Repository Mirror for your Cloud Server and Webhosting

Type:

Package

Title:

Fast Access to Large ASCII Files

Version:

0.8.6

Date:

2024-12-13

Description:

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.

URL:

https://github.com/djvanderlaan/LaF

License:

GPL-3

LazyLoad:

yes

Depends:

methods, utils

Suggests:

testthat, yaml

LinkingTo:

Rcpp

Imports:

Rcpp (≥ 0.11.1)

Collate:

'generics.R' 'laf.R' 'laf_column.R' 'meta.R' 'open.R' 'read_dm_blaise.R' 'stats.R' 'textutils.R' 'types.R' 'utility.R'

RoxygenNote:

7.3.1

Encoding:

UTF-8

NeedsCompilation:

yes

Packaged:

2024-12-13 13:52:15 UTC; dlan@cbsp.nl

Author:

Jan van der Laan

[aut, cre]

Maintainer:

Jan van der Laan <r@eoos.dds.nl>

Repository:

CRAN

Date/Publication:

2024-12-13 16:50:02 UTC

Read records from a large file object into R

Description

When a connection is opened to a "laf" object; this object can then be indexed roughly as one would a data.frame.

Usage

## S4 method for signature 'laf'
x[i, j, drop]

## S4 method for signature 'laf_column'
x[i, j, drop]

Arguments

x

an object of type "laf" or "laf_column".

i

an logical or numeric vector with indices. The rows which should be selected.

j

a numeric vector with the columns to select.

drop

a logical indicating whether or not to convert the result to a vector when only one column is selected. As in when indexing a data.frame.

Select a column from a LaF object

Description

Selecting columns from an laf object works as it does for a data.frame.

Usage

## S4 method for signature 'laf'
x[[i]]

## S4 method for signature 'laf'
x$name

Arguments

x

an object of type laf

i

index of column to select. This should be a numeric or character vector.

name

the name of the column to select.

Value

Returns an object of type laf_column. This object behaves almost the same as an laf object except that is it no longer necessary (or possible) to specify which column should be used for functions that require this.

Go to the beginning of the file

Description

Sets the file pointer to the beginning of the file. The next call to next_block returns the first lines of the file. This method is usually used in combination with next_block.

Usage

begin(x, ...)

## S4 method for signature 'laf'
begin(x, ...)

Arguments

x

an object the supports the begin method, such as an laf object.

...

passed to other methods.

Close the connection to the Large File

Description

Close the connection to the Large File

Usage

## S4 method for signature 'laf'
close(con, ...)

Arguments

con

a "laf" object that can be closed.

...

unused.

Calculate simple statistics of column

Description

Methods for calculating simple statistics of columns of a file: mean, sum, standard deviation, range (min and max), and number of missing values.

Usage

colsum(x, ...)

## S4 method for signature 'laf'
colsum(x, columns, na.rm = TRUE, ...)

## S4 method for signature 'laf_column'
colsum(x, na.rm = TRUE, ...)

colmean(x, ...)

## S4 method for signature 'laf'
colmean(x, columns, na.rm = TRUE, ...)

## S4 method for signature 'laf_column'
colmean(x, na.rm = TRUE, ...)

colfreq(x, ...)

## S4 method for signature 'laf'
colfreq(x, columns, useNA = c("ifany", "always", "no"), ...)

## S4 method for signature 'laf_column'
colfreq(x, na.rm = TRUE, ...)

colrange(x, ...)

## S4 method for signature 'laf'
colrange(x, columns, na.rm = TRUE, ...)

## S4 method for signature 'laf_column'
colrange(x, na.rm = TRUE, ...)

colnmissing(x, ...)

## S4 method for signature 'laf'
colnmissing(x, columns, na.rm = TRUE, ...)

## S4 method for signature 'laf_column'
colnmissing(x, na.rm = TRUE, ...)

Arguments

x

an object of type laf or laf_column.

...

Currently ignored.

columns

a numeric vector with the columns for which the statistics should be calculated.

na.rm

whether or not to ignore missing values. By default missing values are ignored.

useNA

method with which to treat missing values: "ifany" adds a field containing the number of missing values if there are any; "always" will always add a field with the number of missing values even when there are none; "none" will never add a field containing the number of missing values.

