Handling missing data in R

When you work with data, it is most likely you will encounter missing data. Especially in application-oriented fields, available datasets are almost always incomplete. As most analyses require complete data, what to do with the missing values?

An often used and easy solution to handle missing data is deleting incomplete rows and/or columns from the dataset. Is this method the most appropriate one, though? What other methods are available and what is their influence on the outcome of the analysis?

The workshop has the following three parts.

1. Missing Values Analysis
   What amount and what kind of missing data do I have in my dataset? What are possible reasons/mechanisms underlying the missingness?
2. Evaluating Missing Data methods
   How do I know whether I choose the right missing data method? What should I take into account when deciding about a missing data method?
3. Implementing Missing Data Methods
   How can I implement missing data methods in my analysis and/or in my pipeline?

In our next workshop you will learn how to handle missing data in R. This workshop is brought to you by Rianne Schouten.

PROGRAM

17:30 - 18:00 Welcome & Pizza
18:00 - 18:20 Intro to RLadies & AACSB
18:20 - 19:05 Workshop Part 1
19:05 - 19:20 Break
19:20 - 20:05 Workshop Part 2
20:05 - 21:00 Networking & Drinks

ABOUT RIANNE

https://rianneschouten.github.io
https://www.linkedin.com/in/rianne-schouten-5bb29491/

My name is Rianne Schouten and I am a data enthusiast. When I studied Methodology and Statistics, I co-developed a procedure to generate missing data in complete datasets. I implemented the procedure in R-function ampute, and it is now part of imputation package mice.

Then, I worked as an external PhD candidate under supervision of dr. Gerko Vink and prof. dr. Stef van Buuren. My work focused on the effect of missing data on prediction oriented analyses. Unfortunately, I had to quit with my research when I changed jobs and no more funding was available.

Now, I work at Samen Veilig Midden-Nederland (i.e. a youth care organization) as Developer Data & Analytics. It is my job to structure and standardize the analyses of sensitive and highly classified data. I mainly work with databases and visualization software and I lead several projects.

REQUIREMENTS

A basic knowledge of R is required.

CODE OF CONDUCT

We expect all attendees to abide by our Code of Conduct. Anyone who violates this community's Code of Conduct will be refused entry, expelled, and/or any other action deemed appropriate by the Organizer, regardless of event registration, meetup membership or other condition. We are emphatically queer and trans-friendly. This group was created so those who identify as women have a comfortable place to learn.