- Bugfixes Github 384
- Bugfixes
- And ability to convert to Parquet
- Partitioned by folder
- Updated R version to 4
- Fixed bug with data.table syntax
- Auto detection of srckeep in group by
- Global detection for group by and summarise
- Much better NSE support in disk.frame!
- removed
hard_arrange
andhard_group_by
- various API updates
- removed
add_count
method
- removed use of
sysctl
which was violating CRAN policy
- Removed
count
andtally
- Fixed package compatibility
- Bugfix - add_chunk with date column not working
- Minor - added compression and other aprameters to
add_chunk
- General update to dplyr v1
- Remove all
_all/if/at
functions for dplyr v1 - Updated
pull
function to conform to dplyr
- Support for !!! in summarise; github #250
- Added
dplyr::pull
verb
- fixed vignette bug and all vignette are now in static pdf
- fixed bugs with group-by when run with Rscript
- also fixed bugs with multiple group-by columns
- removed bloom filters as they were causing issues with CRAN solaris system for some reason
- removed usage proc/meminfo inline with CRAN policy
- added (experimental) bloomfilter
- urgent bug fix for group-by failing when the number chunks is 1; see Github #241
- experimental one-stage group-by framework!
- bug fixes for data.table trigger by integration with tidyfast
- removed assertthat from imports
- add benchmarkme to Suggests
- got rid of benchmarkme as a dependency
- added
hard_arrange
thanks to Jacky Poon - added more .progress options for joins
- Using data.table::getDTthreads() as default number of workers
- multisession instead of multiprocess as default backend for data.table
- added support for R3.4
- fixed df_get_ram for R < 3.6
- deprecated group_by, arrange, summarise
- add chunk_group_by, chunk_arrange, chunk_summarise
- fit GLMs with
dfglm
- fixed so that dplyr function also work in mutate even with ~ in the name
- fixed disk.frame so that in works in functions too
- Allowed
map
to accept multiple arguments. Thanks Knut Jägersberg for suggestion - Fixed bug where if the CSV is larger than RAM then it fails by adding {LaF} backend
- Added {bigreadr} backend for reading large files by first splitting the file. This is the default behaviour
- Added {LaF} and {readr} chunk readers to
csv_to_disk.frame
- fixed
write_disk.frame(...., shardby)
and othershardby
functions includingrechunk
andshard
- added
df_ram_size()
to accurate determine RAM size for RStudio in R3.6 - added
show_ceremony
andshow_boiler_plate
to show setup code