GRP performs fast, ordered and unordered, groupings of vectors and data frames (or lists of vectors) using radixorderv or group. The output is a list-like object of class 'GRP' which can be printed, plotted and used as an efficient input to all of collapse's fast statistical and transformation functions and operators (see macros .FAST_FUN and .OPERATOR_FUN), as well as to collap, BY and TRA.

fgroup_by is similar to dplyr::group_by but faster and class-agnostic. It creates a grouped data frame with a 'GRP' object attached - for fast dplyr-like programming with collapse's fast functions.

There are also several conversion methods to and from 'GRP' objects. Notable among these is GRP.grouped_df, which returns a 'GRP' object from a grouped data frame created with dplyr::group_by or fgroup_by, and the duo GRP.factor and as_factor_GRP.

gsplit efficiently splits a vector based on a 'GRP' object, and greorder helps to recombine the results. These are the workhorses behind functions like BY, and collap, fsummarise and fmutate when evaluated with base R and user-defined functions.

GRP(X, ...)

# S3 method for default
GRP(X, by = NULL, sort = TRUE, decreasing = FALSE, na.last = TRUE,
    return.groups = TRUE, return.order = sort, method = "auto",
    call = TRUE, ...)

# S3 method for factor
GRP(X, ..., group.sizes = TRUE, drop = FALSE, return.groups = TRUE,
    call = TRUE)

# S3 method for qG
GRP(X, ..., group.sizes = TRUE, return.groups = TRUE, call = TRUE)

# S3 method for pseries
GRP(X, effect = 1L, ..., group.sizes = TRUE, return.groups = TRUE,
    call = TRUE)

# S3 method for pdata.frame
GRP(X, effect = 1L, ..., group.sizes = TRUE, return.groups = TRUE,
    call = TRUE)

# S3 method for grouped_df
GRP(X, ..., return.groups = TRUE, call = TRUE)

# Identify 'GRP' objects
is_GRP(x)

# S3 method for GRP
length(x)                          # Length of data being grouped
GRPN(x, expand = TRUE, ...)        # Group sizes (default: expanded to match data length)
GRPnames(x, force.char = TRUE, sep = ".")  # Group names
as_factor_GRP(x, ordered = FALSE)  # 'GRP'-object to (ordered) factor conversion

# Efficiently split a vector using a 'GRP' object
gsplit(x, g, use.g.names = FALSE, ...)

# Efficiently reorder y = unlist(gsplit(x, g)) such that identical(greorder(y, g), x)
greorder(x, g, ...)

# Fast, class-agnostic pendant to dplyr::group_by for use with fast functions, see details
fgroup_by(.X, ..., sort = TRUE, decreasing = FALSE, na.last = TRUE,
          return.groups = TRUE, return.order = sort, method = "auto")
# Shorthand for fgroup_by
      gby(.X, ..., sort = TRUE, decreasing = FALSE, na.last = TRUE,
          return.groups = TRUE, return.order = sort, method = "auto")

# Get grouping columns from a grouped data frame created with dplyr::group_by or fgroup_by
fgroup_vars(X, return = "data")

# Ungroup grouped data frame created with dplyr::group_by or fgroup_by
fungroup(X, ...)

# S3 method for GRP
print(x, n = 6, ...)

# S3 method for GRP
plot(x, breaks = "auto", type = "s", horizontal = FALSE, ...)

Arguments

X

a vector, list of columns or data frame (default method), or a suitable object (conversion / extractor methods).

.X

a data frame or list.

x, g

a 'GRP' object. For gsplit/greorder, x can be a vector of any type, or NULL to return the integer indices of the groups. gsplit/greorder/GRPN also support vectors or data frames to be passed to g/x.

by

if X is a data frame or list, by can indicate columns to use for the grouping (by default all columns are used). Columns must be passed using a vector of column names, indices, or using a one-sided formula i.e. ~ col1 + col2.

sort

logical. If FALSE, groups are not ordered but simply grouped in the order of first appearance of unique elements / rows. This often provides a performance gain if the data was not sorted beforehand. See also method.

ordered

logical. TRUE adds a class 'ordered' i.e. generates an ordered factor.

decreasing

logical. Should the sort order be increasing or decreasing? Can be a vector of length equal to the number of arguments in X / by (argument passed to radixorderv).

