na_insert() has new argument set to do this by reference.
Some moderate performance improvements to gsplit()/BY() and pivot().
alloc(list(1), 2) now gives list(1, 1) instead of list(list(1), list(1)), which can still be generated with alloc(list(1), 2, simplify = FALSE). This change also affects ftransform()/fmutate(), making, e.g., fmutate(data, y = list(1)) consistent with dplyr::mutate(data, y = list(1)). Thanks @MattAFiedler (#753).
fslice() now works with sf data frames.
collapse 2.1.0, released in March 2025, introduces a fast slicing function, an improved weighted quantile algorithm, a few convenience features, and removes some legacy functions from the package.
Potentially breaking changesFunctions pwNobs, as.factor_GRP, as.factor_qG, is.GRP, is.qG, is.unlistable, is.categorical, is.Date, as.numeric_factor, as.character_factor, and Date_vars, which were renamed in v1.6.0 by either replacing â.â with â_â or using all lower-case letters, and depreciated since then, are now finally removed from the package.
num_vars() (and thus also cat_vars() and collap()) were changed to a simpler C-definition of numeric data types which is more in-line with is.numeric(): is_numeric_C <- function(x) typeof(x) %in% c("integer", "double") && !inherits(x, c("factor", "Date", "POSIXct", "yearmon", "yearqtr")). The previous definition was: is_numeric_C_old <- function(x) typeof(x) %in% c("integer", "double") && (!is.object(x) || inherits(x, c("ts", "units", "integer64"))). Thus, the definition changed from including only certain classes to excluding the most important classes. Thanks @maouw for flagging this (#727).
Fixed some issues using collapse and the tidyverse together, particularly regarding tidyverse methods for âgrouped_dfâ - thanks @NicChr (#645).
More consistent handling of zero-length inputs - they are now also returned in fmean() and fmedian()/fnth() instead of returning NA (#628).
Added function fslice(): a fast alternative to dplyr::slice_[head|tail|min|max] that also works with matrices. Thanks @alinacherkas for the proposal and initial implementation (#725).
Added function groupv() as programmers version of group(), or rather, groupv() is now identical to the former group(), and group() now supports multiple vectors as input e.g. group(v1, v2). This is done for convenience and consistency with radixorder[v](). For backwards compatibility, group() also supports a single list as input.
join() has a new argument require allowing the user to generate messages or errors if the join operation is not successful enough:
join(df1, df2, require = list(x = 0.8, fail = "warning"))
#> Warning: Matched 75.0% of records in table df1 (x), but 80.0% is required
#> left join: df1[id1, id2] 3/4 (75%) <1:1st> df2[id1, id2] 3/4 (75%)
#> id1 id2 name age salary dept
#> 1 1 a John 35 60000 IT
#> 2 1 b Jane 28 NA <NA>
#> 3 2 b Bob 42 55000 Marketing
#> 4 3 c Carl 50 70000 Salespsmat() now has a fill argument to fill empty slots in matrix/array with other elements (default NULL/NA).The weighted quantile algorithm in fquantile()/fnth() was improved to a more theoretically sound method following excellent notes by Matthew Kay. It now also supports quantile type 4, but it does not skip zero weights anymore, as the new algorithm makes it difficult to skip them âon the flyâ. Note that the existing collapse algorithm already had very good properties after a bug fix in v2.0.17, but the new algorithm is more exact and also faster.
The collapse arXiv article has been updated and significantly enhanced. It is an excellent resource to get an overview of the package.
fmatch(factor(NA), NA) now gives 1 instead of NA. Thanks @NicChr (#675).
New developer focused vignette on developing with collapse.
Fixed minor CRAN issues (#676, #702).
Fixed bug with integer64 types in rowbind(). Thanks @arthurgailes for reporting and @aitap for providing a fix (#697).
collapse now also has a Bluesky account at https://bsky.app/profile/rcollapse.bsky.social.
Cases in pivot(..., how = "longer") with no values columns now no longer give an error. Thanks @alvarocombo for flagging this (#663).
Fixed bug in qF(c(4L, 1L, NA), sort = FALSE): hash function failure due to a coding bug. Thanks @mayer79 for flagging this (#666).
If x is already a qG object of the right properties, calling qG(x) now does not copy x anymore. Thanks @mayer79 (https://github.com/mayer79/effectplots/issues/11).
In GRP.default(), the "group.starts" attribute is always returned, even if there is only one group or every observation is its own group. Thanks @JamesThompsonC (#631).
Fixed a bug in pivot() if na.rm = TRUE and how = "wider"|"recast" and there are multiple value columns with different missingness patterns. In this case na_omit(values) was applied with default settings to the original (long) value columns, implying potential loss of information. The fix applies na_omit(values, prop = 1), i.e., only removes completely missing rows.
qDF()/qDT()/qTBL() now allow a length-2 vector of names to row.names.col if X is a named atomic vector, e.g., qDF(fmean(mtcars), c("cars", "mean")) gives the same as pivot(fmean(mtcars, drop = FALSE), names = list("car", "mean")).
Added a subsection on using internal (ad-hoc) grouping to the collapse for tidyverse users vignette.
qsu() now adds a WeightSum column giving the sum of (non-zero or missing) weights if the w argument is used. Thanks @mayer79 for suggesting (#650). For panel data (pid) the âBetweenâ sum of weights is also simply the number of groups, and the âWithinâ sum of weights is the âOverallâ sum of weights divided by the number of groups.
Fixed an inaccuracy in fquantile()/fnth() with weights: As per documentation the target sum is sumwp = (sum(w) - min(w)) * p, however, in practice, the weight of the minimum element of x was used instead of the minimum weight. Since the smallest element in the sample usually has a small weight this was unnoticed for a long while, but thanks to @Jahnic-kb now reported and fixed (#659).
Fixed a bug in recode_char() when regex = TRUE and the default argument was used. Thanks @alinacherkas for both reporting and fixing (#654).
Fixes an installation bug on some Linux systems (conflicting types) (#613).
collapse now enforces string encoding in fmatch() / join(), which caused problems if strings being matched had different encodings (#566, #579, and #618). To avoid noticeable performance implications, checks are done heuristically, i.e., the first, 25th, 50th and 75th percentile and last string of a character vector are checked, and if not UTF8, the entire vector is internally coerced to UTF8 strings before the matching process. In general, character vectors in R can contain strings of different encodings, but this is not the case with most regular data. For performance reasons, collapse assumes that character vectors are uniform in terms of string encoding. Heterogeneous strings should be coerced using tools like stringi::stri_trans_general(x, "latin-ascii").
Fixes a bug using qualified names for fast statistical functions inside across() (#621, thanks @alinacherkas).
collapse now depends on R >= 3.4.0 due to the enforcement of STRICT_R_HEADERS = 1 from R v4.5.0. In particular R API functions were renamed Calloc -> R_Calloc and Free -> R_Free.
Some changes on the C-side to move the package closer to C API compliance (demanded by R-Core). One notable change is that gsplit() no longer supports S4 objects (because SET_S4_OBJECT is not part of the API and asS4() is too expensive for tight loops). I cannot think of a single example where it would be necessary to split an S4 object, but if you do have applications please file an issue.
pivot() has new arguments FUN = "last" and FUN.args = NULL, allowing wide and recast pivots with aggregation (default last value as before). FUN currently supports a single function returning a scalar value. Fast Statistical Functions receive vectorized execution. FUN.args can be used to supply a list of function arguments, including data-length arguments such as weights. There are also a couple of internal functions callable using function strings: "first", "last", "count", "sum", "mean", "min", or "max". These are built into the reshaping C-code and thus extremely fast. Thanks @AdrianAntico for the request (#582).
join() now provides enhanced verbosity, indicating the average order of the join between the two tables, e.g.
join(data.frame(id = c(1, 2, 2, 4)), data.frame(id = c(rep(1,4), 2:3)))
#> left join: x[id] 3/4 (75%) <1.5:1st> y[id] 2/6 (33.3%)
#> id
#> 1 1
#> 2 2
#> 3 2
#> 4 4
join(data.frame(id = c(1, 2, 2, 4)), data.frame(id = c(rep(1,4), 2:3)), multiple = TRUE)
#> left join: x[id] 3/4 (75%) <1.5:2.5> y[id] 5/6 (83.3%)
#> id
#> 1 1
#> 2 1
#> 3 1
#> 4 1
#> 5 2
#> 6 2
#> 7 4In collap(), with multiple functions passed to FUN or catFUN and return = "long", the "Function" column is now generated as a factor variable instead of character (which is more efficient).
Updated âcollapse and sfâ vignette to reflect the recent support for units objects, and added a few more examples.
Fixed a bug in join() where a full join silently became a left join if there are no matches between the tables (#574). Thanks @D3SL for reporting.
Added function group_by_vars(): A standard evaluation version of fgroup_by() that is slimmer and safer for programming, e.g. data |> group_by_vars(ind1) |> collapg(custom = list(fmean = ind2, fsum = ind3)). Or, using magrittr:
library(magrittr)
set_collapse(mask = "manip") # for fgroup_vars -> group_vars
data %>%
group_by_vars(ind1) %>% {
add_vars(
group_vars(., "unique"),
get_vars(., ind2) %>% fmean(keep.g = FALSE) %>% add_stub("mean_"),
get_vars(., ind3) %>% fsum(keep.g = FALSE) %>% add_stub("sum_")
)
}Added function as_integer_factor() to turn factors/factor columns into integer vectors. as_numeric_factor() already exists, but is memory inefficient for most factors where levels can be integers.
join() now internally checks if the rows of the joined datasets match exactly. This check, using identical(m, seq_row(y)), is inexpensive, but, if TRUE, saves a full subset and deep copy of y. Thus join() now inherits the intelligence already present in functions like fsubset(), roworder() and funique() - a key for efficient data manipulation is simply doing less.
In join(), if attr = TRUE, the count option to fmatch() is always invoked, so that the attribute attached always has the same form, regardless of verbose or validate settings.
roworder[v]() has optional setting verbose = 2L to indicate if x is already sorted, making the call to roworder[v]() redundant.
collapse now explicitly supports xts/zoo and units objects and concurrently removes an additional check in the .default method of statistical functions that called the matrix method if is.matrix(x) && !inherits(x, "matrix"). This was a smart solution to account for the fact that xts objects are matrix-based but donât inherit the "matrix" class, thus wrongly calling the default method. The same is the case for units, but here, my recent more intensive engagement with spatial data convinced me that this should be changed. For one, under the previous heuristic solution, it was not possible to call the default method on a units matrix, e.g., fmean.default(st_distance(points_sf)) called fmean.matrix() and yielded a vector. This should not be the case. Secondly, aggregation e.g. fmean(st_distance(points_sf)) or fmean(st_distance(points_sf), g = group_vec) yielded a plain numeric object that lost the units class (in line with the general attribute handling principles). Therefore, I have now decided to remove the heuristic check within the default methods, and explicitly support zoo and units objects. For Fast Statistical Functions, the methods are FUN.zoo <- function(x, ...) if(is.matrix(x)) FUN.matrix(x, ...) else FUN.default(x, ...) and FUN.units <- function(x, ...) if(is.matrix(x)) copyMostAttrib(FUN.matrix(x, ...), x) else FUN.default(x, ...). While the behavior for xts/zoo remains the same, the behavior for units is enhanced, as now the class is preserved in aggregations (the .default method preserves attributes except for ts), and it is possible to manually invoke the .default method on a units matrix and obtain an aggregate statistic. This change may impact computations on other matrix based classes which donât inherit from "matrix" (mts does inherit from "matrix", and I am not aware of any other affected classes, but user code like m <- matrix(rnorm(25), 5); class(m) <- "bla"; fmean(m) will now yield a scalar instead of a vector. Such code must be adjusted to either class(m) <- c("bla", "matrix") or fmean.matrix(m)). Overall, the change makes collapse behave in a more standard and predictable way, and enhances its support for units objects central in the sf ecosystem.
fquantile() now also preserves the attributes of the input, in line with quantile().
