What are error bar plots and how to use and plot them?

What are error bar plots and how to use and plot them?

What are error bar plots and how to use and plot them?

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


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.

Step 1 - Install necessary package and library


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))

Relevant Projects

PySpark Tutorial - Learn to use Apache Spark with Python
PySpark Project-Get a handle on using Python with Spark through this hands-on data processing spark python tutorial.

Credit Card Fraud Detection as a Classification Problem
In this data science project, we will predict the credit card fraud in the transactional dataset using some of the predictive models.

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

Learn to prepare data for your next machine learning project
Text data requires special preparation before you can start using it for any machine learning project.In this ML project, you will learn about applying Machine Learning models to create classifiers and learn how to make sense of textual data.

Perform Time series modelling using Facebook Prophet
In this project, we are going to talk about Time Series Forecasting to predict the electricity requirement for a particular house using Prophet.

Walmart Sales Forecasting Data Science Project
Data Science Project in R-Predict the sales for each department using historical markdown data from the Walmart dataset containing data of 45 Walmart stores.

Predict Employee Computer Access Needs in Python
Data Science Project in Python- Given his or her job role, predict employee access needs using amazon employee database.

Data Science Project-TalkingData AdTracking Fraud Detection
Machine Learning Project in R-Detect fraudulent click traffic for mobile app ads using R data science programming language.

Data Science Project in Python on BigMart Sales Prediction
The goal of this data science project is to build a predictive model and find out the sales of each product at a given Big Mart store.

Resume parsing with Machine learning - NLP with Python OCR and Spacy
In this machine learning resume parser example we use the popular Spacy NLP python library for OCR and text classification.