Methods for fast access to large ASCII files. Currently the following file formats are supported: comma separated format (CSV) and fixed width format. It is assumed that the files are too large to fit into memory, although the package can also be used to efficiently access files that do fit into memory. Methods are provided to access and process files blockwise. Furthermore, an opened file can be accessed as one would an ordinary data.frame. The LaF vignette gives an overview of the functionality provided.
|Author||Jan van der Laan|
|Date of publication||2017-01-02 16:20:19|
|Maintainer||Jan van der Laan <firstname.lastname@example.org>|
begin: Go to the beginning of the file
cindexing: Select a column from a LaF object
close: Close the connection to the Large File
current_line: Get the current line in the file
datamodels: Read and write data models for LaF
detect_dm_csv: Automatically detect data models for CSV-files
determine_nlines: Determine number of lines in a text file
get_lines: Read in specified lines from a text file
goto: Go to specified line in the file
indexing: Read records from a large file object into R
laf-class: Large File object
laf_column-class: Column of a Large File Object
laf_open: Create a connection to a file using a data model.
laf_open_csv: Create a connection to a comma seperated value (CSV) file.
laf_open_fwf: Create a connection to a fixed width file.
levels: Get and change the levels of the column in a Large File...
names: Get and set the names of the columns in a Large File object
ncol: Get the number of columns in a Large File object
next_block: Read the next block of data from a file.
nrow: Get the number of rows in a Large File object
process_blocks: Blockwise processing of file
read_dm_blaise: Read in Blaise data models
read_lines: Read lines from the file
sample_lines: Read in random lines from a text file
show: Print the Large File object to screen
stats: Calculate simple statistics of column