Download Fundamental Data Yahoo Finance.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "shhh <- suppressPackageStartupMessages\n",
    "\n",
    "# A function to collect fundamental data from Yahoo finance (Income Statement, Balance Sheet and Cash Flow statements)\n",
    "\n",
    "getFin <- function(stock){\n",
    "  if (\"rvest\" %in% installed.packages()) {\n",
    "    library(rvest)\n",
    "    }else{\n",
    "      install.packages(\"rvest\")\n",
    "      library(rvest)\n",
    "      }\n",
    "  for (i in 1:length(stock)) {\n",
    "    tryCatch(\n",
    "      {\n",
    "        # Collect the Income Statement data\n",
    "        link <- \"https://finance.yahoo.com/quote/\"\n",
    "        link <- paste0(link, stock[i], \"/financials?p=\", stock[i])\n",
    "        wahis.session <- html_session(link)\n",
    "        p <- wahis.session %>%\n",
    "          html_nodes(xpath = '//*[@id=\"Col1-1-Financials-Proxy\"]/section/div[3]/table')%>%\n",
    "          html_table(fill = TRUE)\n",
    "        IncomeStatement <- p[[1]]\n",
    "        colnames(IncomeStatement) <- paste(IncomeStatement[1,])\n",
    "        IncomeStatement <- IncomeStatement[-c(1,5,12,20,25),]\n",
    "        names_row <- paste(IncomeStatement[,1])\n",
    "        IncomeStatement <- IncomeStatement[,-1]\n",
    "        IncomeStatement <- apply(IncomeStatement, 2, function(x){gsub(\",\",\"\",x)})\n",
    "        IncomeStatement <- as.data.frame(apply(IncomeStatement, 2, as.numeric))\n",
    "        rownames(IncomeStatement) <- paste(names_row)\n",
    "        temp1 <- IncomeStatement\n",
    "        \n",
    "        # Collect the Balance Sheet data\n",
    "        link <- \"https://finance.yahoo.com/quote/\"\n",
    "        link <- paste0(link, stock[i],\"/balance-sheet?p=\", stock[i])\n",
    "        wahis.session <- html_session(link)\n",
    "        p <- wahis.session %>%\n",
    "          html_nodes(xpath = '//*[@id=\"Col1-1-Financials-Proxy\"]/section/div[3]/table')%>%\n",
    "          html_table(fill = TRUE)\n",
    "        BalanceSheet <- p[[1]]\n",
    "        colnames(BalanceSheet) <- BalanceSheet[1,]\n",
    "        BalanceSheet <- BalanceSheet[-c(1,2,17,28),]\n",
    "        names_row <- BalanceSheet[,1]\n",
    "        BalanceSheet <- BalanceSheet[,-1]\n",
    "        BalanceSheet <- apply(BalanceSheet, 2, function(x){gsub(\",\",\"\",x)})\n",
    "        BalanceSheet <- as.data.frame(apply(BalanceSheet, 2, as.numeric))\n",
    "        rownames(BalanceSheet) <- paste(names_row)\n",
    "        temp2 <- BalanceSheet\n",
    "        \n",
    "        # Collect the Cash Flow data\n",
    "        link <- \"https://finance.yahoo.com/quote/\"\n",
    "        link <- paste0(link, stock[i], \"/cash-flow?p=\", stock[i])\n",
    "        wahis.session <- html_session(link)\n",
    "        p <- wahis.session %>%\n",
    "          html_nodes(xpath = '//*[@id=\"Col1-1-Financials-Proxy\"]/section/div[3]/table')%>%\n",
    "          html_table(fill = TRUE)\n",
    "        CashFlow <- p[[1]]\n",
    "        colnames(CashFlow) <- CashFlow[1,]\n",
    "        CashFlow <- CashFlow[-c(1,3,11,16),]\n",
    "        names_row <- CashFlow[,1]\n",
    "        CashFlow <- CashFlow[,-1]\n",
    "        CashFlow <- apply(CashFlow, 2, function(x){gsub(\",\",\"\",x)})\n",
    "        CashFlow <- as.data.frame(apply(CashFlow, 2, as.numeric))\n",
    "        rownames(CashFlow) <- paste(names_row)\n",
    "        temp3 <- CashFlow\n",
    "        \n",
    "        assign(paste0(stock[i],'.f'),value = list(IncomeStatement = temp1, BalanceSheet = temp2, CashFlow = temp3), envir = parent.frame())\n",
    "        },\n",
    "      error = function(cond){\n",
    "        message(stock[i], \"Give error \",cond)\n",
    "        }\n",
    "      )\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>HOG.f.12.31.2018</th><th scope=col>HOG.f.12.31.2017</th><th scope=col>HOG.f.12.31.2016</th><th scope=col>HOG.f.12.31.2015</th><th scope=col>MSFT.f.6.30.2019</th><th scope=col>MSFT.f.6.30.2018</th><th scope=col>MSFT.f.6.30.2017</th><th scope=col>MSFT.f.6.30.2016</th><th scope=col>GOOG.f.12.31.2018</th><th scope=col>GOOG.f.12.31.2017</th><th scope=col>GOOG.f.12.31.2016</th><th scope=col>GOOG.f.12.31.2015</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Total Revenue</th><td>5716875  </td><td>5647224  </td><td>5996458  </td><td>5995402  </td><td>125843000</td><td>110360000</td><td>96571000 </td><td>91154000 </td><td>136819000</td><td>110855000</td><td>90272000 </td><td>74989000 </td></tr>\n",
