{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "SN1jQ6BDYXyCeJWOHW5N",
      "project": "pYq3tTY66ZSR0P222zpE",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import altair as alt\n",
    "from sklearn import *"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "32qIG0xkNauZWMjSpg93",
      "project": "pYq3tTY66ZSR0P222zpE",
      "version": 1
     },
     "outputId": {
      "block": "GSBV8zFQoKntohbTx3gQ",
      "project": "pYq3tTY66ZSR0P222zpE",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>c</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   x  y\n",
       "0  1  a\n",
       "1  2  b\n",
       "2  3  c"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [1,2,3]\n",
    "y = [\"a\", \"b\", \"c\"]\n",
    "df = pd.DataFrame({\"x\": x,\n",
    "                  \"y\" : y})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "vfFm2Xdg8vYuRA3ybv2F",
      "project": "pYq3tTY66ZSR0P222zpE",
      "version": 1
     },
     "outputId": {
      "block": "DdVHENZRJZ9Vdtn8Z5rs",
      "project": "pYq3tTY66ZSR0P222zpE",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "── \u001b[1mAttaching packages\u001b[22m ─────────────────────────────────────── tidyverse 1.2.1 ──\n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2\u001b[39m 3.2.1     \u001b[32m✔\u001b[39m \u001b[34mpurrr  \u001b[39m 0.3.2\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtibble \u001b[39m 2.1.3     \u001b[32m✔\u001b[39m \u001b[34mdplyr  \u001b[39m 0.8.3\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtidyr  \u001b[39m 1.0.2     \u001b[32m✔\u001b[39m \u001b[34mstringr\u001b[39m 1.4.0\n",
      "\u001b[32m✔\u001b[39m \u001b[34mreadr  \u001b[39m 1.3.1     \u001b[32m✔\u001b[39m \u001b[34mforcats\u001b[39m 0.4.0\n",
      "── \u001b[1mConflicts\u001b[22m ────────────────────────────────────────── tidyverse_conflicts() ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mfilter()\u001b[39m masks \u001b[34mstats\u001b[39m::filter()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m    masks \u001b[34mstats\u001b[39m::lag()\n"
     ]
    }
   ],
   "source": [
    "library(tidyverse)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "ud1QbfVkrnwInsamyr8o",
      "project": "pYq3tTY66ZSR0P222zpE",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "mtcars1 <- mtcars %>%\n",
    "ggplot() +\n",
    " geom_point(aes(x = wt, y = hp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "HIUI7oBtvKcqXf7lRcSR",
      "project": "pYq3tTY66ZSR0P222zpE",
      "version": 1
     },
     "outputId": {
      "block": "aLVKKxzpsMbmb4rg0Ugx",
      "project": "pYq3tTY66ZSR0P222zpE",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0gAAANICAIAAAByhViMAAAACXBIWXMAABJ0AAASdAHeZh94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\nAI2g2AEAAGgExQ4AAEAjKHYAAAAa8f9gouLK0Vw8tgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mtcars1 +\n",
    " geom_smooth(aes(x = wt, y = hp), method = \"lm\", se = F) +\n",
    " theme_bw()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "iooxa": {
     "id": {
      "block": "hORuQJTXJAImTikC9IXN",
      "project": "pYq3tTY66ZSR0P222zpE",
      "version": 11
     }
    }
   },
   "source": [
    "An image won't work. How about some text? This works. \n",
    "[curvenote](https://curvenote.com)\n",
    "\n",
    "Text works, images do not. \n",
    "Tables do not work either\n",
    "Tables work only under code fence blocks\n",
    "\n",
    "```\n",
    "| x | y |\n",
    "|----|---|\n",
    "| 1  | 2 |\n",
    "\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "BunFoXWOULGLxJtfscgn",
