Functions and methods for imputing missing values (NA) in tables and list patterned after the tidyverse approach of 'dplyr' and 'rlang'; works with data.tables as well.

**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.

- Over
**80**functions for imputi missing values (See [#Function List] below.) **dplyr**/**tidyverse**compliant inteface:`impute_*`

family of functions for table- or list-based imputations.`impute_*_at`

,`impute_*_all`

and`impute_*_if`

functions

- Uses the *
*na.tools*package to ensure- Type/class and length-safe replacement. (
**tidyimport**will never change produce an object with a different length/nrow or type/class of its target.)

- Type/class and length-safe replacement. (
- General imputation methods
- Generic imputation:
`impute`

,`impute_at`

,`impute_all`

,`impute_if`

- Generic imputation:
- Specialized imputation methods
- Common imputations for:
- constants:
`0`

,`-Inf`

,`Inf`

- univariate, commutative summary functions:
`mean`

,`median`

,`max`

,`min`

,`zero`

- (Coming Soon) univariate, non-commutive/ordered/time-series data:
`loess`

,`locf`

,`locb`

- (Coming Soon) model-based imputation

- constants:
- Support for recursive (lists and table-like structures)
- Support for
`tibble`

- Support for
`data.table`

- Common imputations for:
- Four extensible types of imputations

- recall/track which values have been replaced
`by-group`

calculations- Time-series/ordered/non-commutative methods
- Model-based imputation
- Model-based + by-groups

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

```
R> install.packages("tidyimpute")
```

- Impute by model
- Memorable imputing

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/-Inf`impute_constant`

- Impute with a constant

**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.

`impute_max`

- maximum`impute_minimum`

- minumum`impute_mean`

- mean`impute_median`

- median value`impute_quantile`

- quantile value`impute_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 ordered`impute_locf`

- last observation carried forward, assumes ordered`impute_nocb`

- next observation carried backwards, assumes ordered

**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 ~ ...`

`impute_fit`

,`impute_predict`

- use a model`impute_by_group`

- use by-group imputation

**Generalized (Coming Soon)**

(Impute with column and rows.)

**Future:**

`unimpute`

/`impute_restore`

- restore NAs to the vector; remembering replacement`impute_toggle`

- toggle between`NA`

and replacement values

```
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 )
```

- Add
`NA_explicit_`

as an exported constant for explicit categorical values. - Convert man to use markdown.

- Fix
`na_replace`

(and`na_explicit`

) to add levels for values if they do not already exist. - Add tests
- Fix documentation

- Add na_explicit and na_implicit

- na_replace: revert from using
`ifelse`

because of edge cases - add
`zzz.R`

- add
`NEWS.md`

- add tests for
`na_replace`

`na_replace`

now uses`ifelse`

and prevent recycling`value`