       "\t<tr><th scope=row>Cost of Revenue</th><td>3803576  </td><td>3739199  </td><td>3885527  </td><td>3772841  </td><td> 42910000</td><td> 38353000</td><td>34261000 </td><td>32780000 </td><td> 59549000</td><td> 45583000</td><td>35138000 </td><td>28164000 </td></tr>\n",
       "\t<tr><th scope=row>Gross Profit</th><td>1913299  </td><td>1908025  </td><td>2110931  </td><td>2222561  </td><td> 82933000</td><td> 72007000</td><td>62310000 </td><td>58374000 </td><td> 77270000</td><td> 65272000</td><td>55134000 </td><td>46825000 </td></tr>\n",
       "\t<tr><th scope=row>Research Development</th><td>     NA  </td><td>     NA  </td><td>     NA  </td><td>     NA  </td><td> 16876000</td><td> 14726000</td><td>13037000 </td><td>11988000 </td><td> 21419000</td><td> 16625000</td><td>13948000 </td><td>12282000 </td></tr>\n",
       "\t<tr><th scope=row>Selling General and Administrative</th><td>1090436  </td><td>1016761  </td><td>1061995  </td><td>1040266  </td><td> 23098000</td><td> 22223000</td><td>19942000 </td><td>19198000 </td><td> 24459000</td><td> 19765000</td><td>17470000 </td><td>15183000 </td></tr>\n",
       "\t<tr><th scope=row>Non Recurring</th><td>     NA  </td><td>     NA  </td><td>     NA  </td><td>     NA  </td><td>       NA</td><td>       NA</td><td>      NA </td><td>      NA </td><td>       NA</td><td>       NA</td><td>      NA </td><td>      NA </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & HOG.f.12.31.2018 & HOG.f.12.31.2017 & HOG.f.12.31.2016 & HOG.f.12.31.2015 & MSFT.f.6.30.2019 & MSFT.f.6.30.2018 & MSFT.f.6.30.2017 & MSFT.f.6.30.2016 & GOOG.f.12.31.2018 & GOOG.f.12.31.2017 & GOOG.f.12.31.2016 & GOOG.f.12.31.2015\\\\\n",
       "\\hline\n",
       "\tTotal Revenue & 5716875   & 5647224   & 5996458   & 5995402   & 125843000 & 110360000 & 96571000  & 91154000  & 136819000 & 110855000 & 90272000  & 74989000 \\\\\n",
       "\tCost of Revenue & 3803576   & 3739199   & 3885527   & 3772841   &  42910000 &  38353000 & 34261000  & 32780000  &  59549000 &  45583000 & 35138000  & 28164000 \\\\\n",
       "\tGross Profit & 1913299   & 1908025   & 2110931   & 2222561   &  82933000 &  72007000 & 62310000  & 58374000  &  77270000 &  65272000 & 55134000  & 46825000 \\\\\n",
       "\tResearch Development &      NA   &      NA   &      NA   &      NA   &  16876000 &  14726000 & 13037000  & 11988000  &  21419000 &  16625000 & 13948000  & 12282000 \\\\\n",
       "\tSelling General and Administrative & 1090436   & 1016761   & 1061995   & 1040266   &  23098000 &  22223000 & 19942000  & 19198000  &  24459000 &  19765000 & 17470000  & 15183000 \\\\\n",
       "\tNon Recurring &      NA   &      NA   &      NA   &      NA   &        NA &        NA &       NA  &       NA  &        NA &        NA &       NA  &       NA \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | HOG.f.12.31.2018 | HOG.f.12.31.2017 | HOG.f.12.31.2016 | HOG.f.12.31.2015 | MSFT.f.6.30.2019 | MSFT.f.6.30.2018 | MSFT.f.6.30.2017 | MSFT.f.6.30.2016 | GOOG.f.12.31.2018 | GOOG.f.12.31.2017 | GOOG.f.12.31.2016 | GOOG.f.12.31.2015 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Total Revenue | 5716875   | 5647224   | 5996458   | 5995402   | 125843000 | 110360000 | 96571000  | 91154000  | 136819000 | 110855000 | 90272000  | 74989000  |\n",
       "| Cost of Revenue | 3803576   | 3739199   | 3885527   | 3772841   |  42910000 |  38353000 | 34261000  | 32780000  |  59549000 |  45583000 | 35138000  | 28164000  |\n",
       "| Gross Profit | 1913299   | 1908025   | 2110931   | 2222561   |  82933000 |  72007000 | 62310000  | 58374000  |  77270000 |  65272000 | 55134000  | 46825000  |\n",
       "| Research Development |      NA   |      NA   |      NA   |      NA   |  16876000 |  14726000 | 13037000  | 11988000  |  21419000 |  16625000 | 13948000  | 12282000  |\n",
       "| Selling General and Administrative | 1090436   | 1016761   | 1061995   | 1040266   |  23098000 |  22223000 | 19942000  | 19198000  |  24459000 |  19765000 | 17470000  | 15183000  |\n",
