Tag - R

R

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The course is organized in 3 modules: * Module 1: * * Introduction to R and RStudio * Paths and directories * R basics (syntax, objects) * Functions in R (run, get help) * R scripts * Data types & data structures (vector, matrix, data frame, factor) * Module 2: * * Input / output (read in and write out files) * Library and packages (install, load) * Regular expressions (with grep and gsub) * Conditional statement (if statements) * Repetitive execution (for loops) * Module 3: * * "Base" plotting (scatter plots, histograms, barplots, pie plots, boxplots) * Plotting with ggplot2 (scatter plots, histograms, barplots, dot plots) * Saving plots in files.

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SOFTWARE CARPENTRY LESSONS: * The Unix Shell * Version Control with Git * Programming with Python * Plotting and Programming in Python * Programming with R * R for Reproducible Scientific Analysis DATA CARPENTRY LESSONS: * Ecology Workshop Overview * Data Organization in Spreadsheets for Ecologists * Data Cleaning with OpenRefine for Ecologists * Data Management with SQL for Ecologists * Data Analysis and Visualization in R for Ecologists * Data Analysis and Visualization in Python for Ecologists LIBRARY CARPENTRY LESSONS: * Workshop Overview * Introduction to Working with Data (Regular Expressions) * The UNIX Shell * OpenRefine * Introduction to Git * SQL * Tidy Data * Webscraping * Introduction to Python * Introduction to Data for Archivists

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* SEQUENCING BIOINFORMATICS * BIOINFORMATICS BEYOND SEQUENCES * PROTEOMICS * METABOLOMICS * CLINICAL BIOMARKERS * STATISTICS

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* Understanding the general concepts of ggplot2. * Learning the many options of customizing a ggplot. * Gaining the major steps need to create engaging graphics. * Being able to generate complex plots without the need of post-processing.

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