Get the current line in the file

Description

Get the current line in the file

Usage

current_line(x)

## S4 method for signature 'laf'
current_line(x)

Arguments

x

an object the supports the current_line method, such as an laf object.

Returns the next line that will be read by next_block. The current line can be set by the method goto.

Automatically detect data models for CSV-files

Description

Automatically detect data models for CSV-files. Opening of files using the data models can be done using laf_open.

Usage

detect_dm_csv(
  filename,
  sep = ",",
  dec = ".",
  header = FALSE,
  nrows = 1000,
  nlines = NULL,
  sample = FALSE,
  stringsAsFactors = TRUE,
  factor_fraction = 0.4,
  ...
)

Arguments

filename

character containing the filename of the csv-file.

sep

character vector containing the separator used in the file.

dec

the character used for decimal points.

header

does the first line in the file contain the column names.

nrows

the number of lines that should be read in to detect the column types. The more lines the more likely that the correct types are detected.

nlines

(only needed when the sample option is used) the expected number of lines in the file. If not specified the number of lines in the file is first calculated.

sample

by default the first nrows lines are read in for determining the column types. When sample is used random lines from the file are used. This is more robust, but takes longer.

stringsAsFactors

passed on to read.table. Set to FALSE to read all text columns as character. In that case factor_fraction is ignored.

factor_fraction

the fraction of unique string in a column below which the column is converted to a factor/categorical. For more information see details.

...

additional arguments are passed on to read.table. However, be careful with using these as some of these arguments are not supported by laf_open_csv.

Details

The argument factor_fraction determines the fraction of unique strings below which the column is converted to factor/categorical. If all column need to be converted to character a value larger than one can be used. A value smaller than zero will ensure that all columns will be converted to categorical. Note that LaF stores the levels of a categorical in memory. Therefore, for categorical columns with a very large number of (almost) unique levels can cause memory problems.

Value

read_dm returns a data model which can be used by laf_open. The data model can be written to file using write_dm.

Examples

# Create temporary filename
tmpcsv  <- tempfile(fileext="csv")

# Generate test data
ntest <- 10
column_types <- c("integer", "integer", "double", "string")
testdata <- data.frame(
    a = 1:ntest,
    b = sample(1:2, ntest, replace=TRUE),
    c = round(runif(ntest), 13),
    d = sample(c("jan", "pier", "tjores", "corneel"), ntest, replace=TRUE),
    stringsAsFactors = FALSE
    )
# Write test data to csv file
write.table(testdata, file=tmpcsv, row.names=FALSE, col.names=TRUE, sep=',')

# Detect data model
model <- detect_dm_csv(tmpcsv, header=TRUE)

# Create LaF-object
laf <- laf_open(model)

# Cleanup
file.remove(tmpcsv)

Determine number of lines in a text file

Description

Determine number of lines in a text file

Usage

determine_nlines(filename)

Arguments

filename

character containing the filename of the file of which the lines are to be counted.

Details

The routine counts the number of line endings. If the last line does not end in a line ending, but does contain character, this line is also counted.

The file size is not limited by the amount of memory in the computer.

Value

Returns the number of lines in the file.

Examples

# Create temporary filename
tmpcsv  <- tempfile(fileext="csv")

# Generate file
writeLines(letters[1:20], con=tmpcsv)

# Count the lines
determine_nlines(tmpcsv)

# Cleanup
file.remove(tmpcsv)

Read in specified lines from a text file

Description

Read in specified lines from a text file

Usage

get_lines(filename, line_numbers)

Arguments

filename

character containing the filename of the file from which the lines should be read.

line_numbers

A vector containing the lines that should be read.

Details

Line numbers larger than the number of lines in the file are ignored. Missing values are returned for these.

Value

Returns a character vector with the specified lines.

Examples

# Create temporary filename
tmpcsv  <- tempfile(fileext="csv")

writeLines(letters[1:20], con=tmpcsv)
get_lines(tmpcsv, c(1, 10))

# Cleanup
file.remove(tmpcsv)

Go to specified line in the file

Description

Sets the current line to the line number specified. The next call to next_block will return the data on the specified line in the first row. The number of the current line can be obtained using current_line.

Usage

goto(x, i, ...)

## S4 method for signature 'laf,numeric'
goto(x, i, ...)

Arguments

x

an object the supports the goto method, such as an laf object.

i

the line number .

