Adding error bars to plots can be done using different methods (and different error bar calculations) in R. In this blog post, I want to show you what I think is the easiest way to add error bars to ggplot2 plots in R.
First, you need to create your base plot. Both this post and the video demonstrate making the base plot in R. You can see the dataframe used in both cases.
Adding Error Bar Values to the Plot Dataframe
We are using ggplot2 to make the plot, and what we will end up doing is modifying our base bar plot code by simply adding another line of code to add the object geom_errorbar. What we need to do is to prepare the values for the arguments for geom_errorbar that we will want to set.
The variable named “Mean” in the base plot implies that we might want error bars that represent a certain philosophical margin of error. If we were doing population-based research, we might want this error bar to indicate the 95% confidence interval. If we did that, then we’d want to find the standard deviation of the mean from the original dataset, and multiply that by 1.96 (the z-score) to get the margin of error.
However, in the current use-case which was a lab study, so we decided to use the standard error (SE), which was the standard deviation divided by the square root of the number of units in the group. We decided we would make an error bar spanning 1 SE above the mean to 1 SE below the mean.
The strategy with ggplot2 and geom_errorbar is that when I cam calling geom_errorbar, I need to make sure that each bar in my bar plot has a corresponding SE value available for ggplot2 to use. For me, the easiest way to do that was to create a column in the underlying plot dataframe.
If you access the code files on Github, you’ll see that we are using a data frame called metric_plot_data. If you run the data frame, it will look like this (I included the first two rows):
> metric_plot_data Group Measure Mean SE 1 A Histo CEJ-ABC 159.813741 14.7411291 2 B Histo CEJ-ABC 101.457411 6.4343916
As you can see, I hardcoded the SE in the column titled SE. Now, when I call ggplot2 and refer to geom_errorbar, I can be sure that it puts the right errors on the right bars!
Code for Adding Error Bars
Now let’s look at our ggplot2 code.
ggplot(metric_plot_data, aes(x=Measure, y=Mean, fill=Group)) +
geom_bar(position=position_dodge(), stat="identity", color='black') +
ylab("Mean (mm)") +
xlab("Measurement") +
scale_fill_manual(values=cool_colors) +
geom_errorbar(aes(ymin=Mean-SE, ymax=Mean+SE), width=.2, position=position_dodge(.9))
And here is the plot. Do not be thrown off by scale_fill_manual in the code. This specifies custom colors – you can read more about it here.
I want you to focus on the last line, which is geom_errorbar. Notice in my code that ymin (the minimum y value of the error bar) and ymax (the maximum y value of the error bar) use calculations? Since the mean was a variable, I just made ymin be equal to Mean minus SE, and ymax be Mean plus SE.
But if you had unbalanced error bars (e.g., log transformations), you could theoretically hardcode a y-minimum value and call that ymin_value and another ymax_value and put those as two columns in your data. I’m a huge fan of hardcoding as much as you can mathwise in your plot data frame so that you can make sure you do not make a math error when making your plot and calling up equations on-the-fly.
For this plot, the base plot was a bar plot with position=position_dodge(), so we repeat that formatting for the error bars to ensure they land in the right place. The .9 specifies how much of a dodge, and width=.2 specifies the width of the whisker.
Watch this video to learn how to use the ggsave() command to format the dimensions of your plot. That way, you can control the shape of the output, and ensure all your plot features look correct.
Updated October 10, 2022. Added banners June 16, 2023. Added ggsave video August 29, 2023.
Read all of our data science blog posts!
Apply Weights? It’s Easy in R with the Survey Package!
Apply weights to get weighted proportions and counts! Read my blog post to learn how [...]
Nov
Make Categorical Variable Out of Continuous Variable
Make categorical variables by cutting up continuous ones. But where to put the boundaries? Get [...]
Nov
Remove Rows in R with the Subset Command
Remove rows by criteria is a common ETL operation – and my blog post shows [...]
Oct
CDC Wonder for Studying Vaccine Adverse Events: The Shameful State of US Open Government Data
CDC Wonder is an online query portal that serves as a gateway to many government [...]
Jun
AI Careers: Riding the Bubble
AI careers are not easy to navigate. Read my blog post for foolproof advice for [...]
