notebooks/LeyLabRMisc_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": "true"
   },
   "source": [
    " # Table of Contents\n",
    "<div class=\"toc\" style=\"margin-top: 1em;\"><ul class=\"toc-item\" id=\"toc-level0\"><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#Installing-LeyLabRMisc-R-package-with-renv\" data-toc-modified-id=\"Installing-LeyLabRMisc-R-package-with-renv-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Installing LeyLabRMisc R package with renv</a></span></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#Init\" data-toc-modified-id=\"Init-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Init</a></span></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#Init\" data-toc-modified-id=\"Init-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Init</a></span><ul class=\"toc-item\"><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#df.dims()\" data-toc-modified-id=\"df.dims()-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>df.dims()</a></span></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#as.Num()\" data-toc-modified-id=\"as.Num()-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>as.Num()</a></span></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#summary_x()\" data-toc-modified-id=\"summary_x()-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>summary_x()</a></span></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#unique_n()\" data-toc-modified-id=\"unique_n()-3.4\"><span class=\"toc-item-num\">3.4&nbsp;&nbsp;</span>unique_n()</a></span></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#overlap()\" data-toc-modified-id=\"overlap()-3.5\"><span class=\"toc-item-num\">3.5&nbsp;&nbsp;</span>overlap()</a></span></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#size_objects()\" data-toc-modified-id=\"size_objects()-3.6\"><span class=\"toc-item-num\">3.6&nbsp;&nbsp;</span>size_objects()</a></span></li></ul></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#IO\" data-toc-modified-id=\"IO-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>IO</a></span><ul class=\"toc-item\"><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#list_files()\" data-toc-modified-id=\"list_files()-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>list_files()</a></span></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#make_dir()\" data-toc-modified-id=\"make_dir()-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>make_dir()</a></span></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#write_table()\" data-toc-modified-id=\"write_table()-4.3\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>write_table()</a></span></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#Fread()\" data-toc-modified-id=\"Fread()-4.4\"><span class=\"toc-item-num\">4.4&nbsp;&nbsp;</span>Fread()</a></span></li></ul></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#Plot\" data-toc-modified-id=\"Plot-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Plot</a></span><ul class=\"toc-item\"><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#p.dims()\" data-toc-modified-id=\"p.dims()-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>p.dims()</a></span></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#Plot()\" data-toc-modified-id=\"Plot()-5.2\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>Plot()</a></span></li></ul></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#System\" data-toc-modified-id=\"System-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>System</a></span><ul class=\"toc-item\"><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#bash_job()\" data-toc-modified-id=\"bash_job()-6.1\"><span class=\"toc-item-num\">6.1&nbsp;&nbsp;</span>bash_job()</a></span></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#cat_file()\" data-toc-modified-id=\"cat_file()-6.2\"><span class=\"toc-item-num\">6.2&nbsp;&nbsp;</span>cat_file()</a></span></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#send_email()\" data-toc-modified-id=\"send_email()-6.3\"><span class=\"toc-item-num\">6.3&nbsp;&nbsp;</span>send_email()</a></span></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#conda_info()\" data-toc-modified-id=\"conda_info()-6.4\"><span class=\"toc-item-num\">6.4&nbsp;&nbsp;</span>conda_info()</a></span></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#pipelineInfo\" data-toc-modified-id=\"pipelineInfo-6.5\"><span class=\"toc-item-num\">6.5&nbsp;&nbsp;</span>pipelineInfo</a></span></li></ul></li><li><span><a href=\"http://localhost:7501/notebooks/leylabrmisc/notebooks/LeyLabRMisc_tutorial.ipynb#sessionInfo\" data-toc-modified-id=\"sessionInfo-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>sessionInfo</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#Installing-LeyLabRMisc-R-package-with-renv\" data-toc-modified-id=\"Installing-LeyLabRMisc-R-package-with-renv-1\"><span class=\"toc-item-num\">1&nbsp;&nbsp;</span>Installing LeyLabRMisc R package with renv</a></span></li><li><span><a href=\"#Init\" data-toc-modified-id=\"Init-2\"><span class=\"toc-item-num\">2&nbsp;&nbsp;</span>Init</a></span></li><li><span><a href=\"#Init\" data-toc-modified-id=\"Init-3\"><span class=\"toc-item-num\">3&nbsp;&nbsp;</span>Init</a></span><ul class=\"toc-item\"><li><span><a href=\"#df.dims()\" data-toc-modified-id=\"df.dims()-3.1\"><span class=\"toc-item-num\">3.1&nbsp;&nbsp;</span>df.dims()</a></span></li><li><span><a href=\"#as.Num()\" data-toc-modified-id=\"as.Num()-3.2\"><span class=\"toc-item-num\">3.2&nbsp;&nbsp;</span>as.Num()</a></span></li><li><span><a href=\"#summary_x()\" data-toc-modified-id=\"summary_x()-3.3\"><span class=\"toc-item-num\">3.3&nbsp;&nbsp;</span>summary_x()</a></span></li><li><span><a href=\"#unique_n()\" data-toc-modified-id=\"unique_n()-3.4\"><span class=\"toc-item-num\">3.4&nbsp;&nbsp;</span>unique_n()</a></span></li><li><span><a href=\"#overlap()\" data-toc-modified-id=\"overlap()-3.5\"><span class=\"toc-item-num\">3.5&nbsp;&nbsp;</span>overlap()</a></span></li></ul></li><li><span><a href=\"#IO\" data-toc-modified-id=\"IO-4\"><span class=\"toc-item-num\">4&nbsp;&nbsp;</span>IO</a></span><ul class=\"toc-item\"><li><span><a href=\"#list_files()\" data-toc-modified-id=\"list_files()-4.1\"><span class=\"toc-item-num\">4.1&nbsp;&nbsp;</span>list_files()</a></span></li><li><span><a href=\"#make_dir()\" data-toc-modified-id=\"make_dir()-4.2\"><span class=\"toc-item-num\">4.2&nbsp;&nbsp;</span>make_dir()</a></span></li><li><span><a href=\"#write_table()\" data-toc-modified-id=\"write_table()-4.3\"><span class=\"toc-item-num\">4.3&nbsp;&nbsp;</span>write_table()</a></span></li><li><span><a href=\"#Fread()\" data-toc-modified-id=\"Fread()-4.4\"><span class=\"toc-item-num\">4.4&nbsp;&nbsp;</span>Fread()</a></span></li></ul></li><li><span><a href=\"#Plot\" data-toc-modified-id=\"Plot-5\"><span class=\"toc-item-num\">5&nbsp;&nbsp;</span>Plot</a></span><ul class=\"toc-item\"><li><span><a href=\"#p.dims()\" data-toc-modified-id=\"p.dims()-5.1\"><span class=\"toc-item-num\">5.1&nbsp;&nbsp;</span>p.dims()</a></span></li><li><span><a href=\"#Plot()\" data-toc-modified-id=\"Plot()-5.2\"><span class=\"toc-item-num\">5.2&nbsp;&nbsp;</span>Plot()</a></span></li></ul></li><li><span><a href=\"#System\" data-toc-modified-id=\"System-6\"><span class=\"toc-item-num\">6&nbsp;&nbsp;</span>System</a></span><ul class=\"toc-item\"><li><span><a href=\"#bash_job()\" data-toc-modified-id=\"bash_job()-6.1\"><span class=\"toc-item-num\">6.1&nbsp;&nbsp;</span>bash_job()</a></span></li><li><span><a href=\"#cat_file()\" data-toc-modified-id=\"cat_file()-6.2\"><span class=\"toc-item-num\">6.2&nbsp;&nbsp;</span>cat_file()</a></span></li><li><span><a href=\"#send_email()\" data-toc-modified-id=\"send_email()-6.3\"><span class=\"toc-item-num\">6.3&nbsp;&nbsp;</span>send_email()</a></span></li><li><span><a href=\"#conda_info()\" data-toc-modified-id=\"conda_info()-6.4\"><span class=\"toc-item-num\">6.4&nbsp;&nbsp;</span>conda_info()</a></span></li><li><span><a href=\"#pipelineInfo\" data-toc-modified-id=\"pipelineInfo-6.5\"><span class=\"toc-item-num\">6.5&nbsp;&nbsp;</span>pipelineInfo</a></span></li></ul></li><li><span><a href=\"#sessionInfo\" data-toc-modified-id=\"sessionInfo-7\"><span class=\"toc-item-num\">7&nbsp;&nbsp;</span>sessionInfo</a></span></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Installing LeyLabRMisc R package with renv\n",
    "\n",
    "```\n",
    "conda activate /path/to/my/conda/env/\n",
    "R\n",
    "> renv::install('leylabmpi/LeyLabRMisc')\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Init"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.283126Z",
     "start_time": "2020-03-27T19:50:27.172Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "Attaching package: ‘dplyr’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    filter, lag\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    intersect, setdiff, setequal, union\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘data.table’\n",
      "\n",
      "\n",
      "The following objects are masked from ‘package:dplyr’:\n",
      "\n",
      "    between, first, last\n",
      "\n",
      "\n",
      "\n",
      "Attaching package: ‘tidytable’\n",
      "\n",
      "\n",
      "The following object is masked from ‘package:stats’:\n",
      "\n",
      "    dt\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# load libraries\n",
    "library(dplyr)\n",
    "library(tidyr)\n",
    "library(ggplot2)\n",
    "library(data.table)\n",
    "library(tidytable)\n",
    "library(LeyLabRMisc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.552847Z",
     "start_time": "2020-03-27T19:50:27.177Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# init params\n",
    "## setting threads for data.tables\n",
    "setDTthreads(4)\n",
    "## formatting table printing\n",
    "df.dims()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Init\n",
    "\n",
    "* basic convenience functions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## df.dims()\n",
    "\n",
    "* formatting table printing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.595950Z",
     "start_time": "2020-03-27T19:50:27.181Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 32 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Mazda RX4</th><td>21</td><td>6</td><td>160</td><td>110</td><td>3.9</td><td>2.620</td><td>16.46</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Mazda RX4 Wag</th><td>21</td><td>6</td><td>160</td><td>110</td><td>3.9</td><td>2.875</td><td>17.02</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><th scope=row>Maserati Bora</th><td>15.0</td><td>8</td><td>301</td><td>335</td><td>3.54</td><td>3.57</td><td>14.6</td><td>0</td><td>1</td><td>5</td><td>8</td></tr>\n",
       "\t<tr><th scope=row>Volvo 142E</th><td>21.4</td><td>4</td><td>121</td><td>109</td><td>4.11</td><td>2.78</td><td>18.6</td><td>1</td><td>1</td><td>4</td><td>2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 32 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tMazda RX4 & 21 & 6 & 160 & 110 & 3.9 & 2.620 & 16.46 & 0 & 1 & 4 & 4\\\\\n",
       "\tMazda RX4 Wag & 21 & 6 & 160 & 110 & 3.9 & 2.875 & 17.02 & 0 & 1 & 4 & 4\\\\\n",
       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\tMaserati Bora & 15.0 & 8 & 301 & 335 & 3.54 & 3.57 & 14.6 & 0 & 1 & 5 & 8\\\\\n",
       "\tVolvo 142E & 21.4 & 4 & 121 & 109 & 4.11 & 2.78 & 18.6 & 1 & 1 & 4 & 2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 32 × 11\n",
       "\n",
       "| <!--/--> | mpg &lt;dbl&gt; | cyl &lt;dbl&gt; | disp &lt;dbl&gt; | hp &lt;dbl&gt; | drat &lt;dbl&gt; | wt &lt;dbl&gt; | qsec &lt;dbl&gt; | vs &lt;dbl&gt; | am &lt;dbl&gt; | gear &lt;dbl&gt; | carb &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Mazda RX4 | 21 | 6 | 160 | 110 | 3.9 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |\n",
       "| Mazda RX4 Wag | 21 | 6 | 160 | 110 | 3.9 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| Maserati Bora | 15.0 | 8 | 301 | 335 | 3.54 | 3.57 | 14.6 | 0 | 1 | 5 | 8 |\n",
       "| Volvo 142E | 21.4 | 4 | 121 | 109 | 4.11 | 2.78 | 18.6 | 1 | 1 | 4 | 2 |\n",
       "\n"
      ],
      "text/plain": [
       "              mpg  cyl disp hp  drat wt    qsec  vs am gear carb\n",
       "Mazda RX4     21   6   160  110 3.9  2.620 16.46 0  1  4    4   \n",
       "Mazda RX4 Wag 21   6   160  110 3.9  2.875 17.02 0  1  4    4   \n",
       "⋮             ⋮    ⋮   ⋮    ⋮   ⋮    ⋮     ⋮     ⋮  ⋮  ⋮    ⋮   \n",
       "Maserati Bora 15.0 8   301  335 3.54 3.57  14.6  0  1  5    8   \n",
       "Volvo 142E    21.4 4   121  109 4.11 2.78  18.6  1  1  4    2   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtcars "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.631964Z",
     "start_time": "2020-03-27T19:50:27.184Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 32 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Mazda RX4</th><td>21.0</td><td>6</td><td>160</td><td>110</td><td>3.90</td><td>2.620</td><td>16.46</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Mazda RX4 Wag</th><td>21.0</td><td>6</td><td>160</td><td>110</td><td>3.90</td><td>2.875</td><td>17.02</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Datsun 710</th><td>22.8</td><td>4</td><td>108</td><td> 93</td><td>3.85</td><td>2.320</td><td>18.61</td><td>1</td><td>1</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Hornet 4 Drive</th><td>21.4</td><td>6</td><td>258</td><td>110</td><td>3.08</td><td>3.215</td><td>19.44</td><td>1</td><td>0</td><td>3</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>Hornet Sportabout</th><td>18.7</td><td>8</td><td>360</td><td>175</td><td>3.15</td><td>3.440</td><td>17.02</td><td>0</td><td>0</td><td>3</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><th scope=row>Lotus Europa</th><td>30.4</td><td>4</td><td> 95.1</td><td>113</td><td>3.77</td><td>1.513</td><td>16.9</td><td>1</td><td>1</td><td>5</td><td>2</td></tr>\n",
       "\t<tr><th scope=row>Ford Pantera L</th><td>15.8</td><td>8</td><td>351.0</td><td>264</td><td>4.22</td><td>3.170</td><td>14.5</td><td>0</td><td>1</td><td>5</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Ferrari Dino</th><td>19.7</td><td>6</td><td>145.0</td><td>175</td><td>3.62</td><td>2.770</td><td>15.5</td><td>0</td><td>1</td><td>5</td><td>6</td></tr>\n",
       "\t<tr><th scope=row>Maserati Bora</th><td>15.0</td><td>8</td><td>301.0</td><td>335</td><td>3.54</td><td>3.570</td><td>14.6</td><td>0</td><td>1</td><td>5</td><td>8</td></tr>\n",
       "\t<tr><th scope=row>Volvo 142E</th><td>21.4</td><td>4</td><td>121.0</td><td>109</td><td>4.11</td><td>2.780</td><td>18.6</td><td>1</td><td>1</td><td>4</td><td>2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 32 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tMazda RX4 & 21.0 & 6 & 160 & 110 & 3.90 & 2.620 & 16.46 & 0 & 1 & 4 & 4\\\\\n",
       "\tMazda RX4 Wag & 21.0 & 6 & 160 & 110 & 3.90 & 2.875 & 17.02 & 0 & 1 & 4 & 4\\\\\n",
       "\tDatsun 710 & 22.8 & 4 & 108 &  93 & 3.85 & 2.320 & 18.61 & 1 & 1 & 4 & 1\\\\\n",
       "\tHornet 4 Drive & 21.4 & 6 & 258 & 110 & 3.08 & 3.215 & 19.44 & 1 & 0 & 3 & 1\\\\\n",
       "\tHornet Sportabout & 18.7 & 8 & 360 & 175 & 3.15 & 3.440 & 17.02 & 0 & 0 & 3 & 2\\\\\n",
       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\tLotus Europa & 30.4 & 4 &  95.1 & 113 & 3.77 & 1.513 & 16.9 & 1 & 1 & 5 & 2\\\\\n",
       "\tFord Pantera L & 15.8 & 8 & 351.0 & 264 & 4.22 & 3.170 & 14.5 & 0 & 1 & 5 & 4\\\\\n",
       "\tFerrari Dino & 19.7 & 6 & 145.0 & 175 & 3.62 & 2.770 & 15.5 & 0 & 1 & 5 & 6\\\\\n",
       "\tMaserati Bora & 15.0 & 8 & 301.0 & 335 & 3.54 & 3.570 & 14.6 & 0 & 1 & 5 & 8\\\\\n",
       "\tVolvo 142E & 21.4 & 4 & 121.0 & 109 & 4.11 & 2.780 & 18.6 & 1 & 1 & 4 & 2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 32 × 11\n",
       "\n",
       "| <!--/--> | mpg &lt;dbl&gt; | cyl &lt;dbl&gt; | disp &lt;dbl&gt; | hp &lt;dbl&gt; | drat &lt;dbl&gt; | wt &lt;dbl&gt; | qsec &lt;dbl&gt; | vs &lt;dbl&gt; | am &lt;dbl&gt; | gear &lt;dbl&gt; | carb &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Mazda RX4 | 21.0 | 6 | 160 | 110 | 3.90 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |\n",
       "| Mazda RX4 Wag | 21.0 | 6 | 160 | 110 | 3.90 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |\n",
       "| Datsun 710 | 22.8 | 4 | 108 |  93 | 3.85 | 2.320 | 18.61 | 1 | 1 | 4 | 1 |\n",
       "| Hornet 4 Drive | 21.4 | 6 | 258 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 |\n",
       "| Hornet Sportabout | 18.7 | 8 | 360 | 175 | 3.15 | 3.440 | 17.02 | 0 | 0 | 3 | 2 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| Lotus Europa | 30.4 | 4 |  95.1 | 113 | 3.77 | 1.513 | 16.9 | 1 | 1 | 5 | 2 |\n",
       "| Ford Pantera L | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.170 | 14.5 | 0 | 1 | 5 | 4 |\n",
       "| Ferrari Dino | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.770 | 15.5 | 0 | 1 | 5 | 6 |\n",
       "| Maserati Bora | 15.0 | 8 | 301.0 | 335 | 3.54 | 3.570 | 14.6 | 0 | 1 | 5 | 8 |\n",
       "| Volvo 142E | 21.4 | 4 | 121.0 | 109 | 4.11 | 2.780 | 18.6 | 1 | 1 | 4 | 2 |\n",
       "\n"
      ],
      "text/plain": [
       "                  mpg  cyl disp  hp  drat wt    qsec  vs am gear carb\n",
       "Mazda RX4         21.0 6   160   110 3.90 2.620 16.46 0  1  4    4   \n",
       "Mazda RX4 Wag     21.0 6   160   110 3.90 2.875 17.02 0  1  4    4   \n",
       "Datsun 710        22.8 4   108    93 3.85 2.320 18.61 1  1  4    1   \n",
       "Hornet 4 Drive    21.4 6   258   110 3.08 3.215 19.44 1  0  3    1   \n",
       "Hornet Sportabout 18.7 8   360   175 3.15 3.440 17.02 0  0  3    2   \n",
       "⋮                 ⋮    ⋮   ⋮     ⋮   ⋮    ⋮     ⋮     ⋮  ⋮  ⋮    ⋮   \n",
       "Lotus Europa      30.4 4    95.1 113 3.77 1.513 16.9  1  1  5    2   \n",
       "Ford Pantera L    15.8 8   351.0 264 4.22 3.170 14.5  0  1  5    4   \n",
       "Ferrari Dino      19.7 6   145.0 175 3.62 2.770 15.5  0  1  5    6   \n",
       "Maserati Bora     15.0 8   301.0 335 3.54 3.570 14.6  0  1  5    8   \n",
       "Volvo 142E        21.4 4   121.0 109 4.11 2.780 18.6  1  1  4    2   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.dims(10)\n",
    "mtcars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.663133Z",
