Course Syllabus

FISH 553 Advanced R programming for natural scientists

Course overview

Monday and Wednesday 1:30-1:20pm, room MGH 030. Ten combined lecture/labs.

Instructor: Trevor A. Branch, office: FISH 322B. 

Teaching assistant: Maite Pons,, office hours Thursdays 3:00-4:00pm in FSH 329.

Meetings can also be arranged outside these hours with the TA if you are unable to make either set of office hours. 

Learning objectives

Teaching graduate students how to program in the statistical computing language R.

Required readings

Course handouts and lectures. No required textbook, but participants would benefit from possessing “The Art of R Programming” by Norman Matloff.

Evaluation and grading

Credit/no credit, 2 credits. Credit awarded based on the completion of all assigned weekly homework exercises, which will average 4-6 hours per week.

Course schedule

Lecture 1. Good programming practices, saving plots, creating functions
Lecture 2. Creating functions, scope, globals
Lecture 3. Loops, if-then-else, while statements
Lecture 4. Writing faster R code, editing existing R functions
Lecture 5. Introduction to maximum likelihood estimation
Lecture 6. Minimizing non-linear functions using mle and optim
Lecture 7. Likelihood profiles and confidence intervals
Lecture 8. Bootstrapping, resampling, simulation-estimation, normal vs. t-distribution
Lecture 9. Debugging in R and Rstudio
Lecture 10. Large projects, pitfalls in R

Course Summary:

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