ETS + ARIMA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd03fe6a-7c8e-4c1d-916e-09407337787a",
   "metadata": {},
   "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",
      "Warning message:\n",
      "\"package 'tsibble' was built under R version 4.4.2\"\n",
      "Registered S3 method overwritten by 'tsibble':\n",
      "  method               from \n",
      "  as_tibble.grouped_df dplyr\n",
      "\n",
      "\n",
      "Attaching package: 'tsibble'\n",
      "\n",
      "\n",
      "The following objects are masked from 'package:base':\n",
      "\n",
      "    intersect, setdiff, union\n",
      "\n",
      "\n",
      "── \u001b[1mAttaching packages\u001b[22m ──────────────────────────────────────────────────────────────────────────────────── fpp3 1.0.1 ──\n",
      "\n",
      "\u001b[32m✔\u001b[39m \u001b[34mtibble     \u001b[39m 3.2.1.\u001b[31m9033\u001b[39m     \u001b[32m✔\u001b[39m \u001b[34mtsibbledata\u001b[39m 0.4.1     \n",
      "\u001b[32m✔\u001b[39m \u001b[34mtidyr      \u001b[39m 1.3.1          \u001b[32m✔\u001b[39m \u001b[34mfeasts     \u001b[39m 0.4.1     \n",
      "\u001b[32m✔\u001b[39m \u001b[34mlubridate  \u001b[39m 1.9.3          \u001b[32m✔\u001b[39m \u001b[34mfable      \u001b[39m 0.4.1     \n",
      "\u001b[32m✔\u001b[39m \u001b[34mggplot2    \u001b[39m 3.5.1          \n",
      "\n",
      "Warning message:\n",
      "\"package 'tsibbledata' was built under R version 4.4.2\"\n",
      "Warning message:\n",
      "\"package 'feasts' was built under R version 4.4.2\"\n",
      "Warning message:\n",
      "\"package 'fabletools' was built under R version 4.4.2\"\n",
      "Warning message:\n",
      "\"package 'fable' was built under R version 4.4.2\"\n",
      "── \u001b[1mConflicts\u001b[22m ───────────────────────────────────────────────────────────────────────────────────────── fpp3_conflicts ──\n",
      "\u001b[31m✖\u001b[39m \u001b[34mlubridate\u001b[39m::\u001b[32mdate()\u001b[39m     masks \u001b[34mbase\u001b[39m::date()\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[34mtsibble\u001b[39m::\u001b[32mintersect()\u001b[39m  masks \u001b[34mbase\u001b[39m::intersect()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mlubridate\u001b[39m::\u001b[32minterval()\u001b[39m masks \u001b[34mtsibble\u001b[39m::interval()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mdplyr\u001b[39m::\u001b[32mlag()\u001b[39m          masks \u001b[34mstats\u001b[39m::lag()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mtsibble\u001b[39m::\u001b[32msetdiff()\u001b[39m    masks \u001b[34mbase\u001b[39m::setdiff()\n",
      "\u001b[31m✖\u001b[39m \u001b[34mtsibble\u001b[39m::\u001b[32munion()\u001b[39m      masks \u001b[34mbase\u001b[39m::union()\n",
      "\n"
     ]
    }
   ],
   "source": [
    "library(dplyr)\n",
    "library(tsibble)\n",
    "library(fpp3) \n",
    "library(ggplot2)\n",
    "library(corrplot)\n",
    "library(ggcorrplot)\n",
    "options(repr.plot.width=25)\n",
    "\n",
    "calendar <- read.csv('Datasets/calendar_afcs2024.csv')\n",
    "prices <- read.csv('Datasets/sell_prices_afcs2024.csv')\n",
    "sales_train <- read.csv('Datasets/sales_train_validation_afcs2024.csv')\n",
    "sales_val <- read.csv('Datasets/sales_test_validation_afcs2024.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "76908637-bfb6-452f-846a-f3c516059875",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transpose sale CSV to match forcasting-usable format\n",
    "# After transpose, ensure datatypes are int, surpress row names being selected in df\n",
    "\n",
    "sales_transposed <- t(sales_train)\n",
    "sales_df <- as.data.frame(sales_transposed[-1,]) %>% mutate_all(as.integer)\n",
    "colnames(sales_df) <- sales_transposed[1,]\n",
    "row.names(sales_df) <- NULL\n",
    "\n",
    "#sales_df <- tibble::rownames_to_column(sales_df, \"Day\")\n",
    "#sales_ts <- as_tsibble(sales_df, index = Day)\n",
    "#sales_ts <- as_tsibble(sales_df)\n",
    "\n",
    "# Count totals over the rows, just as a possible informative data about overall food items sales\n",
    "# Add day counter to both df's to make merging possible (otherwise no key)\n",
    "\n",
    "sales_df$total <- rowSums(sales_df)\n",
    "sales_df$day <- 1:nrow(sales_df)\n",
    "calendar$day <- 1:nrow(calendar)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "60143af9-8819-4bf1-83ee-7858233b94a3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Merge dataframes and convert to tsibble \n",
    "\n",
    "calendar_sales <- merge(calendar,sales_df)\n",
    "calendar_sales <- calendar_sales %>% as_tsibble(index = day)\n",
    "calendar_sales$is_event <- ifelse(calendar_sales$event_name_1 != \"NA\", 1, 0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "477c300e-a075-48cd-be48-49fe9ffeac65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RCtjGWiRdxhFsQdjkO0MohWBcnKIFoZ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\nRtwRdw2Tb6ZcY1UbiHv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HQ7mHrD2VNp29Zu9jsmE7YC21h88rvEPbZb1EEomaIE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X9+Lp8eRbxb3qaWzYtWMDH4h7+Wd0qpmf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\nJyV3zVo8ww2stort5e3ivrxL3PMxefDwu9irAjvoYeZ8cfxZ1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tS+ZuSnLO1reELcT7yLK4K4QzuIuwxS60Dc+wdxlzFlahH3wUHcVSDu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\nAAAAABiA/w8WN8hwK6nZ4QAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 1500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Overall Food sales, informative. See cyclic behavior \n",
    "\n",
    "calendar_sales %>% autoplot(total)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "4c2f9763-55db-4b93-9487-8f62d56aa4aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "TRUE"
      ],
      "text/latex": [
       "TRUE"
      ],
      "text/markdown": [
       "TRUE"
      ],
      "text/plain": [
       "[1] TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "is_regular(calendar_sales)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "715cf337-a207-4d17-a17a-b8fe08c70e8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tbl_ts: 5 × 837</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>day</th><th scope=col>date</th><th scope=col>wm_yr_wk</th><th scope=col>weekday</th><th scope=col>wday</th><th scope=col>month</th><th scope=col>year</th><th scope=col>event_name_1</th><th scope=col>event_type_1</th><th scope=col>event_name_2</th><th scope=col>⋯</th><th scope=col>FOODS_3_820_TX_3_validation</th><th scope=col>FOODS_3_821_TX_3_validation</th><th scope=col>FOODS_3_822_TX_3_validation</th><th scope=col>FOODS_3_823_TX_3_validation</th><th scope=col>FOODS_3_824_TX_3_validation</th><th scope=col>FOODS_3_825_TX_3_validation</th><th scope=col>FOODS_3_826_TX_3_validation</th><th scope=col>FOODS_3_827_TX_3_validation</th><th scope=col>total</th><th scope=col>is_event</th></tr>\n",
       "\t<tr><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;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;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</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;int&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;int&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> 331</td><td>12/25/2011</td><td>11148</td><td>Sunday   </td><td>2</td><td>12</td><td>2011</td><td>Christmas</td><td>National</td><td>NA</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>1</td></tr>\n",
       "\t<tr><td> 697</td><td>12/25/2012</td><td>11248</td><td>Tuesday  </td><td>4</td><td>12</td><td>2012</td><td>Christmas</td><td>National</td><td>NA</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>1</td></tr>\n",
       "\t<tr><td>1062</td><td>12/25/2013</td><td>11348</td><td>Wednesday</td><td>5</td><td>12</td><td>2013</td><td>Christmas</td><td>National</td><td>NA</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><td>1427</td><td>12/25/2014</td><td>11447</td><td>Thursday </td><td>6</td><td>12</td><td>2014</td><td>Christmas</td><td>National</td><td>NA</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>4</td><td>1</td></tr>\n",
       "\t<tr><td>1792</td><td>12/25/2015</td><td>11547</td><td>Friday   </td><td>7</td><td>12</td><td>2015</td><td>Christmas</td><td>National</td><td>NA</td><td>⋯</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>1</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tbl\\_ts: 5 × 837\n",
       "\\begin{tabular}{lllllllllllllllllllll}\n",
       " day & date & wm\\_yr\\_wk & weekday & wday & month & year & event\\_name\\_1 & event\\_type\\_1 & event\\_name\\_2 & ⋯ & FOODS\\_3\\_820\\_TX\\_3\\_validation & FOODS\\_3\\_821\\_TX\\_3\\_validation & FOODS\\_3\\_822\\_TX\\_3\\_validation & FOODS\\_3\\_823\\_TX\\_3\\_validation & FOODS\\_3\\_824\\_TX\\_3\\_validation & FOODS\\_3\\_825\\_TX\\_3\\_validation & FOODS\\_3\\_826\\_TX\\_3\\_validation & FOODS\\_3\\_827\\_TX\\_3\\_validation & total & is\\_event\\\\\n",
       " <int> & <chr> & <int> & <chr> & <int> & <int> & <int> & <chr> & <chr> & <chr> & ⋯ & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t  331 & 12/25/2011 & 11148 & Sunday    & 2 & 12 & 2011 & Christmas & National & NA & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 1\\\\\n",
       "\t  697 & 12/25/2012 & 11248 & Tuesday   & 4 & 12 & 2012 & Christmas & National & NA & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 1\\\\\n",
       "\t 1062 & 12/25/2013 & 11348 & Wednesday & 5 & 12 & 2013 & Christmas & National & NA & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 1 & 1\\\\\n",
       "\t 1427 & 12/25/2014 & 11447 & Thursday  & 6 & 12 & 2014 & Christmas & National & NA & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 4 & 1\\\\\n",
       "\t 1792 & 12/25/2015 & 11547 & Friday    & 7 & 12 & 2015 & Christmas & National & NA & ⋯ & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 0 & 2 & 1\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tbl_ts: 5 × 837\n",
       "\n",
       "| day &lt;int&gt; | date &lt;chr&gt; | wm_yr_wk &lt;int&gt; | weekday &lt;chr&gt; | wday &lt;int&gt; | month &lt;int&gt; | year &lt;int&gt; | event_name_1 &lt;chr&gt; | event_type_1 &lt;chr&gt; | event_name_2 &lt;chr&gt; | ⋯ ⋯ | FOODS_3_820_TX_3_validation &lt;int&gt; | FOODS_3_821_TX_3_validation &lt;int&gt; | FOODS_3_822_TX_3_validation &lt;int&gt; | FOODS_3_823_TX_3_validation &lt;int&gt; | FOODS_3_824_TX_3_validation &lt;int&gt; | FOODS_3_825_TX_3_validation &lt;int&gt; | FOODS_3_826_TX_3_validation &lt;int&gt; | FOODS_3_827_TX_3_validation &lt;int&gt; | total &lt;dbl&gt; | is_event &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "|  331 | 12/25/2011 | 11148 | Sunday    | 2 | 12 | 2011 | Christmas | National | NA | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 |\n",
       "|  697 | 12/25/2012 | 11248 | Tuesday   | 4 | 12 | 2012 | Christmas | National | NA | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 |\n",
       "| 1062 | 12/25/2013 | 11348 | Wednesday | 5 | 12 | 2013 | Christmas | National | NA | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 |\n",
       "| 1427 | 12/25/2014 | 11447 | Thursday  | 6 | 12 | 2014 | Christmas | National | NA | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 4 | 1 |\n",
       "| 1792 | 12/25/2015 | 11547 | Friday    | 7 | 12 | 2015 | Christmas | National | NA | ⋯ | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 2 | 1 |\n",
       "\n"
      ],
      "text/plain": [
       "  day  date       wm_yr_wk weekday   wday month year event_name_1 event_type_1\n",
       "1  331 12/25/2011 11148    Sunday    2    12    2011 Christmas    National    \n",
       "2  697 12/25/2012 11248    Tuesday   4    12    2012 Christmas    National    \n",
       "3 1062 12/25/2013 11348    Wednesday 5    12    2013 Christmas    National    \n",
       "4 1427 12/25/2014 11447    Thursday  6    12    2014 Christmas    National    \n",
       "5 1792 12/25/2015 11547    Friday    7    12    2015 Christmas    National    \n",
       "  event_name_2 ⋯ FOODS_3_820_TX_3_validation FOODS_3_821_TX_3_validation\n",
       "1 NA           ⋯ 0                           0                          \n",
       "2 NA           ⋯ 0                           0                          \n",
       "3 NA           ⋯ 0                           0                          \n",
       "4 NA           ⋯ 0                           0                          \n",
       "5 NA           ⋯ 0                           0                          \n",
       "  FOODS_3_822_TX_3_validation FOODS_3_823_TX_3_validation\n",
       "1 0                           0                          \n",
       "2 0                           0                          \n",
       "3 0                           0                          \n",
       "4 0                           0                          \n",
       "5 0                           0                          \n",
       "  FOODS_3_824_TX_3_validation FOODS_3_825_TX_3_validation\n",
       "1 0                           0                          \n",
       "2 0                           0                          \n",
       "3 0                           0                          \n",
       "4 0                           0                          \n",
       "5 0                           0                          \n",
       "  FOODS_3_826_TX_3_validation FOODS_3_827_TX_3_validation total is_event\n",
       "1 0                           0                           2     1       \n",
       "2 0                           0                           2     1       \n",
       "3 0                           0                           1     1       \n",
       "4 0                           0                           4     1       \n",
       "5 0                           0                           2     1       "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tbl_ts: 1 × 837</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>day</th><th scope=col>date</th><th scope=col>wm_yr_wk</th><th scope=col>weekday</th><th scope=col>wday</th><th scope=col>month</th><th scope=col>year</th><th scope=col>event_name_1</th><th scope=col>event_type_1</th><th scope=col>event_name_2</th><th scope=col>⋯</th><th scope=col>FOODS_3_820_TX_3_validation</th><th scope=col>FOODS_3_821_TX_3_validation</th><th scope=col>FOODS_3_822_TX_3_validation</th><th scope=col>FOODS_3_823_TX_3_validation</th><th scope=col>FOODS_3_824_TX_3_validation</th><th scope=col>FOODS_3_825_TX_3_validation</th><th scope=col>FOODS_3_826_TX_3_validation</th><th scope=col>FOODS_3_827_TX_3_validation</th><th scope=col>total</th><th scope=col>is_event</th></tr>\n",
