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課程名稱 |
R在生態資料分析之應用
Ecological Data Analysis in R |
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開課學期 |
111-1 |
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授課對象 |
學程 海洋科學學程 |
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授課教師 |
單偉彌 |
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課號 |
Ocean5098 |
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課程識別碼 |
241EU5050 |
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班次 |
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學分 |
3.0 |
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全/半年 |
半年 |
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必/選修 |
選修 |
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上課時間 |
星期二6,7,8(13:20~16:20) |
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上課地點 |
海研231 |
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備註 |
本課程以英語授課。
總人數上限：15人 |
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課程簡介影片 |
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核心能力關聯 |
核心能力與課程規劃關聯圖 |
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課程大綱
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課程概述 |
This course is designed to teach the usage of R for the analysis of ecological data. It will introduce students to several different analysis options for biological or ecological data (focusing specifically on community-level data) using the free & open-source statistical, mapping, and graphing platform R. Broad topics covered will include: introduction to R language and basic functions / graphics; basic mapping options; diversity measurement; univariate, multivariate, parametric and non-parametric analysis and their basis; functional diversity; and ecological time series analysis. Students will require a laptop for sessions. Schedule is subject to changes according to student progress. |
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課程目標 |
General knowledge in R language, and basic functions
Self-exploration of dataset, and customized analysis
In depth knowledge on community-ecology analysis |
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課程要求 |
Basic knowledge in ecology, biology and informatics |
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預期每週課後學習時數 |
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Office Hours |
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指定閱讀 |
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參考書目 |
Borcard D, Gillet F, Legendre P (2011) Numerical ecology with R. Springer-Verlag New York, 306 p. DOI 10.1007/978-1-4419-7976-6
Paradis E (2002) R for Beginners, Institute des Sciences de l'Evolution University Montpellier II (2002), 72p. Available at http://www.cs.uml.edu/~grinstei/vis2010/R-project%20Documents/Rdebuts_en.pdf
Zuur A, Ieno EN (2007) Analyzing ecological data. Springer-Verlag New York, 672 p. DOI 10.1007/978-0-387-45972-1 |
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評量方式
(僅供參考) |
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週次 |
日期 |
單元主題 |
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Week 1 |
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Introduction: First step [Introduction R, R Studio, Markdown, GitHub] |
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Week 2 |
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Data manipulation, basic graphics and statistical functions [dataset, basic operation, make and edit a plot] |
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Week 3 |
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Data manipulation, basic graphics and statistical functions [dataset, basic operation, make and edit a plot] |
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Week 4 |
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Data exploration [Summarize dataset, preliminary analyses] |
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Week 5 |
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Data exploration [Summarize dataset, preliminary analyses] |
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Week 6 |
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Ecological resemblance [Association coefficient, similarity matrix] |
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Week 7 |
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Numerical classification [Cluster analysis] |
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Week 8 |
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Generalized Linear Model [Linear regression, and GLMs] |
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Week 9 |
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Generalized Linear Model [Linear regression, and GLMs] |
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Week 10 |
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Unconstrained ordination [PCA, PCoA, nMDS] |
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Week 11 |
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Unconstrained ordination [PCA, PCoA, nMDS] |
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Week 12 |
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Constrained ordination [Redundancy and canonical analysis] |
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Week 13 |
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Constrained ordination [Redundancy and canonical analysis] |
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Week 14 |
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From traits to functions |
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