Jun
Descriptive Analysis of Black Friday Death Count Database: Creative Classification
Descriptive analysis of Black Friday Death Count Database provides an example of how creative classification [...]
Nov
Classification Crosswalks: Strategies in Data Transformation
Classification crosswalks are easy to make, and can help you reduce cardinality in categorical variables, [...]
Nov
FAERS Data: Getting Creative with an Adverse Event Surveillance Dashboard
FAERS data are like any post-market surveillance pharmacy data – notoriously messy. But if you [...]
Nov
Dataset Source Documentation: Necessary for Data Science Projects with Multiple Data Sources
Dataset source documentation is good to keep when you are doing an analysis with data [...]
Nov
Joins in Base R: Alternative to SQL-like dplyr
Joins in base R must be executed properly or you will lose data. Read my [...]
Nov
NHANES Data: Pitfalls, Pranks, Possibilities, and Practical Advice
NHANES data piqued your interest? It’s not all sunshine and roses. Read my blog post [...]
Nov
Color in Visualizations: Using it to its Full Communicative Advantage
Color in visualizations of data curation and other data science documentation can be used to [...]
Oct
Defaults in PowerPoint: Setting Them Up for Data Visualizations
Defaults in PowerPoint are set up for slides – not data visualizations. Read my blog [...]
Oct
Text and Arrows in Dataviz Can Greatly Improve Understanding
Text and arrows in dataviz, if used wisely, can help your audience understand something very [...]
Oct
Shapes and Images in Dataviz: Making Choices for Optimal Communication
Shapes and images in dataviz, if chosen wisely, can greatly enhance the communicative value of [...]
Oct
Table Editing in R is Easy! Here Are a Few Tricks…
Table editing in R is easier than in SAS, because you can refer to columns, [...]
Aug
R for Logistic Regression: Example from Epidemiology and Biostatistics
R for logistic regression in health data analytics is a reasonable choice, if you know [...]
1 Comments
Aug
Connecting SAS to Other Applications: Different Strategies
Connecting SAS to other applications is often necessary, and there are many ways to do [...]
Jul
Portfolio Project Examples for Independent Data Science Projects
Portfolio project examples are sometimes needed for newbies in data science who are looking to [...]
Jul
Project Management Terminology for Public Health Data Scientists
Project management terminology is often used around epidemiologists, biostatisticians, and health data scientists, and it’s [...]
Jun
Rapid Application Development Public Health Style
“Rapid application development” (RAD) refers to an approach to designing and developing computer applications. In [...]
Jun
Understanding Legacy Data in a Relational World
Understanding legacy data is necessary if you want to analyze datasets that are extracted from [...]
Jun
Front-end Decisions Impact Back-end Data (and Your Data Science Experience!)
Front-end decisions are made when applications are designed. They are even made when you design [...]
Jun
Reducing Query Cost (and Making Better Use of Your Time)
Reducing query cost is especially important in SAS – but do you know how to [...]
Jun
Curated Datasets: Great for Data Science Portfolio Projects!
Curated datasets are useful to know about if you want to do a data science [...]
May
Statistics Trivia for Data Scientists
Statistics trivia for data scientists will refresh your memory from the courses you’ve taken – [...]
Apr
Management Tips for Data Scientists
Management tips for data scientists can be used by anyone – at work and in [...]
Mar
REDCap Mess: How it Got There, and How to Clean it Up
REDCap mess happens often in research shops, and it’s an analysis showstopper! Read my blog [...]
Mar
GitHub Beginners in Data Science: Here’s an Easy Way to Start!
GitHub beginners – even in data science – often feel intimidated when starting their GitHub [...]
Feb
ETL Pipeline Documentation: Here are my Tips and Tricks!
ETL pipeline documentation is great for team communication as well as data stewardship! Read my [...]
Feb
Benchmarking Runtime is Different in SAS Compared to Other Programs
Benchmarking runtime is different in SAS compared to other programs, where you have to request [...]
Dec
End-to-End AI Pipelines: Can Academics Be Taught How to Do Them?
End-to-end AI pipelines are being created routinely in industry, and one complaint is that academics [...]
