https://github.com/cran/aqp
Tip revision: 8ec6bfd0c9e2f085f1ac5723951fed5e16acb779 authored by Dylan Beaudette on 14 July 2013, 16:14:50 UTC
version 1.5-3
version 1.5-3
Tip revision: 8ec6bfd
TODO
Urgent:
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** ADD to all modules using data.frame, melt, ... ?
## important: change the default behavior of data.frame and melt
opt.original <- options(stringsAsFactors = FALSE)
* determine optimal level of checking in validity method for SoilProfileCollection objects
* make slab() faster / more efficient
* slab() no longer supports weighted-aggregates
* integrate color styling with 'scales' package in plot.SoilProfileCollection
* more aggressive checking of ordering / IDs (especially rbind.SoilProfileCollection)
* Should we move to reshape2? It is faster. (PR)
* S4-cleanup of profile_compare
* raster::extract method
* sp::over method
* rgdal::spTransform method
Interesting:
------------
1. PCA by depth slice
2. user-defined slices for profile_compare()
3. sensitivity analysis on profile_compare()
4. equal-area splines
Optimisation of slab()
-----------------------
0. investigate use of data.table objects instead of data.frame objects
1. investigate .slab.fun.numeric.fast() vs. .slab.fun.numeric.default() for large data sets
2. ideas:
http://jeromyanglim.blogspot.com/2010/02/case-study-in-optimising-code-in-r.html
http://www.noamross.net/blog/2013/4/25/faster-talk.html
https://gist.github.com/noamross/5447008
3. Rprof results:
PctTime Stack
19.160 slab > standardGeneric > slab > aggregate > aggregate.formula > aggregate.data.frame > lapply > FUN > lapply > split > split.default > factor
15.350 slab > standardGeneric > slab > aggregate > aggregate.formula > aggregate.data.frame > lapply > FUN > lapply > FUN > hdquantile > outer > FUN > pbeta
13.064 slab > standardGeneric > slab > slice > standardGeneric > slice > ldply > list_to_dataframe > rbind.fill
4.293 slab > standardGeneric > slab > aggregate > aggregate.formula > eval > eval > model.frame > model.frame.default > sapply > lapply > as.list > as.list.data.frame
4.256 slab > standardGeneric > slab > melt > melt.data.frame > do.call > lapply > FUN > data.frame
3.977 slab > standardGeneric > slab > join > .join_all > cbind > cbind > data.frame
2.918 slab > standardGeneric > slab > slice > standardGeneric > slice > join > .join_first > join.keys > id > id_var > sort > sort.default > sort.int
2.639 slab > standardGeneric > slab > melt > melt.data.frame > do.call > rbind > rbind
2.602 slab > standardGeneric > slab > aggregate > aggregate.formula > aggregate.data.frame
2.026 slab > standardGeneric > slab > join > .join_all > cbind > [ > [.data.frame > make.unique
1.896 slab > standardGeneric > slab > slice > standardGeneric > slice > get.slice > apply > FUN > which
1.821 slab > standardGeneric > slab > aggregate > aggregate.formula > aggregate.data.frame > is.data.frame > [ > [.data.frame > structure
1.635 slab > standardGeneric > slab > melt > melt.data.frame > [[<- > [[<-.data.frame
1.096 slab > standardGeneric > slab > melt > melt.data.frame
0.966 slab > standardGeneric > slab > aggregate > aggregate.formula > aggregate.data.frame > lapply > FUN > lapply > FUN > hdquantile > outer
0.911 slab > standardGeneric > slab > aggregate > aggregate.formula > aggregate.data.frame > as.factor > factor > unique > unique.default
0.873 slab > standardGeneric > slab > slice > standardGeneric > slice > [ > [.data.frame > order
0.799 slab > standardGeneric > slab > slice > standardGeneric > slice > get.slice > apply > FUN
0.688 slab > standardGeneric > slab > aggregate > aggregate.formula > aggregate.data.frame > lapply > FUN > lapply > FUN > hdquantile
0.539 slab > standardGeneric > slab > join > .join_all > join_ids > vapply
Total Time: 107.62 seconds