What are error bar plots and how to use and plot them in R

This recipe explains what are error bar plots and how to use and plot them in R

Recipe Objective

What are error bar plots ? How to use and plot them? Error bars are a visualization function that visualizes the variability of data plotted on the cartesian graph. Error bars help us estimate the uncertainty i.e error in our data. Error bar plots can be drawn over for line plots, bar plots etc. Error bars draw error lines that extend from the end of the bar. The length of this line represents the extend of error in the data. Shorter length indicates that the error is less and less variability in data while longer length of the error lines indicates that the data has greater variability and less reliability. This recipe demonstrates an example of error bar plots.

Explore the BERT Variants - ALBERT vs DistilBERT

Step 1 - Install necessary package and library

library(ggplot2)

Step 2 - Create a dataframe

The data frame consists of categorical type of data with corresponding standard deviation i.e variability of the data. We calculate for 1 standard deviation. Syntax — **ggplot (data) + geom_bar (aes (x,y)) + geom_errorbar (aes(x,y,ymin,ymax)** Data - input data geom_bar - bar plot aes (x,y) — the aes function — creates mapping from data to geom geom_errorbar() - error barplot ymin — the minimum value of the range ymax — the maximum value of the range .

data <- data.frame(values = c(5,10,15,20,25,30), type = c("A","B","C","D","E","F"), sd_dev = c(1.5,2.5,3.5,4.5,5.5,6.5)) print(data)

"data is : "

  values type sd_dev
1      5    A    1.5
2     10    B    2.5
3     15    C    3.5
4     20    D    4.5
5     25    E    5.5
6     30    F    6.5

Step 3 - Plot the bar chart with error bar

ggplot(data)+ geom_bar(aes(x=type,y=values),stat="identity",fill="red")+ geom_errorbar(aes(x=type,ymin=values-sd_dev,ymax=values+sd_dev))

What Users are saying..

profile image

Ray han

Tech Leader | Stanford / Yale University
linkedin profile url

I think that they are fantastic. I attended Yale and Stanford and have worked at Honeywell,Oracle, and Arthur Andersen(Accenture) in the US. I have taken Big Data and Hadoop,NoSQL, Spark, Hadoop... Read More

Relevant Projects

NLP Project on LDA Topic Modelling Python using RACE Dataset
Use the RACE dataset to extract a dominant topic from each document and perform LDA topic modeling in python.

Personalized Medicine: Redefining Cancer Treatment
In this Personalized Medicine Machine Learning Project you will learn to classify genetic mutations on the basis of medical literature into 9 classes.

Build a Review Classification Model using Gated Recurrent Unit
In this Machine Learning project, you will build a classification model in python to classify the reviews of an app on a scale of 1 to 5 using Gated Recurrent Unit.

Deep Learning Project for Beginners with Source Code Part 1
Learn to implement deep neural networks in Python .

Build a Text Classification Model with Attention Mechanism NLP
In this NLP Project, you will learn to build a multi class text classification model with attention mechanism.

Digit Recognition using CNN for MNIST Dataset in Python
In this deep learning project, you will build a convolutional neural network using MNIST dataset for handwritten digit recognition.

Text Classification with Transformers-RoBERTa and XLNet Model
In this machine learning project, you will learn how to load, fine tune and evaluate various transformer models for text classification tasks.

Build an AI Chatbot from Scratch using Keras Sequential Model
In this NLP Project, you will learn how to build an AI Chatbot from Scratch using Keras Sequential Model.

House Price Prediction Project using Machine Learning in Python
Use the Zillow Zestimate Dataset to build a machine learning model for house price prediction.

Learn to Build Generative Models Using PyTorch Autoencoders
In this deep learning project, you will learn how to build a Generative Model using Autoencoders in PyTorch