├── .gitignore ├── LICENSE ├── README.md ├── R_example_for_Jupyter.ipynb ├── R_example_for_RStudio.Rmd ├── binder ├── environment.yml ├── install.R ├── postBuild └── runtime.txt ├── python_example_for_Jupyter.ipynb └── python_example_for_RStudio.Rmd /.gitignore: -------------------------------------------------------------------------------- 1 | # OS generated files 2 | .DS_Store 3 | .DS_Store? 4 | ~$* 5 | .Spotlight* 6 | .Trashes 7 | ehthumbs.db 8 | Thumbs.db 9 | 10 | # exclude R & Rstudio temp files 11 | *.utf8.md 12 | *.knit.md 13 | .Rproj.user 14 | .Rhistory 15 | .Rapp.history 16 | .RData 17 | .Ruserdata 18 | .Rproj.user/ 19 | .Rbuildignore 20 | packrat/lib*/ 21 | packrat/src/ 22 | 23 | # exclude Python and Jupyter temp files 24 | .ipynb_checkpoints 25 | *.pyc 26 | 27 | # exclude authentication tokens 28 | .httr-oauth 29 | 30 | # exclude cache 31 | *cache/* 32 | 33 | # exclude output files 34 | *.pdf 35 | *.html 36 | *.png 37 | *.jpeg 38 | *.jpg 39 | *.pptx 40 | *.docx 41 | *.xlsx 42 | *.csv 43 | 44 | # allow all files in data 45 | !data/* 46 | 47 | # allow all files in documents 48 | !documents/* 49 | 50 | # allow rendered html files in docs 51 | !docs/**/*.html 52 | -------------------------------------------------------------------------------- /LICENSE: -------------------------------------------------------------------------------- 1 | BSD 3-Clause License 2 | 3 | Copyright (c) 2019, Binder Project 4 | All rights reserved. 5 | 6 | Redistribution and use in source and binary forms, with or without 7 | modification, are permitted provided that the following conditions are met: 8 | 9 | * Redistributions of source code must retain the above copyright notice, this 10 | list of conditions and the following disclaimer. 11 | 12 | * Redistributions in binary form must reproduce the above copyright notice, 13 | this list of conditions and the following disclaimer in the documentation 14 | and/or other materials provided with the distribution. 15 | 16 | * Neither the name of the copyright holder nor the names of its 17 | contributors may be used to endorse or promote products derived from 18 | this software without specific prior written permission. 19 | 20 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" 21 | AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE 22 | IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE 23 | DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE 24 | FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL 25 | DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR 26 | SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER 27 | CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, 28 | OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE 29 | OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. 30 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # R + Python Binder Example 2 | 3 | This repo builds on the [r binder](https://github.com/binder-examples/r) and [jupyter lab binder](https://github.com/binder-examples/jupyterlab) and is complementary to the [multi-language-demo binder](https://github.com/binder-examples/multi-language-demo) with examples on using both R and python in both Jupyter Lab and RStudio. 4 | 5 | - Launch in Jupyter Lab: [![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/v2/gh/binder-examples/r_with_python/master?urlpath=lab) 6 | - Launch in RStudio: [![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/v2/gh/binder-examples/r_with_python/master?urlpath=rstudio) 7 | 8 | Example files included: 9 | 10 | - `python_example_for_Jupyter.ipynb` - Notebook file using a python kernel for working in Jupyter 11 | - `R_example_for_Jupyter.ipynb` - Notebook file using an R kernel for working in Jupyter 12 | - `python_example_for_RStudio.Rmd` - RMarkdown file with python code chunks for working in RStudio 13 | - `R_example_for_RStudio.Rmd` - RMarkdown file with R code chunkcs for working in RStudio 14 | 15 | Note: to mix R and python in a single Jupyter notebook, use cell magic as demonstrated in the [multi-language-demo](https://github.com/binder-examples/multi-language-demo) binder examples. To mix R and python in a single RMarkdown file (and exchange information between them), make use of [reticulate](https://rstudio.github.io/reticulate/). The latter requires [RStudio 1.2+](https://www.rstudio.com/products/rstudio/download/preview/) to work interactively, which is not yet installed as the default binder version (i.e. interactive python and python plots will not yet work in RMarkdown and the above example file uses reticulate for plotting in the knitted file). 