| write.big.matrix, read.big.matrix {bigmemory} | R Documentation |
Create a big.matrix by reading from a
suitably-formatted ASCII file, or
write the contents of a big.matrix to a file.
write.big.matrix(x, fileName = NA, row.names = FALSE,
col.names = FALSE, sep=',')
read.big.matrix(fileName, sep = ',', header = FALSE,
col.names = NULL, row.names = NULL, has.row.names=FALSE,
ignore.row.names=FALSE, type = NA,
skip = 0, separated = FALSE, shared = FALSE,
backingfile = NULL, backingpath = NULL,
descriptorfile = NULL, extraCols = NULL)
x |
a big.matrix. |
fileName |
the name of an input/output file. |
sep |
a field delimiter. |
header |
if TRUE, the first line (after a possible skip) should contain column names. |
col.names |
a vector of names, use them even if column names exist in the file. |
row.names |
a vector of names, use them even if row names appear to exist in the file. |
has.row.names |
if TRUE, then the first column contains row names. |
ignore.row.names |
if TRUE when has.row.names==TRUE, the row names will be ignored. |
type |
preferably specified, "integer" for example. |
skip |
number of lines to skip at the head of the file. |
separated |
use separated column organization of the data instead of column-major organization. |
shared |
if TRUE, load the object into shared memory. |
backingfile |
the root name for the file(s) for the cache of x. |
backingpath |
the path to the directory containing the file backing cache. |
descriptorfile |
the file to be used for the description of the filebacked matrix. |
extraCols |
the optional number of extra columns to be appended to the matrix for future use. |
Files must contain only one atomic type
(all integer, for example). You, the user, should know whether
your file has row and/or column names, and various combinations of options
should be helpful in obtaining the desired behavior.
When reading from a file, if type is not specified we try to
make a reasonable guess for you without
making any guarantees at this point.
Unless you have really large integer values, we strongly recommend
you consider "short". If you have something that is essentially
categorical, you might even be able use "char", with huge memory
savings in large data sets.
a big.matrix object is returned by read.big.matrix, while
write.big.matrix creates an output file in the present working directory.
John W. Emerson and Michael J. Kane
# Without specifying the type, this big.matrix x will hold integers.
x <- as.big.matrix(matrix(1:10, 5, 2))
x[2,2] <- NA
x[,]
write.big.matrix(x, "foo.txt")
# Just for fun, I'll read it back in as character (1-byte integers):
y <- read.big.matrix("foo.txt", type="char")
y[,]
# Other examples:
w <- as.big.matrix(matrix(1:10, 5, 2), type='double')
w[1,2] <- NA
w[2,2] <- -Inf
w[3,2] <- Inf
w[4,2] <- NaN
w[,]
write.big.matrix(w, "bar.txt")
w <- read.big.matrix("bar.txt", type="double")
w[,]
w <- read.big.matrix("bar.txt", type="short")
w[,]
# Another example using row names (which we don't like).
x <- as.big.matrix(as.matrix(iris), type='double')
rownames(x) <- as.character(1:nrow(x))
head(x)
write.big.matrix(x, 'IrisData.txt', col.names=TRUE, row.names=TRUE)
y <- read.big.matrix("IrisData.txt", header=TRUE, has.row.names=TRUE)
head(y)
# The following would fail with a dimension mismatch:
if (FALSE) y <- read.big.matrix("IrisData.txt", header=TRUE)