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HBO/WO

MBO

26 januari 2021

COVID-19 data analysis using a parameterized R Markdown and Shiny

This repo is a demo on how to use parameters and an embedded Shiny app in R Markdown

Website

Marc Teunis

2 december 2015

(3/5) Waarom is het raar om met een Gooise r t praten?

Misschien heb je hem zelf. Of misschien erger je je er kapot aan. Feit is dat de Gooise r aan een enorme opmars bezig is. En dat is helemaal niet logisch, zal Marc van Oostendorp (Universiteit Leiden) je uitleggen. Saillant detail is dat zelfs onze voormalige Koningin Beatrix niet immuun is gebleken voor de Gooise-r-terreur.... Benieuwd hoe haar "r" veranderd is? Kijken maarrrr! Deze video is opgenomen door de Universiteit van Nederland: https://www.universiteitvannederland.nl/

Video

Marc van Oostendorp

6 december 2023

Building interactive web apps with the R package shiny

Lecture about app development using the programming language R. Part of the Utrecht University research master course 'Markup Languages and Reproducible Programming in Statistics'. Both the interactive slides, as well as a static pdf version are shared.

Presentatie

Hanne Oberman

2 juli 2025

Data Analysis and Visualization using R

Course is in English Learning outcomes: Apply Core R Programming Skills Learners will be able to write and execute R scripts using basic and complex data types, utilize built-in functions, and implement flow control structures to automate data analysis tasks. 2. Import, Clean, and Manipulate Data Learners will demonstrate the ability to read various data file formats, tidy and transform dataframes using both traditional methods and modern packages such as tidyr and dplyr, and handle date-time and text data efficiently. 3. Visualize Data Effectively Learners will create and customize data visualizations using the ggplot2 package, including revisiting advanced plotting techniques and adding interactivity to graphical outputs. 4. Perform Exploratory Data Analysis (EDA) Learners will conduct EDA using R, applying appropriate statistical summaries and visualizations to uncover patterns, trends, and insights from datasets. 5. Utilize R Packages and Resources Independently Learners will identify, install, and apply relevant R packages to extend functionality, troubleshoot code, and solve data analysis problems, including working through exercises and interpreting provided solutions. Duration Consists of 18 lessons Study points: 5 EC

Website

Michiel Noback

7 maart 2021

Tutorial Part 1- Introduction to sensitivity analysis with R and NetLogo

This resource includes: Docx-file for tutorial part 1 Folder with r- and data-files for the tutorial part 1.

Document

George van Voorn, Guus ten Broeke

2025

A very short introduction to R

Being able to use a programming environment such as R is a valuable skill for our students. R provides opportunities for using larger and more complex datasets in practicals, case studies and theses. Compared to other programming languages, it is relatively easy to learn, which makes it suitable for education. Because R is open access, graduates can keep using it when they can no longer use the university’s software licences. “A (very) short introduction to R” teaches the basics in 1-2 hours with explanations and short exercises. This tutorial is used by lecturers worldwide. We expanded this tutorial to a series of self-study modules of 1-2 hours each. The modules can be used by: Lecturers who would like to use R in their computer practicals, but are afraid that learning R will consume time from the course content. The self-study modules enable these lecturers to use R with little time investment, since they can simply assign students to do (some of) the modules as preparation for their own computer practical. Students who lag behind in programming skills. The background of students in courses becomes increasingly diverse, which sometimes leads to discontent between advanced and less advanced students. With these modules the less advanced students can mend their deficits independently before they come to class, thereby levelling the differences between students and improving the learning experience of all. Students who are writing their BSc, MSc or PhD thesis. Without programming skills, students often fall back on Excel for processing and analysing data (which is often not suitable for the task) and their supervisors spend much time teaching them how to deal with the data. With the self-study modules, the supervisor’s time can be used more effectively to interact with the student about the scientific question to be tackled. Researchers and professionals who want to start using R. The modules are short and free of charge, which lowers the threshold for beginners.

Website

Claudia Brauer

17 mei 2021

The Rest Is History | 53. Game of Thrones

What, if anything, can we learn about medieval history from Game of Thrones? Or is George R R Martin’s hugely popular series of books, and the blockbuster TV series based on them, in danger of supplanting true history in the popular imagination? Tom Holland and Dominic Sandbrook, both huge fans of the series, discuss books, films and history. A Goalhanger Films & Left Peg Media production Producer: Jack Davenport Exec Producer: Tony Pastor

Audio

Tom Holland, Dominic Sandbrook, Jack Davenport, Tony Pastor

27 september 2017

R-link: Deciding on the Use of Public Space

In Arnold van der Valk’s video about the Buttercup Farm in Amsterdam, we saw an example of the competition for urban space that can arise when space is limited. We also saw how the different stakeholders can have very different ideas and visions on how a plot of land should be used. The R-Link research-project studies this topic. In the next video, Dr. Melika Levelt (Amsterdam University of Applied Sciences) will introduce a method from the R-Link project that takes into account the different views of stakeholders in urban space development. Melika will explain how this ‘stakeholder canvas’ may help stakeholders to ask each other the right questions, to identify new stakeholders, and to visualize views and visions in a coherent and helpful manner. LEARNING OBJECTIVES: Describe adaptation; robustness and/or intervention strategies for STE systems. DURATION: 1 short video (~5 min long)

Website

Melika Levelt

1 januari 1970

11: Powerpoint Multipele regressie

In deze powerpoint gaat het over multipele regressie. Causale modellen: confounders en mediatoren Het multipele regressiemodel Model fit (R-kwadraat) en F-toets Deze serie video's is onderdeel van het vak Beschrijvende en Inferentiele Statistiek op de Vrije Universiteit Amsterdam.

Presentatie

Jasper Muis

1 januari 1970

11: Multipele regressie

In deze serie video's gaat het over multipele regressie. Causale modellen: confounders en mediatoren Het multipele regressiemodel Model fit (R-kwadraat) en F-toets Deze serie video's is onderdeel van het vak Beschrijvende en Inferentiele Statistiek op de Vrije Universiteit Amsterdam.

Video

Jasper Muis