{"action":"created","issue":{"url":"https://api.github.com/repos/IndrajeetPatil/ggstatsplot/issues/102","repository_url":"https://api.github.com/repos/IndrajeetPatil/ggstatsplot","labels_url":"https://api.github.com/repos/IndrajeetPatil/ggstatsplot/issues/102/labels{/name}","comments_url":"https://api.github.com/repos/IndrajeetPatil/ggstatsplot/issues/102/comments","events_url":"https://api.github.com/repos/IndrajeetPatil/ggstatsplot/issues/102/events","html_url":"https://github.com/IndrajeetPatil/ggstatsplot/pull/102","id":390247613,"node_id":"MDExOlB1bGxSZXF1ZXN0MjM4MDU0NDY1","number":102,"title":"Outlierfix","user":{"login":"ibecav","id":26557145,"node_id":"MDQ6VXNlcjI2NTU3MTQ1","avatar_url":"https://avatars0.githubusercontent.com/u/26557145?v=4","gravatar_id":"","url":"https://api.github.com/users/ibecav","html_url":"https://github.com/ibecav","followers_url":"https://api.github.com/users/ibecav/followers","following_url":"https://api.github.com/users/ibecav/following{/other_user}","gists_url":"https://api.github.com/users/ibecav/gists{/gist_id}","starred_url":"https://api.github.com/users/ibecav/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ibecav/subscriptions","organizations_url":"https://api.github.com/users/ibecav/orgs","repos_url":"https://api.github.com/users/ibecav/repos","events_url":"https://api.github.com/users/ibecav/events{/privacy}","received_events_url":"https://api.github.com/users/ibecav/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":13,"created_at":"2018-12-12T14:22:20Z","updated_at":"2018-12-13T20:55:42Z","closed_at":null,"author_association":"CONTRIBUTOR","pull_request":{"url":"https://api.github.com/repos/IndrajeetPatil/ggstatsplot/pulls/102","html_url":"https://github.com/IndrajeetPatil/ggstatsplot/pull/102","diff_url":"https://github.com/IndrajeetPatil/ggstatsplot/pull/102.diff","patch_url":"https://github.com/IndrajeetPatil/ggstatsplot/pull/102.patch"},"body":"see issue #101 "},"comment":{"url":"https://api.github.com/repos/IndrajeetPatil/ggstatsplot/issues/comments/447117588","html_url":"https://github.com/IndrajeetPatil/ggstatsplot/pull/102#issuecomment-447117588","issue_url":"https://api.github.com/repos/IndrajeetPatil/ggstatsplot/issues/102","id":447117588,"node_id":"MDEyOklzc3VlQ29tbWVudDQ0NzExNzU4OA==","user":{"login":"ibecav","id":26557145,"node_id":"MDQ6VXNlcjI2NTU3MTQ1","avatar_url":"https://avatars0.githubusercontent.com/u/26557145?v=4","gravatar_id":"","url":"https://api.github.com/users/ibecav","html_url":"https://github.com/ibecav","followers_url":"https://api.github.com/users/ibecav/followers","following_url":"https://api.github.com/users/ibecav/following{/other_user}","gists_url":"https://api.github.com/users/ibecav/gists{/gist_id}","starred_url":"https://api.github.com/users/ibecav/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ibecav/subscriptions","organizations_url":"https://api.github.com/users/ibecav/orgs","repos_url":"https://api.github.com/users/ibecav/repos","events_url":"https://api.github.com/users/ibecav/events{/privacy}","received_events_url":"https://api.github.com/users/ibecav/received_events","type":"User","site_admin":false},"created_at":"2018-12-13T20:55:42Z","updated_at":"2018-12-13T20:55:42Z","author_association":"CONTRIBUTOR","body":"``` r\r\n# Reprex putting all the oulier changes through their paces\r\n# December 13 @ 3:49pm\r\nlibrary(ggstatsplot)\r\n# plot\r\nggstatsplot::ggbetweenstats(\r\n  data = ToothGrowth,\r\n  x = supp,\r\n  y = len,\r\n  plot.type = \"violin\",\r\n  messages = FALSE,\r\n  results.subtitle = FALSE,\r\n  outlier.tagging = TRUE,\r\n  outlier.coef = 0.5,\r\n  mean.plotting = FALSE,\r\n  sample.size.label = FALSE\r\n)\r\n```\r\n\r\n![](https://i.imgur.com/ro5ezoO.png)\r\n\r\n``` r\r\n\r\nggstatsplot::ggbetweenstats(\r\n  data = ToothGrowth,\r\n  