descdata

Computes descriptive statistics for selected numeric columns using
efficient C++ routines.

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

descdata function

<dl> <dt>data</dt>
<dd>A data frame, matrix, or numeric vector. If a vector is
 supplied, it is treated as a single variable and the output's
 first column is named <code>"value"</code>.</dd> <dt>cols</dt>
<dd>Column indices or names of numeric variables to describe.
 If <code>NULL</code>, all numeric columns are used.</dd> <dt>stats</dt>
<dd>Statistic selection from the nine available:
 <code>n</code>, <code>na</code>, <code>mean</code>, <code>sd</code>, <code>median</code>,
 <code>trimmed</code>, <code>min</code>, <code>max</code>, <code>IQR</code>. Can be a
 numeric vector or a character vector of statistic names.</dd> <dt>first</dt>
<dd>Name for the first column of the output, which contains
 the variable identifiers.</dd> <dt>cores</dt>
<dd>Number of CPU cores. <code>NULL</code> (default) means single-threaded execution; pass an integer &gt; 0 to enable OpenMP when <code>nrow(x) * ncol(x) &gt; 1e5</code>.</dd> <dt>verbose</dt>
<dd>Logical; if <code>TRUE</code>, prints timing message.</dd></dl>

Arguments

Chun-Sheng Liang &lt;chun-shengliang@qq.com&gt;

Author

Fast descriptive statistics — descdata

<dl>

 <dt>data</dt>
<dd>A data frame, matrix, or numeric vector. If a vector is
 supplied, it is treated as a single variable and the output's
 first column is named <code>"value"</code>.</dd>

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

 <dt>stats</dt>
<dd>Statistic selection from the nine available:
 <code>n</code>, <code>na</code>, <code>mean</code>, <code>sd</code>, <code>median</code>,
 <code>trimmed</code>, <code>min</code>, <code>max</code>, <code>IQR</code>. Can be a
 numeric vector or a character vector of statistic names.</dd>

 <dt>first</dt>
<dd>Name for the first column of the output, which contains
 the variable identifiers.</dd>

 <dt>cores</dt>
<dd>Number of CPU cores. <code>NULL</code> (default) means single-threaded execution; pass an integer &gt; 0 to enable OpenMP when <code>nrow(x) * ncol(x) &gt; 1e5</code>.</dd>

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

</dl>

descdata: Fast descriptive statistics

Description

Usage

Value

Arguments

Author

Details

References

Examples