     "start_time": "2020-03-27T19:50:27.186Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 32 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>⋯</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>⋯</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Mazda RX4</th><td>21</td><td>6</td><td>160</td><td>110</td><td>⋯</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Mazda RX4 Wag</th><td>21</td><td>6</td><td>160</td><td>110</td><td>⋯</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋱</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><th scope=row>Maserati Bora</th><td>15.0</td><td>8</td><td>301</td><td>335</td><td>⋯</td><td>0</td><td>1</td><td>5</td><td>8</td></tr>\n",
       "\t<tr><th scope=row>Volvo 142E</th><td>21.4</td><td>4</td><td>121</td><td>109</td><td>⋯</td><td>1</td><td>1</td><td>4</td><td>2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 32 × 11\n",
       "\\begin{tabular}{r|lllllllll}\n",
       "  & mpg & cyl & disp & hp & ⋯ & vs & am & gear & carb\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & ⋯ & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tMazda RX4 & 21 & 6 & 160 & 110 & ⋯ & 0 & 1 & 4 & 4\\\\\n",
       "\tMazda RX4 Wag & 21 & 6 & 160 & 110 & ⋯ & 0 & 1 & 4 & 4\\\\\n",
       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋱ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\tMaserati Bora & 15.0 & 8 & 301 & 335 & ⋯ & 0 & 1 & 5 & 8\\\\\n",
       "\tVolvo 142E & 21.4 & 4 & 121 & 109 & ⋯ & 1 & 1 & 4 & 2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 32 × 11\n",
       "\n",
       "| <!--/--> | mpg &lt;dbl&gt; | cyl &lt;dbl&gt; | disp &lt;dbl&gt; | hp &lt;dbl&gt; | ⋯ ⋯ | vs &lt;dbl&gt; | am &lt;dbl&gt; | gear &lt;dbl&gt; | carb &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| Mazda RX4 | 21 | 6 | 160 | 110 | ⋯ | 0 | 1 | 4 | 4 |\n",
       "| Mazda RX4 Wag | 21 | 6 | 160 | 110 | ⋯ | 0 | 1 | 4 | 4 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋱ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| Maserati Bora | 15.0 | 8 | 301 | 335 | ⋯ | 0 | 1 | 5 | 8 |\n",
       "| Volvo 142E | 21.4 | 4 | 121 | 109 | ⋯ | 1 | 1 | 4 | 2 |\n",
       "\n"
      ],
      "text/plain": [
       "              mpg  cyl disp hp  ⋯ vs am gear carb\n",
       "Mazda RX4     21   6   160  110 ⋯ 0  1  4    4   \n",
       "Mazda RX4 Wag 21   6   160  110 ⋯ 0  1  4    4   \n",
       "⋮             ⋮    ⋮   ⋮    ⋮   ⋱ ⋮  ⋮  ⋮    ⋮   \n",
       "Maserati Bora 15.0 8   301  335 ⋯ 0  1  5    8   \n",
       "Volvo 142E    21.4 4   121  109 ⋯ 1  1  4    2   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.dims(4,8)\n",
    "mtcars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.701592Z",
     "start_time": "2020-03-27T19:50:27.189Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 32 × 11</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>mpg</th><th scope=col>cyl</th><th scope=col>disp</th><th scope=col>hp</th><th scope=col>drat</th><th scope=col>wt</th><th scope=col>qsec</th><th scope=col>vs</th><th scope=col>am</th><th scope=col>gear</th><th scope=col>carb</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Mazda RX4</th><td>21</td><td>6</td><td>160</td><td>110</td><td>3.9</td><td>2.620</td><td>16.46</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>Mazda RX4 Wag</th><td>21</td><td>6</td><td>160</td><td>110</td><td>3.9</td><td>2.875</td><td>17.02</td><td>0</td><td>1</td><td>4</td><td>4</td></tr>\n",
       "\t<tr><th scope=row>⋮</th><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><th scope=row>Maserati Bora</th><td>15.0</td><td>8</td><td>301</td><td>335</td><td>3.54</td><td>3.57</td><td>14.6</td><td>0</td><td>1</td><td>5</td><td>8</td></tr>\n",
       "\t<tr><th scope=row>Volvo 142E</th><td>21.4</td><td>4</td><td>121</td><td>109</td><td>4.11</td><td>2.78</td><td>18.6</td><td>1</td><td>1</td><td>4</td><td>2</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 32 × 11\n",
       "\\begin{tabular}{r|lllllllllll}\n",
       "  & mpg & cyl & disp & hp & drat & wt & qsec & vs & am & gear & carb\\\\\n",
       "  & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tMazda RX4 & 21 & 6 & 160 & 110 & 3.9 & 2.620 & 16.46 & 0 & 1 & 4 & 4\\\\\n",
       "\tMazda RX4 Wag & 21 & 6 & 160 & 110 & 3.9 & 2.875 & 17.02 & 0 & 1 & 4 & 4\\\\\n",
       "\t⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\tMaserati Bora & 15.0 & 8 & 301 & 335 & 3.54 & 3.57 & 14.6 & 0 & 1 & 5 & 8\\\\\n",
       "\tVolvo 142E & 21.4 & 4 & 121 & 109 & 4.11 & 2.78 & 18.6 & 1 & 1 & 4 & 2\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 32 × 11\n",
       "\n",
       "| <!--/--> | mpg &lt;dbl&gt; | cyl &lt;dbl&gt; | disp &lt;dbl&gt; | hp &lt;dbl&gt; | drat &lt;dbl&gt; | wt &lt;dbl&gt; | qsec &lt;dbl&gt; | vs &lt;dbl&gt; | am &lt;dbl&gt; | gear &lt;dbl&gt; | carb &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Mazda RX4 | 21 | 6 | 160 | 110 | 3.9 | 2.620 | 16.46 | 0 | 1 | 4 | 4 |\n",
       "| Mazda RX4 Wag | 21 | 6 | 160 | 110 | 3.9 | 2.875 | 17.02 | 0 | 1 | 4 | 4 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| Maserati Bora | 15.0 | 8 | 301 | 335 | 3.54 | 3.57 | 14.6 | 0 | 1 | 5 | 8 |\n",
       "| Volvo 142E | 21.4 | 4 | 121 | 109 | 4.11 | 2.78 | 18.6 | 1 | 1 | 4 | 2 |\n",
       "\n"
      ],
      "text/plain": [
       "              mpg  cyl disp hp  drat wt    qsec  vs am gear carb\n",
       "Mazda RX4     21   6   160  110 3.9  2.620 16.46 0  1  4    4   \n",
       "Mazda RX4 Wag 21   6   160  110 3.9  2.875 17.02 0  1  4    4   \n",
       "⋮             ⋮    ⋮   ⋮    ⋮   ⋮    ⋮     ⋮     ⋮  ⋮  ⋮    ⋮   \n",
       "Maserati Bora 15.0 8   301  335 3.54 3.57  14.6  0  1  5    8   \n",
       "Volvo 142E    21.4 4   121  109 4.11 2.78  18.6  1  1  4    2   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.dims()\n",
    "mtcars"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## as.Num()\n",
    "\n",
    "* A wrapper for `as.numeric(as.character())`, which is useful for working with factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.721557Z",
     "start_time": "2020-03-27T19:50:27.192Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>5</li>\n",
       "\t<li>3</li>\n",
       "\t<li>7</li>\n",
       "\t<li>2</li>\n",
       "\t<li>8</li>\n",
       "</ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<ol class=list-inline>\n",
       "\t\t<li>'2'</li>\n",
       "\t\t<li>'3'</li>\n",
       "\t\t<li>'5'</li>\n",
       "\t\t<li>'7'</li>\n",
       "\t\t<li>'8'</li>\n",
       "\t</ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5\n",
       "\\item 3\n",
       "\\item 7\n",
       "\\item 2\n",
       "\\item 8\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item '2'\n",
       "\\item '3'\n",
       "\\item '5'\n",
       "\\item '7'\n",
       "\\item '8'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5\n",
       "2. 3\n",
       "3. 7\n",
       "4. 2\n",
       "5. 8\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. '2'\n",
       "2. '3'\n",
       "3. '5'\n",
       "4. '7'\n",
       "5. '8'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 5 3 7 2 8\n",
       "Levels: 2 3 5 7 8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fac = factor(c(5,3,7,2,8))\n",
    "fac"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.739318Z",
     "start_time": "2020-03-27T19:50:27.195Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>5</li>\n",
       "\t<li>3</li>\n",
       "\t<li>7</li>\n",
       "\t<li>2</li>\n",
       "\t<li>8</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5\n",
       "\\item 3\n",
       "\\item 7\n",
       "\\item 2\n",
       "\\item 8\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5\n",
       "2. 3\n",
       "3. 7\n",
       "4. 2\n",
       "5. 8\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 5 3 7 2 8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "vec = c(5,3,7,2,8)\n",
    "vec"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.761554Z",
     "start_time": "2020-03-27T19:50:27.198Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>3</li>\n",
       "\t<li>2</li>\n",
       "\t<li>4</li>\n",
       "\t<li>1</li>\n",
       "\t<li>5</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 3\n",
       "\\item 2\n",
       "\\item 4\n",
       "\\item 1\n",
       "\\item 5\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 3\n",
       "2. 2\n",
       "3. 4\n",
       "4. 1\n",
       "5. 5\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 3 2 4 1 5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "as.numeric(fac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.778096Z",
     "start_time": "2020-03-27T19:50:27.200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>5</li>\n",
       "\t<li>3</li>\n",
       "\t<li>7</li>\n",
       "\t<li>2</li>\n",
       "\t<li>8</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5\n",
       "\\item 3\n",
       "\\item 7\n",
       "\\item 2\n",
       "\\item 8\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5\n",
       "2. 3\n",
       "3. 7\n",
       "4. 2\n",
       "5. 8\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 5 3 7 2 8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "as.Num(fac)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.793448Z",
     "start_time": "2020-03-27T19:50:27.203Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>5</li>\n",
       "\t<li>3</li>\n",
       "\t<li>7</li>\n",
       "\t<li>2</li>\n",
       "\t<li>8</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 5\n",
       "\\item 3\n",
       "\\item 7\n",
       "\\item 2\n",
       "\\item 8\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 5\n",
       "2. 3\n",
       "3. 7\n",
       "4. 2\n",
       "5. 8\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 5 3 7 2 8"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "as.numeric(vec)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## summary_x()\n",
    "\n",
    "* `summary()` with more info and better formatting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.811342Z",
     "start_time": "2020-03-27T19:50:27.205Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. \n",
       "  10.40   15.43   19.20   20.09   22.80   33.90 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtcars$mpg %>% summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.831865Z",
     "start_time": "2020-03-27T19:50:27.208Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 1 × 8 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Min.</th><th scope=col>1st Qu.</th><th scope=col>Median</th><th scope=col>Mean</th><th scope=col>3rd Qu.</th><th scope=col>Max.</th><th scope=col>sd</th><th scope=col>sd_err_of_mean</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>.</th><td>10.4</td><td>15.425</td><td>19.2</td><td>20.09062</td><td>22.8</td><td>33.9</td><td>7.928</td><td>3.236</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 8 of type dbl\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & Min. & 1st Qu. & Median & Mean & 3rd Qu. & Max. & sd & sd\\_err\\_of\\_mean\\\\\n",
       "\\hline\n",
       "\t. & 10.4 & 15.425 & 19.2 & 20.09062 & 22.8 & 33.9 & 7.928 & 3.236\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 8 of type dbl\n",
       "\n",
       "| <!--/--> | Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | sd | sd_err_of_mean |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| . | 10.4 | 15.425 | 19.2 | 20.09062 | 22.8 | 33.9 | 7.928 | 3.236 |\n",
       "\n"
      ],
      "text/plain": [
       "  Min. 1st Qu. Median Mean     3rd Qu. Max. sd    sd_err_of_mean\n",
       ". 10.4 15.425  19.2   20.09062 22.8    33.9 7.928 3.236         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtcars$mpg %>% summary_x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.859546Z",
     "start_time": "2020-03-27T19:50:27.211Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 1 × 8 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Min.</th><th scope=col>1st Qu.</th><th scope=col>Median</th><th scope=col>Mean</th><th scope=col>3rd Qu.</th><th scope=col>Max.</th><th scope=col>sd</th><th scope=col>sd_err_of_mean</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Miles per gallon</th><td>10.4</td><td>15.425</td><td>19.2</td><td>20.09062</td><td>22.8</td><td>33.9</td><td>7.928</td><td>3.236</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 8 of type dbl\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & Min. & 1st Qu. & Median & Mean & 3rd Qu. & Max. & sd & sd\\_err\\_of\\_mean\\\\\n",
       "\\hline\n",
       "\tMiles per gallon & 10.4 & 15.425 & 19.2 & 20.09062 & 22.8 & 33.9 & 7.928 & 3.236\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 8 of type dbl\n",
       "\n",
       "| <!--/--> | Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | sd | sd_err_of_mean |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| Miles per gallon | 10.4 | 15.425 | 19.2 | 20.09062 | 22.8 | 33.9 | 7.928 | 3.236 |\n",
       "\n"
      ],
      "text/plain": [
       "                 Min. 1st Qu. Median Mean     3rd Qu. Max. sd    sd_err_of_mean\n",
       "Miles per gallon 10.4 15.425  19.2   20.09062 22.8    33.9 7.928 3.236         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtcars$mpg %>% summary_x('Miles per gallon')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.881649Z",
     "start_time": "2020-03-27T19:50:27.214Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 1 × 8 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Min.</th><th scope=col>1st Qu.</th><th scope=col>Median</th><th scope=col>Mean</th><th scope=col>3rd Qu.</th><th scope=col>Max.</th><th scope=col>sd</th><th scope=col>sd_err_of_mean</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Miles per gallon</th><td>10.4</td><td>15.425</td><td>19.2</td><td>20.09062</td><td>22.8</td><td>33.9</td><td>7.928</td><td>3.236</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 8 of type dbl\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & Min. & 1st Qu. & Median & Mean & 3rd Qu. & Max. & sd & sd\\_err\\_of\\_mean\\\\\n",
       "\\hline\n",
       "\tMiles per gallon & 10.4 & 15.425 & 19.2 & 20.09062 & 22.8 & 33.9 & 7.928 & 3.236\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 8 of type dbl\n",
       "\n",
       "| <!--/--> | Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | sd | sd_err_of_mean |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| Miles per gallon | 10.4 | 15.425 | 19.2 | 20.09062 | 22.8 | 33.9 | 7.928 | 3.236 |\n",
       "\n"
      ],
      "text/plain": [
       "                 Min. 1st Qu. Median Mean     3rd Qu. Max. sd    sd_err_of_mean\n",
       "Miles per gallon 10.4 15.425  19.2   20.09062 22.8    33.9 7.928 3.236         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtcars %>% summary_x('Miles per gallon', mpg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.912297Z",
     "start_time": "2020-03-27T19:50:27.216Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A matrix: 1 × 8 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>Min.</th><th scope=col>1st Qu.</th><th scope=col>Median</th><th scope=col>Mean</th><th scope=col>3rd Qu.</th><th scope=col>Max.</th><th scope=col>sd</th><th scope=col>sd_err_of_mean</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Miles per gallon</th><td>10.4</td><td>15.425</td><td>19.2</td><td>20.09062</td><td>22.8</td><td>33.9</td><td>7.928</td><td>3.236</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 1 × 8 of type dbl\n",
       "\\begin{tabular}{r|llllllll}\n",
       "  & Min. & 1st Qu. & Median & Mean & 3rd Qu. & Max. & sd & sd\\_err\\_of\\_mean\\\\\n",
       "\\hline\n",
       "\tMiles per gallon & 10.4 & 15.425 & 19.2 & 20.09062 & 22.8 & 33.9 & 7.928 & 3.236\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 1 × 8 of type dbl\n",
       "\n",
       "| <!--/--> | Min. | 1st Qu. | Median | Mean | 3rd Qu. | Max. | sd | sd_err_of_mean |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| Miles per gallon | 10.4 | 15.425 | 19.2 | 20.09062 | 22.8 | 33.9 | 7.928 | 3.236 |\n",
       "\n"
      ],
      "text/plain": [
       "                 Min. 1st Qu. Median Mean     3rd Qu. Max. sd    sd_err_of_mean\n",
       "Miles per gallon 10.4 15.425  19.2   20.09062 22.8    33.9 7.928 3.236         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# data.table\n",
    "mtcars %>% as.data.table %>% summary_x('Miles per gallon', mpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## unique_n()\n",
    "\n",
    "* Pretty print of number of unique elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.970244Z",
     "start_time": "2020-03-27T19:50:27.219Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 53940 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>carat</th><th scope=col>cut</th><th scope=col>color</th><th scope=col>clarity</th><th scope=col>depth</th><th scope=col>table</th><th scope=col>price</th><th scope=col>x</th><th scope=col>y</th><th scope=col>z</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;ord&gt;</th><th scope=col>&lt;ord&gt;</th><th scope=col>&lt;ord&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.23</td><td>Ideal  </td><td>E</td><td>SI2</td><td>61.5</td><td>55</td><td>326</td><td>3.95</td><td>3.98</td><td>2.43</td></tr>\n",
       "\t<tr><td>0.21</td><td>Premium</td><td>E</td><td>SI1</td><td>59.8</td><td>61</td><td>326</td><td>3.89</td><td>3.84</td><td>2.31</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>0.86</td><td>Premium</td><td>H</td><td>SI2</td><td>61.0</td><td>58</td><td>2757</td><td>6.15</td><td>6.12</td><td>3.74</td></tr>\n",
       "\t<tr><td>0.75</td><td>Ideal  </td><td>D</td><td>SI2</td><td>62.2</td><td>55</td><td>2757</td><td>5.83</td><td>5.87</td><td>3.64</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 53940 × 10\n",
       "\\begin{tabular}{llllllllll}\n",
       " carat & cut & color & clarity & depth & table & price & x & y & z\\\\\n",
       " <dbl> & <ord> & <ord> & <ord> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0.23 & Ideal   & E & SI2 & 61.5 & 55 & 326 & 3.95 & 3.98 & 2.43\\\\\n",
       "\t 0.21 & Premium & E & SI1 & 59.8 & 61 & 326 & 3.89 & 3.84 & 2.31\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 0.86 & Premium & H & SI2 & 61.0 & 58 & 2757 & 6.15 & 6.12 & 3.74\\\\\n",
       "\t 0.75 & Ideal   & D & SI2 & 62.2 & 55 & 2757 & 5.83 & 5.87 & 3.64\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 53940 × 10\n",
