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  "Title": "Visual Documentation for Data Preprocessing",
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  "Authors@R": "c(person(given = \"Lydia R.\", family = \"Lucchesi\", role = c(\"aut\", \"cre\"), \nemail = \"Lydia.Lucchesi@anu.edu.au\", comment = c(ORCID = \"0000-0002-1901-4301\")),\nperson(given = \"Petra M.\", family = \"Kuhnert\", role = \"ths\"),\nperson(given = \"Jenny L.\", family = \"Davis\", role = \"ths\"),\nperson(given = \"Lexing\", family = \"Xie\", role = \"ths\"))",
  "Maintainer": "Lydia R. Lucchesi <Lydia.Lucchesi@anu.edu.au>",
  "Description": "Data practitioners regularly use the 'R' and 'Python'\nprogramming languages to prepare data for analyses. Thus, they\nencode important data preprocessing decisions in 'R' and\n'Python' code. The 'smallsets' package subsequently decodes\nthese decisions into a Smallset Timeline, a static, compact\nvisualisation of data preprocessing decisions (Lucchesi et al.\n(2022) <doi:10.1145/3531146.3533175>). The visualisation\nconsists of small data snapshots of different preprocessing\nsteps. The 'smallsets' package builds this visualisation from a\nuser's dataset and preprocessing code located in an 'R', 'R\nMarkdown', 'Python', or 'Jupyter Notebook' file. Users simply\nadd structured comments with snapshot instructions to the\npreprocessing code. One optional feature in 'smallsets'\nrequires installation of the 'Gurobi' optimisation software and\n'gurobi' 'R' package, available from <https://www.gurobi.com>.\nMore information regarding the optional feature and 'gurobi'\ninstallation can be found in the 'smallsets' vignette.",
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  "Date/Publication": "2025-01-23 18:31:52 UTC",
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