Semi-Automatic Preprocessing of Messy Data with Change Tracking for Dataset Cleaning

Documentation for package ‘clickR’ version 0.9.39

%<=NA%	leq & not NA
%<NA%	less & NA
%>=NA%	geq & not NA
%>NA%	greater & NA
antimoda	Get anti-mode
bivariate_outliers	Check for bivariate outliers
check_quality	Checks data quality of a variable
cluster_var	Clustering of variables
descriptive	Detailed summary of the data
extreme_values	Extreme values from a numeric vector
fix_all	fix_all
fix_concat	fix_concat
fix_dates	Fix dates
fix_factors	Fix factors imported as numerics
fix_levels	Fix levels
fix_NA	fix_NA
fix_numerics	Fix numeric data
forge	Forge
fxd	Internal function to fix_dates
f_replace	Find and replace
GK_assoc	Computes Goodman and Kruskal's tau
good2go	Good to go
ipboxplot	Improved boxplot
kill.factors	Kill factors
kurtosis	Computes kurtosis
manual_fix	Tracked manual fixes to data
may.numeric	Checks if each value might be numeric
mine.plot	Mine plot
moda	Get mode
moda_cont	Estimates number of modes
mtapply	Multiple tapply
mtcars_messy	Messy Motor Trend Car Road Tests Dataset
nearest	Internal function for descriptive()
nice_names	Nice names
numeros	Brute numeric coercion
outliers	outliers
peek	Peek
prop_may	Gets proportion of most repeated value
prop_min	Gets proportion of least repeated value
remove_empty	remove_empty
restore_changes	Restore changes
scale_01	Scales data between 0 and 1
search_scripts	Search scripts
skewness	Computes skewness
text_date	Internal function for dates with text
track_changes	track_changes
ttrue	True TRUE
unforge	Un-Forge
v_df_changes	Internal function to track_changes
workspace	Explores global environment workspace
workspace_sapply	Applies a function over objects of a specific class