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Last active Dec 17, 2018

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R Markdown to IPython Notebook

This is a proof-of-concept of how an R Markdown Notebook can be converted into an IPython Notebook. The idea is to allow R users to author reproducible documents in R Markdown, but be able to seamlessly convert into HTML or an IPython Notebook, which allows greater interactivity.

The bulk of the work here is done by notedown, a python module that helps create IPython notebooks from markdown. The approach I have taken here is to convert the RMarkdown file into a Markdown file using a custom knitr script, and then using notedown to render it as an IPython notebook.

If you want to test it out, install the notedown module and run make example.ipynb.

R Markdown to IPython Notebook

You can view the converted IPython Notebook on nbviewer

{
"metadata": {
"signature": "sha256:a6c272b48657967d06f009ac5f248bbe12c3862a05cc184c899a8e33e674f3f7"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"%load_ext rmagic"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
" ## Introduction to ggplot2\n",
"\n",
"This is a short demo on how to convert an R Markdown Notebook into an IPython Notebook using knitr and notedown.\n",
"\n",
"Adding a Python Chunk"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def f(x):\n",
" return x + 2\n",
"f(2)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
"4"
]
}
],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is an introduction to [ggplot2](http://github.com/hadley/ggplot2). You can view the source as an R Markdown document, if you are using an IDE like RStudio, or as an IPython notebook, thanks to [notedown](https://github.com/aaren/notedown).\n",
"\n",
"We need to first make sure that we have `ggplot2` and its dependencies installed, using the `install.packages` function.\n",
"\n",
"Now that we have it installed, we can get started by loading it into our workspace"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"library(ggplot2)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"We are now fully set to try and create some amazing plots. \n",
"\n",
"#### Data\n",
"\n",
"We will use the ubiqutous [iris](http://stat.ethz.ch/R-manual/R-patched/library/datasets/html/iris.html) dataset."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"head(iris)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"text": [
" Sepal.Length Sepal.Width Petal.Length Petal.Width Species\n",
"1 5.1 3.5 1.4 0.2 setosa\n",
"2 4.9 3.0 1.4 0.2 setosa\n",
"3 4.7 3.2 1.3 0.2 setosa\n",
"4 4.6 3.1 1.5 0.2 setosa\n",
"5 5.0 3.6 1.4 0.2 setosa\n",
"6 5.4 3.9 1.7 0.4 setosa\n"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"#### Simple Plot\n",
"\n",
"Let us create a simple scatterplot of `Sepal.Length` with `Petal.Length`."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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TsmTJElm7dq0ULlxYOnTo4MgTWqiG8BgJkAAJkAAJ6ETA1TlgTOYfO3ZMevXqpWJQrl+/\nXic2lJUESCAMAYxWDR48WG6//XZl/BMmGw+7TGDNmjVy9913y1133SVYtsOkFwFX34Cxhgzj93gL\nxhg+3oKtCUMxeL03EoZAc+XKZex68hPDY16XMR5wmDvSgX88bfNrn6G/cubMqfotHi6JXPP444/L\njBkzVBGLFy+W+fPny4UXXphIkea1uBcxBO3EfKZZqAc2cB9i3tap3w+s++3Zs6c5pL1y5UpZtmyZ\nqiPZzfXrdwz3IDhH6jN8D+NNripgjJt/+eWXUq1aNXnttdeka9euUqZMGVPWTz75RGBibqTGjRvL\n1Vdfbex68hOwPTRt7hgj/DAg5c6d27EyvVKQn/sMc4qpSBs3bjSrxQ/Unj175KqrrjKPJbLh1/5C\nu/CDjnWtTiTYa1jnk3fu3KkeyIoWLepE8bbK8Gufob/wm5gvX76wPDDKG29yVQHj6bxFixZSrlw5\n1QA8nbVr186UtU+fPuY2NjCsZSz+DjjhoR2/GhvQCMtDN1mMosAI66+//goYRYrx0oSztWnTRjD8\niVS6dGn19uvUdxdvUzTCiq2L8NIyb948lfnaa69VU35O9UNsEvw3l19/F2M1wrLDyprXVQUMxWtY\nj2GBODqJiQRIQH8CeHiuUaOGemBu1qyZFCpUSP9GadiCsWPHmnPw8ITFpBcBVxUw3Jl98cUX8v33\n36vlRTAUYCIBEvAHgWuuucYfDdG4FRhlvOGGGzRuQXqL7qoCxvzUrbfeKidPnow4iZ3eXcDWkwAJ\neIkAlk9u3bpV2atEMr5xU2aMGB45ciTAZsbN+lh2agi4ugzJaFKqbmKjfn6SAAmQQCwEYIcCuxXM\nrTZq1Eg2b94cy2WO5oFxat26daV+/foyaNAgR8tmYd4ikBQF7K0mUxoSIAESCE1g8uTJ8vvvv6uT\nsDB+4403Qmd08ejw4cNNw7qpU6fKunXrXKyNRaeSABVwKumzbhIgAU8RyJs3b4A8qTAcDZbBj0sD\nAyCn8Q4VcBp3PptOAiQQSKBLly7SsGFD5dykVq1acs899wRmSMIe4u8i9i2m7h588EGpmIJoRElo\nJqvIIuCqERYJkwAJkIBOBOBw4a233hIYYmE9cioSrMvhMwHBKPzmDSwVPL1cJ9+Avdw7lI0ESCAl\nBFKlfK2NpfK10vDndmoe8fzJkq0iARJIkMDevXsFhlBQgLfddpsULFgwwRL/e/mqVauU7+pKlSqp\nqGxwneiHhOVK77zzjvIchsAY8GjHpA8BKmB9+oqSkoCvCcCnNPwGIAg70ty5c2XKlCkJtxlLiRAK\nFf4IkLZv3y59+/ZNuFwvFAD/+itWrFCiwOnR9OnTvSAWZYiRAIegYwTFbCRAAu4SQDABQ/miJsyD\nHj9+POFK4YnPUL4o7Ntvv024TC8UsH//flP5Qh5EQ8IIApM+BKiA9ekrSkoCviZQsmRJKV++vNnG\nSy+91BH/8ZdffnmAMRNCo/ohYbi5cuXKZlMQDjIVkZBMAbhhmwCHoG0j4wUkQAJuEIDR0bvvvisT\nJ05Uc8C9evVypBooKcyTfvrpp4I54DvuuMORcr1QyKRJk2TcuHFqDpi+9r3QI/ZkyJYV9ivT3iXu\n5YYbuEOHDrlXgQMl+zXsFsMROnBzJLkIhCPctWuX6TUpydW7Vh3DEbqG1rWC/fq7GGs4Qqzbjidx\nCDoearyGBEiABEiABBIkQAWcIEBeTgIk4H0Cbdq0kQsuuEAwrwzjLicSDJ66desmNWvWlIceekg5\n73CiXJaRPgSogNOnr9lSEkhLAmPGjBGsA8Zs2+HDh6Vfv36OcHj55ZeVRfXBgwflo48+Un+OFMxC\n0oYAFXDadDUbSgLpSQDz5NZ09OhR627c21gGZE379u2z7nKbBKISoAKOiogZSIAEdCbQv39/sUYU\ncspa+M477zQ9dZUtW1Y6duyoMybKngICXIaUAuiskgRIIHkESpQoIT/88IPMnj1batSoIVWqVHGk\ncswnL1iwQMUPrlatmiNrlh0RjIVoQ4AKWJuuoqAkQALxEihQoIDcdNNN8V4e9rrChQsLwhYykUA8\nBDgEHQ81XkMCGhHYsWOHzJw5U/lAdlLszz77TEaMGCG7d++OWixC/I0aNcoR15JRKwuRAYZSeANe\nv359iLM8RAKpIcA34NRwZ60kkBQCq1evVm9+x44dU0OkCG6AodNE03333aeUOsoZP368LFq0SAWR\nD1XuDTfcoPwU49ybb74pP//8s2TPnrxnfxhHtW7dWvAggihIo0ePlrZt24YSlcdIIKkEkvctSGqz\nWBkJkAAIYHkMlC8SAhtg34k0Z84cs5hTp07J22+/be5bNxDhCEECjIQ30Xnz5hm7Sfn8+uuvlfJF\nZViKhHCHTCTgBQJUwF7oBcpAAi4RsAY3QBXB+/FWW6hQoYBLq1evHrBv7OBNN2fOnMau+nTKCCqg\n0Ag75cqVCzgbvB9wkjskkEQCVMBJhM2qSCDZBLp06SJYLnPRRRdJ9+7dBUHbnUgYdoZ1MXwAY/lN\n8+bNwxb74osvCoyV8ubNK1gSlGwFeOWVV8ojjzwisFSGR6yHH344rKw8QQLJJMBgDDZp+9XpOIMx\n2LwRPJCdwRg80Ak2RMBvxznnnGMOh9u41PNZ/fq7yGAMnr/1KCAJkAAJkAAJ2CfAIWj7zHgFCZCA\nDQJbt26VAQMGyN13360soCNdCqtteKqClfXvv/8eKatr5yZMmKBiBsOH9OnTp12rhwWTAJch8R4g\nARJwlQCU6fLly1UdS5YsUUuW4BgjOMGaGtGF9uzZo07Benru3LnB2VzdnzFjhjz11FOqDni5gpw9\nevRwtU4Wnr4E+Aacvn3PlpOA6wSw7Gft2rVmPYcOHQrrEASK11C+uGDTpk1Jd9yxZs0aU1Zs4I2c\niQTcIkAF7BZZlksCJKAcX8Dy2EgXX3yxistr7Fs/S5cuLXXq1DEPNWvWLOn+lVu2bGkum4LTDqvs\npmDcIAGHCHAI2iGQLIYESCA0AbirbNy4sYrFC69YOXKE/9mZNGmSfP7550oJXn/99aELdPEovISh\n/oULF0rt2rUd8RrmorgsWnMC4b8JmjeM4pMACXiDQEZGRsxvkljOcssttyiPVWfOnElJAypXriz4\nYyIBtwlwCNptwiyfBDQiAEOo7du3KwWokdgpExVW0tu2bSOvlPWA3hVTAevdf5SeBBwjAGOp+vXr\nq78OHTrI0aNHHSvbjwVt3LhRGjZsKNdcc416w4efayYSsEOACtgOLeYlAR8TeOWVV8zQgr/88otM\nmzbNx61NvGljx441vVrBWnrq1KmJF8oS0ooAFXBadTcbSwLhCQQHTciVK1f4zDxjWksbKIL5Gcf5\nSQLhCFABhyPD4ySQZgT69esnF154oWp1kyZNGDM3Sv/DwUjVqlVVrgYNGsjNN98c5QqeJoFAArSC\nDuTBPRJIWwIVKlQQxPk9ceKE5M6dO205xNrwMmXKyKxZs8grVmDM9w8CfAP+BxIeIIH0JkDla6//\nycseL+b+HwEq4P+x4BYJkIALBGAd3Lt3b+natausX7/ehRpYJAnoSYBD0Hr2G6UmAW0ING3a1LSu\nbt26tYqIFCoYgzYNoqAk4BABvgE7BJLFkAAJ/JPA2bNnTeWLs3BcATePTCRAAiJUwLwLSIAEXCOQ\nPXt2KViwoFk+AhzUrVvX3OcGCaQzASrgdO59tp0EkkDg448/VkENLrjgAhk3bpwULVo0CbWyChLw\nPgHOAXu/jyghCWhNoFKlSvLZZ59p3QYKTwJuEOAbsBtUWSYJkAAJkAAJRCFABRwFEE+TAAkkj8Ds\n2bNVcAPED/7uu+8cq/i5555Tc89dunRR0Z4cK5gFkUACBKiAE4DHS0mABJwjgOhL/fv3lz/++EMQ\naej+++93JMzfN998I6+99prs2bNHFi9eLMOHD3dOaJZEAgkQoAJOAB4vJQEScI7AkSNHlFtHo0Q4\n8Dh58qSxG/fn33//HXAtFDETCXiBABWwF3qBMpAACUiJEiUCAhrcddddjvikbt68ucAQDAkRnlAu\nEwl4gQCtoL3QC5SBBEhAEcBc7Z133ik5cuQwlWaiaOB1a8aMGbJixQopV66clCxZMtEieT0JOEKA\nCtgRjCyEBEjAKQLVq1dXc79nzpxxqkj1Jl2nTh3HymNBJOAEAQ5BO0GRZZBAGALHjh2TIUOGyIgR\nI8Lk+N/h48ePy/z582Xt2rX/OxhmC/Ojn3/+uWzevDlMDvcPr1u3TubNmyeQ26mUmZmpXFUuXbrU\nqSJdLWfr1q0qhOO+fftcrYeF+5MA34D92a9slQcI4A2uZs2acurUKSXN1KlTZdmyZSElQwzeDh06\nyJo1a9T5J598UkUPCpV59+7d0qZNG/nrr7/UUO3YsWPluuuuC5XVtWOTJ0+WRx55RJVfpUoV+fTT\nTyVPnjwJ14cg94ixi4QlQ08//XTCZbpVwJIlS6Rbt27KUOycc85RzkbOO+88t6pjuT4kwDdgH3Yq\nm+QNAtOmTTOVLySC9W04q178mBvKF3nffPNNfIRMWCsL5YuE4AbvvPNOyHxuHnzrrbfM4hFicNGi\nReZ+vBtok6F8Ucb7778fYBUdb7luXffee++Z/Yk3YHr7cou0f8ulAvZv37JlKSaAuczgBCvcUKlM\nmTIBhyO9SZUtWzYgb/B+wEmXdoLrDJY/nmoLFSok1jCFxYsXV1bL8ZSVjGuCGQTvJ0MG1qE3gYzH\ns5JXmoD5snBvCF6REdaZeOvwW8LwISLVODmf5xVGqeozKJDffvtN/SEqEJxMXHnllSGxFCtWTM49\n91zZsmWLQHEPGzZMChcuHDLv+eefr47v379f6tWrp4aCnRj+DVlZmIO1a9eWTZs2Sc6cOWXAgAGO\nDIGjn9B2OOHAAwgsop1Q7GGakPBhTC/s2rVLvaXffPPN0rNnT/UdClcw2pc3b145fPhwuCzaHk/V\nd8xtYPny5ROE1MQUUbiEh2rkiydlyzJ6yIznQjeuwYL5Q4cOuVG0Y2Xih86PSqpIkSLqx8OPxiR+\n7TMoKSgAY47ZsZs8xQXhxxw/S05aQae4Sap63IeYK96xY4cXxHFUBr9+x/BgjO8XjB7DJYza4GE7\nnsQh6Hio8RoSIAESIAESSJAAFXCCAHl5+hHAMPFDDz0kffv2DTCcSiYJGHi1b99eLW+KNDzmlkx4\nI4Cl9t133y3wtez1NHPmTOUBC8PamOpiIgEvEOAyJC/0AmXQigBcGWINLBKslxcuXJhUYyEsZXrg\ngQdMZrBJGDhwoLmfjI3HHntMLT1CXVgL/PXXX0v58uWTUbXtOlauXClY3oQ0Z84cNZ8H+ZlIINUE\nPKeAMZfg5YT5Ka/LGA8/tAtGWH5tm1PtgpGgoXzBGUuLDhw4oFwcxsM9nmuw7MeasHzJqfZZy420\nvXr1avM0HgBgkIX1wE4kGKxhDtgp85QNGzYEiAXZk80LAsBYB21LRd0BAFzY8evvYkZGhroP3eoz\nzylgrxs4oSO8LmM83y+0y69W0E73WbNmzeSrr75SmGvUqKEMMJJ5T9SvX1/9iBt1tmjRIun3ZMuW\nLcVQbDBAgUWwIU8895/1GqeNsGB5DkMZw/o4FbyM9sGi1ilORple