Set the default path to the definition file.
You can store definitions in a YAML file. Use markdown to create paragraphs, links, and lists. Make sure each new line in a definition is indented two spaces (sorry YAML is a bit picky, but it’s the best human-editable solution).
SESOI: |
Smallest Effect Size of Interest: the smallest effect that is theoretically or practically meaningful
See [Equivalence Testing for Psychological Research](https://doi.org/10.1177/2515245918770963) for a tutorial on methods for choosing an SESOI.
p-value: |
The probability of the observed data, or more extreme data, if the null hypothesis is true. The lower the p-value, the higher the test statistic, and less likely it is to observe the data if the null hypothesis is true.Alternatively, you can add definitions to the file with code. You don’t need to indent new lines if you add definitions this way.
If you want to use the PsyTeachR Glossary, set the path to “psyteachr”. This will produce links to the online glossary when you click on the term, so it’s best to use with the “hover” popup type (see below).
Set the popup type with glossary_popup(); options are
“click” (default), “hover”, and “none”.
If your popup type is “click”, you must add a style with the
glossary_style() function for the popups to work. If you
set the popup type to “hover”, or “none”, you can omit this and the
in-text glossary terms will be styled like other links in your
document.
Set the style at the top of your document (set the code chunk to
results='asis'). The code below shows the default
options.
glossary_style(color = "purple",
text_decoration = "underline",
def_bg = "#333",
def_color = "white")Alternatively, you can add your own CSS to your document (inline or
in an external linked file) to create a more customised appearance. Just
copy the text returned by the glossary_style() function and
customise it.
There are a few ways to customise the glossary term display.
Look up a term from the glossary file with
glossary("alpha"): alphaThe threshold chosen in Neyman-Pearson hypothesis testing to
distinguish test results that lead to the decision to reject the null
hypothesis, or not, based on the desired upper bound of the Type 1 error
rate. An alpha level of 5% is most commonly used, but other alpha levels
can be used as long as they are determined and preregistered by the
researcher before the data is analyzed.
Display a different value for the term with
glossary("alpha", "$\\alpha$"): αThe threshold
chosen in Neyman-Pearson hypothesis testing to distinguish test results
that lead to the decision to reject the null hypothesis, or not, based
on the desired upper bound of the Type 1 error rate. An alpha level of
5% is most commonly used, but other alpha levels can be used as long as
they are determined and preregistered by the researcher before the data
is analyzed.
Use an inline definition instead of the glossary file with
glossary("beta", def = "The second letter of the Greek alphabet"):
betaThe second letter of the Greek
alphabet
Just show the term (no hover) with
glossary("effect size", show = "term"):
effect size‘quantitative
reflection of the magnitude of some phenomenon that is used for the
purpose of addressing a question of interest’ (Kelley & Preacher,
2012)
Just show the definition with
glossary("p-value", show = "def"): The probability of the
observed data, or more extreme data, if the null hypothesis is true. The
lower the p-value, the higher the test statistic, and less likely it is
to observe the data if the null hypothesis is true.
Show the table of terms defined on this page (or since the last
reset) with glossary_table():
| term | definition |
|---|---|
| absolute path | A file path that starts with / and is not appended to the working directory |
| alpha | The threshold chosen in Neyman-Pearson hypothesis testing to distinguish test results that lead to the decision to reject the null hypothesis, or not, based on the desired upper bound of the Type 1 error rate. An alpha level of 5% is most commonly used, but other alpha levels can be used as long as they are determined and preregistered by the researcher before the data is analyzed. |
| beta | The second letter of the Greek alphabet |
| effect size | ‘quantitative reflection of the magnitude of some phenomenon that is used for the purpose of addressing a question of interest’ (Kelley & Preacher, 2012) |
| p-value | The probability of the observed data, or more extreme data, if the null hypothesis is true. The lower the p-value, the higher the test statistic, and less likely it is to observe the data if the null hypothesis is true. |
You can reset the glossary table between sections with
glossary_reset().
Since quarto book chapters are each rendered in a new environment, you will need to load the glossary package for each chapter, and do any setup.
The function add_to_quarto() will set this up for you
(this function is still experimental, so make sure you’ve committed a
version of your project before using). If your working directory is the
quarto project, run the following code to set it up. This will create
and link a css file to setup popup styles, and create a demo
glossary.yml file if you don’t already have one. It will also add the
required setup code to a file called _setup.R, and source this file in
the .Rprofile for this project, so that it will run before each chapter
in the book.
add_to_quarto(quarto_dir = ".",
css_path = "glossary.css",
glossary_path = "glossary.yml",
script_path = "_setup.R")You can set up a book in another directory by changing the
quarto_dir argument. Specify an existing
css_path to append the popup styles to it. You can also
specify an existing glossary path. Set script_path to
FALSE to use a different method for pre-chapter setup.
add_to_quarto(quarto_dir = "~/mybook",
css_path = "style.css",
glossary_path = "my_glossary.yml",
script_path = FALSE)If you don’t use the .Rprofile and _setup.R script method, you will need to start each chapter with a setup chunk that sets the appropriate defaults for your project.
Now, when you display the glossary table, it will show all items
added since the last call to glossary_reset() in the
project.