Скачать или смотреть Calculating Standard Errors and Adding Error Bars in R Using ggplot2 for Long Format Data

Learn how to calculate and plot standard errors alongside error bars in R with ggplot2, specifically when working with long format datasets.
---
This video is based on the question https://stackoverflow.com/q/65959117/ asked by the user 'jo_' ( https://stackoverflow.com/u/14385099/ ) and on the answer https://stackoverflow.com/a/65959260/ provided by the user 'P. Paccioretti' ( https://stackoverflow.com/u/10263697/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: Calculating and plotting standard errors using a dataset in long format

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
Calculating Standard Errors and Adding Error Bars in R Using ggplot2 for Long Format Data

If you're looking to visualize data using bar plots in R with ggplot2, adding error bars can provide valuable information about the precision of your estimates. In this post, we address a common challenge: calculating and displaying standard errors for datasets formatted in a long format. Specifically, we'll delve into a practical example involving the likeability of different ice cream flavors by school.

The Problem

Imagine you have a dataset where you collected responses about the preference for various ice cream flavors (e.g., Strawberry, Vanilla, Chocolate, etc.) across different schools. After preparing your data, you can create a bar plot to visualize the average likeability of each flavor. However, you’re unsure how to add error bars to represent the uncertainty of these averages.

The Challenge

You have the primary code to create the plot, but need guidance on how to calculate the standard error (SE) in the context of long format data using ggplot2.

Solution Overview

To add error bars indicating the standard errors to your bar plot, you can follow these steps:

Calculate the Mean and Standard Error: Utilize the dplyr package to group your data and compute the mean and SE.

Plot with Error Bars: Use ggplot2 to create the bar plot and add error bars using geom_errorbar.

Step 1: Calculate Mean and Standard Error

First, let's use the dplyr library to summarize the dataset. The following snippet will compute the mean and standard error for each flavor:

[[See Video to Reveal this Text or Code Snippet]]

In this code:

group_by(Flavour, variable): Groups the data by flavor and variable.

summarise(): Calculates the mean and standard error (SE) for each group.

MEAN: The average likeability score.

SE: The standard deviation of the scores divided by the square root of the number of responses, giving us a measure of variability.

Step 2: Create the Plot with Error Bars

Next, you can create the plot using ggplot2 and add the error bars. Here's how:

[[See Video to Reveal this Text or Code Snippet]]

In this plotting code:

geom_errorbar(): Adds error bars to the plot. You specify the minimum (ymin) and maximum (ymax) values based on the mean and standard error.

geom_bar(stat = "identity"): Plots the mean values as bars.

Labels and theme settings customize the appearance of your plot.

Conclusion

By following the steps outlined above, you can effectively visualize the likeability of different ice cream flavors while incorporating standard error bars to enhance your analysis. These bars communicate the reliability of the average values and allow viewers to better understand the data's variability.

If you have any questions or need further clarification about the plotting process in R, don’t hesitate to ask!