lecture13.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import plotnine\n",
    "import scipy as sp\n",
    "import statsmodels\n",
    "\n",
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Chi-squared example\n",
    "\n",
    "<https://gist.github.com/kylebgorman/2c2f69c807a3b9a092349d6c6455bed1>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[484, 762],\n",
       "       [477, 468],\n",
       "       [239, 327]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.array([[484, 762], [477, 468], [239, 327]])\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "484"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "762"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 2 is out of bounds for axis 1 with size 2",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-5-c89a660298bc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m: index 2 is out of bounds for axis 1 with size 2"
     ]
    }
   ],
   "source": [
    "d[0,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1246],\n",
       "       [ 945],\n",
       "       [ 566]])"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dparty = d.sum(axis=1, keepdims=True)\n",
    "dparty"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1200, 1557]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dgender = d.sum(axis=0, keepdims=True)\n",
    "dgender"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[542.32861806, 703.67138194],\n",
       "       [411.31664853, 533.68335147],\n",
       "       [246.35473341, 319.64526659]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dind = (dparty @ dgender) / dgender.sum()\n",
    "dind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[484, 762],\n",
       "       [477, 468],\n",
       "       [239, 327]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-58.32861806,  58.32861806],\n",
       "       [ 65.68335147, -65.68335147],\n",
       "       [ -7.35473341,   7.35473341]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d - dind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.27336927,  4.83496668],\n",
       "       [10.48900567,  8.08401208],\n",
       "       [ 0.21956998,  0.16922542]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(d - dind)**2 / dind"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30.07014909575468"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "((d - dind)**2 / dind).sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chi^2:\t\t30.07\n",
      "d.f.:\t\t2\n",
      "P(Chi^2):\t0.0000\n"
     ]
    }
   ],
   "source": [
    "(chi2, p, df, _) = stats.chi2_contingency(d)\n",
    "print(f\"Chi^2:\\t\\t{chi2:.2f}\")\n",
    "print(f\"d.f.:\\t\\t{df}\")\n",
    "print(f\"P(Chi^2):\\t{p:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Fisher exact test example\n",
    "\n",
    "<https://gist.github.com/kylebgorman/2c1a840cbff3ab35a6709528a01e2ff8>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 1],\n",
       "       [1, 3]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.array([[3, 1], [1, 3]])\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 0],\n",
       "       [0, 4]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d = np.array([[4, 0], [0, 4]])\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "P(Fisher):\t0.0286\n"
     ]
    }
   ],
   "source": [
    "(_, p) = stats.fisher_exact(d)\n",
    "print(f\"P(Fisher):\\t{p:.4f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Correlation example\n",
    "\n",
    "<https://gist.github.com/kylebgorman/0fecddad56efbc72f80ef2d3ad53f979>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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>phones</th>\n",
       "      <th>rating</th>\n",
       "      <th>bigram_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>S L EY1 M</td>\n",
       "      <td>5.84</td>\n",
       "      <td>17.468722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>W IH1 S</td>\n",
       "      <td>5.84</td>\n",
       "      <td>11.207508</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>P IH1 N T</td>\n",
       "      <td>5.67</td>\n",
       "      <td>13.045776</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>P AE1 NG K</td>\n",
       "      <td>5.63</td>\n",
