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

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Documentation for package ‘clickR’ version 0.9.39

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%<=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