Suggestion: Add argument `keep_grouping_vars` to cur_data()

[mayer79]

The new cur_data() in dplyr is fantastic. However, it does not yet serve as a full replacement of do. Why? According to its docu, it excludes the grouping variables. I suppose this is to ensure that the same variable name does not appear twice in the resulting data.frame.

Would it be possible to add an argument keep_grouping_vars = FALSE to cur_data where this could be overwritten?

Here is an example:

library(dplyr)

iris %>% 
  group_by(Species) %>% 
  summarize(should_by_five = ncol(cur_data()))

# Output
  Species    should_by_five
  <fct>               <int>
1 setosa                  4
2 versicolor              4
3 virginica               4

This is my suggestion:

iris %>% 
  group_by(Species) %>% 
  summarize(should_by_five = ncol(cur_data(keep_grouping_vars = TRUE)))

The current workaround:

iris %>% 
  group_by(Species) %>% 
  summarize(should_by_five = ncol(bind_cols(cur_group(), cur_data())))

[hadley]

Could you please provide an example of how you’re using do that requires this proposed feature?

[mayer79]

Here, I want to calculate global surrogate trees (within Species) to explain random forest predictions:

library(ranger)
library(dplyr)
library(rpart)

rmse <- function(y, pred) {
  sqrt(mean((y - pred)^2))
}

# Function that calculates surrogate tree to the predictions of the random forest
surrogate <- function(X) {
  simple_fit <- rpart(pred_ ~ Species + Petal.Width + Petal.Length,
                      data = X)
  simple_pred <- predict(simple_fit, X)
  data.frame(rmse = rmse(X[["pred_"]], simple_pred),
             tree = I(list(simple_fit)))
}

# Fit random forest on iris and keep OOB predictions
ir <- iris
complex_fit <- ranger(Sepal.Length ~ Species + Petal.Width + Petal.Length,
                      data = ir)
ir[["pred_"]] <- complex_fit$predictions

# Fit surrogate tree for each "by" group together with performance
group_by(ir, Species) %>%
  do(surrogate(.data)) %>%
  ungroup()

[hadley]

BTW one work around in the meantime is just to cbind(cur_group(), cur_data())

[mayer79]

Thanks for the hint. It actually is my workaround, but now with a better feeling :-).

[hadley]

Question is whether this should be an argument (e.g. cur_data(group_vars = TRUE)) or another function (cur_data_all())

[mayer79]

Maybe even better as own function, yes.

The behaviour of .SD in data.table is as your current implementation:

library(data.table)
ir <- as.data.table(iris)
ir[, ncol(.SD), Species] # All 4

f <- function(X) "Species" %in% colnames(X)
ir[, f(.SD), Species] # All FALSE