An article on collapse has been submitted to the Journal of Statistical Software. The preprint is available through arXiv.
Removed magrittr from most documentation examples (using base pipe).
Improved plot.GRP a little bit - on request of JSS editors.
Fixed a bug in fmatch() when matching integer vectors to factors. This also affected join().
Improved cross-platform compatibility of OpenMP flags. Thanks @kalibera.
Added stub = TRUE argument to the grouped_df methods of Fast Statistical Functions supporting weights, to be able to remove or alter prefixes given to aggregated weights columns if keep.w = TRUE. Globally, users can set st_collapse(stub = FALSE) to disable this prefixing in all statistical functions and operators.
Added functions na_locf() and na_focb() for fast basic C implementations of these procedures (optionally by reference). replace_na() now also has a type argument which supports options "locf" and "focb" (default "const"), similar to data.table::nafill. The implementation also supports character data and list-columns (NULL/empty elements). Thanks @BenoitLondon for suggesting (#489). I note that na_locf() exists in some other packages (such as imputeTS) where it is implemented in R and has additional options. Users should utilize the flexible namespace i.e. set_collapse(remove = "na_locf") to deal with this.
Fixed a bug in weighted quantile estimation (fquantile()) that could lead to wrong/out-of-range estimates in some cases. Thanks @zander-prinsloo for reporting (#523).
Improved right join such that join column names of x instead of y are preserved. This is more consistent with the other joins when join columns in x and y have different names.
More fluent and safe interplay of âmaskâ and âremoveâ options in set_collapse(): it is now seamlessly possible to switch from any combination of âmaskâ and âremoveâ to any other combination without the need of setting them to NULL first.
In pivot(..., values = [multiple columns], labels = "new_labels_column", how = "wieder"), if the columns selected through values already have variable labels, they are concatenated with the new labels provided through "new_labels_col" using " - " as a separator (similar to names where the separator is "_").
whichv() and operators %==%, %!=% now properly account for missing double values, e.g. c(NA_real_, 1) %==% c(NA_real_, 1) yields c(1, 2) rather than 2. Thanks @eutwt for flagging this (#518).
In setv(X, v, R), if the type of R is greater than X e.g. setv(1:10, 1:3, 9.5), then a warning is issued that conversion of R to the lower type (real to integer in this case) may incur loss of information. Thanks @tony-aw for suggesting (#498).
frange() has an option finite = FALSE, like base::range. Thanks @MLopez-Ibanez for suggesting (#511).
varying.pdata.frame(..., any_group = FALSE) now unindexes the result (as should be the case).
Fixed bug in full join if verbose = 0. Thanks @zander-prinsloo for reporting.
Added argument multiple = FALSE to join(). Setting multiple = TRUE performs a multiple-matching join where a row in x is matched to all matching rows in y. The default FALSE just takes the first matching row in y.
Improved recode/replace functions. Notably, replace_outliers() now supports option value = "clip" to replace outliers with the respective upper/lower bounds, and also has option single.limit = "mad" which removes outliers exceeding a certain number of median absolute deviations. Furthermore, all functions now have a set argument which fully applies the transformations by reference.
Functions replace_NA and replace_Inf were renamed to replace_na and replace_inf to make the namespace a bit more consistent. The earlier versions remain available.
Fixed a serious bug in qsu() where higher order weighted statistics were erroneous, i.e. whenever qsu(x, ..., w = weights, higher = TRUE) was invoked, the âSDâ, âSkewâ and âKurtâ columns were wrong (if higher = FALSE the weighted âSDâ is correct). The reason is that there appears to be no straightforward generalization of Welfordâs Online Algorithm to higher-order weighted statistics. This was not detected earlier because the algorithm was only tested with unit weights. The fix involved replacing Welfordâs Algorithm for the higher-order weighted case by a 2-pass method, that additionally uses long doubles for higher-order terms. Thanks @randrescastaneda for reporting.
Fixed some unexpected behavior in t_list() where names âV1â, âV2â, etc. were assigned to unnamed inner lists. It now preserves the missing names. Thanks @orgadish for flagging this.
In join, the if y is an expression e.g. join(x = mtcars, y = subset(mtcars, mpg > 20)), then its name is not extracted but just set to "y". Before, the name of y would be captured as as.character(substitute(y))[1] = "subset" in this case. This is an improvement mainly for display purposes, but could also affect code if there are duplicate columns in both datasets and suffix was not provided in the join call: before, y-columns would be renamed using a (non-sensible) "_subset" suffix, but now using a "_y" suffix. Note that this only concerns cases where y is an expression rather than a single object.
Small performance improvements to %[!]in% operators: %!in% now uses is.na(fmatch(x, table)) rather than fmatch(x, table, 0L) == 0L, and %in%, if exported using set_collapse(mask = "%in%"|"special"|"all") is as.logical(fmatch(x, table, 0L)) instead of fmatch(x, table, 0L) > 0L. The latter are faster because comparison operators >, == with integers additionally need to check for NAâs (= the smallest integer in C).
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 âstubâ (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 transformed matrix or data.frame columns. 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 anymore about overidentification when used with data frames (i.e. using overid = 2 in fmatch()).
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.
Potentially breaking changes.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 NAâs 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 âwideâ and ârecastâ 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 âmaskâ and âremoveâ, giving collapse a flexible namespace in the broadest sense that can be changed at any point within the active session:
âmaskâ 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 itâs 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.
âremoveâ 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 âdigitsâ, âverboseâ and âstable.algoâ, 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 idâs).
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 collapseâs Handling of R Objects: provides an overview of collapseâs (internal) class-agnostic R programming framework.
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).
Fixed minor C/C++ issues flagged by CRANâs detailed checks.
Added functions set_collapse() and get_collapse(), allowing you to globally set defaults for the nthreads and na.rm arguments to all functions in the package. E.g. set_collapse(nthreads = 4, na.rm = FALSE) could be a suitable setting for larger data without missing values. This is implemented using an internal environment by the name of .op, such that these defaults are received using e.g. .op[["nthreads"]], at the computational cost of a few nanoseconds (8-10x faster than getOption("nthreads") which would take about 1 microsecond). .op is not accessible by the user, so function get_collapse() can be used to retrieve settings. Exempt from this are functions .quantile, and a new function .range (alias of frange), which go directly to C for maximum performance in repeated executions, and are not affected by these global settings. Function descr(), which internally calls a bunch of statistical functions, is also not affected by these settings.
Further improvements in thread safety for fsum() and fmean() in grouped computations across data frame columns. All OpenMP enabled functions in collapse can now be considered thread safe i.e. they pass the full battery of tests in multithreaded mode.
collapse 1.9.0 released mid of January 2023, provides improvements in performance and versatility in many areas, as well as greater statistical capabilities, most notably efficient (grouped, weighted) estimation of sample quantiles.
Changes to functionalityAll functions renamed in collapse 1.6.0 are now depreciated, to be removed end of 2023. These functions had already been giving messages since v1.6.0. See help("collapse-renamed").
The lead operator F() is not exported anymore from the package namespace, to avoid clashes with base::F flagged by multiple people. The operator is still part of the package and can be accessed using collapse:::F. I have also added an option "collapse_export_F", such that setting options(collapse_export_F = TRUE) before loading the package exports the operator as before. Thanks @matthewross07 (#100), @edrubin (#194), and @arthurgailes (#347).
Function fnth() has a new default ties = "q7", which gives the same result as quantile(..., type = 7) (Râs default). More details below.
fmode() gave wrong results for singleton groups (groups of size 1) on unsorted data. I had optimized fmode() for singleton groups to directly return the corresponding element, but it did not access the element through the (internal) ordering vector, so the first element/row of the entire vector/data was taken. The same mistake occurred for fndistinct if singleton groups were NA, which were counted as 1 instead of 0 under the na.rm = TRUE default (provided the first element of the vector/data was not NA). The mistake did not occur with data sorted by the groups, because here the data pointer already pointed to the first element of the group. (My apologies for this bug, it took me more than half a year to discover it, using collapse on a daily basis, and it escaped 700 unit tests as well).
Function groupid(x, na.skip = TRUE) returned uninitialized first elements if the first values in x where NA. Thanks for reporting @Henrik-P (#335).
Fixed a bug in the .names argument to across(). Passing a naming function such as .names = function(c, f) paste0(c, "-", f) now works as intended i.e. the function is applied to all combinations of columns (c) and functions (f) using outer(). Previously this was just internally evaluated as .names(cols, funs), which did not work if there were multiple cols and multiple funs. There is also now a possibility to set .names = "flip", which names columns f_c instead of c_f.
fnrow() was rewritten in C and also supports data frames with 0 columns. Similarly for seq_row(). Thanks @NicChr (#344).
Added functions fcount() and fcountv(): a versatile and blazing fast alternative to dplyr::count. It also works with vectors, matrices, as well as grouped and indexed data.
Added function fquantile(): Fast (weighted) continuous quantile estimation (methods 5-9 following Hyndman and Fan (1996)), implemented fully in C based on quickselect and radixsort algorithms, and also supports an ordering vector as optional input to speed up the process. It is up to 2x faster than stats::quantile on larger vectors, but also especially fast on smaller data, where the R overhead of stats::quantile becomes burdensome. For maximum performance during repeated executions, a programmers version .quantile() with different defaults is also provided.
Added function fdist(): A fast and versatile replacement for stats::dist. It computes a full euclidean distance matrix around 4x faster than stats::dist in serial mode, with additional gains possible through multithreading along the distance matrix columns (decreasing thread loads as the matrix is lower triangular). It also supports computing the distance of a matrix with a single row-vector, or simply between two vectors. E.g. fdist(mat, mat[1, ]) is the same as sqrt(colSums((t(mat) - mat[1, ])^2))), but about 20x faster in serial mode, and fdist(x, y) is the same as sqrt(sum((x-y)^2)), about 3x faster in serial mode. In both cases (sub-column level) multithreading is available. Note that fdist does not skip missing values i.e. NAâs will result in NA distances. There is also no internal implementation for integers or data frames. Such inputs will be coerced to numeric matrices.
Added function GRPid() to easily fetch the group id from a grouping object, especially inside grouped fmutate() calls. This addition was warranted especially by the new improved fnth.default() method which allows orderings to be supplied for performance improvements. See commends on fnth() and the example provided below.
fsummarize() was added as a synonym to fsummarise. Thanks @arthurgailes for the PR.