       "\t<tr><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;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;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>⋯</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;int&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;int&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>1599</td><td>6/15/2015</td><td>11520</td><td>Monday</td><td>3</td><td>6</td><td>2015</td><td>NA</td><td>NA</td><td>NA</td><td>⋯</td><td>1</td><td>1</td><td>4</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td><td>4027</td><td>NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tbl\\_ts: 1 × 837\n",
       "\\begin{tabular}{lllllllllllllllllllll}\n",
       " day & date & wm\\_yr\\_wk & weekday & wday & month & year & event\\_name\\_1 & event\\_type\\_1 & event\\_name\\_2 & ⋯ & FOODS\\_3\\_820\\_TX\\_3\\_validation & FOODS\\_3\\_821\\_TX\\_3\\_validation & FOODS\\_3\\_822\\_TX\\_3\\_validation & FOODS\\_3\\_823\\_TX\\_3\\_validation & FOODS\\_3\\_824\\_TX\\_3\\_validation & FOODS\\_3\\_825\\_TX\\_3\\_validation & FOODS\\_3\\_826\\_TX\\_3\\_validation & FOODS\\_3\\_827\\_TX\\_3\\_validation & total & is\\_event\\\\\n",
       " <int> & <chr> & <int> & <chr> & <int> & <int> & <int> & <chr> & <chr> & <chr> & ⋯ & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <int> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\t 1599 & 6/15/2015 & 11520 & Monday & 3 & 6 & 2015 & NA & NA & NA & ⋯ & 1 & 1 & 4 & 0 & 0 & 0 & 2 & 0 & 4027 & NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tbl_ts: 1 × 837\n",
       "\n",
       "| day &lt;int&gt; | date &lt;chr&gt; | wm_yr_wk &lt;int&gt; | weekday &lt;chr&gt; | wday &lt;int&gt; | month &lt;int&gt; | year &lt;int&gt; | event_name_1 &lt;chr&gt; | event_type_1 &lt;chr&gt; | event_name_2 &lt;chr&gt; | ⋯ ⋯ | FOODS_3_820_TX_3_validation &lt;int&gt; | FOODS_3_821_TX_3_validation &lt;int&gt; | FOODS_3_822_TX_3_validation &lt;int&gt; | FOODS_3_823_TX_3_validation &lt;int&gt; | FOODS_3_824_TX_3_validation &lt;int&gt; | FOODS_3_825_TX_3_validation &lt;int&gt; | FOODS_3_826_TX_3_validation &lt;int&gt; | FOODS_3_827_TX_3_validation &lt;int&gt; | total &lt;dbl&gt; | is_event &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1599 | 6/15/2015 | 11520 | Monday | 3 | 6 | 2015 | NA | NA | NA | ⋯ | 1 | 1 | 4 | 0 | 0 | 0 | 2 | 0 | 4027 | NA |\n",
       "\n"
      ],
      "text/plain": [
       "  day  date      wm_yr_wk weekday wday month year event_name_1 event_type_1\n",
       "1 1599 6/15/2015 11520    Monday  3    6     2015 NA           NA          \n",
       "  event_name_2 ⋯ FOODS_3_820_TX_3_validation FOODS_3_821_TX_3_validation\n",
       "1 NA           ⋯ 1                           1                          \n",
       "  FOODS_3_822_TX_3_validation FOODS_3_823_TX_3_validation\n",
       "1 4                           0                          \n",
       "  FOODS_3_824_TX_3_validation FOODS_3_825_TX_3_validation\n",
       "1 0                           0                          \n",
       "  FOODS_3_826_TX_3_validation FOODS_3_827_TX_3_validation total is_event\n",
       "1 2                           0                           4027  NA      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Probably closed on christmas, sales is are aprox 0.\n",
    "# Positive outlier on jun 15 2015, 4027 sales for some reason\n",
    "\n",
    "calendar_sales[calendar_sales$total <=5, ]\n",
    "calendar_sales[calendar_sales$total >=3500, ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "cc7f30fc-f2ff-438f-840c-a72baeb21b30",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1574399 × 3</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>id</th><th scope=col>name</th><th scope=col>value</th></tr>\n",
       "\t<tr><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>FOODS_3_001</td><td> 1</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td> 2</td><td>2</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td> 3</td><td>1</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td> 4</td><td>3</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td> 5</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td> 6</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td> 7</td><td>1</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td> 8</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td> 9</td><td>2</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>10</td><td>1</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>11</td><td>1</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>12</td><td>1</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>13</td><td>1</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>14</td><td>2</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>15</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>16</td><td>3</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>17</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>18</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>19</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>20</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>21</td><td>1</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>22</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>23</td><td>3</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>24</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>25</td><td>3</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>26</td><td>1</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>27</td><td>1</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>28</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>29</td><td>2</td></tr>\n",
       "\t<tr><td>FOODS_3_001</td><td>30</td><td>2</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1884</td><td>4</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1885</td><td>7</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1886</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1887</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1888</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1889</td><td>2</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1890</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1891</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1892</td><td>3</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1893</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1894</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1895</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1896</td><td>1</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1897</td><td>1</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1898</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1899</td><td>1</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1900</td><td>2</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1901</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1902</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1903</td><td>2</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1904</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1905</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1906</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1907</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1908</td><td>1</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1909</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1910</td><td>1</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1911</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1912</td><td>0</td></tr>\n",
       "\t<tr><td>FOODS_3_827</td><td>1913</td><td>3</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1574399 × 3\n",
       "\\begin{tabular}{lll}\n",
       " id & name & value\\\\\n",
       " <chr> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t FOODS\\_3\\_001 &  1 & 0\\\\\n",
       "\t FOODS\\_3\\_001 &  2 & 2\\\\\n",
       "\t FOODS\\_3\\_001 &  3 & 1\\\\\n",
       "\t FOODS\\_3\\_001 &  4 & 3\\\\\n",
       "\t FOODS\\_3\\_001 &  5 & 0\\\\\n",
       "\t FOODS\\_3\\_001 &  6 & 0\\\\\n",
       "\t FOODS\\_3\\_001 &  7 & 1\\\\\n",
       "\t FOODS\\_3\\_001 &  8 & 0\\\\\n",
       "\t FOODS\\_3\\_001 &  9 & 2\\\\\n",
       "\t FOODS\\_3\\_001 & 10 & 1\\\\\n",
       "\t FOODS\\_3\\_001 & 11 & 1\\\\\n",
       "\t FOODS\\_3\\_001 & 12 & 1\\\\\n",
       "\t FOODS\\_3\\_001 & 13 & 1\\\\\n",
       "\t FOODS\\_3\\_001 & 14 & 2\\\\\n",
       "\t FOODS\\_3\\_001 & 15 & 0\\\\\n",
       "\t FOODS\\_3\\_001 & 16 & 3\\\\\n",
       "\t FOODS\\_3\\_001 & 17 & 0\\\\\n",
       "\t FOODS\\_3\\_001 & 18 & 0\\\\\n",
       "\t FOODS\\_3\\_001 & 19 & 0\\\\\n",
       "\t FOODS\\_3\\_001 & 20 & 0\\\\\n",
       "\t FOODS\\_3\\_001 & 21 & 1\\\\\n",
       "\t FOODS\\_3\\_001 & 22 & 0\\\\\n",
       "\t FOODS\\_3\\_001 & 23 & 3\\\\\n",
       "\t FOODS\\_3\\_001 & 24 & 0\\\\\n",
       "\t FOODS\\_3\\_001 & 25 & 3\\\\\n",
       "\t FOODS\\_3\\_001 & 26 & 1\\\\\n",
       "\t FOODS\\_3\\_001 & 27 & 1\\\\\n",
       "\t FOODS\\_3\\_001 & 28 & 0\\\\\n",
       "\t FOODS\\_3\\_001 & 29 & 2\\\\\n",
       "\t FOODS\\_3\\_001 & 30 & 2\\\\\n",
       "\t ⋮ & ⋮ & ⋮\\\\\n",
       "\t FOODS\\_3\\_827 & 1884 & 4\\\\\n",
       "\t FOODS\\_3\\_827 & 1885 & 7\\\\\n",
       "\t FOODS\\_3\\_827 & 1886 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1887 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1888 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1889 & 2\\\\\n",
       "\t FOODS\\_3\\_827 & 1890 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1891 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1892 & 3\\\\\n",
       "\t FOODS\\_3\\_827 & 1893 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1894 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1895 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1896 & 1\\\\\n",
       "\t FOODS\\_3\\_827 & 1897 & 1\\\\\n",
       "\t FOODS\\_3\\_827 & 1898 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1899 & 1\\\\\n",
       "\t FOODS\\_3\\_827 & 1900 & 2\\\\\n",
       "\t FOODS\\_3\\_827 & 1901 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1902 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1903 & 2\\\\\n",
       "\t FOODS\\_3\\_827 & 1904 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1905 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1906 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1907 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1908 & 1\\\\\n",
       "\t FOODS\\_3\\_827 & 1909 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1910 & 1\\\\\n",
       "\t FOODS\\_3\\_827 & 1911 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1912 & 0\\\\\n",
       "\t FOODS\\_3\\_827 & 1913 & 3\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1574399 × 3\n",
       "\n",
       "| id &lt;chr&gt; | name &lt;int&gt; | value &lt;int&gt; |\n",
       "|---|---|---|\n",
       "| FOODS_3_001 |  1 | 0 |\n",
       "| FOODS_3_001 |  2 | 2 |\n",
       "| FOODS_3_001 |  3 | 1 |\n",
       "| FOODS_3_001 |  4 | 3 |\n",
       "| FOODS_3_001 |  5 | 0 |\n",
       "| FOODS_3_001 |  6 | 0 |\n",
       "| FOODS_3_001 |  7 | 1 |\n",
       "| FOODS_3_001 |  8 | 0 |\n",
       "| FOODS_3_001 |  9 | 2 |\n",
       "| FOODS_3_001 | 10 | 1 |\n",
       "| FOODS_3_001 | 11 | 1 |\n",
       "| FOODS_3_001 | 12 | 1 |\n",
       "| FOODS_3_001 | 13 | 1 |\n",
       "| FOODS_3_001 | 14 | 2 |\n",
       "| FOODS_3_001 | 15 | 0 |\n",
       "| FOODS_3_001 | 16 | 3 |\n",
       "| FOODS_3_001 | 17 | 0 |\n",
       "| FOODS_3_001 | 18 | 0 |\n",
       "| FOODS_3_001 | 19 | 0 |\n",
       "| FOODS_3_001 | 20 | 0 |\n",
       "| FOODS_3_001 | 21 | 1 |\n",
       "| FOODS_3_001 | 22 | 0 |\n",
       "| FOODS_3_001 | 23 | 3 |\n",
       "| FOODS_3_001 | 24 | 0 |\n",
       "| FOODS_3_001 | 25 | 3 |\n",
       "| FOODS_3_001 | 26 | 1 |\n",
       "| FOODS_3_001 | 27 | 1 |\n",
       "| FOODS_3_001 | 28 | 0 |\n",
       "| FOODS_3_001 | 29 | 2 |\n",
       "| FOODS_3_001 | 30 | 2 |\n",
       "| ⋮ | ⋮ | ⋮ |\n",
       "| FOODS_3_827 | 1884 | 4 |\n",
       "| FOODS_3_827 | 1885 | 7 |\n",
       "| FOODS_3_827 | 1886 | 0 |\n",
       "| FOODS_3_827 | 1887 | 0 |\n",
       "| FOODS_3_827 | 1888 | 0 |\n",
       "| FOODS_3_827 | 1889 | 2 |\n",
       "| FOODS_3_827 | 1890 | 0 |\n",
       "| FOODS_3_827 | 1891 | 0 |\n",
       "| FOODS_3_827 | 1892 | 3 |\n",
       "| FOODS_3_827 | 1893 | 0 |\n",
       "| FOODS_3_827 | 1894 | 0 |\n",
       "| FOODS_3_827 | 1895 | 0 |\n",
       "| FOODS_3_827 | 1896 | 1 |\n",
       "| FOODS_3_827 | 1897 | 1 |\n",
       "| FOODS_3_827 | 1898 | 0 |\n",
       "| FOODS_3_827 | 1899 | 1 |\n",
       "| FOODS_3_827 | 1900 | 2 |\n",
       "| FOODS_3_827 | 1901 | 0 |\n",
       "| FOODS_3_827 | 1902 | 0 |\n",
       "| FOODS_3_827 | 1903 | 2 |\n",
       "| FOODS_3_827 | 1904 | 0 |\n",
       "| FOODS_3_827 | 1905 | 0 |\n",
       "| FOODS_3_827 | 1906 | 0 |\n",
       "| FOODS_3_827 | 1907 | 0 |\n",
       "| FOODS_3_827 | 1908 | 1 |\n",
       "| FOODS_3_827 | 1909 | 0 |\n",
       "| FOODS_3_827 | 1910 | 1 |\n",
       "| FOODS_3_827 | 1911 | 0 |\n",
       "| FOODS_3_827 | 1912 | 0 |\n",
       "| FOODS_3_827 | 1913 | 3 |\n",
       "\n"
      ],
      "text/plain": [
       "        id          name value\n",
       "1       FOODS_3_001  1   0    \n",
       "2       FOODS_3_001  2   2    \n",
       "3       FOODS_3_001  3   1    \n",
       "4       FOODS_3_001  4   3    \n",
       "5       FOODS_3_001  5   0    \n",
       "6       FOODS_3_001  6   0    \n",
       "7       FOODS_3_001  7   1    \n",
       "8       FOODS_3_001  8   0    \n",
       "9       FOODS_3_001  9   2    \n",
       "10      FOODS_3_001 10   1    \n",
       "11      FOODS_3_001 11   1    \n",
       "12      FOODS_3_001 12   1    \n",
       "13      FOODS_3_001 13   1    \n",
       "14      FOODS_3_001 14   2    \n",
       "15      FOODS_3_001 15   0    \n",
       "16      FOODS_3_001 16   3    \n",
       "17      FOODS_3_001 17   0    \n",
       "18      FOODS_3_001 18   0    \n",
       "19      FOODS_3_001 19   0    \n",
       "20      FOODS_3_001 20   0    \n",
       "21      FOODS_3_001 21   1    \n",
       "22      FOODS_3_001 22   0    \n",
       "23      FOODS_3_001 23   3    \n",
       "24      FOODS_3_001 24   0    \n",
       "25      FOODS_3_001 25   3    \n",
       "26      FOODS_3_001 26   1    \n",
       "27      FOODS_3_001 27   1    \n",
       "28      FOODS_3_001 28   0    \n",
       "29      FOODS_3_001 29   2    \n",
       "30      FOODS_3_001 30   2    \n",
       "⋮       ⋮           ⋮    ⋮    \n",
       "1574370 FOODS_3_827 1884 4    \n",
       "1574371 FOODS_3_827 1885 7    \n",
       "1574372 FOODS_3_827 1886 0    \n",
       "1574373 FOODS_3_827 1887 0    \n",
       "1574374 FOODS_3_827 1888 0    \n",
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       "1574384 FOODS_3_827 1898 0    \n",
       "1574385 FOODS_3_827 1899 1    \n",
