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mde (version 0.3.2)

Missing Data Explorer

Description

Correct identification and handling of missing data is one of the most important steps in any analysis. To aid this process, 'mde' provides a very easy to use yet robust framework to quickly get an idea of where the missing data lies and therefore find the most appropriate action to take. Graham WJ (2009) .

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Version

Install

install.packages('mde')

Monthly Downloads

368

Version

0.3.2

License

GPL-3

Issues

1

Pull Requests

1

Stars

4

Forks

4

Maintainer

Nelson Gonzabato

Last Published

February 10th, 2022

Functions in mde (0.3.2)

get_na_counts

Add columnwise/groupwise counts of missing values
get_na_means

Get mean missingness.
all_na

Checks that all values are NA
column_based_recode

Conditionally Recode NA values based on other Columns
custom_na_recode

Recode NA as another value using a function or a custom equation
recode_as_na_if

Conditionally change all column values to NA
recode_as_na_str

Recode as NA based on string match
recode_as_na

Recode a value as NA
sort_by_missingness

Sort Variables according to missingness
recode_selectors

Helper functions in package mde
recode_as_na_for

Recode Values as NA if they meet defined criteria
dict_recode

Recode Missing Values Dictionary-Style
recode_na_as

Replace missing values with another value
recode_na_if

Recode NA as another value with some conditions
na_counts

Get NA counts for a given character, numeric, factor, etc.
na_summary

An all-in-one missingness report
drop_na_if

Condition based dropping of columns with missing values
drop_row_if

Conditionally drop rows based on percent missingness
recode_as_value

Recode a value as another value
drop_all_na

Drop columns for which all values are NA
percent_missing

Column-wise missingness percentages
recode_helper

Helper functions in package mde
drop_na_at

Drop missing values at columns that match a given pattern
percent_na

percent missing but for vectors.