C API: collapse exports around 40 C functions that provide functionality that is either convenient or rather complicated to implement from scratch. The exported functions can be found at the bottom of src/ExportSymbols.c. The API does not include the Fast Statistical Functions, which I thought are too closely related to how collapse works internally to be of much use to a C programmer (e.g. they expect grouping objects or certain kinds of integer vectors). But you are free to request the export of additional functions, including C++ functions.
fnth() and fmedian() were rewritten in C, with significant gains in performance and versatility. Notably, fnth() now supports (grouped, weighted) continuous quantile estimation like fquantile() (fmedian(), which is a wrapper around fnth(), can also estimate various quantile based weighted medians). The new default for fnth() is ties = "q7", which gives the same result as (f)quantile(..., type = 7) (Râs default). OpenMP multithreading across groups is also much more effective in both the weighted and unweighted case. Finally, fnth.default gained an additional argument o to pass an ordering vector, which can dramatically speed up repeated invocations of the function on the dame data:
# Estimating multiple weighted-grouped quantiles on mpg: pre-computing an ordering provides extra speed.
mtcars %>% fgroup_by(cyl, vs, am) %>%
fmutate(o = radixorder(GRPid(), mpg)) %>% # On grouped data, need to account for GRPid()
fsummarise(mpg_Q1 = fnth(mpg, 0.25, o = o, w = wt),
mpg_median = fmedian(mpg, o = o, w = wt),
mpg_Q3 = fnth(mpg, 0.75, o = o, w = wt))
# Note that without weights this is not always faster. Quickselect can be very efficient, so it depends
# on the data, the number of groups, whether they are sorted (which speeds up radixorder), etc...BY now supports data-length arguments to be passed e.g. BY(mtcars, mtcars$cyl, fquantile, w = mtcars$wt), making it effectively a generic grouped mapply function as well. Furthermore, the grouped_df method now also expands grouping columns for output length > 1.
collap(), which internally uses BY with non-Fast Statistical Functions, now also supports arbitrary further arguments passed down to functions to be split by groups. Thus users can also apply custom weighted functions with collap(). Furthermore, the parsing of the FUN, catFUN and wFUN arguments was improved and brought in-line with the parsing of .fns in across(). The main benefit of this is that Fast Statistical Functions are now also detected and optimizations carried out when passed in a list providing a new name e.g. collap(data, ~ id, list(mean = fmean)) is now optimized! Thanks @ttrodrigz (#358) for requesting this.
descr(), by virtue of fquantile and the improvements to BY, supports full-blown grouped and weighted descriptions of data. This is implemented through additional by and w arguments. The function has also been turned into an S3 generic, with a default and a âgrouped_dfâ method. The âdescrâ methods as.data.frame and print also feature various improvements, and a new compact argument to print.descr, allowing a more compact printout. Users will also notice improved performance, mainly due to fquantile: on the M1 descr(wlddev) is now 2x faster than summary(wlddev), and 41x faster than Hmisc::describe(wlddev). Thanks @statzhero for the request (#355).
radixorder is about 25% faster on characters and doubles. This also benefits grouping performance. Note that group() may still be substantially faster on unsorted data, so if performance is critical try the sort = FALSE argument to functions like fgroup_by and compare.
Most list processing functions are noticeably faster, as checking the data types of elements in a list is now also done in C, and I have made some improvements to collapseâs version of rbindlist() (used in unlist2d(), and various other places).
fsummarise and fmutate gained an ability to evaluate arbitrary expressions that result in lists / data frames without the need to use across(). For example: mtcars |> fgroup_by(cyl, vs, am) |> fsummarise(mctl(cor(cbind(mpg, wt, carb)), names = TRUE)) or mtcars |> fgroup_by(cyl) |> fsummarise(mctl(lmtest::coeftest(lm(mpg ~ wt + carb)), names = TRUE)). There is also the possibility to compute expressions using .data e.g. mtcars |> fgroup_by(cyl) |> fsummarise(mctl(lmtest::coeftest(lm(mpg ~ wt + carb, .data)), names = TRUE)) yields the same thing, but is less efficient because the whole dataset (including âcylâ) is split by groups. For greater efficiency and convenience, you can pre-select columns using a global .cols argument, e.g. mtcars |> fgroup_by(cyl, vs, am) |> fsummarise(mctl(cor(.data), names = TRUE), .cols = .c(mpg, wt, carb)) gives the same as above. Three Notes about this:
fmutate, have the same length as the data (in each group)..data is used, the entire expression (expr) will be turned into a function of .data (function(.data) expr), which means columns are only available when accessed through .data e.g. .data$col1.fsummarise supports computations with mixed result lengths e.g. mtcars |> fgroup_by(cyl) |> fsummarise(N = GRPN(), mean_mpg = fmean(mpg), quantile_mpg = fquantile(mpg)), as long as all computations result in either length 1 or length k vectors, where k is the maximum result length (e.g. for fquantile with default settings k = 5).
List extraction function get_elem() now has an option invert = TRUE (default FALSE) to remove matching elements from a (nested) list. Also the functionality of argument keep.class = TRUE is implemented in a better way, such that the default keep.class = FALSE toggles classes from (non-matched) list-like objects inside the list to be removed.
num_vars() has become a bit smarter: columns of class âtsâ and âunitsâ are now also recognized as numeric. In general, users should be aware that num_vars() does not regard any R methods defined for is.numeric(), it is implemented in C and simply checks whether objects are of type integer or double, and do not have a class. The addition of these two exceptions now guards against two common cases where num_vars() may give undesirable outcomes. Note that num_vars() is also called in collap() to distinguish between numeric (FUN) and non-numeric (catFUN) columns.
Improvements to setv() and copyv(), making them more robust to borderline cases: integer(0) passed to v does nothing (instead of error), and it is also possible to pass a single real index if vind1 = TRUE i.e. passing 1 instead of 1L does not produce an error.
alloc() now works with all types of objects i.e. it can replicate any object. If the input is non-atomic, atomic with length > 1 or NULL, the output is a list of these objects, e.g. alloc(NULL, 10) gives a length 10 list of NULL objects, or alloc(mtcars, 10) gives a list of mtcars datasets. Note that in the latter case the datasets are not deep-copied, so no additional memory is consumed.
missing_cases() and na_omit() have gained an argument prop = 0, indicating the proportion of values missing for the case to be considered missing/to be omitted. The default value of 0 indicates that at least 1 value must be missing. Of course setting prop = 1 indicates that all values must be missing. For data frames/lists the checking is done efficiently in C. For matrices this is currently still implemented using rowSums(is.na(X)) >= max(as.integer(prop * ncol(X)), 1L), so the performance is less than optimal.
missing_cases() has an extra argument count = FALSE. Setting count = TRUE returns the case-wise missing value count (by cols).
Functions frename() and setrename() have an additional argument .nse = TRUE, conforming to the default non-standard evaluation of tagged vector expressions e.g. frename(mtcars, mpg = newname) is the same as frename(mtcars, mpg = "newname"). Setting .nse = FALSE allows newname to be a variable holding a name e.g. newname = "othername"; frename(mtcars, mpg = newname, .nse = FALSE). Another use of the argument is that a (named) character vector can now be passed to the function to rename a (subset of) columns e.g. cvec = letters[1:3]; frename(mtcars, cvec, cols = 4:6, .nse = FALSE) (this works even with .nse = TRUE), and names(cvec) = c("cyl", "vs", "am"); frename(mtcars, cvec, .nse = FALSE). Furthermore, setrename() now also returns the renamed data invisibly, and relabel() and setrelabel() have also gained similar flexibility to allow (named) lists or vectors of variable labels to be passed. Note that these function have no NSE capabilities, so they work essentially like frename(..., .nse = FALSE).
Function add_vars() became a bit more flexible and also allows single vectors to be added with tags e.g. add_vars(mtcars, log_mpg = log(mtcars$mpg), STD(mtcars)), similar to cbind. However add_vars() continues to not replicate length 1 inputs.
Safer multithreading: OpenMP multithreading over parts of the R API is minimized, reducing errors that occurred especially when multithreading across data frame columns. Also the number of threads supplied by the user to all OpenMP enabled functions is ensured to not exceed either of omp_get_num_procs(), omp_get_thread_limit(), and omp_get_max_threads().
Fixed some warnings on rchk and newer C compilers (LLVM clang 10+).
.pseries / .indexed_series methods also change the implicit class of the vector (attached after "pseries"), if the data type changed. e.g. calling a function like fgrowth on an integer pseries changed the data type to double, but the âintegerâ class was still attached after âpseriesâ.
Fixed bad testing for SE inputs in fgroup_by() and findex_by(). See #320.
Added rsplit.matrix method.
descr() now by default also reports 10% and 90% quantiles for numeric variables (in line with STATAâs detailed summary statistics), and can also be applied to âpseriesâ / âindexed_seriesâ. Furthermore, descr() itself now has an argument stepwise such that descr(big_data, stepwise = TRUE) yields computation of summary statistics on a variable-by-variable basis (and the finished âdescrâ object is returned invisibly). The printed result is thus identical to print(descr(big_data), stepwise = TRUE), with the difference that the latter first does the entire computation whereas the former computes statistics on demand.
Function ss() has a new argument check = TRUE. Setting check = FALSE allows subsetting data frames / lists with positive integers without checking whether integers are positive or in-range. For programmers.
Function get_vars() has a new argument rename allowing select-renaming of columns in standard evaluation programming, e.g. get_vars(mtcars, c(newname = "cyl", "vs", "am"), rename = TRUE). The default is rename = FALSE, to warrant full backwards compatibility. See #327.
Added helper function setattrib(), to set a new attribute list for an object by reference + invisible return. This is different from the existing function setAttrib() (note the capital A), which takes a shallow copy of list-like objects and returns the result.
flm and fFtest are now internal generic with an added formula method e.g. flm(mpg ~ hp + carb, mtcars, weights = wt) or fFtest(mpg ~ hp + carb | vs + am, mtcars, weights = wt) in addition to the programming interface. Thanks to Grant McDermott for suggesting.
Added method as.data.frame.qsu, to efficiently turn the default array outputs from qsu() into tidy data frames.
Major improvements to setv and copyv, generalizing the scope of operations that can be performed to all common cases. This means that even simple base R operations such as X[v] <- R can now be done significantly faster using setv(X, v, R).
n and qtab can now be added to options("collapse_mask") e.g. options(collapse_mask = c("manip", "helper", "n", "qtab")). This will export a function n() to get the (group) count in fsummarise and fmutate (which can also always be done using GRPN() but n() is more familiar to dplyr users), and will mask table() with qtab(), which is principally a fast drop-in replacement, but with some different further arguments.
Added C-level helper function all_funs, which fetches all the functions called in an expression, similar to setdiff(all.names(x), all.vars(x)) but better because it takes account of the syntax. For example let x = quote(sum(sum)) i.e. we are summing a column named sum. Then all.names(x) = c("sum", "sum") and all.vars(x) = "sum" so that the difference is character(0), whereas all_funs(x) returns "sum". This function makes collapse smarter when parsing expressions in fsummarise and fmutate and deciding which ones to vectorize.
Fixed a bug in fscale.pdata.frame where the default C++ method was being called instead of the list method (i.e. the method didnât work at all).
Fixed 2 minor rchk issues (the remaining ones are spurious).
fsum has an additional argument fill = TRUE (default FALSE) that initializes the result vector with 0 instead of NA when na.rm = TRUE, so that fsum(NA, fill = TRUE) gives 0 like base::sum(NA, na.rm = TRUE).
Slight performance increase in fmean with groups if na.rm = TRUE (the default).
Significant performance improvement when using base R expressions involving multiple functions and one column e.g. mid_col = (min(col) + max(col)) / 2 or lorentz_col = cumsum(sort(col)) / sum(col) etc. inside fsummarise and fmutate. Instead of evaluating such expressions on a data subset of one column for each group, they are now turned into a function e.g. function(x) cumsum(sort(x)) / sum(x) which is applied to a single vector split by groups.
fsummarise now also adds groupings to transformation functions and operators, which allows full vectorization of more complex tasks involving transformations which are subsequently aggregated. A prime example is grouped bivariate linear model fitting, which can now be done using mtcars |> fgroup_by(cyl) |> fsummarise(slope = fsum(W(mpg), hp) / fsum(W(mpg)^2)). Before 1.8.7 it was necessary to do a mutate step first e.g. mtcars |> fgroup_by(cyl) |> fmutate(dm_mpg = W(mpg)) |> fsummarise(slope = fsum(dm_mpg, hp) / fsum(dm_mpg^2)), because fsummarise did not add groupings to transformation functions like fwithin/W. Thanks to Brodie Gaslam for making me aware of this.
Argument return.groups from GRP.default is now also available in fgroup_by, allowing grouped data frames without materializing the unique grouping columns. This allows more efficient mutate-only operations e.g. mtcars |> fgroup_by(cyl, return.groups = FALSE) |> fmutate(across(hp:carb, fscale)). Similarly for aggregation with dropping of grouping columns mtcars |> fgroup_by(cyl, return.groups = FALSE) |> fmean() is equivalent and faster than mtcars |> fgroup_by(cyl) |> fmean(keep.group_vars = FALSE).