na.last

logical. If missing values are encountered in grouping vector/columns, assign them to the last group (argument passed to radixorderv).

return.groups

logical. Include the unique groups in the created GRP object.

return.order

logical. If sort = TRUE, include the output from radixorderv in the created GRP object. This brings performance improvements in gsplit (and thus also benefits grouped execution of base R functions).

method

character. The algorithm to use for grouping: either "radix", "hash" or "auto". "auto" will chose "radix" when sort = TRUE, yielding ordered grouping via radixorderv, and "hash"-based grouping in first-appearance order via group otherwise. It is possibly to put method = "radix" and sort = FALSE, which will group character data in first appearance order but sort numeric data (a good hybrid option). method = "hash" currently does not support any sorting, thus putting sort = TRUE will simply be ignored.

group.sizes

logical. TRUE tabulates factor levels using tabulate to create a vector of group sizes; FALSE leaves that slot empty when converting from factors.

drop

logical. TRUE efficiently drops unused factor levels beforehand using fdroplevels.

call

logical. TRUE calls match.call and saves it in the final slot of the GRP object.

expand

logical. TRUE returns a vector the same length as the data. FALSE returns the group sizes (computed in first-appearance-order of groups if x is not already a 'GRP' object).

force.char

logical. Always output group names as character vector, even if a single numeric vector was passed to GRP.default.

sep

character. The separator passed to paste when creating group names from multiple grouping variables by pasting them together.

effect

plm / indexed data methods: Select which panel identifier should be used as grouping variable. 1L takes the first variable in the index, 2L the second etc., identifiers can also be passed as a character string. More than one variable can be supplied.

return

an integer or string specifying what fgroup_vars should return. The options are:

Int. String Description
1"data"full grouping columns (default)
2"unique"unique rows of grouping columns
3"names"names of grouping columns
4"indices"integer indices of grouping columns
5"named_indices"named integer indices of grouping columns
6"logical"logical selection vector of grouping columns
7"named_logical"named logical selection vector of grouping columns

use.g.names

logical. TRUE returns a named list, like split. FALSE is slightly more efficient.

n

integer. Number of groups to print out.

breaks

integer. Number of breaks in the histogram of group-sizes.

type

linetype for plot.

horizontal

logical. TRUE arranges plots next to each other, instead of above each other.

...

for fgroup_by: unquoted comma-separated column names, sequences of columns, expressions involving columns, and column names, indices, logical vectors or selector functions. See Examples. For gsplit, greorder and GRPN: further arguments passed to GRP (if g/x is not already a 'GRP' object). For example the by argument could be used if a data frame is passed.

Details

GRP is a central function in the collapse package because it provides, in the form of integer vectors, some key pieces of information to efficiently perform grouped operations at the C/C++ level.

Most statistical function require information about (1) the number of groups (2) an integer group-id indicating which values / rows belong to which group and (3) information about the size of each group. Provided with these, collapse's Fast Statistical Functions pre-allocate intermediate and result vectors of the right sizes and (in most cases) perform grouped statistical computations in a single pass through the data.

The sorting functionality of GRP.default lets groups receive different integer-id's depending on whether the groups are sorted sort = TRUE (FALSE gives first-appearance order), and in which order (argument decreasing). This affects the order of values/rows in the output whenever an aggregation is performed.

Other elements in the object provide information about whether the data was sorted by the variables defining the grouping (6) and the ordering vector (7). These also feed into optimizations in gsplit/greorder that benefit the execution of base R functions across groups.

Complimentary to GRP, the function fgroup_by is a significantly faster and class-agnostic alternative to dplyr::group_by for programming with collapse. It creates a grouped data frame with a 'GRP' object attached in a "groups" attribute. This data frame has classes 'GRP_df', ..., 'grouped_df' and 'data.frame', where ... stands for any other classes the input frame inherits such as 'data.table', 'sf', 'tbl_df', 'indexed_frame' etc.. collapse functions with a 'grouped_df' method respond to 'grouped_df' objects created with either fgroup_by or dplyr::group_by. The method GRP.grouped_df takes the "groups" attribute from a 'grouped_df' and converts it to a 'GRP' object if created with dplyr::group_by.

