Man pages for 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... |
| create_lags | Create lagged variables |
| data | Example data (particle number concentrations in SMEAR I... |
| data1 | Example data (aggregated particle number concentrations,... |
| dataprep | Data preprocessing with multiple steps in one function |
| dataprep-package | dataprep: Fast, Efficient, and Versatile Data Preprocessing... |
| 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... |
| 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... |
| 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... |
| 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 |
