Friday, September 8, 2017 From rOpenSci (https://ropensci.org/blog/2017/09/08/writexl-release/). Except where otherwise noted, content on this site is licensed under the CC-BY license.
The major benefit of writexl over other packages is that it is completely written in C and has absolutely zero dependencies. No Java, Perl or Rtools are required.
write_xlsx function writes a data frame to an xlsx file. You can test that data roundtrips properly by reading it back using the readxl package. Columns containing dates and factors get automatically coerced to character strings.
library(writexl) library(readxl) write_xlsx(iris, "iris.xlsx") # read it back out <- read_xlsx("iris.xlsx")
You can also give it a named list of data frames, in which case each data frame becomes a sheet in the xlsx file:
write_xlsx(list(iris = iris, cars = cars, mtcars = mtcars), "mydata.xlsx")
Performance is good too; in our benchmarks writexl is about twice as fast as openxlsx:
library(microbenchmark) library(nycflights13) microbenchmark( writexl = writexl::write_xlsx(flights, tempfile()), openxlsx = openxlsx::write.xlsx(flights, tempfile()), times = 5 ) ### Unit: seconds ### expr min lq mean median uq max neval ### writexl 8.884712 8.904431 9.103419 8.965643 9.041565 9.720743 5 ### openxlsx 17.166818 18.072527 19.171003 18.669805 18.756661 23.189206 5
The initial version of writexl implements the most important functionality for R users: exporting data frames. However the underlying libxlsxwriter library actually provides far more sophisticated functionality such as custom formatting, writing complex objects, formulas, etc.
Most of this probably won’t be useful to R users. But if you have a well defined use case for exposing some specific features from the library in writexl, open an issue on Github and we’ll look into it!