Introductory Course On R Programming Where Basic Concepts Are Taught

This Is An Intermediate Course On R

IntroductionR is a high-level programming language mainly used for writing scripts. It has strong capabilities for statistical analysis and visualization, and many programming tasks can be completed in very few lines of code. The code then does not even need to be compiled (as is the case in other programming languages like C, C++ and Java), making it ideal for applications where rapid prototyping and automation is desired.


Why learn R? R, because of its powerful features for processing data, is ideally suited for bioinformatics. Fewer lines of code are needed to be written, and the code is intuitive and easy to learn and implement. Reading tab-delimited or comma-separated data files takes 1 line of code. Finding patterns in a long DNA sequence is just 1 line of code. Modules like Bioconductor make it easier to bring powerful functionality at the fingertips of biologists who can then build powerful, automated workflows. R can also be used interactively like Python and Matlab, giving it an edge over Perl in terms of features.


Logistics: This online R course is divided into two levels and comes with pre-recorded videos, handouts, reference cards, examples, data, scripts and quizzes. Enrollees can contact the instructor with questions and get help on the projects. The main topics are listed below. Homework assignments will involve running commands learned in the live lectures.


Pre-requisites: There are no degree prerequisites for this course, but basic knowledge of computers is useful.

Price: $1200 for Commercial/Government enrollees and $600 for Academic researchers and students.


Syllabus:

  • Getting started with programming in R. Installing R
  • Variables: creating, printing, interpolating
  • Operations on variables. Mathematical and logical expressions
  • Reading and writing files
  • If-then-else statements
  • For and while loops
  • List and string operations
  • Pattern matching with regular expressions. DNA sequence operations
  • Writing custom functions
  • Visualizing data with plots

IntroductionR is a high-level programming language mainly used for writing scripts. It has strong capabilities for statistical analysis and visualization, and many programming tasks can be completed in very few lines of code. The code then does not even need to be compiled (as is the case in other programming languages like C, C++ and Java), making it ideal for applications where rapid prototyping and automation is desired.


Why learn R? R, because of its powerful features for processing data, is ideally suited for bioinformatics. Fewer lines of code are needed to be written, and the code is intuitive and easy to learn and implement. Reading tab-delimited or comma-separated data files takes 1 line of code. Finding patterns in a long DNA sequence is just 1 line of code. Modules like Bioconductor make it easier to bring powerful functionality at the fingertips of biologists who can then build powerful, automated workflows. R can also be used interactively like Python and Matlab, giving it an edge over Perl in terms of features.


Logistics: This online R course is divided into two levels and comes with pre-recorded videos, handouts, reference cards, examples, data, scripts and quizzes. Enrollees can contact the instructor with questions and get help on the projects. The main topics are listed below. Homework assignments will involve running commands learned in the live lectures.


Pre-requisites: There are no degree prerequisites for this course, but basic knowledge of computers is useful.

Price: $1200 for Commercial/Government enrollees and $600 for Academic researchers and students.


Syllabus:

  • - Good programming practices, special notes, shortcuts, basic commands you may have forgotten
  • - Case studies in linear regression, t-tests and ANOVA
  • - Advanced graphics with par()
  • - Installing Bioconductor
  • - Using Bioconductor to work with Affymetrix gene expression data
  • - R database drivers for MySQL DBMS
  • - Project: Microarray database with sequence information
  • - Case studies involving gene annotation and literature mining
  • - Exception handling and debugging