{
  "_id": "6a1ea538b25058d4daad8610",
  "Package": "tidycomm",
  "Title": "Data Modification and Analysis for Communication Research",
  "Version": "0.4.2",
  "Authors@R": "c(\nperson(\"Julian\", \"Unkel\", , \"julian.unkel@gmail.com\", role = c(\"aut\", \"cre\"),\ncomment = c(ORCID = \"0000-0001-9568-7041\")),\nperson(\"Mario\", \"Haim\", , \"haim@ifkw.lmu.de\", role = \"aut\",\ncomment = c(ORCID = \"0000-0002-0643-2299\")),\nperson(\"Lara\", \"Kobilke\", , \"lara.kobilke@ifkw.lmu.de\", role = \"aut\",\ncomment = c(ORCID = \"0000-0001-6194-4724\"))\n)",
  "Description": "Provides convenience functions for common data\nmodification and analysis tasks in communication research. This\nincludes functions for univariate and bivariate data analysis,\nindex generation and reliability computation, and intercoder\nreliability tests. All functions follow the style and syntax of\nthe tidyverse, and are construed to perform their computations\non multiple variables at once. Functions for univariate and\nbivariate data analysis comprise summary statistics for\ncontinuous and categorical variables, as well as several tests\nof bivariate association including effect sizes. Functions for\ndata modification comprise index generation and automated\nreliability analysis of index variables. Functions for\nintercoder reliability comprise tests of several intercoder\nreliability estimates, including simple and mean pairwise\npercent agreement, Krippendorff's Alpha (Krippendorff 2004,\nISBN: 9780761915454), and various Kappa coefficients (Brennan &\nPrediger 1981 <doi: 10.1177/001316448104100307>; Cohen 1960\n<doi: 10.1177/001316446002000104>; Fleiss 1971 <doi:\n10.1037/h0031619>).",
  "License": "GPL-3",
  "URL": "https://tidycomm.github.io/tidycomm/",
  "BugReports": "https://github.com/tidycomm/tidycomm/issues",
  "VignetteBuilder": "knitr",
  "Config/testthat/edition": "3",
  "Encoding": "UTF-8",
  "LazyData": "true",
  "Roxygen": "list(markdown = TRUE)",
  "RoxygenNote": "7.3.2",
  "Config/pak/sysreqs": "cmake make libicu-dev libssl-dev libx11-dev\nzlib1g-dev",
  "Repository": "https://tidycomm.r-universe.dev",
  "Date/Publication": "2025-08-25 17:30:32 UTC",
  "RemoteUrl": "https://github.com/tidycomm/tidycomm",
  "RemoteRef": "HEAD",
  "RemoteSha": "74e4510a3de12b85d9fcffea67fd3346646c3d31",
  "NeedsCompilation": "no",
  "Packaged": {
    "Date": "2026-06-02 09:35:24 UTC",
    "User": "root"
  },
  "Author": "Julian Unkel [aut, cre] (ORCID:\n<https://orcid.org/0000-0001-9568-7041>),\nMario Haim [aut] (ORCID: <https://orcid.org/0000-0002-0643-2299>),\nLara Kobilke [aut] (ORCID: <https://orcid.org/0000-0001-6194-4724>)",
  "Maintainer": "Julian Unkel <julian.unkel@gmail.com>",
  "MD5sum": "b92be9897a0704260bd978b6949eade0",
  "_user": "tidycomm",
  "_type": "src",
  "_file": "tidycomm_0.4.2.tar.gz",
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  "_sha256": "86f56c08ead03543d925d13383740e7b4e7750222bd649d8a3df6e61780cb26c",
  "_created": "2026-06-02T09:35:24.000Z",
  "_published": "2026-06-02T09:41:12.721Z",
  "_distro": "noble",
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    "committer": "GitHub <noreply@github.com>",
    "message": "Merge pull request #71 from tidycomm/cran_prep\n\nfinal preparations for cran, such as updating the DESCRIPTION, news.md, and bumbing the vesion number",
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      "package": "car",
      "role": "Imports"
    },
    {
      "package": "dplyr",
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    },
    {
      "package": "fastDummies",
      "role": "Imports"
    },
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      "package": "forcats",
      "role": "Imports"
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      "package": "GGally",
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    {
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    },
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      "package": "MASS",
      "role": "Imports"
    },
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      "package": "MBESS",
      "role": "Imports"
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    },
    {
      "package": "pillar",
      "role": "Imports"
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  "_contributors": [
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    "type": "organization",
    "name": "tidycomm"
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  "_downloads": {
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    "source": "https://cranlogs.r-pkg.org/downloads/total/last-month/tidycomm"
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  "_pkgdown": "https://tidycomm.github.io/tidycomm/",
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  "_rbuild": "4.6.0",
  "_assets": [
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    "extra/citation.html",
    "extra/citation.json",
    "extra/citation.txt",
    "extra/contents.json",
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    "extra/NEWS.txt",
    "extra/readme.html",
    "extra/readme.md",
    "extra/tidycomm.html",
    "manual.pdf"
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  "_realowner": "tidycomm",
