Added swarm bars, forest plot function #180

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Merged

maiyishan merged 166 commits into master from dev

May 8, 2025

.github/ISSUE_TEMPLATE/bug_report.md

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+    ---
+    name: Feature request
+    about: Suggest an idea for this project
+    title: ''
+    labels: enhancement
+    assignees: ''
+    ---
+    **Is your feature request related to a problem? Please describe.**
+    A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+    **Describe the solution you'd like**
+    A clear and concise description of what you want to happen.
+    **Describe alternatives you've considered**
+    A clear and concise description of any alternative solutions or features you've considered.
+    **Is a dataset available for testing out the functionality**
+    If yes, please leave a Google Drive link
+    **Additional context**
+    Add any other context or screenshots about the feature request here.

.github/ISSUE_TEMPLATE/feature_request.md

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+    ---
+    name: Feature request
+    about: Suggest an idea for this project
+    title: ''
+    labels: enhancement
+    assignees: ''
+    ---
+    **Is your feature request related to a problem? Please describe.**
+    A clear and concise description of what the problem is. Ex. I'm always frustrated when [...]
+    **Describe the solution you'd like**
+    A clear and concise description of what you want to happen.
+    **Describe alternatives you've considered**
+    A clear and concise description of any alternative solutions or features you've considered.
+    **Is a dataset available for testing out the functionality**
+    If yes, please leave a Google Drive link
+    **Additional context**
+    Add any other context or screenshots about the feature request here.

.github/workflows/R-CMD-check.yaml

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@@ Expand Up / @@ -14,6 +14,7 @@ jobs: @@
         env:
           GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
           R_KEEP_PKG_SOURCE: yes
+          CI: true
         steps:
           - uses: actions/checkout@v3
@@ Expand Down @@

CONTRIBUTING.md

            
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    @@ -12,13 +12,13 @@ Please note that dabestr is released with a [Contributor Code of Conduct](CODE_O
  
    Ensure the bug was not already reported by searching in [Issues](https://github.com/ACCLAB/dabestr/issues). Be sure to also check that the bug hasn't been addressed in a closed issue.

    If the bug isn't being addressed, open a new one. 

    If the bug isn't being addressed, open a new issue using the Bug report template. 

    Be sure to include a title and clear description, and a [minimally reproducible code sample](http://adv-r.had.co.nz/Reproducibility.html) demonstrating the expected behavior that is not occurring. 

    Be sure to fill in the necessary information, and a [minimally reproducible code sample](http://adv-r.had.co.nz/Reproducibility.html) demonstrating the expected behavior that is not occurring. 

    ## Feature Requests

    You may suggest by opening an issue and adding an Enhancement tag.

    You may suggest by opening an issue using the Feature request template.

    ## Pull requests

DESCRIPTION

            
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    @@ -1,72 +1,77 @@
  
    Package: dabestr

    Type: Package

    Package: dabestr

    Title: Data Analysis using Bootstrap-Coupled Estimation

    Version: 2023.9.12

    Version: 2025.3.14

    Authors@R: c(

      person("Joses W.", "Ho", email = "joseshowh@gmail.com", role = "aut",

             comment = c(ORCID = "0000-0002-9186-6322")),

      person("Kah Seng", "Lian", email = "kahseng@u.nus.edu", role = c("aut")),

      person("Zhuoyu", "Wang", email = "wzhuoyu21@u.nus,edu", role = "aut"),

      person("Jun Yang", "Liao", email = "name@example.com", role = "aut"),

      person("Felicia", "Low", role = "aut", email = "lowminhuifelicia@gmail.com"),

      person("Tayfun", "Tumkaya", role = "aut",

             comment = c(ORCID = "0000-0001-8425-3360")),

      person("Yishan", "Mai", email = "maiyishan@u.duke.nus.edu", role = c("cre", "ctb"),

             comment = c(ORCID = "0000-0002-7199-380X")),

      person("Sangyu", "Xu", role = "ctb",

             comment = c(ORCID = "0000-0002-4927-9204")),

      person("Hyungwon", "Choi", role = "ctb",

             comment = c(ORCID = "0000-0002-6687-3088")),

      person("Adam", "Claridge-Chang", role = "ctb",

             comment = c(ORCID = "0000-0002-4583-3650")),

      person("ACCLAB", role = c("cph", "fnd")))

    Description: Data Analysis using Bootstrap-Coupled ESTimation.

      Estimation statistics is a simple framework that avoids the pitfalls of

      significance testing. It uses familiar statistical concepts: means,

      mean differences, and error bars. More importantly, it focuses on the

      effect size of one's experiment/intervention, as opposed to a false

      dichotomy engendered by P values.

      An estimation plot has two key features:

      1. It presents all datapoints as a swarmplot, which orders each point to

      display the underlying distribution.

      2. It presents the effect size as a bootstrap 95% confidence interval on a

      separate but aligned axes.

      Estimation plots are introduced in Ho et al., Nature Methods 2019, 1548-7105.

      <doi:10.1038/s41592-019-0470-3>.