The 'GRP_df' class in front responds to print.GRP_df which first calls print(fungroup(x), ...) and prints one line below the object indicating the grouping variables, followed, in square brackets, by some statistics on the group sizes: [N | Mean (SD) Min-Max]. The mean is rounded to a full number and the standard deviation (SD) to one digit. Minimum and maximum are only displayed if the SD is non-zero. There also exist a method [.GRP_df which calls NextMethod but makes sure that the grouping information is preserved or dropped depending on the dimensions of the result (subsetting rows or aggregation with data.table drops the grouping object).

GRP.default supports vector and list input and will also return 'GRP' objects if passed. There is also a hidden method GRP.GRP which simply returns grouping objects (no re-grouping functionality is offered).

Apart from GRP.grouped_df there are several further conversion methods:

The conversion of factors to 'GRP' objects by GRP.factor involves obtaining the number of groups calling ng <- fnlevels(f) and then computing the count of each level using tabulate(f, ng). The integer group-id (2) is already given by the factor itself after removing the levels and class attributes and replacing any missing values with ng + 1L. The levels are put in a list and moved to position (4) in the 'GRP' object, which is reserved for the unique groups. Finally, a sortedness check !is.unsorted(id) is run on the group-id to check if the data represented by the factor was sorted (6). GRP.qG works similarly (see also qG), and the 'pseries' and 'pdata.frame' methods simply group one or more factors in the index (selected using the effect argument) .

Creating a factor from a 'GRP' object using as_factor_GRP does not involve any computations, but may involve interacting multiple grouping columns using the paste function to produce unique factor levels.

Value

A list-like object of class `GRP' containing information about the number of groups, the observations (rows) belonging to each group, the size of each group, the unique group names / definitions, whether the groups are ordered and data grouped is sorted or not, the ordering vector used to perform the ordering and the group start positions. The object is structured as follows:

List-index Element-name Content type Content description
[[1]]N.groupsinteger(1)
Number of Groups[[2]]group.idinteger(NROW(X))
An integer group-identifier[[3]]group.sizes
integer(N.groups)Vector of group sizes[[4]]groups
unique(X) or NULLUnique groups (same format as input, except for fgroup_by which uses a plain list, sorted if sort = TRUE), or NULL if return.groups = FALSE[[5]]
group.varscharacterThe names of the grouping variables[[6]]
orderedlogical(2)[1] Whether the groups are ordered: equal to the sort argument in the default method, or TRUE if converted objects inherit a class "ordered" and NA otherwise, [2] Whether the data (X) is already sorted: the result of !is.unsorted(group.id). If sort = FALSE (default method) the second entry is NA.
[[7]]orderinteger(NROW(X)) or NULLOrdering vector from radixorderv (with "starts" attribute), or NULL if return.order = FALSE
[[8]]group.startsinteger(N.groups) or NULL
The first-occurrence positions/rows of the groups. Useful e.g. with ffirst(x, g, na.rm = FALSE). NULL if return.groups = FALSE.[[9]]callmatch.call() or NULL

Examples

## default method
GRP(mtcars$cyl)
#> collapse grouping object of length 32 with 3 ordered groups
#> 
#> Call: GRP.default(X = mtcars$cyl), X is unsorted
#> 
#> Distribution of group sizes: 
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    7.00    9.00   11.00   10.67   12.50   14.00 
#> 
#> Groups with sizes: 
#>  4  6  8 
#> 11  7 14 
GRP(mtcars, ~ cyl + vs + am)       # Or GRP(mtcars, c("cyl","vs","am")) or GRP(mtcars, c(2,8:9))
#> collapse grouping object of length 32 with 7 ordered groups
#> 
#> Call: GRP.default(X = mtcars, by = ~cyl + vs + am), X is unsorted
#> 
#> Distribution of group sizes: 
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   1.000   2.500   3.000   4.571   5.500  12.000 
#> 
#> Groups with sizes: 
#> 4.0.1 4.1.0 4.1.1 6.0.1 6.1.0 8.0.0 8.0.1 
#>     1     3     7     3     4    12     2 
g <- GRP(mtcars, ~ cyl + vs + am)  # Saving the object
print(g)                           # Printing it
#> collapse grouping object of length 32 with 7 ordered groups
#> 
#> Call: GRP.default(X = mtcars, by = ~cyl + vs + am), X is unsorted
#> 
#> Distribution of group sizes: 
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   1.000   2.500   3.000   4.571   5.500  12.000 
#> 
#> Groups with sizes: 
#> 4.0.1 4.1.0 4.1.1 6.0.1 6.1.0 8.0.0 8.0.1 
#>     1     3     7     3     4    12     2 
plot(g)                            # Plotting it