  "_cranurl": true,
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      "date": "2019-09-19"
    },
    {
      "version": "0.1.0",
      "date": "2019-09-22"
    },
    {
      "version": "0.2.1",
      "date": "2021-07-06"
    },
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      "date": "2024-02-22"
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      "version": "0.4.2",
      "date": "2025-08-27"
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  "_exports": [
    "%>%",
    "add_index",
    "categorize_scale",
    "center_scale",
    "correlate",
    "crosstab",
    "describe",
    "describe_cat",
    "design_gray",
    "design_grey",
    "design_lmu",
    "dummify_scale",
    "get_reliability",
    "minmax_scale",
    "model",
    "recode_cat_scale",
    "regress",
    "reverse_scale",
    "setna_scale",
    "t_test",
    "tab_frequencies",
    "tab_percentiles",
    "test_icr",
    "to_correlation_matrix",
    "unianova",
    "visualize",
    "z_scale"
  ],
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      "name": "fbposts",
      "title": "Facebook posts reliability test",
      "object": "fbposts",
      "class": [
        "spec_tbl_df",
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "post_id",
        "coder_id",
        "type",
        "n_pictures",
        "pop_elite",
        "pop_people",
        "pop_othering"
      ],
      "rows": 270,
      "table": true,
      "tojson": true
    },
    {
      "name": "incvlcomments",
      "title": "Incivil Comments Data",
      "object": "incvlcomments",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "participant_num",
        "age",
        "male",
        "high_education",
        "comment_num",
        "issue",
        "profanity",
        "attacks_argument",
        "offensive_stereotyping",
        "violent_threats",
        "offensiveness",
        "adequacy",
        "harm_to_society",
        "deletion_intention",
        "similarity_poster",
        "similarity_group",
        "attitude_gender",
        "attitude_abortion",
        "attitude_migration",
        "attitude_climate",
        "left_right_placement",
        "freedom_of_speech"
      ],
      "rows": 3856,
      "table": true,
      "tojson": true
    },
    {
      "name": "snscomments",
      "title": "SNS Comments data",
      "object": "snscomments",
      "class": [
        "tdcmm",
        "tdcmm",
        "tdcmm",
        "tdcmm",
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "age",
        "gender",
        "education",
        "need_cognition",
        "prior_knowledge",
        "group",
        "issue",
        "order",
        "valence",
        "control_group",
        "medium_evaluation",
        "article_evaluation",
        "comments_quality",
        "comments_valence",
        "article_elaboration"
      ],
      "rows": 630,
      "table": true,
      "tojson": true
    },
    {
      "name": "WoJ",
      "title": "Worlds of Journalism sample data",
      "object": "WoJ",
      "class": [
        "tbl_df",
        "tbl",
        "data.frame"
      ],
      "fields": [
        "country",
        "reach",
        "employment",
        "temp_contract",
        "autonomy_selection",
        "autonomy_emphasis",
        "ethics_1",
        "ethics_2",
        "ethics_3",
        "ethics_4",
        "work_experience",
        "trust_parliament",
        "trust_government",
        "trust_parties",
        "trust_politicians"
      ],
      "rows": 1200,
      "table": true,
      "tojson": true
    }
  ],
  "_help": [
    {
      "page": "add_index",
      "title": "Add index",
      "topics": [
        "add_index"
      ]
    },
    {
      "page": "categorize_scale",
      "title": "Categorize numeric variables into categories",
      "concept": [
        "scaling"
      ],
      "topics": [
        "categorize_scale"
      ]
    },
    {
      "page": "center_scale",
      "title": "Center numeric, continuous variables",
      "concept": [
        "scaling"
      ],
      "topics": [
        "center_scale"
      ]
    },
    {
      "page": "correlate",
      "title": "Compute correlation coefficients",
      "concept": [
        "correlations"
      ],
      "topics": [
        "correlate"
      ]
    },
    {
      "page": "crosstab",
      "title": "Crosstab variables",
      "concept": [
        "categorical"
      ],
      "topics": [
        "crosstab"
      ]
    },
    {
      "page": "describe",
      "title": "Describe numeric variables",
      "concept": [
        "descriptives"
      ],
      "topics": [
        "describe"
      ]
    },
    {
      "page": "describe_cat",
      "title": "Describe categorical variables",
      "concept": [
        "descriptives"
      ],
      "topics": [
        "describe_cat"
      ]
    },
    {
      "page": "design_gray",
      "title": "Gray design",
      "topics": [
        "design_gray"
      ]
    },
    {
      "page": "design_grey",
      "title": "Grey design",
      "topics": [
        "design_grey"
      ]
    },
    {
      "page": "design_lmu",
      "title": "Colorbrewer-inspired design with focus on LMU (lmu.de) green",