Significant speed improvement in qF/qG (factor-generation) for character vectors with more than 100,000 obs and many levels if sort = TRUE (the default). For details see the method argument of ?qF.
Optimizations in fmode and fndistinct for singleton groups.
Fixed some rchk issues found by Thomas Kalibera from CRAN.
faster funique.default method.
group now also internally optimizes on âqGâ objects.
Added function fnunique (yet another alternative to data.table::uniqueN, kit::uniqLen or dplyr::n_distinct, and principally a simple wrapper for attr(group(x), "N.groups")). At present fnunique generally outperforms the others on data frames.
finteraction has an additional argument factor = TRUE. Setting factor = FALSE returns a âqGâ object, which is more efficient if just an integer id but no factor object itself is required.
Operators (see .OPERATOR_FUN) have been improved a bit such that id-variables selected in the .data.frame (by, w or t arguments) or .pdata.frame methods (variables in the index) are not computed upon even if they are numeric (since the default is cols = is.numeric). In general, if cols is a function used to select columns of a certain data type, id variables are excluded from computation even if they are of that data type. It is still possible to compute on id variables by explicitly selecting them using names or indices passed to cols, or including them in the lhs of a formula passed to by.
Further efforts to facilitate adding the group-count in fsummarise and fmutate:
options(collapse_mask = "all") before loading the package, an additional function n() is exported that works just like dplyr:::n().GRPN(). The previous uses of GRPN are unaltered i.e. GRPN can also:
data |> gby(id) |> GRPN() or data %>% gby(id) %>% ftransform(N = GRPN(.)) (note the dot).fsubset(data, GRPN(id) > 10L) or fsubset(data, GRPN(list(id1, id2)) > 10L) or GRPN(data, by = ~ id1 + id2).collapse 1.8.0, released mid of May 2022, brings enhanced support for indexed computations on time series and panel data by introducing flexible âindexed_frameâ and âindexed_seriesâ classes and surrounding infrastructure, sets a modest start to OpenMP multithreading as well as data transformation by reference in statistical functions, and enhances the packages descriptive statistics toolset.
Changes to functionalityFunctions Recode, replace_non_finite, depreciated since collapse v1.1.0 and is.regular, depreciated since collapse v1.5.1 and clashing with a more important function in the zoo package, are now removed.
Fast Statistical Functions operating on numeric data (such as fmean, fmedian, fsum, fmin, fmax, â¦) now preserve attributes in more cases. Previously these functions did not preserve attributes for simple computations using the default method, and only preserved attributes in grouped computations if !is.object(x) (see NEWS section for collapse 1.4.0). This meant that fmin and fmax did not preserve the attributes of Date or POSIXct objects, and none of these functions preserved âunitsâ objects (used a lot by the sf package). Now, attributes are preserved if !inherits(x, "ts"), that is the new default of these functions is to generally keep attributes, except for âtsâ objects where doing so obviously causes an unwanted error (note that âxtsâ and others are handled by the matrix or data.frame method where other principles apply, see NEWS for 1.4.0). An exception are the functions fnobs and fndistinct where the previous default is kept.
Time Series Functions flag, fdiff, fgrowth and psacf/pspacf/psccf (and the operators L/F/D/Dlog/G) now internally process time objects passed to the t argument (where is.object(t) && is.numeric(unclass(t))) via a new function called timeid which turns them into integer vectors based on the greatest common divisor (GCD) (see below). Previously such objects were converted to factor. This can change behavior of code e.g. a âDateâ variable representing monthly data may be regular when converted to factor, but is now irregular and regarded as daily data (with a GCD of 1) because of the different day counts of the months. Users should fix such code by either by calling qG on the time variable (for grouping / factor-conversion) or using appropriate classes e.g. zoo::yearmon. Note that plain numeric vectors where !is.object(t) are still used directly for indexation without passing them through timeid (which can still be applied manually if desired).
BY now has an argument reorder = TRUE, which casts elements in the original order if NROW(result) == NROW(x) (like fmutate). Previously the result was just in order of the groups, regardless of the length of the output. To obtain the former outcome users need to set reorder = FALSE.
options("collapse_DT_alloccol") was removed, the default is now fixed at 100. The reason is that data.table automatically expands the range of overallocated columns if required (so the option is not really necessary), and calling R options from C slows down C code and can cause problems in parallel code.
Fixed a bug in fcumsum that caused a segfault during grouped operations on larger data, due to flawed internal memory allocation. Thanks @Gulde91 for reporting #237.
Fixed a bug in across caused by two function(x) statements being passed in a list e.g. mtcars |> fsummarise(acr(mpg, list(ssdd = function(x) sd(x), mu = function(x) mean(x)))). Thanks @trang1618 for reporting #233.
Fixed an issue in across() when logical vectors were used to select column on grouped data e.g. mtcars %>% gby(vs, am) %>% smr(acr(startsWith(names(.), "c"), fmean)) now works without error.
qsu gives proper output for length 1 vectors e.g. qsu(1).
collapse depends on R > 3.3.0, due to the use of newer C-level macros introduced then. The earlier indication of R > 2.1.0 was only based on R-level code and misleading. Thanks @ben-schwen for reporting #236. I will try to maintain this dependency for as long as possible, without being too restrained by development in Râs C API and the ALTREP system in particular, which collapse might utilize in the future.
Introduction of âindexed_frameâ,âindexed_seriesâ and âindex_dfâ classes: fast and flexible indexed time series and panel data classes that inherit from plmâs âpdata.frameâ, âpseriesâ and âpindexâ classes. These classes take full advantage of collapseâs computational infrastructure, are class-agnostic i.e. they can be superimposed upon any data frame or vector/matrix like object while maintaining most of the functionality of that object, support both time series and panel data, natively handle irregularity, and supports ad-hoc computations inside arbitrary data masking functions and model formulas. This infrastructure comprises of additional functions and methods, and modification of some existing functions and âpdata.frameâ / âpseriesâ methods.
New functions: findex_by/iby, findex/ix, unindex, reindex, is_irregular, to_plm.
New methods: [.indexed_series, [.indexed_frame, [<-.indexed_frame, $.indexed_frame, $<-.indexed_frame, [[.indexed_frame, [[<-.indexed_frame, [.index_df, fsubset.pseries, fsubset.pdata.frame, funique.pseries, funique.pdata.frame, roworder(v) (internal) na_omit (internal), print.indexed_series, print.indexed_frame, print.index_df, Math.indexed_series, Ops.indexed_series.
Modification of âpseriesâ and âpdata.frameâ methods for functions flag/L/F, fdiff/D/Dlog, fgrowth/G, fcumsum, psmat, psacf/pspacf/psccf, fscale/STD, fbetween/B, fwithin/W, fhdbetween/HDB, fhdwithin/HDW, qsu and varying to take advantage of âindexed_frameâ and âindexed_seriesâ while continuing to work as before with âpdata.frameâ and âpseriesâ.
For more information and details see help("indexing").
Added function timeid: Generation of an integer-id/time-factor from time or date sequences represented by integer of double vectors (such as âDateâ, âPOSIXctâ, âtsâ, âyearmonâ, âyearquarterâ or plain integers / doubles) by a numerically quite robust greatest common divisor method (see below). This function is used internally in findex_by, reindex and also in evaluation of the t argument to functions like flag/fdiff/fgrowth whenever is.object(t) && is.numeric(unclass(t)) (see also note above).
Programming helper function vgcd to efficiently compute the greatest common divisor from a vector or positive integer or double values (which should ideally be unique and sorted as well, timeid uses vgcd(sort(unique(diff(sort(unique(na_rm(x)))))))). Precision for doubles is up to 6 digits.
Programming helper function frange: A significantly faster alternative to base::range, which calls both min and max. Note that frange inherits collapseâs global na.rm = TRUE default.
Added function qtab/qtable: A versatile and computationally more efficient alternative to base::table. Notably, it also supports tabulations with frequency weights, and computation of a statistic over combinations of variables. Objects are of class âqtabâ that inherits from âtableâ. Thus all âtableâ methods apply to it.
TRA was rewritten in C, and now has an additional argument set = TRUE which toggles data transformation by reference. The function setTRA was added as a shortcut which additionally returns the result invisibly. Since TRA is usually accessed internally through the like-named argument to Fast Statistical Functions, passing set = TRUE to those functions yields an internal call to setTRA. For example fmedian(num_vars(iris), g = iris$Species, TRA = "-", set = TRUE) subtracts the species-wise median from the numeric variables in the iris dataset, modifying the data in place and returning the result invisibly. Similarly the argument can be added in other workflows such as iris |> fgroup_by(Species) |> fmutate(across(1:2, fmedian, set = TRUE)) or mtcars |> ftransform(mpg = mpg %+=% hp, wt = fsd(wt, cyl, TRA = "replace_fill", set = TRUE)). Note that such chains must be ended by invisible() if no printout is wanted.
Exported helper function greorder, the companion to gsplit to reorder output in fmutate (and now also in BY): let g be a âGRPâ object (or something coercible such as a vector) and x a vector, then greorder orders data in y = unlist(gsplit(x, g)) such that identical(greorder(y, g), x).
fmean, fprod, fmode and fndistinct were rewritten in C, providing performance improvements, particularly in fmode and fndistinct, and improvements for integers in fmean and fprod.
OpenMP multithreading in fsum, fmean, fmedian, fnth, fmode and fndistinct, implemented via an additional nthreads argument. The default is to use 1 thread, which internally calls a serial version of the code in fsum and fmean (thus no change in the default behavior). The plan is to slowly roll this out over all statistical functions and then introduce options to set alternative global defaults. Multi-threading internally works different for different functions, see the nthreads argument documentation of a particular function. Unfortunately I currently cannot guarantee thread safety, as parallelization of complex loops entails some tricky bugs and I have limited time to sort these out. So please report bugs, and if you happen to have experience with OpenMP please consider examining the code and making some suggestions.
TRA has an additional option "replace_NA", e.g. wlddev |> fgroup_by(iso3c) |> fmutate(across(PCGDP:POP, fmedian, TRA = "replace_NA")) performs median value imputation of missing values. Similarly for a matrix X <- matrix(na_insert(rnorm(1e7)), ncol = 100), fmedian(X, TRA = "replace_NA", set = TRUE) (column-wise median imputation by reference).
All Fast Statistical Functions support zero group sizes (e.g. grouping with a factor that has unused levels will always produce an output of length nlevels(x) with 0 or NA elements for the unused levels). Previously this produced an error message with counting/ordinal functions fmode, fndistinct, fnth and fmedian.
âGRPâ objects now also contain a âgroup.startsâ item in the 8âth slot that gives the first positions of the unique groups, and is returned alongside the groups whenever return.groups = TRUE. This now benefits ffirst when invoked with na.rm = FALSE, e.g. wlddev %>% fgroup_by(country) %>% ffirst(na.rm = FALSE) is now just as efficient as funique(wlddev, cols = "country"). Note that no additional computing cost is incurred by preserving the âgroup.startsâ information.
Conversion methods GRP.factor, GRP.qG, GRP.pseries, GRP.pdata.frame and GRP.grouped_df now also efficiently check if grouping vectors are sorted (the information is stored in the âorderedâ element of âGRPâ objects). This leads to performance improvements in gsplit / greorder and dependent functions such as BY and rsplit if factors are sorted.
descr() received some performance improvements (up to 2x for categorical data), and has an additional argument sort.table, allowing frequency tables for categorical variables to be sorted by frequency ("freq") or by table values ("value"). The new default is ("freq"), which presents tables in decreasing order of frequency. A method [.descr was added allowing âdescrâ objects to be subset like a list. The print method was also enhanced, and by default now prints 14 values with the highest frequency and groups the remaining values into a single ... %s Others category. Furthermore, if there are any missing values in the column, the percentage of values missing is now printed behind Statistics. Additional arguments reverse and stepwise allow printing in reverse order and/or one variable at a time.
whichv (and operators %==%, %!=%) now also support comparisons of equal-length arguments e.g. 1:3 %==% 1:3. Note that this should not be used to compare 2 factors.
