Andrew Kostandy comments

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                                            Andrew Kostandy

Error in data.frame(x = full_values$x, y = yy[, iStream * 2], group = as.integer(.group)) :

I am getting the same error on a different dataset. My problem happens when adding the function geom_stream_label() specifically. geom_stream() by itself works fine for me. The strange thing is...

v1.0.1 on CRAN and anaconda?

I add my vote to the above request. Releasing the updated version on CRAN would be great!

Feature Suggestion: `step_other()` ability to work like `fct_lump_n()`

Exactly what Julia said, plus using n allows us to easily know what cardinality level each variable works well with. Otherwise, we would have to figure out the n from...

Feature Suggestion: `step_other()` ability to work like `fct_lump_n()`

I realize what you're saying Julia. (I'm talking about the most occurring n levels not the levels occuring n times which you're referring to above). Anyway, using the threshold of...

Feature Suggestion: `step_other()` ability to work like `fct_lump_n()`

So having the fct_lump_n capability allows us to achieve 2 objectives: 1) Get a fixed number of most popular levels as the optimal value rather than a proportion as a...

Feature Suggestion: add `tidy()` method to `step_other()` to extract non-retained levels

Yes @juliasilge, I think that would be a good solution!

CRAN?

Another request to please release the package on CRAN!

Feature Request: Recipe Step(s) to compute group-meaned and de-meaned variables

So this is how it would be done with dplyr for one variable. I think there could be one recipe step for each of the mutate functions below. Yes, `step_center()`...

Feature Request: Recipe Step(s) to compute group-meaned and de-meaned variables

Yes there would need to be 2 steps because there are various scenarios you may want to do: 1) You may want to keep the variable uncentered and introduce the...

step_woe_bin() for binning numeric and factor predictors

Hi @EmilHvitfeldt, The core of the step is the binning of both factor and numeric variables to get the maximum WOE/IV optimized prediction power from the variable. The description of...