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, 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.

`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.

- 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`

).

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`%=%`

)

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.

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.

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).

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).

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

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.

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

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.

`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.

`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.

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.

- 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.

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).

`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.

- C files are compiled -O3 on Windows, which gives a boost of around 20% for the grouping mechanism applied to character data.

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.

- 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.

- 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_`

.

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.

- 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.

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.

*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.

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.

- 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, 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 1.3.0, released mid August 2020:

`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.

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")`

).

`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, 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:

*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.

- Fixed a bug in
`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 ‘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.

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 ‘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.

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.