Open bedantaguru opened 4 years ago
LCS implemented in Rcpp
I noticed that Rcpp may not be optimal for large data in as_cell_df
Maybe I need to check memory status by memory.limit()
expr | min | lq | mean | median | uq | max | neval |
---|---|---|---|---|---|---|---|
unpivotr::as_cells(df) %>% tidycells::as_cell_df() | 634.28278 | 683.8018 | 778.8232 | 781.1177 | 881.1816 | 910.1206 | 10 |
tidycells::as_cell_df(df, take_col = F) %>% tidycells::as_cell_df() | 809.61787 | 997.8078 | 993.7125 | 1007.7760 | 1031.1863 | 1057.0500 | 10 |
as_cell_df_c2(df) %>% tidycells::as_cell_df() | 99.25656 | 104.2768 | 133.2668 | 113.7746 | 120.1005 | 331.1366 | 10 |
as_cell_df_r(df) %>% tidycells::as_cell_df() | 117.50674 | 123.1997 | 133.3697 | 128.3014 | 136.8042 | 179.8280 | 10 |
as_cell_df_r2(df) %>% tidycells::as_cell_df() | 107.92256 | 109.7794 | 116.9087 | 117.5692 | 121.1555 | 130.4172 | 10 |
as_cell_df_r2
may be a good option to speedup as_cell_df
enhead
alternative is not in Rcpp
Check out tidycells_nightly@Rcpp-dep
While LCS is great, is_attachable is poor as compared to R. Maybe my recent knowledge of C++ is not adequate enough to implement the performant version. Also in my opinion heuristic of this level is better to be kept with R.
As of now, there is no way to save Rcpp::cppFunction()
. The only option is to create a package.
A package directly can't have an optional dependency on {Rcpp}. [It has to be in Imports at least behaviorally]
Hence the best idea is to remove the dependency. LCS is required in name_suggest which is a small and experimental portion of the package.
source code of LCS can be ported as optional module
Need to adopt {Rcpp} whenever possible.
inas_cell_df
in direction attachment (enhead
alternative)