...

additional parameters passed to other methods.

Large File object

Description

A Large File object. This is a reference to a dataset on disk. The data itself is not read into memory (yet). This can be done by the methods for blockwise processing or by indexing the object as a data.frame. The code has been optimised for fast access.

Objects from the Class

Objects can be created by opening a file using one of the methods laf_open_csv or laf_open_fwf. These create a reference to either a CSV file or a fixed width file. The data in these files can either be accessed using blockwise operations using the methods begin, next_block and goto. Or by indexing the laf object as you would a data.frame. In the following example a CSV file is opened and its first column (of type integer) is read into memory:

   laf <- laf_open_csv("file.csv", column_types=c("integer", "double"))
   data <- laf[ , 1]

Column of a Large File Object

Description

Representation of a column in a Large File object. This class itself is a subclass of the class laf. In principle all methods that can be used with a laf object can also be used with a laf_column object except the the column or columns arguments of these methods are not needed.

Objects from the Class

Object of this class are usually created by using the $ operator on laf objects.

Create a connection to a file using a data model.

Description

Uses a data model to create a connection to a file. The data model contains all the information needed to open the file (column types, column widths, etc.).

Usage

laf_open(model, ...)

Arguments

model

a data model, such as one returned by read_dm or detect_dm_csv.

...

additional arguments can be used to overwrite the values specified by the data model. These are listed in the argument documentation for laf_open_csv and laf_open_fwf, e.g. see ignore_failed_conversion.

Details

Depending on the field ‘type’ laf_open uses laf_open_csv and laf_open_fwf to open the file. The data model should contain all information needed by these routines to open the file.

Value

Object of type laf. Values can be extracted from this object using indexing, and methods such as read_lines, next_block.

Examples

# Create some temporary files
tmpcsv  <- tempfile(fileext="csv")
tmp2csv <- tempfile(fileext="csv")
tmpyaml <- tempfile(fileext="yaml")

# Generate test data
ntest <- 10
column_types <- c("integer", "integer", "double", "string")
testdata <- data.frame(
    a = 1:ntest,
    b = sample(1:2, ntest, replace=TRUE),
    c = round(runif(ntest), 13),
    d = sample(c("jan", "pier", "tjores", "corneel"), ntest, replace=TRUE)
    )
# Write test data to csv file
write.table(testdata, file=tmpcsv, row.names=FALSE, col.names=FALSE, sep=',')

# Create LaF-object
laf <- laf_open_csv(tmpcsv, column_types=column_types)

# Write data model to file
write_dm(laf, tmpyaml)

# Read data model and open file
laf <- laf_open(read_dm(tmpyaml))

# Write test data to second csv file
write.table(testdata, file=tmp2csv, row.names=FALSE, col.names=FALSE, sep=',')

# Read data model and open second file, demonstrating the use of the optional
# arguments to laf_open
laf2 <- laf_open(read_dm(tmpyaml), filename=tmp2csv)

# Cleanup
file.remove(tmpcsv)
file.remove(tmp2csv)
file.remove(tmpyaml)

Create a connection to a comma separated value (CSV) file.

Description

A connection to the file filename is created. Column types have to be specified. These are not determined automatically as for example read.csv does. This has been done to increase speed.

Usage

laf_open_csv(
  filename,
  column_types,
  column_names = paste("V", seq_len(length(column_types)), sep = ""),
  sep = ",",
  dec = ".",
  trim = FALSE,
  skip = 0,
  ignore_failed_conversion = FALSE
)

Arguments

filename

character containing the filename of the CSV-file

column_types

character vector containing the types of data in each of the columns. Valid types are: double, integer, categorical and string.

column_names

optional character vector containing the names of the columns.

sep

optional character specifying the field separator used in the file.

dec

optional character specifying the decimal mark.

trim

optional logical specifying whether or not white space at the end of factor levels or character strings should be trimmed.

skip

optional numeric specifying the number of lines at the beginning of the file that should be skipped.

ignore_failed_conversion

ignore (set to NA) fields that could not be converted.

Details

After the connection is created data can be extracted using indexing (as in a normal data.frame) or methods such as read_lines and next_block can be used to read in blocks. For processing the file in blocks the convenience function process_blocks can be used.

The CSV-file should not contain headers. Use the skip option to skip any headers.

