mde v0.2.1

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by Nelson Gonzabato View Source

Missing Data Explorer

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>.

Functions in mde

Name	Description
recode_as_na_for	Recode Values as NA if they meet defined criteria
recode_na_as	Replace missing values with another value
drop_na_at	Drop missing values at columns that match a given pattern
get_na_counts	Add columnwise/groupwise counts of missing values
drop_all_na	Drop columns for which all values are NA
recode_as_na	Recode a value as NA
percent_missing	Columnwise missingness percentages
drop_na_if	Conditon based dropping of columns with missing values
recode_na_if	Recode NA as another value with some conditions
na_summary	An all-in-one missingness report
sort_by_missingness	Sort Variables according to missingness
custom_na_recode	Recode NA as another value using a function or a custom equation
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Vignettes of mde

Name
mde_vignette.rmd
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Details

License	GPL-3
Encoding	UTF-8
LazyData	true
RoxygenNote	7.1.0
URL	https://github.com/Nelson-Gon/mde
BugReports	https://github.com/Nelson-Gon/mde/issues
VignetteBuilder	knitr
NeedsCompilation	no
Packaged	2020-06-27 14:02:34 UTC; Nelson
Repository	CRAN
Date/Publication	2020-06-27 14:40:02 UTC

suggests	covr , knitr , rmarkdown , testthat
imports	dplyr (>= 0.8.9) , tidyr (>= 1.0.3)
depends	R (>= 3.6.0)
Contributors