Rcpp Adoption in various stages

[bedantaguru]

Need to adopt {Rcpp} whenever possible.

• in LCS
• in as_cell_df
• in direction attachment (enhead alternative)

[bedantaguru]

LCS implemented in Rcpp

[bedantaguru]

I noticed that Rcpp may not be optimal for large data in as_cell_df

[bedantaguru]

Maybe I need to check memory status by memory.limit()

[bedantaguru]

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

[bedantaguru]

as_cell_df_r2 may be a good option to speedup as_cell_df

[bedantaguru]

enhead alternative is not in Rcpp

[bedantaguru]

Check out tidycells_nightly@Rcpp-dep

• LCS is in Rcpp (src)
• is_attachable is in Rcpp (src)

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.

Implement #36

[bedantaguru]

source code of LCS can be ported as optional module