fnobs.Rdfnobs 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, ...)a vector, matrix, data frame or grouped data frame (class 'grouped_df').
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.
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.
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.
matrix and data.frame method: Logical. TRUE drops dimensions and returns an atomic vector if g = NULL and TRA = NULL.
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.
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.
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.
## 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