In case of an incomplete line (at line with less columns than it should have): when the line is completely empty the reader stops at that point and considers that as the end of the file. In other cases a warning is issued and the remaining columns are considered empty. For character columns this results in an empty string for numeric columns a NA.

Value

Object of type laf. Values can be extracted from this object using indexing, and methods such as read_lines, next_block.

Examples

# Create temporary filename
tmpcsv  <- tempfile(fileext="csv")

# Generate test data
ntest <- 10
column_types <- c("integer", "integer", "double", "string")
testdata <- data.frame(
    a = 1:ntest,
    b = sample(1:2, ntest, replace=TRUE),
    c = round(runif(ntest), 13),
    d = sample(c("jan", "pier", "tjores", "corneel"), ntest, replace=TRUE)
    )
# Write test data to csv file
write.table(testdata, file=tmpcsv, row.names=FALSE, col.names=FALSE, sep=',')

# Create LaF-object
laf <- laf_open_csv(tmpcsv, column_types=column_types)

# Read from file using indexing
first_column <- laf[ , 1]
first_row    <- laf[1, ]

# Read from file using blockwise operators
begin(laf)
first_block <- next_block(laf, nrows=2)
second_block <- next_block(laf, nrows=2)

# Cleanup
file.remove(tmpcsv)

Create a connection to a fixed width file.

Description

A connection to the file filename is created. Column types have to be specified. These are not determined automatically as for example read.fwf does. This has been done to increase speed.

Usage

laf_open_fwf(
  filename,
  column_types,
  column_widths,
  column_names = paste("V", seq_len(length(column_types)), sep = ""),
  dec = ".",
  trim = TRUE,
  ignore_failed_conversion = FALSE
)

Arguments

filename

character containing the filename of the fixed width file.

column_types

character vector containing the types of data in each of the columns. Valid types are: double, integer, categorical and string.

column_widths

numeric vector containing the width in number of character of each of the columns.

column_names

optional character vector containing the names of the columns.

dec

optional character specifying the decimal mark.

trim

optional logical specifying whether or not whitespace at the end of factor levels or character strings should be trimmed.

ignore_failed_conversion

ignore (set to NA) fields that could not be converted.

Details

After the connection is created data can be extracted using indexing (as in a normal data.frame) or methods such as read_lines and next_block can be used to read in blocks. For processing the file in blocks the (faster) convenience function process_blocks can be used.

Only use ignore_failed_conversion when you are sure that the column specification is correct. Otherwise, this option can hide an incorrect specification.

Value

Object of type laf. Values can be extracted from this object using indexing, and methods such as read_lines, next_block.

Get and change the levels of the column in a Large File object

Description

Get and change the levels of the column in a Large File object

Usage

## S4 method for signature 'laf'
levels(x)

## S4 replacement method for signature 'laf'
levels(x) <- value

## S4 method for signature 'laf_column'
levels(x)

## S4 replacement method for signature 'laf_column'
levels(x) <- value

Arguments

x

a "laf" object.

value

a list with the levels for each column.

Get and set the names of the columns in a Large File object

Description

Get and set the names of the columns in a Large File object

Usage

## S4 method for signature 'laf'
names(x)

## S4 replacement method for signature 'laf'
names(x) <- value

Arguments

x

a "laf" object.

value

a character vector with the new column names

Get the number of columns in a Large File object

Description

Get the number of columns in a Large File object

Usage

## S4 method for signature 'laf'
ncol(x)

Arguments

x

a "laf" object.

Read the next block of data from a file.

Description

Read the next block of data from a file.

Usage

next_block(x, ...)

## S4 method for signature 'laf'
next_block(x, columns = 1:ncol(x), nrows = 5000, ...)

## S4 method for signature 'laf_column'
next_block(x, nrows = 5000, ...)

Arguments

x

an object the supports the next_block method, such as an laf object.

...

passed to other methods.

Reads the next block of lines from a file. The method returns a data.frame. The first line in the data.frame is the line corresponding to the current line in the file. When the end of the file is reached a data.frame with zero rows is returned. This can be used to check whether the end of the file is reached.

columns

an integer vector with the columns that should be read in.

nrows

the (maximum) number of rows to read in one block

Get the number of rows in a Large File object

Description

Get the number of rows in a Large File object

Usage

## S4 method for signature 'laf'
nrow(x)

Arguments

x

a "laf" object.

