Comments (3)
got some speed ups with a combination of using data.table
and fastmatch
microbenchmark(
`old` = dedup_old(x),
`new` = dedup(x),
times = 10
)
#> Unit: milliseconds
#> expr min lq mean median uq max neval
#> old 617.9352 674.4674 709.8393 687.4735 766.6044 832.4115 10
#> new 365.2439 369.0146 395.8602 375.9335 398.7851 524.3056 10
will look for more spots that can be sped up
from scrubr.
This is already an impressive speedup, nice job!
For a data.frame as big as the waxwings one, I'm still afraid that my computer might fail (not because of speed but because of memory?) but I'll try it. 😸
from scrubr.
yeah, it still should be much faster, may try to do some c++ soon (unless all the bottleneck parts are already in it)
from scrubr.
Related Issues (20)
- rename clean_df, misleading name
- fix use of rgbif in examples
- imprecise coords HOT 16
- Collector data source: harvard botanist index HOT 2
- Filtering out Herbaria/Museum locations HOT 6
- move to tibble instead of internal compact data.frame tooling HOT 1
- use case: real world eg of data cleaning with gbif data HOT 4
- use case: data cleaning paper using GBIF data
- When tolerance is specified, dedup() returns larger dataset than original, with many exact duplicates HOT 3
- Extend coord_within for use with any polygon? HOT 1
- Adopt fixnames?
- Filter by biome/ecoregion HOT 9
- coord_within seems to not be working
- imprecise coords: refinements HOT 1
- improve eco_region() fxn: more data sources, etc.
- Fix failing cran checks HOT 13
- How to filter more than one ecoregion in the eco_region function HOT 3
- release new version - check problems
- Test failure with the development version of tibble HOT 1
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