Read a file into R filtering it with an sql statement. Only the filtered portion is processed by R so that files larger than R can otherwise handle can be accommodated.
read.csv.sql(file, sql = "select * from file", header = TRUE, sep = ",", row.names, eol, skip, filter, nrows, field.types, colClasses, dbname = tempfile(), drv = "SQLite", ...) read.csv2.sql(file, sql = "select * from file", header = TRUE, sep = ";", row.names, eol, skip, filter, nrows, field.types, colClasses, dbname = tempfile(), drv = "SQLite", ...)
A file path or a URL (beginning with
filter argument is used and no file is to be input to the filter
file can be omitted,
character string holding an SQL statement. The table representing the
file should be referred to as
Character which ends line.
Skip indicated number of lines in input file.
If specified, this should be a shell/batch command that the input file is piped through. For
read.csv2.sql it is by default the following on non-Windows systems:
tr , .. This translates all commas in the file to dots. On Windows similar functionalty is provided but to do that using a vbscript file that is included with
sqldf to emulate the
Number of rows used to determine column types. It defaults to 50. Using
-1 causes it to use all rows for determining column types.
This argument is rarely needed.
A list whose names are the column names and whose contents are the SQLite types (not the R class names) of the columns. Specifying these types improves how fast it takes. Unless speed is very important this argument is not normally used.
sqldf except that the default is
NULL will put the database in memory which may improve speed
but will limit the size of the database by the available memory.
This argument is ignored.
Currently the only database SQLite supported by
read.csv2.sql is SQLite.
Note that the H2 database has a builtin SQL function,
CSVREAD, which can be used in place of
If the sql statement is a select statement then a data frame is returned.
Reads the indicated file into an sql database creating the database if it does not already exist. Then it applies the sql statement returning the result as a data frame. If the database did not exist prior to this statement it is removed.
Note that it uses facilities of
SQLite to read the file
which are intended for speed and therefore
not as flexible as in R. For example, it does not
recognize quoted fields as special but will regard the quotes as
part of the field. See the
sqldf help for more information.
read.csv2.sql is like
";" and the default
all commas in the file to decimal points (i.e. to dots).
On Windows, if the
filter argument is used and if Rtools is detected
in the registry then the Rtools bin directory is added to the search path
facilitating use of those tools without explicitly setting any the path.
# might need to specify eol= too depending on your system write.csv(iris, "iris.csv", quote = FALSE, row.names = FALSE) iris2 <- read.csv.sql("iris.csv", sql = "select * from file where Species = 'setosa' ")