       "| Non Recurring |      NA   |      NA   |      NA   |      NA   |        NA |        NA |       NA  |       NA  |        NA |        NA |       NA  |       NA  |\n",
       "\n"
      ],
      "text/plain": [
       "                                   HOG.f.12.31.2018 HOG.f.12.31.2017\n",
       "Total Revenue                      5716875          5647224         \n",
       "Cost of Revenue                    3803576          3739199         \n",
       "Gross Profit                       1913299          1908025         \n",
       "Research Development                    NA               NA         \n",
       "Selling General and Administrative 1090436          1016761         \n",
       "Non Recurring                           NA               NA         \n",
       "                                   HOG.f.12.31.2016 HOG.f.12.31.2015\n",
       "Total Revenue                      5996458          5995402         \n",
       "Cost of Revenue                    3885527          3772841         \n",
       "Gross Profit                       2110931          2222561         \n",
       "Research Development                    NA               NA         \n",
       "Selling General and Administrative 1061995          1040266         \n",
       "Non Recurring                           NA               NA         \n",
       "                                   MSFT.f.6.30.2019 MSFT.f.6.30.2018\n",
       "Total Revenue                      125843000        110360000       \n",
       "Cost of Revenue                     42910000         38353000       \n",
       "Gross Profit                        82933000         72007000       \n",
       "Research Development                16876000         14726000       \n",
       "Selling General and Administrative  23098000         22223000       \n",
       "Non Recurring                             NA               NA       \n",
       "                                   MSFT.f.6.30.2017 MSFT.f.6.30.2016\n",
       "Total Revenue                      96571000         91154000        \n",
       "Cost of Revenue                    34261000         32780000        \n",
       "Gross Profit                       62310000         58374000        \n",
       "Research Development               13037000         11988000        \n",
       "Selling General and Administrative 19942000         19198000        \n",
       "Non Recurring                            NA               NA        \n",
       "                                   GOOG.f.12.31.2018 GOOG.f.12.31.2017\n",
       "Total Revenue                      136819000         110855000        \n",
       "Cost of Revenue                     59549000          45583000        \n",
       "Gross Profit                        77270000          65272000        \n",
       "Research Development                21419000          16625000        \n",
       "Selling General and Administrative  24459000          19765000        \n",
       "Non Recurring                             NA                NA        \n",
       "                                   GOOG.f.12.31.2016 GOOG.f.12.31.2015\n",
       "Total Revenue                      90272000          74989000         \n",
       "Cost of Revenue                    35138000          28164000         \n",
       "Gross Profit                       55134000          46825000         \n",
       "Research Development               13948000          12282000         \n",
       "Selling General and Administrative 17470000          15183000         \n",
       "Non Recurring                            NA                NA         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>HOG.f.12.31.2018</th><th scope=col>HOG.f.12.31.2017</th><th scope=col>HOG.f.12.31.2016</th><th scope=col>HOG.f.12.31.2015</th><th scope=col>MSFT.f.6.30.2019</th><th scope=col>MSFT.f.6.30.2018</th><th scope=col>MSFT.f.6.30.2017</th><th scope=col>MSFT.f.6.30.2016</th><th scope=col>GOOG.f.12.31.2018</th><th scope=col>GOOG.f.12.31.2017</th><th scope=col>GOOG.f.12.31.2016</th><th scope=col>GOOG.f.12.31.2015</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Cash And Cash Equivalents</th><td> 544548  </td><td> 338186  </td><td> 425540  </td><td> 400443  </td><td> 11356000</td><td> 11946000</td><td>  7663000</td><td>  6510000</td><td> 16701000</td><td> 10715000</td><td> 12918000</td><td>15409000 </td></tr>\n",
