Credit hours
In-class work per week |
Practice per week |
Credits |
Duration |
Total |
5 |
2 |
8 |
15 weeks |
120 hours |
Instructor
João Luis Ferreira Batista
Objective
To develop proficiency in computing in the R environment for the implementation of quantitative methods in research in Ecology and Forest Resources.
Content
1. Introduction: history and philosophy of working in R environment.
2. Fast course in R: a quick presentation of the basic elements for working in R
3. Functional Structure of R: mathematical functions, logic functions and probability distribution functions
4. R as object oriented programming environment: “everything is object”, types of objects, class and other object attributes
5. The SQL data organization structures: the concept of table, link and query
6. Data in R: reading the data, types of variables, types of data objects
7. Data computation: summaries, aggregation and merging
8. Exploratory graphical data analysis: basic functions and “lattice” package
9. Introduction to Linear Models: statistical “formula”, the rational of statistical modeling in R, linear regression and analysis of variance
10. Simulation: simulation based on probability distribution functions, the simulation of a linear model
11. Resampling: resampling techniques for variance estimation, simulation for confidence envelopes
12. Advanced data computation: indexing, subsetting, data correction, matrix and arrays
13. Programming your own functions in R: function definition, processing and result
14. S language programming: flux control, vectorial procedures, assignment
15. Object oriented programming in S4 language: creating new classes and methods
Bibliography
Bolker, B.M.; Ecological Models and Data in R. Princeton: Princeton Univ. Press, 2008.
Chambers, J.M.; Programming with data. New York: Springer, 1998.
Chambers, J.M.; R Facets. The R Journal, v.1, n.1, p.5-8, 2009.
Chambers, J.M.; Cleveland, W.; Kleiner, B.; Tuckey, P.; Graphical methods for data analysis. Pacific Grove: Wadsworth, 1983.
Cleveland,W.; Elements of graphing data. Pacific Grove: Wadsworth, 1985.
Frost, J. 2013 Regression Analysis: How Do I Interpret R-squared and Assess the Goodness-of-Fit? The Minitab Blog online, http://blog.minitab.com/blog/adventures-in-statistics/regression-analysis-how-do-i-interpret-r-squared-and-assess-the-goodness-of-fit
Kabacoff, R. R in Action. New York: Manning Publication, 2011.
R Development Team. An Introduction to R - Notes on R: A Programming Environment for Data Analysis and Graphics -- Version 3.1.2 (2014-10-31). Manual online, http://brieger.esalq.usp.br/CRAN/doc/manuals/R-intro.pdf
R Development Team. R Data Import/Export—Version 3.1.2 (2014-10-31). Manual online, http://brieger.esalq.usp.br/CRAN/doc/manuals/R-data.pdf
R Development Team. R Language Definition—Version 3.1.2 (2014-10-31). Manual online, http://brieger.esalq.usp.br/CRAN/doc/manuals/R-lang.pdf
Robinson, A.P.; Hamann, J.D. Forest Analytics with R: An Introduction. New York: Springer, 2011.
Venables,W.N.; Smith, D.M.; R Development Team; An Introduction to R—Version 2.9.2 (2009-08-04). Manual online, http://brieger.esalq.usp.br/CRAN/doc/manuals/R-intro.pdf
Venables,W.N.; Ripley, B.D.; Modern Applied Statistics with S. New York: Springer, 2002.
Venables, W.N.; Ripley, B.D.; S Programming. New York: Springer, 2000.