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R is a free programming language and software environment for statistical computing and graphics. R has a wide variety of statistical linear and non-linear modeling and provides numerous graphical techniques.

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dash
tdpetrou
tdpetrou commented Dec 30, 2019

I write tutorials and do lots of live teaching and gravitate mostly to using Jupyter Notebooks, which allow for text and code to be integrated together in one. Using dash on a local instance of Jupyter is essentially no different than using it from a normal .py file.

But, when teaching to people that have no local jupyter instance or don't even have python installed, I've turned to [Binder](myb

jameslamb
jameslamb commented Sep 29, 2019

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
dennislamcv1
dennislamcv1 commented Dec 20, 2019

Problem: Request for a Catboost Tutorial for Regression problems
catboost version: Any version
Operating System: WIndows
CPU: i7

GPU: None

Hi Yandex, I am currently learning how to use Catboost for ML projects. Would love to have a tutorial on Regression problems using real data set consists of mixture of categorical and numerical features.

Please do not use those generic datasets like

Open Source Fast Scalable Machine Learning Platform For Smarter Applications: Deep Learning, Gradient Boosting & XGBoost, Random Forest, Generalized Linear Modeling (Logistic Regression, Elastic Net), K-Means, PCA, Stacked Ensembles, Automatic Machine Learning (AutoML), etc.

  • Updated Feb 22, 2020
  • Jupyter Notebook
colearendt
colearendt commented Jul 3, 2019

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

batpigandme
batpigandme commented Jan 21, 2020
  • Proofread title, description, and parameters
  • Proofread comments in examples
  • Ensure comment headings used to divide into scannable sections
  • Ensure there's one example that shows tidy eval and links to ?dplyr_tidy_eval
  • Ensure there's one example that shows across()
  • Flag examples that seem overly complex for further review
  • Switch out mtcars/iris for
casperdcl
casperdcl commented Jul 16, 2019

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

philip-khor
philip-khor commented Dec 21, 2019

In Section 3.5

The first argument of facet_wrap() should be a formula, which you create with ~ followed by a variable name (here “formula” is the name of a data structure in R, not a synonym for “equation”). The variable that you pass to facet_wrap() should be discrete.

However, the ggplot2 documentation for facet_wrap() states that the formula interface is there for compatibility in fa

maxheld83
maxheld83 commented Jun 24, 2018

options and inline are useful for some knit_print.classFoo(x, inline, options, ...) methods, but the knit_print() generic appears to include no documentation for these arguments.
So when I list them as above, R CMD Build will complain about undocumented arguments.

I can of course supply documentation for these arguments in my package, but since these are really features of knitr, tha

nettoyoussef
nettoyoussef commented Oct 11, 2019

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,

juangomezduaso
juangomezduaso commented May 12, 2019

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
StrikerRUS
StrikerRUS commented Oct 18, 2019

I'm sorry if I missed this functionality, but CLI version hasn't it for sure (I saw the related code only in generate_code_examples.py). I guess it will be very useful to eliminate copy-paste phase, especially for large models.

Of course, piping is a solution, but not for development in Jupyter Notebook, for example.

HerrMo
HerrMo commented Mar 8, 2019

Hi,

in the docu of getTaskData it says under recode.target: "For survival, you may choose to recode the survival times to 'left', 'right' or 'interval2' censored times using 'lcens', 'rcens' or 'icens', respectively."
This is not consistent with the implementation. If one of these options is chosen, e.g.recode.target = "rcens", an error occurs. Instead, recode.traget = "surv" works. See

richardbeare
richardbeare commented Dec 5, 2016

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

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Released August 1993

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