Exploratory Data Analysis in R

Exploratory Data Analysis in R, available at $34.99, has an average rating of 5, with 28 lectures, based on 2 reviews, and has 20 subscribers.

You will learn about Develop a fundamental framework to carry out your own Exploratory Data Analysis The use of scatter plots and how to incorporate linear and non-linear models into your graphics How to evaluate if your data is normal using histograms and probability plots The power of box plots to compare groups This course is ideal for individuals who are If you currently create multiple data visualizations in spreadsheets, youve probably wondered how you could improve your work or how you could work more efficiently. Or, if you have to recreate graphics repeatedly, you might be looking for a tool to make your work more reproducible. This course focuses on the basic techniques used in Exploratory Data Analysis: scatterplots, histograms, probability plots, and box plots. Learning R and ggplot2 will allow you to move beyond spreadsheets and use a professional tool to explore your data effectively. It is particularly useful for If you currently create multiple data visualizations in spreadsheets, youve probably wondered how you could improve your work or how you could work more efficiently. Or, if you have to recreate graphics repeatedly, you might be looking for a tool to make your work more reproducible. This course focuses on the basic techniques used in Exploratory Data Analysis: scatterplots, histograms, probability plots, and box plots. Learning R and ggplot2 will allow you to move beyond spreadsheets and use a professional tool to explore your data effectively.

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Summary

Title: Exploratory Data Analysis in R

Price: $34.99

Average Rating: 5

Number of Lectures: 28

Number of Published Lectures: 28

Number of Curriculum Items: 28

Number of Published Curriculum Objects: 28

Original Price: $27.99

Quality Status: approved

Status: Live

What You Will Learn

Develop a fundamental framework to carry out your own Exploratory Data Analysis

The use of scatter plots and how to incorporate linear and non-linear models into your graphics

How to evaluate if your data is normal using histograms and probability plots

The power of box plots to compare groups

Who Should Attend

If you currently create multiple data visualizations in spreadsheets, youve probably wondered how you could improve your work or how you could work more efficiently. Or, if you have to recreate graphics repeatedly, you might be looking for a tool to make your work more reproducible. This course focuses on the basic techniques used in Exploratory Data Analysis: scatterplots, histograms, probability plots, and box plots. Learning R and ggplot2 will allow you to move beyond spreadsheets and use a professional tool to explore your data effectively.

Target Audiences

If you currently create multiple data visualizations in spreadsheets, youve probably wondered how you could improve your work or how you could work more efficiently. Or, if you have to recreate graphics repeatedly, you might be looking for a tool to make your work more reproducible. This course focuses on the basic techniques used in Exploratory Data Analysis: scatterplots, histograms, probability plots, and box plots. Learning R and ggplot2 will allow you to move beyond spreadsheets and use a professional tool to explore your data effectively.

This example-based course introduces exploratory data analysis (EDA) using R. A primary objective is to apply graphical EDA techniques to representative data sets using the RStudio platform.

I have incorporated datasets from the NIST/SEMATECH e-Handbook of Statistical Methods into this course and adopted their fundamental approach of Exploratory Data Analysis.

We use scatter plots to examine relationships between two variables, determine if there is a linear or non-linear relationship, analyze variations of the dependent variable, and determine if there are outliers in the dataset.

Of course, we need to remember that causality implies association and that association does NOT imply causality.

We will summarise the distribution of a dataset graphically using histograms. This tool can quickly show us the location and spread of the data, and give us a good indication if the data follows a normal distribution, is skewed, has multiple modes or outliers.

An underused, complementary technique to histograms is the probability plot. We will construct probability plots by plotting the data against a theoretical normal distribution. If the data follows a normal distribution, the plot will form a straight line. We will use the normal probability plot to assess whether or not our examples follow a normal distribution.

Finally, we will use box plots to view the variation between different groups within the data.

Aside from scatterplots, most spreadsheet programs do not support these methods, so learning how to do this fundamental analysis in R can improve your ability to explore your data.

Course Curriculum

Chapter 1: Introduction to EDA in R

Lecture 1: Introduction to EDA in R

Chapter 2: Graphical techniques – scatter plots

Lecture 1: 02 Scatter plots – overview presentation

Lecture 2: 02 – Reading data files into RStudio

Lecture 3: 02 – Scatter plots – trend lines

Lecture 4: 02 – Scatter plots – linear models

Lecture 5: 02 – Scatter plots – fitting quadratic data using a linear model

Lecture 6: 02 – Scatter plots – transforming data in ggplot

Lecture 7: 02 – Scatter plots – outliers

Lecture 8: 02 – Scatter plots – Run plots and lag plots

Chapter 3: Graphical techniques – histograms

Lecture 1: 03 – Histograms – overview presentation

Lecture 2: 03 Histograms – getting started

Lecture 3: 03 – Histograms – normal data

Lecture 4: 03 – Histograms – Non-normal, short-tailed

Lecture 5: 03 Histograms – Non-normal, long-tailed

Lecture 6: 03 – Histograms – symmetric and bimodal

Lecture 7: 03 – Histograms – bimodal mixture of two normal distributions

Lecture 8: 03 – Histograms – Non-normal skewed right

Lecture 9: 03 – Histograms – symmetric with outliers

Chapter 4: Graphical techniques – box plots

Lecture 1: 04 – Box Plots – Overview presentation

Lecture 2: 04 – Box Plots – exercises – basics part I

Lecture 3: 04 – Box Plots – exercises – basics part II

Lecture 4: 04 – Box Plots – exercises – comparisons

Chapter 5: Graphical techniques – probability plots

Lecture 1: 05 – Probability Plots – Overview presentation

Lecture 2: 05 – Probability Plots – exercises – normal data

Lecture 3: 05 – Probability Plots – exercises – non-normal distributions (part I)

Lecture 4: 05 – Probability Plots – exercises – non-normal distributions (part II)

Chapter 6: Conclusion to EDA in R

Lecture 1: 06 – Conclusion

Chapter 7: Extra materials for EDA in R

Lecture 1: Extra Materials

Instructors

Ray James Hoobler
Educator | STEM professional | R Enthusiast

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