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Next steps in data analysis with R

Revealing the logic and strengths of R for working with data

Rick Scavetta

New R users typically learn from clean datasets and find it daunting to apply lessons from perfect case studies to completely new datasets they will encounter in the wild. Join expert Rick Scavetta to gain the confidence you need to apply and expand on their basic R knowledge.

What you'll learn-and how you can apply it

By the end of this live online course, you’ll understand:

  • How base R and tidyverse functions work together to create workflows
  • How to use basic functions in the tidyverse to process raw data for typical data analysis questions
  • The most common data structures in R
  • How to query data using logical expressions, indexing, and regular expressions
  • Common pitfalls with vectorization and indexing

And you’ll be able to:

  • Design an exploratory data analysis (EDA) workflow from scratch
  • Search for and implement never-before-used functions
  • Debug common error messages

This training course is for you because...

  • You’ve seen a bit of R in action and want to better understand how it works before delving deeper.
  • You’re currently learning R and would like compact, guided exercises to refresh and solidify your knowledge.
  • You want to approach new datasets with confidence.

Prerequisites

  • An RStudio account (required to complete the in-course exercises)
  • A basic knowledge of data analysis questions and scenarios
  • Familiarity with basic R commands (base package or tidyverse)

Recommended follow-up:

About your instructor

  • Rick Scavetta has worked as an independent data science trainer since 2012. Operating as Scavetta Academy, Rick has a close and recurring presence at primary research institutes all over Germany, including many Max Planck Institutes and Excellence Clusters, in fields as varied as primatology, earth sciences, marine biology, molecular genetics, and behavioral psychology.

Schedule

The timeframes are only estimates and may vary according to how the class is progressing

Introduction (20 minutes)

  • Group discussion: Review of core concepts of data analysis
  • Lecture: Descriptive statistics; inferential statistics; plotting; querying data according to specific criteria; applying transformation and aggregation functions; case studies
  • Q&A

Minichallenges (20 minutes)

  • Lecture: Hurdles to gaining confidence in R
  • Hands-on exercises: Minichallenge I—import difficult data structures; minichallenge II—deal with type mismatches
  • Q&A

Break (5 minutes)

Case study: Part 1 (60 minutes)

  • Lecture: Introduction of new dataset; exploratory data analysis (EDA); developing a strategy from scratch to a reportable solution; descriptive and inferential statistics, plotting; transforming variables, extracting
  • Group discussion: Analytical questions
  • Hands-on exercise: Apply analysis
  • Q&A

Break (5 minutes)

  • Case study: Part 2 (60 minutes)
  • Lecture: Steps in completing our solution; merging data frames; working with lists and results of statistics; reiteration; indexing, logical expressions, and transformation functions
  • Discussion: Strategies for completing and potential problems
  • Hands-on exercises: Apply solutions
  • Q&A

Wrap-up and Q&A (10 minutes)