varying | R Documentation |
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).
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, ...)
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, |
by |
same as |
any_group |
logical. If |
cols |
select columns using column names, indices or a function (e.g. |
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. |
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. |
... |
arguments to be passed to or from other methods. |
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.
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
).
Summary Statistics, Data Transformations, Collapse Overview
## 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))
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