Learn R online – it will be something you will pride yourself with over and over, throughout your life. There are many reasons to learn R online. First, R is an open source software that does statistics. This means that if you learn R online, you will never have to pay for statistical software again. Second, R makes amazing graphics that are very easy to customize. No more fighting with complicated code just to produce a gorgeous figure.
Another reason you should learn R online is because it’s easy – just take my two courses in R on LinkedIn Learning.
Learn R Online: Start with the Basics
Descriptive Healthcare Analytics in R is the first of my two-course series that helps you get started with R. It helps to have a background in beginning statistics, but you really don’t need any experience programming to take this course and hit the ground running with R.
Master the basics of R programming online
Tackle hands-on challenges with real world data
Get LinkedIn Learning course certificate
Here is what it covers:
Chapter 1: What is the BRFSS?
Before you can learn R online, you need some domain knowledge about the example dataset you will use. This chapter introduces you to the Behavioral Risk Factor Surveillance System (BRFSS), an annual cross-sectional health survey done by the United States government that posts its datasets online for you to use to practice your R programming.
Chapter 2: Designing your Metadata
A lot of people don’t realize you have to plan your analysis before you fire up your statistical software and start programming. This chapter shows you how to plan the variables you will ultimately use in your analysis, and develop metadata (like a data dictionary) that will help guide you through your analysis.
Chapter 3: Reading Data in and Applying Exclusions
Most BRFSS analyses only focus on one subpopulation in the dataset – such as veterans, women, or people who have a certain health condition. This chapter shows you how to import your dataset into R, and how to apply exclusions to remove the rows from the dataset that do not belong to the subpopulation of interest to your analysis.
Chapter 4: Preparing for Descriptive Analysis
This chapter shows you how to transform raw data into the variable you will use for your research, and how to prepare table shells to fill in as part of your descriptive analysis.
Chapter 5: Conducting Descriptive Analysis
In this chapter, you will actually execute your descriptive analysis, and prepare the results for interpretation and presentation.
Chapter 6: Descriptive Analysis: Weights and Tests
This bonus chapter explains how you can apply weights to your descriptive analysis, and also, how to apply statistical tests like the chi-square and report the results in your results table.
Advanced R: Now You’re Ready to Tackle Regression Analysis
Once you’ve completed the first course in descriptive analysis in R, you can move on to more advanced approaches covered in Healthcare Analytics: Regression in R.
Build and interpret impressive regression models
Showcase the results of your analysis
Get a head start for graduate school
Chapter 1: Designing your Research
This chapter focuses on establishing your statistical hypotheses a priori, and setting up R to prepare for regression analysis.
Chapter 2: Preparing for Linear Regression
This chapter shows you how to use R to check assumptions and prepare your dataset for linear regression modeling.
Chapter 3: Beginning Linear Regression Modeling
In this chapter, I show you how to conduct the first steps of linear regression modeling using the analytic dataset we prepared in previous chapters.
Chapter 4: Final Linear Regression Modeling
This chapter shows you have to finalize, present, and interpret your linear regression model.
Chapter 5: Preparing for Logistic Regression
What if you are using regression to predict a binary outcome instead of a continuous one? In that case, you want to build a logistic regression model. This chapter shows you how to prepare your data for logistic regression.
Chapter 6: Developing the Logistic Regression Model
This chapter shows you how to finalize, present, and interpret your logistic regression model.
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Learn R online and tackle statistical problems easily with open source software! Take my LinkedIn Learning courses and complete your own unique research projects.
