I will be teaching a one semester hour class at UMKC, Introduction to R (MEDB 5505) on Monday, August 8, 2016 through Friday, August 12, 2016. It runs from 9am to noon on all five days. This is part of a series of classes that cover a basic introduction to statistical packages: data import, data management, simple graphs, and simple descriptive statistics. The other classes (MEDB 5506 and MEDB 5507) cover SPSS and SAS.

Here are some details about this class.

First things first. There are no audits of this class. You have to sign up for credit through UMKC. If you are not currently a student at UMKC, you can apply as a visiting/community student. I’ll include documentation about this process as soon as I get it myself. It’s not difficult, but it has to be done early and it has to be sent to the right place. If you are interested, I can put you in touch with the proper people. Just contact me, preferably by email.

This course is intended to provide a working familiarity with R. You are not expected to have any advanced programming skills, other than the ability to create and modify text files. You need to have a basic understanding of statistical terminology, but no training or experience with advanced statistical methods is necessary. The class will introduce basic methods for data import, data management, simple graphics, and basic statistical analysis.

Here are the course objective:

• Run a simple program in R interactively and using a program file. Explain the advantages and disadvantages of these two methods.
• Access features from the help system. Identify internet resources that can provide additional support.
• Import data from delimited and fixed format text files, spreadsheets (e.g., Excel), and databases (Access, REDCap, and SQL) into R.
• Define the different types of data (nominal, ordinal, interval, ratio) and recognize the important distinctions between categorical and continuous data.
• Name and document variables, with special emphasis on documenting category codes.
• Code missing data values, understand how various statistical methods adapt when there are missing values, and distinguish between listwise deletion and pairwise deletion of missing values.
• Prepare univariate summaries and simple graphics for categorical and continuous variables.
• Incorporate tables and graphs into documents and presentations.
• Transform variables, with special emphasis on recoding and the log transformation.
• Evaluate, both graphically and statistically, the relationship between two continuous variables, between two categorical variables, and between a categorical variable and a continuous variable.
• Merge and combine two separate data files. Explain when to use one-to-one and many-to-one merging. Describe various ways to handle unmatched rows in a merge.
• Restructure longitudinal data from a “one row per subject” format to a “one row per visit” format. Restructure a longitudinal dataset in the opposite direction.
• Conduct an independent analysis of a data set of your own choosing. Prepare a report with a basic summary of your data analysis.