Added some code to the .onLoad function that checks for the existence of a .fastverse configuration file containing a setting for _opt_collapse_mask: If found the code makes sure that the option takes effect before the package is loaded. This means that inside projects using the fastverse and options("collapse_mask") to replace base R / dplyr functions, collapse cannot be loaded without the masking being applied, making it more secure to utilize this feature. For more information about function masking see help("collapse-options") and for .fastverse configuration files see the fastverse vignette.
Added hidden .list methods for fhdwithin/HDW and fhdbetween/HDB. As for the other .FAST_FUN this is just a wrapper for the data frame method and meant to be used on unclassed data frames.
ss() supports unnamed lists / data frames.
The t and w arguments in âgrouped_dfâ methods (NSE) and where formula input is allowed, supports ad-hoc transformations. E.g. wlddev %>% gby(iso3c) %>% flag(t = qG(date)) or L(wlddev, 1, ~ iso3c, ~qG(date)), similarly qsu(wlddev, w = ~ log(POP)), wlddev %>% gby(iso3c) %>% collapg(w = log(POP)) or wlddev %>% gby(iso3c) %>% nv() %>% fmean(w = log(POP)).
Small improvements to group() algorithm, avoiding some cases where the hash function performed badly, particularly with integers.
Function GRPnames now has a sep argument to choose a separator other than ".".
Corrected a C-level bug in gsplit that could lead R to crash in some instances (gsplit is used internally in fsummarise, fmutate, BY and collap to perform computations with base R (non-optimized) functions).
Ensured that BY.grouped_df always (by default) returns grouping columns in aggregations i.e. iris |> gby(Species) |> nv() |> BY(sum) now gives the same as iris |> gby(Species) |> nv() |> fsum().
A . was added to the first argument of functions fselect, fsubset, colorder and fgroup_by, i.e. fselect(x, ...) -> fselect(.x, ...). The reason for this is that over time I added the option to select-rename columns e.g. fselect(mtcars, cylinders = cyl), which was not offered when these functions were created. This presents problems if columns should be renamed into x, e.g. fselect(mtcars, x = cyl) failed, see #221. Renaming the first argument to .x somewhat guards against such situations. I think this change is worthwhile to implement, because it makes the package more robust going forward, and usually the first argument of these functions is never invoked explicitly. I really hope this breaks nobodyâs code.
Added a function GRPN to make it easy to add a column of group sizes e.g. mtcars %>% fgroup_by(cyl,vs,am) %>% ftransform(Sizes = GRPN(.)) or mtcars %>% ftransform(Sizes = GRPN(list(cyl, vs, am))) or GRPN(mtcars, by = ~cyl+vs+am).
Added [.pwcor and [.pwcov, to be able to subset correlation/covariance matrices without loosing the print formatting.
Also ensuring tidyverse examples are in \donttest{} and building without the dplyr testing file to avoid issues with static code analysis on CRAN.
20-50% Speed improvement in gsplit (and therefore in fsummarise, fmutate, collap and BY when invoked with base R functions) when grouping with GRP(..., sort = TRUE, return.order = TRUE). To enable this by default, the default for argument return.order in GRP was set to sort, which retains the ordering vector (needed for the optimization). Retaining the ordering vector uses up some memory which can possibly adversely affect computations with big data, but with big data sort = FALSE usually gives faster results anyway, and you can also always set return.order = FALSE (also in fgroup_by, collap), so this default gives the best of both worlds.
sort.row (replaced by sort in 2020) is now removed from collap. Also arguments return.order and method were added to collap providing full control of the grouping that happens internally.Tests needed to be adjusted for the upcoming release of dplyr 1.0.8 which involves an API change in mutate. fmutate will not take over these changes i.e. fmutate(..., .keep = "none") will continue to work like dplyr::transmute. Furthermore, no more tests involving dplyr are run on CRAN, and I will also not follow along with any future dplyr API changes.
The C-API macro installTrChar (used in the new massign function) was replaced with installChar to maintain backwards compatibility with R versions prior to 3.6.0. Thanks @tedmoorman #213.
Minor improvements to group(), providing increased performance for doubles and also increased performance when the second grouping variable is integer, which turned out to be very slow in some instances.
Removed tests involving the weights package (which is not available on R-devel CRAN checks).
fgroup_by is more flexible, supporting computing columns e.g. fgroup_by(GGDC10S, Variable, Decade = floor(Year / 10) * 10) and various programming options e.g. fgroup_by(GGDC10S, 1:3), fgroup_by(GGDC10S, c("Variable", "Country")), or fgroup_by(GGDC10S, is.character). You can also use column sequences e.g. fgroup_by(GGDC10S, Country:Variable, Year), but this should not be mixed with computing columns. Compute expressions may also not include the : function.
More memory efficient attribute handling in C/C++ (using C-API macro SHALLOW_DUPLICATE_ATTRIB instead of DUPLICATE_ATTRIB) in most places.
Ensured that the base pipe |> is not used in tests or examples, to avoid errors on CRAN checks with older versions of R.
Also adjusted psacf / pspacf / psccf to take advantage of the faster grouping by group.
Fixed minor C/C++ issues flagged in CRAN checks.
Added option ties = "last" to fmode.
Added argument stable.algo to qsu. Setting stable.algo = FALSE toggles a faster calculation of the standard deviation, yielding 2x speedup on large datasets.
Fast Statistical Functions now internally use group for grouping data if both g and TRA arguments are used, yielding efficiency gains on unsorted data.
Ensured that fmutate and fsummarise can be called if collapse is not attached.
collapse 1.7.0, released mid January 2022, brings major improvements in the computational backend of the package, its data manipulation capabilities, and a whole set of new functions that enable more flexible and memory efficient R programming - significantly enhancing the language itself. For the vast majority of codes, updating to 1.7 should not cause any problems.
Changes to functionalitynum_vars is now implemented in C, yielding a massive performance increase over checking columns using vapply(x, is.numeric, logical(1)). It selects columns where (is.double(x) || is.integer(x)) && !is.object(x). This provides the same results for most common classes found in data frames (e.g. factors and date columns are not numeric), however it is possible for users to define methods for is.numeric for other objects, which will not be respected by num_vars anymore. A prominent example are base Râs âtsâ objects i.e. is.numeric(AirPassengers) returns TRUE, but is.object(AirPassengers) is also TRUE so the above yields FALSE, implying - if you happened to work with data frames of âtsâ columns - that num_vars will now not select those anymore. Please make me aware if there are other important classes that are found in data frames and where is.numeric returns TRUE. num_vars is also used internally in collap so this might affect your aggregations.
In flag, fdiff and fgrowth, if a plain numeric vector is passed to the t argument such that is.double(t) && !is.object(t), it is coerced to integer using as.integer(t) and directly used as time variable, rather than applying ordered grouping first. This is to avoid the inefficiency of grouping, and owes to the fact that in most data imported into R with various packages, the time (year) variables are coded as double although they should be integer (I also donât know of any cases where time needs to be indexed by a non-date variable with decimal places). Note that the algorithm internally handles irregularity in the time variable so this is not a problem. Should this break any code, kindly raise an issue on GitHub.
The function setrename now truly renames objects by reference (without creating a shallow copy). The same is true for vlabels<- (which was rewritten in C) and a new function setrelabel. Thus additional care needs to be taken (with use inside functions etc.) as the renaming will take global effects unless a shallow copy of the data was created by some prior operation inside the function. If in doubt, better use frename which creates a shallow copy.
Some improvements to the BY function, both in terms of performance and security. Performance is enhanced through a new C function gsplit, providing split-apply-combine computing speeds competitive with dplyr on a much broader range of R objects. Regarding Security: if the result of the computation has the same length as the original data, names / rownames and grouping columns (for grouped data) are only added to the result object if known to be valid, i.e. if the data was originally sorted by the grouping columns (information recorded by GRP.default(..., sort = TRUE), which is called internally on non-factor/GRP/qG objects). This is because BY does not reorder data after the split-apply-combine step (unlike dplyr::mutate); data are simply recombined in the order of the groups. Because of this, in general, BY should be used to compute summary statistics (unless data are sorted before grouping). The added security makes this explicit.
Added a method length.GRP giving the length of a grouping object. This could break code calling length on a grouping object before (which just returned the length of the list).
Functions renamed in collapse 1.6.0 will now print a message telling you to use the updated names. The functions under the old names will stay around for 1-3 more years.
order instead of sort in function GRP (from a very early version of collapse), is now disabled.fvar, fsd, fscale and qsu) to calculate variances, occurring when initial or final zero weights caused the running sum of weights in the algorithm to be zero, yielding a division by zero and NA as output although a value was expected. These functions now skip zero weights alongside missing weights, which also implies that you can pass a logical vector to the weights argument to very efficiently calculate statistics on a subset of data (e.g. using qsu).Function group was added, providing a low-level interface to a new unordered grouping algorithm based on hashing in C and optimized for Râs data structures. The algorithm was heavily inspired by the great kit package of Morgan Jacob, and now feeds into the package through multiple central functions (including GRP / fgroup_by, funique and qF) when invoked with argument sort = FALSE. It is also used in internal groupings performed in data transformation functions such as fwithin (when no factor or âGRPâ object is provided to the g argument). The speed of the algorithm is very promising (often superior to radixorder), and it could be used in more places still. I welcome any feedback on its performance on different datasets.
Function gsplit provides an efficient alternative to split based on grouping objects. It is used as a new backend to rsplit (which also supports data frame) as well as BY, collap, fsummarise and fmutate - for more efficient grouped operations with functions external to the package.
Added multiple functions to facilitate memory efficient programming (written in C). These include elementary mathematical operations by reference (setop, %+=%, %-=%, %*=%, %/=%), supporting computations involving integers and doubles on vectors, matrices and data frames (including row-wise operations via setop) with no copies at all. Furthermore a set of functions which check a single value against a vector without generating logical vectors: whichv, whichNA (operators %==% and %!=% which return indices and are significantly faster than ==, especially inside functions like fsubset), anyv and allv (allNA was already added before). Finally, functions setv and copyv speed up operations involving the replacement of a value (x[x == 5] <- 6) or of a sequence of values from a equally sized object (x[x == 5] <- y[x == 5], or x[ind] <- y[ind] where ind could be pre-computed vectors or indices) in vectors and data frames without generating any logical vectors or materializing vector subsets.
Function vlengths was added as a more efficient alternative to lengths (without method dispatch, simply coded in C).
Function massign provides a multivariate version of assign (written in C, and supporting all basic vector types). In addition the operator %=% was added as an efficient multiple assignment operator. (It is called %=% and not %<-% to facilitate the translation of Matlab or Python codes into R, and because the zeallot package already provides multiple-assignment operators (%<-% and %->%), which are significantly more versatile, but orders of magnitude slower than %=%)
Fully fledged fmutate function that provides functionality analogous to dplyr::mutate (sequential evaluation of arguments, including arbitrary tagged expressions and across statements). fmutate is optimized to work together with the packages Fast Statistical and Data Transformation Functions, yielding fast, vectorized execution, but also benefits from gsplit for other operations.
across() function implemented for use inside fsummarise and fmutate. It is also optimized for Fast Statistical and Data Transformation Functions, but performs well with other functions too. It has an additional arguments .apply = FALSE which will apply functions to the entire subset of the data instead of individual columns, and thus allows for nesting tibbles and estimating models or correlation matrices by groups etc.. across() also supports an arbitrary number of additional arguments which are split and evaluated by groups if necessary. Multiple across() statements can be combined with tagged vector expressions in a single call to fsummarise or fmutate. Thus the computational framework is pretty general and similar to data.table, although less efficient with big datasets.