Nov
Referring to Columns in R by Name Rather than Number has Pros and Cons
Referring to columns in R can be done using both number and field name syntax. [...]
Oct
The Paste Command in R is Great for Labels on Plots and Reports
The paste command in R is used to concatenate strings. You can leverage the paste [...]
Oct
Coloring Plots in R using Hexadecimal Codes Makes Them Fabulous!
Recoloring plots in R? Want to learn how to use an image to inspire R [...]
Oct
Adding Error Bars to ggplot2 Plots Can be Made Easy Through Dataframe Structure
Adding error bars to ggplot2 in R plots is easiest if you include the width [...]
Oct
AI on the Edge: What it is, and Data Storage Challenges it Poses
“AI on the edge” was a new term for me that I learned from Marc [...]
Jun
Pie Chart ggplot Style is Surprisingly Hard! Here’s How I Did it
Pie chart ggplot style is surprisingly hard to make, mainly because ggplot2 did not give [...]
Apr
Time Series Plots in R Using ggplot2 Are Ultimately Customizable
Time series plots in R are totally customizable using the ggplot2 package, and can come [...]
Apr
Data Curation Solution to Confusing Options in R Package UpSetR
Data curation solution that I posted recently with my blog post showing how to do [...]
Apr
Making Upset Plots with R Package UpSetR Helps Visualize Patterns of Attributes
Making upset plots with R package UpSetR is an easy way to visualize patterns of [...]
6 Comments
Apr
Making Box Plots Different Ways is Easy in R!
Making box plots in R affords you many different approaches and features. My blog post [...]
Mar
Convert CSV to RDS When Using R for Easier Data Handling
Convert CSV to RDS is what you want to do if you are working with [...]
Mar
GPower Case Example Shows How to Calculate and Document Sample Size
GPower case example shows a use-case where we needed to select an outcome measure for [...]
Feb
Querying the GHDx Database: Demonstration and Review of Application
Querying the GHDx database is challenging because of its difficult user interface, but mastering it [...]
Feb
Variable Names in SAS and R Have Different Restrictions and Rules
Variable names in SAS and R are subject to different “rules and regulations”, and these [...]
Feb
Referring to Variables in Processing Data is Different in SAS Compared to R
Referring to variables in processing is different conceptually when thinking about SAS compared to R. [...]
Jan
Counting Rows in SAS and R Use Totally Different Strategies
Counting rows in SAS and R is approached differently, because the two programs process data [...]
Jan
Native Formats in SAS and R for Data Are Different: Here’s How!
Native formats in SAS and R of data objects have different qualities – and there [...]
Jan
SAS-R Integration Example: Transform in R, Analyze in SAS!
Looking for a SAS-R integration example that uses the best of both worlds? I show [...]
Dec
Dumbbell Plot for Comparison of Rated Items: Which is Rated More Highly – Harvard or the U of MN?
Want to compare multiple rankings on two competing items – like hotels, restaurants, or colleges? [...]
2 Comments
Sep
Data for Meta-analysis Need to be Prepared a Certain Way – Here’s How
Getting data for meta-analysis together can be challenging, so I walk you through the simple [...]
Jul
Sort Order, Formats, and Operators: A Tour of The SAS Documentation Page
Get to know three of my favorite SAS documentation pages: the one with sort order, [...]
Nov
Confused when Downloading BRFSS Data? Here is a Guide
I use the datasets from the Behavioral Risk Factor Surveillance Survey (BRFSS) to demonstrate in [...]
2 Comments
Oct
Doing Surveys? Try my R Likert Plot Data Hack!
I love the Likert package in R, and use it often to visualize data. The [...]
3 Comments
Oct
I Used the R Package EpiCurve to Make an Epidemiologic Curve. Here’s How It Turned Out.
With all this talk about “flattening the curve” of the coronavirus, I thought I would [...]
Mar
Which Independent Variables Belong in a Regression Equation? We Don’t All Agree, But Here’s What I Do.
During my failed attempt to get a PhD from the University of South Florida, my [...]
Aug
Adding error bars to ggplot2 in R plots is easiest if you include the width of the error bar as a variable in your plot data. Read my blog post to see an example.
