Advanced and Fast Data Transformation in R
In fnth()/fquantile(), there has been a slight change to the weighted quantile algorithm. As outlined in the documentation, this algorithm gives weighted versions for all continuous quantile methods (type 7-9) in R by replacing sample quantities with their weighted counterparts. E.g., for the default quantile type 7, the continuous (lower) target element is (n - 1) * p. In the weighted algorithm, this became (sum(w) - mean(w)) * p and was compared to the cumulative sum of ordered (by x) weights, to preserve equivalence of the algorithms in cases where the weights are all equal. However, upon a second thought, the use of mean(w) does not really reflect a standard interpretation of the weights as frequencies. I have reasoned that using min(w) instead of mean(w) better reflects such an interpretation, as the minimum (non-zero) weight reflects the size of the smallest sampled unit. So the weighted quantile type 7 target is now (sum(w) - min(w)) * p, and also the other methods have been adjusted accordingly (note that zero weight observations are ignored in the algorithm).
This is more a Note than a change to the package: there is an issue with vctrs that users can encounter using collapse together with the tidyverse (especially ggplot2), which is that collapse internally optimizes computations on factors by giving them an additional "na.included" class if they are known to not contain any missing values. For example pivot(mtcars) gives a "variable" factor which has class c("factor", "na.included"), such that grouping on "variable" in subsequent operations is faster. Unfortunately, pivot(mtcars) |> ggplot(aes(y = value)) + geom_histogram() + facet_wrap( ~ variable) currently gives an error produced by vctrs, because vctrs does not implement a standard S3 method dispatch and thus does not ignore the "na.included" class. It turns out that the only way for me to deal with this is would be to swap the order of classes i.e. c("na.included", "factor"), import vctrs, and implement vec_ptype2 and vec_cast methods for "na.included" objects. This will never happen, as collapse is and will remain independent of the tidyverse. There are two ways you can deal with this: The first way is to remove the "na.included" class for ggplot2 e.g. facet_wrap( ~ set_class(variable, "factor")) or
facet_wrap( ~ factor(variable)) will both work. The second option is to define a function vec_ptype2.factor.factor <- function(x, y, ...) x in your global environment, which avoids vctrs performing extra checks on factor objects.
Fixed a signed integer overflow inside a hash function detected by CRAN checks (changing to unsigned int).
Updated the cheatsheet (see README.md).
Added global option 'stub' (default TRUE) to set_collapse. It is passed to the stub(s) arguments of the statistical operators, B, W, STD, HDW, HDW, L, D, Dlog, G (in .OPERATOR_FUN). By default these operators add a prefix/stub to matrix or data.frame columns transformed by them. Setting set_collapse(stub = FALSE) now allows to switch off this behavior such that columns are not prepended with a prefix by default.
roworder[v]() now also supports grouped data frames, but prints a message indicating that this is inefficient (also for indexed data). An additional argument verbose can be set to 0 to avoid such messages.
%in% with set_collapse(mask = "%in%") does not warn about overidentification when used with data frames.
Fixed several typos in the documentation.
collapse 2.0, released in Mid-October 2023, introduces fast table joins and data reshaping capabilities alongside other convenience functions, and enhances the packages global configurability, including interactive namespace control.
.data is used inside fsummarise() and fmutate(), and .cols = NULL, .data will contain all columns except for grouping columns (in-line with the .SD syntax of data.table). Before, .data contained all columns. The selection in .cols still refers to all columns, thus it is still possible to select all columns using e.g. grouped_data %>% fsummarise(some_expression_involving(.data), .cols = seq_col(.)).qsu(), argument vlabels was renamed to labels. But vlabels will continue to work.fsum(), fmean() and fprod() that returned NA if and only if there was a single integer followed by NA's e.g fsum(c(1L, NA, NA)) erroneously gave NA. This was caused by a C-level shortcut that returned NA when the first element of the vector had been reached (moving from back to front) without encountering any non-NA-values. The bug consisted in the content of the first element not being evaluated in this case. Note that this bug did not occur with real numbers, and also not in grouped execution. Thanks @blset for reporting (#432).Added join(): class-agnostic, vectorized, and (default) verbose joins for R, modeled after the polars API. Two different join algorithms are implemented: a hash-join (default, if sort = FALSE) and a sort-merge-join (if sort = TRUE).