GRPnames(g)                        # Retain group names
#> [1] "4.0.1" "4.1.0" "4.1.1" "6.0.1" "6.1.0" "8.0.0" "8.0.1"
fsum(mtcars, g)                    # Compute the sum of mtcars, grouped by variables cyl, vs and am
#>         mpg cyl   disp   hp  drat     wt   qsec vs am gear carb
#> 4.0.1  26.0   4  120.3   91  4.43  2.140  16.70  0  1    5    2
#> 4.1.0  68.7  12  407.6  254 11.31  8.805  62.91  3  0   11    5
#> 4.1.1 198.6  28  628.6  564 29.04 14.198 130.90  7  7   29   10
#> 6.0.1  61.7  18  465.0  395 11.42  8.265  48.98  0  3   13   14
#> 6.1.0  76.5  24  818.2  461 13.68 13.555  76.86  4  0   14   10
#> 8.0.0 180.6  96 4291.4 2330 37.45 49.249 205.71  0  0   36   37
#>  [ reached 'max' / getOption("max.print") -- omitted 1 rows ]
gsplit(mtcars$mpg, g)              # Use the object to split a vector
#> [[1]]
#> [1] 26
#> 
#> [[2]]
#> [1] 24.4 22.8 21.5
#> 
#> [[3]]
#> [1] 22.8 32.4 30.4 33.9 27.3 30.4 21.4
#> 
#> [[4]]
#> [1] 21.0 21.0 19.7
#> 
#> [[5]]
#> [1] 21.4 18.1 19.2 17.8
#> 
#> [[6]]
#>  [1] 18.7 14.3 16.4 17.3 15.2 10.4 10.4 14.7 15.5 15.2 13.3 19.2
#> 
#> [[7]]
#> [1] 15.8 15.0
#> 
gsplit(NULL, g)                    # The indices of the groups
#> [[1]]
#> [1] 27
#> 
#> [[2]]
#> [1]  8  9 21
#> 
#> [[3]]
#> [1]  3 18 19 20 26 28 32
#> 
#> [[4]]
#> [1]  1  2 30
#> 
#> [[5]]
#> [1]  4  6 10 11
#> 
#> [[6]]
#>  [1]  5  7 12 13 14 15 16 17 22 23 24 25
#> 
#> [[7]]
#> [1] 29 31
#> 
identical(mtcars$mpg,              # greorder and unlist undo the effect of gsplit
          greorder(unlist(gsplit(mtcars$mpg, g)), g))
#> [1] TRUE

## Convert factor to GRP object and vice-versa
GRP(iris$Species)
#> collapse grouping object of length 150 with 3 ordered groups
#> 
#> Call: GRP.factor(X = iris$Species), X is sorted
#> 
#> Distribution of group sizes: 
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>      50      50      50      50      50      50 
#> 
#> Groups with sizes: 
#>     setosa versicolor  virginica 
#>         50         50         50 
as_factor_GRP(g)
#>  [1] 6.0.1 6.0.1 4.1.1 6.1.0 8.0.0 6.1.0 8.0.0 4.1.0 4.1.0 6.1.0 6.1.0 8.0.0
#> [13] 8.0.0 8.0.0 8.0.0 8.0.0 8.0.0 4.1.1 4.1.1 4.1.1 4.1.0 8.0.0 8.0.0 8.0.0
#> [25] 8.0.0 4.1.1 4.0.1 4.1.1 8.0.1 6.0.1 8.0.1 4.1.1
#> Levels: 4.0.1 4.1.0 4.1.1 6.0.1 6.1.0 8.0.0 8.0.1
 