       "      <td>13.723013</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>S T IH1 P</td>\n",
       "      <td>5.53</td>\n",
       "      <td>12.598640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>81</td>\n",
       "      <td>P L OW1 N TH</td>\n",
       "      <td>2.26</td>\n",
       "      <td>22.804817</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>82</td>\n",
       "      <td>TH EY1 P T</td>\n",
       "      <td>2.26</td>\n",
       "      <td>23.380083</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>83</td>\n",
       "      <td>S M IY1 N TH</td>\n",
       "      <td>2.06</td>\n",
       "      <td>25.043146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>84</td>\n",
       "      <td>S P R AA1 R F</td>\n",
       "      <td>2.05</td>\n",
       "      <td>24.031096</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>85</td>\n",
       "      <td>P W AH1 JH</td>\n",
       "      <td>1.74</td>\n",
       "      <td>23.205478</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>86 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           phones  rating  bigram_score\n",
       "0       S L EY1 M    5.84     17.468722\n",
       "1         W IH1 S    5.84     11.207508\n",
       "2       P IH1 N T    5.67     13.045776\n",
       "3      P AE1 NG K    5.63     13.723013\n",
       "4       S T IH1 P    5.53     12.598640\n",
       "..            ...     ...           ...\n",
       "81   P L OW1 N TH    2.26     22.804817\n",
       "82     TH EY1 P T    2.26     23.380083\n",
       "83   S M IY1 N TH    2.06     25.043146\n",
       "84  S P R AA1 R F    2.05     24.031096\n",
       "85     P W AH1 JH    1.74     23.205478\n",
       "\n",
       "[86 rows x 3 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SOURCE = \"albright_hayes_2003.tsv\"\n",
    "\n",
    "d = pd.read_csv(SOURCE, sep=\"\\t\")\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0     17.468722\n",
      "1     11.207508\n",
      "2     13.045776\n",
      "3     13.723013\n",
      "4     12.598640\n",
      "        ...    \n",
      "81    22.804817\n",
      "82    23.380083\n",
      "83    25.043146\n",
      "84    24.031096\n",
      "85    23.205478\n",
      "Name: bigram_score, Length: 86, dtype: float64\n",
      "0     5.84\n",
      "1     5.84\n",
      "2     5.67\n",
      "3     5.63\n",
      "4     5.53\n",
      "      ... \n",
      "81    2.26\n",
      "82    2.26\n",
      "83    2.06\n",
      "84    2.05\n",
      "85    1.74\n",
      "Name: rating, Length: 86, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "x = d.bigram_score\n",
    "y = d.rating\n",
    "\n",
    "print(x)\n",
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson R:\t-0.7457\t(P = 0.0000)\n"
     ]
    }
   ],
   "source": [
    "# The pvalues are usually tiny if there's a lot of data.\n",
    "(stat, p) = stats.pearsonr(x, y)\n",
    "print(f\"Pearson R:\\t{stat:.4f}\\t(P = {p:.4f})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Spearman rho:\t-0.6987\t(P = 0.0000)\n"
     ]
    }
   ],
   "source": [
    "(stat, p) = stats.spearmanr(x, y)\n",
    "print(f\"Spearman rho:\\t{stat:.4f}\\t(P = {p:.4f})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kendall tau:\t-0.4994\t(P = 0.0000)\n"
     ]
    }
   ],
   "source": [
    "(stat, p) = stats.kendalltau(x, y)\n",
    "print(f\"Kendall tau:\\t{stat:.4f}\\t(P = {p:.4f})\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter plot correlation example\n",
    "\n",
    "<https://gist.github.com/kylebgorman/fc138ca5693dc3fd0f51070cd8a596e7>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8772032303561)>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SOURCE = \"albright_hayes_2003.tsv\"\n",
    "\n",
    "d = pd.read_csv(SOURCE, sep=\"\\t\")\n",
    "\n",
    "p = (\n",
    "    plotnine.ggplot(d, plotnine.aes(x=\"bigram_score\", y=\"rating\"))\n",
    "    + plotnine.geom_point()\n",
    "    + plotnine.xlab(\"Bigram probability (-log)\")\n",
    "    + plotnine.ylab(\"Mean rating\")\n",
    "    + plotnine.ylim(1, 7)\n",
    ")\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mim/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8772032303561)>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p += plotnine.geom_smooth(method=\"lm\")\n",
