The default (is back compatible), FALSE , will signal an error, where NA will “automatic” row names and TRUE will call make.
A CSV file contains a number of rows, each containing a number of columns, usually separated by commas.
Each row in a CSV file represents a set of values delimited with a particular delimiter. All rows are homogeneous, that is, each row has the same number of values. Values from all rows with the same index create a column. Values in a single column represent the same type of data.
R base functions provide a write. csv() to export the DataFrame to a CSV file. By default, the exported CSV file contains headers, row index, missing data as NA values, and columns separated by comma delimiter.
write.csv(t, "t.csv", row.names=FALSE)
From ?write.csv
:
row.names: either a logical value indicating whether the row names of
‘x’ are to be written along with ‘x’, or a character vector
of row names to be written.
For completeness, write_csv()
from the readr
package is faster and never writes row names
# install.packages('readr', dependencies = TRUE)
library(readr)
write_csv(t, "t.csv")
If you need to write big data out, use fwrite()
from the data.table
package. It's much faster than both write.csv
and write_csv
# install.packages('data.table')
library(data.table)
fwrite(t, "t.csv")
Below is a benchmark that Edouard published on his site
microbenchmark(write.csv(data, "baseR_file.csv", row.names = F),
write_csv(data, "readr_file.csv"),
fwrite(data, "datatable_file.csv"),
times = 10, unit = "s")
## Unit: seconds
## expr min lq mean median uq max neval
## write.csv(data, "baseR_file.csv", row.names = F) 13.8066424 13.8248250 13.9118324 13.8776993 13.9269675 14.3241311 10
## write_csv(data, "readr_file.csv") 3.6742610 3.7999409 3.8572456 3.8690681 3.8991995 4.0637453 10
## fwrite(data, "datatable_file.csv") 0.3976728 0.4014872 0.4097876 0.4061506 0.4159007 0.4355469 10
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