Display a glossary term with an optional popup of the definition, and add the term to the table created by glossary_table. This function is mainly meant to be used via inline R in R Markdown or quarto documents, e.g.:
`r glossary("Alpha")` does not always have to equal .05.
Usage
glossary(
term,
display = term,
def = NULL,
add_to_table = TRUE,
show = c("term", "def"),
popup = glossary_popup(),
path = glossary_path()
)Arguments
- term
The glossary term to link to, can contain spaces
- display
The text to display (if different than the term)
- def
The short definition to display on hover and in the glossary table; if NULL, this will be looked up from the file in the
pathargument- add_to_table
whether to add to the table created by
glossary_table- show
whether to show the term or just the definition
- popup
whether to show the popup on "click" or "hover" (or "none"); set default with
glossary_popup- path
the path to the glossary file, or NULL for local definitions; set default with
glossary_path
Details
If the path is set to "psyteachr", the glossary term will link to the PsyTeachR glossary. Set show = "def" to just show the definition.
Examples
# set glossary path to example file
path <- system.file("glossary.yml", package = "glossary")
glossary_path(path)
glossary("alpha")
#> [1] "<a class='glossary'>alpha<span class='def'>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.</span></a>"
glossary("alpha", "$\\alpha$")
#> [1] "<a class='glossary'>$\\alpha$<span class='def'>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.</span></a>"
glossary("alpha", def = "The first letter of the Greek alphabet")
#> [1] "<a class='glossary'>alpha<span class='def'>The first letter of the Greek alphabet</span></a>"
glossary("alpha", show = "term")
#> [1] "<a class='glossary'>alpha<span class='def'>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.</span></a>"
glossary("alpha", show = "def")
#> [1] "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."