Added pivot(): fast and easy data reshaping! It supports longer, wider and recast pivoting, including handling of variable labels, through a uniform and parsimonious API. It does not perform data aggregation, and by default does not check if the data is uniquely identified by the supplied ids. Underidentification for 'wide' and 'recast' pivots results in the last value being taken within each group. Users can toggle a duplicates check by setting check.dups = TRUE.
Added rowbind(): a fast class-agnostic alternative to rbind.data.frame() and data.table::rbindlist().
Added fmatch(): a fast match() function for vectors and data frames/lists. It is the workhorse function of join(), and also benefits ckmatch(), %!in%, and new operators %iin% and %!iin% (see below). It is also possible to set_collapse(mask = "%in%") to replace base::"%in%" using fmatch(). Thanks to fmatch(), these operators also all support data frames/lists of vectors, which are compared row-wise.
Added operators %iin% and %!iin%: these directly return indices, i.e. %[!]iin% is equivalent to which(x %[!]in% table). This is useful especially for subsetting where directly supplying indices is more efficient e.g. x[x %[!]iin% table] is faster than x[x %[!]in% table]. Similarly fsubset(wlddev, iso3c %iin% c("DEU", "ITA", "FRA")) is very fast.
Added vec(): efficiently turn matrices or data frames / lists into a single atomic vector. I am aware of multiple implementations in other packages, which are mostly inefficient. With atomic objects, vec() simply removes the attributes without copying the object, and with lists it directly calls C_pivot_longer.
set_collapse() now supports options 'mask' and 'remove', giving collapse a flexible namespace in the broadest sense that can be changed at any point within the active session:
'mask' supports base R or dplyr functions that can be masked into the faster collapse versions. E.g. library(collapse); set_collapse(mask = "unique") (or, equivalently, set_collapse(mask = "funique")) will create unique <- funique in the collapse namespace, export unique() from the namespace, and detach and attach the namespace again so R can find it. The re-attaching also ensures that collapse comes right after the global environment, implying that all it's functions will take priority over other libraries. Users can use fastverse::fastverse_conflicts() to check which functions are masked after using set_collapse(mask = ...). The option can be changed at any time. Using set_collapse(mask = NULL) removes all masked functions from the namespace, and can also be called simply to ensure collapse is at the top of the search path.
'remove' allows removing arbitrary functions from the collapse namespace. E.g. set_collapse(remove = "D") will remove the difference operator D(), which also exists in stats to calculate symbolic and algorithmic derivatives (this is a convenient example but not necessary since collapse::D is S3 generic and will call stats::D() on R calls, expressions or names). This is safe to do as it only modifies which objects are exported from the namespace (it does not truly remove objects from the namespace). This option can also be changed at any time. set_collapse(remove = NULL) will restore the exported namespace.
For both options there exist a number of convenient keywords to bulk-mask / remove functions. For example set_collapse(mask = "manip", remove = "shorthand") will mask all data manipulation functions such as mutate <- fmutate and remove all function shorthands such as mtt (i.e. abbreviations for frequently used functions that collapse supplies for faster coding / prototyping).
set_collapse() also supports options 'digits', 'verbose' and 'stable.algo', enhancing the global configurability of collapse.
qM() now also has a row.names.col argument in the second position allowing generation of rownames when converting data frame-like objects to matrix e.g. qM(iris, "Species") or qM(GGDC10S, 1:5) (interaction of id's).
as_factor_GRP() and finteraction() now have an argument sep = "." denoting the separator used for compound factor labels.
alloc() now has an additional argument simplify = TRUE. FALSE always returns list output.
frename() supports both new = old (pandas, used to far) and old = new (dplyr) style renaming conventions.
across() supports negative indices, also in grouped settings: these will select all variables apart from grouping variables.
