Course at a Glance
R is more than a statistical software. R can be used at all stages of your research, to processthe data collected during the experiment, to make graphics, to analyse them and to write reports and manuscripts (including nice tables, equations, etc.). With a bit of organization, you can perform all these steps within R in an automated manner. In this course, you will learn a work flow to perform all these steps.

Instructors
Gabriel Baud-Bovy gabriel.baud-bovy@iit.it

Hours and Credits 12 hours 4 CF

Synopsis
This course aims at giving to the student a methodology to analyze experimental results, from how to organize data to writing reports. It includes:
- an overview of R
- an introduction to reproducible research with R (knitr, rmarkdown)
- an introduction to several packages for data manipulation (e.g., reshape2, dplyr, tidyr) and graphics (lattice; ggplot2)
- examples of statistical analysis with R
During this course, the student will have to analyze his own data and is expected to read before each course the material that will be made available on this page.

Tools used:

Hardware
- Your laptop

Software
- R (https://cran.r-project.org/)

Syllabus

  • Class 1: Case study, Reproducible research
  • Class 2: R fundamental (R objects, data types, programing)
  • Class 3: Exploratory data analysis and graphical methods in R
  • Class 4: Basic statistics


Final exam
The final exam will consist in the evaluation of a report demonstrating familiarity with the concepts and methods presented in the course.

Prerequisites
- The course assumes some familiarity with programming concepts and data
structures (MATLAB, C/C++, Java or any other programming language). Contact the instructor if you have never programmed anything.
- Install R on your laptop.
- Bring a data set that you want to analyze.

Reading List
- R Core Development Team “An introduction to R".

Venue
IIT-Morego

Course dates
march 20– Sala Montalcini 10-13
march 27 – Sala Volta 14-17
april 3 – Sala Volta 14-17
aprile 10 – Sala Montalcini 10-13

References
Hadley Wickham. Ggplot2: Elegant Graphics for Data Analysis
tidyr: http://vita.had.co.nz/papers/tidy-data.html
Baud-Bovy (2014) Notes on reproducible research with R

Remarks

Software:
The R Project website: http://cran.r-project.org/ (to download R, R packages
knitr and R documentation)
pandoc: http://johnmacfarlane.net/pandoc/index.html
Notepad++: http://notepad-plus-plus.org/
NppToR: http://sourceforge.net/projects/npptor/
MikeTex: http://miktex.org/
R studio: see http://www.rstudio.com/
knitr website: http://yihui.name/knitr/