      "project": "pYq3tTY66ZSR0P222zpE",
      "version": 2
     },
     "outputId": {
      "block": "bhF6qCwdfsHDXVTBezUE",
      "project": "pYq3tTY66ZSR0P222zpE",
      "version": 2
     }
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 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>Valiant</th><td>18.1</td><td>6</td><td>225</td><td>105</td><td>2.76</td><td>3.460</td><td>20.22</td><td>1</td><td>0</td><td>3</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 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",
       "\tValiant & 18.1 & 6 & 225 & 105 & 2.76 & 3.460 & 20.22 & 1 & 0 & 3 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 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",
       "| Valiant | 18.1 | 6 | 225 | 105 | 2.76 | 3.460 | 20.22 | 1 | 0 | 3 | 1 |\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",
       "Valiant           18.1 6   225  105 2.76 3.460 20.22 1  0  3    1   "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(knitr)\n",
    "\n",
    "head(mtcars)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "Gy7KD7G2RrrYTtEylSKy",
      "project": "pYq3tTY66ZSR0P222zpE",
      "version": 1
     },
     "outputId": null
    }
   },
   "outputs": [],
   "source": [
    "library(broom)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "iooxa": {
     "id": {
      "block": "GCT5bwBgmnbwaddKaaEN",
      "project": "pYq3tTY66ZSR0P222zpE",
      "version": 1
     },
     "outputId": {
      "block": "mt0DOw7jfbCy8aY1acIe",
      "project": "pYq3tTY66ZSR0P222zpE",
      "version": 1
     }
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“'tidy.data.frame' is deprecated.\n",
      "See help(\"Deprecated\")”"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A tibble: 11 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>column</th><th scope=col>n</th><th scope=col>mean</th><th scope=col>sd</th><th scope=col>median</th><th scope=col>trimmed</th><th scope=col>mad</th><th scope=col>min</th><th scope=col>max</th><th scope=col>range</th><th scope=col>skew</th><th scope=col>kurtosis</th><th scope=col>se</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&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><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>mpg </td><td>6</td><td> 20.500000</td><td> 1.7663522</td><td> 21.000</td><td> 20.500000</td><td> 1.100</td><td> 18.10</td><td> 22.80</td><td>  4.70</td><td>-0.2588032</td><td>1.786006</td><td> 0.7211103</td></tr>\n",
       "\t<tr><td>cyl </td><td>6</td><td>  6.000000</td><td> 1.2649111</td><td>  6.000</td><td>  6.000000</td><td> 0.000</td><td>  4.00</td><td>  8.00</td><td>  4.00</td><td> 0.0000000</td><td>3.000000</td><td> 0.5163978</td></tr>\n",
       "\t<tr><td>disp</td><td>6</td><td>211.833333</td><td>89.9030960</td><td>192.500</td><td>211.833333</td><td>49.000</td><td>108.00</td><td>360.00</td><td>252.00</td><td> 0.5895813</td><td>2.267444</td><td>36.7027852</td></tr>\n",
       "\t<tr><td>hp  </td><td>6</td><td>117.166667</td><td>29.0889441</td><td>110.000</td><td>117.166667</td><td> 2.500</td><td> 93.00</td><td>175.00</td><td> 82.00</td><td> 1.5702674</td><td>3.874166</td><td>11.8755117</td></tr>\n",
       "\t<tr><td>drat</td><td>6</td><td>  3.440000</td><td> 0.5034680</td><td>  3.500</td><td>  3.440000</td><td> 0.400</td><td>  2.76</td><td>  3.90</td><td>  1.14</td><td>-0.2092434</td><td>1.327855</td><td> 0.2055399</td></tr>\n",
       "\t<tr><td>wt  </td><td>6</td><td>  2.988333</td><td> 0.4632566</td><td>  3.045</td><td>  2.988333</td><td> 0.405</td><td>  2.32</td><td>  3.46</td><td>  1.14</td><td>-0.3112377</td><td>1.624982</td><td> 0.1891237</td></tr>\n",