x = supp,\r\n  y = len,\r\n  plot.type = \"box\",\r\n  results.subtitle = FALSE,\r\n  outlier.tagging = TRUE,\r\n  outlier.coef = 0.5,\r\n  outlier.color = \"blue\",\r\n  messages = FALSE\r\n)\r\n```\r\n\r\n![](https://i.imgur.com/JDQMUGD.png)\r\n\r\n``` r\r\n\r\n\r\nggstatsplot::ggbetweenstats(\r\n  data = ToothGrowth,\r\n  x = supp,\r\n  y = len,\r\n  plot.type = \"boxviolin\",\r\n  results.subtitle = FALSE,\r\n  outlier.tagging = TRUE,\r\n  outlier.coef = 0.5,\r\n  outlier.shape = 12,\r\n  outlier.color = \"blue\",\r\n  notch = TRUE,\r\n  messages = FALSE\r\n)\r\n```\r\n\r\n![](https://i.imgur.com/9d9NQFD.png)\r\n\r\n``` r\r\n\r\n# works grouped\r\nggstatsplot::grouped_ggbetweenstats(\r\n  data = ToothGrowth,\r\n  x = supp,\r\n  y = len,\r\n  grouping.var = dose,\r\n  plot.type = \"violin\",\r\n  results.subtitle = FALSE,\r\n  outlier.tagging = TRUE,\r\n  outlier.coef = 0.5,\r\n  outlier.color = \"blue\",\r\n  messages = FALSE\r\n)\r\n```\r\n\r\n![](https://i.imgur.com/4UcoSK0.png)\r\n\r\n``` r\r\n\r\n# default simplest\r\nggstatsplot::ggbetweenstats(\r\n  data = ggplot2::msleep,\r\n  x = vore,\r\n  y = sleep_total)\r\n#> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.\r\n#> \r\n#> Note: Shapiro-Wilk Normality Test for sleep_total : p-value = 0.154\r\n#> \r\n#> Note: Bartlett's test for homogeneity of variances for factor vore : p-value = 0.104\r\n#> \r\n```\r\n\r\n![](https://i.imgur.com/5jRKzDL.png)\r\n\r\n``` r\r\n\r\n# just add labels\r\nggstatsplot::ggbetweenstats(\r\n  data = ggplot2::msleep,\r\n  x = vore,\r\n  y = sleep_total,\r\n  outlier.tagging = TRUE,\r\n  outlier.label = genus\r\n)\r\n#> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.\r\n#> \r\n#> Note: Shapiro-Wilk Normality Test for sleep_total : p-value = 0.154\r\n#> \r\n#> Note: Bartlett's test for homogeneity of variances for factor vore : p-value = 0.104\r\n#> \r\n```\r\n\r\n![](https://i.imgur.com/t9qGp7m.png)\r\n\r\n``` r\r\n\r\n# works with violins too\r\nggstatsplot::ggbetweenstats(\r\n  data = ggplot2::msleep,\r\n  x = vore,\r\n  y = sleep_total,\r\n  plot.type = \"violin\",\r\n  outlier.tagging = TRUE,\r\n  outlier.label = genus\r\n)\r\n#> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.\r\n#> \r\n#> Note: Shapiro-Wilk Normality Test for sleep_total : p-value = 0.154\r\n#> \r\n#> Note: Bartlett's test for homogeneity of variances for factor vore : p-value = 0.104\r\n#> \r\n```\r\n\r\n![](https://i.imgur.com/t5x6KaU.png)\r\n\r\n``` r\r\n\r\n# get fancier\r\nggstatsplot::ggbetweenstats(\r\n  data = ggplot2::msleep,\r\n  x = vore,\r\n  y = sleep_total,\r\n  outlier.tagging = TRUE,\r\n  outlier.label = genus,\r\n  outlier.color = \"blue\",\r\n  outlier.shape = 15\r\n)\r\n#> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.\r\n#> \r\n#> Note: Shapiro-Wilk Normality Test for sleep_total : p-value = 0.154\r\n#> \r\n#> Note: Bartlett's test for homogeneity of variances for factor vore : p-value = 0.104\r\n#> \r\n```\r\n\r\n![](https://i.imgur.com/CE9E54A.png)\r\n\r\n``` r\r\n\r\n# pure box\r\nggstatsplot::ggbetweenstats(\r\n  data = ggplot2::msleep,\r\n  x = vore,\r\n  y = sleep_total,\r\n  outlier.tagging = TRUE,\r\n  plot.type = \"box\",\r\n  outlier.label = genus,\r\n  outlier.color = \"blue\",\r\n  outlier.shape = 15\r\n)\r\n#> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.\r\n#> \r\n#> Note: Shapiro-Wilk Normality Test for sleep_total : p-value = 0.154\r\n#> \r\n#> Note: Bartlett's test for homogeneity of variances for factor vore : p-value = 0.104\r\n#> \r\n```\r\n\r\n![](https://i.imgur.com/0Lc1tUS.png)\r\n\r\n``` r\r\n\r\n# back to violin changing shape and color\r\nggstatsplot::ggbetweenstats(\r\n  data = ggplot2::msleep,\r\n  x = vore,\r\n  y = sleep_total,\r\n  outlier.tagging = TRUE,\r\n  plot.type = \"violin\",\r\n  outlier.label = genus,\r\n  outlier.color = \"blue\",\r\n  outlier.shape = 15\r\n)\r\n#> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.