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Advanced Courses in Life Sciences

1st Edition

Introduction to R

May 22nd-26th, 2017, Barcelona (Spain)

Introduction to R

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.


Institut Català de Paleontologia Miquel Crusafont

C/ de l’Escola Industrial, nº 23
08201 Sabadell, Barcelona.

Spain – ICP – Sabadell




30 hours on-site.

This course is equivalent to 3 ECTS (European Credit Transfer System) at the Life Science Zurich Graduate School.

The recognition of ECTS by other institutions depends on each university or school.


Places are limited to 20 participants and will be occupied by strict registration order.

Participants who have completed the course will receive a certificate at the end of it.


Ashton Drew instructor for Transmitting Science

Dr. Ashton Drew
KDV Decision Analysis LLC
United States of America



All participants must bring their own personal laptop (Windows, Macintosh) with current versions of R and R Studio pre-installed. If you have any problem installing them, please contact the course coordinator.


Monday, May 22nd, 2017.

  • 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 23rd, 2017.

  • 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 dataframe, then split into four tables by taxa.

Wednesday, May 24th, 2017.

  • Plotting with ggplot2.
    • Brief intro to base and lattice as plotting alternatives.
    • Overview of common specialize plotting packages.
    • Overview of ggplot2 graphic concepts and syntax.
  • Creating and customizing plots.
    • Adjusting labels, colors, 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 spatial data object.
    • Export updated spatial data frame.
  • Exercise: Load a spatial polygon dataset, explore data and generate summary graphs of data, modify dataframe within spatial data object.

Thursday, May 25th, 2017.

  • 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 26th, 2017.

  • How to build your R skills.
    • Using R help, Google, and other online resources.


  • Course Fee
  • Early bird (until November 30th, 2016)
  • 500
  • Regular (after November 30th, 2016)
  • 700
  • This includes course material, coffee breaks and lunches.

This course will be held if at least 50 % of the places are filled.

We offer the possibility of paying in two instalments (contact the course coordinator).


The course will take place in the city of Sabadell, Barcelona (Spain).

You may stay in Barcelona city or Sabadell. You will find information about Hotels and Hostel in Sabadell here. It takes about 45 minutes by public transport to arrive to Sabadell from the centre of Barcelona city. The place of the course is about 15 minutes walking from the train stop.


Former participants will have a 5 % discount on the Course Fee.

Unfortunately there are no scholarships available for this course. However a 20 % discount on the Course Fee is offered for members of some organizations (Organizations with discount). If you want to apply to this discount please indicate it in the Registration form (proof will be asked later).

Spanish unemployed scientists, as well as Spanish PhD students without any grant or scholarship to develop their PhD, could benefit from a 40 % discount on the Course Fee. If you want to ask for this discount, please contact the course coordinator. That would apply for a maximum of 2 places and they will be covered by strict inscription order.

Discounts are not cumulative.


Course Schedule
Monday, May 22nd through Friday, May 26th, 20179:30 to 13:30 Lessons.
13:30 to 15:00 Lunch.
15:00 to 17:00 Lessons.

There will be a coffee breaks each day, halfway through each morning lesson session.

The schedule is approximate; it is possible that the content of one day may run into the next and a working day may be longer than advertised.


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