Added functions relabel and setrelabel to make interactive dealing with variable labels a bit easier. Note that both functions operate by reference. (Through vlabels<- which is implemented in C. Taking a shallow copy of the data frame is useless in this case because variable labels are attributes of the columns, not of the frame). The only difference between the two is that setrelabel returns the result invisibly.
function shortcuts rnm and mtt added for frename and fmutate. across can also be abbreviated using acr.
Added two options that can be invoked before loading of the package to change the namespace: options(collapse_mask = c(...)) can be set to export copies of selected (or all) functions in the package that start with f removing the leading f e.g. fsubset -> subset (both fsubset and subset will be exported). This allows masking base R and dplyr functions (even basic functions such as sum, mean, unique etc. if desired) with collapseâs fast functions, facilitating the optimization of existing codes and allowing you to work with collapse using a more natural namespace. The package has been internally insulated against such changes, but of course they might have major effects on existing codes. Also options(collapse_F_to_FALSE = FALSE) can be invoked to get rid of the lead operator F, which masks base::F (an issue raised by some people who like to use T/F instead of TRUE/FALSE). Read the help page ?collapse-options for more information.
Package loads faster (because I donât fetch functions from some other C/C++ heavy packages in .onLoad anymore, which implied unnecessary loading of a lot of DLLs).
fsummarise is now also fully featured supporting evaluation of arbitrary expressions and across() statements. Note that mixing Fast Statistical Functions with other functions in a single expression can yield unintended outcomes, read more at ?fsummarise.
funique benefits from group in the default sort = FALSE, configuration, providing extra speed and unique values in first-appearance order in both the default and the data frame method, for all data types.
Function ss supports both empty i or j.
The printout of fgroup_by also shows minimum and maximum group size for unbalanced groupings.
In ftransformv/settransformv and fcomputev, the vars argument is also evaluated inside the data frame environment, allowing NSE specifications using column names e.g. ftransformv(data, c(col1, col2:coln), FUN).
qF with option sort = FALSE now generates factors with levels in first-appearance order (instead of a random order assigned by the hash function), and can also be called on an existing factor to recast the levels in first-appearance order. It is also faster with sort = FALSE (thanks to group).
finteraction has argument sort = FALSE to also take advantage of group.
rsplit has improved performance through gsplit, and an additional argument use.names, which can be used to return an unnamed list.
Speedup in vtypes and functions num_vars, cat_vars, char_vars, logi_vars and fact_vars. Note than num_vars behaves slightly differently as discussed above.
vlabels(<-) / setLabels rewritten in C, giving a ~20x speed improvement. Note that they now operate by reference.
vlabels, vclasses and vtypes have a use.names argument. The default is TRUE (as before).
colorder can rename columns on the fly and also has a new mode pos = "after" to place all selected columns after the first selected one, e.g.: colorder(mtcars, cyl, vs_new = vs, am, pos = "after"). The pos = "after" option was also added to roworderv.
add_stub and rm_stub have an additional cols argument to apply a stub to certain columns only e.g. add_stub(mtcars, "new_", cols = 6:9).
namlab has additional arguments N and Ndistinct, allowing to display number of observations and distinct values next to variable names, labels and classes, to get a nice and quick overview of the variables in a large dataset.
copyMostAttrib only copies the "row.names" attribute when known to be valid.
na_rm can now be used to efficiently remove empty or NULL elements from a list.
flag, fdiff and fgrowth produce less messages (i.e. no message if you donât use a time variable in grouped operations, and messages about computations on highly irregular panel data only if data length exceeds 10 million obs.).
The print methods of pwcor and pwcov now have a return argument, allowing users to obtain the formatted correlation matrix, for exporting purposes.
replace_NA, recode_num and recode_char have improved performance and an additional argument set to take advantage of setv to change (some) data by reference. For replace_NA, this feature is mature and setting set = TRUE will modify all selected columns in place and return the data invisibly. For recode_num and recode_char only a part of the transformations are done by reference, thus users will still have to assign the data to preserve changes. In the future, this will be improved so that set = TRUE toggles all transformations to be done by reference.
Use of VECTOR_PTR in C API now gives an error on R-devel even if USE_RINTERNALS is defined. Thus this patch gets rid of all remaining usage of this macro to avoid errors on CRAN checks using the development version of R.
The print method for qsu now uses an apostrophe (â) to designate million digits, instead of a comma (,). This is to avoid confusion with the decimal point, and the typical use of (,) for thousands (which I donât like).
Checks on the gcc11 compiler flagged an additional issue with a pointer pointing to element -1 of a C array (which I had done on purpose to index it with an R integer vector).
collapse 1.6.3CRAN checks flagged a valgrind issue because of comparing an uninitialized value to something.
collapse 1.6.2CRAN maintainers have asked me to remove a line in a Makevars file intended to reduce the size of Rcpp object files (which has been there since version 1.4). So the installed size of the package may now be larger.
collapse 1.6.1A patch for 1.6.0 which fixes issues flagged by CRAN and adds a few handy extras.
Bug FixesPuts examples using the new base pipe |> inside \donttest{} so that they donât fail CRAN tests on older R versions.
Fixes a LTO issue caused by a small mistake in a header file (which does not have any implications to the user but was detected by CRAN checks).
Added a function fcomputev, which allows selecting columns and transforming them with a function in one go. The keep argument can be used to add columns to the selection that are not transformed.
Added a function setLabels as a wrapper around vlabels<- to facilitate setting variable labels inside pipes.
Function rm_stub now has an argument regex = TRUE which triggers a call to gsub and allows general removing of character sequences in column names on the fly.
vlabels<- and setLabels now support list of variable labels or other attributes (i.e. the value is internally subset using [[, not [). Thus they are now general functions to attach a vector or list of attributes to columns in a list / data frame.collapse 1.6.0, released end of June 2021, presents some significant improvements in the user-friendliness, compatibility and programmability of the package, as well as a few function additions.
Changes to Functionalityffirst, flast, fnobs, fsum, fmin and fmax were rewritten in C. The former three now also support list columns (where NULL or empty list elements are considered missing values when na.rm = TRUE), and are extremely fast for grouped aggregation if na.rm = FALSE. The latter three also support and return integers, with significant performance gains, even compared to base R. Code using these functions expecting an error for list-columns or expecting double output even if the input is integer should be adjusted.
collapse now directly supports sf data frames through functions like fselect, fsubset, num_vars, qsu, descr, varying, funique, roworder, rsplit, fcompute etc., which will take along the geometry column even if it is not explicitly selected (mirroring dplyr methods for sf data frames). This is mostly done internally at C-level, so functions remain simple and fast. Existing code that explicitly selects the geometry column is unaffected by the change, but code of the form sf_data %>% num_vars %>% qDF %>% ..., where columns excluding geometry were selected and the object later converted to a data frame, needs to be rewritten as sf_data %>% qDF %>% num_vars %>% .... A short vignette was added describing the integration of collapse and sf.
Iâve received several requests for increased namespace consistency. collapse functions were named to be consistent with base R, dplyr and data.table, resulting in names like is.Date, fgroup_by or settransformv. To me this makes sense, but Iâve been convinced that a bit more consistency is advantageous. Towards that end I have decided to eliminate the â.â notation of base R and to remove some unexpected capitalizations in function names giving some people the impression I was using camel-case. The following functions are renamed: fNobs -> fnobs, fNdistinct -> fndistinct, pwNobs -> pwnobs, fHDwithin -> fhdwithin, fHDbetween -> fhdbetween, as.factor_GRP -> as_factor_GRP, as.factor_qG -> as_factor_qG, is.GRP -> is_GRP, is.qG -> is_qG, is.unlistable -> is_unlistable, is.categorical -> is_categorical, is.Date -> is_date, as.numeric_factor -> as_numeric_factor, as.character_factor -> as_character_factor, Date_vars -> date_vars. This is done in a very careful manner, the others will stick around for a long while (end of 2022), and the generics of fNobs, fNdistinct, fHDbetween and fHDwithin will be kept in the package for an indeterminate period, but their core methods will not be exported beyond 2022. I will start warning about these renamed functions in 2022. In the future I will undogmatically stick to a function naming style with lowercase function names and underslashes where words need to be split. Other function names will be kept. To say something about this: The quick-conversion functions qDF qDT, qM, qF, qG are consistent and in-line with data.table (setDT etc.), and similarly the operators L, F, D, Dlog, G, B, W, HDB, HDW. Iâll keep GRP, BY and TRA, for lack of better names, parsimony and because they are central to the package. The camel case will be kept in helper functions setDimnames etc. because they work like stats setNames and do not modify the argument by reference (like settransform or setrename and various data.table functions). Functions copyAttrib and copyMostAttrib are exports of like-named functions in the C API and thus kept as they are. Finally, I want to keep fFtest the way it is because the F-distribution is widely recognized by a capital F.
Iâve updated the wlddev dataset with the latest data from the World Bank, and also added a variable giving the total population (which may be useful e.g. for population-weighted aggregations across regions). The extra column could invalidate codes used to demonstrate something (I had to adjust some examples, tests and code in vignettes).
Added a function fcumsum (written in C), permitting flexible (grouped, ordered) cumulative summations on matrix-like objects (integer or double typed) with extra methods for grouped data frames and panel series and data frames. Apart from the internal grouping, and an ordering argument allowing cumulative sums in a different order than data appear, fcumsum has 2 options to deal with missing values. The default (na.rm = TRUE) is to skip (preserve) missing values, whereas setting fill = TRUE allows missing values to be populated with the previous value of the cumulative sum (starting from 0).
Added a function alloc to efficiently generate vectors initialized with any value (faster than rep_len).
Added a function pad to efficiently pad vectors / matrices / data.frames with a value (default is NA). This function was mainly created to make it easy to expand results coming from a statistical model fitted on data with missing values to the original length. For example let data <- na_insert(mtcars); mod <- lm(mpg ~ cyl, data), then we can do settransform(data, resid = pad(resid(mod), mod$na.action)), or we could do pad(model.matrix(mod), mod$na.action) or pad(model.frame(mod), mod$na.action) to receive matrices and data frames from model data matching the rows of data. pad is a general function that will also work with mixed-type data. It is also possible to pass a vector of indices matching the rows of the data to pad, in which case pad will fill gaps in those indices with a value/row in the data.
Full data.table support, including reference semantics (set*, :=)!! There is some complex C-level programming behind data.tableâs operations by reference. Notably, additional (hidden) column pointers are allocated to be able to add columns without taking a shallow copy of the data.table, and an ".internal.selfref" attribute containing an external pointer is used to check if any shallow copy was made using base R commands like <-. This is done to avoid even a shallow copy of the data.table in manipulations using := (and is in my opinion not worth it as even large tables are shallow copied by base R (>=3.1.0) within microseconds and all of this complicates development immensely). Previously, collapse treated data.tableâs like any other data frame, using shallow copies in manipulations and preserving the attributes (thus ignoring how data.table works internally). This produced a warning whenever you wanted to use data.table reference semantics (set*, :=) after passing the data.table through a collapse function such as collap, fselect, fsubset, fgroup_by etc. From v1.6.0, I have adopted essential C code from data.table to do the overallocation and generate the ".internal.selfref" attribute, thus seamless workflows combining collapse and data.table are now possible. This comes at a cost of about 2-3 microseconds per function, as to do this I have to shallow copy the data.table again and add extra column pointers and an ".internal.selfref" attribute telling data.table that this table was not copied (it seems to be the only way to do it for now). This integration encompasses all data manipulation functions in collapse, but not the Fast Statistical Functions themselves. Thus you can do agDT <- DT %>% fselect(id, col1:coln) %>% collap(~id, fsum); agDT[, newcol := 1], but you would need to do add a qDT after a function like fsum if you want to use reference semantics without incurring a warning: agDT <- DT %>% fselect(id, col1:coln) %>% fgroup_by(id) %>% fsum %>% qDT; agDT[, newcol := 1]. collapse appears to be the first package that attempts to account for data.tableâs internal working without importing data.table, and qDT is now the fastest way to create a fully functional data.table from any R object. A global option "collapse_DT_alloccol" was added to regulate how many columns collapse overallocates when creating data.tableâs. The default is 100, which is lower than the data.table default of 1024. This was done to increase efficiency of the additional shallow copies, and may be changed by the user.
