data

In discovr: Interactive Tutorials and Data for "Discovering Statistics Using R and RStudio"

To work outside of this tutorial you need to download the following data file:

• jiminy_cricket.csv
• notebook.csv
• exam_anxiety.csv

Set up an r rstudio() project in the way that I recommend in this video, and save the data files to the folder within your project called [data]{.alt}. Place this code in the first code chunk in your r quarto() document:

wish_tib <- here::here("data/jiminy_cricket.csv") |> read_csv()
notebook_tib <- here::here("data/notebook.csv") |> read_csv()
exam_tib <- here::here("data/exam_anxiety.csv") |> read_csv()

Preparing data

To work outside of this tutorial you need to turn categorical variables into factors and set an appropriate baseline category using as_factor() and fct_relevel() from the [forcats]{.pkg} package. For the [wish_tib]{.alt} execute the following code:

wish_tib <- wish_tib |>
  dplyr::mutate(
    strategy = as_factor(strategy),
    time = as_factor(time) |> fct_relevel("Baseline")
  )

For [notebook_tib]{.alt} execute the following code:

notebook_tib <- notebook_tib |>
  dplyr::mutate(
    gender_identity = as_factor(gender_identity),
    film = as_factor(film)
  )

For [exam_tib]{.alt} execute the following code:

exam_tib <- exam_tib |>
  dplyr::mutate(
    id = as_factor(id),
    sex = as_factor(sex)
  )

discovr documentation built on Feb. 5, 2026, 5:07 p.m.