Chun-Sheng Liang <liangchunsheng@lzu.edu.cn>, Hao Wu, Hai-Yan Li, Qiang Zhang, Zhanqing Li, Ke-Bin He, Lanzhou University, Tsinghua University
Efficiently and flexibly preprocess data using a set of data filtering, deletion, and interpolation tools.
These data preprocessing methods are developed based on the principles of completeness, accuracy, threshold method, and linear interpolation and through the setting of constraint conditions, time completion & recovery, and fast & efficient calculation and grouping.
Key preprocessing steps include deletions of variables and observations, outlier removal, and missing values (NA) interpolation, which are dependent on the incomplete and dispersed degrees of raw data.
They clean data more accurately, keep more samples, and add no outliers after interpolation, compared with ordinary methods.
Auto-identification of consecutive NA via run-length based grouping is used in observation deletion, outlier removal, and NA interpolation;
thus, new outliers are not generated in interpolation. Conditional extremum is proposed to realize point-by-point weighed outlier removal that saves non-outliers from being removed.
Plus, time series interpolation with values to refer to within short periods further ensures reliable interpolation.
These methods are based on and improved from the reference: Liang, C.-S., Wu, H., Li, H.-Y., Zhang, Q., Li, Z. & He, K.-B. (2020) <doi:10.1016/j.scitotenv.2020.140923>.
ggplot2, scales, foreach, doParallel, dplyr, reshape2,
data.table, zoo

Data Quality in Epidemiological Research
c(person(given = "University Medicine Greifswald",
role = "cph"),
person(given = "Adrian",
family = "Richter",
role = "aut",
email = "richtera@uni-greifswald.de"),
person(given = "Carsten Oliver",
family = "Schmidt",
role = "aut",
email = "carsten.schmidt@uni-greifswald.de"),
person(given = "Stephan",
family = "Struckmann",
role = c("aut", "cre"),
email = "stephan.struckmann@uni-greifswald.de"))
Data quality assessments guided by a
'data quality framework introduced by Schmidt and colleagues 2021'
<doi:10.1186/s12874-021-01252-7> target the
data quality dimensions integrity, completeness, consistency, and
accuracy. The scope of applicable functions rests on the
availability of extensive metadata which can be provided in
spreadsheet tables. Either standardized (e.g. as 'html5' reports) or
individually tailored reports can be generated. For an introduction
into the specification of corresponding metadata, please refer to the
'package web site'
<https://dataquality.ship-med.uni-greifswald.de/Annotation_of_Metadata.html>.
patchwork, dplyr (>= 1.0.2), emmeans, ggplot2 (>= 2.1.0),
ggpubr, lme4, lubridate, MASS, MultinomialCI, parallelMap,
R.devices, reshape, rlang, robustbase, utils
cowplot (>= 0.9.4), anytime, digest, DT (>= 0.15),
flexdashboard, htmltools, knitr, rmarkdown, rstudioapi,
testthat (>= 2.3.2), tibble, markdown, vdiffr, parallel
University Medicine Greifswald [cph],
Adrian Richter [aut],
Carsten Oliver Schmidt [aut],
Stephan Struckmann [aut, cre]