       "\n",
       "| carat &lt;dbl&gt; | cut &lt;ord&gt; | color &lt;ord&gt; | clarity &lt;ord&gt; | depth &lt;dbl&gt; | table &lt;dbl&gt; | price &lt;int&gt; | x &lt;dbl&gt; | y &lt;dbl&gt; | z &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.23 | Ideal   | E | SI2 | 61.5 | 55 | 326 | 3.95 | 3.98 | 2.43 |\n",
       "| 0.21 | Premium | E | SI1 | 59.8 | 61 | 326 | 3.89 | 3.84 | 2.31 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 0.86 | Premium | H | SI2 | 61.0 | 58 | 2757 | 6.15 | 6.12 | 3.74 |\n",
       "| 0.75 | Ideal   | D | SI2 | 62.2 | 55 | 2757 | 5.83 | 5.87 | 3.64 |\n",
       "\n"
      ],
      "text/plain": [
       "      carat cut     color clarity depth table price x    y    z   \n",
       "1     0.23  Ideal   E     SI2     61.5  55    326   3.95 3.98 2.43\n",
       "2     0.21  Premium E     SI1     59.8  61    326   3.89 3.84 2.31\n",
       "⋮     ⋮     ⋮       ⋮     ⋮       ⋮     ⋮     ⋮     ⋮    ⋮    ⋮   \n",
       "53939 0.86  Premium H     SI2     61.0  58    2757  6.15 6.12 3.74\n",
       "53940 0.75  Ideal   D     SI2     62.2  55    2757  5.83 5.87 3.64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diamonds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:30.987940Z",
     "start_time": "2020-03-27T19:50:27.223Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of unique colors: 7 \n"
     ]
    }
   ],
   "source": [
    "diamonds$color %>% unique_n('colors')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.005034Z",
     "start_time": "2020-03-27T19:50:27.225Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of unique clarities: 8 \n"
     ]
    }
   ],
   "source": [
    "diamonds %>% unique_n('clarities', clarity)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.031263Z",
     "start_time": "2020-03-27T19:50:27.228Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of unique colors: 7 \n"
     ]
    }
   ],
   "source": [
    "diamonds %>% data.table %>% unique_n('colors', color)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.056647Z",
     "start_time": "2020-03-27T19:50:27.230Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of unique cuts: 7 \n"
     ]
    }
   ],
   "source": [
    "diamonds %>% unique_n('cuts', color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## overlap()\n",
    "\n",
    "* Overlap of vectors or table columns \n",
    "* Useful to check for key overlap before joining tables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.073454Z",
     "start_time": "2020-03-27T19:50:27.233Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intersect(x,y): 2 \n",
      "setdiff(x,y): 1 \n",
      "setdiff(y,x): 1 \n",
      "union(x,y): 4 \n"
     ]
    }
   ],
   "source": [
    "overlap(1:3, 2:4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.089061Z",
     "start_time": "2020-03-27T19:50:27.236Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intersect(x,y): 3 \n",
      "setdiff(x,y): 0 \n",
      "setdiff(y,x): 23 \n",
      "union(x,y): 26 \n"
     ]
    }
   ],
   "source": [
    "overlap(c('A', 'B', 'C'), LETTERS)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.104601Z",
     "start_time": "2020-03-27T19:50:27.238Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intersect(x,y): 0 \n",
      "setdiff(x,y): 3 \n",
      "setdiff(y,x): 26 \n",
      "union(x,y): 29 \n"
     ]
    }
   ],
   "source": [
    "overlap(c('A', 'B', 'C'), letters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.170775Z",
     "start_time": "2020-03-27T19:50:27.242Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>Ideal</li>\n",
       "\t<li>Premium</li>\n",
       "\t<li>Good</li>\n",
       "\t<li>Very Good</li>\n",
       "\t<li>Fair</li>\n",
       "</ol>\n",
       "\n",
       "<details>\n",
       "\t<summary style=display:list-item;cursor:pointer>\n",
       "\t\t<strong>Levels</strong>:\n",
       "\t</summary>\n",
       "\t<ol class=list-inline>\n",
       "\t\t<li>'Fair'</li>\n",
       "\t\t<li>'Good'</li>\n",
       "\t\t<li>'Very Good'</li>\n",
       "\t\t<li>'Premium'</li>\n",
       "\t\t<li>'Ideal'</li>\n",
       "\t</ol>\n",
       "</details>"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item Ideal\n",
       "\\item Premium\n",
       "\\item Good\n",
       "\\item Very Good\n",
       "\\item Fair\n",
       "\\end{enumerate*}\n",
       "\n",
       "\\emph{Levels}: \\begin{enumerate*}\n",
       "\\item 'Fair'\n",
       "\\item 'Good'\n",
       "\\item 'Very Good'\n",
       "\\item 'Premium'\n",
       "\\item 'Ideal'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. Ideal\n",
       "2. Premium\n",
       "3. Good\n",
       "4. Very Good\n",
       "5. Fair\n",
       "\n",
       "\n",
       "\n",
       "**Levels**: 1. 'Fair'\n",
       "2. 'Good'\n",
       "3. 'Very Good'\n",
       "4. 'Premium'\n",
       "5. 'Ideal'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] Ideal     Premium   Good      Very Good Fair     \n",
       "Levels: Fair < Good < Very Good < Premium < Ideal"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 53940 × 10</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>carat</th><th scope=col>cut</th><th scope=col>color</th><th scope=col>clarity</th><th scope=col>depth</th><th scope=col>table</th><th scope=col>price</th><th scope=col>x</th><th scope=col>y</th><th scope=col>z</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;ord&gt;</th><th scope=col>&lt;ord&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>0.23</td><td>Ideal  </td><td>E</td><td>SI2</td><td>61.5</td><td>55</td><td>326</td><td>3.95</td><td>3.98</td><td>2.43</td></tr>\n",
       "\t<tr><td>0.21</td><td>Premium</td><td>E</td><td>SI1</td><td>59.8</td><td>61</td><td>326</td><td>3.89</td><td>3.84</td><td>2.31</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>0.86</td><td>Premium</td><td>H</td><td>SI2</td><td>61.0</td><td>58</td><td>2757</td><td>6.15</td><td>6.12</td><td>3.74</td></tr>\n",
       "\t<tr><td>0.75</td><td>Ideal  </td><td>D</td><td>SI2</td><td>62.2</td><td>55</td><td>2757</td><td>5.83</td><td>5.87</td><td>3.64</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 53940 × 10\n",
       "\\begin{tabular}{llllllllll}\n",
       " carat & cut & color & clarity & depth & table & price & x & y & z\\\\\n",
       " <dbl> & <chr> & <ord> & <ord> & <dbl> & <dbl> & <int> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 0.23 & Ideal   & E & SI2 & 61.5 & 55 & 326 & 3.95 & 3.98 & 2.43\\\\\n",
       "\t 0.21 & Premium & E & SI1 & 59.8 & 61 & 326 & 3.89 & 3.84 & 2.31\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 0.86 & Premium & H & SI2 & 61.0 & 58 & 2757 & 6.15 & 6.12 & 3.74\\\\\n",
       "\t 0.75 & Ideal   & D & SI2 & 62.2 & 55 & 2757 & 5.83 & 5.87 & 3.64\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 53940 × 10\n",
       "\n",
       "| carat &lt;dbl&gt; | cut &lt;chr&gt; | color &lt;ord&gt; | clarity &lt;ord&gt; | depth &lt;dbl&gt; | table &lt;dbl&gt; | price &lt;int&gt; | x &lt;dbl&gt; | y &lt;dbl&gt; | z &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|\n",
       "| 0.23 | Ideal   | E | SI2 | 61.5 | 55 | 326 | 3.95 | 3.98 | 2.43 |\n",
       "| 0.21 | Premium | E | SI1 | 59.8 | 61 | 326 | 3.89 | 3.84 | 2.31 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 0.86 | Premium | H | SI2 | 61.0 | 58 | 2757 | 6.15 | 6.12 | 3.74 |\n",
       "| 0.75 | Ideal   | D | SI2 | 62.2 | 55 | 2757 | 5.83 | 5.87 | 3.64 |\n",
       "\n"
      ],
      "text/plain": [
       "      carat cut     color clarity depth table price x    y    z   \n",
       "1     0.23  Ideal   E     SI2     61.5  55    326   3.95 3.98 2.43\n",
       "2     0.21  Premium E     SI1     59.8  61    326   3.89 3.84 2.31\n",
       "⋮     ⋮     ⋮       ⋮     ⋮       ⋮     ⋮     ⋮     ⋮    ⋮    ⋮   \n",
       "53939 0.86  Premium H     SI2     61.0  58    2757  6.15 6.12 3.74\n",
       "53940 0.75  Ideal   D     SI2     62.2  55    2757  5.83 5.87 3.64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diamonds$cut %>% unique\n",
    "diamonds2 = diamonds %>%\n",
    "    mutate(cut = gsub('G', 'g', cut))\n",
    "diamonds2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.200592Z",
     "start_time": "2020-03-27T19:50:27.244Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intersect(x,y): 3 \n",
      "setdiff(x,y): 2 \n",
      "setdiff(y,x): 2 \n",
      "union(x,y): 7 \n"
     ]
    }
   ],
   "source": [
    "overlap(diamonds, diamonds2, cut, cut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.239497Z",
     "start_time": "2020-03-27T19:50:27.249Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "intersect(x,y): 3 \n",
      "setdiff(x,y): 2 \n",
      "setdiff(y,x): 2 \n",
      "union(x,y): 7 \n"
     ]
    }
   ],
   "source": [
    "overlap(diamonds %>% as.data.table, diamonds2 %>% as.data.table,  cut, cut)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.276524Z",
     "start_time": "2020-03-27T19:50:27.251Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'Good'</li>\n",
       "\t<li>'Very Good'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'Good'\n",
       "\\item 'Very Good'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'Good'\n",
       "2. 'Very Good'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"Good\"      \"Very Good\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "overlap(diamonds, diamonds2, cut, cut, to_return='diff_x')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.303892Z",
     "start_time": "2020-03-27T19:50:27.254Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'good'</li>\n",
       "\t<li>'Very good'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'good'\n",
       "\\item 'Very good'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'good'\n",
       "2. 'Very good'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"good\"      \"Very good\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "overlap(diamonds, diamonds2, cut, cut, to_return='diff_y')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.348038Z",
     "start_time": "2020-03-27T19:50:27.257Z"
    },
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 10 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>x</th><th scope=col>y</th><th scope=col>dist</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;fct&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>5</th><td>Good     </td><td>good     </td><td>1</td></tr>\n",
       "\t<tr><th scope=row>8</th><td>Very Good</td><td>Very good</td><td>1</td></tr>\n",
       "\t<tr><th scope=row>9</th><td>Good     </td><td>Fair     </td><td>4</td></tr>\n",
       "\t<tr><th scope=row>1</th><td>Good     </td><td>Ideal    </td><td>5</td></tr>\n",
       "\t<tr><th scope=row>6</th><td>Very Good</td><td>good     </td><td>6</td></tr>\n",
       "\t<tr><th scope=row>7</th><td>Good     </td><td>Very good</td><td>6</td></tr>\n",
       "\t<tr><th scope=row>3</th><td>Good     </td><td>Premium  </td><td>7</td></tr>\n",
       "\t<tr><th scope=row>4</th><td>Very Good</td><td>Premium  </td><td>8</td></tr>\n",
       "\t<tr><th scope=row>2</th><td>Very Good</td><td>Ideal    </td><td>9</td></tr>\n",
       "\t<tr><th scope=row>10</th><td>Very Good</td><td>Fair     </td><td>9</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 10 × 3\n",
       "\\begin{tabular}{r|lll}\n",
       "  & x & y & dist\\\\\n",
       "  & <fct> & <fct> & <dbl>\\\\\n",
       "\\hline\n",
       "\t5 & Good      & good      & 1\\\\\n",
       "\t8 & Very Good & Very good & 1\\\\\n",
       "\t9 & Good      & Fair      & 4\\\\\n",
       "\t1 & Good      & Ideal     & 5\\\\\n",
       "\t6 & Very Good & good      & 6\\\\\n",
       "\t7 & Good      & Very good & 6\\\\\n",
       "\t3 & Good      & Premium   & 7\\\\\n",
       "\t4 & Very Good & Premium   & 8\\\\\n",
       "\t2 & Very Good & Ideal     & 9\\\\\n",
       "\t10 & Very Good & Fair      & 9\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 10 × 3\n",
       "\n",
       "| <!--/--> | x &lt;fct&gt; | y &lt;fct&gt; | dist &lt;dbl&gt; |\n",
       "|---|---|---|---|\n",
       "| 5 | Good      | good      | 1 |\n",
       "| 8 | Very Good | Very good | 1 |\n",
       "| 9 | Good      | Fair      | 4 |\n",
       "| 1 | Good      | Ideal     | 5 |\n",
       "| 6 | Very Good | good      | 6 |\n",
       "| 7 | Good      | Very good | 6 |\n",
       "| 3 | Good      | Premium   | 7 |\n",
       "| 4 | Very Good | Premium   | 8 |\n",
       "| 2 | Very Good | Ideal     | 9 |\n",
       "| 10 | Very Good | Fair      | 9 |\n",
       "\n"
      ],
      "text/plain": [
       "   x         y         dist\n",
       "5  Good      good      1   \n",
       "8  Very Good Very good 1   \n",
       "9  Good      Fair      4   \n",
       "1  Good      Ideal     5   \n",
       "6  Very Good good      6   \n",
       "7  Good      Very good 6   \n",
       "3  Good      Premium   7   \n",
       "4  Very Good Premium   8   \n",
       "2  Very Good Ideal     9   \n",
       "10 Very Good Fair      9   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.dims(20)\n",
    "overlap(diamonds, diamonds2, cut, cut, to_return='diff_fuzzy')\n",
    "df.dims()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## size_objects()\n",
    "* returns the size of objects in a session"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-01T08:15:12.531820Z",
     "start_time": "2020-04-01T08:15:12.373Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>size_objects:</strong> '41.8 Kb'"
      ],
      "text/latex": [
       "\\textbf{size\\textbackslash{}\\_objects:} '41.8 Kb'"
      ],
      "text/markdown": [
       "**size_objects:** '41.8 Kb'"
      ],
      "text/plain": [
       "size_objects \n",
       "   \"41.8 Kb\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To return the size of all objects\n",
    "size_objects(ls())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-04-01T08:15:08.054589Z",
     "start_time": "2020-04-01T08:15:07.878Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<strong>iris:</strong> '6.9 Kb'"
      ],
      "text/latex": [
       "\\textbf{iris:} '6.9 Kb'"
      ],
      "text/markdown": [
       "**iris:** '6.9 Kb'"
      ],
      "text/plain": [
       "    iris \n",
       "\"6.9 Kb\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# To return the size of particular objects\n",
    "size_objects('iris')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IO"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## list_files()\n",
    "\n",
    "* Recursive list of files, with full paths returned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.628461Z",
     "start_time": "2020-03-27T19:50:27.260Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/2_T3_bracken.tsv'</li>\n",
       "\t<li>'/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/36_T6_bracken.tsv'</li>\n",
       "\t<li>'/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/88_T6_bracken.tsv'</li>\n",
       "\t<li>'/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/93_T3_bracken.tsv'</li>\n",
       "\t<li>'/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/93_T7_bracken.tsv'</li>\n",
       "\t<li>'/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/97_T6_bracken.tsv'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '/ebio/abt3\\_projects/software/dev/llmgp/tests/output\\_amy\\_n6\\_GTDB/kraken//by-sample/2\\_T3\\_bracken.tsv'\n",
       "\\item '/ebio/abt3\\_projects/software/dev/llmgp/tests/output\\_amy\\_n6\\_GTDB/kraken//by-sample/36\\_T6\\_bracken.tsv'\n",
       "\\item '/ebio/abt3\\_projects/software/dev/llmgp/tests/output\\_amy\\_n6\\_GTDB/kraken//by-sample/88\\_T6\\_bracken.tsv'\n",
       "\\item '/ebio/abt3\\_projects/software/dev/llmgp/tests/output\\_amy\\_n6\\_GTDB/kraken//by-sample/93\\_T3\\_bracken.tsv'\n",
       "\\item '/ebio/abt3\\_projects/software/dev/llmgp/tests/output\\_amy\\_n6\\_GTDB/kraken//by-sample/93\\_T7\\_bracken.tsv'\n",
       "\\item '/ebio/abt3\\_projects/software/dev/llmgp/tests/output\\_amy\\_n6\\_GTDB/kraken//by-sample/97\\_T6\\_bracken.tsv'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/2_T3_bracken.tsv'\n",
       "2. '/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/36_T6_bracken.tsv'\n",
       "3. '/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/88_T6_bracken.tsv'\n",
       "4. '/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/93_T3_bracken.tsv'\n",
       "5. '/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/93_T7_bracken.tsv'\n",
       "6. '/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/97_T6_bracken.tsv'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/2_T3_bracken.tsv\" \n",
       "[2] \"/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/36_T6_bracken.tsv\"\n",
       "[3] \"/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/88_T6_bracken.tsv\"\n",
       "[4] \"/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/93_T3_bracken.tsv\"\n",
       "[5] \"/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/93_T7_bracken.tsv\"\n",
       "[6] \"/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken//by-sample/97_T6_bracken.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "D = '/ebio/abt3_projects/software/dev/llmgp/tests/output_amy_n6_GTDB/kraken/'\n",
    "F = list_files(D, '*_bracken.tsv')\n",
    "F"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.695603Z",
     "start_time": "2020-03-27T19:50:27.263Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "No. of unique Samples: 6 \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.table: 4871 × 8</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>sample</th><th scope=col>name</th><th scope=col>taxonomy_id</th><th scope=col>taxonomy_lvl</th><th scope=col>kraken_assigned_reads</th><th scope=col>added_reads</th><th scope=col>new_est_reads</th><th scope=col>fraction_total_reads</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>2_T3_bracken.tsv</td><td>s__CAG-170 sp002298695</td><td>159807</td><td>S</td><td> 271</td><td> 22</td><td> 293</td><td>0.00080</td></tr>\n",
       "\t<tr><td>2_T3_bracken.tsv</td><td>s__CAG-103 sp000432375</td><td>145754</td><td>S</td><td>1050</td><td>120</td><td>1170</td><td>0.00318</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>97_T6_bracken.tsv</td><td>s__Bacteroides_A barnesiae </td><td>126209</td><td>S</td><td>136</td><td>6</td><td>142</td><td>0.00034</td></tr>\n",
