If the JSON configuration for allowed runtime reflection contains a class that is not on the class path, the tool tells me to verify that my configuration matches the schema:

error: Error parsing reflection configuration in /home/dsyer/dev/scratch/graal/reflect.json:
Class no.such.Object not found
Verify that the configuration matches the schema described in the -H:PrintFlags=+ output fo

I was reading about persistence.js in the source code and came across this line:

* component ID and will not [?] on with components without an ID.

One unit test in the R package is currently broken. Steps to reproduce on Mac

export CXX=/usr/local/bin/g++-8 CC=/usr/local/bin/gcc-8
Rscript build_r.R
cd R-package/tests
Rscript testthat.R

This results in the following error at the ends of the logs

[LightGBM] [Info] Saving data to binary file /var/folders/xq/wktq4zdx4jd3qdpk34d28m940000gn/T//RtmpiY1DzV/lgb.Dataset_1555

For example, say I want to cross validate with 1000 mcmc samples, but only want it on a single process, the n_jobs=1 parameter for STAN is discarded (as far as I can tell) in the fit in the cross_validation function. Can we have a kwarg parameter in cross_validation that lets us pass arguments to STAN in this function?

Small omission in the guide: it is implied in step 9 that a {} literal should be parsed as a hash-map in the reader, but this is never explicitly stated earlier on. The sentence in question is: "This is basically the functional form of the {} reader literal syntax".

This class could be used instead of cd file https://catboost.ai/docs/concepts/input-data_column-descfile.html when creating Pool from filez. The class should have init function, methods load and save, and Pool init method should be able to use object of this class instead of cd file during initialization.

The docs currently read:

This parameter only matters if you are displaying multiple densities in one plot. If FALSE, the default, each density is computed on the full range of the data. If TRUE, each density is computed over the range of that group: this typically means the estimated x values will not line-up, and hence you won't be able to stack density values.

The remark about this only

It seems that Shiny chooses random ports from 3000-8000 for connections. Some possible improvements that would be desirable:

• document that this is the random port range
• allow specifying a different random port range
• some mechanism for determining if the port is available before it is selected?
• a way to exclude ports from the range

The context here is RStudio Server and users who w

tidyverse/design#107

perhaps it would be nice to provide an example where injected parameters are inside a dictionary or object:

# parameters
import argparse
args = argparse.Namespace()
args_dict = {}

args.a = 1
args_dict['b'] = 2

papermill ... -p args.a 1.618 -p "args_dict['b']" 3.14159

This would of course be useful for making transition between *.py scripts (using e.g

Referring to section section 3.6.1, exercise 3:

"What does show.legend = FALSE do? What happens if you remove it? Why do you think I used it earlier in the chapter?"

I am not sure what the reader should think about while answering the question "What happens if you remove it? ", is it a general question about the usage of show.legend, or is it supposed to refer to a specific chunk of

The cheatsheet graphic linked from the https://github.com/r-lib/devtools/blob/master/README.md is wonderful! But it still references devtools::create, which I understand has been replaced by usethis::create_package(). Time for an update?

Question directly copied from StackOverflow:

With the advent of reticulate, combining R and Python in a single .Rmd document has become increasingly popular among the R community (myself included). Now, my personal workflow usually starts with an R script and, at some point, I create a

This is not an issue, but a recommendation.

I would like to suggest that in the docs available in the Rmarkdown book you include two examples in chapter 15 (parameterized reports):

• passing parameters as raw markdown text for the parameterized reports.
• setting the title using a parameter

This information is available in other parts of the book,

In the Outline (lines 30-33) of Rcpp.Rmd the use of a matrix class is anounced:

• Section @ref(rcpp-intro) teaches you how to write C++ by
  converting simple R functions to their C++ equivalents. You'll learn how
  C++ differs from R, and what the key scalar, vector, and matrix classes
  are called.

Later, line 93 promises to teach how to convert basic functions with

• Matrix inp

Many times when I search for some R plotly documentation, I come across a page with a broken shiny app, which essentially renders that page useless. I usually ignored it, but I realize now that it would have been much more productive to make note and report it! So I'll start now :)

The app here doesn't exist: https://plot.ly/r/shinyapp-linked-click/

• It would be nice to have a list of current contributors and update this list as more people add resources to this repo.

See this SO question: https://stackoverflow.com/questions/56553479/r-predicting-with-new-factor-levels-in-mlr-with-regr-svm-task/56587590#56587590

I did a very dirty fix in the fix-factors branch that works for this example but probably not if more factor levels in newdata have missing values.
In fact, ,fix.factors.prediction sets the missing level to NA rather than to a level existing

Consider changing
[2]: paste0('RStudio version:\\n ', rstudioapi::versionInfo()[[1]])
to
[2]: paste0('RStudio version:\\n ', rstudioapi::versionInfo()$version[[1]])
here.

In addition,
create_edges needs updating to create_edge_df,
create_nodes needs updating to create_node_df ,
combine_edges ne

Hi,
Apologies if this is off topic. I'm struggling to find information about this. Is there any equivalent of ipywidgets allowing basic feedback to R? I'm aware of the likes of plotly and htmlwidgets, but have not been able to identify any mechanism for providing data back to R. There are some comments around the release of ipywidgets 5.0 claiming that recent refactoring is designed to make writi

In tutorial h2o-tutorials/h2o-open-tour-2016/chicago/intro-to-h2o.R:
When I run data <- h2o.importFile(loan_csv), it would not import the data, instead, it returns:
https://raw.githubusercontent.com/h2oai/app-consumer-loan/master/data/loan.csv failed to importError in h2o.importFolder(path, pattern = "", destination_frame = destination_frame, : all files failed to import

I am using