Workshop: Week 3

ImportantInstructions

  • Pick one of the following tasks and work on it as a group, guided by the group navigator. Feel free to work on the others as well if you have time.

  • Each of you should complete the exercises on your laptop, using the RStudio project you created for the course. This includes writing, commenting and executing code in an R script.

  • If you are unsure about anything, please ask us for directions!

  • The tasks are designed for you to practice this week’s R skills.

  • Unless otherwise specified, the data are part of the data archive you downloaded for the Read Data chapter of the textbook. Information about the data can be found on the QML Data website (which is also where you downloaded the data from), by following the “Data docs” links.

NoteTask A: Italian Voice Onset Time

  • Data docs.

  • Read the coretta2018a/ita_egg.rda data in R. .rda files are read using load(). This function works without needing to assign the output to a variable (so it’s different from the other read*() functions, which do require you to assign the output to a variable).

  • Summarise relevant columns, with a particular focus on how VOT (voice onset time, column vot) differs depending on phonological features like place, vowel and so on.

NoteTask B: World Atlas of Language Structures

  • The data from WALS are available through the R package ritwals.

  • Install the package (this is done in two steps: first install devtools and then install ritwals with devtools, see linked package documentation). Then attach it and load the data with data("WALS").

  • Get the number of languages by value (value) for features you are interested in (these are specified in the columns feature_ID and feature). For information on the features see here.

NoteTask C: Massive Auditory Lexical Decision

  • Data docs.

  • Read the tucker2019/mald_1_1.rds data in R.

  • Summarise the following with appropriate summary functions: number of participants, number of words, number of words by lexical status (real vs nonce word, IsWord), reaction times by lexical status, reaction times by lexical status and accuracy, accuracy by lexical status.

  • What can the summaries tell you about reaction times and accuracy based on lexical status? What about individual variation?