# collapse 1.7.2

• 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.

# collapse 1.7.1

• 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

collapse 1.7.0, released mid January 2022, brings major improvements in the computational backend of the package, it’s 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 functionality

• num_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 R’s ‘ts’ 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 ‘ts’ 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 don’t 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.

• The passing of argument order instead of sort in function GRP (from a very early version of collapse), is now disabled.

### Bug Fixes

• Fixed a bug in some functions using Welfords Online Algorithm (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).

#### Basic Computational Infrastructure

• Function group was added, providing a low-level interface to a new unordered grouping algorithm based on hashing in C and optimized for R’s 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 ‘GRP’ 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 it’s 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 %=%)

#### High-Level Features

• 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 collapse’s 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.

### Improvements

• Package loads faster (because I don’t 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 don’t 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.

# collapse 1.6.5

• 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 (’) 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 don’t like).

# collapse 1.6.4

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 1.6.3

CRAN checks flagged a valgrind issue because of comparing an uninitialized value to something.

# collapse 1.6.2

CRAN 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 1.6.1

A patch for 1.6.0 which fixes issues flagged by CRAN and adds a few handy extras.

### Bug Fixes

• Puts examples using the new base pipe |> inside \donttest{} so that they don’t 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.

### Improvements

• 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

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 Functionality

• ffirst, 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.

• I’ve 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 I’ve been convinced that a bit more consistency is advantageous. Towards that end I have decided to eliminate the ‘.’ 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. I’ll 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.

• I’ve 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. ### Improvements • Full data.table support, including reference semantics (set*, :=)!! There is some complex C-level programming behind data.table’s 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.table’s 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.table’s 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.table’s. 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 NA’s 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, ...). I’d 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. # collapse 1.5.3 • A test that occasionally fails on Mac is removed, and all unit testing is now removed from CRAN. collapse has close to 10,000 unit tests covering all central pieces of code. Half of these tests depend on generated data, and for some reasons there is always a test or two that occasionally fail on some operating system (usually not Windows), requiring me to submit a patch. This is not constructive to either the development or the use of this package, therefore tests are now removed from CRAN. They are still run on codecov.io, and every new release is thoroughly tested on Windows. # collapse 1.5.2 ### Changes to Functionality • The first argument of ftransform was renamed to .data from X. This was done to enable the user to transform columns named “X”. 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 “names” 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). ### Bug Fixes • For reasons of efficiency, most statistical and transformation functions used the C macro SHALLOW_DUPLICATE_ATTRIB to copy column attributes in a data frame. Since this macro does not copy S4 object bits, it caused some problems with S4 object columns such as POSIXct (e.g. computing lags/leads, first and last values on these columns). This is now fixed, all statistical functions (apart from fvar and fsd) now use DUPLICATE_ATTRIB and thus preserve S4 object columns (#91). • 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). ### Additions • 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. ### Improvements • C files are compiled -O3 on Windows, which gives a boost of around 20% for the grouping mechanism applied to character data. # collapse 1.5.1 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

collapse 1.5.0, released early January 2021, presents important refinements and some additional functionality.

### Back to CRAN

• I apologize for inconveniences caused by the temporal archival of collapse from December 19, 2020. This archival was caused by the archival of the important lfe package on the 4th of December. collapse depended on lfe for higher-dimensional centering, providing the fhdbetween / 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.

### Bug Fixes

• Segfaults in several Fast Statistical Functions when passed 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_.

### Changes to Functionality

• 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 STATA’s 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 frame’s, these data frame’s will not be searched for matching elements. This default also reflects the more common usage of these functions (extracting entire data frame’s or computed quantities from nested lists rather than searching / subsetting lists of data frame’s). The change also delivers a considerable performance gain.

• Vignettes were outsourced to the website. This nearly halves the size of the source package, and should induce users to appreciate the built-in documentation. The website also makes for much more convenient reading and navigation of these book-style vignettes.

• 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 frame’s / lists).

• Added function allNA for atomic vectors.

• New vignette about using collapse together with data.table, available online.

### Improvements

• Time series functions and operators flag / L / F, fdiff / D / Dlog and fgrowth / G now natively support irregular time series and panels, and feature a ‘complete approach’ 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.

# collapse 1.4.2

• 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

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

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 Functionality

• Deep 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 ‘matrix’ class, while still allowing the user to manually call the default method on matrices (objects with implicit or explicit ‘matrix’ class). The change implies that collapse’s 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.

• Thorough and Controlled Object Conversions: Quick conversion functions 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 ‘tsp’ 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 (‘names’, ‘dim’ and ‘dimnames’ 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 (“-”, “-+”, “/”, “+”, “*”, “%%”, “-%%”) when using TRA() function or the TRA = "..." argument to the Fast Statistical Functions.

• For TRA ‘replace’ and ‘replace_fill’ 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 ‘class’ and ‘levels’ 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. • Reduced Dependency Burden: The dependency on the lfe package was made optional. Functions 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. ### Additions • 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. ### Improvements • Faster internal methods for factors for 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 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 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 1.3.0 collapse 1.3.0, released mid August 2020: ### Changes to Functionality • dapply 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 NaN’s). 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. ### Additions • Added fnth for fast (grouped, weighted) n’th 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")). ### Improvements • fmode and fndistinct have become a bit faster. • fgroup_by now preserves data.table’s. • 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 ‘sorted’ 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 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 collapse 1.2.0, released mid May 2020: ### Changes to Functionality • grouped_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. ### Bug Fixes • Fixed a bug in flag where unused factor levels caused a group size error. ### Additions • 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 ‘GRP’ object to a data frame, but only works with collapse’s 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 ‘group’ object is converted to ‘GRP’ 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. ### Improvements • 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

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 ‘Group.1’, ‘Group.2’, etc…

• 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 id’s 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. Welford’s 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.

# collapse 1.0.0 and earlier

• 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.