Programming enabled with fselect and fgroup_by (you can now pass vectors containing column names or indices). Note that instead of fselect you should use get_vars for standard eval programming.
fselect and fsubset support in-place renaming, e.g. fselect(data, newname = var1, var3:varN), fsubset(data, vark > varp, newname = var1, var3:varN).
collap supports renaming columns in the custom argument, e.g. collap(data, ~ id, custom = list(fmean = c(newname = "var1", "var2"), fmode = c(newname = 3), flast = is_date)).
Performance improvements: fsubset / ss return the data or perform a simple column subset without deep copying the data if all rows are selected through a logical expression. fselect and get_vars, num_vars etc. are slightly faster through data frame subsetting done fully in C. ftransform / fcompute use alloc instead of base::rep to replicate a scalar value which is slightly more efficient.
fcompute now has a keep argument, to preserve several existing columns when computing columns on a data frame.
replace_NA now has a cols argument, so we can do replace_NA(data, cols = is.numeric), to replace NAâs in numeric columns. I note that for big numeric data data.table::setnafill is the most efficient solution.
fhdbetween and fhdwithin have an effect argument in plm methods, allowing centering on selected identifiers. The default is still to center on all panel identifiers.
The plot method for panel series matrices and arrays plot.psmat was improved slightly. It now supports custom colours and drawing of a grid.
settransform and settransformv can now be called without attaching the package e.g. collapse::settransform(data, ...). These errored before when collapse is not loaded because they are simply wrappers around data <- ftransform(data, ...). Iâd like to note from a discussion that avoiding shallow copies with <- (e.g. via :=) does not appear to yield noticeable performance gains. Where data.table is faster on big data this mostly has to do with parallelism and sometimes with algorithms, generally not memory efficiency.
Functions setAttrib, copyAttrib and copyMostAttrib only make a shallow copy of lists, not of atomic vectors (which amounts to doing a full copy and is inefficient). Thus atomic objects are now modified in-place.
Small improvements: Calling qF(x, ordered = FALSE) on an ordered factor will remove the ordered class, the operators L, F, D, Dlog, G, B, W, HDB, HDW and functions like pwcor now work on unnamed matrices or data frames.
The first argument of ftransform was renamed to .data from X. This was done to enable the user to transform columns named âXâ. For the same reason the first argument of frename was renamed to .x from x (not .data to make it explicit that .x can be any R object with a ânamesâ attribute). It is not possible to depreciate X and x without at the same time undoing the benefits of the argument renaming, thus this change is immediate and code breaking in rare cases where the first argument is explicitly set.
The function is.regular to check whether an R object is atomic or list-like is depreciated and will be removed before the end of the year. This was done to avoid a namespace clash with the zoo package (#127).
unlist2d produced a subsetting error if an empty list was present in the list-tree. This is now fixed, empty or NULL elements in the list-tree are simply ignored (#99).A function fsummarize was added to facilitate translating dplyr / data.table code to collapse. Like collap, it is only very fast when used with the Fast Statistical Functions.
A function t_list is made available to efficiently transpose lists of lists.
A small patch for 1.5.0 that:
Fixes a numeric precision issue when grouping doubles (e.g. before qF(wlddev$LIFEEX) gave an error, now it works).
Fixes a minor issue with fhdwithin when applied to pseries and fill = FALSE.
collapse 1.5.0, released early January 2021, presents important refinements and some additional functionality.
Back to CRANfhdbetween / fhdwithin functions for generalized linear projecting / partialling out. To remedy the damage caused by the removal of lfe, I had to rewrite fhdbetween / fhdwithin to take advantage of the demeaning algorithm provided by fixest, which has some quite different mechanics. Beforehand, I made some significant changes to fixest::demean itself to make this integration happen. The CRAN deadline was the 18th of December, and I realized too late that I would not make this. A request to CRAN for extension was declined, so collapse got archived on the 19th. I have learned from this experience, and collapse is now sufficiently insulated that it will not be taken off CRAN even if all suggested packages were removed from CRAN.numeric(0) are fixed (thanks to @eshom and @acylam, #101). The default behavior is that all collapse functions return numeric(0) again, except for fnobs, fndistinct which return 0L, and fvar, fsd which return NA_real_.Functions fhdwithin / HDW and fhdbetween / HDB have been reworked, delivering higher performance and greater functionality: For higher-dimensional centering and heterogeneous slopes, the demean function from the fixest package is imported (conditional on the availability of that package). The linear prediction and partialling out functionality is now built around flm and also allows for weights and different fitting methods.
In collap, the default behavior of give.names = "auto" was altered when used together with the custom argument. Before the function name was always added to the column names. Now it is only added if a column is aggregated with two different functions. I apologize if this breaks any code dependent on the new names, but this behavior just better reflects most common use (applying only one function per column), as well as STATAâs collapse.
For list processing functions like get_elem, has_elem etc. the default for the argument DF.as.list was changed from TRUE to FALSE. This means if a nested lists contains data frameâs, these data frameâs will not be searched for matching elements. This default also reflects the more common usage of these functions (extracting entire data frameâs or computed quantities from nested lists rather than searching / subsetting lists of data frameâs). The change also delivers a considerable performance gain.
Added a set of 10 operators %rr%, %r+%, %r-%, %r*%, %r/%, %cr%, %c+%, %c-%, %c*%, %c/% to facilitate and speed up row- and column-wise arithmetic operations involving a vector and a matrix / data frame / list. For example X %r*% v efficiently multiplies every row of X with v. Note that more advanced functionality is already provided in TRA(), dapply() and the Fast Statistical Functions, but these operators are intuitive and very convenient to use in matrix or matrix-style code, or in piped expressions.
Added function missing_cases (opposite of complete.cases and faster for data frameâs / lists).
Added function allNA for atomic vectors.
New vignette about using collapse together with data.table, available online.
flag / L / F, fdiff / D / Dlog and fgrowth / G now natively support irregular time series and panels, and feature a âcomplete approachâ i.e. values are shifted around taking full account of the underlying time-dimension!Functions pwcor and pwcov can now compute weighted correlations on the pairwise or complete observations, supported by C-code that is (conditionally) imported from the weights package.
fFtest now also supports weights.
collap now provides an easy workaround to aggregate some columns using weights and others without. The user may simply append the names of Fast Statistical Functions with _uw to disable weights. Example: collapse::collap(mtcars, ~ cyl, custom = list(fmean_uw = 3:4, fmean = 8:10), w = ~ wt) aggregates columns 3 through 4 using a simple mean and columns 8 through 10 using the weighted mean.
The parallelism in collap using parallel::mclapply has been reworked to operate at the column-level, and not at the function level as before. It is still not available for Windows though. The default number of cores was set to mc.cores = 2L, which now gives an error on windows if parallel = TRUE.
function recode_char now has additional options ignore.case and fixed (passed to grepl), for enhanced recoding character data based on regular expressions.
rapply2d now has classes argument permitting more flexible use.
na_rm and some other internal functions were rewritten in C. na_rm is now 2x faster than x[!is.na(x)] with missing values and 10x faster without missing values.
An improvement to the [.GRP_df method enabling the use of most data.table methods (such as :=) on a grouped data.table created with fgroup_by.
Some documentation updates by Kevin Tappe.
collapse 1.4.1 is a small patch for 1.4.0 that:
fixes clang-UBSAN and rchk issues in 1.4.0 (minor bugs in compiled code resulting, in this case, from trying to coerce a NaN value to integer, and failing to protect a shallow copy of a variable).
Adds a method [.GRP_df that allows robust subsetting of grouped objects created with fgroup_by (thanks to Patrice Kiener for flagging this).
collapse 1.4.0, released early November 2020, presents some important refinements, particularly in the domain of attribute handling, as well as some additional functionality. The changes make collapse smarter, more broadly compatible and more secure, and should not break existing code.
Changes to FunctionalityDeep Matrix Dispatch / Extended Time Series Support: The default methods of all statistical and transformation functions dispatch to the matrix method if is.matrix(x) && !inherits(x, "matrix") evaluates to TRUE. This specification avoids invoking the default method on classed matrix-based objects (such as multivariate time series of the xts / zoo class) not inheriting a âmatrixâ class, while still allowing the user to manually call the default method on matrices (objects with implicit or explicit âmatrixâ class). The change implies that collapseâs generic statistical functions are now well suited to transform xts / zoo and many other time series and matrix-based classes.
Fully Non-Destructive Piped Workflow: fgroup_by(x, ...) now only adds a class grouped_df, not classes table_df, tbl, grouped_df, and preserves all classes of x. This implies that workflows such as x %>% fgroup_by(...) %>% fmean etc. yields an object xAG of the same class and attributes as x, not a tibble as before. collapse aims to be as broadly compatible, class-agnostic and attribute preserving as possible.
qDF, qDT and qM now have additional arguments keep.attr and class providing precise user control over object conversions in terms of classes and other attributes assigned / maintained. The default (keep.attr = FALSE) yields hard conversions removing all but essential attributes from the object. E.g. before qM(EuStockMarkets) would just have returned EuStockMarkets (because is.matrix(EuStockMarkets) is TRUE) whereas now the time series class and âtspâ attribute are removed. qM(EuStockMarkets, keep.attr = TRUE) returns EuStockMarkets as before.Smarter Attribute Handling: Drawing on the guidance given in the R Internals manual, the following standards for optimal non-destructive attribute handling are formalized and communicated to the user:
The default and matrix methods of the Fast Statistical Functions preserve attributes of the input in grouped aggregations (ânamesâ, âdimâ and âdimnamesâ are suitably modified). If inputs are classed objects (e.g. factors, time series, checked by is.object), the class and other attributes are dropped. Simple (non-grouped) aggregations of vectors and matrices do not preserve attributes, unless drop = FALSE in the matrix method. An exemption is made in the default methods of functions ffirst, flast and fmode, which always preserve the attributes (as the input could well be a factor or date variable).
The data frame methods are unaltered: All attributes of the data frame and columns in the data frame are preserved unless the computation result from each column is a scalar (not computing by groups) and drop = TRUE (the default).
Transformations with functions like flag, fwithin, fscale etc. are also unaltered: All attributes of the input are preserved in the output (regardless of whether the input is a vector, matrix, data.frame or related classed object). The same holds for transformation options modifying the input (â-â, â-+â, â/â, â+â, â*â, â%%â, â-%%â) when using TRA() function or the TRA = "..." argument to the Fast Statistical Functions.
