tidyimport

Comprehensive Library For Handling Missing Values

License CRAN Downloads

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.

Feature List

Upcoming features

Installation

Github (Development Version)

library(devtools)
install_github( "decisionpatterns/tidyimport")

CRAN

R> install.packages("tidyimpute")

Coming Soon …

Function List

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, ...)

Univariate

(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.

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.)

Multivariate (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 ~ ...

Generalized (Coming Soon)

(Impute with column and rows.)

Future:

Examples

tbl <- data.frame( col_1 = letters[1:3], col_2=c(1,NA_real_,3), col_3=3:1)
 
impute( tbl, 2) 
impute_mean( tbl )