    "p"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Anscombe's quartet example\n",
    "\n",
    "<https://gist.github.com/kylebgorman/cba6f20cf241d4451b99d6d8a2fae9c0>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "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>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>y1</th>\n",
       "      <th>y2</th>\n",
       "      <th>y3</th>\n",
       "      <th>y4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>8</td>\n",
       "      <td>8.04</td>\n",
       "      <td>9.14</td>\n",
       "      <td>7.46</td>\n",
       "      <td>6.58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>8</td>\n",
       "      <td>6.95</td>\n",
       "      <td>8.14</td>\n",
       "      <td>6.77</td>\n",
       "      <td>5.76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>13</td>\n",
       "      <td>8</td>\n",
       "      <td>7.58</td>\n",
       "      <td>8.74</td>\n",
       "      <td>12.74</td>\n",
       "      <td>7.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>9</td>\n",
       "      <td>8</td>\n",
       "      <td>8.81</td>\n",
       "      <td>8.77</td>\n",
       "      <td>7.11</td>\n",
       "      <td>8.84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "      <td>8.33</td>\n",
       "      <td>9.26</td>\n",
       "      <td>7.81</td>\n",
       "      <td>8.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>14</td>\n",
       "      <td>8</td>\n",
       "      <td>9.96</td>\n",
       "      <td>8.10</td>\n",
       "      <td>8.84</td>\n",
       "      <td>7.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>8</td>\n",
       "      <td>7.24</td>\n",
       "      <td>6.13</td>\n",
       "      <td>6.08</td>\n",
       "      <td>5.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "      <td>4.26</td>\n",
       "      <td>3.10</td>\n",
       "      <td>5.39</td>\n",
       "      <td>12.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>8</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>8</td>\n",
       "      <td>10.84</td>\n",
       "      <td>9.13</td>\n",
       "      <td>8.15</td>\n",
       "      <td>5.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>4.82</td>\n",
       "      <td>7.26</td>\n",
       "      <td>6.42</td>\n",
       "      <td>7.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>10</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>5.68</td>\n",
       "      <td>4.74</td>\n",
       "      <td>5.73</td>\n",
       "      <td>6.89</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    x1  x2  x3  x4     y1    y2     y3     y4\n",
       "0   10  10  10   8   8.04  9.14   7.46   6.58\n",
       "1    8   8   8   8   6.95  8.14   6.77   5.76\n",
       "2   13  13  13   8   7.58  8.74  12.74   7.71\n",
       "3    9   9   9   8   8.81  8.77   7.11   8.84\n",
       "4   11  11  11   8   8.33  9.26   7.81   8.47\n",
       "5   14  14  14   8   9.96  8.10   8.84   7.04\n",
       "6    6   6   6   8   7.24  6.13   6.08   5.25\n",
       "7    4   4   4  19   4.26  3.10   5.39  12.50\n",
       "8   12  12  12   8  10.84  9.13   8.15   5.56\n",
       "9    7   7   7   8   4.82  7.26   6.42   7.91\n",
       "10   5   5   5   8   5.68  4.74   5.73   6.89"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A Pandas `DataFrame` object, with data copied from R.\n",
    "d = statsmodels.api.datasets.get_rdataset(\"anscombe\").data\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>4</td>\n",
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      ],
      "text/plain": [
       "    facets   x      y\n",
       "id                   \n",
       "0        1  10   8.04\n",
       "1        1   8   6.95\n",
       "2        1  13   7.58\n",
       "3        1   9   8.81\n",
       "4        1  11   8.33\n",
       "5        1  14   9.96\n",
       "6        1   6   7.24\n",
       "7        1   4   4.26\n",
       "8        1  12  10.84\n",
       "9        1   7   4.82\n",
       "10       1   5   5.68\n",
       "0        2  10   9.14\n",
       "1        2   8   8.14\n",
       "2        2  13   8.74\n",
       "3        2   9   8.77\n",
       "4        2  11   9.26\n",
       "5        2  14   8.10\n",
