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.

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 - "replace_NA" | 1 - "replace_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
library(dplyr)
airquality %>% group_by(Month) %>% fnobs()
#> # A tibble: 5 × 6
#>   Month Ozone Solar.R  Wind  Temp   Day
#>   <int> <int>   <int> <int> <int> <int>
#> 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 %>% group_by(country) %>%
           select(PCGDP,LIFEEX,GINI,ODA) %>% fnobs()
#> Adding missing grouping variables: `country`
#> # A tibble: 216 × 5
#>    country             PCGDP LIFEEX  GINI   ODA
#>    <chr>               <int>  <int> <int> <int>
#>  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
#> # … with 206 more rows