![]() ![]() Moreover, Zoom Client needs to be installed for day 2 and 3 (please do not join in a browser window we observe problems then). See below for the RStudio Installation section. Participants must be able to use a laptop/computer capable of running recent RStudio. show some filtered/summarized content of the table.create a RStudio project, knit a R Markdown document.The overall SSA goal will be to prepare an R Markdown document reporting an analysis of a dataset. At the end there will be a general discussion with a Q&A session. A set of tasks will be provided to be solved within 2 hours. The students are asked to type the commands being presented and observe effects (avoid copy-paste own typing is important in order to learn how to respond to mistakes/errors).Ī self study assignment (SSA) will be offered during the last (#8) session. a self-study practice session (with primary exercises and extra exercises) (during the online days with live/chat interactions).a short video for introduction/demonstration.Each session is split into a few small topics, each introduced as follows: ![]() The course will be given a plenary setting. During these sessions, the teachers are continuously available for lecturing and support, either in the lecture hall or in the online chat.Īt the beginning of the week, 15 January 10.00 A.M., the teachers can be contacted to help with R and RStudio installation problems (see below). The course is divided in 8 half-day sessions. On Friday and Monday (day 2 and 3) the lectures will be given online in Zoom. On Thursday and Tuesday (day 1 and 4) the lectures will take place on campus, in the LUMC, in Lecture Hall 3. Visualisation: scatter plots, histograms, boxplots (with tidyverse/ggplot2).Īll study materials are supplied electronically only. The material will be covered in lectures and practical sessions.Data manipulation: filtering, sorting, summarising of a table joining/merging multiple tables (with tidyverse/dplyr and tidyverse/tidyr).R Markdown for building reproducible reports.R data objects: vectors, data frames (tibbles), lists.generate reproducible reports from your own data in HTML, PDF or DOC formats.know where to look for R methods to perform statistical analyses of your own data.understand and write (tidyverse-based) R code.This is not a statistics course! *( see comment below on page). Elementary statistics knowledge is necessary to understand examples. The course assumes no prior programming knowledge. The goal of the course is to teach students how the R language, extended by tidyverse package, can be used to build a report with a simple statistical analysis of data provided in a table. It provides a large repository of statistical analysis methods. Once you're running Ubuntu on Windows, you can follow the steps detailed at Install on Ubuntu/Debian to install recent stable versions of Redis from the official APT repository.What we teach: R is an open-source, free environment/language for statistical computing and graphics. Follow these instructions, and take note of the default Linux distribution it installs. Microsoft provides detailed instructions for installing WSL. For this method to work, you'll need to be running Windows 10 version 2004 and higher or Windows 11. WSL2 lets you run Linux binaries natively on Windows. To install Redis on Windows, you'll first need to enable WSL2 (Windows Subsystem for Linux). However, you can install Redis on Windows for development by following the instructions below. Redis is not officially supported on Windows. ![]()
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