Missing Data Explorer
Conditionally Recode NA values based on other Columns
Recode NA as another value using a function or a custom equation
Recode Missing Values Dictionary-Style
Drop columns for which all values are NA
Drop missing values at columns that match a given pattern
Checks that all values are NA
Condition based dropping of columns with missing values
Conditionally drop rows based on percent missingness
Add columnwise/groupwise counts of missing values
Get mean missingness.
Get NA counts for a given character, numeric, factor, etc.
An all-in-one missingness report
Column-wise missingness percentages
percent missing but for vectors.
Recode a value as NA
Recode Values as NA if they meet defined criteria
Conditionally change all column values to NA
Recode as NA based on string match
Recode a value as another value
Helper functions in package mde
Replace missing values with another value
Recode NA as another value with some conditions
Helper functions in package mde
Sort Variables according to missingness
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) <doi:10.1146/annurev.psych.58.110405.085530>.