      The free-to-view PDF is located at <https://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D>.

        person("Joses W.", "Ho", , "joseshowh@gmail.com", role = "aut",

               comment = c(ORCID = "0000-0002-9186-6322")),

        person("Kah Seng", "Lian", , "kahseng@u.nus.edu", role = "aut"),

        person("Ana Rosa", "Castillo", , "arosacastillo@protonmail.com", role = "aut"),

        person("Zhuoyu", "Wang", , "wzhuoyu21@u.nus,edu", role = "aut"),

        person("Jun Yang", "Liao", , "name@example.com", role = "aut"),

        person("Felicia", "Low", , "lowminhuifelicia@gmail.com", role = "aut"),

        person("Tayfun", "Tumkaya", , role = "aut",

               comment = c(ORCID = "0000-0001-8425-3360")),

        person("Jonathan", "Anns", , role = "ctb",

               comment = c(ORCID = "0009-0005-8349-4986")),

        person("Yishan", "Mai", ,"maiyishan@u.duke.nus.edu", role = c("cre", "ctb"), 

               comment = c(ORCID = "0000-0002-7199-380X")),

        person("Sangyu", "Xu", , role = "ctb",

               comment = c(ORCID = "0000-0002-4927-9204")),

        person("Hyungwon", "Choi", , role = "ctb",

               comment = c(ORCID = "0000-0002-6687-3088")),

        person("Adam", "Claridge-Chang", , role = "ctb",

               comment = c(ORCID = "0000-0002-4583-3650")),

        person("ACCLAB", , role = c("cph", "fnd"))

      )

    Description: Data Analysis using Bootstrap-Coupled ESTimation.  Estimation

        statistics is a simple framework that avoids the pitfalls of

        significance testing. It uses familiar statistical concepts: means,

        mean differences, and error bars. More importantly, it focuses on the

        effect size of one's experiment/intervention, as opposed to a false

        dichotomy engendered by P values.  An estimation plot has two key

        features: 1. It presents all datapoints as a swarmplot, which orders

        each point to display the underlying distribution.  2. It presents the

        effect size as a bootstrap 95% confidence interval on a separate but

        aligned axes.  Estimation plots are introduced in Ho et al., Nature

        Methods 2019, 1548-7105.  <doi:10.1038/s41592-019-0470-3>.  The

        free-to-view PDF is located at

        <https://www.nature.com/articles/s41592-019-0470-3.epdf?author_access_token=Euy6APITxsYA3huBKOFBvNRgN0jAjWel9jnR3ZoTv0Pr6zJiJ3AA5aH4989gOJS_dajtNr1Wt17D0fh-t4GFcvqwMYN03qb8C33na_UrCUcGrt-Z0J9aPL6TPSbOxIC-pbHWKUDo2XsUOr3hQmlRew%3D%3D>.

    License: Apache License (>= 2)

    Encoding: UTF-8

    URL: https://github.com/ACCLAB/dabestr,

        https://acclab.github.io/dabestr/

    URL: https://github.com/ACCLAB/dabestr, https://acclab.github.io/dabestr/

    Depends:

      R (>= 2.10)

        R (>= 2.10)

    Imports: 

      ggplot2, 

      cowplot, 

      tidyr, 

      dplyr, 

      tibble,

      rlang, 

      magrittr, 

      ggbeeswarm,

      effsize,

      grid,

      scales,

      ggsci,

      cli,

      boot,

      stats,

      stringr,

      brunnermunzel,

      methods

    Roxygen: list(markdown = TRUE)

    RoxygenNote: 7.2.3

        boot,

        brunnermunzel,

        cli,

        cowplot,

        dplyr,

        effsize,

        ggbeeswarm,

        ggplot2 (>= 3.5.1),

        ggsci,

        grid,

        magrittr,

        RColorBrewer,

        rlang,

        scales,

        stats,

        stringr,

        tibble,

        tidyr,

        viridisLite

    Suggests: 

        testthat (>= 3.0.0),

        vdiffr,

        kableExtra,

        knitr,

        rmarkdown,

        kableExtra

        testthat (>= 3.0.0),

        vdiffr

    VignetteBuilder: 

        kableExtra,

        knitr

    Config/testthat/edition: 3

    Encoding: UTF-8

    LazyData: true

    VignetteBuilder: knitr, kableExtra

    Roxygen: list(markdown = TRUE)

    RoxygenNote: 7.2.3

NAMESPACE

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@@ Expand Up / @@ -7,9 +7,11 @@ export(cliffs_delta) @@
     export(cohens_d)
     export(cohens_h)
     export(dabest_plot)
+    export(forest_plot)
     export(hedges_g)
     export(load)
     export(mean_diff)
     export(median_diff)
     importFrom(ggplot2,.pt)
     importFrom(magrittr,"%>%")
+    importFrom(rlang,.data)

NEWS.md

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+    # dabestr v2025.3.14
+    This release implements quite a couple of new features, matching up with the current DABEST in python.
+    ### New features
+    * Plots now include swarm bars, contrast bars and delta dots
+    * Forest plots
     # dabestr v2023.9.12
     This release is a complete rebuild of dabestr.
@@ Expand Down @@

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