CRG PhD Course 2017 Introduction to Statistics in R

Course Description

This introductory course to statistics and R is offered in 3 consecutive modules (please see Course Syllabus below), each consisting of A two-hour practicum in a computer class, using R Studio.

Course Objectives

To introduce the basic concepts of statistics and how they can be applied to real-life datasets using R. The students will produce their first scripts that can be re-used when they start analyzing their own data. Knowledge of statistics or R is not required for taking this course. However, familiarity with the material in the previous modules is recommended if the modules are not taken in a sequence.

Course Instructors

• Sarah Bonnin (Module I) sarah.bonnin@crg.eu
• German Demidov (Module II) german.demidov@crg.eu
• Julia Ponomarenko (organizer, Module III) julia.ponomarenko@crg.eu

Time and Location

• Oct 5, 10, 11, 2016. 11:00 - 13:00. PRBB Building. Boinformatics classroom. 468. 4th floor. The hotel wing.

Course Syllabus, Schedule, and Materials

MODULE I. Introduction to R. Basic functions and plotting. Oct 5, 2016.

• Introduction to R programming language:
  • Introduction to R Studio.
  • Data types, variables, packages, handling files/scripts,functions.
• Basic plots in R. The ggplot2 package.
  • Exploratory data analysis: bar-plot, histogram, box-plot, scatter-plot.
  • R Studio ggplot2 cheatsheet
• R Markdown. How to produce html / pdf / Word reports with R.
  • R Studio RMD cheatsheet

• Slides for Module 1: here
• Template for Exercise 4: here

MODULE II. Introduction to Probability & Hypothesis testing. Oct 10, 2016.

• Independence, conditional probability, Bayes formula.
• Distributions, population mean and population variance.
• Central Limit theorem and the Law of large numbers.
• The concept of hypothesis testing, type I and type II error, false discovery rate.
• Significance and confidence level, p-value. Confidence intervals. One-sided and two-sided tests.
• One-sample and two-sample tests for independent and matched samples with known and unknown variance.
• Student t-distribution, assumption of normality.
• Test for proportions.
• Download the zip-file of the module's materials.

MODULE III. Non-parametric tests & Linear regression. Oct 11, 2016.

• Non-parametric tests: Sign test, Wilcoxon sum of ranks test (Mann-Whitney U-test), Wilcoxon signed rank test, Kruskal-Wallis test.
• Kolmogorov-Smirnov (KS) test. Shapiro test for normality. QQ-plot.
• Data transformation.
• Download the zip-file for this part of the practicum.

• Simple linear regression model, residuals, degrees of freedom.
• Interpretation of the slope, correlation, and determination coefficients.
• Standard error and statistical inference in simple linear regression model.
• Analysis of variance (ANOVA). One-way and two-way ANOVA.
• Download the zip-file for the practicum on linear regression and ANOVA.

External Resources

• Nature Web-collection "Statistics for Biologists"
• 100 Statistical Tests.pdf - ResearchGate - just search Google to get a link
• Book "Basics of Statistics" by Jarko Isotalo
• "Introduction to Probability and Statistics using R" by G. Jay Kerns
• R Tutorials by William B. King
• Tutorials "R for basic statistics"
• Blog "R-bloggers"
• StatsBlogs
• Blog "Learning R"
• Blog "R you ready?"
• "R-statistics blog"
• Self-paced online courses from UC Berkeley: Descriptive Statistics. Probability. Inference.
• Online book recommended for the UC Berkeley courses
• Self-paced online course "Explore Statistics with R"
• Online course from Stanford "An Introduction to Statistical Learning with Applications in R"
• Self-paced online course from Microsoft "Intro to R programming"
• Self-paced online course from Harvard "Statistics and R"
• Self-paced online course from Harvard "Statistical Inference and Modeling for High-throughput Experiments"
• VIB "Basic statistics theory" course slides.
• VIB "Basic statistics in R" course. Tutorial, excercises, cheat sheets.