       "\t<tr><th scope=row>Short Term Investments</th><td>  10007  </td><td>     NA  </td><td>   5519  </td><td>  45192  </td><td>122476000</td><td>121718000</td><td>125238000</td><td>106531000</td><td> 92439000</td><td> 91156000</td><td> 73415000</td><td>56517000 </td></tr>\n",
       "\t<tr><th scope=row>Net Receivables</th><td> 306474  </td><td> 329986  </td><td> 285106  </td><td> 247405  </td><td> 29524000</td><td> 26481000</td><td> 22431000</td><td> 18277000</td><td> 21193000</td><td> 18705000</td><td> 15632000</td><td>13459000 </td></tr>\n",
       "\t<tr><th scope=row>Inventory</th><td> 556128  </td><td> 538202  </td><td> 499917  </td><td> 585907  </td><td>  2063000</td><td>  2662000</td><td>  2181000</td><td>  2251000</td><td>  1107000</td><td>   749000</td><td>   268000</td><td>  491000 </td></tr>\n",
       "\t<tr><th scope=row>Other Current Assets</th><td> 124168  </td><td> 156253  </td><td> 155191  </td><td> 171171  </td><td> 10133000</td><td>  6855000</td><td>  5183000</td><td>  6091000</td><td>  4236000</td><td>  2983000</td><td>  3175000</td><td> 1590000 </td></tr>\n",
       "\t<tr><th scope=row>Total Current Assets</th><td>4484442  </td><td>3884742  </td><td>3853852  </td><td>3977883  </td><td>175552000</td><td>169662000</td><td>162696000</td><td>139660000</td><td>135676000</td><td>124308000</td><td>105408000</td><td>90114000 </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & HOG.f.12.31.2018 & HOG.f.12.31.2017 & HOG.f.12.31.2016 & HOG.f.12.31.2015 & MSFT.f.6.30.2019 & MSFT.f.6.30.2018 & MSFT.f.6.30.2017 & MSFT.f.6.30.2016 & GOOG.f.12.31.2018 & GOOG.f.12.31.2017 & GOOG.f.12.31.2016 & GOOG.f.12.31.2015\\\\\n",
       "\\hline\n",
       "\tCash And Cash Equivalents &  544548   &  338186   &  425540   &  400443   &  11356000 &  11946000 &   7663000 &   6510000 &  16701000 &  10715000 &  12918000 & 15409000 \\\\\n",
       "\tShort Term Investments &   10007   &      NA   &    5519   &   45192   & 122476000 & 121718000 & 125238000 & 106531000 &  92439000 &  91156000 &  73415000 & 56517000 \\\\\n",
       "\tNet Receivables &  306474   &  329986   &  285106   &  247405   &  29524000 &  26481000 &  22431000 &  18277000 &  21193000 &  18705000 &  15632000 & 13459000 \\\\\n",
       "\tInventory &  556128   &  538202   &  499917   &  585907   &   2063000 &   2662000 &   2181000 &   2251000 &   1107000 &    749000 &    268000 &   491000 \\\\\n",
       "\tOther Current Assets &  124168   &  156253   &  155191   &  171171   &  10133000 &   6855000 &   5183000 &   6091000 &   4236000 &   2983000 &   3175000 &  1590000 \\\\\n",
       "\tTotal Current Assets & 4484442   & 3884742   & 3853852   & 3977883   & 175552000 & 169662000 & 162696000 & 139660000 & 135676000 & 124308000 & 105408000 & 90114000 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | HOG.f.12.31.2018 | HOG.f.12.31.2017 | HOG.f.12.31.2016 | HOG.f.12.31.2015 | MSFT.f.6.30.2019 | MSFT.f.6.30.2018 | MSFT.f.6.30.2017 | MSFT.f.6.30.2016 | GOOG.f.12.31.2018 | GOOG.f.12.31.2017 | GOOG.f.12.31.2016 | GOOG.f.12.31.2015 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Cash And Cash Equivalents |  544548   |  338186   |  425540   |  400443   |  11356000 |  11946000 |   7663000 |   6510000 |  16701000 |  10715000 |  12918000 | 15409000  |\n",
       "| Short Term Investments |   10007   |      NA   |    5519   |   45192   | 122476000 | 121718000 | 125238000 | 106531000 |  92439000 |  91156000 |  73415000 | 56517000  |\n",
       "| Net Receivables |  306474   |  329986   |  285106   |  247405   |  29524000 |  26481000 |  22431000 |  18277000 |  21193000 |  18705000 |  15632000 | 13459000  |\n",
       "| Inventory |  556128   |  538202   |  499917   |  585907   |   2063000 |   2662000 |   2181000 |   2251000 |   1107000 |    749000 |    268000 |   491000  |\n",
       "| Other Current Assets |  124168   |  156253   |  155191   |  171171   |  10133000 |   6855000 |   5183000 |   6091000 |   4236000 |   2983000 |   3175000 |  1590000  |\n",
       "| Total Current Assets | 4484442   | 3884742   | 3853852   | 3977883   | 175552000 | 169662000 | 162696000 | 139660000 | 135676000 | 124308000 | 105408000 | 90114000  |\n",
       "\n"
      ],
      "text/plain": [
       "                          HOG.f.12.31.2018 HOG.f.12.31.2017 HOG.f.12.31.2016\n",
