Installation of R and RStudio software, including additional packages, is required before the start of the course. Moreover, Zoom Client needs to be installed (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.
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 chat questions and live video answers. 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. (when needed) a short chat question and live video answers session.a self-study practice session (with primary exercises end extra exercises) with chat interactions.a short online (live video) lecture for introduction/demonstration.Each session is split into a few small topics, each introduced as follows: The course is given online in a plenary setting. At the beginning of the week before the course the teachers might be contacted to help with R and RStudio installation problems (see below). The teachers are available for chatting online during all teaching slots. The course is divided in 8 half-day sessions. 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.The course assumes no prior programming knowledge. This is not a statistics course! Elementary statistics knowledge is necessary to understand examples.
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. What we teach: R is an open-source, free environment/language for statistical computing and graphics.
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