View source for CRG Introduction to Statistics and R 2017

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=== Description ===
This is an introductory course to statistics and R programming. For the previous edition of this course, please refer to [https://biocore.crg.eu/wiki/BIST_Introduction_to_Statistics_2016 this page].
<br> The R part is offered in 4 slow-paced practicums for absolute beginners, followed by 3 fast-paced practicums of statistical modules. <br>
For practical exercises we will use R programming language and [https://www.rstudio.com 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.
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=== Course Instructors ===
* [mailto:pervouchine@gmail.com Dmitri Pervouchine] (lectures)
* [mailto:sarah.bonnin@crg.eu Sarah Bonnin] (practicums I - IV) 
* [mailto:estefania.mancini@crg.eu Estefania Mancini] (practicums I - IV) 
* [mailto:german.demidov@crg.eu German Demidov] (practicums V - VII) 
* [mailto:julia.ponomarenko@crg.eu Julia Ponomarenko] (organizer, practicums V - VII)
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=== 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.
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=== Course Syllabus, Schedule, and Materials ===

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==== <b>MODULE 0. Introduction to R. May 25, 26, 29, 30.</b> ====

* <u>PRACTICUM I. Intro to R and R Studio. May 25. 10:00 - 13:00. </u>
** 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.
[[Media:170525_Introduction_to_R_day1.pdf|Slides day1]]
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[[Media:170525_Exercises_day1.pdf|Exercises day1]]
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Correction for [[Media:ex1.R|exercise 1]], [[Media:ex2.R|exercise 2]], [[Media:ex3.R|exercise 3]]



* <u>PRACTICUM II. Data structures in R. May 26. 10:00 - 13:00.</u>
** 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.
[[Media:170526_Introduction_to_R_day2.pdf|Slides day2]]
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[[Media:170526_Exercises_day2.pdf|Exercises day2]]
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* <u>PRACTICUM III. Lists & Packages. May 29. 10:00 - 13:00.</u>
** 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.
[[Media:170529_Introduction_to_R_day3.pdf|Slides day3]]
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[[Media:170529_Exercises_day3.pdf|Exercises day3]]
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* <u>PRACTICUM IV. Plots & Graphics in R. May 30. 10:00 - 13:00.</u>
** 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.
[[Media:170530_Introduction_to_R_day4.pdf|Slides day4]]
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[[Media:170530_Exercises_day4.pdf|Exercises day4]]
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==== <b>MODULE I. Descriptive Statistics & Intro to Probability. June 6. </b> ====

* <u>LECTURE. 10:00 - 13:00.<br></u>
** 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. 
* [[Media:Tables corrected.pdf|STATISTICAL TABLES]]
[[Media:Module_1_Lectures_June_2017.pdf|Lecture 1 slides.]]
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* <u>PRACTICUM. 14:00 - 17:00.</u>
** Descriptive statistics.
** Plots: Bar-plot, histogram, CDF, box-plot, scatter-plot, pie charts etc.
** Distributions, population mean and population variance. 
** QQ-plot. 
** Data transformation.
[[Media:Module1_June_2017.html.zip|Download the zipped html-file for the practicum.]] 
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==== <b>MODULE II. Statistical Inference. June 8. </b> ====

* <u>LECTURE. 10:00 - 13:00.</u>.
** 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. 
[[Media:Module_2_Lectures_June_2017.pdf|Lecture 2 slides.]]
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* <u>PRACTICUM. 14:00 - 17:00.</u>.
** One- and two-sample tests with known and unknown variance, test for proportions, simulation involving confidence intervals and t-distribution. 
** Sample size estimation. 
** Shapiro test for normality. 
** Non-parametric tests. 
** Kolmogorov-Smirnov (KS) test. 
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==== <b>MODULE III. Statistical modeling & Regression. June 9.</b> ====

* <u>LECTURE. 10:00 - 13:00.</u>
** 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. 
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* <u>PRACTICUM. 14:00 - 17:00.</u> 
** Problems on linear regression.
** ANOVA.


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=== External Resources ===
* [http://www.nature.com/collections/qghhqm Nature Web-collection "Statistics for Biologists"] 
* 100 Statistical Tests.pdf - ResearchGate - just search Google to get a link
* [http://data.bits.vib.be/pub/trainingen/StatTheory/Jarko_Isotalo_Concepts.pdf Book "Basics of Statistics" by Jarko Isotalo]
* [https://cran.r-project.org/web/packages/IPSUR/vignettes/IPSUR.pdf "Introduction to Probability and Statistics using R" by G. Jay Kerns]
* [http://ww2.coastal.edu/kingw/statistics/R-tutorials/ R Tutorials by William B. King]
* [http://www.gardenersown.co.uk/Education/Lectures/R/basics.htm Tutorials "R for basic statistics"]
* [http://www.r-bloggers.com Blog "R-bloggers"]
* [http://www.statsblogs.com StatsBlogs]
* [https://learnr.wordpress.com Blog "Learning R"]
* [https://ryouready.wordpress.com Blog "R you ready?"]
* [http://www.r-statistics.com "R-statistics blog"]
* [https://eda.nc3rs.org.uk/experimental-design 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.
* [http://www.sample-size.net/ Sample/effect size online calculators for designing biomedical experiments from UC San Francisco]
* Self-paced online courses from UC Berkeley: [https://www.edx.org/course/introduction-statistics-descriptive-uc-berkeleyx-stat2-1x Descriptive Statistics.] [https://www.edx.org/course/introduction-statistics-probability-uc-berkeleyx-stat2-2x Probability.] [https://www.edx.org/course/introduction-statistics-inference-uc-berkeleyx-stat2-3x Inference.]
* [http://www.stat.berkeley.edu/~stark/SticiGui/ Online book recommended for the UC Berkeley courses]
* [https://www.edx.org/course/explore-statistics-r-kix-kiexplorx-0 Self-paced online course "Explore Statistics with R"]
* [http://www-bcf.usc.edu/~gareth/ISL/ Online course from Stanford "An Introduction to Statistical Learning with Applications in R"]
* [https://www.edx.org/course/introduction-r-programming-microsoft-dat204x-0 Self-paced online course from Microsoft "Intro to R programming"]
* [https://www.edx.org/course/data-analysis-life-sciences-1-statistics-harvardx-ph525-1x Self-paced online course from Harvard "Statistics and R"]
* [https://www.edx.org/course/data-analysis-life-sciences-3-harvardx-ph525-3x Self-paced online course from Harvard "Statistical Inference and Modeling for High-throughput Experiments" ]
* [http://data.bits.vib.be/pub/trainingen/StatTheory/SlidesFullDay.pdf VIB "Basic statistics theory" course slides.]
* [http://students.brown.edu/seeing-theory/ The Seeing Theory website visualizes the fundamental concepts covered in an introductory college statistics, using D3.jc]