## dplyr integration
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:data.table’:
#> 
#>     between, first, last
#> The following object is masked from ‘package:testthat’:
#> 
#>     matches
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union
mtcars %>% group_by(cyl,vs,am) %>% GRP()    # Get GRP object from a dplyr grouped tibble
#> collapse grouping object of length 32 with 7 ordered groups
#> 
#> Call: GRP.grouped_df(X = .), X is unsorted
#> 
#> Distribution of group sizes: 
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>   1.000   2.500   3.000   4.571   5.500  12.000 
#> 
#> Groups with sizes: 
#> 4.0.1 4.1.0 4.1.1 6.0.1 6.1.0 8.0.0 8.0.1 
#>     1     3     7     3     4    12     2 
mtcars %>% group_by(cyl,vs,am) %>% fmean()  # Grouped mean using dplyr grouping
#> # A tibble: 7 × 11
#>     cyl    vs    am   mpg  disp    hp  drat    wt  qsec  gear  carb
#>   <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1     4     0     1  26   120.   91    4.43  2.14  16.7  5     2   
#> 2     4     1     0  22.9 136.   84.7  3.77  2.94  21.0  3.67  1.67
#> 3     4     1     1  28.4  89.8  80.6  4.15  2.03  18.7  4.14  1.43
#> 4     6     0     1  20.6 155   132.   3.81  2.76  16.3  4.33  4.67
#> 5     6     1     0  19.1 205.  115.   3.42  3.39  19.2  3.5   2.5 
#> 6     8     0     0  15.1 358.  194.   3.12  4.10  17.1  3     3.08
#> 7     8     0     1  15.4 326   300.   3.88  3.37  14.6  5     6   
mtcars %>% fgroup_by(cyl,vs,am) %>% fmean() # Faster alternative with collapse grouping
#>   cyl vs am      mpg     disp        hp     drat       wt     qsec     gear
#> 1   4  0  1 26.00000 120.3000  91.00000 4.430000 2.140000 16.70000 5.000000
#> 2   4  1  0 22.90000 135.8667  84.66667 3.770000 2.935000 20.97000 3.666667
#> 3   4  1  1 28.37143  89.8000  80.57143 4.148571 2.028286 18.70000 4.142857
#> 4   6  0  1 20.56667 155.0000 131.66667 3.806667 2.755000 16.32667 4.333333
#> 5   6  1  0 19.12500 204.5500 115.25000 3.420000 3.388750 19.21500 3.500000
#> 6   8  0  0 15.05000 357.6167 194.16667 3.120833 4.104083 17.14250 3.000000
#>       carb
#> 1 2.000000
#> 2 1.666667
#> 3 1.428571
#> 4 4.666667
#> 5 2.500000
#> 6 3.083333
#>  [ reached 'max' / getOption("max.print") -- omitted 1 rows ]

mtcars %>% fgroup_by(cyl,vs,am)            # Print method for grouped data frame
#>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb
#> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4
#> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1
#> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1
#> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2
#> Valiant           18.1   6  225 105 2.76 3.460 20.22  1  0    3    1
#>  [ reached 'max' / getOption("max.print") -- omitted 26 rows ]
#> 
#> Grouped by:  cyl, vs, am  [7 | 5 (3.8) 1-12] 

library(magrittr)
## Adding a column of group sizes.
mtcars %>% fgroup_by(cyl,vs,am) %>% fsummarise(Sizes = GRPN())
#>   cyl vs am Sizes
#> 1   4  0  1     1
#> 2   4  1  0     3
#> 3   4  1  1     7
#> 4   6  0  1     3
#> 5   6  1  0     4
#> 6   8  0  0    12
#> 7   8  0  1     2
mtcars %>% fgroup_by(cyl,vs,am) %>% fmutate(Sizes = GRPN())
#>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb Sizes
#> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4     3
#> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4     3
#> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1     7
#> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1     4
#> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2    12
#>  [ reached 'max' / getOption("max.print") -- omitted 27 rows ]
#> 
#> Grouped by:  cyl, vs, am  [7 | 5 (3.8) 1-12] 
# Note: can also set options(collapse_mask = "n") to use n() instead, see help("collapse-options")
# Other usage modes:
mtcars %>% fgroup_by(cyl,vs,am) %>% ftransform(Sizes = GRPN(.))
#>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb Sizes
#> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4     3
#> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4     3
#> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1     7
#> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1     4
#> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2    12
#>  [ reached 'max' / getOption("max.print") -- omitted 27 rows ]
#> 
#> Grouped by:  cyl, vs, am  [7 | 5 (3.8) 1-12] 
mtcars %>% ftransform(Sizes = GRPN(list(cyl,vs,am)))  # Same thing, slightly more efficient
#>                    mpg cyl disp  hp drat    wt  qsec vs am gear carb Sizes
#> Mazda RX4         21.0   6  160 110 3.90 2.620 16.46  0  1    4    4     3
#> Mazda RX4 Wag     21.0   6  160 110 3.90 2.875 17.02  0  1    4    4     3
#> Datsun 710        22.8   4  108  93 3.85 2.320 18.61  1  1    4    1     7
#> Hornet 4 Drive    21.4   6  258 110 3.08 3.215 19.44  1  0    3    1     4
#> Hornet Sportabout 18.7   8  360 175 3.15 3.440 17.02  0  0    3    2    12
#>  [ reached 'max' / getOption("max.print") -- omitted 27 rows ]