+HT6O+aFNkGG/Pnzq1UvkfrM\nunTOrty0grZJzK83Gq2gY78R8BYMpwuYe8U8LL6kyU544/zhhx/UsG+4pU1uy/Tll18KXDG2bt3a\n0QAHTitgcMC8PeRFVKRrr73WbTQhy8dvB62gQ6Lx7EG3raCpgG12PRWwTWAeyO7XPuMyJA/cXDZE\noAK2AcsjWd1WwLSC9khHU4zUE8AbrR/XQWMuFf6jnZpTNXoKQ7pwmpHOCUPKYMtEAvEQoAKOhxqv\n8R0BRCGCd6fLL79cBg0a5Jv2QTlgzvOKK64QBKY3fEgn2sDx48fLpZdeKk2bNhXMSUMRpVtCJCoM\nZ4MtpiKMOeZ048D2xk+ACjh+drzSRwQQdefIkSOqRYha9PPPP/uidQiaYBhLwS3mxIkTHWnXCy+8\nYJazfft2mTBhgrmfLhuvvvqqmgNHe3/99VeZMmVKujSd7XSIABWwQyBZjN4EsNzAmoL3red02g5u\nB5bBOJFgMW9NTpVrLdPr226x9Xq7KZ9zBJz5NjonD0sigZQQGDp0qBiW4HfccYdgeZEfUvfu3dVQ\nMdpyySWXSI8ePRxp1sMPP2wGHqhYsaJj5ToiXJIK6dOnj1x44YWqNliiIyADEwnYIUAraDu0svL6\n1aLWUD5+NEKKtc+MNZrxRjaxeSslnN2OFTSCnBQsWDDhOq0FwAEH7pcSJUpYDye87cYypISFilAA\n5n5jWQtKK+gIED16ilbQHu0YiuU/AhhG1UX52qXvtPJF/VCUTitfu+3yQv5YlK8X5KQM3iPgOU9Y\n3kNEiUggOQTg1AIOPkqXLi3t2rVTbgvD1Qx3mDCwqlChgjRq1ChcNtvH586dK6tWrVLWzdWqVbN9\nvR8ugAHeggULpE6dOiresh/axDZ4kwAVsDf7hVKlGYH9+/crpbt3717VcvgrfuSRR0JSwNrbtm3b\nCjxyIT311FNy++23h8xr5+B7770n//73v9UlL730knz++edSuXJlO0Von/fHH3+UTp06mWumYekM\nt5tMJOAGARphuUGVZZKATQI//fSTGMoXlyJqT7j07bffmsoXeQy/1OHyx3rcWg6CkKOedEuI6mR1\nWBKpH9KNDdvrPAEqYOeZskQSsE2gatWqkjt3bvM6BDcIly677LKAU8H7ASdt7ASXE0kGG8VqlTWY\nQa1atbSSn8LqRSDj8azkFZERKNsYVvOKTMFywPAE1p9+S7DQ9Gs0JB36DEZS8KiE+79x48YyePDg\nAIVsvd8wR4z52bx586qh6HvuuUeC16Ra88e6DS9gKBMBA/r165eyoAXGmmLrm2isbUg0H5YVwboc\nD0OdO3dWQ/vBa57jrQP3Ifj60WOWDt+xePoNRplYHQE3teESwkzGa7zJZUjhqIY5HuuSljCXe/Yw\nlyF5tmvCCmZnGVLYQjx4Aj/mUL5nzpzxoHTxi8RlSPGzS9WVXIaUKvKslwRIgARIgARcJMA5YBfh\nsmgSSDWBgwcPqmVKGFpF4ABYWzuREIjg1ltvlWuuuUYQmMHrCb6rIWu3bt1k165dSRcXS8zgKatK\nlSryyiuvJL1+VuhNAlTA3uwXSkUCjhB48MEH5Y8//lDzWH/++af079/fkXL/7//+T77//nvZtm2b\nIJAF1g57NX333XeCZVWQFZbdzz77bNJFHTZsmFpbjD4YOXKk/PDDD0mXgRV6jwAVsPf6hBKRgGME\ngt/2nIpdG1yuU2EOHWu4paBgWYP3LVld2wyuM3jftYpZsKcJUAF7unsoHAkkRuCBBx4wgyagJFg3\nO5F69eplllu9enVPe4xq1qyZnH/++arZOXPmTEngiJ49e5qW6hiGhqU7EwnQCtrmPUAraJvAPJDd\nr30WqxX0pk2b5Msvv5TmzZubisiJbsHQ9o4dOwTLl7AUw6nkhhU0ljjCxSRcd5YtW9YpUW2Vg1EC\nBK9A9Cjrmm9bhXg0s1+/Y25bQVMB27yh/XqjcRmSzRvBA9ljVcAeENWWCG4oYFsCuJQZvx1YY42H\nFr8lv/4uuq2AOQTtt28C20MCQQT+/vtv+eabb2TPnj1BZ7y3i/W/y5Ytk19++SWqcHCXuWjRIlm/\nfn3UvMxAAl4kwGAMXuwVykQCDhHYsGGDdOzYUYx4wFOmTBG4vfRqwhz19OnTlXjdu3cXWFuHSvBG\nB09VCJ6ANHTo0JTM7YaSjcdIIFYCfAOOlRTzkYCGBKBwoXyR8Il9rybMkRrKFzK+/fbbYV0A4g3Z\nUL7I+8Ybb+CDiQS0IkAFrFV3UVgSsEegVKlSAReULFkyYN9LO4UKFQrwqVu0aNGwxl3nnnuuaYWN\nNni5XV5iTFm8RYAK2Fv9QWlIwFECiBN8yy23qAADiHOLYV2vJhjyvPzyy2qIHEub4DEqXCCE8uXL\nK4caWF6EIBYjRozwarMoFwmEJUAr6LBoQp/wq7UfraBD97eXj9IK2su980/ZaAX9TyZeP0IraK/3\nEOUjARIgARIggTgIcAg6Dmg6XIKlJ49nhXq+//77Y1rSoUObICNic06YMEHuvfdemTx5smNiHzhw\nQJ566inp27evLF26NGK58Cn88MMPy4ABAwRWxqlIkBUOHRAQwan4sljWgyHg3r17BxhDJdo+GFc9\n+uijyg/16tWrEy2O15OAbwhwCNpmV+oyBI2oL3A8j1SgQAHlCB7DzOGSLkPQ7777rvoxN9oBhdG6\ndWtjN+RnLH0Ghf7FF1+o65Ef62Zh6BMqtWrVStasWaNOIc+CBQvCGguFuj7RY++99578+9//Noup\nU6eOTJ061dyPd2P06NHy4osvmpd/8MEHan7VPBDnBpZB/fTTT+pqOKJAcIRIAczpiCNO0Cm8LJbv\nWArFi7tqDkHHjS69L1y+fLkJAG9IcEfoh7RixYqAZgTvB5y0sWMt5/jx42HfbE+ePGkqXxSPtzv8\nJTMZD1ZGnRs3bjQ2E/q0MkBBwfvxFA7HGitXrjQvhStGhOZjIgESEOEQtE/vAvj9NRKMdapVq2bs\nav0Jx/pGgoVskyZNjN2EPq28sKSlRo0aIcuDz2OrI/2LL75YypQpEzKvWwcx7GxNDRo0sO7GvW1l\ngHYifnCiCX1k7TPEJTYCIyRaNq8nAd0JcAjaZg/qMtRy5swZmTZtmuzdu1duvPFGKV68eMSW6jIE\njUZgjhbxVOvXry81a9aM2C6cjKXPMLcMJxAIE9euXbuI60pPnDghH330kXIScdNNN0nBggWjyuB0\nhjlz5iinGnhQ+Ne//uVY8Rh6xzwtHmwQtceJhLnljz/+WI4cOSIdOnSQSFMhqI9D0E5QT24ZsXzH\nkiuRM7W5PQRNBWyzn/x6o+mkgG12WUwK2G6ZXsjPZUhe6IXYZcBvB4MxxM7LCzndVsBxDUHjDaRl\ny5ZqwXylSpXE+Js1a5YXmFEGEjAJYA4SFs5MzhPAKMvBgwedL9hGiXCvCb/QsSTIipEOJhLwCoG4\nFPAdd9whtWvXlrfeeksNg8G/LP6uvvpqr7SLcpCAMpaqV6+eelDs0aOHYCiUyRkC33//vdStW1dN\nATz44IOCB51kp8cee0zN1SMeMYbOwyUo6LvuukvJivuBS6HCkeLxZBOISwFjXnHYsGFy5ZVXqmDc\n+ALgL9rcTrIbx/rSm8CoUaNk586dCsK8efNk9uzZ6Q3EwdYPHz5cBZdHkZjfhUJOZlq1apVMmjRJ\nVYm34CeffDJs9V9++aVgzhwJc/wjR44Mm5cnSCCZBOJSwLBq/Oyzz5IpJ+siARLwEIFUvPFam2+n\nfjt5rXVwmwTcJhCzAsYTLuKI4g8L6du3b68sRY1j+OQbhtvdxfLtEOjfv79pzYwlNbBbYHKGwJAh\nQ6Rw4cKqMPwWYDQsmQnBGm677TZVJRzNwNNWuNSiRQu57rrr1OkSJUooD2bh8vI4CSSTQMxW0Bjm\nWbt2bUTZYIwFK794E9wnoh4vJ1pBe7l3/ikbjG6wbChv3rz/PKn5kVRbQWNuFUuLDEXsFE47y5Bg\nWIW+zZkzZ9Tq9+/fLwh5mD17zO8dUcu0k4FW0HZoeSOv21bQOWJtJtY6wugCCYoSglkTvAHRwtBK\nhNteIIAfWzwUwrsVk7MEoCidVr52JYRCjTXRRiVWUsyXLAK2HgXxI4a/Ro0aqU9j/+jRo8rpPxwZ\nMJEACehLAH6tX3vttbCuOONpGazP4at64sSJKV+2FI/8vIYE3CIQ8xswBIDT+7lz5ypZrEN6cDdX\ntmzZiJaIbjWA5ZIACThDAEpy0KBBqjBYkM+cOVPgOjLRhKhRM2bMUMUgkATKxdszEwmkOwFbb8Aw\n5cfTbOfOndUntvGHuaAtW7Y45rou3TuF7SeBVBCwGlEi6MT8+fMTFgMWyFgGZCSEb/z999+NXX6S\nQFoTsKWA8aaLJ9fNmzdL5cqVzT88JeMPzjlgjci54LS+p9h4TQlceumlAZLD0jjRhN+MSy65xCwG\n87AwHmMiARIQycgK2v64XRCwhs7IyFDKFnFn8+fPL1gY/8ILL6gAAFDQDRs2tFusHDt2TPDk7eWE\nB5BYXd95uR3BssFCEz+WfjRW8mufwQAJVshOPfDCmQ6siVHu/fff71ikKSwBw3e7YsWK8swzz6jp\nquD7z7pvWCn7bf0u7kNM3SE8qN+SX79jiFttrKQI12eIHBYpvnW463A85mVI1kLKlSunYnxaLSBv\nuOEGNX+Ep1trMHjrddG2uQwpGiH3zjMYg3ts3So51cuQ3GoXfsyhfOFr2k+Jy5D06023lyHZGoI2\n8OFG2rZtm7GrviyY28GTAJ50rYrZzMQNEiABEiABEiABk0BcCnjw4MFqKRI80SAWKbxgYXgJc0iI\nPdupUyezAm6QgA4EYBjUpUsXNez6/vvvRxQZlrwXX3yxuu9hLexUeuedd1T9t99+u/zxxx9OFcty\nSIAEPEogriFotAUB0WHdCC9DCGCO+SPMR+3evVsp43jayyHoeKg5c026D0HfcsstgjCbSJgL//rr\nr+X8888PCRcGh9Z512XLlknx4sVD5o314Pr16wUuE410zTXXmMEGjGPBnxyCDibi7X0OQXu7f0JJ\n5/YQdNyL8erUqSP4syYYY+GPiQR0I7B9+3ZTZMw/ImpOKAUMpzNW5YuLsAQvUQW8Y8cOs35sWOUJ\nOMEdEiAB3xCIawgawRgaN24s1apVk4suusj8s64j9A0hNiQtCPTs2dNsZ82aNdWSOvOAZQPWjhh+\nNlKpUqWkVq1axm7cn1dddVVAuYhfzEQCJOBvAnENQSPoAn6w4JISFotGYjAGg4R+n+k+BI0eW7du\nncCnOSL7wKAwUvrggw/UlEv37t0dc+6P6RxEHStdurRaYx+pfpzjEHQ0Qt46zyFob/VHLNK4PQRt\nWwFjeK5kyZJqiA5zZU4mzgE7SdNeWVTA9nh5ITcVsBd6IXYZqIBjZ+WVnG4rYNtD0FC6MBaZMGGC\n551meKUTKYceBDZt2iQwqIrF0cqaNWvkl19+idowPLD+/PPP6u06amZmIAESSCsCthUw6CBmb69e\nvVRIQixBMv44B5xW946vGvvuu++qJUA333yzWo4USQk/99xz0qpVK7Xkrn///hE53HfffdKhQwdp\n2bKlOLlkKWKlPEkCJKAFAdtD0GjVr7/+GvLtl3PAWvR5SCHTfQgarlNhzWwkrPWFYVRwQvARGGFZ\nFfSiRYvUvG1wXqzlhZ2EkXLnzi2rV692bM6YQ9AGWT0+OQStRz9ZpXR7CPp/FlTWWqNsw0oUCT9C\n+/fvF/x4W42xolzO0yTgOQLnnntugAIuUaJESBnhK/mcc85R692RAUq1YMGCIfPiewFjLsO/OZYq\nGX6OQ17AgyRAAmlFIK4haARbwBB0mTJl5NVXXxUMww0fPjytwLGx/iKAIAFYToS41k888UTEOLhj\nxoxRS++wTvjFF1+UAgUKhIQBl6zPP/+8VKhQQS3ZGz16dMh8PEgCJJCeBOIagr7uuuukadOmUrRo\nUfUmgOALbdq0kQ8//DChmMC0gk7dTZjuQ9CpIx9/zRyCjp9dKq7kEHQqqCdWp9tD0LbfgGHVidCD\nAwcOFNxQSOXLlxe48ps7d27I1mJ95eTJk0Oe40ESIAESIAESSEcCthUwliFhyG3FihUmL7jmmzFj\nRkhDFLju+/TTT5XltHkBN0ggCQQQd/WOO+6QunXrpswCGVHCBg0apIa14SfdqQQ/7F27dpX//Oc/\nAsOwSAlOQxDfF4ZlTCRAAt4hEJcR1ogRIwRWo5gDg1HK2LFjlWUohqGDE4almzdvrjz8BJ/DciZr\nzE8Yq2RkZARn89Q+jGi8LmM8wAzjID+1DcoXa3CRMG9buXJlFTgkHj7xXHP8+HFBxDBD8S5fvlym\nTZsWT1EB16BN99xzj3kMD7kDBgww960bePgdMmSIOjR9+nQVEL5jx47WLJ7bNu5FzwmWoEBoF15g\n/PQdM5D4+XfRzT6LSwHjC1y9enX5/PPP1dM31jniKXzfvn0BTukRXQZGLTDWCpVgwAWn90bC2kr4\nmGZKPgHcZEjhDIqSL1HiNSLEoDVhuVCfPn2sh1zdXrt2ral8URGW78HNZKIK5qOPPgqQG0ub8D0L\nlRBlyZrwRh4urzUft90hgO8Z+bvD1o1S0V958+aNGOMeI23xprgUMCozgjAYFUMJd+7c2YwFjKdy\nDEs3adJEeReCgRXmgnGdkTA0Z03I8+eff1oPeW4b8954s/Fb8qMRFoae58yZY3YVRmiSeX9hCRJC\nF27cuFHJgIfLrVu3mvLEu4G429blTfXr1w/bLjCwpiuuuCJsXmu+VG5jSSNsTayjY6mUx6m6aYTl\nFMnklROrEVa8EsWtgKNViGGWtm3bqmxYL4z9aA7uo5XJ8yRgh8C4cePk2WeflZUrVwos9evVq2fn\n8oTzQpFMnTpVMAeLMJ3wsuVEglLHUPaSJUvUEiesSgiXGjRooOrH2z8ci4RyLhLuWh4nARJwl4Br\nChgOCox4wYgw89tvv4WMr+pu81h6uhN4+OGHlbV+qkYt4LTj3nvvdbwbEAq0WbNmagonmhEW3nrx\nx0QCJOAtAjEr4GhDQjgfLsHLkNVoJFw+HvcfgWPHjikFaMwxe7mFsOaHIaCxvM4pWRFmECNA9Bbn\nFFGWQwL+IBDzMqT58+cri2dYPYf6c8K60x9I2QoQwAPZgw8+qKzjMewJAyQvp8WLF6sRG/h5hics\npxIMDWGweNlllymbCKfKZTkkQAL6E4jZExae4q0Wy6GaDl+3+fLlC3UqpmP0hBUTJlcyOW2EtWDB\nArUG1xAWQe7ff/99Yzepn7EYzl1//fUCq2UjffbZZwJjp0QSpl7QbiOBsbEsyjiWyCc9YSVCL/nX\n0ggr+cwTrTFWIyzovnhSzEPQmNOFxysmEoiFQLAFa/B+LGUkM481uhHqDd6PR5bgNmMfIwM6DMfH\n015eQwIkYI9AzEPQ0YrFEPXOnTujZeP5NCEARy0wEkIqVKiQPPTQQ55uOYy1jNEbrHNHYIZEE9b8\nGuuOMW0zdOhQKt9EofJ6EvARgZiHoKO1GVaW+IG54YYbomUNe55D0GHRuH7C6SFoQ2B4gUJUoFQu\nQYtlCBryYpoFi+ox7ORkQshOKGAsRXIycQjaSZrul8UhaPcZO12DZ4agozUMXq+YSCCYQLi4usH5\nvLCPaRb8OZ3wcMNEAiRAAsEEYp4DXrZsmfTs2TP4+oD9kSNHKr/PAQe5QwIpJPDdd9/Jpk2bpFGj\nRlKuXLkUSsKqSYAESCCQQMwKuEqVKoIlFZFS1apVI53mORJIKoF3331XHn30UVUnfFzPnj2bfniT\n2gOsjARIIBKBmBUw5vHgczZSSpW3oUgy8Vz6EoAvciNhbvebb76RLl26GIf4SQIkQAIpJRCXFfTX\nX3+twhFinSScDMAtXsmSJVV0pJS2hpWTgIXAJZdcYtkT5RQk4AB3SIAESCCFBGJ+A7bK2Lt3b+Vk\nAWHQoIAxvIfhvhtvvNGajdskkFIC8MQF62P4IUdgEHijYiIBEiABrxCwrYDhSODAgQPyyCOPyNtv\nv60MXO6//361BhhzbIjpy0QCXiCAOJ6DBw9OaTAGL3CgDCRAAt4kYHsIGl58sJ4RSrhmzZoCK1Ok\nokWLej7OqDe7gFKRAAmQAAmkIwHbb8CAhPBqNWrUkN9//102b94st9xyi8ydO1fFJ01HiGyzNwns\n3btXHnvsMdmwYYO0b99eMHXCRAIkQAJeIWD7DRiCYz0wLEwRXg0GWTDGeuutt4QOB7zSrZQDBJ57\n7jllGLh+/XoZMWKELFy4kGBIgARIwDMEbClgLDPCH5waYF0wtmH9PGDAADUfPH36dM80jIKQwLZt\n2wIgbN26NWCfOyRAAiSQSgK2FHDr1q0Fhi0rV65Un9jGH6yg8XZRr169VLaFdZNAAIHbbrtNsmf/\n7y2OwAhGcIiATNwhARIggRQRsDUHPGfOHEFItTvuuEO98Roy40fO+KEzjvGTBFJNADF+v/rqK9my\nZYuKboSoTEwkQAIk4BUCthQwLKAx7zt58mQlP2KmItIL5n6pgL3SpZTDSuCCCy5QDjjopc1Khdsk\nQAJeIGBrCNoQGJbPvXr1kjJlyij/0P3795fhw4cbp/lJAloSwBzxL7/8okZ5tGwAhSYBEtCKQFwK\nuEePHoI3iyeffFI1dtCgQeqtGNamTCSgI4EpU6bItddeq7y5de3alUpYx06kzCSgGQHbChiesFat\nWiUDBw5UHobQ3vLly5trgTVrP8UlAUXglVdekbNnz6rtxYsXyw8//EAyJEACJOAqAdsKGPPAsHpe\nsWKFKRh+uLAuGJamTCSgI4FixYoFiB28H3CSOyRAAiTgAAFbRlhGfXBq0LBhQzn//POVs/uxY8cq\nQ5c2bdoYWfhJAloRePrppwVTKbt27ZJ77rlHKlWqpJX8FJYESEA/AnEp4I4dO6ooSJ9//rmcOnVK\nOnToIJUrV9av9ZSYBP5/AgipSUcyvB1IgASSScCWAsayI/xIIbg5Qg/CAxYTCZAACZAACZCAfQK2\nFDDedJcuXaqWH8Gx/cyZM9VQtP1qeYWXCOzYsUMtI4OBHZyslC1b1kvieU6W3bt3C6ZdTpw4oZbj\nVahQwXMyUiASIAHvE4hZAW/atEkWLVqkIsucc8458uijj8ro0aOpgL3fx1El7Natm+pXZITnKATY\ngLEdU2gCWAO/fPlydXLevHmCv5w5c4bOzKMkQAIkEIZAzFbQcGyPAAxQvkgtWrRQoQjDlMvDmhA4\nePCgqXwhMh60/v77b02kT76YJ0+eNJUvasf3YufOnckXhDWSAAloTyBmBQxjK+tTPhTxsWPHtAeQ\n7g2Af+RatWqZGKpXry7Fixc397kRSCBXrlxSv3598yCMDzlkb+LgBgmQgA0CMQ9Bo0zD9zO2Dx06\npBwXwBe0kfLnzx+gpI3j/PQ2gTfffFM++eQTwRww5vmZIhPA/C/8oeNtuHPnzvSDHhkXz5IACYQh\nYEsBYw7YGII2yrPuw51fp06djFP81IQA3oL79eun5n337dunidSpExMPmnfddVfqBGDNJEACviAQ\nswJu0KBB1LlBeMhiIgESIAESIAESiE4gZgWMMIRFixaNXiJzkAAJkAAJkAAJRCUQsxFW1JKYgQRI\ngARIgARIIGYCVMAxo2JGEiABEiABEnCOABWwcyxZEgmQAAmQAAnETIAKOGZUzEgCJEACJEACzhGg\nAnaOJUsiARIgARIggZgJxGwFHXOJzOgJAnCqAb/Oe/fulVatWgnW+oZL8HKGNdy4plGjRpI7d+5w\nWXmcBEiABEjAIQJUwA6B9FoxTzzxhLz11ltKrPHjxwtiN4dTrHfffbfMnz9f5YWbxXfeecdrzaE8\nJEACJOA7AhyC9l2X/rdBn376qdmyjRs3yurVq8196waCMRjKF8cXLlwY1eGK9XpukwAJkAAJxEeA\nCjg+bp6/qmrVqqaMefLkkfLly5v71o2CBQsGBBMoWbLkP9yNWvNzmwRIgARIwBkCHIJ2hqPnShk1\napSMHDlSvc1iiLlYsWIhZUTc3wkTJsgrr7yi5oB79+7N4AIhSfEgCZAACThLgArYWZ6eKa1UqVJK\nAcciEOI8Y74YypjBGGIhxjwkQAIkkDgBDkEnzpAlkAAJkAAJkIBtAlTAtpHxAhIgARIgARJInAAV\ncOIMWQIJkAAJkAAJ2CZABWwbGS8gARIgARIggcQJUAEnzpAlkAAJkAAJkIBtAlTAtpHxAhIgARIg\nARJInAAVcOIMWQIJkAAJkAAJ2CZABWwbGS8gARIgARIggcQJUAEnzpAlkAAJkAAJkIBtAlTAtpHx\nAhIgARIgARJInAAVcOIMWQIJkAAJkAAJ2CZABWwbGS8gARIgARIggcQJUAEnzpAlkAAJkAAJkIBt\nAlTAtpHxAhIgARIgARJInADDESbOMK1K2Lhxo7z99ttSpEgR6dmzpxQqVCit2s/GkgAJkIBTBKiA\nnSKZBuUcOXJEbr31VtmzZ49q7fLly2XixIlp0HI2kQRIgAScJ+A5BZwnTx7nW+lgiTly5BCvyxhP\nc9GubNmyRWzbunXrTOWLOpYuXRoxfzxyuHGNX/sM/ZU7d27JyMhwA1vKysyePbtkZmaqv5QJ4ULF\nuXLlErTNr78ffmwXvlu4F91qm+cU8PHjx1249Z0rEh3hdRnjaS3ahR/0SG0rX768lCpVSnbu3Kmq\naNCgQcT88cjhxjV+7TP8MJw4cUJOnTrlBraUlYkHJrTtzJkzKZPBrYrPnj2rxXfGbvv9+h3Lnz+/\nnD59OmKfFShQwC4uM7/nFLApGTc8RyBv3rzy4YcfynvvvSeFCxeW22+/3XMyUiASIAES0IUAFbAu\nPeUROcuWLSsPPfSQR6ShGCRAAiSgLwEuQ9K37yg5CZAACZCAxgSogDXuPIpOAiRAAiSgLwEqYH37\njpKTAAmQAAloTIAKWOPOo+gkQAIkQAL6EqAC1rfvKDkJkAAJkIDGBKiANe48ik4CJEACJKAvASpg\nffuOkpMACZAACWhMgApY486j6CRAAiRAAvoSoALWt+8oOQmQAAmQgMYEqIA17jyKTgIkQAIkoC8B\nKmB9+46SkwAJkAAJaEyACljjzqPoJEACJEAC+hKgAta37yg5CZAACZCAxgSogDXuPIpOAiRAAiSg\nLwEqYH37jpKTAAmQAAloTIAKWOPOo+gkQAIkQAL6EqAC1rfvKDkJkAAJkIDGBKiANe48ik4CJEAC\nJKAvASpgffuOkpMACZAACWhMgApY486j6CRAAiRAAvoSoALWt+8oOQmQAAmQgMYEqIA17jyKTgIk\nQAIkoC8BKmB9+46SkwAJkAAJaEyACljjzqPoJEACJEAC+hKgAta37yg5CZAACZCAxgSogDXuPIpO\nAiRAAiSgLwEqYH37jpKTAAmQAAloTIAKWOPOo+gkQAIkQAL6EqAC1rfvKDkJkAAJkIDGBKiANe48\nik4CJEACJKAvASpgffuOkpMACZAACWhMgApY486j6CRAAiRAAvoSoALWt+8oOQmQAAmQgMYEqIA1\n7jyKTgIkQAIkoC8BKmB9+46SkwAJkAAJaEyACljjzqPoJEACJEAC+hKgAta37yg5CZAACZCAxgSo\ngDXuPIpOAiRAAiSgLwEqYH37jpKTAAmQAAloTIAKWOPOo+gkQAIkQAL6EqAC1rfvKDkJkAAJkIDG\nBKiANe48ik4CJEACJKAvASpgffuOkpMACZAACWhMgApY486j6CRAAiRAAvoSoALWt+8oOQmQAAmQ\ngMYEqIA17jyKTgIkQAIkoC8BKmB9+46SkwAJkAAJaEyACljjzqPoJEACJEAC+hKgAta37yg5CZAA\nCZCAxgSogDXuPIpOAiRAAiSgLwEqYH37jpKTAAmQAAloTIAKWOPOo+gkQAIkQAL6EqAC1rfvKDkJ\nkAAJkIDGBKiANe48ik4CJEACJKAvASpgffuOkpMACZAACWhMgApY486j6CRAAiRAAvoSoALWt+8o\nOQmQAAmQgMYEqIA17jyKTgIkQAIkoC8BKmB9+46SkwAJkAAJaEwgh5uyHzp0SD7//HPZv3+/lClT\nRlq1aiU5crhapZvNYdkkQAIkQAIk4BgBV9+A586dK5UrV5Z7771XCfzzzz87JjgLIgESIAESIAGd\nCbj6OtqoUSMpUKCA4pMzZ071JmyFdfbsWeuuZGZmBuxzhwRIgARIgAT8SiBbltJzXett2LBBPvzw\nQ+nfv7/kzZvXZDls2DDZtWuXud+2bVtp1qyZuc8NEiABEiABEvAyAUy1FixYMC4RXVfAq1evlhkz\nZsjdd98tRYoUCRDy5MmTYn0LPnjwoBw7diwgj9d28uTJI8ePH/eaWAnLY/QN5uv9lvzaZ2XLlpW/\n/vpLTp065asug50I3gvOnDnjq3blzp1bihYtKjt27PBVu9AYv37HihUrJtBTULLhUv78+aV48eLh\nTkc87uoQ9Nq1a+Wrr76Sf/3rX5IvX75/CJIrV66AY0eOHPH8MDR+GJIwaBDAJRk7aFO2bNl82zY/\n9hnuCz/ej0ab/NhnRtuS8Z1OZh1+bpfxPXODp6sKGMPOp0+fljFjxijZL7/8cmnevLkb7WCZJEAC\nJEACJKAVAVcV8KOPPqoVDApLAiRAAiRAAski4OoypGQ1Il3q2bRpk8BQ7dJLLxUYsKUivf3221Kr\nVi1p3LixLF26NBUisE4SIAES8AUBKmCNuvGZZ56RFStWyOHDh+WNN96QhQsXJlX67du3y+OPP66W\nk23evFkGDx6c1PpZGQmQAAn4iQAVsEa9eeDAgQBpg/cDTrqwA8VvNYxJdv0uNIlFkgAJkEDKCDqR\nQ1cAABnvSURBVFABpwy9/Yr79OkjWMqAVLNmTWnSpIn9QhK4okqVKtKmTRtVAiymsa6biQRIgARI\nID4CrhphxScSrwpHAJ7FFi1aJDt37hQow1T41X7ppZekb9++ysMZ/HszkQAJkAAJxEeACjg+bim7\nCgv58ZfKBOXPRAIkQAIkkBgBDkEnxo9XkwAJkAAJkEBcBKiA48LGi0iABEiABEggMQJUwInx49Uk\nQAIkQAIkEBcBKuC4sPEiEiABEiABEkiMABVwYvx4NQmQAAmQAAnERYAKOC5svIgESIAESIAEEiNA\nBZwYP15NAiRAAiRAAnERoAKOC1v6XrRkyRLp3r279OvXT7Zu3Zp0EMePH5dbb71VeQKDHGfPnk26\nDBs3bpT77rtPevXqJb/++mvS62eFJEAC/iBARxz+6MektGL//v3Ss2dPOXr0qKoPCvijjz5KSt1G\nJXB/+f3336vdb775RoYOHSpPP/20cTopn3fffbf8/vvvqq6ffvpJFi9ebLoITYoArIQESMAXBPgG\n7ItuTE4jtm3bZipf1Lh+/frkVGypZcOGDZY9kdWrVwfsu71z8uRJQVhII+3bt092795t7PKTBEiA\nBGImQAUcMypmhAvKqlWrmiDatWtnbidr4+abbw6o6vbbbw/Yd3snV65c0rp1a7OaOnXqSNmyZc19\nbpAACZBArAQ4BB0rKeaTnDlzyocffigzZ86UwoULS7NmzZJO5Z577pFy5crJF198Ie3bt5fGjRsn\nXYbRo0dLy5Yt5cSJE0oZIzIUEwmQAAnYJZAtK75rpt2L3Mr/999/y6FDh9wq3pFy8+TJIzAE8lsq\nUqSIQJFgSNVvya99dt5558muXbvk1KlTvuoyRPnCz9KZM2d81S7ch+ecc47s2LHDV+1CY/z6HStW\nrJj6fh08eDBsnxUoUECKFy8e9nykExyCjkQnSecwr/jnn3/GZNG7d+9eWbZsmeOSwboZoQ5jSQiH\nGOmGjKUM5iEBEiCBdCdABZziOwBLWho2bCjXXnuttGrVSg4cOBBWogkTJkjt2rUF86CXXXZZgEFU\n2ItiOHHdddfJ9ddfr+qHLJHSI488IldffbVg7jPZFtCR5OI5EiABEtCNABVwinvs9ddfV8OIEGPd\nunXywQcfhJUIc49GgqIeM2aMsRv3J95mrVa9W7Zskd9++y1keXhYmDx5sjqHYc/hw4eHzMeDJEAC\nJEAC0QlQAUdn5GoOzJ1YU+7cua27AdswgrKm/PnzW3fj2g5VH+Y0QqXgvMH7oa7hMRIgARIggdAE\nqIBDc0na0T59+sill16q6sMw9C233BK27mHDhgkMVJAqVqyovDGFzRzjCRiFXHXVVWZuDHGXKlXK\n3LduwOjnoYceEizFgXFCsh1gWGXhNgmQAAnoToBW0DZ70C1rPwzpBr/hhhMNnqjy5csX7nRcx1Ee\nrKCPHDkS9frTp0+bDwJRM3sgg1t9luqm0Qo61T1gr37ch7SCtscs1blpBZ3qHkhS/bEqX4jjtPJF\nmXirxV8syXgLjyUv85AACZAACYQmwCHo0FwSPoplPc8995zMmTMn4bLiKQDrqXv37q2GtH/88ceI\nRWBJ0ahRo+T555+PaIWNQubNmyfXXHONckQRzQUj2t6xY0cZOHCg4K05UoKM4DVr1qxI2dS5F198\nUW688UYZN25c1LxuZDh8+LCMHTtWGcFh7ToTCZAACcRDgEPQNqnFMpy5dOnSgLlcWC8n220jlCR8\nNyNlz55d8EBQokSJkK2FMvvll1/UuUsuuURmzJgRMh8sprEEyUgwwlq7dq2xG/C5fPnygDZj2dQn\nn3wSkMfYQV7IYEQ2euaZZ6Rz587G6YBPzIO/8cYb5rEhQ4YIvGNFSrH0WaTrg8916dJFBWDA8Qsv\nvFB55crIyAjO5vo+h6BdR+xoBRyCdhRnUgrjEHRSMDtbybfffhtQIKL2JDtt377drBKKLdybON5+\nDeWLC1atWiV79uwxr7VuTJkyxbqrXDEGHLDsTJs2zbInsmbNmoB96853331nKl8cj8Rr7ty51kuV\nW8yAAy7vwGkKoh8ZCUuzrKyN4/wkARIggWgEOAQdjVAc5+vWrRtwFZxWJDtZXaPBuCqcg41ChQoJ\ngiwY6YILLlAWzsa+9TP4LT7SvHWLFi2slyqr7YADlp1gPldccYXlbOBm8Llw7Qq8yrk9zJPjbd5I\nCMRQunRpY5efJEACJBAzgYzHs1LMuV3OeOzYMcEbhpcTDJCizWdiiRCUGoylunbtqgLIJ9thP7xq\nYWgXMmBIt1atWmGxNm/eXFlgQxHidoBSDpXgLxpDMpivxfZ7770Xdlgbw6N4CPjjjz/UMquJEyeG\njZkLJYahbwzRYegZzMLxatq0qfKli3lYDFs//PDDoUQNOBZLnwVcEGUHMiBVr15dsYVlayoS+glW\n68bQfSpkcKNOTJkgechNvSPNxH2YN29ewb3rt+T0d8wrfPD7ie8XAq+ES3goj9cwlnPA4aiGOe70\nfGKYapJ+mMEYko484Qo5B5wwwqQWwDngpOJ2pDLOATuCkYWQAAmQAAmQgLcIpP0cMIZGMWeLIdsV\nK1Y40jsYYsLcJOZTUfbWrVsdKReWzBj2RLkYfo2UELf3oosuUla6//rXvyJllRdeeEENJeNpb8SI\nERHz9u/fX5WJsjEEHSmhXHjWgqxWf9ORrnHyHIyjbr31VjW8/eSTTzpZNMsiARIggYQJpPUQ9ObN\nmwMCukdagmOQjmUI+v7775fp06cblyijnXBLcMxMMWzAAMm69haRiXr16hXySihI63w6LJiDjcNw\nIWIbV6tWLaAMzB0XLFgw4Bh2gpcWwQhr/fr1/8iHAz/88IN06tTJPNeoUSPBw04yEx4WPv30U7PK\n8ePHS5MmTcx93Tc4BK1XD3IIWq/+grQcgnaxz4KDzyPWrhMpeBlPpBCDduqDkZo1RQrsHWwoZqwJ\ntl6PbTjsCE7h5A2uL7gOaznBLINZW/O6tR1cZ/C+W/WyXBIgARKIhUBaD0HXrFnTXJ4Dq9toQ7Wx\nAEUeOIcwLDmxP2DAAHwknLp3726Wgafpvn37mvvBG4jvayRYI7dt29bYDfiEcw4jGARO4G0Yb1ah\nEpYWnXvuueapZs2amdvBGwgsAb5IeFO+9957g7O4vn/XXXcpy2pUVLlyZeW9y/VKWQEJkAAJxEgg\nrYegwQgm5nA+gaUk4RSPlWUsQ9DIj7fgr776Sho0aBBTudY6Im3DoQXmqtu0aRPV9H3ZsmXKGxaU\nr/WBIFT5mF8Gi3r16oU6bR5DHnjKgiK2RlEyM1g28Ia8cuVKtU62ZMmSljPJ20Q//PXXX2re2m/h\nEzkEnbz7yImaOATtBMXkluH2EHTaK2C73RmrArZbbqrzcxlSqnvAfv1UwPaZpfIKKuBU0o+vbrcV\ncFoPQaNLMAcKa91gN4uhugtuGxEsYMOGDaFOx31sy5YtMnv27LAuIOMuOMYL4fAA7jPnz5+vhfMD\nzGeDF95soyW4ikSfoe+YSIAESMBLBHJ4SZhky4K4uldeeaUYxk2wXJ40aVJIMfbv36+GffHjj/li\nBFgIN68asoAwBzH0261bN2WxDM9GkKF8+fJhcrtz+MEHHzQDJWBo+6WXXnKnIgdK/fnnn5W3LHim\nKVCggHz88cdqfjdU0TNnzlTz5Bg2L1WqlBo6xxMtEwmQAAl4gUBavwFjaZChfNEZixYtCtsnCAJg\nWBLjjfGdd94Jm9fOiffff99cLoS3NOuyGTvlxJsXa5atS6Qwv+tla+GpU6eabuEgOxRwuPTuu++a\nbhoRyQlz8kwkQAIk4BUCaa2Aq1atGtAP8NMaLgUbaJUrVy5cVlvHg992nSo3ViHgw9T6Vli4cOGQ\na4BjLc/tfMF8gvet9QefC9635uU2CZAACSSbQFoHYyhTpoxgaHndunVK6bz88stSoUKFkH2AgAFw\nToF5RwxbDx06NKoVcsiCgg5iqQ4sdU+dOiU333yz9OjRI2wggqBLHdnFcDq8VSFoAnggcEPwQ4Ej\nFTlUSI0aNdQbOhyIwMMWljeFs/BGuzBqgfM9e/aUDh06OCSFN4phMAZv9EOsUjAYQ6ykvJOPwRi8\n0xdKElpBe6xDYhDHr31GK+gYOt9DWWgF7aHOiFEUWkHHCIrZSIAESIAESEAnAmk9B6xTR7kpK5bq\nYOgbnrbC+XZ2s34/l/3rr7/KPffco7yhORWUw8+82DYSSCcCab0MKZ06OlxbYdENxWsoByyLWrhw\nYdh51XDl8Pg/CcDCHmxhZ4CEhxtYmTORAAmQAAjwDTjN7wM4IjGUL1BguY6XlyHp1F0Ih2goX8gN\nYz+sSWYiARIgARCgAk7z+wCWtIhdbCT4d7YuSzKO89M+gYoVK6r4zcaVCJARzmLbyMNPEiCB9CHA\nIej06euwLX399dfl66+/Vm4omzZtGjYfT9gjkJGRIXC08tlnn0n+/PmlVatW9gpgbhIgAV8ToAL2\ndffG1jhECerSpYtaf8zh59iYxZoLirdz586xZmc+EiCBNCLg2yFozL3RAX8a3clsKgmQAAloRsCX\nChgerWrVqqX+3nzzTc26hOKSAAmQAAmkAwHfKeADBw7I888/r/oOFqfPPvusGewgHTqUbSQBEiAB\nEtCDgO8UMKxMYfxiJOzT8tSgwU8SIAESIAGvEPCdAkbAhMcee0xy5coliG40bNgwgRN0JhIgARIg\nARLwEgFfaqY77rhDWZ4i0g+Vr5duN8pCAiRAAiRgEPClAkbjcubMabSRnyRAAiRAAiTgOQK+G4L2\nCuGffvpJRo0aJd98841XRKIcJEACJEACHiLg2zfgVDL+8ccfpVOnTsqzFOR46aWXpE2bNqkUiXWT\nAAmQAAl4jIDnFDCCVns5YU45mowLFiwwlS/agrfgm266ycvNUnPlmDOP1jZPNyKMcLH0WZhLPX0Y\n/QUvZlarf08LHKNwWLWAKF3481OCYSjaxu+YPr2K7xbuQ7f6zHMK+Pjx457uHXRENBlr1qwZ0Abs\nR7sm4IIU7KBd+EH3upzxoImlz+IpN9XX4IfhxIkTcurUqVSL4mj9eGBC286cOeNouV4oDL4J+B3z\nQk/EJgNcyZ4+fTpinxUoUCC2wkLk8pwCDiGjdocaN24so0ePlnnz5knt2rXltttu064NFJgESIAE\nSMBdAlTALvFt166d4I+JBEiABEiABEIRoBV0KCo8RgIkQAIkQAIuE6ACdhkwiycBEiABEiCBUASo\ngENR4TESIAESIAEScJkAFbDLgFk8CZAACZAACYQiQAUcigqPkQAJkAAJkIDLBKiAXQbM4kmABEiA\nBEggFAEq4FBUeIwESIAESIAEXCZABewyYBZPAiRAAiRAAqEIUAGHosJjJEACJEACJOAyASpglwGz\neBIgARIgARIIRYAKOBQVHiMBEiABEiABlwlQAbsMmMWTAAmQAAmQQCgCVMChqPAYCZAACZAACbhM\ngArYZcAsngRIgARIgARCEaACDkWFx0iABEiABEjAZQJUwC4DZvEkQAIkQAIkEIpAtsysFOoEj6UX\ngVmzZsmZM2ekTZs26dVwjVs7bNgw6dmzp5QuXVrjVqSP6L/99ptMnz5dHnjggfRptOYt/eCDD9T3\nq2HDhq60JIcrpbJQ7QicPn1a8MekD4ETJ04In5/16a+zZ8/KyZMn9RGYksqpU6fUi4lbKDgE7RZZ\nlksCJEACJEACEQhQAUeAw1MkQAIkQAIk4BYBzgG7RVazcnft2qWGM0uVKqWZ5Okr7tq1a6VixYqS\nJ0+e9IWgUcuPHDkiO3bskEqVKmkkdXqLunXrVvX9Kl68uCsgqIBdwcpCSYAESIAESCAyAQ5BR+bD\nsyRAAiRAAiTgCgEqYFewslASIAESIAESiEyAy5Ai80mLs19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}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The basic idea in `ggplot2` is to map different plot aesthetics to variables in the dataset. In this plot, we map the x-axis to the variable `Sepal.Length` and the y-axis to the variable `Petal.Length`.\n",
"\n",
"#### Add Color\n",
"\n",
"Now suppose, we want to color the points based on the `Species`. `ggplot2` makes it really easy, since all you need to do is map the aesthetic `color` to the variable `Species`."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point(aes(color = Species))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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fxjws9ZGNPR3t3J+muI7lW7Ol8uWMbV0W+nZXpuIyYpGhsjHFq3y5vKWb/LnH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\n"
}
],
"prompt_number": 6
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note that I could have included the color mapping right inside the `ggplot` line, in which case this mapping would have been applicable globally through all layers. If that doesn't make any sense to you right now, don't worry, as we will get there by the end of this tutorial.\n",
"\n",
"#### Add Line\n",
"\n",
"We are interested in the relationship between `Petal.Length` and `Sepal.Length`. So, let us fit a regression line through the scatterplot. Now, before you start thinking you need to run a `lm` command and gather the predictions using `predict`, I will ask you to stop right there and read the next line of code."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point() + geom_smooth(method = \"lm\", \n",
" se = F)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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R7t27V4oXLx5Ldqc8i6UZmGPGJgpGQp74lpPaVY/IY3ftVAZX3s97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\nAhKQgATyCCjAebwcLQEJSEACEihC4A85/ZRqzwwRnwAAAABJRU5ErkJggg==\n"
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"If you are like me when the first time I ran this, you might be thinking this is voodoo! I thought so too, but apparently it is not. It is the beauty of `ggplot2` and the underlying notion of grammar of graphics.\n",
"\n",
"You can extend this idea further and have a regression line plotted for each `Species`."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"%%R\n",
"ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) + geom_point() + \n",
" geom_smooth(method = \"lm\", se = F)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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2jzJHdrf3hNVZxuNtGuUG6Broqkm5Hle+3LARKc1lh4fUS2cdHm8wMgQBPyWA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}
],
"prompt_number": 8
}
],
"metadata": {}
}
]
}
%load_ext rmagic

