collapse (version 1.7.6)

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 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: 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

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.

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 performed by mapping the data to a sparse-array 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)
# }
# NOT RUN {
 <!-- % No code relying on suggested package -->
## method for grouped data frames - created with dplyr::group_by or fgroup_by
library(dplyr)
airquality %>% group_by(Month) %>% fndistinct()
wlddev %>% group_by(country) %>%
             select(PCGDP,LIFEEX,GINI,ODA) %>% fndistinct()
# }