varying: Fast Check of Variation in Data

In collapse: Advanced and Fast Data Transformation

Fast Check of Variation in Data

Description

varying is a generic function that (column-wise) checks for variation in the values of x, (optionally) within the groups g (e.g. a panel-identifier).

Usage

varying(x, ...)

## Default S3 method:
varying(x, g = NULL, any_group = TRUE, use.g.names = TRUE, ...)

## S3 method for class 'matrix'
varying(x, g = NULL, any_group = TRUE, use.g.names = TRUE, drop = TRUE, ...)

## S3 method for class 'data.frame'
varying(x, by = NULL, cols = NULL, any_group = TRUE, use.g.names = TRUE, drop = TRUE, ...)

# Methods for indexed data / compatibility with plm:

## S3 method for class 'pseries'
varying(x, effect = 1L, any_group = TRUE, use.g.names = TRUE, ...)

## S3 method for class 'pdata.frame'
varying(x, effect = 1L, cols = NULL, any_group = TRUE, use.g.names = TRUE,
        drop = TRUE, ...)

# Methods for grouped data frame / compatibility with dplyr:

## S3 method for class 'grouped_df'
varying(x, any_group = TRUE, use.g.names = FALSE, drop = TRUE,
        keep.group_vars = TRUE, ...)

# Methods for grouped data frame / compatibility with sf:

## S3 method for class 'sf'
varying(x, by = NULL, cols = NULL, any_group = TRUE, use.g.names = TRUE, drop = TRUE, ...)

Arguments

x	a vector, matrix, data frame, 'indexed_series' ('pseries'), 'indexed_frame' ('pdata.frame') or grouped data frame ('grouped_df'). Data must not be numeric.
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.
by	same as g, but also allows one- or two-sided formulas i.e. ~ group1 + group2 or var1 + var2 ~ group1 + group2. See Examples
any_group	logical. If !is.null(g), FALSE will check and report variation in all groups, whereas the default TRUE only checks if there is variation within any group. See Examples.
cols	select columns using column names, indices or a function (e.g. is.numeric). Two-sided formulas passed to by overwrite cols.
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 methods: Logical. TRUE drops dimensions and returns an atomic vector if the result is 1-dimensional.
effect	plm methods: Select the panel identifier by which variation in the data should be examined. 1L takes the first variable in the index, 2L the second etc.. Index variables can also be called by name. More than one index variable can be supplied, which will be interacted.
keep.group_vars	grouped_df method: Logical. FALSE removes grouping variables after computation.
...	arguments to be passed to or from other methods.

Details

Without groups passed to g, varying simply checks if there is any variation in the columns of x and returns TRUE for each column where this is the case and FALSE otherwise. A set of data points is defined as varying if it contains at least 2 distinct non-missing values (such that a non-0 standard deviation can be computed on numeric data). varying checks for variation in both numeric and non-numeric data.

If groups are supplied to g (or alternatively a grouped_df to x), varying can operate in one of 2 modes:

• If any_group = TRUE (the default), varying checks each column for variation in any of the groups defined by g, and returns TRUE if such within-variation was detected and FALSE otherwise. Thus only one logical value is returned for each column and the computation on each column is terminated as soon as any variation within any group was found.

• If any_group = FALSE, varying runs through the entire data checking each group for variation and returns, for each column in x, a logical vector reporting the variation check for all groups. If a group contains only missing values, a NA is returned for that group.

The sf method simply ignores the geometry column.

Value

A logical vector or (if !is.null(g) and any_group = FALSE), a matrix or data frame of logical vectors indicating whether the data vary (over the dimension supplied by g).

See Also

Summary Statistics, Data Transformations, Collapse Overview

Examples

## Checks overall variation in all columns
varying(wlddev)

## Checks whether data are time-variant i.e. vary within country
varying(wlddev, ~ country)

## Same as above but done for each country individually, countries without data are coded NA
head(varying(wlddev, ~ country, any_group = FALSE))

collapse documentation built on Nov. 3, 2024, 9:08 a.m.