       "\t<tr><td>97_T6_bracken.tsv</td><td>s__Pauljensenia sp000466265</td><td>139772</td><td>S</td><td> 13</td><td>2</td><td> 15</td><td>0.00004</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 4871 × 8\n",
       "\\begin{tabular}{llllllll}\n",
       " sample & name & taxonomy\\_id & taxonomy\\_lvl & kraken\\_assigned\\_reads & added\\_reads & new\\_est\\_reads & fraction\\_total\\_reads\\\\\n",
       " <chr> & <chr> & <int> & <chr> & <int> & <int> & <int> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 2\\_T3\\_bracken.tsv & s\\_\\_CAG-170 sp002298695 & 159807 & S &  271 &  22 &  293 & 0.00080\\\\\n",
       "\t 2\\_T3\\_bracken.tsv & s\\_\\_CAG-103 sp000432375 & 145754 & S & 1050 & 120 & 1170 & 0.00318\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 97\\_T6\\_bracken.tsv & s\\_\\_Bacteroides\\_A barnesiae  & 126209 & S & 136 & 6 & 142 & 0.00034\\\\\n",
       "\t 97\\_T6\\_bracken.tsv & s\\_\\_Pauljensenia sp000466265 & 139772 & S &  13 & 2 &  15 & 0.00004\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 4871 × 8\n",
       "\n",
       "| sample &lt;chr&gt; | name &lt;chr&gt; | taxonomy_id &lt;int&gt; | taxonomy_lvl &lt;chr&gt; | kraken_assigned_reads &lt;int&gt; | added_reads &lt;int&gt; | new_est_reads &lt;int&gt; | fraction_total_reads &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| 2_T3_bracken.tsv | s__CAG-170 sp002298695 | 159807 | S |  271 |  22 |  293 | 0.00080 |\n",
       "| 2_T3_bracken.tsv | s__CAG-103 sp000432375 | 145754 | S | 1050 | 120 | 1170 | 0.00318 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 97_T6_bracken.tsv | s__Bacteroides_A barnesiae  | 126209 | S | 136 | 6 | 142 | 0.00034 |\n",
       "| 97_T6_bracken.tsv | s__Pauljensenia sp000466265 | 139772 | S |  13 | 2 |  15 | 0.00004 |\n",
       "\n"
      ],
      "text/plain": [
       "     sample            name                        taxonomy_id taxonomy_lvl\n",
       "1    2_T3_bracken.tsv  s__CAG-170 sp002298695      159807      S           \n",
       "2    2_T3_bracken.tsv  s__CAG-103 sp000432375      145754      S           \n",
       "⋮    ⋮                 ⋮                           ⋮           ⋮           \n",
       "4870 97_T6_bracken.tsv s__Bacteroides_A barnesiae  126209      S           \n",
       "4871 97_T6_bracken.tsv s__Pauljensenia sp000466265 139772      S           \n",
       "     kraken_assigned_reads added_reads new_est_reads fraction_total_reads\n",
       "1     271                   22          293          0.00080             \n",
       "2    1050                  120         1170          0.00318             \n",
       "⋮    ⋮                     ⋮           ⋮             ⋮                   \n",
       "4870 136                   6           142           0.00034             \n",
       "4871  13                   2            15           0.00004             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = list_files(D, '*_bracken.tsv') %>% \n",
    "    files_to_list(label_index=0) %>%\n",
    "    lapply(Fread) %>%\n",
    "    data.table::rbindlist(idcol='sample')\n",
    "\n",
    "dt %>% unique_n('Samples', sample)\n",
    "dt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.757229Z",
     "start_time": "2020-03-27T19:50:27.266Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'2_T3_bracken.tsv'</li>\n",
       "\t<li>'36_T6_bracken.tsv'</li>\n",
       "\t<li>'88_T6_bracken.tsv'</li>\n",
       "\t<li>'93_T3_bracken.tsv'</li>\n",
       "\t<li>'93_T7_bracken.tsv'</li>\n",
       "\t<li>'97_T6_bracken.tsv'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item '2\\_T3\\_bracken.tsv'\n",
       "\\item '36\\_T6\\_bracken.tsv'\n",
       "\\item '88\\_T6\\_bracken.tsv'\n",
       "\\item '93\\_T3\\_bracken.tsv'\n",
       "\\item '93\\_T7\\_bracken.tsv'\n",
       "\\item '97\\_T6\\_bracken.tsv'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. '2_T3_bracken.tsv'\n",
       "2. '36_T6_bracken.tsv'\n",
       "3. '88_T6_bracken.tsv'\n",
       "4. '93_T3_bracken.tsv'\n",
       "5. '93_T7_bracken.tsv'\n",
       "6. '97_T6_bracken.tsv'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"2_T3_bracken.tsv\"  \"36_T6_bracken.tsv\" \"88_T6_bracken.tsv\"\n",
       "[4] \"93_T3_bracken.tsv\" \"93_T7_bracken.tsv\" \"97_T6_bracken.tsv\""
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt %>% dt_distinct(sample) %>% dt_pull(sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## make_dir()\n",
    "\n",
    "* create directory (if needed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.826630Z",
     "start_time": "2020-03-27T19:50:27.269Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directory already exists: /ebio/abt3_projects/software/dev/tmp/TEST_DIR/ \n"
     ]
    }
   ],
   "source": [
    "D = '/ebio/abt3_projects/software/dev/tmp/TEST_DIR/'\n",
    "make_dir(D) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.840920Z",
     "start_time": "2020-03-27T19:50:27.272Z"
    },
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#unlink(D, recursive = TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## write_table()\n",
    "\n",
    "* Write tab-delim table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:31.915043Z",
     "start_time": "2020-03-27T19:50:27.276Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File written: /ebio/abt3_projects/software/dev/tmp/TEST_DIR//bracken_combined.tsv \n"
     ]
    }
   ],
   "source": [
    "out_file = file.path(D, 'bracken_combined.tsv')\n",
    "dt %>% write_table(out_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fread()\n",
    "\n",
    "* `fread()` with `sep = \"\\t\", check.names = TRUE`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:32.194509Z",
     "start_time": "2020-03-27T19:50:27.279Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.table: 100 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>V1</th><th scope=col>V2</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>UniRef50_Q6GZX4</td><td>Putative transcription factor 001R</td></tr>\n",
       "\t<tr><td>UniRef50_Q6GZW6</td><td>Putative helicase 009L            </td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>UniRef50_P46731</td><td>18 kDa antigen 2</td></tr>\n",
       "\t<tr><td>UniRef50_P12809</td><td>18 kDa antigen  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 100 × 2\n",
       "\\begin{tabular}{ll}\n",
       " V1 & V2\\\\\n",
       " <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t UniRef50\\_Q6GZX4 & Putative transcription factor 001R\\\\\n",
       "\t UniRef50\\_Q6GZW6 & Putative helicase 009L            \\\\\n",
       "\t ⋮ & ⋮\\\\\n",
       "\t UniRef50\\_P46731 & 18 kDa antigen 2\\\\\n",
       "\t UniRef50\\_P12809 & 18 kDa antigen  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 100 × 2\n",
       "\n",
       "| V1 &lt;chr&gt; | V2 &lt;chr&gt; |\n",
       "|---|---|\n",
       "| UniRef50_Q6GZX4 | Putative transcription factor 001R |\n",
       "| UniRef50_Q6GZW6 | Putative helicase 009L             |\n",
       "| ⋮ | ⋮ |\n",
       "| UniRef50_P46731 | 18 kDa antigen 2 |\n",
       "| UniRef50_P12809 | 18 kDa antigen   |\n",
       "\n"
      ],
      "text/plain": [
       "    V1              V2                                \n",
       "1   UniRef50_Q6GZX4 Putative transcription factor 001R\n",
       "2   UniRef50_Q6GZW6 Putative helicase 009L            \n",
       "⋮   ⋮               ⋮                                 \n",
       "99  UniRef50_P46731 18 kDa antigen 2                  \n",
       "100 UniRef50_P12809 18 kDa antigen                    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "infile = '/ebio/abt3_projects2/databases_no-backup/humann2/utility_mapping/map_uniref50_name.txt.bz2'\n",
    "cmd = glue::glue('bzip2 -c -d {file} | head -n 100', file=infile)\n",
    "fread(cmd=cmd, sep='\\t', fill=TRUE, header=FALSE)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:32.276659Z",
     "start_time": "2020-03-27T19:50:27.281Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.table: 99 × 2</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>UniRef50_Q6GZX4</th><th scope=col>Putative.transcription.factor.001R</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>UniRef50_Q6GZW6</td><td>Putative helicase 009L                       </td></tr>\n",
       "\t<tr><td>UniRef50_Q6GZV6</td><td>Putative serine/threonine-protein kinase 019R</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>UniRef50_P46731</td><td>18 kDa antigen 2</td></tr>\n",
       "\t<tr><td>UniRef50_P12809</td><td>18 kDa antigen  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.table: 99 × 2\n",
       "\\begin{tabular}{ll}\n",
       " UniRef50\\_Q6GZX4 & Putative.transcription.factor.001R\\\\\n",
       " <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t UniRef50\\_Q6GZW6 & Putative helicase 009L                       \\\\\n",
       "\t UniRef50\\_Q6GZV6 & Putative serine/threonine-protein kinase 019R\\\\\n",
       "\t ⋮ & ⋮\\\\\n",
       "\t UniRef50\\_P46731 & 18 kDa antigen 2\\\\\n",
       "\t UniRef50\\_P12809 & 18 kDa antigen  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.table: 99 × 2\n",
       "\n",
       "| UniRef50_Q6GZX4 &lt;chr&gt; | Putative.transcription.factor.001R &lt;chr&gt; |\n",
       "|---|---|\n",
       "| UniRef50_Q6GZW6 | Putative helicase 009L                        |\n",
       "| UniRef50_Q6GZV6 | Putative serine/threonine-protein kinase 019R |\n",
       "| ⋮ | ⋮ |\n",
       "| UniRef50_P46731 | 18 kDa antigen 2 |\n",
       "| UniRef50_P12809 | 18 kDa antigen   |\n",
       "\n"
      ],
      "text/plain": [
       "   UniRef50_Q6GZX4 Putative.transcription.factor.001R           \n",
       "1  UniRef50_Q6GZW6 Putative helicase 009L                       \n",
       "2  UniRef50_Q6GZV6 Putative serine/threonine-protein kinase 019R\n",
       "⋮  ⋮               ⋮                                            \n",
       "98 UniRef50_P46731 18 kDa antigen 2                             \n",
       "99 UniRef50_P12809 18 kDa antigen                               "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "infile = '/ebio/abt3_projects2/databases_no-backup/humann2/utility_mapping/map_uniref50_name.txt.bz2'\n",
    "cmd = glue::glue('bzip2 -c -d {file} | head -n 100', file=infile)\n",
    "Fread(cmd=cmd)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## p.dims()\n",
    "\n",
    "* Formatting plot dimensions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:32.309375Z",
     "start_time": "2020-03-27T19:50:27.284Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 150 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>Sepal.Length</th><th scope=col>Sepal.Width</th><th scope=col>Petal.Length</th><th scope=col>Petal.Width</th><th scope=col>Species</th></tr>\n",
       "\t<tr><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;fct&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>5.1</td><td>3.5</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>4.9</td><td>3.0</td><td>1.4</td><td>0.2</td><td>setosa</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>6.2</td><td>3.4</td><td>5.4</td><td>2.3</td><td>virginica</td></tr>\n",
       "\t<tr><td>5.9</td><td>3.0</td><td>5.1</td><td>1.8</td><td>virginica</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 150 × 5\n",
       "\\begin{tabular}{lllll}\n",
       " Sepal.Length & Sepal.Width & Petal.Length & Petal.Width & Species\\\\\n",
       " <dbl> & <dbl> & <dbl> & <dbl> & <fct>\\\\\n",
       "\\hline\n",
       "\t 5.1 & 3.5 & 1.4 & 0.2 & setosa\\\\\n",
       "\t 4.9 & 3.0 & 1.4 & 0.2 & setosa\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 6.2 & 3.4 & 5.4 & 2.3 & virginica\\\\\n",
       "\t 5.9 & 3.0 & 5.1 & 1.8 & virginica\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 150 × 5\n",
       "\n",
       "| Sepal.Length &lt;dbl&gt; | Sepal.Width &lt;dbl&gt; | Petal.Length &lt;dbl&gt; | Petal.Width &lt;dbl&gt; | Species &lt;fct&gt; |\n",
       "|---|---|---|---|---|\n",
       "| 5.1 | 3.5 | 1.4 | 0.2 | setosa |\n",
       "| 4.9 | 3.0 | 1.4 | 0.2 | setosa |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 6.2 | 3.4 | 5.4 | 2.3 | virginica |\n",
       "| 5.9 | 3.0 | 5.1 | 1.8 | virginica |\n",
       "\n"
      ],
      "text/plain": [
       "    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",
       "⋮   ⋮            ⋮           ⋮            ⋮           ⋮        \n",
       "149 6.2          3.4         5.4          2.3         virginica\n",
       "150 5.9          3.0         5.1          1.8         virginica"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:34.254836Z",
     "start_time": "2020-03-27T19:50:27.286Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+gAAAMgCAIAAAA/et9qAAAACXBIWXMAAB7CAAAewgFu0HU+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\ngjsAAACgAAR3AAAAQAEI7gAAAIAC/H/3rTe99qgWAQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 500
      },
      "text/plain": {
       "height": 400,
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p = iris %>%\n",
    "    ggplot(aes(Sepal.Length, Sepal.Width, color=Species)) +\n",
    "    geom_point() +\n",
    "    theme_bw()\n",
    "\n",
    "p.dims(5,4)\n",
    "plot(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:34.658089Z",
     "start_time": "2020-03-27T19:50:27.288Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAlgAAAGQCAIAAAD9V4nPAAAACXBIWXMAAB7CAAAewgFu0HU+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\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 200,
       "width": 300
      },
      "text/plain": {
       "height": 200,
       "width": 300
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.dims(3,2)\n",
    "plot(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:35.125046Z",
     "start_time": "2020-03-27T19:50:27.291Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABdwAAASwCAIAAABuFh+mAAAACXBIWXMAAC4jAAAuIwF4pT92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\nBf8HU5ikv4aHN7wAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 600,
       "width": 750
      },
      "text/plain": {
       "height": 600,
       "width": 750
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.dims(5,4,res=300)\n",
    "plot(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot()\n",
    "\n",
    "* Creating plot & writing the object to a file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:35.715729Z",
     "start_time": "2020-03-27T19:50:27.293Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+gAAAMgCAIAAAA/et9qAAAACXBIWXMAAB7CAAAewgFu0HU+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\ngjsAAACgAAR3AAAAQAEI7gAAAIAC/H/3rTe99qgWAQAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 400,
       "width": 500
      },
      "text/plain": {
       "height": 400,
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.dims(5,4)\n",
    "plot(p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:36.097305Z",
     "start_time": "2020-03-27T19:50:27.296Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "File written: /ebio/abt3_projects/Georg_animal_feces/tmp/ipynb_sandbox/.figures/9215c5be3ee16139071640e63ab5e5d8.RDS \n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA+gAAAGQCAIAAACyL902AAAACXBIWXMAAB7CAAAewgFu0HU+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\n6AEk7gAAAAAAeuD/Po1a1b5ZcB4AAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 200,
       "width": 500
      },
      "text/plain": {
       "height": 200,
       "width": 500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "p.dims(5,2)\n",
    "Plot(p)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# System"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## bash_job()\n",
    "\n",
    "* Wrapper around `system2()` that auto-activates a conda env"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:40.023111Z",
     "start_time": "2020-03-27T19:50:27.299Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "total 24K\n",
      "-rw-r--r-- 1 nyoungblut abt3 24K Mar 27 17:18 LeyLabRMisc_tutorial.ipynb"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bash_job('ls -thlc', conda_env='py3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:50:55.650118Z",
     "start_time": "2020-03-27T19:50:27.301Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "# packages in environment at /ebio/abt3_projects/software/miniconda3_gt4.4/envs/py3:\n",
      "#\n",
      "# Name                    Version                   Build  Channel\n",
      "_r-mutex                  1.0.1               anacondar_1    conda-forge\n",
      "appdirs                   1.4.3                      py_1    conda-forge\n",
      "asn1crypto                0.24.0                     py_1    conda-forge\n",
      "attrs                     18.1.0                     py_1    conda-forge\n",
      "automat                   0.7.0                    py36_0    conda-forge\n",
      "backcall                  0.1.0                      py_0    conda-forge\n",
      "blas                      1.1                    openblas    conda-forge\n",
      "bleach                    2.1.3                      py_0    conda-forge\n",
      "bzip2                     1.0.6                h470a237_2    conda-forge\n",
      "ca-certificates           2019.11.28           hecc5488_0    conda-forge\n",
      "cairo                     1.14.12              he56eebe_3    conda-forge\n",
      "certifi                   2019.11.28       py36h9f0ad1d_1    conda-forge\n",
      "cffi                      1.11.5           py36h5e8e0c9_1    conda-forge\n",
      "chardet                   3.0.4           py36h9f0ad1d_1006    conda-forge\n",
      "constantly                15.1.0                     py_0    conda-forge\n",
      "cryptography              2.3              py36hdffb7b8_0    conda-forge\n",
      "cryptography-vectors      2.3                      py36_1    conda-forge\n",
      "curl                      7.61.0               h93b3f91_1    conda-forge\n",
      "decorator                 4.3.0                      py_0    conda-forge\n",
      "entrypoints               0.2.3                    py36_1    conda-forge\n",
      "fontconfig                2.13.0               h0c61d53_4    conda-forge\n",