16 | 17 | ## Binder Setup 18 | 19 | Modify the files in the `binder` sub-directory to specify required dependencies for R and python. Binder will generate a docker image for your repository after every change to the repo and then will re-use the docker image on subsequent launches. This means that the first time you launch binder for the latest version of your branch, it may take some time to launch (especially with a lot of R dependencies which are all compiled from source) but will be fast for everyone else afterwards. Switching between `RStudio` vs. `Jupyter Lab` as the IDE is accomplished easily by changing the binder link as described below - the required dependencies for both are automatically installed and do not need to be explicitly listed in the respective configuration files. 20 | 21 | **Important**: binder will *only* work for public repositories. If your repository is private, you will have to make it public in the repository settings before you can launch it in binder. 22 | 23 | ### R 24 | - modify the `binder/runtime.txt` file to specify which R date snapshot should be used (the [MRAN](https://mran.microsoft.com/documents/rro/reproducibility) network keeps a daily snapshot) 25 | - modify the `binder/install.R` file to make sure all dependencies are specified. Dependencies can be from CRAN, bioconductor or GitHub. Since GitHub hosted libraries are not part of the MRAN snapshot, it is best to specify a commit or release tag to ensure that a compatible version of the package is installed in the binder. 26 | 27 | ### Python 28 | 29 | - modify the `binder/environment.yml` file to specify the dependencies. The file is a standard [conda environment config file](https://conda.io/docs/user-guide/tasks/manage-environments.html#create-env-file-manually) and thus supports conda packages, version definitions, multiple source channels as well as pip installations. 30 | - modify the `binder/postBuild` file for any JupyterLab extensions or other direct install commands 31 | 32 | ## Binder Link 33 | 34 | ### Jupyter Lab 35 | 36 | - modify the following link to launch your repo in an RStudio binder (`USER`, `REPO`, `BRANCH`): `http://mybinder.org/v2/gh/USER/REPO/BRANCH?urlpath=lab` 37 | - modify the following markdown code to create a launch badge like the one at the top this README: `[![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/v2/gh/USER/REPO/BRANCH?urlpath=lab)` 38 | 39 | ### RStudio 40 | 41 | - modify the following link to launch your repo in an RStudio binder (`USER`, `REPO`, `BRANCH`): `http://mybinder.org/v2/gh/USER/REPO/BRANCH?urlpath=rstudio` 42 | - modify the following markdown code to create a launch badge like the one at the top of this README: `[![Binder](http://mybinder.org/badge.svg)](http://mybinder.org/v2/gh/USER/REPO/BRANCH?urlpath=rstudio)` 43 | -------------------------------------------------------------------------------- /R_example_for_Jupyter.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# $\\LaTeX$ Math\n", 8 | "\n", 9 | "This is just markdown that can include latex math.