# Fast Check of Variation in Data

`varying.Rd`

`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, ...)
# S3 method for default
varying(x, g = NULL, any_group = TRUE, use.g.names = TRUE, ...)
# S3 method for matrix
varying(x, g = NULL, any_group = TRUE, use.g.names = TRUE, drop = TRUE, ...)
# S3 method for 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 pseries
varying(x, effect = 1L, any_group = TRUE, use.g.names = TRUE, ...)
# S3 method for 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 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 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`

).

## Examples

```
## Checks overall variation in all columns
varying(wlddev)
#> country iso3c date year decade region income OECD PCGDP LIFEEX
#> TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE TRUE
#> GINI ODA POP
#> TRUE TRUE TRUE
## Checks whether data are time-variant i.e. vary within country
varying(wlddev, ~ country)
#> iso3c date year decade region income OECD PCGDP LIFEEX GINI ODA
#> FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE TRUE
#> POP
#> TRUE
## Same as above but done for each country individually, countries without data are coded NA
head(varying(wlddev, ~ country, any_group = FALSE))
#> iso3c date year decade region income OECD PCGDP LIFEEX GINI
#> Afghanistan FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE NA
#> Albania FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE
#> Algeria FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE TRUE TRUE
#> American Samoa FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE NA NA
#> Andorra FALSE TRUE TRUE TRUE FALSE FALSE FALSE TRUE NA NA
#> ODA POP
#> Afghanistan TRUE TRUE
#> Albania TRUE TRUE
#> Algeria TRUE TRUE
#> American Samoa NA TRUE
#> Andorra NA TRUE
#> [ reached 'max' / getOption("max.print") -- omitted 1 rows ]
```