RStudio addins and R functions that make copy-pasting vectors and tables to text painless.

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>.

Do most of the painful data preparation for a data science project with a minimum amount of code; Take advantages of 'data.table' efficiency and use some algorithmic trick in order to perform data preparation in a time and RAM efficient way.

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 website'
    <https://dataquality.qihs.uni-greifswald.de/VIN_Annotation_of_Metadata.html>.