\n", 10 | "\n", 11 | "$$\n", 12 | "\\begin{align}\n", 13 | "\\dot{x} & = \\sigma(y-x) \\\\\n", 14 | "\\dot{y} & = \\rho x - y - xz \\\\\n", 15 | "\\dot{z} & = -\\beta z + xy\n", 16 | "\\end{align}\n", 17 | "$$" 18 | ] 19 | }, 20 | { 21 | "cell_type": "markdown", 22 | "metadata": {}, 23 | "source": [ 24 | "# System Info\n" 25 | ] 26 | }, 27 | { 28 | "cell_type": "code", 29 | "execution_count": 1, 30 | "metadata": {}, 31 | "outputs": [ 32 | { 33 | "data": { 34 | "text/plain": [ 35 | "R version 3.5.1 (2018-07-02)\n", 36 | "Platform: x86_64-apple-darwin15.6.0 (64-bit)\n", 37 | "Running under: macOS High Sierra 10.13.1\n", 38 | "\n", 39 | "Matrix products: default\n", 40 | "BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib\n", 41 | "LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib\n", 42 | "\n", 43 | "locale:\n", 44 | "[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8\n", 45 | "\n", 46 | "attached base packages:\n", 47 | "[1] stats graphics grDevices utils datasets methods base \n", 48 | "\n", 49 | "loaded via a namespace (and not attached):\n", 50 | " [1] compiler_3.5.1 magrittr_1.5 IRdisplay_0.5.0 \n", 51 | " [4] pbdZMQ_0.3-3 tools_3.5.1 htmltools_0.3.6 \n", 52 | " [7] base64enc_0.1-3 crayon_1.3.4 Rcpp_0.12.18 \n", 53 | "[10] uuid_0.1-2 stringi_1.2.4 IRkernel_0.8.12.9000\n", 54 | "[13] jsonlite_1.5 stringr_1.3.1 digest_0.6.18 \n", 55 | "[16] repr_0.15.0 evaluate_0.11 " 56 | ] 57 | }, 58 | "metadata": {}, 59 | "output_type": "display_data" 60 | } 61 | ], 62 | "source": [ 63 | "# session info\n", 64 | "sessionInfo()" 65 | ] 66 | }, 67 | { 68 | "cell_type": "markdown", 69 | "metadata": {}, 70 | "source": [ 71 | "# Data" 72 | ] 73 | }, 74 | { 75 | "cell_type": "code", 76 | "execution_count": 3, 77 | "metadata": {}, 78 | "outputs": [ 79 | { 80 | "data": { 81 | "text/html": [ 82 | "\n", 83 | "\n", 84 | "\n", 85 | "\t\n", 86 | "\t\n", 87 | "\t\n", 88 | "\t\n", 89 | "\t\n", 90 | "\t\n", 91 | "\t\n", 92 | "\t\n", 93 | "\t\n", 94 | "\t\n", 95 | "\t\n", 96 | "\t\n", 97 | "\t\n", 98 | "\t\n", 99 | "\t\n", 100 | "\t\n", 101 | "\t\n", 102 | "\t\n", 103 | "\t\n", 104 | "\t\n", 105 | "\t\n", 106 | "\t\n", 107 | "\t\n", 108 | "\t\n", 109 | "\t\n", 110 | "\t\n", 111 | "\t\n", 112 | "\t\n", 113 | "\t\n", 114 | "\t\n", 115 | "\t\n", 116 | "\t\n", 117 | "\t\n", 118 | "\t\n", 119 | "\t\n", 120 | "\t\n", 121 | "\t\n", 122 | "\t\n", 123 | "\t\n", 124 | "\t\n", 125 | "\t\n", 126 | "\t\n", 127 | "\t\n", 128 | "\t\n", 129 | "\t\n", 130 | "\t\n", 131 | "\t\n", 132 | "\t\n", 133 | "\t\n", 134 | "\t\n", 135 | "\t\n", 136 | "\t\n", 137 | "\t\n", 138 | "\t\n", 139 | "\t\n", 140 | "\t\n", 141 | "\t\n", 142 | "\t\n", 143 | "\t\n", 144 | "\t\n", 145 | "\t\n", 146 | "\n", 147 | "
Sepal.LengthSepal.WidthPetal.LengthPetal.WidthSpecies
5.1 3.5 1.4 0.2 setosa
4.9 3.0 1.4 0.2 setosa
4.7 3.2 1.3 0.2 setosa
4.6 3.1 1.5 0.2 setosa
5.0 3.6 1.4 0.2 setosa
5.4 3.9 1.7 0.4 setosa
4.6 3.4 1.4 0.3 setosa
5.0 3.4 1.5 0.2 setosa
4.4 2.9 1.4 0.2 setosa
4.9 3.1 1.5 0.1 setosa
5.4 3.7 1.5 0.2 setosa
4.8 3.4 1.6 0.2 setosa
4.8 3.0 1.4 0.1 setosa
4.3 3.0 1.1 0.1 setosa
5.8 4.0 1.2 0.2 setosa
5.7 4.4 1.5 0.4 setosa
5.4 3.9 1.3 0.4 setosa
5.1 3.5 1.4 0.3 setosa
5.7 3.8 1.7 0.3 setosa
5.1 3.8 1.5 0.3 setosa
5.4 3.4 1.7 0.2 setosa
5.1 3.7 1.5 0.4 setosa
4.6 3.6 1.0 0.2 setosa
5.1 3.3 1.7 0.5 setosa
4.8 3.4 1.9 0.2 setosa
5.0 3.0 1.6 0.2 setosa
5.0 3.4 1.6 0.4 setosa
5.2 3.5 1.5 0.2 setosa
5.2 3.4 1.4 0.2 setosa
4.7 3.2 1.6 0.2 setosa
⋮⋮⋮⋮⋮
6.9 3.2 5.7 2.3 virginica
5.6 2.8 4.9 2.0 virginica
7.7 2.8 6.7 2.0 virginica
6.3 2.7 4.9 1.8 virginica
6.7 3.3 5.7 2.1 virginica
7.2 3.2 6.0 1.8 virginica
6.2 2.8 4.8 1.8 virginica
6.1 3.0 4.9 1.8 virginica
6.4 2.8 5.6 2.1 virginica
7.2 3.0 5.8 1.6 virginica
7.4 2.8 6.1 1.9 virginica
7.9 3.8 6.4 2.0 virginica
6.4 2.8 5.6 2.2 virginica
6.3 2.8 5.1 1.5 virginica
6.1 2.6 5.6 1.4 virginica
7.7 3.0 6.1 2.3 virginica
6.3 3.4 5.6 2.4 virginica
6.4 3.1 5.5 1.8 virginica
6.0 3.0 4.8 1.8 virginica
6.9 3.1 5.4 2.1 virginica
6.7 3.1 5.6 2.4 virginica
6.9 3.1 5.1 2.3 virginica
5.8 2.7 5.1 1.9 virginica
6.8 3.2 5.9 2.3 virginica
6.7 3.3 5.7 2.5 virginica
6.7 3.0 5.2 2.3 virginica
6.3 2.5 5.0 1.9 virginica
6.5 3.0 5.2 2.0 virginica
6.2 3.4 5.4 2.3 virginica
5.9 3.0 5.1 1.8 virginica