Blockwise processing of file

Description

Reads the specified file block by block and feeds each block to the specified function.

Usage

process_blocks(x, fun, ...)

## S4 method for signature 'laf'
process_blocks(
  x,
  fun,
  columns = 1:ncol(x),
  nrows = 5000,
  allow_interupt = FALSE,
  progress = FALSE,
  ...
)

Arguments

x

an object the supports the process_blocks method, such as an laf object.

fun

a function to apply to each block (see details).

...

additional parameters are passed on to fun.

columns

an integer vector with the columns that should be read in.

nrows

the (maximum) number of rows to read in one block

allow_interupt

when TRUE the function fun is expected to return a list. The second element is the result of the function. The first element should be a logical value indication whether process_blocks should continue (FALSE) or stop (TRUE). When interrupted the function is not called a last time with an empty data.frame to finalize the result.

progress

show a progress bar. Note that this triggers a calculation of the number of lines in the file which for CSV files can take some time. When numeric code is used as the style of the progress bar (see txtProgressBar).

Details

The function should accept as the first argument the next block of data. When the end of the file is reached this is an empty (zero row) data.frame. As the second argument the function should accept the output of the previous call to the function. The first time the function is called the second argument has the value NULL.

Read and write data models for LaF

Description

Using these routines data models can be written and read. These data models can be used to create LaF object without the need to specify all arguments (column names, column types etc.). Opening of files using the data models can be done using laf_open.

Usage

read_dm(modelfile, ...)

write_dm(model, modelfile)

Arguments

modelfile

character containing the filename of the file the model is to be written to/read from.

...

additional arguments are added to the data model or, when they are also present in the file are used to overwrite the values specified in the file.

model

a data model or an object of type laf. See details for more information.

Details

A data model is a list containing information which open routine should be used (e.g. laf_open_csv or laf_open_fwf), and the arguments needed for these routines. Required elements are ‘type’, which can (currently) be ‘csv’, or ‘fwf’, and ‘columns’, which should be a data.frame containing at least the columns ‘name’ and ‘type’, and for fwf ‘width’. These columns correspond to the arguments column_names, column_types and column_widths respectively. Other arguments of the laf_open_* routines can be specified as additional elements of the list.

write_dm can also be used to write a data model that is created from an object of type laf. This is probably one of the easiest ways to create a data model.

The data model is stored in a text file in YAML format which is a format in which data structures can be stored in a readable and editable format.

Value

read_dm returns a data model which can be used by laf_open.

Examples

# Create some temporary files
tmpcsv  <- tempfile(fileext="csv")
tmp2csv <- tempfile(fileext="csv")
tmpyaml <- tempfile(fileext="yaml")

# Generate test data
ntest <- 10
column_types <- c("integer", "integer", "double", "string")
testdata <- data.frame(
    a = 1:ntest,
    b = sample(1:2, ntest, replace=TRUE),
    c = round(runif(ntest), 13),
    d = sample(c("jan", "pier", "tjores", "corneel"), ntest, replace=TRUE)
    )
# Write test data to csv file
write.table(testdata, file=tmpcsv, row.names=FALSE, col.names=FALSE, sep=',')

# Create LaF-object
laf <- laf_open_csv(tmpcsv, column_types=column_types)

# Write data model to stdout() (screen)
write_dm(laf, stdout())

# Write data model to file
write_dm(laf, tmpyaml)

# Read data model and open file
laf2 <- laf_open(read_dm(tmpyaml))

# Write test data to second csv file
write.table(testdata, file=tmp2csv, row.names=FALSE, col.names=FALSE, sep=',')

# Read data model and open seconde file, demonstrating the use of the optional
# arguments to read_dm
laf2 <- laf_open(read_dm(tmpyaml, filename=tmp2csv))

# Cleanup
file.remove(tmpcsv)
file.remove(tmp2csv)
file.remove(tmpyaml)

Read in Blaise data models

Description

Read in Blaise data models

Usage

read_dm_blaise(filename, datafilename = NA, encoding = "latin1")

Arguments

filename

the filename of the file containing the data model.

datafilename

the filename of the data file to which the data model belongs.

encoding

the encoding used in the file. See readLines.

