Fast, Efficient, and Versatile Data Preprocessing and Reshaping with 'C++', 'OpenMP' & 'SIMD'

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Documentation for package ‘dataprep’ version 0.1.8

• DESCRIPTION file.
• User guides, package vignettes and other documentation.
• Package NEWS.

Help Pages

dataprep-package	dataprep: Fast, Efficient, and Versatile Data Preprocessing and Reshaping with 'C++', 'OpenMP' & 'SIMD'
balance_panel	Balance panel data
bin_data	Discretize continuous variables into bins
clean_strings	Clean and standardize character columns
condextr	Remove outliers using point-by-point weighed outlier removal by conditional extremum
create_lags	Create lagged variables
data	Example data (particle number concentrations in SMEAR I Varrio forest)
data1	Example data (aggregated particle number concentrations, SMEAR I Varrio forest)
dataprep	Data preprocessing with multiple steps in one function
data_report	Generate a simple data quality report
day_night_flag	Day/night flag
dcast	Cast a long-format data.frame into a wide format
decompose_ts	Simple time series decomposition
deduplicate	Remove duplicate observations
descdata	Fast descriptive statistics
descplot	View descriptive statistics via plot
detect_outliers	Detect outliers using multiple methods
detrend_ts	Remove linear trend from time series
drift_detect	Sensor drift detection
dry_run	Simulate preprocessing and report changes without modifying data
encode_categorical	Encode categorical variables
filter_high_cor	Remove highly correlated variables
filter_low_var	Remove low-variance (near-constant) variables
impute_missing	Impute missing values
log_returns	Logarithmic returns for financial time series
melt	Fast wide-to-long data reshaping with flexible ID/measure specification
na_diagnose	Diagnose missing value patterns in data
obsedele	Delete observations with excessive consecutive missing values
optisolu	Find optimal combination of interval and times for condextr
percdata	Calculate top and bottom percentiles of selected variables
percoutl	Traditional percentile-based outlier removal
percplot	Plot top and bottom percentiles of selected variables
phys_filter	Physical limit filtering
prep_fit	Build a preprocessing plan on training data to prevent data leakage
prep_transform	Apply a preprocessing plan to new data
remove_diurnal_cycle	Remove diurnal cycle
resample_time	Resample time series to a coarser period
roll_apply	Apply rolling window statistics
sample_data	Random sampling with optional stratification
season_flag	Season flag
shorvalu	Interpolation with values to refer to within short periods
transform_data	Transform and standardize numeric variables
validate_data	Validate data against a set of rules
varidele	Delete variables containing too many missing values
winsorize	Winsorize outliers by capping extreme values
zerona	Turn zeros to missing values