Fast (Grouped) Observation Count for Matrix-Like Objects

fnobs is a generic function that (column-wise) computes the number of non-missing values in x, (optionally) grouped by g. It is much faster than sum(!is.na(x)). The TRA argument can further be used to transform x using its (grouped) observation count.

Usage

fnobs(x, ...)

# S3 method for default
fnobs(x, g = NULL, TRA = NULL, use.g.names = TRUE, ...)

# S3 method for matrix
fnobs(x, g = NULL, TRA = NULL, use.g.names = TRUE, drop = TRUE, ...)

# S3 method for data.frame
fnobs(x, g = NULL, TRA = NULL, use.g.names = TRUE, drop = TRUE, ...)

# S3 method for grouped_df
fnobs(x, TRA = NULL, 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.
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.

Details

fnobs preserves all attributes of non-classed vectors / columns, and only the 'label' attribute (if available) of classed vectors / columns (i.e. dates or factors). When applied to data frames and matrices, the row-names are adjusted as necessary.

Value

Integer. The number of non-missing observations in x, grouped by g, or (if TRA is used) x transformed by its number of non-missing observations, grouped by g.

Examples

## default vector method
fnobs(airquality$Solar.R)                   # Simple Nobs
#> [1] 146
fnobs(airquality$Solar.R, airquality$Month) # Grouped Nobs
#>  5  6  7  8  9 
#> 27 30 31 28 30 

## data.frame method
fnobs(airquality)
#>   Ozone Solar.R    Wind    Temp   Month     Day 
#>     116     146     153     153     153     153 
fnobs(airquality, airquality$Month)
#>   Ozone Solar.R Wind Temp Month Day
#> 5    26      27   31   31    31  31
#> 6     9      30   30   30    30  30
#> 7    26      31   31   31    31  31
#> 8    26      28   31   31    31  31
#> 9    29      30   30   30    30  30
fnobs(wlddev)                               # Works with data of all types!
#> country   iso3c    date    year  decade  region  income    OECD   PCGDP  LIFEEX 
#>   13176   13176   13176   13176   13176   13176   13176   13176    9470   11670 
#>    GINI     ODA     POP 
#>    1744    8608   12919 
head(fnobs(wlddev, wlddev$iso3c))
#>     country iso3c date year decade region income OECD PCGDP LIFEEX GINI ODA POP
#> ABW      61    61   61   61     61     61     61   61    32     60    0  20  60
#> AFG      61    61   61   61     61     61     61   61    18     60    0  60  60
#> AGO      61    61   61   61     61     61     61   61    40     60    3  58  60
#> ALB      61    61   61   61     61     61     61   61    40     60    9  32  60
#> AND      61    61   61   61     61     61     61   61    50      0    0   0  60
#>  [ reached 'max' / getOption("max.print") -- omitted 1 rows ]

## matrix method
aqm <- qM(airquality)
fnobs(aqm)                                  # Also works for character or logical matrices
#>   Ozone Solar.R    Wind    Temp   Month     Day 
#>     116     146     153     153     153     153 
fnobs(aqm, airquality$Month)
#>   Ozone Solar.R Wind Temp Month Day
#> 5    26      27   31   31    31  31
#> 6     9      30   30   30    30  30
#> 7    26      31   31   31    31  31
#> 8    26      28   31   31    31  31
#> 9    29      30   30   30    30  30

## method for grouped data frames - created with dplyr::group_by or fgroup_by
airquality |> fgroup_by(Month) |> fnobs()
#>   Month Ozone Solar.R Wind Temp Day
#> 1     5    26      27   31   31  31
#> 2     6     9      30   30   30  30
#> 3     7    26      31   31   31  31
#> 4     8    26      28   31   31  31
#> 5     9    29      30   30   30  30
wlddev |> fgroup_by(country) |>
           fselect(PCGDP,LIFEEX,GINI,ODA) |> fnobs()
#>                country PCGDP LIFEEX GINI ODA
#> 1          Afghanistan    18     60    0  60
#> 2              Albania    40     60    9  32
#> 3              Algeria    60     60    3  60
#> 4       American Samoa    17      0    0   0
#> 5              Andorra    50      0    0   0
#> 6               Angola    40     60    3  58
#> 7  Antigua and Barbuda    43     60    0  47
#> 8            Argentina    60     60   31  60
#> 9              Armenia    30     60   20  29
#> 10               Aruba    32     60    0  20
#> 11           Australia    60     60   10   0
#> 12             Austria    60     60   21   0
#> 13          Azerbaijan    30     60    6  29
#> 14        Bahamas, The    60     60    0  43
#>  [ reached 'max' / getOption("max.print") -- omitted 202 rows ]