Sta-R-tistics

Introduction to the basics of R and statistics with the aim of making you a statistics staR. This course is divided into modules that cover various topics:

1. Module 1: R Basics
   1. R Basics 1 (interfaces, RStudio, variable and data types, loading data, conditionals)
   2. R Basics 2 (order, filter, select, summarize, mutate with dplyr, loops, functions)
2. Module 2: An Introduction to Statistics
   1. Intro to Stats 1 (Descriptive stats, Confidence Intervals, Hypothesis testing, effect sizes)
   2. Intro to Stats 2 (bootstrapping, permutations, ANOVAs)
   3. Intro to Stats 3 (regression models, Hierarchical regressions, model fitting, model selection)
3. Bayesian Statictics
4. Graphing and Visualization (ggplot, heatmaps, corrplot)

See the tentative schedule below:

1. Basics I (R interfaces, variables and data types, data loading) [Feb 17; 6-8pm]
2. Basics II (loops, data manipulation and summarizing, functions) [week of Feb 24; 5-7pm]
   [ Reading Week - NO CLASS ]
   
3. Statistics I (descriptive stats, hypothesis testing, chi-square, effect sizes) [March 10; 5-7pm]
4. Statistics II (bootstrapping, permutations, ANOVAs) [March 17; 5-7pm]
5. Statistics III (regression models, model fitting, model selection) [March 24; 5-7pm] [ April Exams - NO CLASS ]
   
6. Bayesian Statistics I (comparing Bayesian vs inferential statistics) [Wednesday May 4; 5-7pm]
7. Bayesian Statistics II (MCMC sampling, t-test, Bayesian regression models) [Wednesday May 11; 5-7pm]
8. Graphing and Visualization (ggplot, heatmaps, corrplot, etc.) [May 19th]
   

If you have any questions please contact [email protected] or GSAN at [email protected]

Data for the class was taken from the following sources:

https://www.kaggle.com/ruslankl/mice-protein-expression

https://www.kaggle.com/osmi/mental-health-in-tech-survey

https://data.world/kcmillersean/billboard-hot-100-1958-2017

https://figshare.com/articles/dataset/Flanker_Stroop_mouse-tracking_data/11320100

Helpful resoruces for coding in R and statistics:

Basics:

https://bookdown.org/ndphillips/YaRrr/
R Studio cheat sheets: https://www.rstudio.com/resources/cheatsheets/
Tidyverse: https://www.tidyverse.org/learn/
Data manipulation: https://statsandr.com/blog/data-manipulation-in-r/

Visualization:

Colorbrewer: https://colorbrewer2.org/
The R Graph Gallery: https://www.r-graph-gallery.com/
R Charts: https://r-charts.com/ IGraph: https://igraph.org/r/html/latest/igraph_demo.html
Circlize: https://github.com/jokergoo/circlize

Package repositories:

CRAN: https://cran.r-project.org/web/packages/available_packages_by_name.html
Bioconductor: https://www.bioconductor.org/packages/release/BiocViews.html#___Software

Bioinformatics Training:

Bioinformatics.ca: https://bioinformatics.ca/workshops/
Calcul Quebec: https://www.calculquebec.ca/en/academic-research-services/procedures/
MiCM: https://www.mcgill.ca/micm/training/workshops-series/workshop-materials

Platforms:

Github (https://github.com/) : Many packages in development live here, can report issues, participate in discussion forums and host your own code.
Compute Canada (https://www.computecanada.ca/research-portal/): Canadian computational resources consortium, maintains supercomputing clusters that you can access for free, provides a lot of technical support.

Frequentist statistics:

Resource for ANOVA: https://statsandr.com/blog/anova-in-r/
Resources for plotting and assumption testing: https://remi-theriault.com/tutorials/
Resources for hierarchical models: https://github.com/mkfreeman/hierarchical-models/blob/master/generate-data.R/
http://mfviz.com/hierarchical-models/
https://m-clark.github.io/mixed-models-with-R/

Bayesian statistics:

https://www.statlect.com/fundamentals-of-statistics/Markov-Chain-Monte-Carlo-diagnostics https://cran.r-project.org/web/packages/BEST/vignettes/BEST.pdf https://jkkweb.sitehost.iu.edu/articles/Kruschke2013JEPG.pdf https://www.youtube.com/watch?v=fhw1j1Ru2i0 http://journals.sagepub.com/doi/10.1177/2515245918771304 https://cran.r-project.org/web/packages/bridgesampling/vignettes/bridgesampling_stan_ttest.html

Statistics Consulting at McGill:

https://www.mcgill.ca/mathstat/consulting/services