Includes functions that researchers or practitioners may use to clean
    raw data, transferring html, xlsx, txt data file into other formats. And it
    also can be used to manipulate text variables, extract numeric variables from
    text variables and other variable cleaning processes. It is originated from a
    author's project which focuses on creative performance in online education
    environment. The resulting paper of that study will be published soon.

Flexible and efficient cleaning of data with interactivity.
  'datacleanr' facilitates best practices in data analyses and reproducibility with built-in features and by translating interactive/manual operations to code. 
  The package is designed for interoperability, and so seamlessly fits into reproducible analyses pipelines in 'R'.

Access the 'Google Data Commons API V2' <https://docs.datacommons.org/api/rest/v2/>.
    Data Commons provides programmatic access to statistical and demographic data from
    dozens of sources organized in a knowledge graph.

A tool developed with the 'Golem' framework which provides 
                     an easier way to check cells differences between two data 
                     frames. The user provides two data frames for comparison, 
                     selects IDs variables identifying each row of input data, 
                     then clicks a button to perform the comparison. Several 
                     'R' package functions are used to describe the data 
                     and perform the comparison in the server of the 
                     application. The main ones are comparedf() from 'arsenal' 
                     and skim() from 'skimr'. For more details see the 
                     description of comparedf() from the 'arsenal' package and 
                     that of skim() from the 'skimr' package.

Set of 'shiny'-based 'RStudio' add-ins for interactively creating datasets through
  graphical interfaces. Users can adjust graphical elements to define data
  generation parameters, and the resulting datasets can be saved to temporary
  RDS files for further analysis or visualization. Methods are described in:
  Chang et al. (2024) <https://CRAN.R-project.org/package=shiny>;
  Bostock et al. (2011) <doi:10.1109/TVCG.2011.185>.