       "Cash And Cash Equivalents  544548           338186           425540         \n",
       "Short Term Investments      10007               NA             5519         \n",
       "Net Receivables            306474           329986           285106         \n",
       "Inventory                  556128           538202           499917         \n",
       "Other Current Assets       124168           156253           155191         \n",
       "Total Current Assets      4484442          3884742          3853852         \n",
       "                          HOG.f.12.31.2015 MSFT.f.6.30.2019 MSFT.f.6.30.2018\n",
       "Cash And Cash Equivalents  400443           11356000         11946000       \n",
       "Short Term Investments      45192          122476000        121718000       \n",
       "Net Receivables            247405           29524000         26481000       \n",
       "Inventory                  585907            2063000          2662000       \n",
       "Other Current Assets       171171           10133000          6855000       \n",
       "Total Current Assets      3977883          175552000        169662000       \n",
       "                          MSFT.f.6.30.2017 MSFT.f.6.30.2016 GOOG.f.12.31.2018\n",
       "Cash And Cash Equivalents   7663000          6510000         16701000        \n",
       "Short Term Investments    125238000        106531000         92439000        \n",
       "Net Receivables            22431000         18277000         21193000        \n",
       "Inventory                   2181000          2251000          1107000        \n",
       "Other Current Assets        5183000          6091000          4236000        \n",
       "Total Current Assets      162696000        139660000        135676000        \n",
       "                          GOOG.f.12.31.2017 GOOG.f.12.31.2016 GOOG.f.12.31.2015\n",
       "Cash And Cash Equivalents  10715000          12918000         15409000         \n",
       "Short Term Investments     91156000          73415000         56517000         \n",
       "Net Receivables            18705000          15632000         13459000         \n",
       "Inventory                    749000            268000           491000         \n",
       "Other Current Assets        2983000           3175000          1590000         \n",
       "Total Current Assets      124308000         105408000         90114000         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>HOG.f.12.31.2018</th><th scope=col>HOG.f.12.31.2017</th><th scope=col>HOG.f.12.31.2016</th><th scope=col>HOG.f.12.31.2015</th><th scope=col>MSFT.f.6.30.2019</th><th scope=col>MSFT.f.6.30.2018</th><th scope=col>MSFT.f.6.30.2017</th><th scope=col>MSFT.f.6.30.2016</th><th scope=col>GOOG.f.12.31.2018</th><th scope=col>GOOG.f.12.31.2017</th><th scope=col>GOOG.f.12.31.2016</th><th scope=col>GOOG.f.12.31.2015</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Net Income</th><td>531451  </td><td>521759  </td><td>692164  </td><td> 752207 </td><td>39240000</td><td>16571000</td><td>25489000</td><td>20539000</td><td>30736000</td><td>12662000</td><td>19478000</td><td>16348000</td></tr>\n",
       "\t<tr><th scope=row>Depreciation</th><td>230209  </td><td>222188  </td><td>209555  </td><td> 198074 </td><td>11600000</td><td> 9900000</td><td> 7800000</td><td> 5878000</td><td> 9029000</td><td> 6899000</td><td> 6100000</td><td> 5024000</td></tr>\n",
       "\t<tr><th scope=row>Adjustments To Net Income</th><td>150035  </td><td>226144  </td><td>131961  </td><td>  54670 </td><td>-2521000</td><td>-3054000</td><td> 1342000</td><td> 6229000</td><td> 3298000</td><td> 8284000</td><td> 7158000</td><td> 5609000</td></tr>\n",
       "\t<tr><th scope=row>Changes In Accounts Receivables</th><td>  9143  </td><td>-18149  </td><td>-45934  </td><td> -13665 </td><td>-2812000</td><td>-3862000</td><td>-1216000</td><td>  562000</td><td>-2169000</td><td>-3768000</td><td>-2578000</td><td>-2094000</td></tr>\n",
       "\t<tr><th scope=row>Changes In Liabilities</th><td>196192  </td><td> 10128  </td><td> 38237  </td><td> 138823 </td><td> 4694000</td><td> 7070000</td><td> 3901000</td><td> 2653000</td><td> 1438000</td><td> 1121000</td><td>  333000</td><td>  246000</td></tr>\n",
