Centro de Ciências do Mar (CCMAR)General information
The course is addressed to people who were perhaps more confused than enlightened by their lectures in statistics and who have never used a computer for much more than word processing and data entry. From this starting point, it slowly but surely instils an understanding of mathematics, statistics and programming, sufficient for initiating research in ecology. The course’s practical value is enhanced by extensive use of biological examples and the computer language R for graphics, programming and data analysis. R is a language and environment for statistical computing and graphics Ecological research is becoming increasingly quantitative, yet students often opt out of courses in mathematics and statistics, unwittingly limiting their ability to carry out research in the future. This course provides a practical introduction to quantitative ecology for students and practitioners who have realised that they need this opportunity.
R is a language and environment for statistical computing and graphics (http://www.r-project.org). R provides a wide variety of statistical (linear and non-linear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques. Many users think of R as a statistics system. Furthermore, it is an environment within which statistical techniques are implemented. R can be extended (easily) via packages, covering a very wide range of modern statistics and much more. It has become the 'lingua franca' among statisticians, and is increasingly being used for data analysis among researchers. Many advanced or recent statistical and graphical/visualisation techniques are only available in R.
Sessions
aRound. Installation and management of R packages. Review of packages. Input and output of data.
useR. R session. Elements of the language.
gRaphics. Plotting functions and parameters.
autoR. Basic R programming and automatize tasks.
wRite. Preparing statistical reports using R.
Rspatial. Analysis of vector and raster cartography, also connecting with GRASS and QGIS.
aRtistics. Experimental design. Hypothesis contrast, ANOVA. Basic multivariate statistics.
multivaR. Diversity and multivariate analysis: ordination and gradient analyses, ENFA (Ecological Niche Factor Analysis), habitat suitability maps, metapopulation simulations.
lineaR. Construction, optimization and evaluation of linear models. Representation and spatial interpretation.
modelaRt. Construction, optimization and evaluation of non-linear models. Representation and spatial interpretation.
Course format
This course is divided into 10 theoretical-practical sessions of 4 hours long, including assignments through which you can practice your mastery under supervision.
We will provide students with a selection of data sets with which to work, however participants are encouraged to bring their own data.
The focus will be on giving the participants practical experience with the software. The course material will be a blend of introductory lectures on R and practical sessions.
The objective is to review the state-of-the-art statistical methods for analysis of ecological data, demonstrating the power of open source statistical software. We will provide hands-on experience for standard data analysis (cookbook), enabling participants to use the software on their own problems (take-home software)
Content
The highlighted icons, represent the fields of education (in compliance with ISCED Classification) engaged during this course/programme.
Venue
Faro, Portugal
Application
Click here to apply: https://docs.google.com/forms/d/1l9zsM75FxTpYeWaBpbj8GS1ezImIYBnk4AUiMD1tm_Y/vie...
Cost:
<p><u>I<strong>nscriptions</strong></u><br /> Students: 250 €<br /> Other participants: 350 €</p> <p><u><strong>Grants</strong></u><br /> We offer 4 grants for students. The grant will cover the half of the expenses for the course.<br /> If you are interested, you should submit a short CV, a justification of your situation and a letter of motivation, explaining why and how do you think this course will improve you and your professional development, and how the grant will help you.</p>
Prerequisites:
Participants should attend with their own PC and have the last version of R installed https://www.cran.r-project.org/). It is also recommended to install Rstudio (https://www.rstudio.com/). No previous experience with the software is required.
This course requires some prior experience in statistics and elemental mathematics. Knowing object-oriented programming is not needed
Application Procedure:
Deadlines
Call for grants:Until 26th February
Resolution of grants:29th February
Inscriptions:January - April
Qualification
The course is addressed to people who were perhaps more confused than enlightened by their lectures in statistics and who have never used a computer for much more than word processing and data entry. From this starting point, it slowly but surely instils an understanding of mathematics, statistics and programming, sufficient for initiating research in ecology. The course’s practical value is enhanced by extensive use of biological examples and the computer language R for graphics, programming and data analysis. R is a language and environment for statistical computing and graphics Ecological research is becoming increasingly quantitative, yet students often opt out of courses in mathematics and statistics, unwittingly limiting their ability to carry out research in the future. This course provides a practical introduction to quantitative ecology for students and practitioners who have realised that they need this opportunity.
R is a language and environment for statistical computing and graphics (http://www.r-project.org). R provides a wide variety of statistical (linear and non-linear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques. Many users think of R as a statistics system. Furthermore, it is an environment within which statistical techniques are implemented. R can be extended (easily) via packages, covering a very wide range of modern statistics and much more. It has become the 'lingua franca' among statisticians, and is increasingly being used for data analysis among researchers. Many advanced or recent statistical and graphical/visualisation techniques are only available in R.
Sessions
aRound. Installation and management of R packages. Review of packages. Input and output of data.
useR. R session. Elements of the language.
gRaphics. Plotting functions and parameters.
autoR. Basic R programming and automatize tasks.
wRite. Preparing statistical reports using R.
Rspatial. Analysis of vector and raster cartography, also connecting with GRASS and QGIS.
aRtistics. Experimental design. Hypothesis contrast, ANOVA. Basic multivariate statistics.
multivaR. Diversity and multivariate analysis: ordination and gradient analyses, ENFA (Ecological Niche Factor Analysis), habitat suitability maps, metapopulation simulations.
lineaR. Construction, optimization and evaluation of linear models. Representation and spatial interpretation.
modelaRt. Construction, optimization and evaluation of non-linear models. Representation and spatial interpretation.
Course format
This course is divided into 10 theoretical-practical sessions of 4 hours long, including assignments through which you can practice your mastery under supervision.
We will provide students with a selection of data sets with which to work, however participants are encouraged to bring their own data.
Deadlines
Call for grants:Until 26th February
Resolution of grants:29th February
Inscriptions:January - April
The focus will be on giving the participants practical experience with the software. The course material will be a blend of introductory lectures on R and practical sessions.
The objective is to review the state-of-the-art statistical methods for analysis of ecological data, demonstrating the power of open source statistical software. We will provide hands-on experience for standard data analysis (cookbook), enabling participants to use the software on their own problems (take-home software)
Participants should attend with their own PC and have the last version of R installed https://www.cran.r-project.org/). It is also recommended to install Rstudio (https://www.rstudio.com/). No previous experience with the software is required.
This course requires some prior experience in statistics and elemental mathematics. Knowing object-oriented programming is not needed