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       "1574396 FOODS_3_827 1910 1    \n",
       "1574397 FOODS_3_827 1911 0    \n",
       "1574398 FOODS_3_827 1912 0    \n",
       "1574399 FOODS_3_827 1913 3    "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A data.frame: 1969 × 13</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>date</th><th scope=col>wm_yr_wk</th><th scope=col>weekday</th><th scope=col>wday</th><th scope=col>month</th><th scope=col>year</th><th scope=col>event_name_1</th><th scope=col>event_type_1</th><th scope=col>event_name_2</th><th scope=col>event_type_2</th><th scope=col>snap_TX</th><th scope=col>day</th><th scope=col>name</th></tr>\n",
       "\t<tr><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;chr&gt;</th><th scope=col>&lt;chr&gt;</th><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;int&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1/29/2011</td><td>11101</td><td>Saturday </td><td>1</td><td>1</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td> 1</td><td> 1</td></tr>\n",
       "\t<tr><td>1/30/2011</td><td>11101</td><td>Sunday   </td><td>2</td><td>1</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td> 2</td><td> 2</td></tr>\n",
       "\t<tr><td>1/31/2011</td><td>11101</td><td>Monday   </td><td>3</td><td>1</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td> 3</td><td> 3</td></tr>\n",
       "\t<tr><td>2/1/2011 </td><td>11101</td><td>Tuesday  </td><td>4</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>1</td><td> 4</td><td> 4</td></tr>\n",
       "\t<tr><td>2/2/2011 </td><td>11101</td><td>Wednesday</td><td>5</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td> 5</td><td> 5</td></tr>\n",
       "\t<tr><td>2/3/2011 </td><td>11101</td><td>Thursday </td><td>6</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>1</td><td> 6</td><td> 6</td></tr>\n",
       "\t<tr><td>2/4/2011 </td><td>11101</td><td>Friday   </td><td>7</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td> 7</td><td> 7</td></tr>\n",
       "\t<tr><td>2/5/2011 </td><td>11102</td><td>Saturday </td><td>1</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>1</td><td> 8</td><td> 8</td></tr>\n",
       "\t<tr><td>2/6/2011 </td><td>11102</td><td>Sunday   </td><td>2</td><td>2</td><td>2011</td><td>SuperBowl    </td><td>Sporting</td><td>NA</td><td>NA</td><td>1</td><td> 9</td><td> 9</td></tr>\n",
       "\t<tr><td>2/7/2011 </td><td>11102</td><td>Monday   </td><td>3</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>1</td><td>10</td><td>10</td></tr>\n",
       "\t<tr><td>2/8/2011 </td><td>11102</td><td>Tuesday  </td><td>4</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td>11</td><td>11</td></tr>\n",
       "\t<tr><td>2/9/2011 </td><td>11102</td><td>Wednesday</td><td>5</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>1</td><td>12</td><td>12</td></tr>\n",
       "\t<tr><td>2/10/2011</td><td>11102</td><td>Thursday </td><td>6</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td>13</td><td>13</td></tr>\n",
       "\t<tr><td>2/11/2011</td><td>11102</td><td>Friday   </td><td>7</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>1</td><td>14</td><td>14</td></tr>\n",
       "\t<tr><td>2/12/2011</td><td>11103</td><td>Saturday </td><td>1</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>1</td><td>15</td><td>15</td></tr>\n",
       "\t<tr><td>2/13/2011</td><td>11103</td><td>Sunday   </td><td>2</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>1</td><td>16</td><td>16</td></tr>\n",
       "\t<tr><td>2/14/2011</td><td>11103</td><td>Monday   </td><td>3</td><td>2</td><td>2011</td><td>ValentinesDay</td><td>Cultural</td><td>NA</td><td>NA</td><td>0</td><td>17</td><td>17</td></tr>\n",
       "\t<tr><td>2/15/2011</td><td>11103</td><td>Tuesday  </td><td>4</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>1</td><td>18</td><td>18</td></tr>\n",
       "\t<tr><td>2/16/2011</td><td>11103</td><td>Wednesday</td><td>5</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td>19</td><td>19</td></tr>\n",
       "\t<tr><td>2/17/2011</td><td>11103</td><td>Thursday </td><td>6</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td>20</td><td>20</td></tr>\n",
       "\t<tr><td>2/18/2011</td><td>11103</td><td>Friday   </td><td>7</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td>21</td><td>21</td></tr>\n",
       "\t<tr><td>2/19/2011</td><td>11104</td><td>Saturday </td><td>1</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td>22</td><td>22</td></tr>\n",
       "\t<tr><td>2/20/2011</td><td>11104</td><td>Sunday   </td><td>2</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td>23</td><td>23</td></tr>\n",
       "\t<tr><td>2/21/2011</td><td>11104</td><td>Monday   </td><td>3</td><td>2</td><td>2011</td><td>PresidentsDay</td><td>National</td><td>NA</td><td>NA</td><td>0</td><td>24</td><td>24</td></tr>\n",
       "\t<tr><td>2/22/2011</td><td>11104</td><td>Tuesday  </td><td>4</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td>25</td><td>25</td></tr>\n",
       "\t<tr><td>2/23/2011</td><td>11104</td><td>Wednesday</td><td>5</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td>26</td><td>26</td></tr>\n",
       "\t<tr><td>2/24/2011</td><td>11104</td><td>Thursday </td><td>6</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td>27</td><td>27</td></tr>\n",
       "\t<tr><td>2/25/2011</td><td>11104</td><td>Friday   </td><td>7</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td>28</td><td>28</td></tr>\n",
       "\t<tr><td>2/26/2011</td><td>11105</td><td>Saturday </td><td>1</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td>29</td><td>29</td></tr>\n",
       "\t<tr><td>2/27/2011</td><td>11105</td><td>Sunday   </td><td>2</td><td>2</td><td>2011</td><td>NA           </td><td>NA      </td><td>NA</td><td>NA</td><td>0</td><td>30</td><td>30</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><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>5/21/2016</td><td>11617</td><td>Saturday </td><td>1</td><td>5</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1940</td><td>1940</td></tr>\n",
       "\t<tr><td>5/22/2016</td><td>11617</td><td>Sunday   </td><td>2</td><td>5</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1941</td><td>1941</td></tr>\n",
       "\t<tr><td>5/23/2016</td><td>11617</td><td>Monday   </td><td>3</td><td>5</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1942</td><td>1942</td></tr>\n",
       "\t<tr><td>5/24/2016</td><td>11617</td><td>Tuesday  </td><td>4</td><td>5</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1943</td><td>1943</td></tr>\n",
       "\t<tr><td>5/25/2016</td><td>11617</td><td>Wednesday</td><td>5</td><td>5</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1944</td><td>1944</td></tr>\n",
       "\t<tr><td>5/26/2016</td><td>11617</td><td>Thursday </td><td>6</td><td>5</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1945</td><td>1945</td></tr>\n",
       "\t<tr><td>5/27/2016</td><td>11617</td><td>Friday   </td><td>7</td><td>5</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1946</td><td>1946</td></tr>\n",
       "\t<tr><td>5/28/2016</td><td>11618</td><td>Saturday </td><td>1</td><td>5</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1947</td><td>1947</td></tr>\n",
       "\t<tr><td>5/29/2016</td><td>11618</td><td>Sunday   </td><td>2</td><td>5</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1948</td><td>1948</td></tr>\n",
       "\t<tr><td>5/30/2016</td><td>11618</td><td>Monday   </td><td>3</td><td>5</td><td>2016</td><td>MemorialDay   </td><td>National </td><td>NA          </td><td>NA      </td><td>0</td><td>1949</td><td>1949</td></tr>\n",
       "\t<tr><td>5/31/2016</td><td>11618</td><td>Tuesday  </td><td>4</td><td>5</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1950</td><td>1950</td></tr>\n",
       "\t<tr><td>6/1/2016 </td><td>11618</td><td>Wednesday</td><td>5</td><td>6</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>1</td><td>1951</td><td>1951</td></tr>\n",
       "\t<tr><td>6/2/2016 </td><td>11618</td><td>Thursday </td><td>6</td><td>6</td><td>2016</td><td>NBAFinalsStart</td><td>Sporting </td><td>NA          </td><td>NA      </td><td>0</td><td>1952</td><td>1952</td></tr>\n",
       "\t<tr><td>6/3/2016 </td><td>11618</td><td>Friday   </td><td>7</td><td>6</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>1</td><td>1953</td><td>1953</td></tr>\n",
       "\t<tr><td>6/4/2016 </td><td>11619</td><td>Saturday </td><td>1</td><td>6</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1954</td><td>1954</td></tr>\n",
       "\t<tr><td>6/5/2016 </td><td>11619</td><td>Sunday   </td><td>2</td><td>6</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>1</td><td>1955</td><td>1955</td></tr>\n",
       "\t<tr><td>6/6/2016 </td><td>11619</td><td>Monday   </td><td>3</td><td>6</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>1</td><td>1956</td><td>1956</td></tr>\n",
       "\t<tr><td>6/7/2016 </td><td>11619</td><td>Tuesday  </td><td>4</td><td>6</td><td>2016</td><td>Ramadan starts</td><td>Religious</td><td>NA          </td><td>NA      </td><td>1</td><td>1957</td><td>1957</td></tr>\n",
       "\t<tr><td>6/8/2016 </td><td>11619</td><td>Wednesday</td><td>5</td><td>6</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1958</td><td>1958</td></tr>\n",
       "\t<tr><td>6/9/2016 </td><td>11619</td><td>Thursday </td><td>6</td><td>6</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>1</td><td>1959</td><td>1959</td></tr>\n",
       "\t<tr><td>6/10/2016</td><td>11619</td><td>Friday   </td><td>7</td><td>6</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1960</td><td>1960</td></tr>\n",
       "\t<tr><td>6/11/2016</td><td>11620</td><td>Saturday </td><td>1</td><td>6</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>1</td><td>1961</td><td>1961</td></tr>\n",
       "\t<tr><td>6/12/2016</td><td>11620</td><td>Sunday   </td><td>2</td><td>6</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>1</td><td>1962</td><td>1962</td></tr>\n",
       "\t<tr><td>6/13/2016</td><td>11620</td><td>Monday   </td><td>3</td><td>6</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>1</td><td>1963</td><td>1963</td></tr>\n",
       "\t<tr><td>6/14/2016</td><td>11620</td><td>Tuesday  </td><td>4</td><td>6</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1964</td><td>1964</td></tr>\n",
       "\t<tr><td>6/15/2016</td><td>11620</td><td>Wednesday</td><td>5</td><td>6</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>1</td><td>1965</td><td>1965</td></tr>\n",
       "\t<tr><td>6/16/2016</td><td>11620</td><td>Thursday </td><td>6</td><td>6</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1966</td><td>1966</td></tr>\n",
       "\t<tr><td>6/17/2016</td><td>11620</td><td>Friday   </td><td>7</td><td>6</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1967</td><td>1967</td></tr>\n",
       "\t<tr><td>6/18/2016</td><td>11621</td><td>Saturday </td><td>1</td><td>6</td><td>2016</td><td>NA            </td><td>NA       </td><td>NA          </td><td>NA      </td><td>0</td><td>1968</td><td>1968</td></tr>\n",
       "\t<tr><td>6/19/2016</td><td>11621</td><td>Sunday   </td><td>2</td><td>6</td><td>2016</td><td>NBAFinalsEnd  </td><td>Sporting </td><td>Father's day</td><td>Cultural</td><td>0</td><td>1969</td><td>1969</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 1969 × 13\n",
       "\\begin{tabular}{lllllllllllll}\n",
       " date & wm\\_yr\\_wk & weekday & wday & month & year & event\\_name\\_1 & event\\_type\\_1 & event\\_name\\_2 & event\\_type\\_2 & snap\\_TX & day & name\\\\\n",
       " <chr> & <int> & <chr> & <int> & <int> & <int> & <chr> & <chr> & <chr> & <chr> & <int> & <int> & <int>\\\\\n",
       "\\hline\n",
       "\t 1/29/2011 & 11101 & Saturday  & 1 & 1 & 2011 & NA            & NA       & NA & NA & 0 &  1 &  1\\\\\n",
       "\t 1/30/2011 & 11101 & Sunday    & 2 & 1 & 2011 & NA            & NA       & NA & NA & 0 &  2 &  2\\\\\n",
       "\t 1/31/2011 & 11101 & Monday    & 3 & 1 & 2011 & NA            & NA       & NA & NA & 0 &  3 &  3\\\\\n",
       "\t 2/1/2011  & 11101 & Tuesday   & 4 & 2 & 2011 & NA            & NA       & NA & NA & 1 &  4 &  4\\\\\n",
       "\t 2/2/2011  & 11101 & Wednesday & 5 & 2 & 2011 & NA            & NA       & NA & NA & 0 &  5 &  5\\\\\n",
       "\t 2/3/2011  & 11101 & Thursday  & 6 & 2 & 2011 & NA            & NA       & NA & NA & 1 &  6 &  6\\\\\n",
       "\t 2/4/2011  & 11101 & Friday    & 7 & 2 & 2011 & NA            & NA       & NA & NA & 0 &  7 &  7\\\\\n",
       "\t 2/5/2011  & 11102 & Saturday  & 1 & 2 & 2011 & NA            & NA       & NA & NA & 1 &  8 &  8\\\\\n",
       "\t 2/6/2011  & 11102 & Sunday    & 2 & 2 & 2011 & SuperBowl     & Sporting & NA & NA & 1 &  9 &  9\\\\\n",
       "\t 2/7/2011  & 11102 & Monday    & 3 & 2 & 2011 & NA            & NA       & NA & NA & 1 & 10 & 10\\\\\n",
       "\t 2/8/2011  & 11102 & Tuesday   & 4 & 2 & 2011 & NA            & NA       & NA & NA & 0 & 11 & 11\\\\\n",
       "\t 2/9/2011  & 11102 & Wednesday & 5 & 2 & 2011 & NA            & NA       & NA & NA & 1 & 12 & 12\\\\\n",
       "\t 2/10/2011 & 11102 & Thursday  & 6 & 2 & 2011 & NA            & NA       & NA & NA & 0 & 13 & 13\\\\\n",
       "\t 2/11/2011 & 11102 & Friday    & 7 & 2 & 2011 & NA            & NA       & NA & NA & 1 & 14 & 14\\\\\n",
       "\t 2/12/2011 & 11103 & Saturday  & 1 & 2 & 2011 & NA            & NA       & NA & NA & 1 & 15 & 15\\\\\n",
       "\t 2/13/2011 & 11103 & Sunday    & 2 & 2 & 2011 & NA            & NA       & NA & NA & 1 & 16 & 16\\\\\n",
       "\t 2/14/2011 & 11103 & Monday    & 3 & 2 & 2011 & ValentinesDay & Cultural & NA & NA & 0 & 17 & 17\\\\\n",
       "\t 2/15/2011 & 11103 & Tuesday   & 4 & 2 & 2011 & NA            & NA       & NA & NA & 1 & 18 & 18\\\\\n",
       "\t 2/16/2011 & 11103 & Wednesday & 5 & 2 & 2011 & NA            & NA       & NA & NA & 0 & 19 & 19\\\\\n",
       "\t 2/17/2011 & 11103 & Thursday  & 6 & 2 & 2011 & NA            & NA       & NA & NA & 0 & 20 & 20\\\\\n",
       "\t 2/18/2011 & 11103 & Friday    & 7 & 2 & 2011 & NA            & NA       & NA & NA & 0 & 21 & 21\\\\\n",
       "\t 2/19/2011 & 11104 & Saturday  & 1 & 2 & 2011 & NA            & NA       & NA & NA & 0 & 22 & 22\\\\\n",
       "\t 2/20/2011 & 11104 & Sunday    & 2 & 2 & 2011 & NA            & NA       & NA & NA & 0 & 23 & 23\\\\\n",
       "\t 2/21/2011 & 11104 & Monday    & 3 & 2 & 2011 & PresidentsDay & National & NA & NA & 0 & 24 & 24\\\\\n",
       "\t 2/22/2011 & 11104 & Tuesday   & 4 & 2 & 2011 & NA            & NA       & NA & NA & 0 & 25 & 25\\\\\n",
       "\t 2/23/2011 & 11104 & Wednesday & 5 & 2 & 2011 & NA            & NA       & NA & NA & 0 & 26 & 26\\\\\n",
       "\t 2/24/2011 & 11104 & Thursday  & 6 & 2 & 2011 & NA            & NA       & NA & NA & 0 & 27 & 27\\\\\n",
