Instructor notes

This lesson uses mostly combined.csv. The 3 other csv files: plots.csv, species.csv and surveys.csv are only needed for the lesson on databases. combined.csv is downloaded directly in the chapter “Starting with Data” and does not need to be downloaded before hand. It however requires that there is a decent internet connection in the room where the workshop is being taught. To facilitate the download process, the chunk of code that includes the URL where the csv file lives, and where the file should go and be named is included in the code handout (see next paragraph). Using this approach ensures that the file will be where the lesson expects it to be, and teaches good/reproducible practice of automating the download. If the learners haven’t created the data/ directory and/or are not in the correct working directory, the download.file command will produce an error. Therefore, it is important to use the stickies at this point. The code handout (a link to download it is also available on the top bar of the lesson website) is useful for Data Carpentry workshops. It includes an outline of the lesson content, the text for the challenges, the links for the files that need to be downloaded for the lesson, and pieces of code that may be difficult to type for learners with no programming experience/who are unfamiliar with R’s syntax. We encourage you to distribute it to the learners at the beginning of the lesson. As an instructor, we encourage you to do the live coding directly in this file, so the participants can follow along. Some learners may have previous R installations. On Mac, if a new install is performed, the learner’s system will create a symbolic link, pointing to the new install as ‘Current.’ Sometimes this process does not occur, and, even though a new R is installed and can be accessed via the R console, RStudio does not find it. The net result of this is that the learner’s RStudio will be running an older R install. This will cause package installations to fail. This can be fixed at the terminal. First, check for the appropriate R installation in the library; We are currently using R 3.4.x. If it isn’t there, they will need to install it. If it is present, you will need to set the symbolic link to Current to point to the 3.4.x directory:

Instructor notes https://tess.elixir-europe.org/materials/instructor-notes This lesson uses mostly combined.csv. The 3 other csv files: plots.csv, species.csv and surveys.csv are only needed for the lesson on databases. combined.csv is downloaded directly in the chapter “Starting with Data” and does not need to be downloaded before hand. It however requires that there is a decent internet connection in the room where the workshop is being taught. To facilitate the download process, the chunk of code that includes the URL where the csv file lives, and where the file should go and be named is included in the code handout (see next paragraph). Using this approach ensures that the file will be where the lesson expects it to be, and teaches good/reproducible practice of automating the download. If the learners haven’t created the data/ directory and/or are not in the correct working directory, the download.file command will produce an error. Therefore, it is important to use the stickies at this point. The code handout (a link to download it is also available on the top bar of the lesson website) is useful for Data Carpentry workshops. It includes an outline of the lesson content, the text for the challenges, the links for the files that need to be downloaded for the lesson, and pieces of code that may be difficult to type for learners with no programming experience/who are unfamiliar with R’s syntax. We encourage you to distribute it to the learners at the beginning of the lesson. As an instructor, we encourage you to do the live coding directly in this file, so the participants can follow along. Some learners may have previous R installations. On Mac, if a new install is performed, the learner’s system will create a symbolic link, pointing to the new install as ‘Current.’ Sometimes this process does not occur, and, even though a new R is installed and can be accessed via the R console, RStudio does not find it. The net result of this is that the learner’s RStudio will be running an older R install. This will cause package installations to fail. This can be fixed at the terminal. First, check for the appropriate R installation in the library; We are currently using R 3.4.x. If it isn’t there, they will need to install it. If it is present, you will need to set the symbolic link to Current to point to the 3.4.x directory: