drift_detect

Detects potential sensor drift by comparing rolling window statistics (mean or standard deviation) to their long-term baseline. Returns a logical matrix indicating time points where the rolling statistic deviates beyond a threshold.

Fast, efficient, and versatile preprocessing and reshaping of tabular
and time-series data. Most heavy routines are implemented in 'C++' via
'Rcpp', with optional 'OpenMP' parallelization and 'SIMD' acceleration
('AVX2' / 'AVX-512') on supported hardware. The 0.1.8 release rewrites
the cleaning routines in 'C++' and delivers a 1.1–1146× speedup over
0.1.5. The 'melt()' and 'dcast()' reshaping functions achieve a
0.6×–1628.9× speedup for 'melt()' and a 1.9×–799.8× speedup for
'dcast()' relative to every one of the seven major alternatives in
the R and Python ecosystems, at every tested scale (from 1,000 to
100,000,000 rows), and produce output identical to 'reshape2',
'data.table', 'tidyr', 'pandas', 'polars', 'dask', and 'duckdb'.
Core preprocessing steps include variable deletion by missing
fraction, observation deletion by consecutive missing runs,
point-by-point weighted outlier removal via conditional extremum,
traditional percentile-based outlier removal, and linear
interpolation within short time periods. The package also provides
fast reshaping, descriptive statistics, missing-value diagnosis,
multiple imputation strategies, winsorization, several outlier
detection methods (IQR, MAD, percentile), data transformation and
standardization, categorical encoding, duplicate removal, data
validation, data quality reporting, and stratified sampling.
Feature-engineering helpers cover binning, high-correlation and
low-variance filtering, and string cleaning. Time-series tools
cover detrending, diurnal-cycle removal, rolling statistics, lag
creation, resampling, simple decomposition, day/night and season
flags, log returns, drift detection, and panel balancing.
Fit/transform-style machine-learning interfaces prevent data
leakage during preprocessing. Methods are based on, and improved
from: Liang, C.-S., Wu, H., Li, H.-Y., Zhang, Q., Li, Z. & He,
K.-B. (2020) <doi:10.1016/j.scitotenv.2020.140923>. This work
was supported by the National Natural Science Foundation of China
(No. 12301674).

Chun-Sheng Liang

dataprep

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

Hao Wu

Hai-Yan Li

Qiang Zhang

Zhanqing Li

Ke-Bin He

drift_detect function

<dl> <dt>data</dt>
<dd>A data frame, matrix, or numeric vector.</dd> <dt>cols</dt>
<dd>Column indices or names of numeric variables. If <code>NULL</code>, all numeric columns are used.</dd> <dt>window</dt>
<dd>Window size for rolling statistics.</dd> <dt>threshold</dt>
<dd>Threshold in units of standard deviation. Values with absolute z-score above this are flagged as drift.</dd> <dt>method</dt>
<dd>Rolling statistic to use: <code>"mean"</code> or <code>"sd"</code>.</dd> <dt>group</dt>
<dd>Optional grouping column for group-wise detection.</dd> <dt>date_col</dt>
<dd>Optional time column (not required for calculation).</dd> <dt>verbose</dt>
<dd>Logical; if <code>TRUE</code>, prints progress message.</dd></dl>

Arguments

Sensor drift detection — drift_detect

<dl>

 <dt>data</dt>
<dd>A data frame, matrix, or numeric vector.</dd>

 <dt>cols</dt>
<dd>Column indices or names of numeric variables. If <code>NULL</code>, all numeric columns are used.</dd>

 <dt>window</dt>
<dd>Window size for rolling statistics.</dd>

 <dt>threshold</dt>
<dd>Threshold in units of standard deviation. Values with absolute z-score above this are flagged as drift.</dd>

 <dt>method</dt>
<dd>Rolling statistic to use: <code>"mean"</code> or <code>"sd"</code>.</dd>

 <dt>group</dt>
<dd>Optional grouping column for group-wise detection.</dd>

 <dt>date_col</dt>
<dd>Optional time column (not required for calculation).</dd>

 <dt>verbose</dt>
<dd>Logical; if <code>TRUE</code>, prints progress message.</dd>

</dl>

drift_detect: Sensor drift detection

Description

Usage

Value

Arguments

Examples