       "\t 2/25/2011 & 11104 & Friday    & 7 & 2 & 2011 & NA            & NA       & NA & NA & 0 & 28 & 28\\\\\n",
       "\t 2/26/2011 & 11105 & Saturday  & 1 & 2 & 2011 & NA            & NA       & NA & NA & 0 & 29 & 29\\\\\n",
       "\t 2/27/2011 & 11105 & Sunday    & 2 & 2 & 2011 & NA            & NA       & NA & NA & 0 & 30 & 30\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 5/21/2016 & 11617 & Saturday  & 1 & 5 & 2016 & NA             & NA        & NA           & NA       & 0 & 1940 & 1940\\\\\n",
       "\t 5/22/2016 & 11617 & Sunday    & 2 & 5 & 2016 & NA             & NA        & NA           & NA       & 0 & 1941 & 1941\\\\\n",
       "\t 5/23/2016 & 11617 & Monday    & 3 & 5 & 2016 & NA             & NA        & NA           & NA       & 0 & 1942 & 1942\\\\\n",
       "\t 5/24/2016 & 11617 & Tuesday   & 4 & 5 & 2016 & NA             & NA        & NA           & NA       & 0 & 1943 & 1943\\\\\n",
       "\t 5/25/2016 & 11617 & Wednesday & 5 & 5 & 2016 & NA             & NA        & NA           & NA       & 0 & 1944 & 1944\\\\\n",
       "\t 5/26/2016 & 11617 & Thursday  & 6 & 5 & 2016 & NA             & NA        & NA           & NA       & 0 & 1945 & 1945\\\\\n",
       "\t 5/27/2016 & 11617 & Friday    & 7 & 5 & 2016 & NA             & NA        & NA           & NA       & 0 & 1946 & 1946\\\\\n",
       "\t 5/28/2016 & 11618 & Saturday  & 1 & 5 & 2016 & NA             & NA        & NA           & NA       & 0 & 1947 & 1947\\\\\n",
       "\t 5/29/2016 & 11618 & Sunday    & 2 & 5 & 2016 & NA             & NA        & NA           & NA       & 0 & 1948 & 1948\\\\\n",
       "\t 5/30/2016 & 11618 & Monday    & 3 & 5 & 2016 & MemorialDay    & National  & NA           & NA       & 0 & 1949 & 1949\\\\\n",
       "\t 5/31/2016 & 11618 & Tuesday   & 4 & 5 & 2016 & NA             & NA        & NA           & NA       & 0 & 1950 & 1950\\\\\n",
       "\t 6/1/2016  & 11618 & Wednesday & 5 & 6 & 2016 & NA             & NA        & NA           & NA       & 1 & 1951 & 1951\\\\\n",
       "\t 6/2/2016  & 11618 & Thursday  & 6 & 6 & 2016 & NBAFinalsStart & Sporting  & NA           & NA       & 0 & 1952 & 1952\\\\\n",
       "\t 6/3/2016  & 11618 & Friday    & 7 & 6 & 2016 & NA             & NA        & NA           & NA       & 1 & 1953 & 1953\\\\\n",
       "\t 6/4/2016  & 11619 & Saturday  & 1 & 6 & 2016 & NA             & NA        & NA           & NA       & 0 & 1954 & 1954\\\\\n",
       "\t 6/5/2016  & 11619 & Sunday    & 2 & 6 & 2016 & NA             & NA        & NA           & NA       & 1 & 1955 & 1955\\\\\n",
       "\t 6/6/2016  & 11619 & Monday    & 3 & 6 & 2016 & NA             & NA        & NA           & NA       & 1 & 1956 & 1956\\\\\n",
       "\t 6/7/2016  & 11619 & Tuesday   & 4 & 6 & 2016 & Ramadan starts & Religious & NA           & NA       & 1 & 1957 & 1957\\\\\n",
       "\t 6/8/2016  & 11619 & Wednesday & 5 & 6 & 2016 & NA             & NA        & NA           & NA       & 0 & 1958 & 1958\\\\\n",
       "\t 6/9/2016  & 11619 & Thursday  & 6 & 6 & 2016 & NA             & NA        & NA           & NA       & 1 & 1959 & 1959\\\\\n",
       "\t 6/10/2016 & 11619 & Friday    & 7 & 6 & 2016 & NA             & NA        & NA           & NA       & 0 & 1960 & 1960\\\\\n",
       "\t 6/11/2016 & 11620 & Saturday  & 1 & 6 & 2016 & NA             & NA        & NA           & NA       & 1 & 1961 & 1961\\\\\n",
       "\t 6/12/2016 & 11620 & Sunday    & 2 & 6 & 2016 & NA             & NA        & NA           & NA       & 1 & 1962 & 1962\\\\\n",
       "\t 6/13/2016 & 11620 & Monday    & 3 & 6 & 2016 & NA             & NA        & NA           & NA       & 1 & 1963 & 1963\\\\\n",
       "\t 6/14/2016 & 11620 & Tuesday   & 4 & 6 & 2016 & NA             & NA        & NA           & NA       & 0 & 1964 & 1964\\\\\n",
       "\t 6/15/2016 & 11620 & Wednesday & 5 & 6 & 2016 & NA             & NA        & NA           & NA       & 1 & 1965 & 1965\\\\\n",
       "\t 6/16/2016 & 11620 & Thursday  & 6 & 6 & 2016 & NA             & NA        & NA           & NA       & 0 & 1966 & 1966\\\\\n",
       "\t 6/17/2016 & 11620 & Friday    & 7 & 6 & 2016 & NA             & NA        & NA           & NA       & 0 & 1967 & 1967\\\\\n",
       "\t 6/18/2016 & 11621 & Saturday  & 1 & 6 & 2016 & NA             & NA        & NA           & NA       & 0 & 1968 & 1968\\\\\n",
       "\t 6/19/2016 & 11621 & Sunday    & 2 & 6 & 2016 & NBAFinalsEnd   & Sporting  & Father's day & Cultural & 0 & 1969 & 1969\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 1969 × 13\n",
       "\n",
       "| date &lt;chr&gt; | wm_yr_wk &lt;int&gt; | weekday &lt;chr&gt; | wday &lt;int&gt; | month &lt;int&gt; | year &lt;int&gt; | event_name_1 &lt;chr&gt; | event_type_1 &lt;chr&gt; | event_name_2 &lt;chr&gt; | event_type_2 &lt;chr&gt; | snap_TX &lt;int&gt; | day &lt;int&gt; | name &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| 1/29/2011 | 11101 | Saturday  | 1 | 1 | 2011 | NA            | NA       | NA | NA | 0 |  1 |  1 |\n",
       "| 1/30/2011 | 11101 | Sunday    | 2 | 1 | 2011 | NA            | NA       | NA | NA | 0 |  2 |  2 |\n",
       "| 1/31/2011 | 11101 | Monday    | 3 | 1 | 2011 | NA            | NA       | NA | NA | 0 |  3 |  3 |\n",
       "| 2/1/2011  | 11101 | Tuesday   | 4 | 2 | 2011 | NA            | NA       | NA | NA | 1 |  4 |  4 |\n",
       "| 2/2/2011  | 11101 | Wednesday | 5 | 2 | 2011 | NA            | NA       | NA | NA | 0 |  5 |  5 |\n",
       "| 2/3/2011  | 11101 | Thursday  | 6 | 2 | 2011 | NA            | NA       | NA | NA | 1 |  6 |  6 |\n",
       "| 2/4/2011  | 11101 | Friday    | 7 | 2 | 2011 | NA            | NA       | NA | NA | 0 |  7 |  7 |\n",
       "| 2/5/2011  | 11102 | Saturday  | 1 | 2 | 2011 | NA            | NA       | NA | NA | 1 |  8 |  8 |\n",
       "| 2/6/2011  | 11102 | Sunday    | 2 | 2 | 2011 | SuperBowl     | Sporting | NA | NA | 1 |  9 |  9 |\n",
       "| 2/7/2011  | 11102 | Monday    | 3 | 2 | 2011 | NA            | NA       | NA | NA | 1 | 10 | 10 |\n",
       "| 2/8/2011  | 11102 | Tuesday   | 4 | 2 | 2011 | NA            | NA       | NA | NA | 0 | 11 | 11 |\n",
       "| 2/9/2011  | 11102 | Wednesday | 5 | 2 | 2011 | NA            | NA       | NA | NA | 1 | 12 | 12 |\n",
       "| 2/10/2011 | 11102 | Thursday  | 6 | 2 | 2011 | NA            | NA       | NA | NA | 0 | 13 | 13 |\n",
       "| 2/11/2011 | 11102 | Friday    | 7 | 2 | 2011 | NA            | NA       | NA | NA | 1 | 14 | 14 |\n",
       "| 2/12/2011 | 11103 | Saturday  | 1 | 2 | 2011 | NA            | NA       | NA | NA | 1 | 15 | 15 |\n",
       "| 2/13/2011 | 11103 | Sunday    | 2 | 2 | 2011 | NA            | NA       | NA | NA | 1 | 16 | 16 |\n",
       "| 2/14/2011 | 11103 | Monday    | 3 | 2 | 2011 | ValentinesDay | Cultural | NA | NA | 0 | 17 | 17 |\n",
       "| 2/15/2011 | 11103 | Tuesday   | 4 | 2 | 2011 | NA            | NA       | NA | NA | 1 | 18 | 18 |\n",
       "| 2/16/2011 | 11103 | Wednesday | 5 | 2 | 2011 | NA            | NA       | NA | NA | 0 | 19 | 19 |\n",
       "| 2/17/2011 | 11103 | Thursday  | 6 | 2 | 2011 | NA            | NA       | NA | NA | 0 | 20 | 20 |\n",
       "| 2/18/2011 | 11103 | Friday    | 7 | 2 | 2011 | NA            | NA       | NA | NA | 0 | 21 | 21 |\n",
       "| 2/19/2011 | 11104 | Saturday  | 1 | 2 | 2011 | NA            | NA       | NA | NA | 0 | 22 | 22 |\n",
       "| 2/20/2011 | 11104 | Sunday    | 2 | 2 | 2011 | NA            | NA       | NA | NA | 0 | 23 | 23 |\n",
       "| 2/21/2011 | 11104 | Monday    | 3 | 2 | 2011 | PresidentsDay | National | NA | NA | 0 | 24 | 24 |\n",
       "| 2/22/2011 | 11104 | Tuesday   | 4 | 2 | 2011 | NA            | NA       | NA | NA | 0 | 25 | 25 |\n",
       "| 2/23/2011 | 11104 | Wednesday | 5 | 2 | 2011 | NA            | NA       | NA | NA | 0 | 26 | 26 |\n",
       "| 2/24/2011 | 11104 | Thursday  | 6 | 2 | 2011 | NA            | NA       | NA | NA | 0 | 27 | 27 |\n",
       "| 2/25/2011 | 11104 | Friday    | 7 | 2 | 2011 | NA            | NA       | NA | NA | 0 | 28 | 28 |\n",
       "| 2/26/2011 | 11105 | Saturday  | 1 | 2 | 2011 | NA            | NA       | NA | NA | 0 | 29 | 29 |\n",
       "| 2/27/2011 | 11105 | Sunday    | 2 | 2 | 2011 | NA            | NA       | NA | NA | 0 | 30 | 30 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 5/21/2016 | 11617 | Saturday  | 1 | 5 | 2016 | NA             | NA        | NA           | NA       | 0 | 1940 | 1940 |\n",
       "| 5/22/2016 | 11617 | Sunday    | 2 | 5 | 2016 | NA             | NA        | NA           | NA       | 0 | 1941 | 1941 |\n",
       "| 5/23/2016 | 11617 | Monday    | 3 | 5 | 2016 | NA             | NA        | NA           | NA       | 0 | 1942 | 1942 |\n",
       "| 5/24/2016 | 11617 | Tuesday   | 4 | 5 | 2016 | NA             | NA        | NA           | NA       | 0 | 1943 | 1943 |\n",
       "| 5/25/2016 | 11617 | Wednesday | 5 | 5 | 2016 | NA             | NA        | NA           | NA       | 0 | 1944 | 1944 |\n",
       "| 5/26/2016 | 11617 | Thursday  | 6 | 5 | 2016 | NA             | NA        | NA           | NA       | 0 | 1945 | 1945 |\n",
       "| 5/27/2016 | 11617 | Friday    | 7 | 5 | 2016 | NA             | NA        | NA           | NA       | 0 | 1946 | 1946 |\n",
       "| 5/28/2016 | 11618 | Saturday  | 1 | 5 | 2016 | NA             | NA        | NA           | NA       | 0 | 1947 | 1947 |\n",
       "| 5/29/2016 | 11618 | Sunday    | 2 | 5 | 2016 | NA             | NA        | NA           | NA       | 0 | 1948 | 1948 |\n",
       "| 5/30/2016 | 11618 | Monday    | 3 | 5 | 2016 | MemorialDay    | National  | NA           | NA       | 0 | 1949 | 1949 |\n",
       "| 5/31/2016 | 11618 | Tuesday   | 4 | 5 | 2016 | NA             | NA        | NA           | NA       | 0 | 1950 | 1950 |\n",
       "| 6/1/2016  | 11618 | Wednesday | 5 | 6 | 2016 | NA             | NA        | NA           | NA       | 1 | 1951 | 1951 |\n",
       "| 6/2/2016  | 11618 | Thursday  | 6 | 6 | 2016 | NBAFinalsStart | Sporting  | NA           | NA       | 0 | 1952 | 1952 |\n",
       "| 6/3/2016  | 11618 | Friday    | 7 | 6 | 2016 | NA             | NA        | NA           | NA       | 1 | 1953 | 1953 |\n",
       "| 6/4/2016  | 11619 | Saturday  | 1 | 6 | 2016 | NA             | NA        | NA           | NA       | 0 | 1954 | 1954 |\n",
       "| 6/5/2016  | 11619 | Sunday    | 2 | 6 | 2016 | NA             | NA        | NA           | NA       | 1 | 1955 | 1955 |\n",
       "| 6/6/2016  | 11619 | Monday    | 3 | 6 | 2016 | NA             | NA        | NA           | NA       | 1 | 1956 | 1956 |\n",
       "| 6/7/2016  | 11619 | Tuesday   | 4 | 6 | 2016 | Ramadan starts | Religious | NA           | NA       | 1 | 1957 | 1957 |\n",
       "| 6/8/2016  | 11619 | Wednesday | 5 | 6 | 2016 | NA             | NA        | NA           | NA       | 0 | 1958 | 1958 |\n",
       "| 6/9/2016  | 11619 | Thursday  | 6 | 6 | 2016 | NA             | NA        | NA           | NA       | 1 | 1959 | 1959 |\n",
       "| 6/10/2016 | 11619 | Friday    | 7 | 6 | 2016 | NA             | NA        | NA           | NA       | 0 | 1960 | 1960 |\n",
       "| 6/11/2016 | 11620 | Saturday  | 1 | 6 | 2016 | NA             | NA        | NA           | NA       | 1 | 1961 | 1961 |\n",
       "| 6/12/2016 | 11620 | Sunday    | 2 | 6 | 2016 | NA             | NA        | NA           | NA       | 1 | 1962 | 1962 |\n",
       "| 6/13/2016 | 11620 | Monday    | 3 | 6 | 2016 | NA             | NA        | NA           | NA       | 1 | 1963 | 1963 |\n",
       "| 6/14/2016 | 11620 | Tuesday   | 4 | 6 | 2016 | NA             | NA        | NA           | NA       | 0 | 1964 | 1964 |\n",
       "| 6/15/2016 | 11620 | Wednesday | 5 | 6 | 2016 | NA             | NA        | NA           | NA       | 1 | 1965 | 1965 |\n",
       "| 6/16/2016 | 11620 | Thursday  | 6 | 6 | 2016 | NA             | NA        | NA           | NA       | 0 | 1966 | 1966 |\n",
       "| 6/17/2016 | 11620 | Friday    | 7 | 6 | 2016 | NA             | NA        | NA           | NA       | 0 | 1967 | 1967 |\n",
       "| 6/18/2016 | 11621 | Saturday  | 1 | 6 | 2016 | NA             | NA        | NA           | NA       | 0 | 1968 | 1968 |\n",
       "| 6/19/2016 | 11621 | Sunday    | 2 | 6 | 2016 | NBAFinalsEnd   | Sporting  | Father's day | Cultural | 0 | 1969 | 1969 |\n",
       "\n"
      ],
      "text/plain": [
       "     date      wm_yr_wk weekday   wday month year event_name_1   event_type_1\n",
       "1    1/29/2011 11101    Saturday  1    1     2011 NA             NA          \n",
       "2    1/30/2011 11101    Sunday    2    1     2011 NA             NA          \n",
       "3    1/31/2011 11101    Monday    3    1     2011 NA             NA          \n",
       "4    2/1/2011  11101    Tuesday   4    2     2011 NA             NA          \n",
       "5    2/2/2011  11101    Wednesday 5    2     2011 NA             NA          \n",
       "6    2/3/2011  11101    Thursday  6    2     2011 NA             NA          \n",
       "7    2/4/2011  11101    Friday    7    2     2011 NA             NA          \n",
       "8    2/5/2011  11102    Saturday  1    2     2011 NA             NA          \n",
       "9    2/6/2011  11102    Sunday    2    2     2011 SuperBowl      Sporting    \n",
       "10   2/7/2011  11102    Monday    3    2     2011 NA             NA          \n",
       "11   2/8/2011  11102    Tuesday   4    2     2011 NA             NA          \n",
       "12   2/9/2011  11102    Wednesday 5    2     2011 NA             NA          \n",
       "13   2/10/2011 11102    Thursday  6    2     2011 NA             NA          \n",
       "14   2/11/2011 11102    Friday    7    2     2011 NA             NA          \n",
       "15   2/12/2011 11103    Saturday  1    2     2011 NA             NA          \n",
       "16   2/13/2011 11103    Sunday    2    2     2011 NA             NA          \n",
       "17   2/14/2011 11103    Monday    3    2     2011 ValentinesDay  Cultural    \n",
       "18   2/15/2011 11103    Tuesday   4    2     2011 NA             NA          \n",
       "19   2/16/2011 11103    Wednesday 5    2     2011 NA             NA          \n",
       "20   2/17/2011 11103    Thursday  6    2     2011 NA             NA          \n",
       "21   2/18/2011 11103    Friday    7    2     2011 NA             NA          \n",
       "22   2/19/2011 11104    Saturday  1    2     2011 NA             NA          \n",
       "23   2/20/2011 11104    Sunday    2    2     2011 NA             NA          \n",
       "24   2/21/2011 11104    Monday    3    2     2011 PresidentsDay  National    \n",
       "25   2/22/2011 11104    Tuesday   4    2     2011 NA             NA          \n",
       "26   2/23/2011 11104    Wednesday 5    2     2011 NA             NA          \n",
       "27   2/24/2011 11104    Thursday  6    2     2011 NA             NA          \n",
       "28   2/25/2011 11104    Friday    7    2     2011 NA             NA          \n",
       "29   2/26/2011 11105    Saturday  1    2     2011 NA             NA          \n",
       "30   2/27/2011 11105    Sunday    2    2     2011 NA             NA          \n",
       "⋮    ⋮         ⋮        ⋮         ⋮    ⋮     ⋮    ⋮              ⋮           \n",
       "1940 5/21/2016 11617    Saturday  1    5     2016 NA             NA          \n",
       "1941 5/22/2016 11617    Sunday    2    5     2016 NA             NA          \n",
       "1942 5/23/2016 11617    Monday    3    5     2016 NA             NA          \n",
       "1943 5/24/2016 11617    Tuesday   4    5     2016 NA             NA          \n",
       "1944 5/25/2016 11617    Wednesday 5    5     2016 NA             NA          \n",
       "1945 5/26/2016 11617    Thursday  6    5     2016 NA             NA          \n",
       "1946 5/27/2016 11617    Friday    7    5     2016 NA             NA          \n",
       "1947 5/28/2016 11618    Saturday  1    5     2016 NA             NA          \n",
       "1948 5/29/2016 11618    Sunday    2    5     2016 NA             NA          \n",
       "1949 5/30/2016 11618    Monday    3    5     2016 MemorialDay    National    \n",
       "1950 5/31/2016 11618    Tuesday   4    5     2016 NA             NA          \n",
       "1951 6/1/2016  11618    Wednesday 5    6     2016 NA             NA          \n",
       "1952 6/2/2016  11618    Thursday  6    6     2016 NBAFinalsStart Sporting    \n",
       "1953 6/3/2016  11618    Friday    7    6     2016 NA             NA          \n",
       "1954 6/4/2016  11619    Saturday  1    6     2016 NA             NA          \n",
       "1955 6/5/2016  11619    Sunday    2    6     2016 NA             NA          \n",
       "1956 6/6/2016  11619    Monday    3    6     2016 NA             NA          \n",
       "1957 6/7/2016  11619    Tuesday   4    6     2016 Ramadan starts Religious   \n",
       "1958 6/8/2016  11619    Wednesday 5    6     2016 NA             NA          \n",
       "1959 6/9/2016  11619    Thursday  6    6     2016 NA             NA          \n",
       "1960 6/10/2016 11619    Friday    7    6     2016 NA             NA          \n",
       "1961 6/11/2016 11620    Saturday  1    6     2016 NA             NA          \n",
       "1962 6/12/2016 11620    Sunday    2    6     2016 NA             NA          \n",
       "1963 6/13/2016 11620    Monday    3    6     2016 NA             NA          \n",
       "1964 6/14/2016 11620    Tuesday   4    6     2016 NA             NA          \n",
       "1965 6/15/2016 11620    Wednesday 5    6     2016 NA             NA          \n",
       "1966 6/16/2016 11620    Thursday  6    6     2016 NA             NA          \n",
       "1967 6/17/2016 11620    Friday    7    6     2016 NA             NA          \n",
       "1968 6/18/2016 11621    Saturday  1    6     2016 NA             NA          \n",
       "1969 6/19/2016 11621    Sunday    2    6     2016 NBAFinalsEnd   Sporting    \n",
       "     event_name_2 event_type_2 snap_TX day  name\n",
       "1    NA           NA           0        1    1  \n",
       "2    NA           NA           0        2    2  \n",
       "3    NA           NA           0        3    3  \n",
       "4    NA           NA           1        4    4  \n",
       "5    NA           NA           0        5    5  \n",
       "6    NA           NA           1        6    6  \n",
       "7    NA           NA           0        7    7  \n",
       "8    NA           NA           1        8    8  \n",
       "9    NA           NA           1        9    9  \n",
       "10   NA           NA           1       10   10  \n",
       "11   NA           NA           0       11   11  \n",
       "12   NA           NA           1       12   12  \n",
       "13   NA           NA           0       13   13  \n",
       "14   NA           NA           1       14   14  \n",
       "15   NA           NA           1       15   15  \n",
       "16   NA           NA           1       16   16  \n",
       "17   NA           NA           0       17   17  \n",
       "18   NA           NA           1       18   18  \n",
       "19   NA           NA           0       19   19  \n",
       "20   NA           NA           0       20   20  \n",
       "21   NA           NA           0       21   21  \n",
       "22   NA           NA           0       22   22  \n",
       "23   NA           NA           0       23   23  \n",
       "24   NA           NA           0       24   24  \n",
       "25   NA           NA           0       25   25  \n",
       "26   NA           NA           0       26   26  \n",
       "27   NA           NA           0       27   27  \n",
       "28   NA           NA           0       28   28  \n",
       "29   NA           NA           0       29   29  \n",
       "30   NA           NA           0       30   30  \n",
       "⋮    ⋮            ⋮            ⋮       ⋮    ⋮   \n",
       "1940 NA           NA           0       1940 1940\n",
       "1941 NA           NA           0       1941 1941\n",
       "1942 NA           NA           0       1942 1942\n",
       "1943 NA           NA           0       1943 1943\n",
       "1944 NA           NA           0       1944 1944\n",
       "1945 NA           NA           0       1945 1945\n",
       "1946 NA           NA           0       1946 1946\n",
       "1947 NA           NA           0       1947 1947\n",
       "1948 NA           NA           0       1948 1948\n",
       "1949 NA           NA           0       1949 1949\n",
       "1950 NA           NA           0       1950 1950\n",
       "1951 NA           NA           1       1951 1951\n",
       "1952 NA           NA           0       1952 1952\n",
       "1953 NA           NA           1       1953 1953\n",
       "1954 NA           NA           0       1954 1954\n",
       "1955 NA           NA           1       1955 1955\n",
       "1956 NA           NA           1       1956 1956\n",
       "1957 NA           NA           1       1957 1957\n",
       "1958 NA           NA           0       1958 1958\n",
       "1959 NA           NA           1       1959 1959\n",
       "1960 NA           NA           0       1960 1960\n",
       "1961 NA           NA           1       1961 1961\n",
       "1962 NA           NA           1       1962 1962\n",
       "1963 NA           NA           1       1963 1963\n",
       "1964 NA           NA           0       1964 1964\n",
       "1965 NA           NA           1       1965 1965\n",
       "1966 NA           NA           0       1966 1966\n",
       "1967 NA           NA           0       1967 1967\n",
       "1968 NA           NA           0       1968 1968\n",
       "1969 Father's day Cultural     0       1969 1969"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "calendar2 <- calendar \n",
    "calendar2$name <- 1:nrow(calendar)\n",
    "sales <- sales_train %>% pivot_longer (2:ncol(sales_train)) \n",
    "sales$name <- gsub(\"d_\", \"\", sales$name)\n",
    "sales$id <- gsub(\"_TX_3_validation\", \"\" ,sales$id)\n",
    "sales$name <- as.integer(sales$name)\n",
    "\n",
    "sales\n",
    "calendar2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "99bab811-a455-4c3a-b5b3-02bc65c80870",
   "metadata": {},
   "outputs": [],
   "source": [
    "calendar2_sales <- left_join(sales,calendar2, by='name') \n",
    "calendar2_sales$date <- as.Date(calendar2_sales$date, format = \"%m/%d/%Y\")\n",
    "calendar2_sales <- calendar2_sales %>% as_tsibble(index = date , key = id)\n",
    "calendar2_sales <- left_join(calendar2_sales, prices, by = c(\"wm_yr_wk\" = \"wm_yr_wk\", \"id\"=\"item_id\"))\n",
    "#calendar2_sales$is_event <- ifelse(calendar2_sales$event_name_1 != \"NA\", 1, 0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "478715e1-9f8c-40fe-8dcd-1cd7da9b6d0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "weekdays <- list('Monday', 'Tuesday', 'Wednesday', 'Thursday')\n",
    "weekend <- list('Friday','Saturday')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3a6b03e3-f667-435b-896f-a504984c89ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "calendar2_sales$is_weekend <- ifelse(calendar2_sales$weekday %in% weekend, 1, 0)\n",
    "calendar2_sales$event_name_1 <- replace(calendar2_sales$event_name_1, is.na(calendar2_sales$event_name_1), 0)\n",
    "calendar2_sales$is_event <- ifelse(calendar2_sales$event_name_1 != 0, 1, 0)\n",
    "\n",
    "final_data <- calendar2_sales[c(\"id\",\"name\",\"value\",'date','wday','wm_yr_wk','weekday','month', 'year','event_name_1','event_type_1','snap_TX','sell_price','is_event','is_weekend')]\n",
    "final_data_numeric <- final_data[c('id','name','value','month','sell_price','is_event','is_weekend')]\n",
    "\n",
    "final_data_numeric_2 <- final_data[c('id','name','value','month','sell_price','is_event','is_weekend')]\n",
    "final_data_numeric_2$id <- gsub(\"FOODS_3_\", \"\" ,final_data_numeric_2$id)\n",
    "\n",
    "final_data_numeric_3 <- final_data[c('value','wday','month','sell_price','is_event','is_weekend', 'snap_TX')]\n",
    "final_data_numeric_4 <- final_data[c('value','wday','month','sell_price','is_event','is_weekend', 'snap_TX')]\n",
    "names(final_data_numeric_4)[names(final_data_numeric_4) == 'value'] <- 'Amount sold'\n",
    "\n",
    "final_data_numeric$value <- as.double(final_data_numeric$value)\n",
    "final_data_numeric$name <- as.double(final_data_numeric$name)\n",
    "final_data_numeric$month <- as.double(final_data_numeric$month)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "b91ea751-0832-48da-83f5-7e094320d528",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 1574399 × 7</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>name</th><th scope=col>Amount sold</th><th scope=col>month</th><th scope=col>sell_price</th><th scope=col>is_event</th><th scope=col>is_weekend</th><th scope=col>snap_TX</th></tr>\n",
       "\t<tr><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><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;int&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td> 1</td><td>0</td><td>1</td><td>2.28</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td> 2</td><td>2</td><td>1</td><td>2.28</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td> 3</td><td>1</td><td>1</td><td>2.28</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td> 4</td><td>3</td><td>2</td><td>2.28</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td> 5</td><td>0</td><td>2</td><td>2.28</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td> 6</td><td>0</td><td>2</td><td>2.28</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td> 7</td><td>1</td><td>2</td><td>2.28</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td> 8</td><td>0</td><td>2</td><td>2.28</td><td>0</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><td> 9</td><td>2</td><td>2</td><td>2.28</td><td>1</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><td>10</td><td>1</td><td>2</td><td>2.28</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>11</td><td>1</td><td>2</td><td>2.28</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>12</td><td>1</td><td>2</td><td>2.28</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>13</td><td>1</td><td>2</td><td>2.28</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>14</td><td>2</td><td>2</td><td>2.28</td><td>0</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><td>15</td><td>0</td><td>2</td><td>2.28</td><td>0</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><td>16</td><td>3</td><td>2</td><td>2.28</td><td>0</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><td>17</td><td>0</td><td>2</td><td>2.28</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>18</td><td>0</td><td>2</td><td>2.28</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>19</td><td>0</td><td>2</td><td>2.28</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>20</td><td>0</td><td>2</td><td>2.28</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>21</td><td>1</td><td>2</td><td>2.28</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>22</td><td>0</td><td>2</td><td>2.28</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>23</td><td>3</td><td>2</td><td>2.28</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>24</td><td>0</td><td>2</td><td>2.28</td><td>1</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>25</td><td>3</td><td>2</td><td>2.28</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>26</td><td>1</td><td>2</td><td>2.28</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>27</td><td>1</td><td>2</td><td>2.28</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>28</td><td>0</td><td>2</td><td>2.28</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>29</td><td>2</td><td>2</td><td>2.28</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>30</td><td>2</td><td>2</td><td>2.28</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td><td>⋮</td></tr>\n",
       "\t<tr><td>1884</td><td>4</td><td>3</td><td>1</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>1885</td><td>7</td><td>3</td><td>1</td><td>1</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>1886</td><td>0</td><td>3</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1887</td><td>0</td><td>3</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1888</td><td>0</td><td>3</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1889</td><td>2</td><td>3</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1890</td><td>0</td><td>4</td><td>1</td><td>0</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><td>1891</td><td>0</td><td>4</td><td>1</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>1892</td><td>3</td><td>4</td><td>1</td><td>0</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><td>1893</td><td>0</td><td>4</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1894</td><td>0</td><td>4</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>1895</td><td>0</td><td>4</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>1896</td><td>1</td><td>4</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>1897</td><td>1</td><td>4</td><td>1</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>1898</td><td>0</td><td>4</td><td>1</td><td>0</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><td>1899</td><td>1</td><td>4</td><td>1</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>1900</td><td>2</td><td>4</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>1901</td><td>0</td><td>4</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>1902</td><td>0</td><td>4</td><td>1</td><td>0</td><td>0</td><td>1</td></tr>\n",
       "\t<tr><td>1903</td><td>2</td><td>4</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1904</td><td>0</td><td>4</td><td>1</td><td>0</td><td>1</td><td>1</td></tr>\n",