\n" 148 | ], 149 | "text/latex": [ 150 | "\\begin{tabular}{r|lllll}\n", 151 | " Sepal.Length & Sepal.Width & Petal.Length & Petal.Width & Species\\\\\n", 152 | "\\hline\n", 153 | "\t 5.1 & 3.5 & 1.4 & 0.2 & setosa\\\\\n", 154 | "\t 4.9 & 3.0 & 1.4 & 0.2 & setosa\\\\\n", 155 | "\t 4.7 & 3.2 & 1.3 & 0.2 & setosa\\\\\n", 156 | "\t 4.6 & 3.1 & 1.5 & 0.2 & setosa\\\\\n", 157 | "\t 5.0 & 3.6 & 1.4 & 0.2 & setosa\\\\\n", 158 | "\t 5.4 & 3.9 & 1.7 & 0.4 & setosa\\\\\n", 159 | "\t 4.6 & 3.4 & 1.4 & 0.3 & setosa\\\\\n", 160 | "\t 5.0 & 3.4 & 1.5 & 0.2 & setosa\\\\\n", 161 | "\t 4.4 & 2.9 & 1.4 & 0.2 & setosa\\\\\n", 162 | "\t 4.9 & 3.1 & 1.5 & 0.1 & setosa\\\\\n", 163 | "\t 5.4 & 3.7 & 1.5 & 0.2 & setosa\\\\\n", 164 | "\t 4.8 & 3.4 & 1.6 & 0.2 & setosa\\\\\n", 165 | "\t 4.8 & 3.0 & 1.4 & 0.1 & setosa\\\\\n", 166 | "\t 4.3 & 3.0 & 1.1 & 0.1 & setosa\\\\\n", 167 | "\t 5.8 & 4.0 & 1.2 & 0.2 & setosa\\\\\n", 168 | "\t 5.7 & 4.4 & 1.5 & 0.4 & setosa\\\\\n", 169 | "\t 5.4 & 3.9 & 1.3 & 0.4 & setosa\\\\\n", 170 | "\t 5.1 & 3.5 & 1.4 & 0.3 & setosa\\\\\n", 171 | "\t 5.7 & 3.8 & 1.7 & 0.3 & setosa\\\\\n", 172 | "\t 5.1 & 3.8 & 1.5 & 0.3 & setosa\\\\\n", 173 | "\t 5.4 & 3.4 & 1.7 & 0.2 & setosa\\\\\n", 174 | "\t 5.1 & 3.7 & 1.5 & 0.4 & setosa\\\\\n", 175 | "\t 4.6 & 3.6 & 1.0 & 0.2 & setosa\\\\\n", 176 | "\t 5.1 & 3.3 & 1.7 & 0.5 & setosa\\\\\n", 177 | "\t 4.8 & 3.4 & 1.9 & 0.2 & setosa\\\\\n", 178 | "\t 5.0 & 3.0 & 1.6 & 0.2 & setosa\\\\\n", 179 | "\t 5.0 & 3.4 & 1.6 & 0.4 & setosa\\\\\n", 180 | "\t 5.2 & 3.5 & 1.5 & 0.2 & setosa\\\\\n", 181 | "\t 5.2 & 3.4 & 1.4 & 0.2 & setosa\\\\\n", 182 | "\t 4.7 & 3.2 & 1.6 & 0.2 & setosa\\\\\n", 183 | "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n", 184 | "\t 6.9 & 3.2 & 5.7 & 2.3 & virginica\\\\\n", 185 | "\t 5.6 & 2.8 & 4.9 & 2.0 & virginica\\\\\n", 186 | "\t 7.7 & 2.8 & 6.7 & 2.0 & virginica\\\\\n", 187 | "\t 6.3 & 2.7 & 4.9 & 1.8 & virginica\\\\\n", 188 | "\t 6.7 & 3.3 & 5.7 & 2.1 & virginica\\\\\n", 189 | "\t 7.2 & 3.2 & 6.0 & 1.8 & virginica\\\\\n", 190 | "\t 6.2 & 2.8 & 4.8 & 1.8 & virginica\\\\\n", 191 | "\t 6.1 & 3.0 & 4.9 & 1.8 & virginica\\\\\n", 192 | "\t 6.4 & 2.8 & 5.6 & 2.1 & virginica\\\\\n", 193 | "\t 7.2 & 3.0 & 5.8 & 1.6 & virginica\\\\\n", 194 | "\t 7.4 & 2.8 & 6.1 & 1.9 & virginica\\\\\n", 195 | "\t 7.9 & 3.8 & 6.4 & 2.0 & virginica\\\\\n", 196 | "\t 6.4 & 2.8 & 5.6 & 2.2 & virginica\\\\\n", 197 | "\t 6.3 & 2.8 & 5.1 & 1.5 & virginica\\\\\n", 198 | "\t 6.1 & 2.6 & 5.6 & 1.4 & virginica\\\\\n", 199 | "\t 7.7 & 3.0 & 6.1 & 2.3 & virginica\\\\\n", 200 | "\t 6.3 & 3.4 & 5.6 & 2.4 & virginica\\\\\n", 201 | "\t 6.4 & 3.1 & 5.5 & 1.8 & virginica\\\\\n", 202 | "\t 6.0 & 3.0 & 4.8 & 1.8 & virginica\\\\\n", 203 | "\t 6.9 & 3.1 & 5.4 & 2.1 & virginica\\\\\n", 204 | "\t 6.7 & 3.1 & 5.6 & 2.4 & virginica\\\\\n", 205 | "\t 6.9 & 3.1 & 5.1 & 2.3 & virginica\\\\\n", 206 | "\t 5.8 & 2.7 & 5.1 & 1.9 & virginica\\\\\n", 207 | "\t 6.8 & 3.2 & 5.9 & 2.3 & virginica\\\\\n", 208 | "\t 6.7 & 3.3 & 5.7 & 2.5 & virginica\\\\\n", 209 | "\t 6.7 & 3.0 & 5.2 & 2.3 & virginica\\\\\n", 210 | "\t 6.3 & 2.5 & 5.0 & 1.9 & virginica\\\\\n", 211 | "\t 6.5 & 3.0 & 5.2 & 2.0 & virginica\\\\\n", 212 | "\t 6.2 & 3.4 & 5.4 & 2.3 & virginica\\\\\n", 213 | "\t 5.9 & 3.0 & 5.1 & 1.8 & virginica\\\\\n", 214 | "\\end{tabular}\n" 215 | ], 216 | "text/markdown": [ 217 | "\n", 218 | "Sepal.Length | Sepal.Width | Petal.Length | Petal.Width | Species | \n", 219 | "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", 220 | "| 5.1 | 3.5 | 1.4 | 0.2 | setosa | \n", 221 | "| 4.9 | 3.0 | 1.4 | 0.2 | setosa | \n", 222 | "| 4.7 | 3.2 | 1.3 | 0.2 | setosa | \n", 223 | "| 4.6 | 3.1 | 1.5 | 0.2 | setosa | \n", 224 | "| 5.0 | 3.6 | 1.4 | 0.2 | setosa | \n", 225 | "| 5.4 | 3.9 | 1.7 | 0.4 | setosa | \n", 226 | "| 4.6 | 3.4 | 1.4 | 0.3 | setosa | \n", 227 | "| 5.0 | 3.4 | 1.5 | 0.2 | setosa | \n", 228 | "| 4.4 | 2.9 | 1.4 | 0.2 | setosa | \n", 229 | "| 4.9 | 3.1 | 1.5 | 0.1 | setosa | \n", 230 | "| 5.4 | 3.7 | 1.5 | 0.2 | setosa | \n", 231 | "| 4.8 | 3.4 | 1.6 | 0.2 | setosa | \n", 232 | "| 4.8 | 3.0 | 1.4 | 0.1 | setosa | \n", 233 | "| 4.3 | 3.0 | 1.1 | 0.1 | setosa | \n", 234 | "| 5.8 | 4.0 | 1.2 | 0.2 | setosa | \n", 235 | "| 5.7 | 4.4 | 1.5 | 0.4 | setosa | \n", 236 | "| 5.4 | 3.9 | 1.3 | 0.4 | setosa | \n", 237 | "| 5.1 | 3.5 | 1.4 | 0.3 | setosa | \n", 238 | "| 5.7 | 3.8 | 1.7 | 0.3 | setosa | \n", 239 | "| 5.1 | 3.8 | 1.5 | 0.3 | setosa | \n", 240 | "| 5.4 | 3.4 | 1.7 | 0.2 | setosa | \n", 241 | "| 5.1 | 3.7 | 1.5 | 0.4 | setosa | \n", 242 | "| 4.6 | 3.6 | 1.0 | 0.2 | setosa | \n", 243 | "| 5.1 | 3.3 | 1.7 | 0.5 | setosa | \n", 244 | "| 4.8 | 3.4 | 1.9 | 0.2 | setosa | \n", 245 | "| 5.0 | 3.0 | 1.6 | 0.2 | setosa | \n", 246 | "| 5.0 | 3.4 | 1.6 | 0.4 | setosa | \n", 247 | "| 5.2 | 3.5 | 1.5 | 0.2 | setosa | \n", 248 | "| 5.2 | 3.4 | 1.4 | 0.2 | setosa | \n", 249 | "| 4.7 | 3.2 | 1.6 | 0.2 | setosa | \n", 250 | "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | \n", 251 | "| 6.9 | 3.2 | 5.7 | 2.3 | virginica | \n", 252 | "| 5.6 | 2.8 | 4.9 | 2.0 | virginica | \n", 253 | "| 7.7 | 2.8 | 6.7 | 2.0 | virginica | \n", 254 | "| 6.3 | 2.7 | 4.9 | 1.8 | virginica | \n", 255 | "| 6.7 | 3.3 | 5.7 | 2.1 | virginica | \n", 256 | "| 7.2 | 3.2 | 6.0 | 1.8 | virginica | \n", 257 | "| 6.2 | 2.8 | 4.8 | 1.8 | virginica | \n", 258 | "| 6.1 | 3.0 | 4.9 | 1.8 | virginica | \n", 259 | "| 6.4 | 2.8 | 5.6 | 2.1 | virginica | \n", 260 | "| 7.2 | 3.0 | 5.8 | 1.6 | virginica | \n", 261 | "| 7.4 | 2.8 | 6.1 | 1.9 | virginica | \n", 262 | "| 7.9 | 3.8 | 6.4 | 2.0 | virginica | \n", 263 | "| 6.4 | 2.8 | 5.6 | 2.2 | virginica | \n", 264 | "| 6.3 | 2.8 | 5.1 | 1.5 | virginica | \n", 265 | "| 6.1 | 2.6 | 5.6 | 1.4 | virginica | \n", 266 | "| 7.7 | 3.0 | 6.1 | 2.3 | virginica | \n", 267 | "| 6.3 | 3.4 | 5.6 | 2.4 | virginica | \n", 268 | "| 6.4 | 3.1 | 5.5 | 1.8 | virginica | \n", 269 | "| 6.0 | 3.0 | 4.8 | 1.8 | virginica | \n", 270 | "| 6.9 | 3.1 | 5.4 | 2.1 | virginica | \n", 271 | "| 6.7 | 3.1 | 5.6 | 2.4 | virginica | \n", 272 | "| 6.9 | 3.1 | 5.1 | 2.3 | virginica | \n", 273 | "| 5.8 | 2.7 | 5.1 | 1.9 | virginica | \n", 274 | "| 6.8 | 3.2 | 5.9 | 2.3 | virginica | \n", 275 | "| 6.7 | 3.3 | 5.7 | 2.5 | virginica | \n", 276 | "| 6.7 | 3.0 | 5.2 | 2.3 | virginica | \n", 277 | "| 6.3 | 2.5 | 5.0 | 1.9 | virginica | \n", 278 | "| 6.5 | 3.0 | 5.2 | 2.0 | virginica | \n", 279 | "| 6.2 | 3.4 | 5.4 | 2.3 | virginica | \n", 280 | "| 5.9 | 3.0 | 5.1 | 1.8 | virginica | \n", 281 | "\n", 282 | "\n" 283 | ], 284 | "text/plain": [ 285 | " Sepal.Length Sepal.Width Petal.Length Petal.Width Species \n", 286 | "1 5.1 3.5 1.4 0.2 setosa \n", 287 | "2 4.9 3.0 1.4 0.2 setosa \n", 288 | "3 4.7 3.2 1.3 0.2 setosa \n", 289 | "4 4.6 3.1 1.5 0.2 setosa \n", 290 | "5 5.0 3.6 1.4 0.2 setosa \n", 291 | "6 5.4 3.9 1.7 0.4 setosa \n", 292 | "7 4.6 3.4 1.4 0.3 setosa \n", 293 | "8 5.0 3.4 1.5 0.2 setosa \n", 294 | "9 4.4 2.9 1.4 0.2 setosa \n", 295 | "10 4.9 3.1 1.5 0.1 setosa \n", 296 | "11 5.4 3.7 1.5 0.2 setosa \n", 297 | "12 4.8 3.4 1.6 0.2 setosa \n", 298 | "13 4.8 3.0 1.4 0.1 setosa \n", 299 | "14 4.3 3.0 1.1 0.1 setosa \n", 300 | "15 5.8 4.0 1.2 0.2 setosa \n", 301 | "16 5.7 4.4 1.5 0.4 setosa \n", 302 | "17 5.4 3.9 1.3 0.4 setosa \n", 303 | "18 5.1 3.5 1.4 0.3 setosa \n", 304 | "19 5.7 3.8 1.7 0.3 setosa \n", 305 | "20 5.1 3.8 1.5 0.3 setosa \n", 306 | "21 5.4 3.4 1.7 0.2 setosa \n", 307 | "22 5.1 3.7 1.5 0.4 setosa \n", 308 | "23 4.6 3.6 1.0 0.2 setosa \n", 309 | "24 5.1 3.3 1.7 0.5 setosa \n", 310 | "25 4.8 3.4 1.9 0.2 setosa \n", 311 | "26 5.0 3.0 1.6 0.2 setosa \n", 312 | "27 5.0 3.4 1.6 0.4 setosa \n", 313 | "28 5.2 3.5 1.5 0.2 setosa \n", 314 | "29 5.2 3.4 1.4 0.2 setosa \n", 315 | "30 4.7 3.2 1.6 0.2 setosa \n", 316 | "⋮ ⋮ ⋮ ⋮ ⋮ ⋮ \n", 317 | "121 6.9 3.2 5.7 2.3 virginica\n", 318 | "122 5.6 2.8 4.9 2.0 virginica\n", 319 | "123 7.7 2.8 6.7 2.0 virginica\n", 320 | "124 6.3 2.7 4.9 1.8 virginica\n", 321 | "125 6.7 3.3 5.7 2.1 virginica\n", 322 | "126 7.2 3.2 6.0 1.8 virginica\n", 323 | "127 6.2 2.8 4.8 1.8 virginica\n", 324 | "128 6.1 3.0 4.9 1.8 virginica\n", 325 | "129 6.4 2.8 5.6 2.1 virginica\n", 326 | "130 7.2 3.0 5.8 1.6 virginica\n", 327 | "131 7.4 2.8 6.1 1.9 virginica\n", 328 | "132 7.9 3.8 6.4 2.0 virginica\n", 329 | "133 6.4 2.8 5.6 2.2 virginica\n", 330 | "134 6.3 2.8 5.1 1.5 virginica\n", 331 | "135 6.1 2.6 5.6 1.4 virginica\n", 332 | "136 7.7 3.0 6.1 2.3 virginica\n", 333 | "137 6.3 3.4 5.6 2.4 virginica\n", 334 | "138 6.4 3.1 5.5 1.8 virginica\n", 335 | "139 6.0 3.0 4.8 1.8 virginica\n", 336 | "140 6.9 3.1 5.4 2.1 virginica\n", 337 | "141 6.7 3.1 5.6 2.4 virginica\n", 338 | "142 6.9 3.1 5.1 2.3 virginica\n", 339 | "143 5.8 2.7 5.1 1.9 virginica\n", 340 | "144 6.8 3.2 5.9 2.3 virginica\n", 341 | "145 6.7 3.3 5.7 2.5 virginica\n", 342 | "146 6.7 3.0 5.2 2.3 virginica\n", 343 | "147 6.3 2.5 5.0 1.9 virginica\n", 344 | "148 6.5 3.0 5.2 2.0 virginica\n", 345 | "149 6.2 3.4 5.4 2.3 virginica\n", 346 | "150 5.9 3.0 5.1 1.8 virginica" 347 | ] 348 | }, 349 | "metadata": {}, 350 | "output_type": "display_data" 351 | } 352 | ], 353 | "source": [ 354 | "# data\n", 355 | "iris" 356 | ] 357 | }, 358 | { 359 | "cell_type": "markdown", 360 | "metadata": {}, 361 | "source": [ 362 | "# Plot" 363 | ] 364 | }, 365 | { 366 | "cell_type": "code", 367 | "execution_count": 4, 368 | "metadata": {}, 369 | "outputs": [ 370 | { 371 | "data": {}, 372 | "metadata": {}, 373 | "output_type": "display_data" 374 | }, 375 | { 376 | "data": { 377 | "image/png": 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NFj/msmVB6mk0A0BKz57DMaQsxLAiRA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Et1KK8zl4tN/diIs0v4+kqPkpo/tasTx3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\n798ft9xyix7ZSy+9hC+++EKvTmq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bJu\nEiABEiABEiABEiCBkCNABTrkTikHRAIkQAIkQAIkQAIk4E0C/w9qkVA3ePULPwAAAABJRU5E\nrkJggg==", 378 | "text/plain": [ 379 | "plot without title" 380 | ] 381 | }, 382 | "metadata": {}, 383 | "output_type": "display_data" 384 | } 385 | ], 386 | "source": [ 387 | "library(ggplot2)\n", 388 | "options(repr.plot.width=6, repr.plot.height=4)\n", 389 | "ggplot(iris) + \n", 390 | " aes(Sepal.Length, Sepal.Width, color = Species) +\n", 391 | " geom_point() + \n", 392 | " theme_bw()" 393 | ] 394 | } 395 | ], 396 | "metadata": { 397 | "kernelspec": { 398 | "display_name": "R", 399 | "language": "R", 400 | "name": "ir" 401 | }, 402 | "language_info": { 403 | "codemirror_mode": "r", 404 | "file_extension": ".r", 405 | "mimetype": "text/x-r-source", 406 | "name": "R", 407 | "pygments_lexer": "r", 408 | "version": "3.5.1" 409 | }, 410 | "toc-autonumbering": true 411 | }, 412 | "nbformat": 4, 413 | "nbformat_minor": 2 414 | } 415 | -------------------------------------------------------------------------------- /R_example_for_RStudio.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Example" 3 | subtitle: "SUBTITLE" 4 | author: "AUTHORS" 5 | date: "`r format(Sys.Date(), '%d %b %Y')`" 6 | output: 7 | html_document: 8 | df_print: paged 9 | number_sections: yes 10 | toc: yes 11 | toc_float: true 12 | toc_depth: 3 13 | code_folding: show 14 | editor_options: 15 | chunk_output_type: inline 16 | --- 17 | 18 | # $\LaTeX$ Math 19 | 20 | This is just markdown that can include latex math. 21 | 22 | $$ 23 | \begin{align} 24 | \dot{x} & = \sigma(y-x) \\ 25 | \dot{y} & = \rho x - y - xz \\ 26 | \dot{z} & = -\beta z + xy 27 | \end{align} 28 | $$ 29 | 30 | # System Info 31 | 32 | ```{r} 33 | # session info 34 | sessionInfo() 35 | ``` 36 | 37 | # Data 38 | 39 | ```{r} 40 | # data 41 | iris 42 | ``` 43 | 44 | # Plot 45 | 46 | ```{r, fig.width=6, fig.height=4} 47 | library(ggplot2) 48 | ggplot(iris) + 49 | aes(Sepal.Length, Sepal.Width, color = Species) + 50 | geom_point() + 51 | theme_bw() 52 | ``` 53 | 54 | -------------------------------------------------------------------------------- /binder/environment.yml: -------------------------------------------------------------------------------- 1 | dependencies: 2 | - matplotlib 3 | - numpy 4 | - pandas 5 | -------------------------------------------------------------------------------- /binder/install.R: -------------------------------------------------------------------------------- 1 | ### install regular packages 2 | 3 | install.packages("reticulate") # python support in RMarkdown 4 | install.packages("ggplot2") # for plotting 5 | install.packages(c("rmarkdown", "caTools", "bitops")) # for knitting 6 | 7 | ### install bioconductor packages 8 | # install.packages("BiocManager") 9 | # BiocManager::install("package") 10 | 11 | ### install GitHub packages (tag = commit, branch or release tag) 12 | # install.packages("devtools") 13 | # devtools::install_github("user/repo", ref = "tag") -------------------------------------------------------------------------------- /binder/postBuild: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | 3 | # Install JupyterLab extension 4 | jupyter labextension install @jupyterlab/toc 5 | 6 | # Remove output from Jupyter notebooks 7 | jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace python_example_for_Jupyter.ipynb 8 | jupyter nbconvert --ClearOutputPreprocessor.enabled=True --inplace R_example_for_Jupyter.ipynb 9 | -------------------------------------------------------------------------------- /binder/runtime.txt: -------------------------------------------------------------------------------- 1 | r-2018-11-01 -------------------------------------------------------------------------------- /python_example_for_Jupyter.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# $\\LaTeX$ Math\n", 8 | "This is just markdown that can include latex math.\n", 9 | "\n", 10 | "\\begin{align}\n", 11 | "\\dot{x} & = \\sigma(y-x) \\\\\n", 12 | "\\dot{y} & = \\rho x - y - xz \\\\\n", 13 | "\\dot{z} & = -\\beta z + xy\n", 14 | "\\end{align}" 15 | ] 16 | }, 17 | { 18 | "cell_type": "markdown", 19 | "metadata": {}, 20 | "source": [ 21 | "# System Info" 22 | ] 23 | }, 24 | { 25 | "cell_type": "code", 26 | "execution_count": 1, 27 | "metadata": {}, 28 | "outputs": [ 29 | { 30 | "name": "stdout", 31 | "output_type": "stream", 32 | "text": [ 33 | "{'commit_hash': '7f10f7bb3',\n", 34 | " 'commit_source': 'installation',\n", 35 | " 'default_encoding': 'UTF-8',\n", 36 | " 'ipython_path': '/Users/sk/anaconda3/envs/class/lib/python3.6/site-packages/IPython',\n", 37 | " 'ipython_version': '6.4.0',\n", 38 | " 'os_name': 'posix',\n", 39 | " 'platform': 'Darwin-17.2.0-x86_64-i386-64bit',\n", 40 | " 'sys_executable': '/Users/sk/anaconda3/envs/class/bin/python',\n", 41 | " 'sys_platform': 'darwin',\n", 42 | " 'sys_version': '3.6.5 |Anaconda custom (64-bit)| (default, Apr 26 2018, '\n", 43 | " '08:42:37) \\n'\n", 44 | " '[GCC 4.2.1 Compatible Clang 4.0.1 (tags/RELEASE_401/final)]'}\n" 45 | ] 46 | } 47 | ], 48 | "source": [ 49 | "import IPython\n", 50 | "print(IPython.sys_info())" 51 | ] 52 | }, 53 | { 54 | "cell_type": "markdown", 55 | "metadata": {}, 56 | "source": [ 57 | "# Data" 58 | ] 59 | }, 60 | { 61 | "cell_type": "code", 62 | "execution_count": 5, 63 | "metadata": {}, 64 | "outputs": [ 65 | { 66 | "data": { 67 | "text/html": [ 68 | "
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" 400 | ], 401 | "text/plain": [ 402 | " t x\n", 403 | "0 0.00 1.000000\n", 404 | "1 0.01 1.062791\n", 405 | "2 0.02 1.125333\n", 406 | "3 0.03 1.187381\n", 407 | "4 0.04 1.248690\n", 408 | "5 0.05 1.309017\n", 409 | "6 0.06 1.368125\n", 410 | "7 0.07 1.425779\n", 411 | "8 0.08 1.481754\n", 412 | "9 0.09 1.535827\n", 413 | "10 0.10 1.587785\n", 414 | "11 0.11 1.637424\n", 415 | "12 0.12 1.684547\n", 416 | "13 0.13 1.728969\n", 417 | "14 0.14 1.770513\n", 418 | "15 0.15 1.809017\n", 419 | "16 0.16 1.844328\n", 420 | "17 0.17 1.876307\n", 421 | "18 0.18 1.904827\n", 422 | "19 0.19 1.929776\n", 423 | "20 0.20 1.951057\n", 424 | "21 0.21 1.968583\n", 425 | "22 0.22 1.982287\n", 426 | "23 0.23 1.992115\n", 427 | "24 0.24 1.998027\n", 428 | "25 0.25 2.000000\n", 429 | "26 0.26 1.998027\n", 430 | "27 0.27 1.992115\n", 431 | "28 0.28 1.982287\n", 432 | "29 0.29 1.968583\n", 433 | ".. ... ...