Introduction to ggplot2

This is a short demo on how to convert an R Markdown Notebook into an IPython Notebook using knitr and notedown.

Adding a Python Chunk

def f(x):
  return x + 2
f(2)

This is an introduction to ggplot2. You can view the source as an R Markdown document, if you are using an IDE like RStudio, or as an IPython notebook, thanks to notedown.

We need to first make sure that we have ggplot2 and its dependencies installed, using the install.packages function.

Now that we have it installed, we can get started by loading it into our workspace

library(ggplot2)

We are now fully set to try and create some amazing plots.

Data

We will use the ubiqutous iris dataset.

head(iris)

Simple Plot

Let us create a simple scatterplot of Sepal.Length with Petal.Length.

ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point()

The basic idea in ggplot2 is to map different plot aesthetics to variables in the dataset. In this plot, we map the x-axis to the variable Sepal.Length and the y-axis to the variable Petal.Length.

Add Color

Now suppose, we want to color the points based on the Species. ggplot2 makes it really easy, since all you need to do is map the aesthetic color to the variable Species.

ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point(aes(color = Species))

Note that I could have included the color mapping right inside the ggplot line, in which case this mapping would have been applicable globally through all layers. If that doesn't make any sense to you right now, don't worry, as we will get there by the end of this tutorial.

Add Line

We are interested in the relationship between Petal.Length and Sepal.Length. So, let us fit a regression line through the scatterplot. Now, before you start thinking you need to run a lm command and gather the predictions using predict, I will ask you to stop right there and read the next line of code.

ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) + geom_point() + geom_smooth(method = "lm", 
    se = F)

If you are like me when the first time I ran this, you might be thinking this is voodoo! I thought so too, but apparently it is not. It is the beauty of ggplot2 and the underlying notion of grammar of graphics.

You can extend this idea further and have a regression line plotted for each Species.

ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) + geom_point() + 
    geom_smooth(method = "lm", se = F)
## Introduction to ggplot2
This is a short demo on how to convert an R Markdown Notebook into an IPython Notebook using knitr and notedown.
Adding a Python Chunk
```{r engine="python"}
def f(x):
return x + 2
f(2)
```
This is an introduction to [ggplot2](http://github.com/hadley/ggplot2). You can view the source as an R Markdown document, if you are using an IDE like RStudio, or as an IPython notebook, thanks to [notedown](https://github.com/aaren/notedown).
We need to first make sure that we have `ggplot2` and its dependencies installed, using the `install.packages` function.
Now that we have it installed, we can get started by loading it into our workspace
```{r}
library(ggplot2)
```
We are now fully set to try and create some amazing plots.
#### Data
We will use the ubiqutous [iris](http://stat.ethz.ch/R-manual/R-patched/library/datasets/html/iris.html) dataset.
```{r}
head(iris)
```
#### Simple Plot
Let us create a simple scatterplot of `Sepal.Length` with `Petal.Length`.
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) +
geom_point()
```
The basic idea in `ggplot2` is to map different plot aesthetics to variables in the dataset. In this plot, we map the x-axis to the variable `Sepal.Length` and the y-axis to the variable `Petal.Length`.
#### Add Color
Now suppose, we want to color the points based on the `Species`. `ggplot2` makes it really easy, since all you need to do is map the aesthetic `color` to the variable `Species`.
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) +
geom_point(aes(color = Species))
```
Note that I could have included the color mapping right inside the `ggplot` line, in which case this mapping would have been applicable globally through all layers. If that doesn't make any sense to you right now, don't worry, as we will get there by the end of this tutorial.
#### Add Line
We are interested in the relationship between `Petal.Length` and `Sepal.Length`. So, let us fit a regression line through the scatterplot. Now, before you start thinking you need to run a `lm` command and gather the predictions using `predict`, I will ask you to stop right there and read the next line of code.
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Petal.Length)) +
geom_point() +
geom_smooth(method = 'lm', se = F)
```
If you are like me when the first time I ran this, you might be thinking this is voodoo! I thought so too, but apparently it is not. It is the beauty of `ggplot2` and the underlying notion of grammar of graphics.
You can extend this idea further and have a regression line plotted for each `Species`.
```{r}
ggplot(iris, aes(x = Sepal.Length, y = Petal.Length, color = Species)) +
geom_point() +
geom_smooth(method = 'lm', se = F)
```
#!/usr/bin/Rscript
library(knitr)
opts_chunk$set(eval = FALSE)
rmd = paste("```\n%load_ext rmagic\n```\n\n",
paste(readLines('example.Rmd'), collapse = '\n'),
collapse = '\n'
)
knit(text = rmd, output = 'example.md')
example.md: example.Rmd
./knit
example.ipynb: example.md
notedown example.md | sed 's/%%r/%%R/' > example.ipynb
@aaren

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aaren commented Mar 7, 2014

I can get this executing without error in the ipython notebook but I can't get the plots to appear without using print.

I get no output with this:

%%R
head(iris)

But I do get output with this:

%%R
out <- head(iris)
print(out)

Any ideas?

This is the first time I've used R.

@ramnathv

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Owner Author

ramnathv commented Apr 6, 2014

I am using IPython 2.0. I had the same problems with the earlier version on IPython. Can you update and check if the problem still persists?

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aaren commented Apr 11, 2014

That fixed it. Now working for me with IPython 2.0

@lecy

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lecy commented Mar 7, 2016

Very nice! Building on this, here is a simple R function to call the conversion from within the R environment:

https://github.com/lecy/RMD-to-Jupyter/blob/master/README.md

library(tools)

rmd2jupyter <- function( filename, path=getwd() ) 
{
  path_in <- paste( path, "/", filename, " ", sep="" )
  path_out <- paste(path, "/", file_path_sans_ext(filename), ".ipynb", sep="")
  full_shell <- paste("notedown ", path_in, " --rmagic --run > ", path_out, sep="")
  shell(full_shell)
}

# download the example.Rmd file from this repository
# setwd(...) to where the example.Rmd file is located

rmd2jupyter( "example.Rmd" )
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