       "\t<tr><th scope=row>Changes In Inventories</th><td>-31059  </td><td>-20584  </td><td> 85072  </td><td>-155222 </td><td>  597000</td><td> -465000</td><td>   50000</td><td>  600000</td><td>      NA</td><td>      NA</td><td>      NA</td><td>      NA</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & HOG.f.12.31.2018 & HOG.f.12.31.2017 & HOG.f.12.31.2016 & HOG.f.12.31.2015 & MSFT.f.6.30.2019 & MSFT.f.6.30.2018 & MSFT.f.6.30.2017 & MSFT.f.6.30.2016 & GOOG.f.12.31.2018 & GOOG.f.12.31.2017 & GOOG.f.12.31.2016 & GOOG.f.12.31.2015\\\\\n",
       "\\hline\n",
       "\tNet Income & 531451   & 521759   & 692164   &  752207  & 39240000 & 16571000 & 25489000 & 20539000 & 30736000 & 12662000 & 19478000 & 16348000\\\\\n",
       "\tDepreciation & 230209   & 222188   & 209555   &  198074  & 11600000 &  9900000 &  7800000 &  5878000 &  9029000 &  6899000 &  6100000 &  5024000\\\\\n",
       "\tAdjustments To Net Income & 150035   & 226144   & 131961   &   54670  & -2521000 & -3054000 &  1342000 &  6229000 &  3298000 &  8284000 &  7158000 &  5609000\\\\\n",
       "\tChanges In Accounts Receivables &   9143   & -18149   & -45934   &  -13665  & -2812000 & -3862000 & -1216000 &   562000 & -2169000 & -3768000 & -2578000 & -2094000\\\\\n",
       "\tChanges In Liabilities & 196192   &  10128   &  38237   &  138823  &  4694000 &  7070000 &  3901000 &  2653000 &  1438000 &  1121000 &   333000 &   246000\\\\\n",
       "\tChanges In Inventories & -31059   & -20584   &  85072   & -155222  &   597000 &  -465000 &    50000 &   600000 &       NA &       NA &       NA &       NA\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | HOG.f.12.31.2018 | HOG.f.12.31.2017 | HOG.f.12.31.2016 | HOG.f.12.31.2015 | MSFT.f.6.30.2019 | MSFT.f.6.30.2018 | MSFT.f.6.30.2017 | MSFT.f.6.30.2016 | GOOG.f.12.31.2018 | GOOG.f.12.31.2017 | GOOG.f.12.31.2016 | GOOG.f.12.31.2015 |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| Net Income | 531451   | 521759   | 692164   |  752207  | 39240000 | 16571000 | 25489000 | 20539000 | 30736000 | 12662000 | 19478000 | 16348000 |\n",
       "| Depreciation | 230209   | 222188   | 209555   |  198074  | 11600000 |  9900000 |  7800000 |  5878000 |  9029000 |  6899000 |  6100000 |  5024000 |\n",
       "| Adjustments To Net Income | 150035   | 226144   | 131961   |   54670  | -2521000 | -3054000 |  1342000 |  6229000 |  3298000 |  8284000 |  7158000 |  5609000 |\n",
       "| Changes In Accounts Receivables |   9143   | -18149   | -45934   |  -13665  | -2812000 | -3862000 | -1216000 |   562000 | -2169000 | -3768000 | -2578000 | -2094000 |\n",
       "| Changes In Liabilities | 196192   |  10128   |  38237   |  138823  |  4694000 |  7070000 |  3901000 |  2653000 |  1438000 |  1121000 |   333000 |   246000 |\n",
       "| Changes In Inventories | -31059   | -20584   |  85072   | -155222  |   597000 |  -465000 |    50000 |   600000 |       NA |       NA |       NA |       NA |\n",
       "\n"
      ],
      "text/plain": [
       "                                HOG.f.12.31.2018 HOG.f.12.31.2017\n",
       "Net Income                      531451           521759          \n",
       "Depreciation                    230209           222188          \n",
       "Adjustments To Net Income       150035           226144          \n",
       "Changes In Accounts Receivables   9143           -18149          \n",
       "Changes In Liabilities          196192            10128          \n",
       "Changes In Inventories          -31059           -20584          \n",
       "                                HOG.f.12.31.2016 HOG.f.12.31.2015\n",
       "Net Income                      692164            752207         \n",
       "Depreciation                    209555            198074         \n",
       "Adjustments To Net Income       131961             54670         \n",
       "Changes In Accounts Receivables -45934            -13665         \n",
       "Changes In Liabilities           38237            138823         \n",
       "Changes In Inventories           85072           -155222         \n",
       "                                MSFT.f.6.30.2019 MSFT.f.6.30.2018\n",
       "Net Income                      39240000         16571000        \n",
       "Depreciation                    11600000          9900000        \n",
