powered by
Read in chunks from a large file with row/column filtering to obtain a reasonable-sized data.frame.
big.read.table( file, nrows = 1e+05, sep = ",", header = TRUE, row.names = NULL, cols = NULL, rowfilter = NULL, as.is = TRUE, estimate = FALSE )
the name of the file, obviously
the chunk size; consider reducing this if there are lots of columns
by default we expect a CSV file
is TRUE by default
TRUE
I really dislike row names
for filtering column by name or number (supporting negative indexing)
a function that is assumed to take a chunk as a data frame and return a smaller data frame (with fewer rows), separately from the column filtering.
TRUE by default
do a preliminary estimation of the work to be done, and then have a chance to bail out if it looks like a bad idea
# NOT RUN { data(CO2) write.csv(CO2, "CO2.csv", row.names=FALSE) x <- big.read.table("CO2.csv", nrows=10) unlink("CO2.csv") head(x) # }
Run the code above in your browser using DataLab