\r\n#> \r\n#> Note: Shapiro-Wilk Normality Test for sleep_total : p-value = 0.154\r\n#> \r\n#> Note: Bartlett's test for homogeneity of variances for factor vore : p-value = 0.104\r\n#> \r\n```\r\n\r\n![](https://i.imgur.com/ZmV4vVS.png)\r\n\r\n``` r\r\n\r\n# both\r\nggstatsplot::ggbetweenstats(\r\n  data = ggplot2::msleep,\r\n  x = vore,\r\n  y = sleep_total,\r\n  outlier.tagging = TRUE,\r\n  plot.type = \"boxviolin\",\r\n  outlier.label = genus,\r\n  outlier.color = \"blue\",\r\n  outlier.shape = 15\r\n)\r\n#> Note: 95% CI for effect size estimate was computed with 100 bootstrap samples.\r\n#> \r\n#> Note: Shapiro-Wilk Normality Test for sleep_total : p-value = 0.154\r\n#> \r\n#> Note: Bartlett's test for homogeneity of variances for factor vore : p-value = 0.104\r\n#> \r\n```\r\n\r\n![](https://i.imgur.com/qfryOZb.png)\r\n\r\n``` r\r\n\r\n# and of course my original issue on a large dataset\r\n# box\r\nggstatsplot::ggbetweenstats(\r\n  data = movies_long,\r\n  x = mpaa,\r\n  y = rating,\r\n  plot.type = \"box\",\r\n  conf.level = 0.99,\r\n  xlab = \"MPAA Rating\",\r\n  ylab = \"IMDB Rating\",\r\n  pairwise.comparisons = TRUE,\r\n  pairwise.annotation = \"p.value\",\r\n  p.adjust.method = \"fdr\",\r\n  outlier.tagging = TRUE,\r\n  outlier.label = title,\r\n  nboot = 10,\r\n  ggtheme = ggthemes::theme_few(),\r\n  ggstatsplot.layer = FALSE,\r\n  bf.message = TRUE\r\n)\r\n#> Note: 99% CI for effect size estimate was computed with 10 bootstrap samples.\r\n#> \r\n#> # tibble [3 × 11]\r\n#>   group1 group2 mean.difference conf.low conf.high    se t.value    df\r\n#>   <chr>  <chr>            <dbl>    <dbl>     <dbl> <dbl>   <dbl> <dbl>\r\n#> 1 R      PG-13           -0.219   -0.375    -0.064 0.047   3.31  1142.\r\n#> 2 R      PG              -0.323   -0.573    -0.074 0.075   3.05   277.\r\n#> 3 PG-13  PG              -0.104   -0.362     0.154 0.077   0.952  309.\r\n#> # ... with 3 more variables: p.value <dbl>, significance <chr>,\r\n#> #   p.value.label <chr>\r\n#> Note: Shapiro-Wilk Normality Test for rating : p-value = < 0.001\r\n#> \r\n#> Note: Bartlett's test for homogeneity of variances for factor mpaa : p-value = 0.004\r\n#> \r\n```\r\n\r\n![](https://i.imgur.com/oush4dZ.png)\r\n\r\n``` r\r\n\r\n# violin\r\nggstatsplot::ggbetweenstats(\r\n  data = movies_long,\r\n  x = mpaa,\r\n  y = rating,\r\n  plot.type = \"violin\",\r\n  conf.level = 0.99,\r\n  xlab = \"MPAA Rating\",\r\n  ylab = \"IMDB Rating\",\r\n  pairwise.comparisons = TRUE,\r\n  pairwise.annotation = \"p.value\",\r\n  p.adjust.method = \"fdr\",\r\n  outlier.tagging = TRUE,\r\n  outlier.label = title,\r\n  nboot = 10,\r\n  ggtheme = ggthemes::theme_few(),\r\n  ggstatsplot.layer = FALSE,\r\n  bf.message = TRUE\r\n)\r\n#> Note: 99% CI for effect size estimate was computed with 10 bootstrap samples.\r\n#> \r\n#> # tibble [3 × 11]\r\n#>   group1 group2 mean.difference conf.low conf.high    se t.value    df\r\n#>   <chr>  <chr>            <dbl>    <dbl>     <dbl> <dbl>   <dbl> <dbl>\r\n#> 1 R      PG-13           -0.219   -0.375    -0.064 0.047   3.31  1142.\r\n#> 2 R      PG              -0.323   -0.573    -0.074 0.075   3.05   277.\r\n#> 3 PG-13  PG              -0.104   -0.362     0.154 0.077   0.952  309.\r\n#> # ... with 3 more variables: p.value <dbl>, significance <chr>,\r\n#> #   p.value.label <chr>\r\n#> Note: Shapiro-Wilk Normality Test for rating : p-value = < 0.001\r\n#> \r\n#> Note: Bartlett's test for homogeneity of variances for factor mpaa : p-value = 0.004\r\n#> \r\n```\r\n\r\n![](https://i.imgur.com/Afkx3hD.png)\r\n\r\n<sup>Created on 2018-12-13 by the [reprex package](https://reprex.tidyverse.org) (v0.2.1)</sup>"}}
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