       "Adjustments To Net Income       -2521000         -3054000        \n",
       "Changes In Accounts Receivables -2812000         -3862000        \n",
       "Changes In Liabilities           4694000          7070000        \n",
       "Changes In Inventories            597000          -465000        \n",
       "                                MSFT.f.6.30.2017 MSFT.f.6.30.2016\n",
       "Net Income                      25489000         20539000        \n",
       "Depreciation                     7800000          5878000        \n",
       "Adjustments To Net Income        1342000          6229000        \n",
       "Changes In Accounts Receivables -1216000           562000        \n",
       "Changes In Liabilities           3901000          2653000        \n",
       "Changes In Inventories             50000           600000        \n",
       "                                GOOG.f.12.31.2018 GOOG.f.12.31.2017\n",
       "Net Income                      30736000          12662000         \n",
       "Depreciation                     9029000           6899000         \n",
       "Adjustments To Net Income        3298000           8284000         \n",
       "Changes In Accounts Receivables -2169000          -3768000         \n",
       "Changes In Liabilities           1438000           1121000         \n",
       "Changes In Inventories                NA                NA         \n",
       "                                GOOG.f.12.31.2016 GOOG.f.12.31.2015\n",
       "Net Income                      19478000          16348000         \n",
       "Depreciation                     6100000           5024000         \n",
       "Adjustments To Net Income        7158000           5609000         \n",
       "Changes In Accounts Receivables -2578000          -2094000         \n",
       "Changes In Liabilities            333000            246000         \n",
       "Changes In Inventories                NA                NA         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Select the tickers we wish to collect\n",
    "symbols <- c(\"GOOG\", \"MSFT\", \"HOG\")\n",
    "\n",
    "# Apply the function\n",
    "suppressWarnings(tryCatch(getFin(symbols)))\n",
    "\n",
    "symbols.f <- sapply(symbols, function(x) { paste0(x, \".f\") })\n",
    "\n",
    "tickers <- list2env(mget(symbols.f))\n",
    "\n",
    "IS <- lapply(tickers, \"[[\", \"IncomeStatement\")\n",
    "BS <- lapply(tickers, \"[[\", \"BalanceSheet\")\n",
    "CF <- lapply(tickers, \"[[\", \"CashFlow\")\n",
    "\n",
    "IS <- as.data.frame(IS)\n",
    "BS <- as.data.frame(BS)\n",
    "CF <- as.data.frame(CF)\n",
    "\n",
    "head(IS)\n",
    "head(BS)\n",
    "head(CF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>symbol</th><th scope=col>year</th><th scope=col>Total.Revenue</th><th scope=col>Cost.of.Revenue</th><th scope=col>Gross.Profit</th><th scope=col>Research.Development</th><th scope=col>Selling.General.and.Administrative</th><th scope=col>Non.Recurring</th><th scope=col>Others</th><th scope=col>Total.Operating.Expenses</th><th scope=col>...</th><th scope=col>Investments</th><th scope=col>Other.Cash.flows.from.Investing.Activities</th><th scope=col>Total.Cash.Flows.From.Investing.Activities</th><th scope=col>Dividends.Paid</th><th scope=col>Sale.Purchase.of.Stock</th><th scope=col>Net.Borrowings</th><th scope=col>Other.Cash.Flows.from.Financing.Activities</th><th scope=col>Total.Cash.Flows.From.Financing.Activities</th><th scope=col>Effect.Of.Exchange.Rate.Changes</th><th scope=col>Change.In.Cash.and.Cash.Equivalents</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>HOG      </td><td>2018     </td><td>  5716875</td><td> 3803576 </td><td> 1913299 </td><td>      NA </td><td> 1090436 </td><td>NA       </td><td>NA       </td><td> 4894012 </td><td>...      </td><td> -10007  </td><td>-11598   </td><td>  -662269</td><td>  -245810</td><td>NA       </td><td>   618128</td><td>     NA  </td><td>   -14763</td><td> -15351  </td><td> 513538  </td></tr>\n",
       "\t<tr><td>HOG      </td><td>2017     </td><td>  5647224</td><td> 3739199 </td><td> 1908025 </td><td>      NA </td><td> 1016761 </td><td>NA       </td><td>NA       </td><td> 4755960 </td><td>...      </td><td>   6916  </td><td>   547   </td><td>  -562468</td><td>  -251862</td><td>NA       </td><td>   155511</td><td>   8458  </td><td>  -550261</td><td>  26747  </td><td> -80921  </td></tr>\n",