       "6        2   6   6.13\n",
       "7        2   4   3.10\n",
       "8        2  12   9.13\n",
       "9        2   7   7.26\n",
       "10       2   5   4.74\n",
       "0        3  10   7.46\n",
       "1        3   8   6.77\n",
       "2        3  13  12.74\n",
       "3        3   9   7.11\n",
       "4        3  11   7.81\n",
       "5        3  14   8.84\n",
       "6        3   6   6.08\n",
       "7        3   4   5.39\n",
       "8        3  12   8.15\n",
       "9        3   7   6.42\n",
       "10       3   5   5.73\n",
       "0        4   8   6.58\n",
       "1        4   8   5.76\n",
       "2        4   8   7.71\n",
       "3        4   8   8.84\n",
       "4        4   8   8.47\n",
       "5        4   8   7.04\n",
       "6        4   8   5.25\n",
       "7        4  19  12.50\n",
       "8        4   8   5.56\n",
       "9        4   8   7.91\n",
       "10       4   8   6.89"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Converts from \"wide\" to \"long\" format.\n",
    "d[\"id\"] = d.index\n",
    "d = pd.wide_to_long(d, [\"x\", \"y\"], i=\"id\", j=\"facets\")\n",
    "d.reset_index(level=\"facets\", inplace=True)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/mim/anaconda3/lib/python3.7/site-packages/numpy/core/fromnumeric.py:2495: FutureWarning: Method .ptp is deprecated and will be removed in a future version. Use numpy.ptp instead.\n",
      "  return ptp(axis=axis, out=out, **kwargs)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8772025913285)>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "p = (\n",
    "    plotnine.ggplot(d, plotnine.aes(x=\"x\", y=\"y\"))\n",
    "    + plotnine.geom_point()\n",
    "    + plotnine.geom_smooth(method=\"lm\", se=False, fullrange=True)\n",
    "    + plotnine.facet_wrap(\"facets\")\n",
    "    + plotnine.scale_x_continuous(breaks=[4, 6, 8, 10, 12, 14, 16, 18])\n",
    "    + plotnine.scale_y_continuous(breaks=[4, 6, 8, 10, 12])\n",
    "    + plotnine.xlab(\"\")\n",
    "    + plotnine.ylab(\"\")\n",
    ")\n",
    "p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<ggplot: (8772025259237)>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(\n",
    "    plotnine.ggplot(d, plotnine.aes(x=\"x\", y=\"y\", alpha=\"facets\"))\n",
    "    + plotnine.geom_point()\n",
    "#     + plotnine.facet_wrap(\"facets\")\n",
    "#     + plotnine.geom_smooth(method=\"lm\", se=False, fullrange=True)\n",
    "#     + plotnine.scale_x_continuous(breaks=[4, 6, 8, 10, 12, 14, 16, 18])\n",
    "#     + plotnine.scale_y_continuous(breaks=[4, 6, 8, 10, 12])\n",
    "#     + plotnine.xlab(\"\")\n",
    "#     + plotnine.ylab(\"\")\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "def correlate(x, y):\n",
    "    (stat, p) = stats.pearsonr(x, y)\n",
    "    print(f\"Pearson R:\\t{stat:.4f}\\t(P = {p:.4f})\")\n",
    "    (stat, p) = stats.spearmanr(x, y)\n",
    "    print(f\"Spearman rho:\\t{stat:.4f}\\t(P = {p:.4f})\")\n",
    "    (stat, p) = stats.kendalltau(x, y)\n",
    "    print(f\"Kendall tau:\\t{stat:.4f}\\t(P = {p:.4f})\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson R:\t0.8164\t(P = 0.0022)\n",
      "Spearman rho:\t0.8182\t(P = 0.0021)\n",
      "Kendall tau:\t0.6364\t(P = 0.0057)\n"
     ]
    }
   ],
   "source": [
    "d = statsmodels.api.datasets.get_rdataset(\"anscombe\").data\n",
    "\n",
    "correlate(d['x1'], d['y1'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson R:\t0.8162\t(P = 0.0022)\n",
      "Spearman rho:\t0.6909\t(P = 0.0186)\n",
      "Kendall tau:\t0.5636\t(P = 0.0165)\n"
     ]
    }
   ],
   "source": [
    "correlate(d['x2'], d['y2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson R:\t0.8163\t(P = 0.0022)\n",
      "Spearman rho:\t0.9909\t(P = 0.0000)\n",
      "Kendall tau:\t0.9636\t(P = 0.0000)\n"
     ]
    }
   ],
   "source": [
    "correlate(d['x3'], d['y3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson R:\t0.8165\t(P = 0.0022)\n",
      "Spearman rho:\t0.5000\t(P = 0.1173)\n",
      "Kendall tau:\t0.4264\t(P = 0.1138)\n"
     ]
    }
   ],
   "source": [
    "correlate(d['x4'], d['y4'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "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": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}