       "\t<tr><td>1905</td><td>0</td><td>4</td><td>1</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>1906</td><td>0</td><td>4</td><td>1</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>1907</td><td>0</td><td>4</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1908</td><td>1</td><td>4</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1909</td><td>0</td><td>4</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1910</td><td>1</td><td>4</td><td>1</td><td>0</td><td>0</td><td>0</td></tr>\n",
       "\t<tr><td>1911</td><td>0</td><td>4</td><td>1</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>1912</td><td>0</td><td>4</td><td>1</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "\t<tr><td>1913</td><td>3</td><td>4</td><td>1</td><td>0</td><td>1</td><td>0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 1574399 × 7\n",
       "\\begin{tabular}{lllllll}\n",
       " name & Amount sold & month & sell\\_price & is\\_event & is\\_weekend & snap\\_TX\\\\\n",
       " <int> & <int> & <int> & <dbl> & <dbl> & <dbl> & <int>\\\\\n",
       "\\hline\n",
       "\t  1 & 0 & 1 & 2.28 & 0 & 1 & 0\\\\\n",
       "\t  2 & 2 & 1 & 2.28 & 0 & 1 & 0\\\\\n",
       "\t  3 & 1 & 1 & 2.28 & 0 & 0 & 0\\\\\n",
       "\t  4 & 3 & 2 & 2.28 & 0 & 0 & 1\\\\\n",
       "\t  5 & 0 & 2 & 2.28 & 0 & 0 & 0\\\\\n",
       "\t  6 & 0 & 2 & 2.28 & 0 & 0 & 1\\\\\n",
       "\t  7 & 1 & 2 & 2.28 & 0 & 1 & 0\\\\\n",
       "\t  8 & 0 & 2 & 2.28 & 0 & 1 & 1\\\\\n",
       "\t  9 & 2 & 2 & 2.28 & 1 & 1 & 1\\\\\n",
       "\t 10 & 1 & 2 & 2.28 & 0 & 0 & 1\\\\\n",
       "\t 11 & 1 & 2 & 2.28 & 0 & 0 & 0\\\\\n",
       "\t 12 & 1 & 2 & 2.28 & 0 & 0 & 1\\\\\n",
       "\t 13 & 1 & 2 & 2.28 & 0 & 0 & 0\\\\\n",
       "\t 14 & 2 & 2 & 2.28 & 0 & 1 & 1\\\\\n",
       "\t 15 & 0 & 2 & 2.28 & 0 & 1 & 1\\\\\n",
       "\t 16 & 3 & 2 & 2.28 & 0 & 1 & 1\\\\\n",
       "\t 17 & 0 & 2 & 2.28 & 1 & 0 & 0\\\\\n",
       "\t 18 & 0 & 2 & 2.28 & 0 & 0 & 1\\\\\n",
       "\t 19 & 0 & 2 & 2.28 & 0 & 0 & 0\\\\\n",
       "\t 20 & 0 & 2 & 2.28 & 0 & 0 & 0\\\\\n",
       "\t 21 & 1 & 2 & 2.28 & 0 & 1 & 0\\\\\n",
       "\t 22 & 0 & 2 & 2.28 & 0 & 1 & 0\\\\\n",
       "\t 23 & 3 & 2 & 2.28 & 0 & 1 & 0\\\\\n",
       "\t 24 & 0 & 2 & 2.28 & 1 & 0 & 0\\\\\n",
       "\t 25 & 3 & 2 & 2.28 & 0 & 0 & 0\\\\\n",
       "\t 26 & 1 & 2 & 2.28 & 0 & 0 & 0\\\\\n",
       "\t 27 & 1 & 2 & 2.28 & 0 & 0 & 0\\\\\n",
       "\t 28 & 0 & 2 & 2.28 & 0 & 1 & 0\\\\\n",
       "\t 29 & 2 & 2 & 2.28 & 0 & 1 & 0\\\\\n",
       "\t 30 & 2 & 2 & 2.28 & 0 & 1 & 0\\\\\n",
       "\t ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮ & ⋮\\\\\n",
       "\t 1884 & 4 & 3 & 1 & 0 & 1 & 0\\\\\n",
       "\t 1885 & 7 & 3 & 1 & 1 & 1 & 0\\\\\n",
       "\t 1886 & 0 & 3 & 1 & 0 & 0 & 0\\\\\n",
       "\t 1887 & 0 & 3 & 1 & 0 & 0 & 0\\\\\n",
       "\t 1888 & 0 & 3 & 1 & 0 & 0 & 0\\\\\n",
       "\t 1889 & 2 & 3 & 1 & 0 & 0 & 0\\\\\n",
       "\t 1890 & 0 & 4 & 1 & 0 & 1 & 1\\\\\n",
       "\t 1891 & 0 & 4 & 1 & 0 & 1 & 0\\\\\n",
       "\t 1892 & 3 & 4 & 1 & 0 & 1 & 1\\\\\n",
       "\t 1893 & 0 & 4 & 1 & 0 & 0 & 0\\\\\n",
       "\t 1894 & 0 & 4 & 1 & 0 & 0 & 1\\\\\n",
       "\t 1895 & 0 & 4 & 1 & 0 & 0 & 1\\\\\n",
       "\t 1896 & 1 & 4 & 1 & 0 & 0 & 1\\\\\n",
       "\t 1897 & 1 & 4 & 1 & 0 & 1 & 0\\\\\n",
       "\t 1898 & 0 & 4 & 1 & 0 & 1 & 1\\\\\n",
       "\t 1899 & 1 & 4 & 1 & 0 & 1 & 0\\\\\n",
       "\t 1900 & 2 & 4 & 1 & 0 & 0 & 1\\\\\n",
       "\t 1901 & 0 & 4 & 1 & 0 & 0 & 1\\\\\n",
       "\t 1902 & 0 & 4 & 1 & 0 & 0 & 1\\\\\n",
       "\t 1903 & 2 & 4 & 1 & 0 & 0 & 0\\\\\n",
       "\t 1904 & 0 & 4 & 1 & 0 & 1 & 1\\\\\n",
       "\t 1905 & 0 & 4 & 1 & 0 & 1 & 0\\\\\n",
       "\t 1906 & 0 & 4 & 1 & 0 & 1 & 0\\\\\n",
       "\t 1907 & 0 & 4 & 1 & 0 & 0 & 0\\\\\n",
       "\t 1908 & 1 & 4 & 1 & 0 & 0 & 0\\\\\n",
       "\t 1909 & 0 & 4 & 1 & 0 & 0 & 0\\\\\n",
       "\t 1910 & 1 & 4 & 1 & 0 & 0 & 0\\\\\n",
       "\t 1911 & 0 & 4 & 1 & 0 & 1 & 0\\\\\n",
       "\t 1912 & 0 & 4 & 1 & 0 & 1 & 0\\\\\n",
       "\t 1913 & 3 & 4 & 1 & 0 & 1 & 0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 1574399 × 7\n",
       "\n",
       "| name &lt;int&gt; | Amount sold &lt;int&gt; | month &lt;int&gt; | sell_price &lt;dbl&gt; | is_event &lt;dbl&gt; | is_weekend &lt;dbl&gt; | snap_TX &lt;int&gt; |\n",
       "|---|---|---|---|---|---|---|\n",
       "|  1 | 0 | 1 | 2.28 | 0 | 1 | 0 |\n",
       "|  2 | 2 | 1 | 2.28 | 0 | 1 | 0 |\n",
       "|  3 | 1 | 1 | 2.28 | 0 | 0 | 0 |\n",
       "|  4 | 3 | 2 | 2.28 | 0 | 0 | 1 |\n",
       "|  5 | 0 | 2 | 2.28 | 0 | 0 | 0 |\n",
       "|  6 | 0 | 2 | 2.28 | 0 | 0 | 1 |\n",
       "|  7 | 1 | 2 | 2.28 | 0 | 1 | 0 |\n",
       "|  8 | 0 | 2 | 2.28 | 0 | 1 | 1 |\n",
       "|  9 | 2 | 2 | 2.28 | 1 | 1 | 1 |\n",
       "| 10 | 1 | 2 | 2.28 | 0 | 0 | 1 |\n",
       "| 11 | 1 | 2 | 2.28 | 0 | 0 | 0 |\n",
       "| 12 | 1 | 2 | 2.28 | 0 | 0 | 1 |\n",
       "| 13 | 1 | 2 | 2.28 | 0 | 0 | 0 |\n",
       "| 14 | 2 | 2 | 2.28 | 0 | 1 | 1 |\n",
       "| 15 | 0 | 2 | 2.28 | 0 | 1 | 1 |\n",
       "| 16 | 3 | 2 | 2.28 | 0 | 1 | 1 |\n",
       "| 17 | 0 | 2 | 2.28 | 1 | 0 | 0 |\n",
       "| 18 | 0 | 2 | 2.28 | 0 | 0 | 1 |\n",
       "| 19 | 0 | 2 | 2.28 | 0 | 0 | 0 |\n",
       "| 20 | 0 | 2 | 2.28 | 0 | 0 | 0 |\n",
       "| 21 | 1 | 2 | 2.28 | 0 | 1 | 0 |\n",
       "| 22 | 0 | 2 | 2.28 | 0 | 1 | 0 |\n",
       "| 23 | 3 | 2 | 2.28 | 0 | 1 | 0 |\n",
       "| 24 | 0 | 2 | 2.28 | 1 | 0 | 0 |\n",
       "| 25 | 3 | 2 | 2.28 | 0 | 0 | 0 |\n",
       "| 26 | 1 | 2 | 2.28 | 0 | 0 | 0 |\n",
       "| 27 | 1 | 2 | 2.28 | 0 | 0 | 0 |\n",
       "| 28 | 0 | 2 | 2.28 | 0 | 1 | 0 |\n",
       "| 29 | 2 | 2 | 2.28 | 0 | 1 | 0 |\n",
       "| 30 | 2 | 2 | 2.28 | 0 | 1 | 0 |\n",
       "| ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ | ⋮ |\n",
       "| 1884 | 4 | 3 | 1 | 0 | 1 | 0 |\n",
       "| 1885 | 7 | 3 | 1 | 1 | 1 | 0 |\n",
       "| 1886 | 0 | 3 | 1 | 0 | 0 | 0 |\n",
       "| 1887 | 0 | 3 | 1 | 0 | 0 | 0 |\n",
       "| 1888 | 0 | 3 | 1 | 0 | 0 | 0 |\n",
       "| 1889 | 2 | 3 | 1 | 0 | 0 | 0 |\n",
       "| 1890 | 0 | 4 | 1 | 0 | 1 | 1 |\n",
       "| 1891 | 0 | 4 | 1 | 0 | 1 | 0 |\n",
       "| 1892 | 3 | 4 | 1 | 0 | 1 | 1 |\n",
       "| 1893 | 0 | 4 | 1 | 0 | 0 | 0 |\n",
       "| 1894 | 0 | 4 | 1 | 0 | 0 | 1 |\n",
       "| 1895 | 0 | 4 | 1 | 0 | 0 | 1 |\n",
       "| 1896 | 1 | 4 | 1 | 0 | 0 | 1 |\n",
       "| 1897 | 1 | 4 | 1 | 0 | 1 | 0 |\n",
       "| 1898 | 0 | 4 | 1 | 0 | 1 | 1 |\n",
       "| 1899 | 1 | 4 | 1 | 0 | 1 | 0 |\n",
       "| 1900 | 2 | 4 | 1 | 0 | 0 | 1 |\n",
       "| 1901 | 0 | 4 | 1 | 0 | 0 | 1 |\n",
       "| 1902 | 0 | 4 | 1 | 0 | 0 | 1 |\n",
       "| 1903 | 2 | 4 | 1 | 0 | 0 | 0 |\n",
       "| 1904 | 0 | 4 | 1 | 0 | 1 | 1 |\n",
       "| 1905 | 0 | 4 | 1 | 0 | 1 | 0 |\n",
       "| 1906 | 0 | 4 | 1 | 0 | 1 | 0 |\n",
       "| 1907 | 0 | 4 | 1 | 0 | 0 | 0 |\n",
       "| 1908 | 1 | 4 | 1 | 0 | 0 | 0 |\n",
       "| 1909 | 0 | 4 | 1 | 0 | 0 | 0 |\n",
       "| 1910 | 1 | 4 | 1 | 0 | 0 | 0 |\n",
       "| 1911 | 0 | 4 | 1 | 0 | 1 | 0 |\n",
       "| 1912 | 0 | 4 | 1 | 0 | 1 | 0 |\n",
       "| 1913 | 3 | 4 | 1 | 0 | 1 | 0 |\n",
       "\n"
      ],
      "text/plain": [
       "        name Amount sold month sell_price is_event is_weekend snap_TX\n",
       "1        1   0           1     2.28       0        1          0      \n",
       "2        2   2           1     2.28       0        1          0      \n",
       "3        3   1           1     2.28       0        0          0      \n",
       "4        4   3           2     2.28       0        0          1      \n",
       "5        5   0           2     2.28       0        0          0      \n",
       "6        6   0           2     2.28       0        0          1      \n",
       "7        7   1           2     2.28       0        1          0      \n",
       "8        8   0           2     2.28       0        1          1      \n",
       "9        9   2           2     2.28       1        1          1      \n",
       "10      10   1           2     2.28       0        0          1      \n",
       "11      11   1           2     2.28       0        0          0      \n",
       "12      12   1           2     2.28       0        0          1      \n",
       "13      13   1           2     2.28       0        0          0      \n",
       "14      14   2           2     2.28       0        1          1      \n",
       "15      15   0           2     2.28       0        1          1      \n",
       "16      16   3           2     2.28       0        1          1      \n",
       "17      17   0           2     2.28       1        0          0      \n",
       "18      18   0           2     2.28       0        0          1      \n",
       "19      19   0           2     2.28       0        0          0      \n",
       "20      20   0           2     2.28       0        0          0      \n",
       "21      21   1           2     2.28       0        1          0      \n",
       "22      22   0           2     2.28       0        1          0      \n",
       "23      23   3           2     2.28       0        1          0      \n",
       "24      24   0           2     2.28       1        0          0      \n",
       "25      25   3           2     2.28       0        0          0      \n",
       "26      26   1           2     2.28       0        0          0      \n",
       "27      27   1           2     2.28       0        0          0      \n",
       "28      28   0           2     2.28       0        1          0      \n",
       "29      29   2           2     2.28       0        1          0      \n",
       "30      30   2           2     2.28       0        1          0      \n",
       "⋮       ⋮    ⋮           ⋮     ⋮          ⋮        ⋮          ⋮      \n",
       "1574370 1884 4           3     1          0        1          0      \n",
       "1574371 1885 7           3     1          1        1          0      \n",
       "1574372 1886 0           3     1          0        0          0      \n",
       "1574373 1887 0           3     1          0        0          0      \n",
       "1574374 1888 0           3     1          0        0          0      \n",
       "1574375 1889 2           3     1          0        0          0      \n",
       "1574376 1890 0           4     1          0        1          1      \n",
       "1574377 1891 0           4     1          0        1          0      \n",
       "1574378 1892 3           4     1          0        1          1      \n",
       "1574379 1893 0           4     1          0        0          0      \n",
       "1574380 1894 0           4     1          0        0          1      \n",
       "1574381 1895 0           4     1          0        0          1      \n",
       "1574382 1896 1           4     1          0        0          1      \n",
       "1574383 1897 1           4     1          0        1          0      \n",
       "1574384 1898 0           4     1          0        1          1      \n",
       "1574385 1899 1           4     1          0        1          0      \n",
       "1574386 1900 2           4     1          0        0          1      \n",
       "1574387 1901 0           4     1          0        0          1      \n",
       "1574388 1902 0           4     1          0        0          1      \n",
       "1574389 1903 2           4     1          0        0          0      \n",
       "1574390 1904 0           4     1          0        1          1      \n",
       "1574391 1905 0           4     1          0        1          0      \n",
       "1574392 1906 0           4     1          0        1          0      \n",
       "1574393 1907 0           4     1          0        0          0      \n",
       "1574394 1908 1           4     1          0        0          0      \n",
       "1574395 1909 0           4     1          0        0          0      \n",
       "1574396 1910 1           4     1          0        0          0      \n",
       "1574397 1911 0           4     1          0        1          0      \n",
       "1574398 1912 0           4     1          0        1          0      \n",
       "1574399 1913 3           4     1          0        1          0      "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "final_data_numeric_4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "84662a80-f581-4ce2-9e2c-063b3f0d45c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A matrix: 6 × 6 of type dbl</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>name</th><th scope=col>value</th><th scope=col>month</th><th scope=col>sell_price</th><th scope=col>is_event</th><th scope=col>is_weekend</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>name</th><td>1.00</td><td> 0.01</td><td> 0.03</td><td> 0.02</td><td> 0.00</td><td> 0.00</td></tr>\n",
       "\t<tr><th scope=row>value</th><td>0.01</td><td> 1.00</td><td> 0.00</td><td>-0.13</td><td> 0.00</td><td> 0.03</td></tr>\n",
       "\t<tr><th scope=row>month</th><td>0.03</td><td> 0.00</td><td> 1.00</td><td> 0.00</td><td>-0.07</td><td> 0.00</td></tr>\n",
       "\t<tr><th scope=row>sell_price</th><td>0.02</td><td>-0.13</td><td> 0.00</td><td> 1.00</td><td> 0.00</td><td> 0.00</td></tr>\n",
       "\t<tr><th scope=row>is_event</th><td>0.00</td><td> 0.00</td><td>-0.07</td><td> 0.00</td><td> 1.00</td><td>-0.04</td></tr>\n",
       "\t<tr><th scope=row>is_weekend</th><td>0.00</td><td> 0.03</td><td> 0.00</td><td> 0.00</td><td>-0.04</td><td> 1.00</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A matrix: 6 × 6 of type dbl\n",
       "\\begin{tabular}{r|llllll}\n",
       "  & name & value & month & sell\\_price & is\\_event & is\\_weekend\\\\\n",
       "\\hline\n",
       "\tname & 1.00 &  0.01 &  0.03 &  0.02 &  0.00 &  0.00\\\\\n",
       "\tvalue & 0.01 &  1.00 &  0.00 & -0.13 &  0.00 &  0.03\\\\\n",
       "\tmonth & 0.03 &  0.00 &  1.00 &  0.00 & -0.07 &  0.00\\\\\n",
       "\tsell\\_price & 0.02 & -0.13 &  0.00 &  1.00 &  0.00 &  0.00\\\\\n",
       "\tis\\_event & 0.00 &  0.00 & -0.07 &  0.00 &  1.00 & -0.04\\\\\n",
       "\tis\\_weekend & 0.00 &  0.03 &  0.00 &  0.00 & -0.04 &  1.00\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A matrix: 6 × 6 of type dbl\n",
       "\n",
       "| <!--/--> | name | value | month | sell_price | is_event | is_weekend |\n",
       "|---|---|---|---|---|---|---|\n",
       "| name | 1.00 |  0.01 |  0.03 |  0.02 |  0.00 |  0.00 |\n",
       "| value | 0.01 |  1.00 |  0.00 | -0.13 |  0.00 |  0.03 |\n",
       "| month | 0.03 |  0.00 |  1.00 |  0.00 | -0.07 |  0.00 |\n",
       "| sell_price | 0.02 | -0.13 |  0.00 |  1.00 |  0.00 |  0.00 |\n",
       "| is_event | 0.00 |  0.00 | -0.07 |  0.00 |  1.00 | -0.04 |\n",
       "| is_weekend | 0.00 |  0.03 |  0.00 |  0.00 | -0.04 |  1.00 |\n",
       "\n"
      ],
      "text/plain": [
       "           name value month sell_price is_event is_weekend\n",
       "name       1.00  0.01  0.03  0.02       0.00     0.00     \n",
       "value      0.01  1.00  0.00 -0.13       0.00     0.03     \n",
       "month      0.03  0.00  1.00  0.00      -0.07     0.00     \n",
       "sell_price 0.02 -0.13  0.00  1.00       0.00     0.00     \n",
       "is_event   0.00  0.00 -0.07  0.00       1.00    -0.04     \n",
       "is_weekend 0.00  0.03  0.00  0.00      -0.04     1.00     "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cormat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6cec34b9-ef2b-4fa6-8747-47f930b04de1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate the correlation matrix\n",
    "cor_matrix <- cor(mtcars)\n",
    "# Create a basic correlation heatmap using corrplot\n",
    "corrplot(cor_matrix, method = \"color\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "8578c802-d032-4d87-bbb0-ca7213e73c08",
   "metadata": {},
   "outputs": [
    {
     "data": {
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1h2db78SeyXyQj3Jl8UYDTCHZi46uHe\nLa/rLwzfLqv+R6wej7jXOMHTgKmG+6b2siuAbxHuAB+76jd7ua1bdNvKv308rnGfy3aTtVws\nay+7AvgO4Q7MxP222qYvt/0q96uaS2W62kG9qb3d5OJm9RC+q25VsXyrv8WJTzALcCThDszB\nRb+PX8XdGg9fv94JmOqH4q7udpP9+Wv7D3c+qJbV9d9CuAOxCXdg8i5v+92313U/PHp5VbPm\ntpNfXL3ubvcr6G/rHdQ/wVsAhCbcgYm7PezWWPXzo/f7E/OsK+7jvl6f5pym1Rw++rqt+T8P\nTvAWAKEJd2DKdv1x8OX2vmbK7fodX1bbugf0d5XXaJxgeXi/kH5XO9wrv8UJFuoDfJ1wByZs\n2Vd7H1k1w71fG35f78vv1T5lZ/3l4evuftd/yqDqUpnab3GChfoA3yDcgQnruu3TlXpvsj+q\nv9ruaq5Cr71NY/3l4fv/ZXDd/01U+18T9d/CKnogNuEOTNhJjrg/uNufH2k13V1lTrA8/Ga5\n/y1qVW/jmvpvYRU9EJtwB6bscY37Xf3Ququ6q8x15V1lTrA8fA78mIDYhDswcafYVWZx2W8r\ns6q4q8x13VN2nmAF+hz4MQGxCXdg8g77uG/qbfuy/91gW7Osa5/5p+7y8P6brvxfcIK3WJxk\noT7ANwh3YA5qnjn1qv7Zneo3adXl4bMJ91Ms1Af4OuEOzMT9tlK4d8vriZ8cCYBZEO4AH7Ol\nNwAhCHcAAJgA4Q4AABMg3AEAYAKEOwC0k1p/A8B0CHcAaCal1t8BMB3CHQBaSY64A+WEOwA0\nkiyVAY4g3IFofqK+n6nuF1T386ur/9/wC35OdT+3vmzspg+8N97ffQDgR8IdiEa4FxHuEcN9\neLylj8b7hw8C5IQ7EI1wLyLchTtwboQ7EI1wLyLchTtwboQ7EI1wLyLchTtwboQ7EI1wLyLc\nI4b7LzQofTTeP3wQICfcgWiEexHhLtyBcyPcgWiEexHhLtyBcyPcgWiEexHhHjHcf+FBqdVc\nAmZGuAPRCPciwl24A+dGuAPRCPciwl24A+dGuAPRCPciwl24A+dGuAPRCPciwj1iuP8ig1Kr\nuQTMjHAHohHuRYS7cAfOjXAHohHuRYS7cAfOjXAHohHuRYR7xHD/RQelVnMJmBnhDkQj3IsI\nd+EOnBvhDkQj3IsId+EOnBvhDkQj3IsId+EOnBvhDkQj3IsI94jh/osNSm1mEjA7wh2IRrgX\nEe7CHTg3wh2IRrgXEe7CHTg3wh2IRrgXEe4Rw/0XH5RazSVgZoQ7EI1wLyLchTtwboQ7EI1w\nLyLchTtwboQ7EI1wLyLcI4b7LzEotZpLwMwIdyAa4V5EuAt34NwIdyAa4V5EuAt34NwIdyAa\n4V5EuAt34NwIdyAa4V5EuEcM919yUGo1l4CZEe5ANMK9iHAX7sC5Ee5ANMK9iHAX7sC5Ee5A\nNMK9iHCPGO6/1KDUai4BMyPcgWiEexHhLtyBcyPcgWiEexHhLtyBcyPcgWiEexHhLtyBcyPc\ngWiEexHhHjHcf+lBqdVcAmZGuAPRCPciwl24A+dGuAPRCPciwl24A+dGuAPRCPciwj1iuP8y\ng1KruQTMjHAHohHuRYS7cAfOjXAHohHuRYS7cAfOjXAHPrPsll974e6Tx7tu6KpwLyPchTtw\nboQ78Ild13WfJfigVffJE4T7dwj3iOH+yw5KX5k/AG8Id+ATV922u/rKCzvhXpNwF+7AuRHu\nwCe65WL5WYIPv1C41yTchTtwboQ78LHbbrvYdrf91Ye6vu6W14uH2w939m5W3erm8aHHy667\n2Oyf1PWev8xu3XXr3dCLtsuHLybcjybcI4b7LzcoVZuewHkR7sDH1t3d4q5b91e77rqP8b7B\nD+W+v7J/LA/3ZX/n9etwv9nf6m7evmh/cyPcjybchTtwboQ78KHL/ZYyy+5y0Yf2+rJP8P3l\nsj8Yv7xf3C/7w/F5uO8fXr1e/rLs7vvnr9686OmmcD+WcBfuwLkR7sCHbveH1g9rZbrubn95\nsThE92a/28yuP3qeh/vd87WXL/OyMc0PL9rsn74T7kcT7sIdODfCHfjQah/W9/2x8qzOX5X5\ny9Whawfbrtvc37+8/PkJ2c0nwr2IcI8Y7r/8oFRhZgLnSLgDH7nonlx8K9wX1/3K9+WFcB+N\ncBfuwLkR7sBHrp/D/fp74b5Y7LarbOW7cP8u4S7cgXMj3IGPrPYL2vsj76u34f60XH3zeOfd\nh+E+/KLDzTvhfjThHjHcf4VBqcrkBM6PcAc+cN/39d66u38T7tkGMavuZnG5/jHcL56/0Kp/\n0tCuMju7ynyRcBfuwLkR7sAHts+bweyez5KUXb5syX7zvBv7y8Orbr+V5N7tYb3N3eLNPu6b\n/taVcD+acBfuwLkR7sAHlsv86ptwX9wsH0+C2n/49OqHNe53q5dwP5w59W7x6kWHp147c+qX\nCPeI4f4rDkpjT0zgTAl3IBrhXkS4C3fg3Ah3IBrhXkS4C3fg3Ah3IBrhXkS4C3fg3Ah3IBrh\nXkS4Rwz3X2lQajWXgJkR7kA0wr2IcBfuwLkR7kA0wr2IcBfuwLkR7kA0wr2IcI8Y7r/yoNRq\nLgEzI9yBaIR7EeEu3IFzI9yBaIR7EeE+8XBPKb1zA+Adwh2IRrgXEe7TDvf0+O/bGwDvEe5A\nNMK9iHCPGO6/yqD0dpSn7PL1DYB3CXcgGuFeRLgLd+DcCHcgGuFeRLhPKNzTmzXsKbu0VAYo\nJNyBaIR7EeEeMdx/1UHp7ShP2eXCh1OBMsIdiEa4FxHuMwr3tHDEHSgg3IFohHsR4T6fcH91\nA+Bdwh2IRrgXEe7CHTg3wh2IRrgXEe4Rw/1XG5TejvKUXQp3oJBwB6IR7kWE+6TD3QmYgC8Q\n7kA0wr2IcJ92uD9vJJPyGwAfEe5ANMK9iHCPGO6/+qDUai4BMyPcgWiEexHhLtyBcyPcgWiE\nexHhLtyBcyPcgWiEexHhLtyBcyPcgWiEexHhHjHcf41BqdVcAmZGuAPRCPciwl24A+dGuAPR\nCPciwl24A+dGuAPRCPciwj1iuP+ag1KruQTMjHAHohHuRYS7cAfOjXAHohHuRYS7cAfOjXAH\nohHuRYR7xHD/tQalVnMJmBnhDkQj3IsId+EOnBvhDkQj3IsId+EOnBvhDkQj3IsId+EOnBvh\nDkQj3IsI94jh/msPSq3mEjAzwh2IRrgXEe7CHTg3wh2IRrgXEe7CHTg3wh2IRrgXEe4Rw/3X\nGZRazSVgZoQ7EI1wLyLchTtwboQ7EI1wLyLchTtwboQ7EI1wLyLchTtwboQ7EI1wLyLcI4b7\nrzsotZpLwMwIdyCa+lX9M/V/NThB9Fb30/X9ZHUn+DllY1e4A1UJdyAa4R6EcC+TjV3hDlQl\n3IFohHsQwr1MNnbTrzcotZpLwMwIdyAa4R6EcC+TjV3hDlQl3IFohHsQwr1MNnaFO1CVcAei\nEe5BCPcy2dgV7kBVwh2IRrgHIdzLZGM3/fqDUqu5BMyMcAeiEe5BCPcy2dgV7kBVwh2IRrgH\nIdzLZGNXuANVCXcgGuEehHAvk43d9BsMSq3mEjAzwh2IRrgHIdzLZGNXuANVCXcgGuEehHAv\nk41d4Q5UJdyBaIR7EMK9TDZ20284KLWaS8DMCHcgGuEehHAvk41d4Q5UJdyBaIR7EMK9TDZ2\nhTtQlXAHohHuQQj3MtnYFe5AVcIdiEa4ByHcy2RjN/1Gg1KruQTMjHAHohHuQQj3MtnYFe5A\nVcIdiEa4ByHcy2RjV7gDVQl3IBrhHoRwL5ON3fQbD0qt5hIwM8IdiEa4ByHcy2RjV7gDVQl3\nIBrhHoRwL5ONXeEOVCXcgWiEexDCvUw2doU7UJVwB6IR7kEI9zLZ2E2/yaDUai4BMyPcgWiE\nexDCvUw2doU7UJVwB6IR7kEI9zLZ2BXuQFXCHYhGuAch3MtkYzf9poNSq7kEzIxwB6IR7kEI\n9zLZ2BXuQFXCHYhGuAch3MtkY1e4A1UJdyAa4R6EcC+TjV3hDlQl3IFohHsQwr1MNnbTbzYo\nNZpKwNwIdyAa4R6EcC+TjV3hDlQl3IFohHsQwr1MNnaFO1CVcAeiEe5BCPcy2dhNv/mg1Gou\nATMj3IFohHsQwr1MNnaPCfeU0js3AN4h3IFohHsQwr1MNnaPCPf0+O/bGwDvEe5ANMI9COFe\nJhu75eGessvXNwDeJdyBaIR7EMK9TDZ2028xKL0d5Sm7lOxAIeEORCPcgxDuZbKx++Vwt8Yd\nKCHcgWiEexDCvUw2dt8L9/QmzFN2udg/+MMTAN4S7kA0wj0I4V4mG7vptxyU3o7ylF1a4w4U\nEu7AiXXdJ08Q7kEI9zLZ2BXuQFXCHTgx4T4Vwr1MNnaFO1CVcAdOTLhPhXAvk43d9FsNSm9H\necouhTtQSLgDJybcp0K4l8nGbnm4OwET8AXCHajkrrvq/9h1u/6Pq+5usdguu+0h3Hebrltu\nF4vLbrV/8tOfPeEehHAvkw36I8L9eQfIlN8A+IhwB2pZ7g+tXz20+oNuuVisuwebPtyvu72H\nRzZ90C8Wt9318+uEexDCvUw25o8Jd4CjCXegluvudtGvjHlI9n2Y33bL+8X9sg/3rn/otr+2\nOxyXv+ounl8n3IMQ7mWyMZ9+60HppBMPmC/hDtRy0a37BTOb7r4/2H7xeHB997LGfX9t1V32\nV19Wygj3KIR7mWzMC3egKuEOVLN+aPJtd99dHxr+sdgPf1zsrtf7azf9Ipm7bKWMcI9CuJfJ\nhrxwB6oS7kA1u4ccX64Wq9Vh1Uwe7uvDIvdF/7HUZb+q5mWljHCPQriXyYZ8+m0GpRPOOmDO\nhDtQT7e667aLbXe5Xw+ThftVt7rZXRzu2Ha7vu1fCPcghHuZbOwKd6Aq4Q7Us+2uHqp893DZ\nfwJ1s98Y8u7w4dRFvwZ+H+733fo+Xykj3KMQ7mWysSvcgaqEO1DPQ6QfDrXvk32X7ypzt7hf\nPx6CX3XLfKWMcI9CuJfJxq5wB6oS7kBFq/1uMev9jpD9IfcHV32ubw9L3A97uO+6fE8Z4R6G\ncC+Tjd302w5Kp5pwwMwJd6Ci6/3Zlw6X/ZXnM6c+5Pv6btdt+nsvu1crZYR7FMK9TDZ2hTtQ\nlXAHWtt1r1bKCPcohHuZbOwKd6Aq4Q60tu5uXt0W7kEI9zLZ2E2/3aB00gkFzJdwB9rquv7k\nTDnhHoRwL5ONXeEOVCXcgbaWh4XuGeEehHAvk41d4Q5UJdyBaIR7EMK9TDZ2hTtQlXAHohHu\nQQj3MtnYTb/9oNRqLgEzI9yBaIR7EMK9TDZ2hTtQlXAHohHuQQj3MtnYFe5AVcIdiEa4ByHc\ny2RjN/0Og1KruQTMjHAHohHuQQj3MtnYFe5AVcIdiEa4ByHcy2RjV7gDVQl3IBrhHoRwL5ON\n3fQ7Dkqt5hIwM8IdiEa4ByHcy2RjV7gDVQl3IBrhHoRwL5ONXeEOVCXcgWiEexDCvUw2doU7\nUJVwB6IR7kEI9zLZ2E2/86DUai4BMyPcgWiEexDCvUw2doU7UJVwB6IR7kEI9zLZ2BXuQFXC\nHYhGuAch3MtkYzf9roNSq7kEzIxwB6IR7kEI9zLZ2BXuQFXCHYhGuAch3MtkY1e4A1UJdyAa\n4R6EcC+TjV3hDlQl3IFohHsQwr1MNnbT7z4otZpLwMwIdyAa4R6EcC+TjV3hDlQl3IFohHsQ\nwr1MNnaFO1CVcAeiEe5BCPcy2dhNv+eg1GouATMj3IFohHsQwr1MNnaFO1CVcAeiEe5BCPcy\n2dgV7kBVwh2IRrgHIdzLZGNXuANVCXcgGuEehHAvk43d9HsPSq3mEjAzwh2IRrgHIdzLZGNX\nuANVCXcgGuEehHAvk41d4Q5UJdyBaIR7EMK9TDZ20+87KLWaS8DMCHcgGuEehHAvk41d4Q5U\nJdyBaIR7EMK9TDZ2hTtQlXAHohHuQQj3MtnYFe5AVcIdiEa4ByHcy2RjN/3+g1KruQTMjHAH\nohHuQQj3MtnYFe5AVcIdiEa4ByHcy2RjV7gDVQl3IBrhHoRwL5ON3fQHDkqt5hIwM8IdiEa4\nByHcy2RjV7gDVQl3IBrhHoRwL5ONXeEOVCXcgWhOkFr1zeFXg5+qr/5/xAlkYzf9wYNSq7kE\nzIxwB6Jp3WGjEO7CXbgDYxPuQDStO2wUwl24C3dgbMIdiKZ1h41CuAt34Q6MTbgD0bTusFEI\n9/MM9z90UGo1l4CZEe5ANK07bBTCXbh/Eu4pvb578EkAOeEORNO6w0Yh3IX7x+GeFq9bPQ09\nCeAV4Q5E07rDRiHczzPc//BB6e0oT9nl4drAkwBeE+5ANK07bBTCXbgfE+7JUhmggHAHomnd\nYaMQ7sJduANjE+5ANK07bBTCXbhn4Z5+/CTqD+GeFsIdKCDcgWhad9gohPt5hvsfOSi9HeUp\nuzz8MfAkgNeEOxBN6w4bhXAX7sXhnl8CfEC4A9G07rBRCHfhXh7ujypPLGD6hDsQTesOG4Vw\nP89w/6MHpbejPGWXizfXAQYJdyCa1h02CuEu3D8M97efRx16EsArwh2IpnWHjUK4C/ePw33x\ntDbm6cHBJwHkhDsQTesOG4VwF+6fhDvA0YQ7EE3rDhuFcD/PcP9jB6VWcwmYGeEORNO6w0Yh\n3IW7cAfGJtyBaFp32CiEu3AX7sDYhDsQTesOG4VwP89w/+MHpVZzCZgZ4Q5E07rDRiHchbtw\nB8Ym3IFoWnfYKIS7cBfuwNiEOxBN6w4bhXA/z3D/EwelVnMJmBnhDkTTusNGIdyFu3AHxibc\ngWhad9gohLtwF+7A2IQ7EE3rDhuFcBfuwh0Ym3AHomndYaMQ7ucZ7n/SoNRqLgEzI9yBaFp3\n2CiEu3AX7sDYhDsQTesOG4VwF+7CHRibcAeiad1hoxDu5xnuf8qg1GouATMj3IFoWnfYKIS7\ncBfuwNiEOxBN6w4bhXAX7sIdGJtwB6Jp3WGjEO7CXbgDYxPuQDStO2wUwv08w/1PG5RazSVg\nZoQ7EE3rDhuFcBfuwh0Ym3AHomndYaMQ7sJduANjE+5ANK07bBTC/TzD/c8YlFrNJWBmhDsQ\nTesOG4VwF+7CHRibcAeiad1hoxDuwl24A2MT7kAzu/6i6368u3WHjUK4C3fhDoxNuAOtrPbJ\nLtyF+8RlYzf9WYPSqeYUMHPCHWilE+7CfQ6ysSvcgaqEO9CKcBfu9f8jTiAbu8IdqEq4AxU8\n1Ph1t7xeLLZdt93fc7PqVjeHhy42+4e63v6p2/3tZ607bBTC/TzD/c8ZlE439YBZE+5ABV13\n3Wf5bt1f9uW+v9Kt9w8t+6vXL+G+6a/chSyF4wAAIABJREFUvLy4dYeNQrgLd+EOjE24AxU8\nNPrl4ubxcrlY3HbL+8X9srt9fmj1slTm6faT1h02CuEu3IU7MDbhDlTQdXf7y4vFoc83Xb/1\n464/5P700Eu4P91+0rrDRiHchbtwB8Ym3IEK8s+dvjT6q1x/+6QnrTtsFML9PMP9zx+UTjDn\ngHMg3IEKhLtwF+7CHRibcAcqEO7CXbgLd2Bswh2o4Mcmf1rjvhHuwn3W4f4XDkonmXXA/Al3\noIIfm/zVrjJPd758dlW4C/cJywa+cAeqEu5ABW8Opuf7uD/duer6nSKFu3CfuGzgC3egKuEO\nVPB2FczN8vnMqU+XdyvhLtxnIBv46S8ZlE4x6YAzINyBaFp32CiEu3AX7sDYhDsQTesOG4Vw\nF+7CHRibcAeiad1hoxDuwl24A2MT7kA0rTtsFML9PMP9Lx+UWs0lYGaEOxBN6w4bhXAX7sId\nGJtwB6Jp3WGjEO7CXbgDYxPuQDStO2wUwv08w/2vGpRazSVgZoQ7EE3rDhuFcBfuwh0Ym3AH\nomndYaMQ7sJduANjE+5ANK07bBTCXbgLd2Bswh2IpnWHjUK4n2e4/zWDUqu5BMyMcAeiad1h\noxDuwl24A2MT7kA0rTtsFMJduAt3YGzCHYimdYeNQrifZ7j/9YNSq7kEzIxwB6Jp3WGjEO7C\nXbgDYxPuQDStO2wUwl24C3dgbMIdiKZ1h41CuAt34Q6MTbgD0bTusFEI9/MM979pUGo1l4CZ\nEe5ANK07bBTCXbh/Eu4ppVc3Bp8EkBPuQDStO2wUwl24fxzu6fHftzcA3iPcgWhad9gohPt5\nhvvfOii9HeUpu3x9A+Bdwh2IpnWHjUK4C/fycF+8vQEwQLgD0bTusFEId+Eu3IGxCXcgmtYd\nNgrhLtyzcH/72dOUXS7eXAcYJNyBaFp32CiE+3mG+98+KL0d5Sm7XLy5DjBIuAPRtO6wUQh3\n4X5cuA88B+AHwh2IpnWHjUK4C/ejwn3gKQA/Eu5ANK07bBTC/TzD/e8alN6O8pRdLnQ7UEa4\nA9G07rBRCHfh/mG4vz0BE8CnhDsQTesOG4VwF+4fh/viaaOZtL/6dt8ZgDeEOxBN6w4bhXA/\nz3D/ewalVnMJmBnhDkTTusNGIdyFu3AHxibcgWhad9gohLtwF+7A2IQ7EE3rDhuFcBfuwh0Y\nm3AHomndYaMQ7ucZ7j97UGo1l4CZEe5ANK07bBTCXbgLd2Bswh2IpnWHjUK4C3fhDoxNuAPR\ntO6wUQj38wz3f2BQajWXgJkR7kA0rTtsFMJduAt3YGzCHYimdYeNQrgLd+EOjE24A9G07rBR\nCHfhLtyBsQl3IJrWHTYK4X6e4f4PD0qt5hIwM8IdiObn19c69cYwh18N5iEbu8IdqEq4A9EI\n9yLCPYps7Ap3oCrhDkQj3IsI9yiysZv+0UGp0VQC5ka4A9EI9yLCPYps7Ap3oCrhDkQj3IsI\n9yiysSvcgaqEOxCNcC8i3KPIxq5wB6oS7kA0wr2IcI8iG7vpnxiUWs0lYGaEOxCNcC8i3KPI\nxq5wB6oS7kA0wr2IcI8iG7vCHahKuAPRCPciwj2KbOymf2pQajWXgJkR7kA0wr2IcI8iG7vC\nHahKuAPRCPciwj2KbOwKd6Aq4Q5EI9yLCPcosrGb/tlBqdVcAmZGuAPRCPciwj2KbOwKd6Aq\n4Q5EI9yLCPcosrEr3IGqhDsQjXAvItyjyMaucAeqEu5ANMK9iHCPIhu76V8YlFrNJWBmhDsQ\njXAvItyjyMaucAeqEu5ANMK9iHCPIhu7wh2oSrgD0Qj3IsI9imzspn9pUGo1l4CZEe5ANMK9\niHCPIhu7wh2oSrgD0Qj3IsI9imzsCnegKuEORCPciwj3KLKxK9yBqoQ7EI1wLyLco8jGbvpX\nBqVWcwmYGeEORCPciwj3KLKxK9yBqoQ7EI1wLyLco8jGrnAHqhLuQDTCvYhwjyIbu+lfH5Ra\nzSVgZoQ7EI1wLyLco8jGrnAHqhLuQDTCvYhwjyIbu8IdqEq4A9EI9yLCPYps7Ap3oCrhDkQj\n3IsI9yiysZv+rUGp1VwCZka4A9EI9yLCPYps7Ap3oCrhDkQj3IsI9yiysSvcgaqEOxCNcC8i\n3KPIxm76dwelVnMJmBnhDkQj3IsI9yiysSvcgaqEOxCNcC8i3KPIxq5wB6oS7kA0wr2IcI8i\nG7vCHahKuAPRCPciwj2KbOymnzMotZpLwMwIdyAa4V5EuEeRjV3hDlQl3IFohHsR4R5FNnaF\nO1CVcAeiEe5FhHsU2dhNP3dQajWXgJkR7kA0wr2IcI8iG7vCHahKuAPj6brDv58+6UPCvYhw\njyIbu8IdqEq4A+MR7ick3KPIxm76eYPS9+YVwCPhDoynKNw/JdyLCPcosrEr3IGqhDswHuF+\nQsI9imzsHh/uKX3wIMBrwh34mt2669a7/dWbVbe86a8MhPvDzW233B6uXq66zeMTtstuffH6\n1U+EexHhHkU2do8O9/T4L0AJ4Q58yU231xf3Zn9tvXgn3K9fHn145vbwhHV/5/Ly1aufCPci\nwj2KbOym4Wek96ZRyi4BPiXcgS9ZdveLxW23Wix23fpycbnudu+E+/J+cb/sbvur68vF4Qm3\n/dWrh4rPX/1EuBcR7lFkw124A1UJd+BLuufU3nR9jl92m3fCvX/e7vDo3eGe/jV3/UuWr179\nRLgXEe5RZMP9vXBP7yxlT9klwKeEO/Al267b3N/317on761xf/rj7dXXr34i3IsI9yiy4Z7+\nv0HpvWmUskuATwl34Guul/0q9Qvh3opwjyIb7sIdqEq4A1+12676Ne5Zc38p3N98XeFeRLhH\nkY1d4Q5UJdyBbzisV9/lN9+Ee7+wfdddvQr3dbbGfbf4gXAvItyjyMaucAeqEu7Al6z6fWL2\nu8rc9vvGLG7e/XDqYVeZ3atwv+n3ktn2u8pkr34i3IsI9yiy4Z7+30HpvWmUskuATwl34Etu\nDyvT+8Pp+z3ZH5e7D4T7/uHN4lW4Z/u4v7z6iXAvItyjyIb7seHuBEzAUYQ78DX7M6fu93fs\nz33aXfXlPbzGfdOtns+r+vxHvyvNxetXPxHuRYR7FNnYPTrcF+/sEwkwRLgDVQ18+PQzwr2I\ncI8iG7vpZwal0eYTcN6EO1CVcK9FuEeRjV3hDlQl3IHRdS+EezXCPYps7Ap3oCrhDoxOuJ+C\ncI8iG7vCHahKuAPRCPciwj2KbOymnx6UWs0lYGaEOxCNcC8i3KPIxq5wB6oS7kA0wr2IcI8i\nG7vCHahKuAPRCPciwj2KbOym/2dQajWXgJkR7kA0wr2IcI8iG7vCHahKuAPRCPciwj2KbOwK\nd6Aq4Q5EI9yLCPcosrGb/u9BqdVcAmZGuAPRCPciwj2KbOwKd6Aq4Q5EI9yLCPcosrEr3IGq\nhDsQjXAvItyjyMaucAeqEu5ANMK9iHCPIhu76acGpVZzCZgZ4Q5EI9yLCPcosrEr3IGqhDsQ\njXAvItyjyMaucAeqEu5ANMK9iHCPIhu76f8alFrNJWBmhDsQjXAvItyjyMaucAeqEu5ANMK9\niHCPIhu7wh2oSrgD0Qj3IsI9imzsCnegKuEORCPciwj3KLKxm/7PQanVXAJmRrgD0Qj3IsI9\nimzsCnegKuEORCPciwj3KLKxK9yBqoQ7EI1wLyLco8jGbvo/BqVWcwmYGeEORCPciwj3KLKx\nK9yBqoQ7EI1wLyLco8jGrnAHqhLuQDTCvYhwjyIbu8IdqEq4A9EI9yLCPYps7Kb/fVBqNZeA\nmRHuQDTCvYhwjyIbu8IdqEq4A9EI9yLCPYps7Ap3oCrhDkQj3IsI9yiysZv+t0Gp1VwCZka4\nA9EI9yLCPYps7Ap3oCrhDkQj3IsI9yiysSvcgaqEOxCNcC8i3KPIxq5wB6oS7kA0wr2IcI8i\nG7tpeLylVnMJmBnhDkQj3IsI9yiysSvcgaqEOxCNcC8i3KPIxq5wB6oS7kA0wr2IcI8iG7vp\nfx2UWs0lYGaEOxCNcC8i3KPIxq5wB6oS7kA0wr2IcI8iG7vCHahKuAPRnCC1frq6n6qu/k+p\n/q8GP1H/L+Ln1ZeN3fS/DEqt5hIwM8IdiKZ+kQr3IsJduAOxCHcgmvpFKtyLCHfhDsQi3IFo\n6hepcC8i3IU7EItwB6KpX6TCvYhwPz7c/+dBqdVcAmZGuAPR1C9S4V5EuAt3IBbhDkRTv0iF\nexHhLtyBWIQ7EE39IhXuRYT78eH+Pw1KreYSMDPCHYimfpEK9yLCXbgDsQh3IJr6RSrciwh3\n4Q7EItyBaOoXqXAvItyFOxCLcAeiqV+kwr2IcD8+3P/HQanVXAJmRrgD0dQvUuFeRLgLdyAW\n4Q5EU79IhXsR4S7cgViEOxBN/SIV7kWE+/Hh/j8MSq3mEjAzwh2Ipn6RCvciwl24A7EIdyCa\n+kUq3IsI98rhnlJ6deOdpwE8Ee5ANPWLVLgXEe51wz09/vv2BsAw4Q5EU79IhXsR4X58uP/3\ng9LQOE/Z5esbAO8Q7kA09YtUuBcR7qcL98XbGwBvCHcgmvpFKtyLCPfRwn1oAXvKLl/dBfAe\n4Q5EU79IhXsR4X58uP93g9LQOE/Z5eLNdYABwh2Ipn6RCvciwl24A7EIdyCa+kUq3IsI99OG\n++CzADLCHYimfpEK9yLC/fhw/28Hpdcj/LDi/XDny0M/PAngLeEORFO/SIV7EeFeKdwfn5td\nLnQ7UEK4A9HUL1LhXkS4Vw33tydgAviEcAeiqV+kwr2IcK8b7ounPSLTYfXMwJaRAK8IdyCa\n+kUq3IsI9+PD/b8ZlFrNJWBmhDsQTf0iFe5FhLtwB2IR7kA09YtUuBcR7sIdiEW4A9HUL1Lh\nXkS4Hx/u//Wg1GouATMj3IFo6hepcC8i3IU7EItwB6KpX6TCvYhwF+5ALMIdiKZ+kQr3IsJd\nuAOxCHcgmvpFKtyLCPfjw/2/GpRazSVgZoQ7EE39IhXuRYS7cAdiEe5ANPWLVLgXEe7CHYhF\nuAPR1C9S4V5EuB8f7v/loNRqLgEzI9yBaOoXqXAvItyFOxCLcAeiqV+kwr2IcBfuQCzCHYim\nfpEK9yLCXbgDsQh34Lu67ttfYpffqF+kwr2IcD8+3P+LQenbMwSgJ9yB7/p+uK9efYX6RSrc\niwh34Q7EItyB9jrhLtwrEe7AjAh3oD3hLtxrOXG4/+eD0mnnEzBbwh34rn1279Zdt979+NDN\nqlveLBaX3Wp/c9VdPt/Xv+5i0y2v+ytdnu71i1S4FxHuwh2IRbgD39VH980+vrub149s9neu\nF4t1d/Fw86K/+nzfw+uW/dVr4S7c6xHuwIwId+C7+uhedveLxe3jgfUnu259ubhcd7uHR64f\nbl8/XHu57+F1D1dv+tdYKiPcaxHuwIwId+C7+uzuujfLZPqD65eLfp3M5uHxPun7zWPy+7q7\n51fnL6tfpMK9iHA/Ptx/clCqN/uAsyLcge/qs3vbdZv7+zcPPFosrrqLxUW3fXXfIdeFu3Cv\nSbgDMyLcge/aZ/d1v159efHDA8+RftddP8T9nXAX7sId4KuEO/Bdj9m9265+WOOe9/hy1f/z\n6j7hLtzr/0WcONz/s0Fp3BkHnC3hDnzXS3b/cA7VTbbwfdvd7D+gmt0n3IV7/b8I4Q7MiHAH\nvqvP7lV3+3ZXmdtued/vFLlZ9FtBdvuPpWb35eGer7GpX6TCvYhwF+5ALMId+K4+vW8PC9fv\nXj+y3t95WPm+2u/dnt/3Eu6rhzteXlW/SIV7EeF+fLj/p4NS1QkInA/hDnzXy5lT73586OYh\nya8OR9Nvn9bIPN/3Eu53K+Eu3OsQ7sCMCHcgmvpFKtyLCHfhDsQi3IFo6hepcC8i3IU7EItw\nB0bVddlO7V9Tv0iFexHhfny4/yeD0rfnFUBPuAOjEu4Hwl24C3dgbMIdiKZ+kQr3IsJduAOx\nCHcgmvpFKtyLCPfjw/0/