      "freetype                  2.8.1                hfa320df_1    conda-forge\n",
      "gettext                   0.19.8.1                      0    conda-forge\n",
      "glib                      2.55.0               h464dc38_2    conda-forge\n",
      "gmp                       6.1.2                hfc679d8_0    conda-forge\n",
      "graphite2                 1.3.11               hfc679d8_0    conda-forge\n",
      "gsl                       2.2.1           blas_openblas_4  [blas_openblas]  conda-forge\n",
      "harfbuzz                  1.8.5                h2bb21d5_0    conda-forge\n",
      "html5lib                  1.0.1                      py_0    conda-forge\n",
      "hyperlink                 17.3.1                     py_0    conda-forge\n",
      "icu                       58.2                 hfc679d8_0    conda-forge\n",
      "idna                      2.7                      py36_2    conda-forge\n",
      "incremental               17.5.0                     py_0    conda-forge\n",
      "ipykernel                 4.8.2                    py36_0    conda-forge\n",
      "ipython                   6.5.0                    py36_0    conda-forge\n",
      "ipython_genutils          0.2.0                      py_1    conda-forge\n",
      "jedi                      0.12.1                   py36_0    conda-forge\n",
      "jinja2                    2.10                       py_1    conda-forge\n",
      "jpeg                      9c                   h470a237_0    conda-forge\n",
      "jsonschema                2.6.0                    py36_1    conda-forge\n",
      "jupyter_client            5.2.3                      py_1    conda-forge\n",
      "jupyter_core              4.4.0                      py_0    conda-forge\n",
      "krb5                      1.14.6                        0    conda-forge\n",
      "libffi                    3.2.1                         3    conda-forge\n",
      "libgcc                    7.2.0                h69d50b8_2  \n",
      "libgcc-ng                 7.2.0                hdf63c60_3  \n",
      "libgfortran               3.0.0                         1    https://repo.anaconda.com/pkgs/free\n",
      "libgfortran-ng            7.2.0                hdf63c60_3  \n",
      "libiconv                  1.15                 h470a237_1    conda-forge\n",
      "libopenblas               0.2.20               h9ac9557_7  \n",
      "libpng                    1.6.35               ha92aebf_0    conda-forge\n",
      "libsodium                 1.0.16                        0    conda-forge\n",
      "libssh2                   1.8.0                h5b517e9_2    conda-forge\n",
      "libstdcxx-ng              7.2.0                hdf63c60_3  \n",
      "libtiff                   4.0.9                he6b73bb_1    conda-forge\n",
      "libuuid                   1.0.3                         1    conda-forge\n",
      "libxcb                    1.13                 h470a237_2    conda-forge\n",
      "libxml2                   2.9.8                h422b904_2    conda-forge\n",
      "markupsafe                1.0                      py36_0    conda-forge\n",
      "mistune                   0.8.3            py36h470a237_2    conda-forge\n",
      "nb_conda                  2.2.1                    py36_0    conda-forge\n",
      "nb_conda_kernels          2.1.1                    py36_1    conda-forge\n",
      "nbconvert                 5.3.1                      py_1    conda-forge\n",
      "nbformat                  4.4.0                      py_1    conda-forge\n",
      "ncurses                   6.1                  hfc679d8_1    conda-forge\n",
      "notebook                  5.6.0                    py36_0    conda-forge\n",
      "numpy                     1.15.0          py36_blas_openblashd3ea46f_200  [blas_openblas]  conda-forge\n",
      "numpy-base                1.14.3           py36h0ea5e3f_1  \n",
      "openblas                  0.2.20                        8    conda-forge\n",
      "openssl                   1.0.2o               h470a237_1    conda-forge\n",
      "pandas                    0.23.4           py36hf8a1672_0    conda-forge\n",
      "pandoc                    2.2.2                         1    conda-forge\n",
      "pandocfilters             1.4.2                      py_1    conda-forge\n",
      "pango                     1.40.14              h9105a7a_2    conda-forge\n",
      "parso                     0.3.1                      py_0    conda-forge\n",
      "pcre                      8.41                 h470a237_2    conda-forge\n",
      "pexpect                   4.6.0                    py36_0    conda-forge\n",
      "pickleshare               0.7.4                    py36_0    conda-forge\n",
      "pip                       18.0                     py36_1    conda-forge\n",
      "pixman                    0.34.0                        2    conda-forge\n",
      "prometheus_client         0.3.0                      py_0    conda-forge\n",
      "prompt_toolkit            1.0.15                   py36_0    conda-forge\n",
      "psutil                    5.4.8            py36h470a237_0    conda-forge\n",
      "pthread-stubs             0.4                  h470a237_1    conda-forge\n",
      "ptyprocess                0.6.0                    py36_0    conda-forge\n",
      "pyasn1                    0.4.4                      py_0    conda-forge\n",
      "pyasn1-modules            0.2.1                      py_0    conda-forge\n",
      "pycparser                 2.18                       py_1    conda-forge\n",
      "pygments                  2.2.0                      py_1    conda-forge\n",
      "pyhamcrest                1.9.0                      py_2    conda-forge\n",
      "pyopenssl                 18.0.0                   py36_0    conda-forge\n",
      "pysocks                   1.7.1            py36h9f0ad1d_1    conda-forge\n",
      "python                    3.6.6                h5001a0f_0    conda-forge\n",
      "python-dateutil           2.7.3                      py_0    conda-forge\n",
      "python_abi                3.6                     1_cp36m    conda-forge\n",
      "pytz                      2018.5                     py_0    conda-forge\n",
      "pyzmq                     17.1.0           py36hae99301_0    conda-forge\n",
      "r-assertthat              0.2.0                  r3.4.1_0    conda-forge\n",
      "r-base                    3.4.1                h4fe35fd_8    conda-forge\n",
      "r-base64enc               0.1_3            r341h470a237_1    conda-forge\n",
      "r-bh                      1.66.0_1               r3.4.1_0    conda-forge\n",
      "r-bindr                   0.1.1                  r3.4.1_0    conda-forge\n",
      "r-bindrcpp                0.2.2            r341h9d2a408_0    conda-forge\n",
      "r-bit                     1.1_12           r341h470a237_1    conda-forge\n",
      "r-bit64                   0.9_5            r341h470a237_1    conda-forge\n",
      "r-blob                    1.1.1                  r3.4.1_0    conda-forge\n",
      "r-cli                     1.0.0                  r3.4.1_0    conda-forge\n",
      "r-crayon                  1.3.4                  r3.4.1_0    conda-forge\n",
      "r-dbi                     1.0.0                    r341_0    conda-forge\n",
      "r-dbplyr                  1.2.2                    r341_0    conda-forge\n",
      "r-digest                  0.6.15           r341h470a237_1    conda-forge\n",
      "r-dplyr                   0.7.6            r341h9d2a408_0    conda-forge\n",
      "r-evaluate                0.10.1                 r3.4.1_0    conda-forge\n",
      "r-glue                    1.3.0            r341h470a237_1    conda-forge\n",
      "r-htmltools               0.3.6            r341hfc679d8_1    conda-forge\n",
      "r-irdisplay               0.4.4                  r3.4.1_0    conda-forge\n",
      "r-irkernel                0.8.12                   r341_0    conda-forge\n",
      "r-jsonlite                1.5              r341h470a237_1    conda-forge\n",
      "r-magrittr                1.5                    r3.4.1_0    conda-forge\n",
      "r-memoise                 1.1.0                  r3.4.1_0    conda-forge\n",
      "r-pbdzmq                  0.3_2                  r3.4.1_0    conda-forge\n",
      "r-pillar                  1.2.2            r341h6115d3f_1    conda-forge\n",
      "r-pkgconfig               2.0.1                  r3.4.1_0    conda-forge\n",
      "r-plogr                   0.2.0            r341h6115d3f_0    conda-forge\n",
      "r-prettyunits             1.0.2                  r3.4.1_0    conda-forge\n",
      "r-purrr                   0.2.4            r341h470a237_1    conda-forge\n",
      "r-r6                      2.2.2                  r3.4.1_0    conda-forge\n",
      "r-rcpp                    0.12.17          r341h9d2a408_1    conda-forge\n",
      "r-repr                    0.15                     r341_0    conda-forge\n",
      "r-rlang                   0.2.1            r341h470a237_1    conda-forge\n",
      "r-rsqlite                 2.0              r341hfc679d8_1    conda-forge\n",
      "r-stringi                 1.2.3            r341hfc679d8_1    conda-forge\n",
      "r-stringr                 1.3.1                    r341_0    conda-forge\n",
      "r-tibble                  1.4.2            r341h470a237_1    conda-forge\n",
      "r-tidyselect              0.2.4            r341hfc679d8_1    conda-forge\n",
      "r-utf8                    1.1.3            r341h470a237_1    conda-forge\n",
      "r-uuid                    0.1_2                    r341_0    conda-forge\n",
      "readline                  7.0                  haf1bffa_1    conda-forge\n",
      "requests                  2.23.0                   py36_0    conda-forge\n",
      "rpy2                      2.9.4           py36r341h24bf2e0_0    conda-forge\n",
      "send2trash                1.5.0                      py_0    conda-forge\n",
      "service_identity          17.0.0                     py_0    conda-forge\n",
      "setuptools                40.0.0                   py36_1    conda-forge\n",
      "simplegeneric             0.8.1                      py_1    conda-forge\n",
      "six                       1.11.0                   py36_1    conda-forge\n",
      "sqlite                    3.24.0               h2f33b56_0    conda-forge\n",
      "tabulate                  0.8.6                      py_0    conda-forge\n",
      "terminado                 0.8.1                    py36_0    conda-forge\n",
      "testpath                  0.3.1                    py36_0    conda-forge\n",
      "tk                        8.6.8                         0    conda-forge\n",
      "tornado                   5.1              py36h470a237_1    conda-forge\n",
      "traitlets                 4.3.2                    py36_0    conda-forge\n",
      "twisted                   18.7.0           py36h14c3975_1  \n",
      "tzlocal                   1.5.1                      py_0    conda-forge\n",
      "urllib3                   1.25.7                   py36_0    conda-forge\n",
      "wcwidth                   0.1.7                      py_1    conda-forge\n",
      "webencodings              0.5                      py36_0    conda-forge\n",
      "wheel                     0.31.1                   py36_1    conda-forge\n",
      "xorg-kbproto              1.0.7                h470a237_2    conda-forge\n",
      "xorg-libice               1.0.9                h470a237_4    conda-forge\n",
      "xorg-libsm                1.2.2                h470a237_5    conda-forge\n",
      "xorg-libx11               1.6.5                h470a237_2    conda-forge\n",
      "xorg-libxau               1.0.8                h470a237_6    conda-forge\n",
      "xorg-libxdmcp             1.1.2                h470a237_7    conda-forge\n",
      "xorg-libxext              1.3.3                h470a237_4    conda-forge\n",
      "xorg-libxrender           0.9.10               h470a237_2    conda-forge\n",
      "xorg-libxt                1.1.5                h470a237_2    conda-forge\n",
      "xorg-renderproto          0.11.1               h470a237_2    conda-forge\n",
      "xorg-xextproto            7.3.0                h470a237_2    conda-forge\n",
      "xorg-xproto               7.0.31               h470a237_7    conda-forge\n",
      "xz                        5.2.4                h470a237_1    conda-forge\n",
      "zeromq                    4.2.5                hfc679d8_5    conda-forge\n",
      "zlib                      1.2.11               h470a237_3    conda-forge\n",
      "zope.interface            4.5.0            py36h470a237_0    conda-forge"
     ]
    },
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bash_job('conda list', conda_env='py3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:51:03.177558Z",
     "start_time": "2020-03-27T19:50:27.304Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not a conda environment: /ebio/abt3_projects/Georg_animal_feces/phyloseq\n",
      "# packages in environment at /ebio/abt3_projects/software/miniconda3_gt4.4:\n",
      "#\n",
      "# Name                    Version                   Build  Channel\n",
      "_libgcc_mutex             0.1                        main  \n",
      "_r-mutex                  1.0.0               anacondar_1  \n",
      "appdirs                   1.4.3                      py_1    conda-forge\n",
      "asn1crypto                1.3.0                    py36_0  \n",
      "attrs                     19.3.0                     py_0  \n",
      "automat                   0.7.0                    py36_0    conda-forge\n",
      "backcall                  0.1.0                    py36_0  \n",
      "binutils_impl_linux-64    2.31.1               h6176602_1  \n",
      "binutils_linux-64         2.31.1               h6176602_9  \n",
      "biopython                 1.73                     pypi_0    pypi\n",
      "blas                      1.0                         mkl  \n",
      "bleach                    3.1.0                    py36_0  \n",
      "bwidget                   1.9.11                        1  \n",
      "bzip2                     1.0.8                h7b6447c_0  \n",
      "ca-certificates           2020.1.1                      0  \n",
      "cairo                     1.14.12              h8948797_3  \n",
      "certifi                   2019.11.28               py36_0  \n",
      "cffi                      1.14.0           py36h2e261b9_0  \n",
      "chardet                   3.0.4                 py36_1003  \n",
      "click                     7.0                      pypi_0    pypi\n",
      "coloredlogs               10.0                     pypi_0    pypi\n",
      "colormath                 3.0.0                    pypi_0    pypi\n",
      "conda                     4.8.2                    py36_0  \n",
      "conda-env                 2.6.0                         1    conda-forge\n",
      "conda-package-handling    1.6.0            py36h7b6447c_0  \n",
      "constantly                15.1.0                     py_0    conda-forge\n",
      "cryptography              2.8              py36h1ba5d50_0  \n",
      "cryptography-vectors      2.3                      py36_1    conda-forge\n",
      "curl                      7.65.3               hbc83047_0  \n",
      "cycler                    0.10.0                   pypi_0    pypi\n",
      "dbus                      1.13.12              h746ee38_0  \n",
      "decorator                 4.4.1                      py_0  \n",
      "defusedxml                0.6.0                      py_0  \n",
      "entrypoints               0.3                      py36_0  \n",
      "expat                     2.2.6                he6710b0_0  \n",
      "fontconfig                2.13.0               h9420a91_0  \n",
      "freetype                  2.9.1                h8a8886c_1  \n",
      "fribidi                   1.0.5                h7b6447c_0  \n",
      "future                    0.18.2                   pypi_0    pypi\n",
      "gcc_impl_linux-64         7.3.0                habb00fd_1  \n",
      "gcc_linux-64              7.3.0                h553295d_9  \n",
      "gettext                   0.19.8.1          hc5be6a0_1002    conda-forge\n",
      "gfortran_impl_linux-64    7.3.0                hdf63c60_1  \n",
      "gfortran_linux-64         7.3.0                h553295d_9  \n",
      "glib                      2.63.1               h5a9c865_0  \n",
      "gmp                       6.1.2                h6c8ec71_1  \n",
      "graphite2                 1.3.13               h23475e2_0  \n",
      "gsl                       2.4                  h14c3975_4  \n",
      "gst-plugins-base          1.12.5               hde13a9d_0    conda-forge\n",
      "gstreamer                 1.12.5               h61a6719_0    conda-forge\n",
      "gxx_impl_linux-64         7.3.0                hdf63c60_1  \n",
      "gxx_linux-64              7.3.0                h553295d_9  \n",
      "harfbuzz                  1.8.8                hffaf4a1_0  \n",
      "html5lib                  1.0.1                      py_0    conda-forge\n",
      "humanfriendly             4.18                     pypi_0    pypi\n",
      "humann2                   0.11.2                   pypi_0    pypi\n",
      "hyperlink                 17.3.1                     py_0    conda-forge\n",
      "icu                       58.2                 h9c2bf20_1  \n",
      "idna                      2.8                      py36_0  \n",
      "importlib_metadata        1.5.0                    py36_0  \n",
      "incremental               17.5.0                     py_0    conda-forge\n",
      "ipykernel                 5.1.4            py36h39e3cac_0  \n",
      "ipython                   7.12.0           py36h5ca1d4c_0  \n",
      "ipython_genutils          0.2.0                    py36_0  \n",
      "ipywidgets                7.5.1                      py_0  \n",
      "jedi                      0.16.0                   py36_0  \n",
      "jinja2                    2.11.1                     py_0  \n",
      "jpeg                      9c                h14c3975_1001    conda-forge\n",