## Various options for programming and interactive use
fgroup_by(GGDC10S, Variable, Decade = floor(Year / 10) * 10) %>% head(3)
#>   Country Regioncode             Region Variable Year AGR MIN MAN PU CON WRT
#> 1     BWA        SSA Sub-saharan Africa       VA 1960  NA  NA  NA NA  NA  NA
#> 2     BWA        SSA Sub-saharan Africa       VA 1961  NA  NA  NA NA  NA  NA
#> 3     BWA        SSA Sub-saharan Africa       VA 1962  NA  NA  NA NA  NA  NA
#>   TRA FIRE GOV OTH SUM
#> 1  NA   NA  NA  NA  NA
#> 2  NA   NA  NA  NA  NA
#> 3  NA   NA  NA  NA  NA
fgroup_by(GGDC10S, 1:3, 5) %>% head(3)
#>   Country Regioncode             Region Variable Year AGR MIN MAN PU CON WRT
#> 1     BWA        SSA Sub-saharan Africa       VA 1960  NA  NA  NA NA  NA  NA
#> 2     BWA        SSA Sub-saharan Africa       VA 1961  NA  NA  NA NA  NA  NA
#> 3     BWA        SSA Sub-saharan Africa       VA 1962  NA  NA  NA NA  NA  NA
#>   TRA FIRE GOV OTH SUM
#> 1  NA   NA  NA  NA  NA
#> 2  NA   NA  NA  NA  NA
#> 3  NA   NA  NA  NA  NA
fgroup_by(GGDC10S, c("Variable", "Country")) %>% head(3)
#>   Country Regioncode             Region Variable Year AGR MIN MAN PU CON WRT
#> 1     BWA        SSA Sub-saharan Africa       VA 1960  NA  NA  NA NA  NA  NA
#> 2     BWA        SSA Sub-saharan Africa       VA 1961  NA  NA  NA NA  NA  NA
#> 3     BWA        SSA Sub-saharan Africa       VA 1962  NA  NA  NA NA  NA  NA
#>   TRA FIRE GOV OTH SUM
#> 1  NA   NA  NA  NA  NA
#> 2  NA   NA  NA  NA  NA
#> 3  NA   NA  NA  NA  NA
fgroup_by(GGDC10S, is.character) %>% head(3)
#>   Country Regioncode             Region Variable Year AGR MIN MAN PU CON WRT
#> 1     BWA        SSA Sub-saharan Africa       VA 1960  NA  NA  NA NA  NA  NA
#> 2     BWA        SSA Sub-saharan Africa       VA 1961  NA  NA  NA NA  NA  NA
#> 3     BWA        SSA Sub-saharan Africa       VA 1962  NA  NA  NA NA  NA  NA
#>   TRA FIRE GOV OTH SUM
#> 1  NA   NA  NA  NA  NA
#> 2  NA   NA  NA  NA  NA
#> 3  NA   NA  NA  NA  NA
fgroup_by(GGDC10S, Country:Variable, Year) %>% head(3)
#>   Country Regioncode             Region Variable Year AGR MIN MAN PU CON WRT
#> 1     BWA        SSA Sub-saharan Africa       VA 1960  NA  NA  NA NA  NA  NA
#> 2     BWA        SSA Sub-saharan Africa       VA 1961  NA  NA  NA NA  NA  NA
#> 3     BWA        SSA Sub-saharan Africa       VA 1962  NA  NA  NA NA  NA  NA
#>   TRA FIRE GOV OTH SUM
#> 1  NA   NA  NA  NA  NA
#> 2  NA   NA  NA  NA  NA
#> 3  NA   NA  NA  NA  NA
fgroup_by(GGDC10S, Country:Region, Var = Variable, Year) %>% head(3)
#>   Country Regioncode             Region Variable Year AGR MIN MAN PU CON WRT
#> 1     BWA        SSA Sub-saharan Africa       VA 1960  NA  NA  NA NA  NA  NA
#> 2     BWA        SSA Sub-saharan Africa       VA 1961  NA  NA  NA NA  NA  NA
#> 3     BWA        SSA Sub-saharan Africa       VA 1962  NA  NA  NA NA  NA  NA
#>   TRA FIRE GOV OTH SUM
#> 1  NA   NA  NA  NA  NA
#> 2  NA   NA  NA  NA  NA
#> 3  NA   NA  NA  NA  NA

