Error bars not at top of graph r1/17/2024 ![]() So I have excel on my computer, and I've downloaded and installed all available updates, but it looks a little different from what I'm used to. They are identifiable with a special user flair.Ī community since MaAsking a question? Describe if you are using Excel (include version and operating system!), Google Sheets, or another spreadsheet application. Occasionally Microsoft developers will post or comment. Recent ClippyPoint Milestones !Ĭongratulations and thank you to these contributors Date Include a screenshot, use the tableit website, or use the ExcelToReddit converter (courtesy of u/tirlibibi17) to present your data. NOTE: For VBA, you can select code in your VBA window, press Tab, then copy and paste that into your post or comment. To keep Reddit from mangling your formulas and other code, display it using inline-code or put it in a code-block This will award the user a ClippyPoint and change the post's flair to solved. OPs can (and should) reply to any solutions with: Solution Verified Only text posts are accepted you can have images in Text posts.Use the appropriate flair for non-questions.Post titles must be specific to your problem.The violin plot is another possibility if you have a large sample size to display. The boxplot with jitter is a good one for a relatively small amount of data. ![]() But if you know the individual data points, show them. Of course it is not possible if you only have summary statistics. It is better to avoid error bars as much as you can. It is quite obvious that the 3 metrics report very different visualizations and conclusions.Īlways specify which metrics you used for the error bars Workaround P3 <- ggplot(my_sum) + geom_bar( aes( x=Species, y=mean), stat= "identity", fill= "#69b3a2", alpha= 0.7, width= 0.6) + geom_errorbar( aes( x=Species, ymin=mean -ic, ymax=mean +ic), width= 0.4, colour= "black", alpha= 0.9, size= 1) + ggtitle( "confidence interval") + theme( P2 <- ggplot(my_sum) + geom_bar( aes( x=Species, y=mean), stat= "identity", fill= "#69b3a2", alpha= 0.7, width= 0.6) + geom_errorbar( aes( x=Species, ymin=mean -se, ymax=mean +se), width= 0.4, colour= "black", alpha= 0.9, size= 1) + ggtitle( "standard error") + theme( ) + theme_ipsum() + xlab( "") + ylab( "Sepal Length") P1 <- ggplot(my_sum) + geom_bar( aes( x=Species, y=mean), stat= "identity", fill= "#69b3a2", alpha= 0.7, width= 0.6) + geom_errorbar( aes( x=Species, ymin=mean -sd, ymax=mean +sd), width= 0.4, colour= "black", alpha= 0.9, size= 1) + ggtitle( "standard deviation") + theme( My_sum % group_by(Species) %>% summarise( Calculated as the root square of the variance Standard Deviation (SD) represents the amount of dispersion of the variable.Here is an overview of their definitions and how to calculate them on a simple vector in R. Three different types of values are commonly used for error bars, sometimes giving very different results. The second issue with error bars is that they are used to show different metrics, and it is not always clear which one is being shown. Thus, the same barplot with error bars can in fact tell very different stories, hidden to the reader.Īlways show your individual data points if you can #showyourdata What is an error bar? Both groups can have the same kind of distribution ( B), one group can have outliers ( C), one group can have a bimodal distribution ( D), or groups can have unequal sample sizes: The same barplot with error bars (left) can represent several situations. It illustrates that the full data may suggest different conclusions than the summary statistics. Here is a figure from a paper in PLOS Biology. The first issue with error bars is that they hide information. For instance, it appears that measurements in group B are more precise than in group E. The black error bar gives information on how the individual observations are dispersed around the average. The bar heights represent their mean value. In the graphic above 5 groups are reported. ) + ggtitle( "A barplot with error bar") + xlab( "") # Plot ggplot(data) + geom_bar( aes( x=name, y=value), stat= "identity", fill= "#69b3a2", alpha= 0.7, width= 0.5) + geom_errorbar( aes( x=name, ymin=value -sd, ymax=value +sd), width= 0.4, colour= "black", alpha= 0.9, size= 1) + theme_ipsum() + theme( ![]()
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