Code
sacr_rec %>% step_profile(all_predictors(), profile = vars(sqft)) %>% prep(
data = Sacramento)
Condition
Error in `step_profile()`:
Caused by error in `prep()`:
! The profiled variable cannot be in the list of variables to be fixed. `sqft` was in both.
Code
sacr_rec %>% step_profile(sqft, beds, price, profile = vars(zip, beds)) %>%
prep(data = Sacramento)
Condition
Error in `step_profile()`:
Caused by error in `prep()`:
x `profile` should select only one column
i 2 columns were selected: `zip` and `beds`.
Code
sacr_rec %>% step_profile(city, profile = vars(sqft), pct = -1) %>% prep(data = Sacramento)
Condition
Error in `step_profile()`:
! `pct` must be a number between 0 and 1, not the number -1.
Code
sacr_rec %>% step_profile(city, profile = vars(sqft), grid = 1:3) %>% prep(
data = Sacramento)
Condition
Error in `step_profile()`:
x `grid` should have 2 elements, not 3.
i See ?step_profile (`?recipes::step_profile()`) for information.
Code
sacr_rec %>% step_profile(city, profile = vars(sqft), grid = list(pctl = 1,
len = 2)) %>% prep(data = Sacramento)
Condition
Error in `step_profile()`:
! `grid$pctl` must be `TRUE` or `FALSE`, not the number 1.
Code
fixed(rep(c(TRUE, FALSE), each = 5))
Condition
Error in `fixed()`:
! No method for determining a value to fix for objects of class: <logical>.
Code
recipe(~., data = mtcars) %>% step_profile(grid = list(pctl = TRUE, not_len = 100))
Condition
Error in `step_profile()`:
x `grid` should have two named elements len and pctl, not "not_len" and "pctl".
i See ?step_profile (`?recipes::step_profile()`) for information.
Code
rec
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 10
-- Operations
* Profiling data set for: mpg
Code
rec
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
outcome: 1
predictor: 10
-- Training information
Training data contained 32 data points and no incomplete rows.
-- Operations
* Profiling data set for: mpg | Trained
Code
print(rec)
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
predictor: 10
-- Operations
* Profiling data set for: sqft
Code
prep(rec)
Message
-- Recipe ----------------------------------------------------------------------
-- Inputs
Number of variables by role
predictor: 10
-- Training information
Training data contained 20 data points and no incomplete rows.
-- Operations
* Profiling data set for: sqft | Trained
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