (as in a csv file).
- cont
Logical - are the values continuous (TRUE, default) or categories (FALSE)? See Details.
- header
Logical indicating whether the first line of the file is a header; default TRUE.
- rownames
Default (TRUE) indicates that the first column of the file includes names of rows. If a character vector is given here, these names are used as rownames; if FALSE, no rownames are used.
- overwrite
Logical: if a file with the given name exists, should it be overwritten or not? If NULL, the user will be prompt for input.
Details
The environmental data such as methylation data can be large if the information
is stored on per-SNP basis. Thus, when data is large, this function reads it
in andcreates a special ff object that stores the data without limiting the
memory available. This can take time but needs to be performed only once.
Later on, one can use the envDataLoad function to load the appropriate
data from .ffData file saved to disk, which is a quick process.
Details
If 'file.out' is not given, the default is NULL and the output filenames are
constructed based on the input filenames. The '_env' suffix is added to the
base name and the .ffData file is written to disk. This file contains
all the information needed to restore the ffdf object by calling
envDataLoad function later on.
If 'cont' is TRUE (default), the output data will be a list of ff matrices containing single-precision values. However, before using this data as stratification values, the user needs to create categories - this can be done manually or with the provided envDataCategorize function.