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Fast Factor Generation, Interactions and Vector Grouping

qF.Rd

qF, shorthand for 'quick-factor' implements very fast factor generation from atomic vectors using either radix ordering or index hashing followed by sorting.

qG, shorthand for 'quick-group', generates a kind of factor-light without the levels attribute but instead an attribute providing the number of levels. Optionally the levels / groups can be attached, but without converting them to character (which can have large performance implications). Objects have a class 'qG'.

finteraction generates a factor or 'qG' object by interacting multiple vectors or factors. In that process missing values are always replaced with a level and unused levels/combinations are always dropped.

collapse internally makes optimal use of factors and 'qG' objects when passed as grouping vectors to statistical functions (g/by, or t arguments) i.e. typically no further grouping or ordering is performed and objects are used directly by statistical C/C++ code.

Usage

qF(x, ordered = FALSE, na.exclude = TRUE, sort = .op[["sort"]], drop = FALSE,
   keep.attr = TRUE, method = "auto")

qG(x, ordered = FALSE, na.exclude = TRUE, sort = .op[["sort"]],
   return.groups = FALSE, method = "auto")

is_qG(x)

as_factor_qG(x, ordered = FALSE, na.exclude = TRUE)

finteraction(..., factor = TRUE, ordered = FALSE, sort = factor && .op[["sort"]],
             method = "auto", sep = ".")
itn(...) # Shorthand for finteraction

Arguments

x

a atomic vector, factor or quick-group.

ordered

logical. Adds a class 'ordered'.

na.exclude

logical. TRUE preserves missing values (i.e. no level is generated for NA). FALSE attaches an additional class "na.included" which is used to skip missing value checks performed before sending objects to C/C++. See Details.

sort

logical. TRUE sorts the levels in ascending order (like factor); FALSE provides the levels in order of first appearance, which can be significantly faster. Note that if a factor is passed as input, only sort = FALSE takes effect and unused levels will be dropped (as factors usually have sorted levels and checking sortedness can be expensive).

drop

logical. If x is a factor, TRUE efficiently drops unused factor levels beforehand using fdroplevels.

keep.attr

logical. If TRUE and x has additional attributes apart from 'levels' and 'class', these are preserved in the conversion to factor.

method

an integer or character string specifying the method of computation:

Int. String Description
1"auto"automatic selection: if(is.double(x) && sort) "radix" else if(sort && length(x) < 1e5) "rcpp_hash" else "hash".
2"radix"use radix ordering to generate factors. Supports sort = FALSE only for character vectors. See Details.
3"hash"use hashing to generate factors. Since v1.8.3 this is a fast hybrid implementation using group and radix ordering applied to the unique elements. See Details.
4"rcpp_hash"the previous "hash" algorithm prior to v1.8.3: uses Rcpp::sugar::sort_unique and Rcpp::sugar::match. Only supports sort = TRUE.

Note that for finteraction, method = "hash" is always unsorted and method = "rcpp_hash" is not available.

return.groups

logical. TRUE returns the unique elements / groups / levels of x in an attribute called "groups". Unlike qF, they are not converted to character.

factor

logical. TRUE returns an factor, FALSE returns a 'qG' object.

sep

character. The separator passed to paste when creating factor levels from multiple grouping variables.

...

multiple atomic vectors or factors, or a single list of equal-length vectors or factors. See Details.

Details

Whenever a vector is passed to a Fast Statistical Function such as fmean(mtcars, mtcars$cyl), is is grouped using qF, or qG if use.g.names = FALSE.

qF is a combination of as.factor and factor. Applying it to a vector i.e. qF(x) gives the same result as as.factor(x). qF(x, ordered = TRUE) generates an ordered factor (same as factor(x, ordered = TRUE)), and qF(x, na.exclude = FALSE) generates a level for missing values (same as factor(x, exclude = NULL)). An important addition is that qF(x, na.exclude = FALSE) also adds a class 'na.included'. This prevents collapse functions from checking missing values in the factor, and is thus computationally more efficient. Therefore factors used in grouped operations should preferably be generated using qF(x, na.exclude = FALSE). Setting sort = FALSE gathers the levels in first-appearance order (unless method = "radix" and x is numeric, in which case the levels are always sorted). This often gives a noticeable speed improvement.

There are 3 internal methods of computation: radix ordering, hashing, and Rcpp sugar hashing. Radix ordering is done by combining the functions radixorder and groupid. It is generally faster than hashing for large numeric data and pre-sorted data (although there are exceptions). Hashing uses group, followed by radixorder on the unique elements if sort = TRUE. It is generally fastest for character data. Rcpp hashing uses Rcpp::sugar::sort_unique and Rcpp::sugar::match. This is often less efficient than the former on large data, but the sorting properties (relying on std::sort) may be superior in borderline cases where radixorder fails to deliver exact lexicographic ordering of factor levels.