      "jsonschema                3.2.0                    py36_0  \n",
      "jupyter                   1.0.0                      py_1    conda-forge\n",
      "jupyter_client            5.3.4                    py36_0  \n",
      "jupyter_console           6.1.0                      py_0  \n",
      "jupyter_contrib_core      0.3.3                      py_2    conda-forge\n",
      "jupyter_contrib_nbextensions 0.5.0                    py36_0    conda-forge\n",
      "jupyter_core              4.6.1                    py36_0  \n",
      "jupyter_highlight_selected_word 0.2.0                 py36_1000    conda-forge\n",
      "jupyter_latex_envs        1.4.4                 py36_1000    conda-forge\n",
      "jupyter_nbextensions_configurator 0.4.1                    py36_0    conda-forge\n",
      "jupyterlab                0.35.4                   py36_0    conda-forge\n",
      "jupyterlab_server         0.2.0                      py_0    conda-forge\n",
      "kiwisolver                1.1.0                    pypi_0    pypi\n",
      "krb5                      1.16.4               h173b8e3_0  \n",
      "ld_impl_linux-64          2.33.1               h53a641e_7    conda-forge\n",
      "libarchive                3.3.3             hc3f970e_1004    conda-forge\n",
      "libblas                   3.8.0               14_openblas    conda-forge\n",
      "libcblas                  3.8.0               14_openblas    conda-forge\n",
      "libcurl                   7.65.3               h20c2e04_0  \n",
      "libedit                   3.1.20181209         hc058e9b_0  \n",
      "libffi                    3.2.1                hd88cf55_4  \n",
      "libgcc                    7.2.0                h69d50b8_2  \n",
      "libgcc-ng                 9.1.0                hdf63c60_0  \n",
      "libgfortran               3.0.0                         1    https://repo.anaconda.com/pkgs/free\n",
      "libgfortran-ng            7.3.0                hdf63c60_0  \n",
      "libiconv                  1.15              h516909a_1005    conda-forge\n",
      "libopenblas               0.3.7                h6e990d7_3    conda-forge\n",
      "libpng                    1.6.37               hbc83047_0  \n",
      "libsodium                 1.0.17               h516909a_0    conda-forge\n",
      "libssh2                   1.8.2                h1ba5d50_0  \n",
      "libstdcxx-ng              9.1.0                hdf63c60_0  \n",
      "libtiff                   4.0.10            h9022e91_1002    conda-forge\n",
      "libuuid                   1.0.3                h1bed415_2  \n",
      "libxcb                    1.13                 h1bed415_1  \n",
      "libxml2                   2.9.9                hea5a465_1  \n",
      "libxslt                   1.1.33               h7d1a2b0_0  \n",
      "lxml                      4.5.0            py36hefd8a0e_0  \n",
      "lz4-c                     1.8.3             he1b5a44_1001    conda-forge\n",
      "lzo                       2.10                 h49e0be7_2  \n",
      "lzstring                  1.0.4                    pypi_0    pypi\n",
      "make                      4.2.1                h1bed415_1  \n",
      "markdown                  3.1.1                    pypi_0    pypi\n",
      "markupsafe                1.1.1            py36h7b6447c_0  \n",
      "matplotlib                3.1.1                    pypi_0    pypi\n",
      "mistune                   0.8.4            py36h7b6447c_0  \n",
      "more-itertools            7.2.0                      py_0    conda-forge\n",
      "multiqc                   1.8                      pypi_0    pypi\n",
      "nb_conda                  2.2.1                    py36_0    conda-forge\n",
      "nb_conda_kernels          2.2.2                    py36_0  \n",
      "nbconvert                 5.6.1                    py36_0  \n",
      "nbformat                  5.0.4                      py_0  \n",
      "ncbi-genome-download      0.2.9                    pypi_0    pypi\n",
      "ncurses                   6.1                  he6710b0_1  \n",
      "networkx                  2.4                      pypi_0    pypi\n",
      "notebook                  6.0.3                    py36_0  \n",
      "numpy                     1.16.2                   pypi_0    pypi\n",
      "openblas                  0.2.20                        8    conda-forge\n",
      "openssl                   1.1.1d               h7b6447c_4  \n",
      "pandas                    0.24.2                   pypi_0    pypi\n",
      "pandoc                    2.2.3.2                       0  \n",
      "pandocfilters             1.4.2                    py36_1  \n",
      "pango                     1.42.4               h049681c_0  \n",
      "parso                     0.6.1                      py_0  \n",
      "pcre                      8.43                 he6710b0_0  \n",
      "pexpect                   4.8.0                    py36_0  \n",
      "pickleshare               0.7.5                    py36_0  \n",
      "pip                       20.0.2                   py36_1  \n",
      "pixman                    0.38.0               h7b6447c_0  \n",
      "prometheus_client         0.7.1                      py_0  \n",
      "prompt_toolkit            3.0.3                      py_0  \n",
      "pthread-stubs             0.4               h14c3975_1001    conda-forge\n",
      "ptyprocess                0.6.0                    py36_0  \n",
      "pyasn1                    0.4.4                      py_0    conda-forge\n",
      "pyasn1-modules            0.2.1                      py_0    conda-forge\n",
      "pycosat                   0.6.3            py36h7b6447c_0  \n",
      "pycparser                 2.19                     py36_0  \n",
      "pygments                  2.5.2                      py_0  \n",
      "pyhamcrest                1.9.0                      py_2    conda-forge\n",
      "pyopenssl                 19.1.0                   py36_0  \n",
      "pyparsing                 2.4.5                    pypi_0    pypi\n",
      "pyqt                      5.9.2            py36h05f1152_2  \n",
      "pyrsistent                0.15.7           py36h7b6447c_0  \n",
      "pysam                     0.15.2                   pypi_0    pypi\n",
      "pysocks                   1.7.1                    py36_0  \n",
      "python                    3.6.8                h0371630_0  \n",
      "python-dateutil           2.8.0                    pypi_0    pypi\n",
      "python-libarchive-c       2.8                   py36_1004    conda-forge\n",
      "pytz                      2018.9                   pypi_0    pypi\n",
      "pyyaml                    5.3              py36h7b6447c_0  \n",
      "pyzmq                     18.1.1           py36he6710b0_0  \n",
      "qt                        5.9.7                h5867ecd_1  \n",
      "qtconsole                 4.6.0                      py_1  \n",
      "r-assertthat              0.2.0                  r3.4.1_0    conda-forge\n",
      "r-backports               1.1.4             r36h96ca727_0  \n",
      "r-base                    3.6.1                h9bb98a2_1  \n",
      "r-base64enc               0.1_3            r341h470a237_1    conda-forge\n",
      "r-bh                      1.66.0_1               r3.4.1_0    conda-forge\n",
      "r-bindr                   0.1.1                  r3.4.1_0    conda-forge\n",
      "r-bindrcpp                0.2.2            r341h9d2a408_0    conda-forge\n",
      "r-bit                     1.1_12           r341h470a237_1    conda-forge\n",
      "r-bit64                   0.9_5            r341h470a237_1    conda-forge\n",
      "r-blob                    1.1.1                  r3.4.1_0    conda-forge\n",
      "r-cli                     1.0.0                  r3.4.1_0    conda-forge\n",
      "r-crayon                  1.3.4                  r3.4.1_0    conda-forge\n",
      "r-dbi                     1.0.0                    r341_0    conda-forge\n",
      "r-dbplyr                  1.2.2                    r341_0    conda-forge\n",
      "r-digest                  0.6.15           r341h470a237_1    conda-forge\n",
      "r-dplyr                   0.7.6            r341h9d2a408_0    conda-forge\n",
      "r-ellipsis                0.3.0             r35hcdcec82_0    conda-forge\n",
      "r-evaluate                0.10.1                 r3.4.1_0    conda-forge\n",
      "r-fansi                   0.4.0             r36h96ca727_0  \n",
      "r-glue                    1.3.0            r341h470a237_1    conda-forge\n",
      "r-htmltools               0.3.6            r341hfc679d8_1    conda-forge\n",
      "r-irdisplay               0.4.4                  r3.4.1_0    conda-forge\n",
      "r-irkernel                0.8.15                    r36_0  \n",
      "r-jsonlite                1.5              r341h470a237_1    conda-forge\n",
      "r-magrittr                1.5                    r3.4.1_0    conda-forge\n",
      "r-memoise                 1.1.0                  r3.4.1_0    conda-forge\n",
      "r-pbdzmq                  0.3_2                  r3.4.1_0    conda-forge\n",
      "r-pillar                  1.2.2            r341h6115d3f_1    conda-forge\n",
      "r-pkgconfig               2.0.1                  r3.4.1_0    conda-forge\n",
      "r-plogr                   0.2.0            r341h6115d3f_0    conda-forge\n",
      "r-prettyunits             1.0.2                  r3.4.1_0    conda-forge\n",
      "r-purrr                   0.2.4            r341h470a237_1    conda-forge\n",
      "r-r6                      2.2.2                  r3.4.1_0    conda-forge\n",
      "r-rcpp                    0.12.17          r341h9d2a408_1    conda-forge\n",
      "r-repr                    0.15                     r341_0    conda-forge\n",
      "r-rlang                   0.2.1            r341h470a237_1    conda-forge\n",
      "r-rsqlite                 2.0              r341hfc679d8_1    conda-forge\n",
      "r-stringi                 1.2.3            r341hfc679d8_1    conda-forge\n",
      "r-stringr                 1.3.1                    r341_0    conda-forge\n",
      "r-tibble                  1.4.2            r341h470a237_1    conda-forge\n",
      "r-tidyselect              0.2.4            r341hfc679d8_1    conda-forge\n",
      "r-utf8                    1.1.3            r341h470a237_1    conda-forge\n",
      "r-uuid                    0.1_2                    r341_0    conda-forge\n",
      "r-vctrs                   0.2.0             r35hcdcec82_1    conda-forge\n",
      "r-zeallot                 0.1.0             r36h6115d3f_0  \n",
      "readline                  7.0                  h7b6447c_5  \n",
      "requests                  2.22.0                   py36_1  \n",
      "rpy2                      2.9.4           py36r36h481b005_0  \n",
      "ruamel_yaml               0.15.87          py36h7b6447c_0  \n",
      "send2trash                1.5.0                    py36_0  \n",
      "service_identity          17.0.0                     py_0    conda-forge\n",
      "setuptools                45.2.0                   py36_0  \n",
      "simplegeneric             0.8.1                      py_1    conda-forge\n",
      "simplejson                3.17.0                   pypi_0    pypi\n",
      "sip                       4.19.8           py36hf484d3e_0  \n",
      "six                       1.14.0                   py36_0  \n",
      "spectra                   0.0.11                   pypi_0    pypi\n",
      "sqlite                    3.31.1               h7b6447c_0  \n",
      "terminado                 0.8.3                    py36_0  \n",
      "testpath                  0.4.4                      py_0  \n",
      "tk                        8.6.8                hbc83047_0  \n",
      "tktable                   2.10                 h14c3975_0  \n",
      "tornado                   6.0.3            py36h7b6447c_3  \n",
      "tqdm                      4.42.1                     py_0  \n",
      "traitlets                 4.3.3                    py36_0  \n",
      "twisted                   18.7.0           py36h14c3975_1  \n",
      "tzlocal                   2.0.0                      py_0    conda-forge\n",
      "urllib3                   1.25.8                   py36_0  \n",
      "wcwidth                   0.1.8                      py_0  \n",
      "webencodings              0.5.1                    py36_1  \n",
      "wheel                     0.34.2                   py36_0  \n",
      "widgetsnbextension        3.5.1                    py36_0  \n",
      "xorg-kbproto              1.0.7             h14c3975_1002    conda-forge\n",
      "xorg-libice               1.0.10               h516909a_0    conda-forge\n",
      "xorg-libsm                1.2.3             h84519dc_1000    conda-forge\n",
      "xorg-libx11               1.6.9                h516909a_0    conda-forge\n",
      "xorg-libxau               1.0.9                h14c3975_0    conda-forge\n",
      "xorg-libxdmcp             1.1.3                h516909a_0    conda-forge\n",
      "xorg-libxext              1.3.4                h516909a_0    conda-forge\n",
      "xorg-libxrender           0.9.10            h516909a_1002    conda-forge\n",
      "xorg-libxt                1.1.5                h470a237_2    conda-forge\n",
      "xorg-renderproto          0.11.1            h14c3975_1002    conda-forge\n",
      "xorg-xextproto            7.3.0             h14c3975_1002    conda-forge\n",
      "xorg-xproto               7.0.31            h14c3975_1007    conda-forge\n",
      "xz                        5.2.4                h14c3975_4  \n",
      "yaml                      0.1.7                had09818_2  \n",
      "zeromq                    4.3.2                he1b5a44_2    conda-forge\n",
      "zipp                      2.2.0                      py_0  \n",
      "zlib                      1.2.11               h7b6447c_3  \n",
      "zope.interface            4.5.0            py36h470a237_0    conda-forge\n",
      "zstd                      1.3.3                h84994c4_0  "
     ]
    },
    {
     "data": {
      "text/plain": [
       "NULL"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "conda_env = '/ebio/abt3_projects/Georg_animal_feces/phyloseq'\n",
    "bash_job('conda list', conda_env=conda_env)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## cat_file()\n",
    "\n",
    "* Pretty print a text file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:51:03.300771Z",
     "start_time": "2020-03-27T19:50:27.307Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LeyLab R misc\n",
      "==============\n",
      "\n",
      "Misc R functions, rmarkdown templates, etc. for making life a bit easier in the Ley Lab\n",
      "\n",
      "Feel free to contribute! Just clone the repo, make some changes, commit, and create a pull request.\n",
      "\n",
      "# Install\n",
      "\n",
      "## `renv` R package\n",
      "\n",
      "`renv::install(\"leylabmpi/LeyLabRMisc\")`\n",
      "\n",
      "## `devtools` R package\n",
      "\n",
      "`devtools::install_github(\"leylabmpi/LeyLabRMisc\")`\n",
      "\n",
      "## `source()` function\n",
      "\n",
      "* Clone the repo: `git clone git@github.com:leylabmpi/LeyLabRMisc.git`\n",
      "* In your R session, use `source(\"path/to/repo/init.R\")`\n",
      "  * **Note:** you will need to source each `*.R` file individually\n",
      "\n",
      "### Submodules\n",
      "\n",
      "* If this repo will be inside another git repo, then use `git submodule`\n",
      "  * See [this stack overflow post](https://stackoverflow.com/questions/1811730/how-do-i-work-with-a-git-repository-within-another-repository)\n",
      "\n",
      "# Functions\n",
      "\n",
      "* `R/init.R`\n",
      "  * General functions that could be sourced at the start of a Jupyter Notebook or Rmarkdown file\n",
      "* `R/itol.R`\n",
      "  * Functions for creating input files for iTOL\n",
      "* `R/phyloseq.R`\n",
      "  * Helper functions for working with phyloseq\n",
      "\n",
      "# Templates\n",
      "\n",
      "Rmarkdown templates are in `inst/rmarkdown/templates/`\n",
      "\n",
      "To use a template:\n",
      "\n",
      "* Install this package\n",
      "* Restart R\n",
      "* Go to `File => New File => R Markdow ... => From Template`\n",
      "\n",
      "# Examples\n",
      "\n",
      "## Metagenomes\n",
      "\n",
      "### Reading in bracken tables\n",
      "\n",
      "```\n",
      "library(dplyr)\n",
      "library(ggplot2)\n",
      "library(data.table)\n",
      "library(tidytable)\n",
      "library(LeyLabRMisc)\n",
      "# listing files\n",
      "brk_cls_files = list_files(profile_dir, 'all-combined-bracken.tsv') \n",
      "brk_cls_files %>% length\n",
      "# reading tables\n",
      "brk_cls = brk_cls_files %>% file_list(-2) %>%\n",
      "    plyr::llply(read_bracken) %>%\n",
      "    data.table::rbindlist(use.names=TRUE, idcol='dataset')\n",
      "brk_cls\n",
      "```\n",
      "\n",
      "# TODO\n"
     ]
    }
   ],
   "source": [
    "cat_file('/ebio/abt3_projects/software/dev/LeyLabRMisc/README.md')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## send_email()\n",
    "\n",
    "* Send yourself an email (username automatically determined)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:51:04.345061Z",
     "start_time": "2020-03-27T19:50:27.310Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Sending email to: nyoungblut@tuebingen.mpg.de\n",
      "\n"
     ]
    }
   ],
   "source": [
    "send_email('http://127.0.0.1:9705/notebooks/tmp/ipynb_sandbox/LeyLabRMisc_tutorial.ipynb')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## conda_info()\n",
    "\n",
    "* `conda list` on an environment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:51:15.346182Z",
     "start_time": "2020-03-27T19:50:27.312Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Not a conda environment: /ebio/abt3_projects/Georg_animal_feces/tidyverse\n",
      "# packages in environment at /ebio/abt3_projects/software/miniconda3_gt4.4:\n",
      "#\n",
      "# Name                    Version                   Build  Channel\n",
      "_libgcc_mutex             0.1                        main  \n",
