Workflow of Data Analysis
Using R

A 3-Day Remote Seminar Taught by Bianca Manago, Ph.D.

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Statistical analyses are only as good as the data that go into them. This is why the majority of time on any data analysis project should be spent, not on conducting the analyses (i.e., actually running the model), but instead on the steps needed to prepare the data for analysis. There are dozens of decisions that go into data management.  If not properly documented or considered, those decisions can produce erroneous results or preclude replication.

This seminar is designed to teach researchers how to prepare data for analysis in a way that is both accurate and replicable. By following these principles, your data analytic projects will be both well-planned and executed. The scope of the seminar ranges from such broad topics as developing research plans to the detailed minutia of planning variable names.

Starting March 18, we are offering this seminar as a 3-day synchronous*, remote workshop. Each day will consist of a 4-hour live lecture held via the free video-conferencing software Zoom. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.

Each lecture session will conclude with a hands-on exercise reviewing the content covered, to be completed on your own. An additional session will be held Thursday and Friday afternoons as an “office hour”, where you can review the exercise results with the instructor and ask any questions.

*We understand that scheduling is difficult during this unpredictable time. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for two weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.

More Details About the Course Content

This seminar is for researchers who are trying to establish or improve their workflow. I do not expect participants to be expert programmers; this seminar should be accessible to very novice R users, while still being useful to more advanced users. Lessons from this seminar balance ease of use with proper functioning, introducing researchers to useful tools, e.g., dual-pane browsers, macro programs, plain text editors, RStudio, and GitHub. For those who are already familiar with these tools, this seminar will teach you how to optimize them. Lessons from this seminar should make conducting research less painful, more efficient, more accurate, and reproducible.

This is a hands-on seminar with ample opportunities to plan and practice your workflow.

Some highlights include:

  • Planning (analyses, sensitivity analyses, variable construction, etc.)
  • Directory structure
  • Data preservation
  • Documentation
  • Dual workflow (separating data management and analyses)
  • Writing robust script files
  • Using log files
  • Variable naming
  • Value labeling
  • Reproducibility and replication
  • Examining data

Computing

The empirical examples and exercises in this course will emphasize R. To fully benefit from the course, you should use your own computer with R installed. You should also download and install RStudio, a front-end for R that makes it easier to work with. This software is free and available for Windows, Mac, and Linux platforms. For those who prefer Stata, equivalent Stata code will be provided on request.

If you’d like to take this course but are concerned that you don’t know enough R, there are excellent online resources for learning the basics. Here are our recommendations.

Who Should Register?

This course is for anyone who wants to improve the efficiency and accuracy of their data analysis and presentation. You should have experience with data analysis, as well as familiarity with the R programming language.

Outline

PART 1: INTRODUCTION TO WORKFLOW

  1. What is “workflow”?
  2. Why care about WF?
  3. WF and replication
  4. Steps in and principles of WF

PART 2: PLAN, ORGANIZE, DOCUMENT, AND PRESERVE

  1. Planning research projects in the:
    a. Large (overall questions, project checklist, and timeline)
    b. Middle (data cleaning, analyses, tables, and figures)
    c. Small (naming variables, naming files, value labels, and order of
    analyses/cleaning)
  2. Organizing files and folders
  3. Documentation
  4. Preserving data and preventing loss
  5. Replication

PART 3: SCRIPT FILES IN R

  1. Strengths and weaknesses of R for workflow
  2. Dual workflow
  3. Robust script files
  4. Legible script files
  5. Automation in script files

PART 4: CLEANING, LABELING, & MISSING DATA

  1. Naming and labeling variables
  2. Missing data
  3. Merging data
  4. Verifying data

PART 5: ANALYZING & PRESENTING FINDINGS

  1. Principles of data analysis
  2. Documenting provenance
  3. The posting principle
  4. Presenting findings

PART 6: COLLABORATION

  1. Key factors in collaboration
  2. Introducing workflow with co-authors
  3. Coordinating workflow with multiple authors

Reviews of Workflow of Data Analysis Using R

“Bianca was a great, knowledgeable entertaining teacher.  She really made what could have been very dry material seem exciting.  The knowledge I gained in this course will revolutionize the way I approach my research projects.”
  Laura Prichett, Johns Hopkins

“This was a very informative seminar.  I would suggest if for all Data Managers, Analysts and Study Team Members.  There are many practices suggested that would beneficial to a study team.”
  Angela Green, Johns Hopkins University

Seminar information

Thursday, March 18, 2021 –
Saturday, March 20, 2021

Each day will follow this schedule:

10:00am-2:00pm ET: Live lecture via Zoom

4:00pm-5:00pm ET: Live “office hour” via Zoom (Thursday and Friday only)

Payment Information

The fee of $895 includes all course materials.

PayPal and all major credit cards are accepted.

Our Tax ID number is 26-4576270.