collapse (version 1.2.1)

fNdistinct: Fast (Grouped) Distinct Value Count for Matrix-Like Objects

Description

fNdistinct is a generic function that (column-wise) computes the number of distinct values in x, (optionally) grouped by g. It is significantly faster than length(unique(x)). The TRA argument can further be used to transform x using its (grouped) distinct value count.

Usage

fNdistinct(x, ...)
# S3 method for default
fNdistinct(x, g = NULL, TRA = NULL, na.rm = TRUE,
           use.g.names = TRUE, ...)
# S3 method for matrix
fNdistinct(x, g = NULL, TRA = NULL, na.rm = TRUE,
           use.g.names = TRUE, drop = TRUE, ...)
# S3 method for data.frame
fNdistinct(x, g = NULL, TRA = NULL, na.rm = TRUE,
           use.g.names = TRUE, drop = TRUE, ...)
# S3 method for grouped_df
fNdistinct(x, TRA = NULL, na.rm = TRUE,
           use.g.names = FALSE, keep.group_vars = TRUE, ...)

Arguments

x

a vector, matrix, data.frame or grouped tibble (dplyr::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: 1 - "replace_fill" | 2 - "replace" | 3 - "-" | 4 - "-+" | 5 - "/" | 6 - "%" | 7 - "+" | 8 - "*" | 9 - "%%" | 10 - "-%%". See TRA.

na.rm

logical. TRUE: Skip missing values in x (faster computation). FALSE: Also consider 'NA' as one distinct value.

use.g.names

make group-names and add to the result as names (vector method) or row-names (matrix and data.frame method). No row-names are generated for data.tables and grouped tibbles.

drop

matrix and data.frame method: drop dimensions and return 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.

Value

Integer. The number of distinct values in x, grouped by g, or (if TRA is used) x transformed by its distinct value count, grouped by g.

Details

fNdistinct implements a fast algorithm to find the number of distinct values utilizing index- hashing implemented in the Rcpp::sugar::IndexHash class.

If na.rm = TRUE (the default), missing values will be skipped yielding substantial performance gains in data with many missing values. If na.rm = TRUE, missing values will simply be treated as any other value and read into the hash-map. Thus with the former, a numeric vector c(1.25,NaN,3.56,NA) will have a distinct value count of 2, whereas the latter will return a distinct value count of 4.

Grouped computations are currently performed by mapping the data to a sparse-array directed by g and then hash-mapping each group. This is often not much slower than using a larger hash-map for the entire data when g = NULL.

fNdistinct 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.

See Also

fNobs, Fast Statistical Functions, Collapse Overview

Examples

# NOT RUN {
## default vector method
fNdistinct(airquality$Solar.R)                   # Simple distinct value count
fNdistinct(airquality$Solar.R, airquality$Month) # Grouped distinct value count

## data.frame method
fNdistinct(airquality)
fNdistinct(airquality, airquality$Month)
fNdistinct(wlddev)                               # Works with data of all types!
head(fNdistinct(wlddev, wlddev$iso3c))

## matrix method
aqm <- qM(airquality)
fNdistinct(aqm)                                  # Also works for character or logical matrices
fNdistinct(aqm, airquality$Month)

## method for grouped tibbles - for use with dplyr:
library(dplyr)
airquality %>% group_by(Month) %>% fNdistinct
wlddev %>% group_by(country) %>%
             select(PCGDP,LIFEEX,GINI,ODA) %>% fNdistinct

# }