      "_r-mutex                  1.0.0               anacondar_1  \n",
      "appdirs                   1.4.3                      py_1    conda-forge\n",
      "asn1crypto                1.3.0                    py36_0  \n",
      "attrs                     19.3.0                     py_0  \n",
      "automat                   0.7.0                    py36_0    conda-forge\n",
      "backcall                  0.1.0                    py36_0  \n",
      "binutils_impl_linux-64    2.31.1               h6176602_1  \n",
      "binutils_linux-64         2.31.1               h6176602_9  \n",
      "biopython                 1.73                     pypi_0    pypi\n",
      "blas                      1.0                         mkl  \n",
      "bleach                    3.1.0                    py36_0  \n",
      "bwidget                   1.9.11                        1  \n",
      "bzip2                     1.0.8                h7b6447c_0  \n",
      "ca-certificates           2020.1.1                      0  \n",
      "cairo                     1.14.12              h8948797_3  \n",
      "certifi                   2019.11.28               py36_0  \n",
      "cffi                      1.14.0           py36h2e261b9_0  \n",
      "chardet                   3.0.4                 py36_1003  \n",
      "click                     7.0                      pypi_0    pypi\n",
      "coloredlogs               10.0                     pypi_0    pypi\n",
      "colormath                 3.0.0                    pypi_0    pypi\n",
      "conda                     4.8.2                    py36_0  \n",
      "conda-env                 2.6.0                         1    conda-forge\n",
      "conda-package-handling    1.6.0            py36h7b6447c_0  \n",
      "constantly                15.1.0                     py_0    conda-forge\n",
      "cryptography              2.8              py36h1ba5d50_0  \n",
      "cryptography-vectors      2.3                      py36_1    conda-forge\n",
      "curl                      7.65.3               hbc83047_0  \n",
      "cycler                    0.10.0                   pypi_0    pypi\n",
      "dbus                      1.13.12              h746ee38_0  \n",
      "decorator                 4.4.1                      py_0  \n",
      "defusedxml                0.6.0                      py_0  \n",
      "entrypoints               0.3                      py36_0  \n",
      "expat                     2.2.6                he6710b0_0  \n",
      "fontconfig                2.13.0               h9420a91_0  \n",
      "freetype                  2.9.1                h8a8886c_1  \n",
      "fribidi                   1.0.5                h7b6447c_0  \n",
      "future                    0.18.2                   pypi_0    pypi\n",
      "gcc_impl_linux-64         7.3.0                habb00fd_1  \n",
      "gcc_linux-64              7.3.0                h553295d_9  \n",
      "gettext                   0.19.8.1          hc5be6a0_1002    conda-forge\n",
      "gfortran_impl_linux-64    7.3.0                hdf63c60_1  \n",
      "gfortran_linux-64         7.3.0                h553295d_9  \n",
      "glib                      2.63.1               h5a9c865_0  \n",
      "gmp                       6.1.2                h6c8ec71_1  \n",
      "graphite2                 1.3.13               h23475e2_0  \n",
      "gsl                       2.4                  h14c3975_4  \n",
      "gst-plugins-base          1.12.5               hde13a9d_0    conda-forge\n",
      "gstreamer                 1.12.5               h61a6719_0    conda-forge\n",
      "gxx_impl_linux-64         7.3.0                hdf63c60_1  \n",
      "gxx_linux-64              7.3.0                h553295d_9  \n",
      "harfbuzz                  1.8.8                hffaf4a1_0  \n",
      "html5lib                  1.0.1                      py_0    conda-forge\n",
      "humanfriendly             4.18                     pypi_0    pypi\n",
      "humann2                   0.11.2                   pypi_0    pypi\n",
      "hyperlink                 17.3.1                     py_0    conda-forge\n",
      "icu                       58.2                 h9c2bf20_1  \n",
      "idna                      2.8                      py36_0  \n",
      "importlib_metadata        1.5.0                    py36_0  \n",
      "incremental               17.5.0                     py_0    conda-forge\n",
      "ipykernel                 5.1.4            py36h39e3cac_0  \n",
      "ipython                   7.12.0           py36h5ca1d4c_0  \n",
      "ipython_genutils          0.2.0                    py36_0  \n",
      "ipywidgets                7.5.1                      py_0  \n",
      "jedi                      0.16.0                   py36_0  \n",
      "jinja2                    2.11.1                     py_0  \n",
      "jpeg                      9c                h14c3975_1001    conda-forge\n",
      "jsonschema                3.2.0                    py36_0  \n",
      "jupyter                   1.0.0                      py_1    conda-forge\n",
      "jupyter_client            5.3.4                    py36_0  \n",
      "jupyter_console           6.1.0                      py_0  \n",
      "jupyter_contrib_core      0.3.3                      py_2    conda-forge\n",
      "jupyter_contrib_nbextensions 0.5.0                    py36_0    conda-forge\n",
      "jupyter_core              4.6.1                    py36_0  \n",
      "jupyter_highlight_selected_word 0.2.0                 py36_1000    conda-forge\n",
      "jupyter_latex_envs        1.4.4                 py36_1000    conda-forge\n",
      "jupyter_nbextensions_configurator 0.4.1                    py36_0    conda-forge\n",
      "jupyterlab                0.35.4                   py36_0    conda-forge\n",
      "jupyterlab_server         0.2.0                      py_0    conda-forge\n",
      "kiwisolver                1.1.0                    pypi_0    pypi\n",
      "krb5                      1.16.4               h173b8e3_0  \n",
      "ld_impl_linux-64          2.33.1               h53a641e_7    conda-forge\n",
      "libarchive                3.3.3             hc3f970e_1004    conda-forge\n",
      "libblas                   3.8.0               14_openblas    conda-forge\n",
      "libcblas                  3.8.0               14_openblas    conda-forge\n",
      "libcurl                   7.65.3               h20c2e04_0  \n",
      "libedit                   3.1.20181209         hc058e9b_0  \n",
      "libffi                    3.2.1                hd88cf55_4  \n",
      "libgcc                    7.2.0                h69d50b8_2  \n",
      "libgcc-ng                 9.1.0                hdf63c60_0  \n",
      "libgfortran               3.0.0                         1    https://repo.anaconda.com/pkgs/free\n",
      "libgfortran-ng            7.3.0                hdf63c60_0  \n",
      "libiconv                  1.15              h516909a_1005    conda-forge\n",
      "libopenblas               0.3.7                h6e990d7_3    conda-forge\n",
      "libpng                    1.6.37               hbc83047_0  \n",
      "libsodium                 1.0.17               h516909a_0    conda-forge\n",
      "libssh2                   1.8.2                h1ba5d50_0  \n",
      "libstdcxx-ng              9.1.0                hdf63c60_0  \n",
      "libtiff                   4.0.10            h9022e91_1002    conda-forge\n",
      "libuuid                   1.0.3                h1bed415_2  \n",
      "libxcb                    1.13                 h1bed415_1  \n",
      "libxml2                   2.9.9                hea5a465_1  \n",
      "libxslt                   1.1.33               h7d1a2b0_0  \n",
      "lxml                      4.5.0            py36hefd8a0e_0  \n",
      "lz4-c                     1.8.3             he1b5a44_1001    conda-forge\n",
      "lzo                       2.10                 h49e0be7_2  \n",
      "lzstring                  1.0.4                    pypi_0    pypi\n",
      "make                      4.2.1                h1bed415_1  \n",
      "markdown                  3.1.1                    pypi_0    pypi\n",
      "markupsafe                1.1.1            py36h7b6447c_0  \n",
      "matplotlib                3.1.1                    pypi_0    pypi\n",
      "mistune                   0.8.4            py36h7b6447c_0  \n",
      "more-itertools            7.2.0                      py_0    conda-forge\n",
      "multiqc                   1.8                      pypi_0    pypi\n",
      "nb_conda                  2.2.1                    py36_0    conda-forge\n",
      "nb_conda_kernels          2.2.2                    py36_0  \n",
      "nbconvert                 5.6.1                    py36_0  \n",
      "nbformat                  5.0.4                      py_0  \n",
      "ncbi-genome-download      0.2.9                    pypi_0    pypi\n",
      "ncurses                   6.1                  he6710b0_1  \n",
      "networkx                  2.4                      pypi_0    pypi\n",
      "notebook                  6.0.3                    py36_0  \n",
      "numpy                     1.16.2                   pypi_0    pypi\n",
      "openblas                  0.2.20                        8    conda-forge\n",
      "openssl                   1.1.1d               h7b6447c_4  \n",
      "pandas                    0.24.2                   pypi_0    pypi\n",
      "pandoc                    2.2.3.2                       0  \n",
      "pandocfilters             1.4.2                    py36_1  \n",
      "pango                     1.42.4               h049681c_0  \n",
      "parso                     0.6.1                      py_0  \n",
      "pcre                      8.43                 he6710b0_0  \n",
      "pexpect                   4.8.0                    py36_0  \n",
      "pickleshare               0.7.5                    py36_0  \n",
      "pip                       20.0.2                   py36_1  \n",
      "pixman                    0.38.0               h7b6447c_0  \n",
      "prometheus_client         0.7.1                      py_0  \n",
      "prompt_toolkit            3.0.3                      py_0  \n",
      "pthread-stubs             0.4               h14c3975_1001    conda-forge\n",
      "ptyprocess                0.6.0                    py36_0  \n",
      "pyasn1                    0.4.4                      py_0    conda-forge\n",
      "pyasn1-modules            0.2.1                      py_0    conda-forge\n",
      "pycosat                   0.6.3            py36h7b6447c_0  \n",
      "pycparser                 2.19                     py36_0  \n",
      "pygments                  2.5.2                      py_0  \n",
      "pyhamcrest                1.9.0                      py_2    conda-forge\n",
      "pyopenssl                 19.1.0                   py36_0  \n",
      "pyparsing                 2.4.5                    pypi_0    pypi\n",
      "pyqt                      5.9.2            py36h05f1152_2  \n",
      "pyrsistent                0.15.7           py36h7b6447c_0  \n",
      "pysam                     0.15.2                   pypi_0    pypi\n",
      "pysocks                   1.7.1                    py36_0  \n",
      "python                    3.6.8                h0371630_0  \n",
      "python-dateutil           2.8.0                    pypi_0    pypi\n",
      "python-libarchive-c       2.8                   py36_1004    conda-forge\n",
      "pytz                      2018.9                   pypi_0    pypi\n",
      "pyyaml                    5.3              py36h7b6447c_0  \n",
      "pyzmq                     18.1.1           py36he6710b0_0  \n",
      "qt                        5.9.7                h5867ecd_1  \n",
      "qtconsole                 4.6.0                      py_1  \n",
      "r-assertthat              0.2.0                  r3.4.1_0    conda-forge\n",
      "r-backports               1.1.4             r36h96ca727_0  \n",
      "r-base                    3.6.1                h9bb98a2_1  \n",
      "r-base64enc               0.1_3            r341h470a237_1    conda-forge\n",
      "r-bh                      1.66.0_1               r3.4.1_0    conda-forge\n",
      "r-bindr                   0.1.1                  r3.4.1_0    conda-forge\n",
      "r-bindrcpp                0.2.2            r341h9d2a408_0    conda-forge\n",
      "r-bit                     1.1_12           r341h470a237_1    conda-forge\n",
      "r-bit64                   0.9_5            r341h470a237_1    conda-forge\n",
      "r-blob                    1.1.1                  r3.4.1_0    conda-forge\n",
      "r-cli                     1.0.0                  r3.4.1_0    conda-forge\n",
      "r-crayon                  1.3.4                  r3.4.1_0    conda-forge\n",
      "r-dbi                     1.0.0                    r341_0    conda-forge\n",
      "r-dbplyr                  1.2.2                    r341_0    conda-forge\n",
      "r-digest                  0.6.15           r341h470a237_1    conda-forge\n",
      "r-dplyr                   0.7.6            r341h9d2a408_0    conda-forge\n",
      "r-ellipsis                0.3.0             r35hcdcec82_0    conda-forge\n",
      "r-evaluate                0.10.1                 r3.4.1_0    conda-forge\n",
      "r-fansi                   0.4.0             r36h96ca727_0  \n",
      "r-glue                    1.3.0            r341h470a237_1    conda-forge\n",
      "r-htmltools               0.3.6            r341hfc679d8_1    conda-forge\n",
      "r-irdisplay               0.4.4                  r3.4.1_0    conda-forge\n",
      "r-irkernel                0.8.15                    r36_0  \n",
      "r-jsonlite                1.5              r341h470a237_1    conda-forge\n",
      "r-magrittr                1.5                    r3.4.1_0    conda-forge\n",
      "r-memoise                 1.1.0                  r3.4.1_0    conda-forge\n",
      "r-pbdzmq                  0.3_2                  r3.4.1_0    conda-forge\n",
      "r-pillar                  1.2.2            r341h6115d3f_1    conda-forge\n",
      "r-pkgconfig               2.0.1                  r3.4.1_0    conda-forge\n",
      "r-plogr                   0.2.0            r341h6115d3f_0    conda-forge\n",
      "r-prettyunits             1.0.2                  r3.4.1_0    conda-forge\n",
      "r-purrr                   0.2.4            r341h470a237_1    conda-forge\n",
      "r-r6                      2.2.2                  r3.4.1_0    conda-forge\n",
      "r-rcpp                    0.12.17          r341h9d2a408_1    conda-forge\n",
      "r-repr                    0.15                     r341_0    conda-forge\n",
      "r-rlang                   0.2.1            r341h470a237_1    conda-forge\n",
      "r-rsqlite                 2.0              r341hfc679d8_1    conda-forge\n",
      "r-stringi                 1.2.3            r341hfc679d8_1    conda-forge\n",
      "r-stringr                 1.3.1                    r341_0    conda-forge\n",
      "r-tibble                  1.4.2            r341h470a237_1    conda-forge\n",
      "r-tidyselect              0.2.4            r341hfc679d8_1    conda-forge\n",
      "r-utf8                    1.1.3            r341h470a237_1    conda-forge\n",
      "r-uuid                    0.1_2                    r341_0    conda-forge\n",
      "r-vctrs                   0.2.0             r35hcdcec82_1    conda-forge\n",
      "r-zeallot                 0.1.0             r36h6115d3f_0  \n",
      "readline                  7.0                  h7b6447c_5  \n",
      "requests                  2.22.0                   py36_1  \n",
      "rpy2                      2.9.4           py36r36h481b005_0  \n",
      "ruamel_yaml               0.15.87          py36h7b6447c_0  \n",
      "send2trash                1.5.0                    py36_0  \n",
      "service_identity          17.0.0                     py_0    conda-forge\n",
      "setuptools                45.2.0                   py36_0  \n",
      "simplegeneric             0.8.1                      py_1    conda-forge\n",
      "simplejson                3.17.0                   pypi_0    pypi\n",
      "sip                       4.19.8           py36hf484d3e_0  \n",
      "six                       1.14.0                   py36_0  \n",
      "spectra                   0.0.11                   pypi_0    pypi\n",
      "sqlite                    3.31.1               h7b6447c_0  \n",
      "terminado                 0.8.3                    py36_0  \n",
      "testpath                  0.4.4                      py_0  \n",
      "tk                        8.6.8                hbc83047_0  \n",
      "tktable                   2.10                 h14c3975_0  \n",
      "tornado                   6.0.3            py36h7b6447c_3  \n",
      "tqdm                      4.42.1                     py_0  \n",
      "traitlets                 4.3.3                    py36_0  \n",
      "twisted                   18.7.0           py36h14c3975_1  \n",
      "tzlocal                   2.0.0                      py_0    conda-forge\n",
      "urllib3                   1.25.8                   py36_0  \n",
      "wcwidth                   0.1.8                      py_0  \n",
      "webencodings              0.5.1                    py36_1  \n",
      "wheel                     0.34.2                   py36_0  \n",
      "widgetsnbextension        3.5.1                    py36_0  \n",
      "xorg-kbproto              1.0.7             h14c3975_1002    conda-forge\n",
      "xorg-libice               1.0.10               h516909a_0    conda-forge\n",
      "xorg-libsm                1.2.3             h84519dc_1000    conda-forge\n",
      "xorg-libx11               1.6.9                h516909a_0    conda-forge\n",
      "xorg-libxau               1.0.9                h14c3975_0    conda-forge\n",
      "xorg-libxdmcp             1.1.3                h516909a_0    conda-forge\n",
      "xorg-libxext              1.3.4                h516909a_0    conda-forge\n",
      "xorg-libxrender           0.9.10            h516909a_1002    conda-forge\n",
