SICSS Workshop "Data Science with R"
This course with Dr. Paul Schmidt is designed for natural scientists who primarily want to use R for statistical data analysis and to process data comprehensibly and present it professionally. Basic knowledge of R is required.
The course's main objective is to impart the R syntax and statistical knowledge in an intuitive and applicable manner. Special emphasis is placed on practical application. Therefore, the methods and underlying theory listed below are explained using illustrative examples.
For your preparation, please find some relevant notes here.
Module 1 | Sep 3, 2024, 9 am - 1 pm online
Modern Data Wrangling with the tidyverse
Discover the power of the tidyverse for data transformation. Learn how to convert raw data files (e.g., .csv or .xlsx) into neatly organized tables, utilizing key tools such as pipes (%>%), tibbles, and dplyr verbs to enhance your data processing capabilities. This crucial step, often required before any statistical analysis begins but rarely mentioned, will streamline your workflow and improve your results.
Module 2 | Sep 5, 2024, 9 am - 1 pm online
A more intuitive understanding of basic statisics in R Perform and understand statistical methods such as linear regression, ANOVA, and t-tests. Discuss the appropriate usage of these methods, the importance and pitfalls of p-values, and e.g. the differences between t-tests and Tukey tests.
Module 3 | Sep 6, 2024, 9 am - 1 pm online
Analyze data from a block design with repeated measures over time Learn to analyze time-series data from a randomized complete block design published in a peer-reviewed publication. Understand how to account for serial correlation in repeated measures, using mixed models with glmmTMB and nlme packages. This module includes practical steps to import and format data, perform descriptive statistics, visualize trends, and build advanced models to interpret complex time?series data.
Credit Points: 1.0