      "topics": [
        "design_lmu"
      ]
    },
    {
      "page": "dummify_scale",
      "title": "Convert categorical variables to dummy variables",
      "concept": [
        "scaling"
      ],
      "topics": [
        "dummify_scale"
      ]
    },
    {
      "page": "fbposts",
      "title": "Facebook posts reliability test",
      "topics": [
        "fbposts"
      ]
    },
    {
      "page": "get_reliability",
      "title": "Get reliability estimates of index variables",
      "concept": [
        "reliability"
      ],
      "topics": [
        "get_reliability"
      ]
    },
    {
      "page": "incvlcomments",
      "title": "Incivil Comments Data",
      "topics": [
        "incvlcomments"
      ]
    },
    {
      "page": "minmax_scale",
      "title": "Rescale numeric continuous variables to new minimum/maximum boundaries",
      "concept": [
        "scaling"
      ],
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        "minmax_scale"
      ]
    },
    {
      "page": "recode_cat_scale",
      "title": "Recode one or more categorical variables into new categories",
      "concept": [
        "scaling"
      ],
      "topics": [
        "recode_cat_scale"
      ]
    },
    {
      "page": "regress",
      "title": "Compute linear regression",
      "topics": [
        "regress"
      ]
    },
    {
      "page": "reverse_scale",
      "title": "Reverse numeric, logical, or date/time continuous variables",
      "concept": [
        "scaling"
      ],
      "topics": [
        "reverse_scale"
      ]
    },
    {
      "page": "setna_scale",
      "title": "Set specified values to NA in selected variables or entire data frame",
      "concept": [
        "scaling"
      ],
      "topics": [
        "setna_scale"
      ]
    },
    {
      "page": "snscomments",
      "title": "SNS Comments data",
      "topics": [
        "snscomments"
      ]
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      "title": "Compute t-tests",
      "concept": [
        "t-test"
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    {
      "page": "tab_frequencies",
      "title": "Tabulate frequencies",
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      ]
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        "descriptives"
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        "tab_percentiles"
      ]
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      "page": "test_icr",
      "title": "Perform an intercoder reliability test",
      "concept": [
        "intercoder reliability"
      ],
      "topics": [
        "test_icr"
      ]
    },
    {
      "page": "to_correlation_matrix",
      "title": "Create correlation matrix",
      "concept": [
        "correlation"
      ],
      "topics": [
        "to_correlation_matrix"
      ]
    },
    {
      "page": "unianova",
      "title": "Compute one-way ANOVAs",
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        "ANOVA"
      ],
      "topics": [
        "unianova"
      ]
    },
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      "page": "visualize",
      "title": "Visualize tidycomm output",
      "concept": [
        "visualize"
      ],
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        "visualize",
        "visualize.tdcmm_crrltn",
        "visualize.tdcmm_ctgrcl",
        "visualize.tdcmm_dscrb",
        "visualize.tdcmm_nnv",
        "visualize.tdcmm_prcntl",
        "visualize.tdcmm_rgrssn",
        "visualize.tdcmm_ttst"
      ]
    },
    {
      "page": "WoJ",
      "title": "Worlds of Journalism sample data",
      "topics": [
        "WoJ"
      ]
    },
    {
      "page": "z_scale",
      "title": "Z-standardize numeric, continuous variables",
      "concept": [
        "scaling"
      ],
      "topics": [
        "z_scale"
      ]
    }
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  "_readme": "https://github.com/tidycomm/tidycomm/raw/HEAD/README.md",
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    "RcppParallel",
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    "reformulas",
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    "rpf",
    "rstudioapi",
    "S7",
    "scales",
    "sem",
    "semTools",
    "SparseM",
    "StanHeaders",
    "stringi",
    "stringr",
    "survival",
    "tibble",
    "tidyr",
    "tidyselect",
    "timechange",
    "timeDate",
    "tzdb",
    "urca",
    "utf8",
    "vctrs",
    "viridisLite",
    "vroom",
    "withr",
    "xtable",
    "zoo"
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  "_vignettes": [
    {
      "source": "v02_bivariate.Rmd",
      "filename": "v02_bivariate.html",
      "title": "Bivariate analysis of continuous and/or categorical variables",
      "engine": "knitr::rmarkdown",
      "headings": [
        "Compute contingency tables and Chi-square tests",
        "Compute t-Tests",
        "Compute one-way ANOVAs",
        "Compute correlation tables and matrices",
        "Compute linear regressions"
      ],
      "created": "2019-09-20 20:07:09",
      "modified": "2024-02-16 16:51:38",
      "commits": 2
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