rsample (version 0.0.2)

tidy.rsplit: Tidy Resampling Object

Description

The `tidy` function from the broom package can be used on `rset` and `rsplit` objects to generate tibbles with which rows are in the analysis and assessment sets.

Usage

# S3 method for rsplit
tidy(x, unique_ind = TRUE, ...)
# S3 method for rset
tidy(x, ...)
# S3 method for vfold_cv
tidy(x, ...)
# S3 method for nested_cv
tidy(x, ...)

Arguments

x

A `rset` or `rsplit` object

unique_ind

Should unique row identifiers be returned? For example, if `FALSE` then bootstrapping results will include multiple rows in the sample for the same row in the original data.

...

Not currently used.

Value

A tibble with columns `Row` and `Data`. The latter has possible values "Analysis" or "Assessment". For `rset` inputs, identification columns are also returned but their names and values depend on the type of resampling. `vfold_cv` contains a column "Fold" and, if repeats are used, another called "Repeats". `bootstraps` and `mc_cv` use the column "Resample".

Details

Note that for nested resampling, the rows of the inner resample, named `inner_Row`, are *relative* row indices and do not correspond to the rows in the original data set.

Examples

# NOT RUN {
library(ggplot2)
theme_set(theme_bw())

set.seed(4121)
cv <- tidy(vfold_cv(mtcars, v = 5))
ggplot(cv, aes(x = Fold, y = Row, fill = Data)) + 
  geom_tile() + scale_fill_brewer()
  
set.seed(4121)
rcv <- tidy(vfold_cv(mtcars, v = 5, repeats = 2))
ggplot(rcv, aes(x = Fold, y = Row, fill = Data)) + 
  geom_tile() + facet_wrap(~Repeat) + scale_fill_brewer()
  
set.seed(4121)
mccv <- tidy(mc_cv(mtcars, times = 5))
ggplot(mccv, aes(x = Resample, y = Row, fill = Data)) + 
  geom_tile() + scale_fill_brewer() 
  
set.seed(4121)
bt <- tidy(bootstraps(mtcars, time = 5))
ggplot(bt, aes(x = Resample, y = Row, fill = Data)) + 
  geom_tile() + scale_fill_brewer()
  
dat <- data.frame(day = 1:30)
# Resample by week instead of day
ts_cv <- rolling_origin(dat, initial = 7, assess = 7, 
                        skip = 6, cumulative = FALSE)
ts_cv <- tidy(ts_cv)
ggplot(ts_cv, aes(x = Resample, y = factor(Row), fill = Data)) +
  geom_tile() + scale_fill_brewer()
# }