# Andreas Jürg Papritz: Catalogue data in Autumn Semester 2016

Name | Dr. Andreas Jürg Papritz |

Address | I. f. Biogeochemie/Schadstoffdyn. ETH Zürich, CHN E 35.2 Universitätstrasse 16 8092 Zürich SWITZERLAND |

Telephone | +41 44 633 60 72 |

andreas.papritz@env.ethz.ch | |

URL | http://www.step.ethz.ch/people/scientific-staff/andreas-papritz |

Department | Environmental Systems Science |

Relationship | Lecturer |

Number | Title | ECTS | Hours | Lecturers | |
---|---|---|---|---|---|

401-6215-00L | Using R for Data Analysis and Graphics (Part I) | 1 credit | 1G | A. Drewek, A. J. Papritz | |

Abstract | The course provides the first part an introduction to the statistical software R for scientists. Topics covered are data generation and selection, graphical and basic statistical functions, creating simple functions, basic types of objects. | ||||

Objective | The students will be able to use the software R for simple data analysis. | ||||

Content | The course provides the first part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R. Part I of the course covers the following topics: - What is R? - R Basics: reading and writing data from/to files, creating vectors & matrices, selecting elements of dataframes, vectors and matrices, arithmetics; - Types of data: numeric, character, logical and categorical data, missing values; - Simple (statistical) functions: summary, mean, var, etc., simple statistical tests; - Writing simple functions; - Introduction to graphics: scatter-, boxplots and other high-level plotting functions, embellishing plots by title, axis labels, etc., adding elements (lines, points) to existing plots. The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org Note: Part I of UsingR is complemented and extended by Part II, which is offered during the second part of the semester and which can be taken independently from Part I. | ||||

Lecture notes | An Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf | ||||

Prerequisites / Notice | The course resources will be provided via the Moodle web learning platform Please login (with your ETH (or other University) username+password) at https://moodle-app2.let.ethz.ch/enrol/users.php?id=1145 Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration. | ||||

401-6217-00L | Using R for Data Analysis and Graphics (Part II) | 1 credit | 1G | A. Drewek, A. J. Papritz | |

Abstract | The course provides the second part an introduction to the statistical software R for scientists. Topics are data generation and selection, graphical functions, important statistical functions, types of objects, models, programming and writing functions. Note: This part builds on "Using R... (Part I)", but can be taken independently if the basics of R are already known. | ||||

Objective | The students will be able to use the software R efficiently for data analysis. | ||||

Content | The course provides the second part of an introduction to the statistical software R for scientists. R is free software that contains a huge collection of functions with focus on statistics and graphics. If one wants to use R one has to learn the programming language R - on very rudimentary level. The course aims to facilitate this by providing a basic introduction to R. Part II of the course builds on part I and covers the following additional topics: - Elements of the R language: control structures (if, else, loops), lists, overview of R objects, attributes of R objects; - More on R functions; - Applying functions to elements of vectors, matrices and lists; - Object oriented programming with R: classes and methods; - Tayloring R: options - Extending basic R: packages The course focuses on practical work at the computer. We will make use of the graphical user interface RStudio: www.rstudio.org | ||||

Lecture notes | An Introduction to R. http://stat.ethz.ch/CRAN/doc/contrib/Lam-IntroductionToR_LHL.pdf | ||||

Prerequisites / Notice | Basic knowledge of R equivalent to "Using R .. (part 1)" ( = 401-6215-00L ) is a prerequisite for this course. The course resources will be provided via the Moodle web learning platform Please login (with your ETH (or other University) username+password) at https://moodle-app2.let.ethz.ch/enrol/users.php?id=1145 Choose the course "Using R for Data Analysis and Graphics" and follow the instructions for registration. | ||||

401-6233-00L | Spatial Statistics Special Students "University of Zurich (UZH)" in the Master Program in Biostatistics at UZH cannot register for this course unit electronically. Forward the lecturer's written permission to attend to the Registrar's Office. Alternatively, the lecturer may also send an email directly to registrar@ethz.ch. The Registrar's Office will then register you for the course. | 1 credit | 1G | A. J. Papritz | |

Abstract | In many research fields, spatially referenced data are collected. When analysing such data the focus is either on exploring their structure (dependence on explanatory variables, autocorrelation) and/or on spatial prediction. The course provides an introduction to geostatistical methods that are useful for such purposes. | ||||

Objective | The course will provide an overview of the basic concepts and stochastic models that are commonly used to model spatial data. In addition, the participants will learn a number of geostatistical techniques and acquire some familiarity with software that is useful for analysing spatial data. | ||||

Content | After an introductory discussion of the types of problems and the kind of data that arise in environmental research, an introduction into linear geostatistics (models: stationary and intrinsic random processes, modelling large-scale spatial patterns by regression, modelling autocorrelation by variogram; kriging: mean-square prediction of spatial data) will be taught. The lectures will be complemented by data analyses that the participants have to do themselves. | ||||

Lecture notes | Slides, descriptions of the problems for the data analyses and worked-out solutions to them will be provided. | ||||

Literature | P.J. Diggle & P.J. Ribeiro Jr. 2007. Model-based Geostatistics. Springer |