fslice function

Fast Slicing of Matrix-Like Objects

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

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.

• 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.

Returns

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
fslice(mtcars, n = 3, how = "last")      # Last 3 rows
fslice(mtcars, n = 0.1)                  # First 10% of rows

# Using order.by
fslice(mtcars, n = 3, how = "min", order.by = mpg)  # 3 cars with lowest mpg
fslice(mtcars, n = 3, how = "max", order.by = mpg)  # 3 cars with highest mpg

# With grouping
mtcars |> fslice(cyl, n = 2)                        # First 2 cars per cylinder
mtcars |> fslice(cyl, n = 2, sort = TRUE)           # with sorting (slightly less efficient)
mtcars |> fslice(cyl, n = 2, how = "min", order.by = mpg)  # 2 lowest mpg cars per cylinder

# Using with.ties
mtcars |> fslice(cyl, n = 1, how = "min", order.by = mpg, with.ties = TRUE)

# With grouped data
mtcars |>
  fgroup_by(cyl) |>
  fslice(n = 2, how = "max", order.by = mpg)        # 2 highest mpg cars per cylinder