Course information

Course title
Introduction to R
University / Organization
Transmitting Science
Academic level
Both Msc and PhD
Transmitting Science
Course director
Course lecturer(s)
Dr Ashton Drew (KDV Decision Analysis LLC, United States of America)
Course aim
The aim of this course is to give an introduction to R addressed to people that has never used R. By the end of the course, the participants should be able to do the following in R: Import / export data-bases. Manage data sets. Carry out basic statistic analyses with R. Draw high quality graphs. Program specific functions. Guided practice with R – Students are encouraged to bring a dataset with them along with a “previously completed” statistical analysis or graphic. Ideally something fairly introductory and simple from the student’s own field of practice that you’ve worked with in Excel, SAS, or elsewhere. We will use last lesson session to review all the steps to ensure students can load, check, tidy data and then perform the basic statistics or generate the graphs common in their respective disciplines. This time also usually provides opportunity to troubleshoot and learn to navigate web resources to find solutions to errors. Extra datasets will be available for students that prefer not to bring their own work or who want extra practice at specific skills.
Course contents / programme
Program Monday, May 21st, 2018. Orientation to R and R Studio. Introduction to R programming language. Basic data objects: Values, vectors, data frames, lists. Programming syntax. Packages and libraries. Working directory and environments. Comments, indents, and other good practice. Loading data into R. Reading xlsx, txt, and csv files. Quick summary commands to check data quality. Quick plot commands to check data quality. Reproducible Research Methods in R. Orientation to R Markdown. Exercise: Load and tidy some data within R Markdown. Tuesday, May 22nd, 2018. Restructuring data. Adding, deleting, renaming variables. Changing long to short format (and vice versa). Joining data frames. Subsetting data. Conditional programming. Logical operators. If else statements. For loops. Exercise: Load and tidy a species and a habitat dataset, join to create a single species-habitat data frame, then split into four tables by taxa. Wednesday, May 23rd, 2018. Plotting with ggplot2. Brief intro to base and lattice as plotting alternatives. Overview of common specialize plotting packages. Overview of ggplot2 graphics concepts and syntax. Creating and customizing plots. Adjusting labels, colours, and shapes. Using groups and handling legends. Integrate data from multiple sources. Handling data from spatial data objects. Load and view a raster data file. Load and view a vector data file. Summarize and manipulate the data frame component of the spatial data object. Export updated spatial data frame. Exercise: Load a spatial polygon dataset, explore data and generate summary graphs of data, modify data frame within spatial data object. Thursday, May 24th, 2018. Exploratory data analysis with R. Regression packages and simple procedures. Clustering packages and simple procedures. Probability Distributions. Writing custom functions. Exercise Option 1A: Load and tidy a dataset, perform an unsupervised and supervised clustering. Exercise Option 1B: Load and tidy a dataset, perform linear regression and ANOVA. Exercise 2: Change your code from exercise 1 into a custom function. Friday, May 25th, 2018. How to build your R skills. Using R help, Google, and other online resources.
Required Knowledge and preparation
Teaching methods
Course type
Consecutive days
Online course
Duration (net days)
May 21st-25th, 2018
Course fee
Direct e-mail to register
Paper submission required?
Logo of institution
front approval