Details

The function reads the data model from file and returns a list that can be used by laf_open to open the file for reading. Only a subset of the most common features found in Blaise files are supported. If part of the data model can not be parsed a warning is given.

Value

Returns a data model (which is a list containing all the relevant information to open a file using laf_open. When the file contains more than one data model a list of data models is returned and a warning issued.

Examples

# Create some temporary files
tmpdat <- tempfile(fileext="dat")
tmpbla <- tempfile(fileext="bla")

# Generate test data
lines <- c(
    " 1M 1.45Rotterdam ",
    " 2F12.00Amsterdam ",
    " 3  .22 Berlin    ",
    "  M22   Paris     ",
    " 4F12345London    ",
    " 5M     Copenhagen",
    " 6M-12.1          ",
    " 7F   -1Oslo      ")
writeLines(lines, con=tmpdat)

# Create a file containing the data model
writeLines(c( 
    "DATAMODEL test", 
    "FIELDS", 
    "  id     : INTEGER[2]", 
    "  gender : STRING[1]", 
    "  x      : REAL[5] {comment}", 
    "  city   : STRING[10]", 
    "ENDMODEL"), con=tmpbla)
model <- read_dm_blaise(tmpbla, datafilename=tmpdat)
laf <- laf_open(model)

# Cleanup
file.remove(tmpbla)
file.remove(tmpdat)

Read lines from the file

Description

Reads the specified lines and columns from the data file.

Usage

read_lines(x, ...)

## S4 method for signature 'laf'
read_lines(x, rows, columns = 1:ncol(x), ...)

## S4 method for signature 'laf_column'
read_lines(x, rows, columns = 1:ncol(x), ...)

Arguments

x

an object the supports the read_lines method, such as an laf object.

...

passed on to other methods.

rows

a numeric vector with the rows that should be read from the file.

columns

an integer vector with the columns that should be read in.

Details

Note that when scanning through the complete file next_block is much faster. Also note that random file access can be slow (and is always much slower than sequential file access), especially for certain file types such as comma separated. Reading is generally faster when the lines that should be read are sorted.

Read in random lines from a text file

Description

Read in random lines from a text file

Usage

sample_lines(filename, n, nlines = NULL)

Arguments

filename

character containing the filename of the file from which the lines should be read.

n

The number of lines that should be sampled from the file.

nlines

The total number of lines in the file. If not specified or NULL the number of lines is first determined using determine_nlines.

Details

When nlines is not specified, the total number of lines is first determined. This can take quite some time. Therefore, specifying the number of lines can cause a significant speed up. It can also be used to sample lines from the first nlines line by specifying a value for nlines that is smaller than the number of lines in the file.

Value

Returns a character vector with the sampled lines.

Examples

# Create temporary filename
tmpcsv  <- tempfile(fileext="csv")

writeLines(letters[1:20], con=tmpcsv)
sample_lines(tmpcsv, 10)

# Cleanup
file.remove(tmpcsv)

Print the Large File object to screen

Description

Print the Large File object to screen

Print a column of a Large File object to screen

Usage

## S4 method for signature 'laf'
show(object)

## S4 method for signature 'laf_column'
show(object)

Arguments

object

the object to print to screen.

Read records from a large file object into R

Description

Usage

Arguments

Select a column from a LaF object

Description

Usage

Arguments

Value

Go to the beginning of the file

Description

Usage

Arguments

Close the connection to the Large File

Description

Usage

Arguments

Calculate simple statistics of column

Description

Usage

Arguments

Get the current line in the file

Description

Usage

Arguments

Automatically detect data models for CSV-files

Description

Usage

Arguments

Details

Value

See Also

Examples

Determine number of lines in a text file

Description

Usage

Arguments

Details

Value

See Also

Examples

Read in specified lines from a text file

Description

Usage

Arguments

Details

Value

See Also

Examples

Go to specified line in the file

Description

Usage

Arguments

Large File object

Description

Objects from the Class

Column of a Large File Object

Description

Objects from the Class

Create a connection to a file using a data model.

Description

Usage

Arguments

Details

Value

See Also

Examples

Create a connection to a comma separated value (CSV) file.

Description

Usage

Arguments

Details

Value

See Also

Examples

Create a connection to a fixed width file.

Description

Usage

Arguments

Details