\n", 434 | "170 1.70 0.048943\n", 435 | "171 1.71 0.031417\n", 436 | "172 1.72 0.017713\n", 437 | "173 1.73 0.007885\n", 438 | "174 1.74 0.001973\n", 439 | "175 1.75 0.000000\n", 440 | "176 1.76 0.001973\n", 441 | "177 1.77 0.007885\n", 442 | "178 1.78 0.017713\n", 443 | "179 1.79 0.031417\n", 444 | "180 1.80 0.048943\n", 445 | "181 1.81 0.070224\n", 446 | "182 1.82 0.095173\n", 447 | "183 1.83 0.123693\n", 448 | "184 1.84 0.155672\n", 449 | "185 1.85 0.190983\n", 450 | "186 1.86 0.229487\n", 451 | "187 1.87 0.271031\n", 452 | "188 1.88 0.315453\n", 453 | "189 1.89 0.362576\n", 454 | "190 1.90 0.412215\n", 455 | "191 1.91 0.464173\n", 456 | "192 1.92 0.518246\n", 457 | "193 1.93 0.574221\n", 458 | "194 1.94 0.631875\n", 459 | "195 1.95 0.690983\n", 460 | "196 1.96 0.751310\n", 461 | "197 1.97 0.812619\n", 462 | "198 1.98 0.874667\n", 463 | "199 1.99 0.937209\n", 464 | "\n", 465 | "[200 rows x 2 columns]" 466 | ] 467 | }, 468 | "execution_count": 5, 469 | "metadata": {}, 470 | "output_type": "execute_result" 471 | } 472 | ], 473 | "source": [ 474 | "import pandas as pd\n", 475 | "import numpy as np\n", 476 | "curve = pd.DataFrame(np.arange(0.0, 2.0, 0.01), columns=list('t'))\n", 477 | "curve['x'] = 1 + np.sin(2 * np.pi * curve['t'])\n", 478 | "curve" 479 | ] 480 | }, 481 | { 482 | "cell_type": "markdown", 483 | "metadata": {}, 484 | "source": [ 485 | "# Plot" 486 | ] 487 | }, 488 | { 489 | "cell_type": "code", 490 | "execution_count": 10, 491 | "metadata": {}, 492 | "outputs": [ 493 | { 494 | "data": { 495 | "text/plain": [ 496 | "" 497 | ] 498 | }, 499 | "execution_count": 10, 500 | "metadata": {}, 501 | "output_type": "execute_result" 502 | }, 503 | { 504 | "data": { 505 | "image/png": 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\n", 506 | "text/plain": [ 507 | "
" 508 | ] 509 | }, 510 | "metadata": {}, 511 | "output_type": "display_data" 512 | } 513 | ], 514 | "source": [ 515 | "import matplotlib.pyplot as plt\n", 516 | "plt.rcParams['figure.figsize'] = [8, 4]\n", 517 | "curve.plot.line(x = 't', y = 'x')" 518 | ] 519 | } 520 | ], 521 | "metadata": { 522 | "kernelspec": { 523 | "display_name": "Python 3", 524 | "language": "python", 525 | "name": "python3" 526 | }, 527 | "language_info": { 528 | "codemirror_mode": { 529 | "name": "ipython", 530 | "version": 3 531 | }, 532 | "file_extension": ".py", 533 | "mimetype": "text/x-python", 534 | "name": "python", 535 | "nbconvert_exporter": "python", 536 | "pygments_lexer": "ipython3", 537 | "version": "3.6.5" 538 | }, 539 | "toc-autonumbering": true 540 | }, 541 | "nbformat": 4, 542 | "nbformat_minor": 2 543 | } 544 | -------------------------------------------------------------------------------- /python_example_for_RStudio.Rmd: -------------------------------------------------------------------------------- 1 | --- 2 | title: "Example" 3 | subtitle: "SUBTITLE" 4 | author: "AUTHORS" 5 | output: 6 | html_document: 7 | df_print: paged 8 | number_sections: yes 9 | toc: yes 10 | toc_float: true 11 | toc_depth: 3 12 | code_folding: show 13 | editor_options: 14 | chunk_output_type: inline 15 | --- 16 | 17 | # $\LaTeX$ Math 18 | 19 | This is just markdown that can include latex math. 20 | 21 | $$ 22 | \begin{align} 23 | \dot{x} & = \sigma(y-x) \\ 24 | \dot{y} & = \rho x - y - xz \\ 25 | \dot{z} & = -\beta z + xy 26 | \end{align} 27 | $$ 28 | 29 | # System Info 30 | 31 | ```{python} 32 | import IPython 33 | print(IPython.sys_info()) 34 | ``` 35 | 36 | # Data 37 | 38 | ```{python} 39 | import pandas as pd 40 | import numpy as np 41 | curve = pd.DataFrame(np.arange(0.0, 2.0, 0.01), columns=list('t')) 42 | curve['x'] = 1 + np.sin(2 * np.pi * curve['t']) 43 | ``` 44 | 45 | ```{r} 46 | # access python variable via reticulate - requires RStudio 1.2+ to work interactively 47 | library(reticulate) 48 | py$curve 49 | ``` 50 | 51 | # Plot 52 | 53 | ```{r, fig.width=8, fig.height=4} 54 | # python plots (e.g. matplotlib) require RStudio 1.2+ for proper rendering 55 | # using reticulate and ggplot instead (will work only in knitted document) 56 | library(ggplot2) 57 | ggplot(py$curve) + 58 | aes(x = t, y = x) + 59 | geom_line() + 60 | theme_bw() 61 | ``` --------------------------------------------------------------------------------