HpRazSVgZoQ7EE39IhXuRYS7cAdiEe5ANPWLVLgXEe7CHYhFuAPR\n1C9S4V5EuAt3IBbhDkRTv0iFexHhfny4/0eDUqu5BMyMcAeiqV+kwr2IcBfuQCzCHYimfpEK\n9yLCXbgDsQh3IJr6RSrciwj348P9PxyUWs0lYGaEOxBN/SIV7kWEu3AHYhHuQDT1i1S4FxHu\nwh2IRbgD0dQvUuFeRLgLdyAW4Q5EU79IhXsR4X58uP8Hg1KruQTMjHAHoqlfpMK9iHAX7kAs\nwh2Ipn6RCvciwl24A7EIdyCa+kUq3IsI9+PD/d8flFrNJWBmhDsQTf0iFe5FhLtwB2IR7kA0\n9YtUuBcR7sIdiEW4A9HUL1LhXkS4C3cgFuEORFO/SIV7EeF+fLj/e4NSq7kEzIxwB6KpX6TC\nvYhwF+5ALMIdiKZ+kQr3IsJduAOxCHcgmvpFKtyLCPfjw/3fGZRazSVgZoQ7EE39IhXuRYR7\n5XBP6fUD7zwN4IlwB6KpX6TCvYhwrxvuafG61dPw0wCeCXcgmvpFKtyLCPfjw/3fHpSGxnnK\nLg/XBp8G8EK4A9HUL1LhXkS4nzLck6UywKeEOxBN/SIV7kWEu3AHYhHuQDT1i1S4FxHuo4V7\n+vFzqIsfwz0thDvwKeEORFO/SIV7EeF+fLj/m4PS0DhP2eXhj8GnAbwQ7kA09YtUuBcR7icL\n9/wS4F3CHYimfpEK9yLCvVK4HxbOHO5Mz/cMLacBeEW4A9HUL1LhXkS4Hx/u/8agNDTOU3a5\neHMdYIBwB6KpX6TCvYhwrxrubz+POvw0gGfCHYimfpEK9yLCvW64L57Wxjw9/M7TAJ4IdyCa\n+kUq3IsI98rhDnAk4Q5EU79IhXsR4X58uP9rg1KruQTMjHAHoqlfpMK9iHAX7kAswh2I5ufU\n95PV1c/q+upX9U/X/9VAuAMzItyBaIR7EML9+HD/VwelVnMJmBnhDkQj3IMQ7sIdiEW4A9EI\n9yCEu3AHYhHuQDTCPQjhLtyBWIQ7EI1wD0K4Hx/u//Kg1GouATMj3IFohHsQwl24A7EIdyAa\n4R6EcBfuQCzCHYhGuAch3I8P939xUGo1l4CZEe5ANMI9COEu3IFYhDsQjXAPQrgLdyAW4Q5E\nI9yDEO7Hh/s/Pyi1mkvAzAh3IBrhHoRwF+5ALMIdiEa4ByHchTsQi3AHohHuQQh34Q7EItyB\naIR7EML9+HD/5walVnMJmBnhDkQj3IMQ7sIdiEW4A9EI9yCEu3AHYhHuQDTCPQjhfny4/zOD\nUqu5BMyMcAeiEe5BCHfhDsQi3IFohHsQwl24A7EIdyAa4R6EcBfuQCzCHYhGuAch3I8P9396\nUGo1l4CZEe5ANMI9COEu3IFYhDsQjXAPQrgLdyAW4Q5EI9yDEO7Hh/s/OSi1mkvAzAh3IBrh\nHoRwF+5ALMIdiEa4ByHchTsQi3AHohHuQQh34Q7EItyBaIR7EML9+HD/xwelVnMJmBnhDkQj\n3IMQ7sIdiEW4A9EI9yCEu3AHYhHuQDTCPQjhfny4/2ODUqu5BMyMcAeiEe5BCHfhDsQi3IFo\nhHsQwl24A7EIdyAa4R6EcBfuQCzCHYhGuAch3I8P939kUGo1l4CZEe5ANMI9COEu3IFYhDsQ\njXAPQrgLdyAW4Q5EI9yDEO7Hh/s/NCi1mkvAzAh3IBrhHoRwF+5ALMId+EzX1X91/izhHoRw\nF+5ALMId+IxwF+7CvTDc/8FB6TszCOCZcAfqEu6TJdyFOxCLcAfqEu6TJdyFOxCLcAc+s4/q\n3brr1rv87rvuqv9j1+3vveruFoubVbe8OTz6fPWQ5Nvu+vWdF5tueb1/5nbZbYV7RMJduAOx\nCHfgM31U33R7N/n9y31sXz1kd/+k5WKx2T9n3d98ubpP8u3+an7nsr/al/u6v7IR7gEJ9+PD\n/e8flCpOT+CcCHfgM31UL7v7xeK2W+X3X3e3+0cfkv3hoevFrltfLi7X/RH47Gr/6kO3v7rz\n4epN/+Vuu+X94n4p3AMS7sIdiEW4A5/po7rrdm/uv+hz/K7b9E2/7i4Wm+7y4d7LbrPIrz68\nevt0FD678+7xC2/213bCPSDhLtyBWIQ78JnDMfNuc3//wwPrhxLfdvfd9aHhuyc/XF0fMv31\nnU9f+LHYhXtAwv34cP/7BqWa8xM4I8Id+Mw+qq/7RenLi1cP7B6SfblarFaHVTPvhXu3PCyx\nEe4TI9yFOxCLcAc+8xjVu+3q9Rr3h0dWd912se0uV/0qmCy+X129u+8eV8P/8Lhwj024C3cg\nFuEOfOYlqn/ck33bXXW7xe7hst8ZcvOyDj672r/mev8B1h/ufFrj3t95J9wDEu7CHYhFuAOf\n6aN61R8zv/3xiPtDbx8Ote/re79BzOKm//BpdnWf5Kt+58cf79xf7uwqE5ZwPz7c/95BqfYc\nBc6EcAc+00f17WF1+t0PDx0Wz6z3B9Qft2Q/LIR/ubpP8vt94P9w5+Fyv7n7lXAPSLhXDveU\n0js3AAYJd+AzL2dO/bHbF9f7sy8dLhf7M6N2Vxevrx6S/Lo/zv7jnY8POXNqUMK9brinx3/f\n3gAYJtyBaIR7EML9+HD/uweloXGessvXNwDeIdyBaIR7EML91OEO8DHhDhyl67Lt2OsQ7kEI\n91OGuzXuwOeEO3AU4S7chfvR4Z4Gsjxll4v9w2+eAvCacAeiEe5BCPfjw/3vHJSGxnnKLq1x\nB4oIdyAa4R6EcBfuQCzCHYhGuAch3CuF+2HhzOHOx4de3QB4h3AHohHuQQj348P97xiUhsZ5\nyi6FO1BEuAPRCPcghHvVcHcCJuBowh2IRrgHIdzrhvvzDpApvwHwPuEORCPcgxDux4f73zYo\ntZpLwMwIdyAa4R6EcBfuQCzCHYhGuAch3IU7EItwB6IR7kEId+EOxCLcgWiEexDC/fhw/1sG\npVZzCZgZ4Q5EI9yDEO7CHYhFuAPRCPcghLtwB2IR7kA0wj0I4X58uP/Ng1KruQTMjHAHohHu\nQQh34Q7EItyBaIR7EMJduAOxCHcgGuEehHAX7kAswh2IRrgHIdyPD/e/cVBqNZeAmRHuQDTC\nPQjhLtyBWIQ7EI1wD0K4C3cgFuEORCPcgxDux4f73zAotZpLwMwIdyAa4R6EcBfuQCzCHYhG\nuAch3IU7EItwB6IR7kEId+EOxCLcgWiEexDC/fhw/+sGpVZzCZgZ4Q5EI9yDEO7CHYhFuAPR\nCPcghLtwB2IR7kA0wj0I4X58uP+1g1KruQTMjHAHohHuQQh34Q7EItyBaIR7EMJduAOxCHcg\nGuEehHAX7kAswh2IRrgHIdyPD/e/elBqNZeAmRHuQDTCPQjhLtyBWIQ7EI1wD0K4C3cgFuEO\nRCPcgxDux4f7XzkotZpLwMwIdyAa4R6EcBfuQCzCHYhGuAch3IU7EItwB6IR7kEI9+PD/a8Y\nlFrNJWBmhDsQzc+tr3UST8MJmre++r8a/EQ2doU7UJVwB6IR7kG0bu5RCHdgRoQ7EI1wD6J1\nc49CuAMzItyBaIR7EK2bexQnDve/bFBqNJWAuRHuQDTCPYjWzT0K4Q7MiHAHohHuQbRu7lEI\nd2BGhDsQjXAPonVzj+LE4f6XDkqt5hIwM8IdiEa4B9G6uUch3IEZEe5ANMI9iNbNPQrhDsyI\ncAeiEe5BtG7uUQh3YEaEOxCNcA+idXOP4sTh/hcPSq3mEjAzwh2IRrgH0bq5RyHcgRkR7kA0\nwj2I1s09CuEOzIhwB6IR7kG0bu5RnDjc/6JBqdVcAmZGuAPRCPcgWjf3KIQ7MCPCHYhGuAfR\nurlHIdyBGRHuQDTCPYjWzT0K4Q7MiHAHohHuQbRu7lGcONz/gkGp1VwCZka4A9EI9yBaN/co\nhDswI8IdiEa4B9G6uUch3IEZEe5ANMI9iNbNPYoTh/ufNyi1mkvAzAh3IBrhHkTr5h6FcAdm\nRLgD0Qj3IFo39yiEOzAjwh2IRrgH0bq5R3HicP9zB6VWcwmYGeEORCPcg2jd3KMQ7sCMCHcg\nGuEeROvmHkXkcE8pvXMDYJBwB6IR7kG0bu5RBA739Pjv2xsAw4Q7EI1wD6J1c4/ixOH+Zw9K\nQ+M8ZZevbwC8Q7gD0Qj3IFo39yiEOzAjwh2IRrgH0bq5RxEj3NPACvaUXVoqAxQR7kA0wj2I\n1s09ihOH+585KA2N85RdLnw4FSgh3IFohHsQrZt7FFMJ97RwxB34lHAHohHuQbRu7lFMJNxf\n3QB4h3AHohHuQbRu7lFEDPfDivfDnY8PvboB8A7hDkQj3INo3dyjOHG4/+mD0tA4T9mlcAeK\nCHcgGuEeROvmHkXccHcCJuBowh2IRrgH0bq5RxE43J83kkn5DYD3CXcgGuEeROvmHsWJw/1P\nHZRazSVgZoQ7EI1wD6J1c49CuAMzItyBaIR7EK2bexTCHZgR4Q5EI9yDaN3coxDuwIwId+C0\nusziqlvv71x3Vy/PEO5BtG7uUZw43P/kQemkUwyYL+EOnNarcF8su5uH+266ZfYM4R5E6+Ye\nhXAHZkS4Aw30zb5313UXi8tld5c9KNyDaN3coxDuwIwId6CB53DfL5bZ5AtlhHsYrZt7FCcO\n9z9hUDrVxAJmTrgD37Bbd916t+hL/GLTLa/39226brld7O9cbB+v/uAl3BfL7vrVQhnhHkbr\n5h6FcAdmRLgDX3dzWKp+05f4sr/2UO7Xh/v6XH+43V9dv31hFu53D8+4e/WgcA+idXOPQrgD\nMyLcga9bdveLxW236kt8ffnQ8ftrt/19fZk/1Pz94n7Z3/GDLNwXV68Xygj3MFo39yiEOzAj\nwh34uq7bPV+7W+Q9/hju/cO7bjPwwpfryx9Wygj3KFo39yhOHO5/3KA00oQDzp1wB75u23Wb\n+/v+2qHED5cXu+v1Y7gvXu59Jbvrqvvhs6nCPYrWzT0K4Q7MiHAHvuG6X9m+vHgV7uunPdqL\nwv2uWy5+WOQu3INo3dyjEO7AjAj9ykS6AAAETklEQVR34Ft229VhZXt/o7+86lY3u4vicO9X\nwL8+/5Jwj6J1c4/ixOH+xwxKFWYecI6EO/BdfYW/hPv+2lO490fSdz9++HSRhfvVfgH8+tVT\nhHsQrZt7FMIdmBHhDnzd6rCDzOp1uN8t7tevdpXZvXnhU7jfdd3lYt/52WIZ4R5E6+YehXAH\nZkS4A193e1jOfpeH+7bL7tyvd3+7qcxzuC/7PeAXi9eLZYR7EK2bexQnDvc/alCqNAGBcyPc\ngW/Ynzn1ZSPI/eVVf9d+D8iHm5tudTPwusdwv3o+OVO+WEa4B9G6uUch3IEZEe5ANQOfSi0h\n3INo3dyjEO7AjAh3oBrhPm2tm3sUwh2YEeEOVJOfSPXZ5y8T7kG0bu5RnDjc/4hBafzJBZwl\n4Q5UI9ynrXVzj0K4AzMi3IFohHsQrZt7FMIdmBHhDkQj3INo3dyjOHG4/2GDUqOpBMyNcAei\nEe5BtG7uUQh3YEaEOxCNcA+idXOPQrgDMyLcgWiEexCtm3sUwh2YEeEORCPcg2jd3KM4cbj/\nIYNSq7kEzIxwB6IR7kG0bu5RCHdgRoQ7EI1wD6J1c49CuAMzItyBaIR7EK2bexQnDvc/aFBq\nNZeAmRHuQDTCPYjWzT0K4Q7MiHAHohHuQbRu7lEId2BGhDsQjXAPonVzj0K4AzMi3IFohHsQ\nrZt7FCcO9z9gUGo1l4CZEe5ANMI9iNbNPQrhDsyIcAeiEe5BtG7uUQh3YEaEOxCNcA+idXOP\n4sTh/vsNSq3mEjAzwh2IRrgH0bq5RyHcgRkR7kA0wj2I1s09CuEOzIhwB6IR7kG0bu5RCHdg\nRoQ7EI1wD6J1c4/ixOH++wxKreYSMDPCHYhGuAfRurlHIdyBGRHuQDTCPYjWzT0K4Q7MiHAH\nohHuQbRu7lGcONx/r0Gp1VwCZka4A9EI9yBaN/cohDswI8IdiEa4B9G6uUch3IEZEe5ANMI9\niNbNPYoTh/vvMSi1mkvAzAh3IBrhHkTr5h6FcAdmRLgD0Qj3IFo39yiEOzAjwh2IRrgH0bq5\nRyHcgRkR7kA0wj2I1s09ihOH++82KLWaS8DMCHcgGuEeROvmHoVwB2ZEuAPRCPcgWjf3KIQ7\nMCPCHYhGuAfRurlHceJw/10GpVZzCZgZ4Q5EI9yDaN3coxDuwIwIdyAa4R5E6+YexSTC/bPH\nAQ6EOxCNcA+idXOPYgrhnj55HOCRcAeiEe5BtG7uUZw43H+nQenDAZ8ccQcKCXcgGuEeROvm\nHkX8cE+WygClhDsQjXAPonVzj+LE4f6BD4b8R48BvBDuANBUav0NABMh3AGgqdT6GwAmQrgD\nQFOp9TcATIRwB4AGXha+p5bfBjAhwh0AmkqtvwFgIoQ7ADSVWn8DwEQIdwBoKrX+BoCJEO4A\nADABwh0AACZAuAMAwAQIdwAAmADhDgAAEyDcAQBgAoQ7AABMgHAHAIAJEO4AADAB/z9QkOFS\nVcQb0QAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title \"\""
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 1500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "cormat <- round(cor(final_data_numeric_4, use = \"pairwise.complete.obs\"),2)\n",
    "corrplot(cormat, method = \"color\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "47f05ceb-3dee-4bae-9706-58fbe2927333",
   "metadata": {},
   "outputs": [],
   "source": [
    "to_forecast <- final_data_numeric %>% as_tsibble(key = \"id\", index=\"name\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1d10f48d-df9f-4ef1-8d90-7b5daf05767f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x<- to_forecast %>% model(NAIVE(value)) %>% forecast(h=28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0d5daa64-8a47-465b-b27d-9828c72750e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "fc <- x %>% filter(id == \"FOODS_3_827\" | id == \"FOODS_3_001\")\n",
    "#l <- fc[c(\"id\",\"name\", \".mean\")]\n",
    "#l <- pivot_wider(fc)\n",
    "#pivot_wider(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "b7a704d3-86b3-4acc-a8fb-a6f973be75f3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"2 errors (1 unique) encountered for ARIMA(value)\n",
      "[2] incorrect number of subscripts on matrix\n",
      "\"\n"
     ]
    }
   ],
   "source": [
    "#ARIMA final_data_numeric\n",
    "arima <- fc %>% model(ARIMA(value)) #%>% forecast(h=28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "aa90f44a-a017-4c0f-a701-7fb11cbefe4f",
   "metadata": {},
   "outputs": [],
   "source": [
    "x <-as_tsibble(final_data_numeric, index=\"name\", key=\"id\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "0755aeaf-3a1c-4ac2-a992-67afb7da53a9",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n",
      "Warning message in sqrt(diag(best$var.coef)):\n",
      "\"NaNs produced\"\n"
     ]
    }
   ],
   "source": [
    "arima <- x %>% model(ARIMA(value)) %>% forecast(h=28)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "8df183f7-024b-4a46-aacf-24ea719a57e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "arima_rounded <- arima\n",
    "arima_rounded$.mean <- ceiling(arima_rounded$.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "34488602-1d02-4137-a506-fed9d4f872d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "arima_rounded2 <- arima[c('id', 'name','.mean')]\n",
    "arima_rounded2$.mean <- ceiling(arima_rounded$.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "359ec805-ac5c-4e6e-a689-2aa97e4d46bc",
   "metadata": {},
   "outputs": [],
   "source": [
    "sales_val_long <- sales_val %>% pivot_longer (2:ncol(sales_val))\n",
    "sales_val_long$name <- gsub (\"d_\", \"\" ,sales_val_long$name)\n",
    "sales_val_long$id <- gsub (\"_TX_3_validation\", \"\" ,sales_val_long$id)\n",
    "sales_val_long$name <- as.integer(sales_val_long$name)\n",
    "sales_val_long <-as_tsibble(sales_val_long, index=\"name\", key=\"id\" )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "d6c3ccb7-4e7d-41a8-b8e3-ea1c1a26eba2",
   "metadata": {},
   "outputs": [],
   "source": [
    "arima_results <- summary(accuracy(arima,sales_val_long) %>% select(ME:MAE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "649c6075-d941-4989-b1e6-19db341c2eba",
   "metadata": {},
   "outputs": [],
   "source": [
    "croston_results <- summary(accuracy(croston,sales_val_long) %>% select(ME:MAE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8abc3b12-3f5e-4a0a-b5e2-c3ef8d400df3",
   "metadata": {},
   "outputs": [],
   "source": [
    "arima_rounded_results <- summary(accuracy(arima_rounded,sales_val_long) %>% select(ME:MAE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "a41453be-c3cd-4736-be83-57968697a802",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       ME                RMSE             MAE         \n",
       " Min.   :-5.56427   Min.   : 0.000   Min.   : 0.0000  \n",
       " 1st Qu.:-0.09409   1st Qu.: 0.672   1st Qu.: 0.5326  \n",
       " Median : 0.07435   Median : 1.134   Median : 0.8709  \n",
       " Mean   : 0.26161   Mean   : 1.827   Mean   : 1.3997  \n",
       " 3rd Qu.: 0.34871   3rd Qu.: 1.957   3rd Qu.: 1.4982  \n",
       " Max.   :18.10714   Max.   :25.588   Max.   :19.6274  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arima_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "985e6045-5bef-4655-8d62-aa52992234d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       ME                RMSE             MAE         \n",
       " Min.   :-5.56427   Min.   : 0.000   Min.   : 0.0000  \n",
       " 1st Qu.:-0.09409   1st Qu.: 0.672   1st Qu.: 0.5326  \n",
       " Median : 0.07435   Median : 1.134   Median : 0.8709  \n",
       " Mean   : 0.26161   Mean   : 1.827   Mean   : 1.3997  \n",
       " 3rd Qu.: 0.34871   3rd Qu.: 1.957   3rd Qu.: 1.4982  \n",
       " Max.   :18.10714   Max.   :25.588   Max.   :19.6274  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arima_rounded_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "068e46d7-fd64-4168-9dea-ceb9fefa0b30",
   "metadata": {},
   "outputs": [],
   "source": [
    "ETS_flat <- to_forecast %>% \n",
    "model(ETS(value)) %>% forecast(h=28)\n",
    "ETS_flat$.mean <- ceiling(ETS_flat$.mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "6375f0ac-20bb-455a-aca2-e4ab3229d578",
   "metadata": {},
   "outputs": [],
   "source": [
    "ETS_flat_results <- summary(accuracy(ETS_flat,sales_val_long) %>% select(ME:MAE))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "bb8137b1-fc07-455e-a5f1-f46ee298a276",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "       ME                 RMSE              MAE         \n",
       " Min.   :-18.63255   Min.   : 0.0000   Min.   : 0.0000  \n",
       " 1st Qu.: -0.09992   1st Qu.: 0.6584   1st Qu.: 0.5395  \n",
       " Median :  0.06525   Median : 1.1288   Median : 0.8665  \n",
       " Mean   :  0.20009   Mean   : 1.8890   Mean   : 1.4639  \n",
       " 3rd Qu.:  0.34544   3rd Qu.: 1.9765   3rd Qu.: 1.5012  \n",
       " Max.   : 18.10714   Max.   :25.5422   Max.   :20.0569  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ETS_flat_results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "38f8d5e2-a082-48cd-b195-2baccbb05aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "FOODS_3_001 =arima_rounded2 %>% filter(id == \"FOODS_3_109\") "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "00b985fb-f51c-4387-8c12-1236560dad7a",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Plot variable not specified, automatically selected `.vars = .mean`\n"
     ]
    },
    {
     "data": {
      "image/png": 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pbcb7O33S53jN4pcrdUehvus86lLLnfxnBn0/ROkbul0ttwn3UuZcn9Jrvb7XLH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\nQmCC",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 1500
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "autoplot(as_tsibble(FOODS_3_001, index=name, key=id))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "34cd750a-c1ff-4056-bf6f-91632a8b0d56",
   "metadata": {},
   "outputs": [],
   "source": [
    "arima_rounded$id <- paste0(arima_rounded$id,\"_TX_3_validation\")\n",
    "write.csv2(arima_rounded, \"ARIMA_submission.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "84a52fc9-940e-4109-876b-f1338667bfb4",
   "metadata": {},
   "outputs": [],
   "source": [
    "arima_sub <- arima_rounded[c('id','name','.mean')]\n",
    "arima_sub <- arima_sub %>% pivot_wider(names_from = name, values_from = .mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "d3c81a3b-af1b-4eeb-b3cc-6ec9187d66d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "ETS_flat$id <- paste0(ETS_flat$id,\"_TX_3_validation\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "4eae26cb-772d-4082-b0e6-f8c8d9a67f4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "ETS_sub <- ETS_flat[c('id','name','.mean')]\n",
    "ETS_sub <- ETS_sub %>% pivot_wider(names_from = name, values_from = .mean)\n",
    "\n",
    "write.csv2(ETS_sub, \"ETS_Flat_submission.csv\")"
   ]
  }
 ],
 "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": "4.4.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}