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Fast (Grouped) First and Last Value for Matrix-Like Objects

ffirst_flast.Rd

ffirst and flast are S3 generic functions that (column-wise) returns the first and last values in x, (optionally) grouped by g. The TRA argument can further be used to transform x using its (groupwise) first and last values.

Usage

ffirst(x, ...)
flast(x, ...)

# Default S3 method
ffirst(x, g = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
       use.g.names = TRUE, ...)
# Default S3 method
flast(x, g = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
      use.g.names = TRUE, ...)

# S3 method for class 'matrix'
ffirst(x, g = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
       use.g.names = TRUE, drop = TRUE, ...)
# S3 method for class 'matrix'
flast(x, g = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
      use.g.names = TRUE, drop = TRUE, ...)

# S3 method for class 'data.frame'
ffirst(x, g = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
       use.g.names = TRUE, drop = TRUE, ...)
# S3 method for class 'data.frame'
flast(x, g = NULL, TRA = NULL, na.rm = .op[["na.rm"]],
      use.g.names = TRUE, drop = TRUE, ...)

# S3 method for class 'grouped_df'
ffirst(x, TRA = NULL, na.rm = .op[["na.rm"]],
       use.g.names = FALSE, keep.group_vars = TRUE, ...)
# S3 method for class 'grouped_df'
flast(x, TRA = NULL, na.rm = .op[["na.rm"]],
      use.g.names = FALSE, keep.group_vars = TRUE, ...)

Arguments

x

a vector, matrix, data frame or grouped data frame (class 'grouped_df').

g

a factor, GRP object, atomic vector (internally converted to factor) or a list of vectors / factors (internally converted to a GRP object) used to group x.

TRA

an integer or quoted operator indicating the transformation to perform: 0 - "na" | 1 - "fill" | 2 - "replace" | 3 - "-" | 4 - "-+" | 5 - "/" | 6 - "%" | 7 - "+" | 8 - "*" | 9 - "%%" | 10 - "-%%". See TRA.

na.rm

logical. TRUE skips missing values and returns the first / last non-missing value i.e. if the first (1) / last (n) value is NA, take the second (2) / second-to-last (n-1) value etc..

use.g.names

logical. Make group-names and add to the result as names (default method) or row-names (matrix and data frame methods). No row-names are generated for data.table's.

drop

matrix and data.frame method: Logical. TRUE drops dimensions and returns an atomic vector if g = NULL and TRA = NULL.

keep.group_vars

grouped_df method: Logical. FALSE removes grouping variables after computation.

...

arguments to be passed to or from other methods. If TRA is used, passing set = TRUE will transform data by reference and return the result invisibly.

Value

ffirst returns the first value in x, grouped by g, or (if TRA is used) x transformed by its first value, grouped by g. Similarly flast returns the last value in x, ...

Note

Both functions are significantly faster if na.rm = FALSE, particularly ffirst which can take direct advantage of the 'group.starts' elements in GRP objects.

See also

Fast Statistical Functions, Collapse Overview

Examples

## default vector method
ffirst(airquality$Ozone)                   # Simple first value
#> [1] 41
ffirst(airquality$Ozone, airquality$Month) # Grouped first value
#>   5   6   7   8   9 
#>  41  29 135  39  96 
ffirst(airquality$Ozone, airquality$Month,
       na.rm = FALSE)                      # Grouped first, but without skipping initial NA's
#>   5   6   7   8   9 
#>  41  NA 135  39  96 

## data.frame method
ffirst(airquality)
#>   Ozone Solar.R    Wind    Temp   Month     Day 
#>    41.0   190.0     7.4    67.0     5.0     1.0 
ffirst(airquality, airquality$Month)
#>   Ozone Solar.R Wind Temp Month Day
#> 5    41     190  7.4   67     5   1
#> 6    29     286  8.6   78     6   1
#> 7   135     269  4.1   84     7   1
#> 8    39      83  6.9   81     8   1
#> 9    96     167  6.9   91     9   1
ffirst(airquality, airquality$Month, na.rm = FALSE) # Again first Ozone measurement in month 6 is NA
#>   Ozone Solar.R Wind Temp Month Day
#> 5    41     190  7.4   67     5   1
#> 6    NA     286  8.6   78     6   1
#> 7   135     269  4.1   84     7   1
#> 8    39      83  6.9   81     8   1
#> 9    96     167  6.9   91     9   1

## matrix method
aqm <- qM(airquality)
ffirst(aqm)
#>   Ozone Solar.R    Wind    Temp   Month     Day 
#>    41.0   190.0     7.4    67.0     5.0     1.0 
ffirst(aqm, airquality$Month) # etc..
#>   Ozone Solar.R Wind Temp Month Day
#> 5    41     190  7.4   67     5   1
#> 6    29     286  8.6   78     6   1
#> 7   135     269  4.1   84     7   1
#> 8    39      83  6.9   81     8   1
#> 9    96     167  6.9   91     9   1
 
## method for grouped data frames - created with dplyr::group_by or fgroup_by
library(dplyr)
airquality |> group_by(Month) |> ffirst()
#> # A tibble: 5 × 6
#>   Month Ozone Solar.R  Wind  Temp   Day
#>   <int> <int>   <int> <dbl> <int> <int>
#> 1     5    41     190   7.4    67     1
#> 2     6    29     286   8.6    78     1
#> 3     7   135     269   4.1    84     1
#> 4     8    39      83   6.9    81     1
#> 5     9    96     167   6.9    91     1
airquality |> group_by(Month) |> select(Ozone) |> ffirst(na.rm = FALSE)
#> Adding missing grouping variables: `Month`
#> # A tibble: 5 × 2
#>   Month Ozone
#>   <int> <int>
#> 1     5    41
#> 2     6    NA
#> 3     7   135
#> 4     8    39
#> 5     9    96

# Note: All examples generalize to flast.