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Fast Slicing of Matrix-Like Objects

fslice.Rd

A fast function to extract rows from a matrix or data frame-like object (by groups).

Usage

fslice(x, ..., n = 1, how = "first", order.by = NULL,
       na.rm = .op[["na.rm"]], sort = FALSE, with.ties = FALSE)

fslicev(x, cols = NULL, n = 1, how = "first", order.by = NULL,
        na.rm = .op[["na.rm"]], sort = FALSE, with.ties = FALSE, ...)

Arguments

x

a matrix, data frame or list-like object, including 'grouped_df'.

...

for fslice: names or sequences of columns to group by - passed to fselect. If x is a matrix: atomic vectors to group x. Can be empty to operate on (un)grouped data. For fslicev: further arguments passed to GRP (such as decreasing, na.last, method).

cols

select columns to group by, using column names, indices, a logical vector or a selector function (e.g. is_categorical). It can also be a list of vectors, or, if x is a matrix, a single vector.

n

integer or proportion (if < 1). Number of rows to select from each group. If a proportion is provided, it is converted to the equivalent number of rows using max(1, round(n * nrow(x))) or max(1, round(n * nrow(x) / N.groups)) for grouped data.

how

character. Method to select rows. One of:

  • "first": select first n rows

  • "last": select last n rows

  • "min": select n rows with minimum values of order.by

  • "max": select n rows with maximum values of order.by

order.by

vector or column name to order by when how is "min" or "max". Must be same length as rows in x. In fslice it must not be quoted.

na.rm

logical. If TRUE, missing values in order.by are removed before selecting rows.

sort

logical. If TRUE, sort selected rows on the grouping columns. FALSE uses first-appearance order (including grouping columns if how is "first" or "last") - fastest.

with.ties

logical. If TRUE and how is "min" or "max", returns all rows with the extreme value. Currently only supported for n = 1 and sort = FALSE.

Value

A subset of x containing the selected rows.

See also

fsubset, fcount, Data Frame Manipulation, Collapse Overview

Examples

# Basic usage
fslice(mtcars, n = 3)                    # First 3 rows
#>                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
fslice(mtcars, n = 3, how = "last")      # Last 3 rows
#>                mpg cyl disp  hp drat   wt qsec vs am gear carb
#> Ferrari Dino  19.7   6  145 175 3.62 2.77 15.5  0  1    5    6
#> Maserati Bora 15.0   8  301 335 3.54 3.57 14.6  0  1    5    8
#> Volvo 142E    21.4   4  121 109 4.11 2.78 18.6  1  1    4    2
fslice(mtcars, n = 0.1)                  # First 10% of rows
#>                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

# Using order.by
fslice(mtcars, n = 3, how = "min", order.by = mpg)  # 3 cars with lowest mpg
#>                      mpg cyl disp  hp drat    wt  qsec vs am gear carb
#> Cadillac Fleetwood  10.4   8  472 205 2.93 5.250 17.98  0  0    3    4
#> Lincoln Continental 10.4   8  460 215 3.00 5.424 17.82  0  0    3    4
#> Camaro Z28          13.3   8  350 245 3.73 3.840 15.41  0  0    3    4
fslice(mtcars, n = 3, how = "max", order.by = mpg)  # 3 cars with highest mpg
#>                 mpg cyl disp hp drat    wt  qsec vs am gear carb
#> Toyota Corolla 33.9   4 71.1 65 4.22 1.835 19.90  1  1    4    1
#> Fiat 128       32.4   4 78.7 66 4.08 2.200 19.47  1  1    4    1
#> Honda Civic    30.4   4 75.7 52 4.93 1.615 18.52  1  1    4    2

# With grouping
mtcars |> fslice(cyl, n = 2)                        # First 2 cars per cylinder
#>                    mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Mazda RX4         21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag     21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#> Datsun 710        22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> Duster 360        14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
#> Merc 240D         24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
mtcars |> fslice(cyl, n = 2, sort = TRUE)           # with sorting (slightly less efficient)
#>                    mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Datsun 710        22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
#> Merc 240D         24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
#> Mazda RX4         21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#> Mazda RX4 Wag     21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
#> Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
#> Duster 360        14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
mtcars |> fslice(cyl, n = 2, how = "min", order.by = mpg)  # 2 lowest mpg cars per cylinder
#>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
#> Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
#> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2
#> Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
#> Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
#> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4

# Using with.ties
mtcars |> fslice(cyl, n = 1, how = "min", order.by = mpg, with.ties = TRUE)
#>                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
#> Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
#> Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
#> Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

# With grouped data
mtcars |>
  fgroup_by(cyl) |>
  fslice(n = 2, how = "max", order.by = mpg)        # 2 highest mpg cars per cylinder
#>                    mpg cyl  disp  hp drat    wt  qsec vs am gear carb
#> Toyota Corolla    33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
#> Fiat 128          32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
#> Hornet 4 Drive    21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
#> Mazda RX4         21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
#> Pontiac Firebird  19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
#> Hornet Sportabout 18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2