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 heterogenous 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, and also made available as PDF versions for download there. 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.

Additions

• 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, released mid 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 barebones (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.

• Adds a method [.GRP_df that allows robust subsetting of grouped objects created with fgroup_by (thanks to Patrice Kiener for flagging this).

• 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.

• Some documentation updates by Kevin Tappe.

collapse 1.3.2, released mid September 2020, is a minor update:

• 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 minor patch for 1.3.0:

• Adjusted unit tests that fail on certain operating systems (mostly because of numeric precision issues). This update contains no changes to code or functionality.

collapse 1.3.0, released mid August 2020, is another major update:

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.

• The shortcut gvr was created for get_vars(..., regex = TRUE). Also a helper .c was introduced for non-standard concatenation (i.e. .c(a, b) == c("a", "b")).

Improvements

• 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.

• fmode and fNdistinct have become a bit faster.

• fgroup_by now preserves data.table's.

• 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.

• 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, released end of May 2020, is a patch for 1.2.0:

• 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, is a major update of the package - changes and additions:

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.

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).

• 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.

• 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.

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.

collapse 1.1.0 released 01.04.2020 - some small fixes and additions:

• 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. Note: fmedian will also support weights as soon as I am able to implement a sufficiently fast (i.e. linear time) algorithm. I also hope to introduce (weighted) quantiles. I am happy for any help with these features.

• 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 Martin 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 used. Seamless integration of collapse with dplyr, plm and data.table. CRAN release of collapse 1.0.0 on 19th March 2020.