Comprehensive Library For Handling Missing Values
tidyimpute is tidtverse/dplyr compliant toolkit for imputing missing
values (NA) values in list-like and table-like structures including data.tables.
It had two goals: 1) extend existing na.*
functions from the stats packages
and 2) provide dplyr/tidyverse compliant methods for tables and lists.
This package is based on the handy na.tools package which provides tools for working with missing values in vectors.
impute_*
family of functions for table- or list-based imputations.impute_*_at
, impute_*_all
and impute_*_if
functions impute
, impute_at
, impute_all
, impute_if
0
, -Inf
, Inf
mean
, median
, max
, min
, zero
loess
, locf
, locb
tibble
data.table
by-group
calculationslibrary(devtools)
install_github( "decisionpatterns/tidyimport")
R> install.packages("tidyimpute")
There are four types of imputation methods. They are distinguished by how the replacement values are calculated. Each is described below as well as describing each of the methods used.
Constants
In "constant" imputation methods, missing values are replaced by an
a priori selected constant value. The vector containingmissing values
is not used to calculate the replacement value. These take the form: na.fun(x, ...)
impute_zero
- 0 impute_inf
/ impute_neginf
- Inf/-Infimpute_constant
- Impute with a constantUnivariate
(Impute using function(s) of the target variable; When imputing in a table this is also called column-based imputation since the values used to derive the imputed come from the single column alone.)
In "univariate" replacement methods, values are calculated using
only the target vector, ie the one containing the missing values. The functions
for performing the imputation are nominally univariate summary functions.
Generally, the ordering of the vector does not affect imputed values. In general,
one value is used to replace all missing values (NA
) for a variable.
impute_max
- maximum impute_minimum
- minumum impute_mean
- mean impute_median
- median valueimpute_quantile
- quantile valueimpute_sample
- randomly sampled value via bootstrap.Ordered Univariate (Coming Soon)
(Impute using function(s) of the target variable. Variable ordering relevant. This is a super class of the previous column-based imputation.)
In "ordered univariate" methods, replacement valuse are calculated from the vector that is assumed to be ordered. These types are very often used with time-series data. (Many of these functions are taken from or patterned after functions in the zoo package.)
impute_loess
- loess smoother, assumes values are orderedimpute_locf
- last observation carried forward, assumes ordered impute_nocb
- next observation carried backwards, assumes orderedMultivariate (Coming Soon)
(Impute with multiple variables from the same observation. In tables, this is also called row-based imputation because imputed values derive from other measurement for the same observation. )
In "Multivariate" imputation, any value from the same row (observation) can be
used to derive the replacement value. This is generally implemented as a model
traing from the data with var ~ ...
impute_fit
,impute_predict
- use a model impute_by_group
- use by-group imputationGeneralized (Coming Soon)
(Impute with column and rows.)
Future:
unimpute
/impute_restore
- restore NAs to the vector; remembering
replacementimpute_toggle
- toggle between NA
and replacement valuestbl <- data.frame( col_1 = letters[1:3], col_2=c(1,NA_real_,3), col_3=3:1)
impute( tbl, 2)
impute_mean( tbl )
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