FREE - Introduction To Statistics Using R And Rstudio (IRRS03R) PR statistics have made the previous recordings of their 2 day Introduction to Statistics Using R and Rstudio FREE. The reason behind this is nearly all of our courses require some prior knowledge and experience with R and statistics.
https://www.prstatistics.com/course/introduction-to-statistics-using-r-and-rstudio-irrs03r/ You will receive details of how to access the recordings and materials in the confirmation email when you book. ABOUT THIS COURSE - In this two day course, we provide a comprehensive introduction to R and how it can be used for data science and statistics. We begin by providing a thorough introduction to RStudio, which is the most popular and powerful interfaces for using R. We then introduce all the fundamentals of the R language and R environment: variables and assignment, data structures, operators, functions, scripts, packages, projects, etc. We then provide an introduction to data processing and formatting (aka, data wrangling), an introduction to data visualization, an introduction to RMarkdown, and an introduction to some of the most widely used statistical methods such as linear regression, Anovas, etc. From this course, you will gain a comprehensive introduction to R, which will serve as a foundation for progressing further with R to any kind of data analysis, data science, or statistics. Day 1 - Approx. 6 Hours Topic 1: The What and Why of R. We’ll start by briefly explaining what R is, what is used for, and why is has become so popular. Topic 2: Guided tour of RStudio. RStudio is the most widely used interface to R. We will provide a tour of all its parts and features and how to use it effectively. Topic 3: First steps in R. Now, we cover all the fundamentals of R and the R environment. These include variables and assignment, data structures such as vectors, data frames, lists, etc, operations on data structures, functions, scripts, installing and loading packages, using RStudio projects, reading in data, etc. This topic will be detailed so that everyone obtains a solid grasp on these fundamentals, which makes all subsequent learning much easier. Day 2 - Approx. 6 Hours Topic 4: Introducing wrangling. Data wrangling, which is the art of cleaning and restructuring data is a big topic. Here, we just provide an introduction (subsequent courses in this series will cover wrangling in depth). Here, we will primarily focus on filtering, slicing, selecting, renaming, and mutating data frames. Topic 5: Data visualization. Data visualization is another big and important topics. Here, we just provide an introduction, specifically an introduction to ggplot (subsequent courses in this serious will cover visualization in depth). We’ll cover scatterplots, boxplots, histograms, and their variants. Topic 6: RMarkdown. RMarkdown is a powerful tool for creating reproducible research reports, as well as slides, scientific website, posters, etc. In an RMarkdown document, we mix R code and the narrative text of the report, and the outputs of the R code, including figures, are included in the final document. Topic 7: Introduction to Statistics using R. There are many thousands of statistical methods built into R. Here, we will simply provide an introduction to some of the most widely used methods. In particular, we will cover linear regression, Anova, and some other simple test. The aim of this section is to get a sense of how statistical analysis is done in a R, and how to perform some of the most widely used methods. -- Oliver Hooker PhD. PR statistics [[alternative HTML version deleted]] _______________________________________________ R-sig-ecology mailing list R-sig-ecology@r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-ecology