TRA() allows shorthands "NA" for "replace_NA" and "fill" for "replace_fill".
group() experienced a minor speedup with >= 2 vectors as the first two vectors are now hashed jointly.
fquantile() with names = TRUE adds up to 1 digit after the comma in the percent-names, e.g. fquantile(airmiles, probs = 0.001) generates appropriate names (not 0% as in the previous version).
New vignette on collapse's handling of R objects.
print.descr() with groups and option perc = TRUE (the default) also shows percentages of the group frequencies for each variable.
funique(mtcars[NULL, ], sort = TRUE) gave an error (for data frame with zero rows). Thanks @NicChr (#406).
Added SIMD vectorization for fsubset().
vlengths() now also works for strings, and is hence a much faster version of both lengths() and nchar(). Also for atomic vectors the behavior is like lengths(), e.g. vlengths(rnorm(10)) gives rep(1L, 10).
In collap[v/g](), the ... argument is now placed after the custom argument instead of after the last argument, in order to better guard against unwanted partial argument matching. In particular, previously the n argument passed to fnth was partially matched to na.last. Thanks @ummel for alerting me of this (#421).
Using DATAPTR_RO to point to R lists because of the use of ALTLISTS on R-devel.
Replacing != loop controls for SIMD loops with < to ensure compatibility on all platforms. Thanks @albertus82 (#399).
Improvements in get_elem()/has_elem(): Option invert = TRUE is implemented more robustly, and a function passed to get_elem()/has_elem() is now applied to all elements in the list, including elements that are themselves list-like. This enables the use of inherits to find list-like objects inside a broader list structure e.g. get_elem(l, inherits, what = "lm") fetches all linear model objects inside l.
Fixed a small bug in descr() introduced in v1.9.0, producing an error if a data frame contained no numeric columns - because an internal function was not defined in that case. Also, POSIXct columns are handled better in print - preserving the time zone (thanks @cdignam-chwy #392).
fmean() and fsum() with g = NULL, as well as TRA(), setop(), and related operators %r+%, %+=% etc., setv() and fdist() now utilize Single Instruction Multiple Data (SIMD) vectorization by default (if OpenMP is enabled), enabling potentially very fast computing speeds. Whether these instructions are utilized during compilation depends on your system. In general, if you want to max out collapse on your system, consider compiling from source with CFLAGS += -O3 -march=native -fopenmp and CXXFLAGS += -O3 -march=native in your .R/Makevars.
Added functions fduplicated() and any_duplicated(), for vectors and lists / data frames. Thanks @NicChr (#373)
sort option added to set_collapse() to be able to set unordered grouping as a default. E.g. setting set_collapse(sort = FALSE) will affect collap(), BY(), GRP(), fgroup_by(), qF(), qG(), finteraction(), qtab() and internal use of these functions for ad-hoc grouping in fast statistical functions. Other uses of sort, for example in funique() where the default is sort = FALSE, are not affected by the global default setting.
Fixed a small bug in group() / funique() resulting in an unnecessary memory allocation error in rare cases. Thanks @NicChr (#381).
Further fix to an Address Sanitizer issue as required by CRAN (eliminating an unused out of bounds access at the end of a loop).
qsu() finally has a grouped_df method.
Added options option("collapse_nthreads") and option("collapse_na.rm"), which allow you to load collapse with different defaults e.g. through an .Rprofile or .fastverse configuration file. Once collapse is loaded, these options take no effect, and users need to use set_collapse() to change .op[["nthreads"]] and .op[["na.rm"]] interactively.
Exported method plot.psmat() (can be useful to plot time series matrices).