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

Jingwei Li

Graduate Research assistance at Stony Brook University
linkedin profile url

ProjectPro is an awesome platform that helps me learn much hands-on industrial experience with a step-by-step walkthrough of projects. There are two primary paths to learn: Data Science and Big Data.... Read More

Relevant Projects

Time Series Classification Project for Elevator Failure Prediction
In this Time Series Project, you will predict the failure of elevators using IoT sensor data as a time series classification machine learning problem.

MLOps Project to Build Search Relevancy Algorithm with SBERT
In this MLOps SBERT project you will learn to build and deploy an accurate and scalable search algorithm on AWS using SBERT and ANNOY to enhance search relevancy in news articles.

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

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 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.

Predict Churn for a Telecom company using Logistic Regression
Machine Learning Project in R- Predict the customer churn of telecom sector and find out the key drivers that lead to churn. Learn how the logistic regression model using R can be used to identify the customer churn in telecom dataset.

Customer Churn Prediction Analysis using Ensemble Techniques
In this machine learning churn project, we implement a churn prediction model in python using ensemble techniques.

Time Series Forecasting Project-Building ARIMA Model in Python
Build a time series ARIMA model in Python to forecast the use of arrival rate density to support staffing decisions at call centres.

Build a Multi Class Image Classification Model Python using CNN
This project explains How to build a Sequential Model that can perform Multi Class Image Classification in Python using CNN

Learn to Build an End-to-End Machine Learning Pipeline - Part 2
In this Machine Learning Project, you will learn how to build an end-to-end machine learning pipeline for predicting truck delays, incorporating Hopsworks' feature store and Weights and Biases for model experimentation.