## Note that you can create a grouped data frame without materializing the unique grouping columns
fgroup_by(GGDC10S, Variable, Country, return.groups = FALSE) %>% fmutate(across(AGR:SUM, fscale))
#>   Country Regioncode             Region Variable Year AGR MIN MAN PU CON WRT
#> 1     BWA        SSA Sub-saharan Africa       VA 1960  NA  NA  NA NA  NA  NA
#> 2     BWA        SSA Sub-saharan Africa       VA 1961  NA  NA  NA NA  NA  NA
#> 3     BWA        SSA Sub-saharan Africa       VA 1962  NA  NA  NA NA  NA  NA
#> 4     BWA        SSA Sub-saharan Africa       VA 1963  NA  NA  NA NA  NA  NA
#>   TRA FIRE GOV OTH SUM
#> 1  NA   NA  NA  NA  NA
#> 2  NA   NA  NA  NA  NA
#> 3  NA   NA  NA  NA  NA
#> 4  NA   NA  NA  NA  NA
#>  [ reached 'max' / getOption("max.print") -- omitted 5023 rows ]
#> 
#> Grouped by:  Variable, Country  [85 | 59 (7.7) 4-65] 
fgroup_by(GGDC10S, Variable, Country, return.groups = FALSE) %>% fselect(AGR:SUM) %>% fmean()
#>           AGR        MIN         MAN          PU         CON         WRT
#> 1   1419.8013   52.08903  1931.76020  101.720936   742.40444  1982.17750
#> 2    964.2103   56.03295   235.03321    5.346433   122.78267   281.51638
#> 3  17191.3529  206.02389  6991.37096  364.573404  3524.73837  8509.46124
#> 4    188.0574   10.47964    18.05585    3.093238    25.34108    36.32971
#> 5    701.9112  101.41742   624.90303   29.358568   296.00226   694.66129
#> 6 287744.2384 7049.52662 67143.63401 1606.064456 20851.66641 28908.10661
#>            TRA       FIRE         GOV         OTH         SUM
#> 1   648.511948  627.79291  2043.47128   992.44753  10542.1770
#> 2   115.472843   44.56442          NA   395.56503   2220.5242
#> 3  2054.373148 4413.54448  5307.28010  5710.26650  54272.9850
#> 4     8.362217   15.33622    61.10484    27.60635    393.7666
#> 5   258.240876  272.43836          NA  1003.43964   3982.3726
#> 6 13928.607381 4929.26308 22669.24815 30990.11860 485820.4737
#>  [ reached 'max' / getOption("max.print") -- omitted 79 rows ]

## Note also that setting sort = FALSE on unsorted data can be much faster... if not required...
library(microbenchmark)
microbenchmark(gby(GGDC10S, Variable, Country), gby(GGDC10S, Variable, Country, sort = FALSE))
#> Warning: less accurate nanosecond times to avoid potential integer overflows
#> Unit: microseconds
#>                                           expr    min      lq      mean  median
#>                gby(GGDC10S, Variable, Country) 99.835 105.903 121.79050 109.839
#>  gby(GGDC10S, Variable, Country, sort = FALSE) 33.538  39.032  46.82159  41.533
#>        uq     max neval
#>  115.0255 981.909   100
#>   49.5895 159.818   100