       "\t<tr><td>HOG      </td><td>2016     </td><td>  5996458</td><td> 3885527 </td><td> 2110931 </td><td>      NA </td><td> 1061995 </td><td>NA       </td><td>NA       </td><td> 4947522 </td><td>...      </td><td>  40014  </td><td>   411   </td><td>  -392731</td><td>  -252321</td><td>NA       </td><td>   -78256</td><td>   2251  </td><td>  -777885</td><td>  -9443  </td><td>  -5720  </td></tr>\n",
       "\t<tr><td>HOG      </td><td>2015     </td><td>  5995402</td><td> 3772841 </td><td> 2222561 </td><td>      NA </td><td> 1040266 </td><td>NA       </td><td>NA       </td><td> 4813107 </td><td>...      </td><td>  11507  </td><td>  7474   </td><td>  -915848</td><td>  -249262</td><td>NA       </td><td>  1397161</td><td>  14878  </td><td>  -354064</td><td> -14677  </td><td>-184471  </td></tr>\n",
       "\t<tr><td>MSFT     </td><td>2019     </td><td>125843000</td><td>42910000 </td><td>82933000 </td><td>16876000 </td><td>23098000 </td><td>NA       </td><td>NA       </td><td>82884000 </td><td>...      </td><td> 540000  </td><td>    NA   </td><td>-15773000</td><td>-13811000</td><td>NA       </td><td> -4000000</td><td>-675000  </td><td>-36887000</td><td>-115000  </td><td>-590000  </td></tr>\n",
       "\t<tr><td>MSFT     </td><td>2018     </td><td>110360000</td><td>38353000 </td><td>72007000 </td><td>14726000 </td><td>22223000 </td><td>NA       </td><td>NA       </td><td>75302000 </td><td>...      </td><td>6557000  </td><td>-98000   </td><td> -6061000</td><td>-12699000</td><td>NA       </td><td>-10201000</td><td>-971000  </td><td>-33590000</td><td>  50000  </td><td>4283000  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
       " symbol & year & Total.Revenue & Cost.of.Revenue & Gross.Profit & Research.Development & Selling.General.and.Administrative & Non.Recurring & Others & Total.Operating.Expenses & ... & Investments & Other.Cash.flows.from.Investing.Activities & Total.Cash.Flows.From.Investing.Activities & Dividends.Paid & Sale.Purchase.of.Stock & Net.Borrowings & Other.Cash.Flows.from.Financing.Activities & Total.Cash.Flows.From.Financing.Activities & Effect.Of.Exchange.Rate.Changes & Change.In.Cash.and.Cash.Equivalents\\\\\n",
       "\\hline\n",
       "\t HOG       & 2018      &   5716875 &  3803576  &  1913299  &       NA  &  1090436  & NA        & NA        &  4894012  & ...       &  -10007   & -11598    &   -662269 &   -245810 & NA        &    618128 &      NA   &    -14763 &  -15351   &  513538  \\\\\n",
       "\t HOG       & 2017      &   5647224 &  3739199  &  1908025  &       NA  &  1016761  & NA        & NA        &  4755960  & ...       &    6916   &    547    &   -562468 &   -251862 & NA        &    155511 &    8458   &   -550261 &   26747   &  -80921  \\\\\n",
       "\t HOG       & 2016      &   5996458 &  3885527  &  2110931  &       NA  &  1061995  & NA        & NA        &  4947522  & ...       &   40014   &    411    &   -392731 &   -252321 & NA        &    -78256 &    2251   &   -777885 &   -9443   &   -5720  \\\\\n",
       "\t HOG       & 2015      &   5995402 &  3772841  &  2222561  &       NA  &  1040266  & NA        & NA        &  4813107  & ...       &   11507   &   7474    &   -915848 &   -249262 & NA        &   1397161 &   14878   &   -354064 &  -14677   & -184471  \\\\\n",
       "\t MSFT      & 2019      & 125843000 & 42910000  & 82933000  & 16876000  & 23098000  & NA        & NA        & 82884000  & ...       &  540000   &     NA    & -15773000 & -13811000 & NA        &  -4000000 & -675000   & -36887000 & -115000   & -590000  \\\\\n",
       "\t MSFT      & 2018      & 110360000 & 38353000  & 72007000  & 14726000  & 22223000  & NA        & NA        & 75302000  & ...       & 6557000   & -98000    &  -6061000 & -12699000 & NA        & -10201000 & -971000   & -33590000 &   50000   & 4283000  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| symbol | year | Total.Revenue | Cost.of.Revenue | Gross.Profit | Research.Development | Selling.General.and.Administrative | Non.Recurring | Others | Total.Operating.Expenses | ... | Investments | Other.Cash.flows.from.Investing.Activities | Total.Cash.Flows.From.Investing.Activities | Dividends.Paid | Sale.Purchase.of.Stock | Net.Borrowings | Other.Cash.Flows.from.Financing.Activities | