To visualise a model, it is very useful to be able to generate an
evenly spaced grid of points from the data. `data_grid`

helps you
do this by wrapping around `expand()`

.

`data_grid(data, ..., .model = NULL)`

data

A data frame

...

Variables passed on to `expand()`

.model

A model. If supplied, any predictors needed for the model
not present in `...`

will be filled in with "typical" values.

`seq_range()`

for generating ranges from continuous
variables.

# NOT RUN { data_grid(mtcars, vs, am) # For continuous variables, seq_range is useful data_grid(mtcars, mpg = seq_range(mpg, 10)) # If you optionally supply a model, missing predictors will # be filled in with typical values mod <- lm(mpg ~ wt + cyl + vs, data = mtcars) data_grid(mtcars, .model = mod) data_grid(mtcars, cyl = seq_range(cyl, 9), .model = mod) # }