       "\t<tr><td>qsec</td><td>6</td><td> 18.128333</td><td> 1.5210314</td><td> 17.815</td><td> 18.128333</td><td> 1.075</td><td> 16.46</td><td> 20.22</td><td>  3.76</td><td> 0.2585718</td><td>1.476108</td><td> 0.6209585</td></tr>\n",
       "\t<tr><td>vs  </td><td>6</td><td>  0.500000</td><td> 0.5477226</td><td>  0.500</td><td>  0.500000</td><td> 0.500</td><td>  0.00</td><td>  1.00</td><td>  1.00</td><td> 0.0000000</td><td>1.000000</td><td> 0.2236068</td></tr>\n",
       "\t<tr><td>am  </td><td>6</td><td>  0.500000</td><td> 0.5477226</td><td>  0.500</td><td>  0.500000</td><td> 0.500</td><td>  0.00</td><td>  1.00</td><td>  1.00</td><td> 0.0000000</td><td>1.000000</td><td> 0.2236068</td></tr>\n",
       "\t<tr><td>gear</td><td>6</td><td>  3.500000</td><td> 0.5477226</td><td>  3.500</td><td>  3.500000</td><td> 0.500</td><td>  3.00</td><td>  4.00</td><td>  1.00</td><td> 0.0000000</td><td>1.000000</td><td> 0.2236068</td></tr>\n",
       "\t<tr><td>carb</td><td>6</td><td>  2.166667</td><td> 1.4719601</td><td>  1.500</td><td>  2.166667</td><td> 0.500</td><td>  1.00</td><td>  4.00</td><td>  3.00</td><td> 0.5190377</td><td>1.439290</td><td> 0.6009252</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 11 × 13\n",
       "\\begin{tabular}{r|lllllllllllll}\n",
       " column & n & mean & sd & median & trimmed & mad & min & max & range & skew & kurtosis & se\\\\\n",
       " <chr> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t mpg  & 6 &  20.500000 &  1.7663522 &  21.000 &  20.500000 &  1.100 &  18.10 &  22.80 &   4.70 & -0.2588032 & 1.786006 &  0.7211103\\\\\n",
       "\t cyl  & 6 &   6.000000 &  1.2649111 &   6.000 &   6.000000 &  0.000 &   4.00 &   8.00 &   4.00 &  0.0000000 & 3.000000 &  0.5163978\\\\\n",
       "\t disp & 6 & 211.833333 & 89.9030960 & 192.500 & 211.833333 & 49.000 & 108.00 & 360.00 & 252.00 &  0.5895813 & 2.267444 & 36.7027852\\\\\n",
       "\t hp   & 6 & 117.166667 & 29.0889441 & 110.000 & 117.166667 &  2.500 &  93.00 & 175.00 &  82.00 &  1.5702674 & 3.874166 & 11.8755117\\\\\n",
       "\t drat & 6 &   3.440000 &  0.5034680 &   3.500 &   3.440000 &  0.400 &   2.76 &   3.90 &   1.14 & -0.2092434 & 1.327855 &  0.2055399\\\\\n",
       "\t wt   & 6 &   2.988333 &  0.4632566 &   3.045 &   2.988333 &  0.405 &   2.32 &   3.46 &   1.14 & -0.3112377 & 1.624982 &  0.1891237\\\\\n",
       "\t qsec & 6 &  18.128333 &  1.5210314 &  17.815 &  18.128333 &  1.075 &  16.46 &  20.22 &   3.76 &  0.2585718 & 1.476108 &  0.6209585\\\\\n",
       "\t vs   & 6 &   0.500000 &  0.5477226 &   0.500 &   0.500000 &  0.500 &   0.00 &   1.00 &   1.00 &  0.0000000 & 1.000000 &  0.2236068\\\\\n",
       "\t am   & 6 &   0.500000 &  0.5477226 &   0.500 &   0.500000 &  0.500 &   0.00 &   1.00 &   1.00 &  0.0000000 & 1.000000 &  0.2236068\\\\\n",
       "\t gear & 6 &   3.500000 &  0.5477226 &   3.500 &   3.500000 &  0.500 &   3.00 &   4.00 &   1.00 &  0.0000000 & 1.000000 &  0.2236068\\\\\n",
       "\t carb & 6 &   2.166667 &  1.4719601 &   1.500 &   2.166667 &  0.500 &   1.00 &   4.00 &   3.00 &  0.5190377 & 1.439290 &  0.6009252\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 11 × 13\n",
       "\n",
       "| column &lt;chr&gt; | n &lt;dbl&gt; | mean &lt;dbl&gt; | sd &lt;dbl&gt; | median &lt;dbl&gt; | trimmed &lt;dbl&gt; | mad &lt;dbl&gt; | min &lt;dbl&gt; | max &lt;dbl&gt; | range &lt;dbl&gt; | skew &lt;dbl&gt; | kurtosis &lt;dbl&gt; | se &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| mpg  | 6 |  20.500000 |  1.7663522 |  21.000 |  20.500000 |  1.100 |  18.10 |  22.80 |   4.70 | -0.2588032 | 1.786006 |  0.7211103 |\n",
