Difference between revisions of "CRG PhD Course 2017 Introduction to Statistics in R"

Revision as of 15:10, 2 October 2017

Course Description

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

Course Objectives

To introduce or to refresh 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, II) sarah.bonnin@crg.eu
• Julia Ponomarenko (organizer, Module II, III) julia.ponomarenko@crg.eu

Time and Location

• Oct 3, 4, 5, 2017. 11:00 - 13:00. PRBB Building. Boinformatics classroom. 468. 4th floor. The hotel wing.

Course Syllabus, Schedule, and Materials

MODULE I. Introduction to R. Oct 3, 2017.

• Introduction to R programming language and R Studio:
  • Introduction to R studio: explore environment variable, navigate the history of commands, navigate directory and file structure, workspace and files.
  • Basics of R language: syntax, special characters, simple arithmetic in R console, create/delete and manipulate an object.
  • R scripts: create and run, comment.
• Functions in R.
  • Input/output: read and write a file, change and create a directory (functions: setwd, getwd).
• Data structures in R (functions: class, dim, sum)
  • Vectors and factors: create, modify, subset, manipulate, compare.
  • Matrices and data frames: create, access/extract/subset, modify, arithmetic, conversions, check and name dimensions.
  • Lists: create, access/extract/subset, modify.
  • Missing values: how to deal with NA values (functions: is.na, na.omit).
• Slides for Module I: Open the pdf file.

MODULE II. Descriptive Statistics & Plots in R. Oct 4, 2017.

• Packages in R: find, install, load, explore/find functions and documentation, get help on functions.
• Basic plots in R: bar-plots, histograms, box-plots, scatter-plots.
• Exploratory data analysis and descriptive statistical functions: summary, mean, sd, min, max, quantile.
• OUTCOME:
  • Install the packages "diamonds" and "WriteXLS".
  • Write a script that manipulates the diamonds data frame, writes it into an Excel file, produces and saves plots.
• Slides for Module II, Part 1:
• Slides for Module II, Part 2: Download the zipped html-file for the practicum.
• The ggplot2 cheatsheet

MODULE III. Introduction to Statistical Inference. Oct 5, 2017.

• 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.
• 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 of the module's materials.

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"
• Guide and tool for design and analysis of biological experiments from the UK's National Center for the Replacement Refinement and Reduction of Animals in Research (NC3R), covering topics of control for cofounding variables, sample size, effect size, a standardised effect size, power of statistical tests, multiple testing.
• Sample/effect size online calculators for designing biomedical experiments from UC San Francisco
• 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"
• The Seeing Theory website visualizes the fundamental concepts covered in an introductory college statistics, using D3.jc