      "xorg-libxt                1.1.5                h470a237_2    conda-forge\n",
      "xorg-renderproto          0.11.1            h14c3975_1002    conda-forge\n",
      "xorg-xextproto            7.3.0             h14c3975_1002    conda-forge\n",
      "xorg-xproto               7.0.31            h14c3975_1007    conda-forge\n",
      "xz                        5.2.4                h14c3975_4  \n",
      "yaml                      0.1.7                had09818_2  \n",
      "zeromq                    4.3.2                he1b5a44_2    conda-forge\n",
      "zipp                      2.2.0                      py_0  \n",
      "zlib                      1.2.11               h7b6447c_3  \n",
      "zope.interface            4.5.0            py36h470a237_0    conda-forge\n",
      "zstd                      1.3.3                h84994c4_0  "
     ]
    }
   ],
   "source": [
    "conda_env = '/ebio/abt3_projects/Georg_animal_feces/tidyverse'\n",
    "condaInfo(conda_env)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pipelineInfo\n",
    "\n",
    "* basic info about a LL* pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:51:15.542729Z",
     "start_time": "2020-03-27T19:50:27.315Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "LLA\n",
      "====\n",
      "\n",
      "Ley Lab Genome analysis pipeline (LLG)\n",
      "\n",
      "* Version: 0.1.3\n",
      "* Authors:\n",
      "  * Nick Youngblut <nyoungb2@gmail.com>\n",
      "* Maintainers:\n",
      "  * Nick Youngblut <nyoungb2@gmail.com>\n",
      "\n",
      "--- conda envs ---\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/gtdbtk.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- pigz\n",
      "- bioconda::gtdbtk\n",
      "\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/checkm.yaml <==\n",
      "channels: !!python/tuple\n",
      "- bioconda\n",
      "dependencies:\n",
      "- python=2.7\n",
      "- pigz\n",
      "- bioconda::prodigal\n",
      "- bioconda::pplacer\n",
      "- bioconda::checkm-genome\n",
      "\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/quast.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- bioconda::seqkit\n",
      "- bioconda::quast=5.0.0\n",
      "\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/sourmash.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- bioconda::sourmash=2.0.0a4\n",
      "\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/fastqc.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- bioconda::seqtk\n",
      "- bioconda::fastqc=0.11.7\n",
      "\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/multiqc.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- bioconda::multiqc=1.4\n",
      "\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/hadley.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- conda-forge::r-ape\n",
      "- conda-forge::r-dplyr\n",
      "- conda-forge::r-tidyr\n",
      "- conda-forge::r-ggplot2\n",
      "\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/bbmap.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- pigz\n",
      "- bioconda::bbmap=37.78\n",
      "\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/ani.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- pigz\n",
      "- bioconda::dashing\n",
      "- bioconda::fastani\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/prokka.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- bioconda::prokka\n",
      "\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/prodigal.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- bioconda::prodigal\n",
      "- pip:\n",
      "  - biopython-convert\n",
      "\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/eggnog-mapper.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- pigz\n",
      "- bioconda::eggnog-mapper\n",
      "\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/phylophlan.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- pigz\n",
      "- numpy\n",
      "- python=3.7\n",
      "- conda-forge::biopython\n",
      "- bioconda::dendropy\n",
      "- bioconda::blast\n",
      "- bioconda::diamond\n",
      "- bioconda::vsearch\n",
      "- bioconda::mafft\n",
      "- bioconda::muscle\n",
      "- bioconda::trimal\n",
      "- bioconda::muscle\n",
      "- bioconda::raxml\n",
      "- bioconda::fasttree\n",
      "\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/drep.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- pigz\n",
      "- bioconda::mash\n",
      "- bioconda::checkm-genome\n",
      "- bioconda::drep\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/antismash.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- pigz\n",
      "- bioconda::antismash>=5.1.2\n",
      "\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/deepbgc.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- pigz\n",
      "- bioconda::deepbgc=0.1.18\n",
      "\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/abricate.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- bioconda::abricate\n",
      "\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/rRNA.yaml <==\n",
      "channels:\n",
      "- qiime2/label/r2020.2\n",
      "- conda-forge\n",
      "- bioconda\n",
      "- defaults\n",
      "dependencies:\n",
      "- barrnap\n",
      "- _libgcc_mutex=0.1\n",
      "- _openmp_mutex=4.5\n",
      "- _r-mutex=1.0.1\n",
      "- alsa-lib=1.1.5\n",
      "- arb-bio-tools=6.0.6\n",
      "- attrs=19.3.0\n",
      "- backcall=0.1.0\n",
      "- bibtexparser=1.1.0\n",
      "- binutils_impl_linux-64=2.33.1\n",
      "- binutils_linux-64=2.33.1\n",
      "- bioconductor-biobase=2.42.0\n",
      "- bioconductor-biocgenerics=0.28.0\n",
      "- bioconductor-biocparallel=1.16.6\n",
      "- bioconductor-biostrings=2.50.2\n",
      "- bioconductor-dada2=1.10.0\n",
      "- bioconductor-delayedarray=0.8.0\n",
      "- bioconductor-genomeinfodb=1.18.1\n",
      "- bioconductor-genomeinfodbdata=1.2.1\n",
      "- bioconductor-genomicalignments=1.18.1\n",
      "- bioconductor-genomicranges=1.34.0\n",
      "- bioconductor-iranges=2.16.0\n",
      "- bioconductor-rsamtools=1.34.0\n",
      "- bioconductor-s4vectors=0.20.1\n",
      "- bioconductor-shortread=1.40.0\n",
      "- bioconductor-summarizedexperiment=1.12.0\n",
      "- bioconductor-xvector=0.22.0\n",
      "- bioconductor-zlibbioc=1.28.0\n",
      "- biom-format=2.1.8\n",
      "- blas=2.14\n",
      "- blast=2.9.0\n",
      "- bleach=3.1.1\n",
      "- bokeh=1.4.0\n",
      "- boost=1.68.0\n",
      "- boost-cpp=1.68.0\n",
      "- bwidget=1.9.14\n",
      "- bzip2=1.0.8\n",
      "- ca-certificates=2019.11.28\n",
      "- cachecontrol=0.12.5\n",
      "- cairo=1.16.0\n",
      "- certifi=2019.11.28\n",
      "- cffi=1.13.2\n",
      "- chardet=3.0.4\n",
      "- click=7.0\n",
      "- cryptography=2.8\n",
      "- curl=7.68.0\n",
      "- cutadapt=2.8\n",
      "- cycler=0.10.0\n",
      "- cython=0.29.15\n",
      "- dbus=1.13.6\n",
      "- deblur=1.1.0\n",
      "- decorator=4.4.1\n",
      "- defusedxml=0.6.0\n",
      "- dendropy=4.4.0\n",
      "- dnaio=0.4.1\n",
      "- emperor=1.0.0\n",
      "- entrypoints=0.3\n",
      "- expat=2.2.9\n",
      "- fastcluster=1.1.26\n",
      "- fasttree=2.1.10\n",
      "- fontconfig=2.13.1\n",
      "- freetype=2.10.0\n",
      "- future=0.18.2\n",
      "- gcc_impl_linux-64=7.3.0\n",
      "- gcc_linux-64=7.3.0\n",
      "- gettext=0.19.8.1\n",
      "- gfortran_impl_linux-64=7.3.0\n",
      "- gfortran_linux-64=7.3.0\n",
      "- giflib=5.2.1\n",
      "- glib=2.58.3\n",
      "- gmp=6.2.0\n",
      "- gneiss=0.4.6\n",
      "- gnutls=3.6.5\n",
      "- graphite2=1.3.13\n",
      "- gsl=2.5\n",
      "- gst-plugins-base=1.14.5\n",
      "- gstreamer=1.14.5\n",
      "- gxx_impl_linux-64=7.3.0\n",
      "- gxx_linux-64=7.3.0\n",
      "- h5py=2.10.0\n",
      "- harfbuzz=2.4.0\n",
      "- hdf5=1.10.5\n",
      "- hdmedians=0.13\n",
      "- hmmer=3.1b2\n",
      "- icu=58.2\n",
      "- idna=2.9\n",
      "- ijson=2.6.1\n",
      "- importlib_metadata=1.5.0\n",
      "- ipykernel=5.1.4\n",
      "- ipython=7.12.0\n",
      "- ipython_genutils=0.2.0\n",
      "- ipywidgets=7.5.1\n",
      "- iqtree=1.6.12\n",
      "- jedi=0.16.0\n",
      "- jinja2=2.11.1\n",
      "- joblib=0.14.1\n",
      "- jpeg=9c\n",
      "- jsonschema=3.2.0\n",
      "- jupyter_client=6.0.0\n",
      "- jupyter_core=4.6.3\n",
      "- kiwisolver=1.1.0\n",
      "- krb5=1.16.4\n",
      "- lcms2=2.9\n",
      "- ld_impl_linux-64=2.33.1\n",
      "- libarbdb=6.0.6\n",
      "- libblas=3.8.0\n",
      "- libcblas=3.8.0\n",
      "- libcurl=7.68.0\n",
      "- libedit=3.1.20170329\n",
      "- libffi=3.2.1\n",
      "- libgcc=7.2.0\n",
      "- libgcc-ng=9.2.0\n",
      "- libgfortran-ng=7.3.0\n",
      "- libgomp=9.2.0\n",
      "- libiconv=1.15\n",
      "- liblapack=3.8.0\n",
      "- liblapacke=3.8.0\n",
      "- libopenblas=0.3.7\n",
      "- libpng=1.6.37\n",
      "- libsodium=1.0.17\n",
      "- libssh2=1.8.2\n",
      "- libstdcxx-ng=9.2.0\n",
      "- libtiff=4.1.0\n",
      "- libuuid=2.32.1\n",
      "- libxcb=1.13\n",
      "- libxml2=2.9.9\n",
      "- lockfile=0.12.2\n",
      "- lz4-c=1.8.3\n",
      "- mafft=7.310\n",
      "- make=4.3\n",
      "- markupsafe=1.1.1\n",
      "- matplotlib=3.1.1\n",
      "- matplotlib-base=3.1.1\n",
      "- mistune=0.8.4\n",
      "- more-itertools=8.2.0\n",
      "- msgpack-python=1.0.0\n",
      "- natsort=7.0.1\n",
      "- nbconvert=5.6.1\n",
      "- nbformat=5.0.4\n",
      "- ncurses=6.1\n",
      "- nettle=3.4.1\n",
      "- networkx=2.4\n",
      "- nose=1.3.7\n",
      "- notebook=6.0.3\n",
      "- numpy=1.18.1\n",
      "- olefile=0.46\n",
      "- openjdk=11.0.1\n",
      "- openssl=1.1.1d\n",
      "- packaging=20.1\n",
      "- pandas=0.25.3\n",
      "- pandoc=2.9.2\n",
      "- pandocfilters=1.4.2\n",
      "- pango=1.40.14\n",
      "- parso=0.6.1\n",
      "- patsy=0.5.1\n",
      "- pcre=8.44\n",
      "- perl=5.26.2\n",
      "- perl-archive-tar=2.32\n",
      "- perl-carp=1.38\n",
      "- perl-common-sense=3.74\n",
      "- perl-compress-raw-bzip2=2.087\n",
      "- perl-compress-raw-zlib=2.087\n",
      "- perl-exporter=5.72\n",
      "- perl-exporter-tiny=1.002001\n",
      "- perl-extutils-makemaker=7.36\n",
      "- perl-io-compress=2.087\n",
      "- perl-io-zlib=1.10\n",
      "- perl-json=4.02\n",
      "- perl-json-xs=2.34\n",
      "- perl-list-moreutils=0.428\n",
      "- perl-list-moreutils-xs=0.428\n",
      "- perl-pathtools=3.75\n",
      "- perl-scalar-list-utils=1.52\n",
      "- perl-types-serialiser=1.0\n",
      "- perl-xsloader=0.24\n",
      "- pexpect=4.8.0\n",
      "- pickleshare=0.7.5\n",
      "- pigz=2.3.4\n",
      "- pillow=7.0.0\n",
      "- pip=20.0.2\n",
      "- pixman=0.38.0\n",
      "- pluggy=0.12.0\n",
      "- prometheus_client=0.7.1\n",
      "- prompt_toolkit=3.0.3\n",
      "- psutil=5.7.0\n",
      "- pthread-stubs=0.4\n",
      "- ptyprocess=0.6.0\n",
      "- py=1.8.1\n",
      "- pycparser=2.19\n",
      "- pygments=2.5.2\n",
      "- pyopenssl=19.1.0\n",
      "- pyparsing=2.4.6\n",
      "- pyqt=5.9.2\n",
      "- pyrsistent=0.15.7\n",
      "- pysocks=1.7.1\n",
      "- pytest=5.3.5\n",
      "- python=3.6.7\n",
      "- python-dateutil=2.8.1\n",
      "- pytz=2019.3\n",
      "- pyyaml=5.3\n",
      "- pyzmq=19.0.0\n",
      "- q2-alignment=2020.2.0\n",
      "- q2-composition=2020.2.0\n",
      "- q2-cutadapt=2020.2.0\n",
      "- q2-dada2=2020.2.0\n",
      "- q2-deblur=2020.2.0\n",
      "- q2-demux=2020.2.0\n",
      "- q2-diversity=2020.2.0\n",
      "- q2-emperor=2020.2.0\n",
      "- q2-feature-classifier=2020.2.0\n",
      "- q2-feature-table=2020.2.0\n",
      "- q2-fragment-insertion=2020.2.0\n",
      "- q2-gneiss=2020.2.0\n",
      "- q2-longitudinal=2020.2.0\n",
      "- q2-metadata=2020.2.0\n",
      "- q2-phylogeny=2020.2.0\n",
      "- q2-quality-control=2020.2.0\n",
      "- q2-quality-filter=2020.2.0\n",
      "- q2-sample-classifier=2020.2.0\n",
      "- q2-taxa=2020.2.0\n",
      "- q2-types=2020.2.0\n",
      "- q2-vsearch=2020.2.0\n",
      "- q2cli=2020.2.0\n",
      "- q2templates=2020.2.0\n",
      "- qiime2=2020.2.0\n",
      "- qt=5.9.7\n",
      "- r-assertthat=0.2.1\n",
      "- r-backports=1.1.5\n",
      "- r-base=3.5.1\n",
      "- r-bh=1.72.0_3\n",
      "- r-bitops=1.0_6\n",
      "- r-cli=2.0.2\n",
      "- r-cluster=2.1.0\n",
      "- r-colorspace=1.4_1\n",
      "- r-crayon=1.3.4\n",
      "- r-data.table=1.12.6\n",
      "- r-digest=0.6.25\n",
      "- r-ellipsis=0.3.0\n",
      "- r-fansi=0.4.1\n",
      "- r-farver=2.0.3\n",
      "- r-formatr=1.7\n",
      "- r-futile.logger=1.4.3\n",
      "- r-futile.options=1.0.1\n",
      "- r-ggplot2=3.2.1\n",
      "- r-glue=1.3.1\n",
      "- r-gtable=0.3.0\n",
      "- r-hwriter=1.3.2\n",
      "- r-labeling=0.3\n",
      "- r-lambda.r=1.2.4\n",
      "- r-lattice=0.20_40\n",
      "- r-latticeextra=0.6_28\n",
      "- r-lazyeval=0.2.2\n",
      "- r-lifecycle=0.1.0\n",
      "- r-magrittr=1.5\n",
      "- r-mass=7.3_51.5\n",
      "- r-matrix=1.2_18\n",
      "- r-matrixstats=0.55.0\n",
      "- r-mgcv=1.8_31\n",
      "- r-munsell=0.5.0\n",
      "- r-nlme=3.1_144\n",
      "- r-permute=0.9_5\n",
      "- r-pillar=1.4.3\n",
      "- r-pkgconfig=2.0.3\n",
      "- r-plyr=1.8.5\n",
      "- r-r6=2.4.1\n",
      "- r-rcolorbrewer=1.1_2\n",
      "- r-rcpp=1.0.3\n",
      "- r-rcppparallel=4.4.4\n",
      "- r-rcurl=1.98_1.1\n",
      "- r-reshape2=1.4.3\n",
      "- r-rlang=0.4.4\n",
      "- r-scales=1.1.0\n",
      "- r-snow=0.4_3\n",
      "- r-stringi=1.4.3\n",
      "- r-stringr=1.4.0\n",
      "- r-tibble=2.1.3\n",
      "- r-utf8=1.1.4\n",
      "- r-vctrs=0.2.3\n",
      "- r-vegan=2.5_6\n",
      "- r-viridislite=0.3.0\n",
      "- r-withr=2.1.2\n",
      "- r-zeallot=0.1.0\n",
      "- raxml=8.2.12\n",
      "- readline=8.0\n",
      "- requests=2.23.0\n",
      "- scikit-bio=0.5.5\n",
      "- scikit-learn=0.22.1\n",
      "- scipy=1.4.1\n",
      "- seaborn=0.10.0\n",
      "- send2trash=1.5.0\n",
      "- sepp=4.3.10\n",
      "- setuptools=45.2.0\n",
      "- sina=1.6.0\n",
      "- sip=4.19.8\n",
      "- six=1.14.0\n",
      "- sortmerna=2.0\n",
      "- sqlite=3.30.1\n",
      "- statsmodels=0.11.1\n",
      "- tbb=2019.9\n",
      "- terminado=0.8.3\n",
      "- testpath=0.4.4\n",
      "- tk=8.6.10\n",
      "- tktable=2.10\n",
      "- tornado=6.0.3\n",
      "- traitlets=4.3.3\n",
      "- tzlocal=2.0.0\n",
      "- unifrac=0.10.0\n",
      "- urllib3=1.25.7\n",
      "- vsearch=2.7.0\n",
      "- wcwidth=0.1.8\n",
      "- webencodings=0.5.1\n",
      "- wheel=0.34.2\n",
      "- widgetsnbextension=3.5.1\n",
      "- xopen=0.8.4\n",
      "- xorg-fixesproto=5.0\n",
      "- xorg-inputproto=2.3.2\n",
      "- xorg-kbproto=1.0.7\n",
      "- xorg-libice=1.0.10\n",
      "- xorg-libsm=1.2.3\n",
      "- xorg-libx11=1.6.9\n",
      "- xorg-libxau=1.0.9\n",
      "- xorg-libxdmcp=1.1.3\n",
      "- xorg-libxext=1.3.4\n",
      "- xorg-libxfixes=5.0.3\n",
      "- xorg-libxi=1.7.10\n",
      "- xorg-libxrender=0.9.10\n",
      "- xorg-libxtst=1.2.3\n",
      "- xorg-recordproto=1.14.2\n",
      "- xorg-renderproto=0.11.1\n",
      "- xorg-xextproto=7.3.0\n",
      "- xorg-xproto=7.0.31\n",
      "- xz=5.2.4\n",
      "- yaml=0.2.2\n",
      "- zeromq=4.3.2\n",
      "- zipp=3.0.0\n",
      "- zlib=1.2.11\n",
      "- zstd=1.4.4\n",
      "==> /ebio/abt3_projects/software/dev/llg//bin/envs/traitar.yaml <==\n",
      "channels:\n",
      "- conda-forge\n",
      "- bioconda\n",
      "dependencies:\n",
      "- scipy\n",
      "- pandas\n",
      "- numpy\n",
      "- matplotlib\n",
      "- pigz\n",
      "- bioconda::hmmer\n",
      "- pip:\n",
      "  - traitar3>=0.1.1"
     ]
    }
   ],
   "source": [
    "pipelineInfo('/ebio/abt3_projects/software/dev/llg/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# sessionInfo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2020-03-27T19:51:15.599222Z",
     "start_time": "2020-03-27T19:50:27.318Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "R version 3.6.2 (2019-12-12)\n",
       "Platform: x86_64-conda_cos6-linux-gnu (64-bit)\n",
       "Running under: Ubuntu 18.04.3 LTS\n",
       "\n",
       "Matrix products: default\n",
       "BLAS/LAPACK: /ebio/abt3_projects/Georg_animal_feces/envs/tidyverse/lib/libopenblasp-r0.3.7.so\n",
       "\n",
       "locale:\n",
       " [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              \n",
       " [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    \n",
       " [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   \n",
       " [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 \n",
       " [9] LC_ADDRESS=C               LC_TELEPHONE=C            \n",
       "[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       \n",
       "\n",
       "attached base packages:\n",
       "[1] stats     graphics  grDevices utils     datasets  methods   base     \n",
       "\n",
       "other attached packages:\n",
       "[1] LeyLabRMisc_0.1.3 tidytable_0.3.2   data.table_1.12.8 ggplot2_3.2.1    \n",
       "[5] tidyr_1.0.0       dplyr_0.8.3      \n",
       "\n",
       "loaded via a namespace (and not attached):\n",
       " [1] Rcpp_1.0.3       pillar_1.4.3     compiler_3.6.2   base64enc_0.1-3 \n",
       " [5] tools_3.6.2      zeallot_0.1.0    digest_0.6.23    uuid_0.1-2      \n",
       " [9] jsonlite_1.6     evaluate_0.14    tibble_2.1.3     lifecycle_0.1.0 \n",
       "[13] gtable_0.3.0     pkgconfig_2.0.3  rlang_0.4.2      IRdisplay_0.7.0 \n",
       "[17] IRkernel_1.1     repr_1.0.2       withr_2.1.2      vctrs_0.2.1     \n",
       "[21] grid_3.6.2       tidyselect_0.2.5 glue_1.3.1       R6_2.4.1        \n",
       "[25] pbdZMQ_0.3-3     farver_2.0.2     purrr_0.3.3      magrittr_1.5    \n",
       "[29] backports_1.1.5  scales_1.1.0     htmltools_0.4.0  assertthat_0.2.1\n",
       "[33] colorspace_1.4-1 labeling_0.3     lazyeval_0.2.2   munsell_0.5.0   \n",
       "[37] crayon_1.3.4    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sessionInfo()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.4.1"
  },
  "notify_time": "30",
  "toc": {
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "toc_cell": true,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}