Difference between revisions of "BCH2024-2012"
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==Links and Material== | ==Links and Material== | ||
*The course [[R tutorial]] | *The course [[R tutorial]] | ||
+ | |||
+ | Lecture slides | ||
+ | * [http://biochemistry.utoronto.ca/steipe/abc/CourseMaterials/R_Module4_Regression.pdf Module 4 - Regression (PDF, 1.9 MB)] | ||
+ | |||
'''R''' scripts for ... | '''R''' scripts for ... | ||
* ... [http://biochemistry.utoronto.ca/steipe/abc/CourseMaterials/Module_1.R Module 1 (Landscape)] | * ... [http://biochemistry.utoronto.ca/steipe/abc/CourseMaterials/Module_1.R Module 1 (Landscape)] | ||
* ... [http://biochemistry.utoronto.ca/steipe/abc/CourseMaterials/Module_2.R Module 2 and 3 (EDA, Plotting)] | * ... [http://biochemistry.utoronto.ca/steipe/abc/CourseMaterials/Module_2.R Module 2 and 3 (EDA, Plotting)] | ||
− | + | * ... [http://biochemistry.utoronto.ca/steipe/abc/CourseMaterials/Module_4.R Module 4 (Regression)] | |
Data sets: | Data sets: |
Revision as of 00:03, 28 August 2012
Exploratory Data Analysis using R
This is the 2012 BCH2024 course on Exploratory data analysis using R taught by Boris Steipe at the University of Toronto.
Contents
Organization
All sessions will take place in MSB 3290.
Meeting times are:
- Thursday, Aug. 23 14:00-16:00
- Friday, Aug. 24 14:00-16:00
- Tuesday, Aug. 28 10:00-12:00
- Thursday, Aug. 30 14:00-16:00
In addition to the course sessions, there are a number of lab modules for you to work through at home.
Auditors are welcome IF they commit to participating actively in all sessions.
Grading will be 50% course participation and 50% mini project.
Course participation is:
- Being prepared for and actively participating in class.
- Contributing to e-mail discussion of questions arising from the course material or the lab modules.
The mini project comprises:
- Search for an R package that implements an analysis relevant to EDA in the scope your thesis project. Alternatively you can come up with a creative, interesting use of the default R functionality in script form, or as a function that you write.
- Summarize what you plan to do, eMail me your proposal and have me sign off on the suitability of the proposal.
- Develop a script that guides a user through installation of the package (or defines your function) and executes a typical use case. Don't forget to include a sample dataset. Make sure your script is extensively annotated with comments and that you include notes on the interpretation of results.
- Submit your script and sample data to me no later than Friday morning, Aug. 31.
Links and Material
- The course R tutorial
Lecture slides
R scripts for ...
Data sets: