CRG Introduction to Statistics and R 2017

This is an introductory course to statistics and R programming. For the previous edition of this course, please refer to this page.
The R part is offered in 4 slow-paced practicums for absolute beginners, followed by 3 fast-paced practicums of statistical modules.
For practical exercises we will use R programming language and R Studio.

The statistics material is offered in 3 consecutive modules (please see Course Syllabus below), each containing a morning lecture and an afternoon practicum in a computer class. These practicums are focused on using statistics in R, with the purpose to demonstrate and reinforce understanding of concepts introduced in the lectures, rather than teaching R programming.

Course Instructors

Dmitri Pervouchine (lectures)
Sarah Bonnin (practicums I - IV)
Estefania Mancini (practicums I - IV)
German Demidov (practicums V - VII)
Julia Ponomarenko (organizer, practicums V - VII)

Dates, Time and Location

Module 0. Introduction to R. May 25, 26, 29, 30, 2017.
- 10:00 - 13:00.
- PRBB. Boinformatics classroom (468). 4th floor. The hotel/North wing.

Modules I, II, III. Introduction to Statistics. June 6, 8, 9, 2017.
- LECTURES.
  - 10:00 - 13:00.
  - PRBB. Ramon y Cajal.
- PRACTICUMS.
  - 14:00 - 17:00.
  - PRBB. Boinformatics classroom (468). 4th floor. The hotel/North wing.

Course Syllabus, Schedule, and Materials

MODULE 0. Introduction to R. May 25, 26, 29, 30.

PRACTICUM I. Intro to R and R Studio. May 25. 10:00 - 13:00.
- Introduction to R studio: explore environment variable, navigate the history of commands, navigate directory and file structure, workspace and files.
- Simple arithmetic in R console.
- Create and delete an object.
- Introduction to data types and the "vector" data structure.
- Create and run a short script.
- Read and write a file.
- OUTCOME: Write a script that creates (and enters) a directory, process a simple manipulation and write into a file.

Slides day1
Exercises day1
Correction for exercise 1, exercise 2, exercise 3

PRACTICUM II. Data structures in R. May 26. 10:00 - 13:00.
- More on vectors and factors.
- Matrices and data frames: create, access/extract/subset, modify, arithmetic, conversions, check and name dimensions.
- OUTCOME: Produce a script that reads matrices and data frames, manipulate them, read and write files.

Slides day2
Exercises day2

PRACTICUM III. Lists & Packages. May 29. 10:00 - 13:00.
- More on data structures. Lists: create, access/extract/subset, modify.
- Packages: find, install, load, explore/find functions and documentation, get help on functions.
- OUTCOME: Install the packages "diamonds" and "WriteXLS". Use them in a script that manipulates the diamonds data frame and writes it into an Excel file.

Slides day3
Exercises day3

PRACTICUM IV. Plots & Graphics in R. May 30. 10:00 - 13:00.
- Basic plotting: scatter plots, box plots, histograms, density plots. Changing colors, points shapes, titles, labels, legend, axes, etc.
- Introduction to ggplot2 package: structure of ggplot2 commands, scatter plots.
- OUTCOME: Write a script that produces, customizes, and saves plots in files.

Slides day4
Exercises day4

MODULE I. Descriptive Statistics & Intro to Probability. June 6.

LECTURE. 10:00 - 13:00.
- Exploratory data analysis and graphical displays.
- Samples, measures of center and spread, percentiles, odds ratio.
- Outliers and robustness.
- Independence, conditional probability, Bayes formula.
- Distributions, population mean and population variance, Binomial, Poisson, and Normal distribution.
- Central Limit theorem and the Law of large numbers.
- Continuity correction.
- Sampling with and without replacement.
- Correction for finite population size.
STATISTICAL TABLES

Lecture 1 slides.

PRACTICUM. 14:00 - 17:00.
- Descriptive statistics.
- Plots: Bar-plot, histogram, CDF, box-plot, scatter-plot, pie charts etc.
- Distributions, population mean and population variance.

Download the zipped html-file for the practicum.

MODULE II. Statistical Inference. June 8.

LECTURE. 10:00 - 13:00..
- The concept of hypothesis testing, type I and type II error, false discovery rate.
- Significance and confidence level, p-value.
- One-sided and two-sided tests and confidence intervals.
- Sampling distribution, estimators, standard error.
- Normal probabilities in application to p-value.
- One-sample and two-sample tests for independent and matched samples with known variance.
- The case of unknown variance and Student t-distribution, assumption of normality.
- Pooled variance and equal variances assumption.
- Estimation of variance.
- Fisher test for variance equality.
- Non-parametric tests: Sign test, Wilcoxon sum of ranks test (Mann-Whitney U-test), Wilcoxon signed rank test.
- Chi-square test for goodness of fit, chi-square test for independence.
- Sample size estimation.

Lecture 2 slides.

PRACTICUM. 14:00 - 17:00..
- One- and two-sample tests with known and unknown variance.
- Test for proportions.
- Confidence intervals and t-distribution.
- Fisher test.
- Sample size estimation.

Download the zipped html-file for the practicum Part 1.
Download the zipped html-file for the practicum Part 2.

MODULE III. Statistical modeling & Regression. June 9.

LECTURE. 10:00 - 13:00.
- Simple linear regression model, residuals, degrees of freedom, least squares method, correlation coefficient, variance decomposition, determination coefficient.
- 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.
- Beyond simple regression models: multiple regression, logistic regression.
- Correction for multiple testing, family-wise error rate.

Lecture 3 slides.

PRACTICUM. 14:00 - 17:00.
- QQ-plot.
- Tests for normality.
- Data transformation.
- Non-parametric tests.
- Problems on linear regression.
- ANOVA.