highlightword Example

Simple usage

Adding a fenced with with .fragment .highlightword and the word you need, plus what any valid CSS style you want applied. The following div added to this slide

::: {.fragment .highlightword word="LinearRegression()" style="background:yellow;"}
:::

will highlight like so when slides are advanced:

from vetiver import VetiverModel
from vetiver.data import mtcars
from sklearn.linear_model import LinearRegression

model = LinearRegression().fit(mtcars.drop(columns="mpg"), mtcars["mpg"])
v = VetiverModel(model, model_name = "cars_linear", 
                 prototype_data = mtcars.drop(columns="mpg"))
v.description

Number argument

The first instance of the word will be matched by default. Set number argument to change that

::: {.fragment .highlightword word="VetiverModel" number=2 style="background:yellow;"}
:::

to have the second instance highlighted

from vetiver import VetiverModel
from vetiver.data import mtcars
from sklearn.linear_model import LinearRegression

model = LinearRegression().fit(mtcars.drop(columns="mpg"), mtcars["mpg"])
v = VetiverModel(model, model_name = "cars_linear", 
                 prototype_data = mtcars.drop(columns="mpg"))
v.description

chunk argument

The first code chunk will be search by default.

::: {.fragment .highlightword word="VetiverModel" chunk=2 style="background:yellow;"}
:::

chunk 1: Set chunk argument to change that.

from vetiver import VetiverModel
from vetiver.data import mtcars
from sklearn.linear_model import LinearRegression

chunk 2: notice that we didn’t set number=2 since this is the first instance of the word in this chunk.

model = LinearRegression().fit(mtcars.drop(columns="mpg"), mtcars["mpg"])
v = VetiverModel(model, model_name = "cars_linear", 
                 prototype_data = mtcars.drop(columns="mpg"))
v.description

fragments

This highlighting is still revealjs fragments, so can change the ordering as well

::: {.fragment .highlightword fragment-index=1 word="VetiverModel" number=2 style="background:yellow;"}
:::

::: {.fragment .highlightword fragment-index=1 word="v.description" number=1 style="background:pink;"}
:::

To make things out of order, or the same time

from vetiver import VetiverModel
from vetiver.data import mtcars
from sklearn.linear_model import LinearRegression

model = LinearRegression().fit(mtcars.drop(columns="mpg"), mtcars["mpg"])
v = VetiverModel(model, model_name = "cars_linear", 
                 prototype_data = mtcars.drop(columns="mpg"))
v.description