Introduction to Techniques of Study in Experimental Sciences with R.

Image from A.J.Canepa

Introduction to Techniques of Study in Experimental Sciences

To install the theoretical-practical abilities that allow students and professionals to face questions related to the knowledge of the natural environment (with emphasis on the marine environment); allowing the successful development of a research program, taking and analyzing data and delivering results.

The course is aimed at students, researchers or professionals related to the study of the natural environment who wish to broaden their theoretical knowledge and practical skills of how to deal with the study of natural systems and the effect of the humans over it.

This course is proposed as a formative complement applied to the experimental design, experimental development and data analysis. We understand that it is especially useful for the writing of Thesis (Bachellor, Master and PhD), scientific articles and professional works where descriptive statistics and experimental analysis are necessary. Examples of ecology (terrestrial and marine), but also of other scientific disciplines (environmental sciences, chemistry, medicine, etc.)
The activity will consist on computer based theoretical-practical sessions and complemented with practices in laboratory and field sampling.

Introduction to programming in R and RStudio

Introduction to R Software and RStudio Graphical Interface.
Download, installation and maintenance R, RStudio and other packages.
Workspace and scripting management.
Basic object-oriented programming.
Import data from different platforms.
Basic processing of data for statistical purposes

Experimental design and sampling

Theory of sampling, Replication, Pseudo-replication.
Experimental control. BACI (Before After Control Impact) design.
Practical activities of data collection design.
Campaigns / Experiments with Real Examples

Statistical Data Exploration with R

Introduction to the “ggplot2” package.
Visual exploration of data for statistical purposes.
Violation of the assumptions of linear models: Normality, Homogeneity, Independence

Data analysis with R

Introduction to statistical models.
Linear models.
Generalized linear models

Group work and final discussion

Presentation of the problem to be evaluated by the students.
Work in discrete groups using what has been learned so far.
Presentation of the results. Final discussion of results

OVERVIEW

This course is proposed as a formative complement applied to the experimental design, experimental development and data analysis. We understand that it is especially useful for the writing of Thesis (Bachellor, Master and PhD), scientific articles and professional works where descriptive statistics and experimental analysis are necessary. Examples of ecology (terrestrial and marine), but also of other scientific disciplines (environmental sciences, chemistry, medicine, etc.)
The activity will consist on computer based theoretical-practical sessions and complemented with practices in laboratory and field sampling.

CONTENTS

Introduction to programming in R and RStudio

Introduction to R Software and RStudio Graphical Interface.
Download, installation and maintenance R, RStudio and other packages.
Workspace and scripting management.
Basic object-oriented programming.
Import data from different platforms.
Basic processing of data for statistical purposes

Experimental design and sampling

Theory of sampling, Replication, Pseudo-replication.
Experimental control. BACI (Before After Control Impact) design.
Practical activities of data collection design.
Campaigns / Experiments with Real Examples

Statistical Data Exploration with R

Introduction to the “ggplot2” package.
Visual exploration of data for statistical purposes.
Violation of the assumptions of linear models: Normality, Homogeneity, Independence

Introduction to Techniques of Study in Experimental Sciences with R.

Image from A.J.Canepa