       "| cyl  | 6 |   6.000000 |  1.2649111 |   6.000 |   6.000000 |  0.000 |   4.00 |   8.00 |   4.00 |  0.0000000 | 3.000000 |  0.5163978 |\n",
       "| disp | 6 | 211.833333 | 89.9030960 | 192.500 | 211.833333 | 49.000 | 108.00 | 360.00 | 252.00 |  0.5895813 | 2.267444 | 36.7027852 |\n",
       "| hp   | 6 | 117.166667 | 29.0889441 | 110.000 | 117.166667 |  2.500 |  93.00 | 175.00 |  82.00 |  1.5702674 | 3.874166 | 11.8755117 |\n",
       "| drat | 6 |   3.440000 |  0.5034680 |   3.500 |   3.440000 |  0.400 |   2.76 |   3.90 |   1.14 | -0.2092434 | 1.327855 |  0.2055399 |\n",
       "| wt   | 6 |   2.988333 |  0.4632566 |   3.045 |   2.988333 |  0.405 |   2.32 |   3.46 |   1.14 | -0.3112377 | 1.624982 |  0.1891237 |\n",
       "| qsec | 6 |  18.128333 |  1.5210314 |  17.815 |  18.128333 |  1.075 |  16.46 |  20.22 |   3.76 |  0.2585718 | 1.476108 |  0.6209585 |\n",
       "| vs   | 6 |   0.500000 |  0.5477226 |   0.500 |   0.500000 |  0.500 |   0.00 |   1.00 |   1.00 |  0.0000000 | 1.000000 |  0.2236068 |\n",
       "| am   | 6 |   0.500000 |  0.5477226 |   0.500 |   0.500000 |  0.500 |   0.00 |   1.00 |   1.00 |  0.0000000 | 1.000000 |  0.2236068 |\n",
       "| gear | 6 |   3.500000 |  0.5477226 |   3.500 |   3.500000 |  0.500 |   3.00 |   4.00 |   1.00 |  0.0000000 | 1.000000 |  0.2236068 |\n",
       "| carb | 6 |   2.166667 |  1.4719601 |   1.500 |   2.166667 |  0.500 |   1.00 |   4.00 |   3.00 |  0.5190377 | 1.439290 |  0.6009252 |\n",
       "\n"
      ],
      "text/plain": [
       "   column n mean       sd         median  trimmed    mad    min    max   \n",
       "1  mpg    6  20.500000  1.7663522  21.000  20.500000  1.100  18.10  22.80\n",
       "2  cyl    6   6.000000  1.2649111   6.000   6.000000  0.000   4.00   8.00\n",
       "3  disp   6 211.833333 89.9030960 192.500 211.833333 49.000 108.00 360.00\n",
       "4  hp     6 117.166667 29.0889441 110.000 117.166667  2.500  93.00 175.00\n",
       "5  drat   6   3.440000  0.5034680   3.500   3.440000  0.400   2.76   3.90\n",
       "6  wt     6   2.988333  0.4632566   3.045   2.988333  0.405   2.32   3.46\n",
       "7  qsec   6  18.128333  1.5210314  17.815  18.128333  1.075  16.46  20.22\n",
       "8  vs     6   0.500000  0.5477226   0.500   0.500000  0.500   0.00   1.00\n",
       "9  am     6   0.500000  0.5477226   0.500   0.500000  0.500   0.00   1.00\n",
       "10 gear   6   3.500000  0.5477226   3.500   3.500000  0.500   3.00   4.00\n",
       "11 carb   6   2.166667  1.4719601   1.500   2.166667  0.500   1.00   4.00\n",
       "   range  skew       kurtosis se        \n",
       "1    4.70 -0.2588032 1.786006  0.7211103\n",
       "2    4.00  0.0000000 3.000000  0.5163978\n",
       "3  252.00  0.5895813 2.267444 36.7027852\n",
       "4   82.00  1.5702674 3.874166 11.8755117\n",
       "5    1.14 -0.2092434 1.327855  0.2055399\n",
       "6    1.14 -0.3112377 1.624982  0.1891237\n",
       "7    3.76  0.2585718 1.476108  0.6209585\n",
       "8    1.00  0.0000000 1.000000  0.2236068\n",
       "9    1.00  0.0000000 1.000000  0.2236068\n",
       "10   1.00  0.0000000 1.000000  0.2236068\n",
       "11   3.00  0.5190377 1.439290  0.6009252"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "tidy(head(mtcars))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "iooxa": {
   "id": {
    "block": "hWmL2NFVU5Q8zURcyA1f",
    "project": "pYq3tTY66ZSR0P222zpE",
    "version": 23
   }
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}