![]() Finally, participants will be taught how to apply basic techniques of statistical inference from experimental data, including analysis of variance, linear models, t-test and testing the hypothesis behind these models. ![]() Participants will also learn how to customize the appearance of plots (labels, colors, legends, margins) so that they are publication ready. Participants will also learn how to visualize data using the basic R plotting tools and we will cover common statistical plots (scatter plots, bar plots, box plots, histograms), with an emphasis on exploratory data analysis (detecting outliers, visualize correlations and patterns) as well as visualizing results of statistical models. ![]() This will include understanding how to prepare data in a spreadsheet such that it can be imported efficiently into R Next, participants will learn how to import and export data and will get to know the test datasets that R provides for practicing their skills.A good understanding on R data types facilitates further learning and understanding of R code This will be followed by a strong foundation on the basic types of data in R (vectors, matrices, lists, data frames) and how to work with them (access data, modify, filter).They will learn how to work with variables to store data, and how to apply functions to data. ![]()
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