For TRA âreplaceâ and âreplace_fillâ options, the data type of the STATS is preserved, not of x. This provides better results particularly with functions like fnobs and fndistinct. E.g. previously fnobs(letters, TRA = "replace") would have returned the observation counts coerced to character, because letters is character. Now the result is integer typed. For attribute handling this means that the attributes of x are preserved unless x is a classed object and the data types of x and STATS do not match. An exemption to this rule is made if x is a factor and an integer (non-factor) replacement is offered to STATS. In that case the attributes of x are copied exempting the âclassâ and âlevelsâ attribute, e.g. so that fnobs(iris$Species, TRA = "replace") gives an integer vector, not a (malformed) factor. In the unlikely event that STATS is a classed object, the attributes of STATS are preserved and the attributes of x discarded.
fhdwithin / fhdbetween can only perform higher-dimensional centering if lfe is available. Linear prediction and centering with a single factor (among a list of covariates) is still possible without installing lfe. This change means that collapse now only depends on base R and Rcpp and is supported down to R version 2.10.Added function rsplit for efficient (recursive) splitting of vectors and data frames.
Added function fdroplevels for very fast missing level removal + added argument drop to qF and GRP.factor, the default is drop = FALSE. The addition of fdroplevels also enhances the speed of the fFtest function.
fgrowth supports annualizing / compounding growth rates through added power argument.
A function flm was added for bare bones (weighted) linear regression fitting using different efficient methods: 4 from base R (.lm.fit, solve, qr, chol), using fastLm from RcppArmadillo (if installed), or fastLm from RcppEigen (if installed).
Added function qTBL to quickly convert R objects to tibble.
helpers setAttrib, copyAttrib and copyMostAttrib exported for fast attribute handling in R (similar to attributes<-(), these functions return a shallow copy of the first argument with the set of attributes replaced, but do not perform checks for attribute validity like attributes<-(). This can yield large performance gains with big objects).
helper cinv added wrapping the expression chol2inv(chol(x)) (efficient inverse of a symmetric, positive definite matrix via Choleski factorization).
A shortcut gby is now available to abbreviate the frequently used fgroup_by function.
A print method for grouped data frames of any class was added.
funique, fmode and fndistinct.The grouped_df methods for flag, fdiff, fgrowth now also support multiple time variables to identify a panel e.g. data %>% fgroup_by(region, person_id) %>% flag(1:2, list(month, day)).
More security features for fsubset.data.frame / ss, ss is now internal generic and also supports subsetting matrices.
In some functions (like na_omit), passing double values (e.g. 1 instead of integer 1L) or negative indices to the cols argument produced an error or unexpected behavior. This is now fixed in all functions.
Fixed a bug in helper function all_obj_equal occurring if objects are not all equal.
Some performance improvements through increased use of pointers and C API functions.
collapse 1.3.2, released mid September 2020:
Fixed a small bug in fndistinct for grouped distinct value counts on logical vectors.
Additional security for ftransform, which now efficiently checks the names of the data and replacement arguments for uniqueness, and also allows computing and transforming list-columns.
Added function ftransformv to facilitate transforming selected columns with function - a very efficient replacement for dplyr::mutate_if and dplyr::mutate_at.
frename now allows additional arguments to be passed to a renaming function.
collapse 1.3.1, released end of August 2020, is a patch for v1.3.0 that takes care of some unit test failures on certain operating systems (mostly because of numeric precision issues). It provides no changes to the code or functionality.
collapse 1.3.0collapse 1.3.0, released mid August 2020:
Changes to Functionalitydapply and BY now drop all unnecessary attributes if return = "matrix" or return = "data.frame" are explicitly requested (the default return = "same" still seeks to preserve the input data structure).
unlist2d now saves integer rownames if row.names = TRUE and a list of matrices without rownames is passed, and id.factor = TRUE generates a normal factor not an ordered factor. It is however possible to write id.factor = "ordered" to get an ordered factor id.
fdiff argument logdiff renamed to log, and taking logs is now done in R (reduces size of C++ code and does not generate as many NaNâs). logdiff may still be used, but it may be deactivated in the future. Also in the matrix and data.frame methods for flag, fdiff and fgrowth, columns are only stub-renamed if more than one lag/difference/growth rate is computed.
Added fnth for fast (grouped, weighted) nâth element/quantile computations.
Added roworder(v) and colorder(v) for fast row and column reordering.
Added frename and setrename for fast and flexible renaming (by reference).
Added function fungroup, as replacement for dplyr::ungroup, intended for use with fgroup_by.
fmedian now supports weights, computing a decently fast (grouped) weighted median based on radix ordering.
fmode now has the option to compute min and max mode, the default is still simply the first mode.
fwithin now supports quasi-demeaning (added argument theta) and can thus be used to manually estimate random-effects models.
funique is now generic with a default vector and data.frame method, providing fast unique values and rows of data. The default was changed to sort = FALSE.
The shortcut gvr was created for get_vars(..., regex = TRUE).
A helper .c was introduced for non-standard concatenation (i.e. .c(a, b) == c("a", "b")).
fmode and fndistinct have become a bit faster.
fgroup_by now preserves data.tableâs.
ftransform now also supports a data.frame as replacement argument, which automatically replaces matching columns and adds unmatched ones. Also ftransform<- was created as a more formal replacement method for this feature.
collap columns selected through cols argument are returned in the order selected if keep.col.order = FALSE. Argument sort.row is depreciated, and replace by argument sort. In addition the decreasing and na.last arguments were added and handed down to GRP.default.
radixorder âsortedâ attribute is now always attached.
stats::D which is masked when collapse is attached, is now preserved through methods D.expression and D.call.
GRP option call = FALSE to omit a call to match.call -> minor performance improvement.
Several small performance improvements through rewriting some internal helper functions in C and reworking some R code.
Performance improvements for some helper functions, setRownames / setColnames, na_insert etc.
Increased scope of testing statistical functions. The functionality of the package is now secured by 7700 unit tests covering all central bits and pieces.
collapse 1.2.1, released end of May 2020:
Minor fixes for 1.2.0 issues that prevented correct installation on Mac OS X and a vignette rebuilding error on solaris.
fmode.grouped_df with groups and weights now saves the sum of the weights instead of the max (this makes more sense as the max only applies if all elements are unique).
collapse 1.2.0, released mid May 2020:
Changes to Functionalitygrouped_df methods for fast statistical functions now always attach the grouping variables to the output in aggregations, unless argument keep.group_vars = FALSE. (formerly grouping variables were only attached if also present in the data. Code hinged on this feature should be adjusted)
qF ordered argument default was changed to ordered = FALSE, and the NA level is only added if na.exclude = FALSE. Thus qF now behaves exactly like as.factor.
Recode is depreciated in favor of recode_num and recode_char, it will be removed soon. Similarly replace_non_finite was renamed to replace_Inf.
In mrtl and mctl the argument ret was renamed return and now takes descriptive character arguments (the previous version was a direct C++ export and unsafe, code written with these functions should be adjusted).
GRP argument order is depreciated in favor of argument decreasing. order can still be used but will be removed at some point.
flag where unused factor levels caused a group size error.Added a suite of functions for fast data manipulation:
fselect selects variables from a data frame and is equivalent but much faster than dplyr::select.fsubset is a much faster version of base::subset to subset vectors, matrices and data.frames. The function ss was also added as a faster alternative to [.data.frame.ftransform is a much faster update of base::transform, to transform data frames by adding, modifying or deleting columns. The function settransform does all of that by reference.fcompute is equivalent to ftransform but returns a new data frame containing only the columns computed from an existing one.na_omit is a much faster and enhanced version of base::na.omit.replace_NA efficiently replaces missing values in multi-type data.Added function fgroup_by as a much faster version of dplyr::group_by based on collapse grouping. It attaches a âGRPâ object to a data frame, but only works with collapseâs fast functions. This allows dplyr like manipulations that are fully collapse based and thus significantly faster, i.e. data %>% fgroup_by(g1,g2) %>% fselect(cola,colb) %>% fmean. Note that data %>% dplyr::group_by(g1,g2) %>% dplyr::select(cola,colb) %>% fmean still works, in which case the dplyr âgroupâ object is converted to âGRPâ as before. However data %>% fgroup_by(g1,g2) %>% dplyr::summarize(...) does not work.
Added function varying to efficiently check the variation of multi-type data over a dimension or within groups.
Added function radixorder, same as base::order(..., method = "radix") but more accessible and with built-in grouping features.
Added functions seqid and groupid for generalized run-length type id variable generation from grouping and time variables. seqid in particular strongly facilitates lagging / differencing irregularly spaced panels using flag, fdiff etc.
fdiff now supports quasi-differences i.e. \(x_t - \rho x_{t-1}\) and quasi-log differences i.e. \(log(x_t) - \rho log(x_{t-1})\). an arbitrary \(\rho\) can be supplied.
Added a Dlog operator for faster access to log-differences.
Faster grouping with GRP and faster factor generation with added radix method + automatic dispatch between hash and radix method. qF is now ~ 5x faster than as.factor on character and around 30x faster on numeric data. Also qG was enhanced.
Further slight speed tweaks here and there.
collap now provides more control for weighted aggregations with additional arguments w, keep.w and wFUN to aggregate the weights as well. The defaults are keep.w = TRUE and wFUN = fsum. A specialty of collap remains that keep.by and keep.w also work for external objects passed, so code of the form collap(data, by, FUN, catFUN, w = data$weights) will now have an aggregated weights vector in the first column.
qsu now also allows weights to be passed in formula i.e. qsu(data, by = ~ group, pid = ~ panelid, w = ~ weights).
fgrowth has a scale argument, the default is scale = 100 which provides growth rates in percentage terms (as before), but this may now be changed.
All statistical and transformation functions now have a hidden list method, so they can be applied to unclassed list-objects as well. An error is however provided in grouped operations with unequal-length columns.
collapse 1.1.0 released early April 2020:
Fixed remaining gcc10, LTO and valgrind issues in C/C++ code, and added some more tests (there are now ~ 5300 tests ensuring that collapse statistical functions perform as expected).
Fixed the issue that supplying an unnamed list to GRP(), i.e. GRP(list(v1, v2)) would give an error. Unnamed lists are now automatically named âGroup.1â, âGroup.2â, etcâ¦
Fixed an issue where aggregating by a single id in collap() (i.e. collap(data, ~ id1)), the id would be coded as factor in the aggregated data.frame. All variables including idâs now retain their class and attributes in the aggregated data.
Added weights (w) argument to fsum and fprod.
Added an argument mean = 0 to fwithin / W. This allows simple and grouped centering on an arbitrary mean, 0 being the default. For grouped centering mean = "overall.mean" can be specified, which will center data on the overall mean of the data. The logical argument add.global.mean = TRUE used to toggle this in collapse 1.0.0 is therefore depreciated.
Added arguments mean = 0 (the default) and sd = 1 (the default) to fscale / STD. These arguments now allow to (group) scale and center data to an arbitrary mean and standard deviation. Setting mean = FALSE will just scale data while preserving the mean(s). Special options for grouped scaling are mean = "overall.mean" (same as fwithin / W), and sd = "within.sd", which will scale the data such that the standard deviation of each group is equal to the within- standard deviation (= the standard deviation computed on the group-centered data). Thus group scaling a panel-dataset with mean = "overall.mean" and sd = "within.sd" harmonizes the data across all groups in terms of both mean and variance. The fast algorithm for variance calculation toggled with stable.algo = FALSE was removed from fscale. Welfordâs numerically stable algorithm used by default is fast enough for all practical purposes. The fast algorithm is still available for fvar and fsd.
Added the modulus (%%) and subtract modulus (-%%) operations to TRA().
Added the function finteraction, for fast interactions, and as_character_factor to coerce a factor, or all factors in a list, to character (analogous to as_numeric_factor). Also exported the function ckmatch, for matching with error message showing non-matched elements.
First version of the package featuring only the functions collap and qsu based on code shared by Sebastian Krantz on R-devel, February 2019.
Major rework of the package using Rcpp and data.table internals, introduction of fast statistical functions and operators and expansion of the scope of the package to a broad set of data transformation and exploration tasks. Several iterations of enhancing speed of R code. Seamless integration of collapse with dplyr, plm and data.table. CRAN release of collapse 1.0.0 on 19th March 2020.
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