Regarding speed: In general qF is around 5x faster than as.factor on character data and about 30x faster on numeric data. Automatic method dispatch typically does a good job delivering optimal performance.

qG is in the first place a programmers function. It generates a factor-'light' class 'qG' consisting of only an integer grouping vector and an attribute providing the number of groups. It is slightly faster and more memory efficient than GRP for grouping atomic vectors, and also convenient as it can be stored in a data frame column, which are the main reasons for its existence.

finteraction is simply a wrapper around as_factor_GRP(GRP.default(X)), where X is replaced by the arguments in '...' combined in a list (so its not really an interaction function but just a multivariate grouping converted to factor, see GRP for computational details). In general: All vectors, factors, or lists of vectors / factors passed can be interacted. Interactions always create a level for missing values and always drop unused levels.

Value

qF returns an (ordered) factor. qG returns an object of class 'qG': an integer grouping vector with an attribute "N.groups" indicating the number of groups, and, if return.groups = TRUE, an attribute "groups" containing the vector of unique groups / elements in x corresponding to the integer-id. finteraction can return either.

Note

An efficient alternative for character vectors with multithreading support is provided by kit::charToFact.

qG(x, sort = FALSE, na.exclude = FALSE, method = "hash") internally calls group(x) which can also be used directly and also supports multivariate groupings where x can be a list of vectors.

Neither qF nor qG reorder groups / factor levels. An exception was added in v1.7, when calling qF(f, sort = FALSE) on a factor f, the levels are recast in first appearance order. These objects can however be converted into one another using qF/qG or the direct method as_factor_qG (called inside qF). It is also possible to add a class 'ordered' (ordered = TRUE) and to create am extra level / integer for missing values (na.exclude = FALSE) if factors or 'qG' objects are passed to qF or qG.

See also

group, groupid, GRP, Fast Grouping and Ordering, Collapse Overview

Examples

cylF <- qF(mtcars$cyl)     # Factor from atomic vector
cylG <- qG(mtcars$cyl)     # Quick-group from atomic vector
cylG                       # See the simple structure of this object
#>  [1] 2 2 1 2 3 2 3 1 1 2 2 3 3 3 3 3 3 1 1 1 1 3 3 3 3 1 1 1 3 2 3 1
#> attr(,"N.groups")
#> [1] 3
#> attr(,"class")
#> [1] "qG"

cf  <- qF(wlddev$country)  # Bigger data
cf2 <- qF(wlddev$country, na.exclude = FALSE)  # With na.included class
dat <- num_vars(wlddev)
 
# cf2 is faster in grouped operations because no missing value check is performed
library(microbenchmark)
microbenchmark(fmax(dat, cf), fmax(dat, cf2))
#> Unit: microseconds
#>            expr    min      lq     mean median      uq     max neval
#>   fmax(dat, cf) 88.806 89.3390 90.70881 89.585 89.8515 117.260   100
#>  fmax(dat, cf2) 84.091 84.6445 86.43825 84.911 85.3210 142.967   100

finteraction(mtcars$cyl, mtcars$vs)  # Interacting two variables (can be factors)
#>  [1] 6.0 6.0 4.1 6.1 8.0 6.1 8.0 4.1 4.1 6.1 6.1 8.0 8.0 8.0 8.0 8.0 8.0 4.1 4.1
#> [20] 4.1 4.1 8.0 8.0 8.0 8.0 4.1 4.0 4.1 8.0 6.0 8.0 4.1
#> Levels: 4.0 4.1 6.0 6.1 8.0
head(finteraction(mtcars))           # A more crude example..
#> [1] 21.6.160.110.3.9.2.62.16.46.0.1.4.4    
#> [2] 21.6.160.110.3.9.2.875.17.02.0.1.4.4   
#> [3] 22.8.4.108.93.3.85.2.32.18.61.1.1.4.1  
#> [4] 21.4.6.258.110.3.08.3.215.19.44.1.0.3.1
#> [5] 18.7.8.360.175.3.15.3.44.17.02.0.0.3.2 
#> [6] 18.1.6.225.105.2.76.3.46.20.22.1.0.3.1 
#> 32 Levels: 10.4.8.460.215.3.5.424.17.82.0.0.3.4 ...

finteraction(mtcars$cyl, mtcars$vs, factor = FALSE) # Returns 'qG', by default unsorted
#>  [1] 1 1 2 3 4 3 4 2 2 3 3 4 4 4 4 4 4 2 2 2 2 4 4 4 4 2 5 2 4 1 4 2
#> attr(,"N.groups")
#> [1] 5
#> attr(,"class")
#> [1] "qG"          "na.included"
group(mtcars[c("cyl", "vs")]) # Same thing. Use whatever syntax is more convenient
#>  [1] 1 1 2 3 4 3 4 2 2 3 3 4 4 4 4 4 4 2 2 2 2 4 4 4 4 2 5 2 4 1 4 2
#> attr(,"N.groups")
#> [1] 5
#> attr(,"class")
#> [1] "qG"          "na.included"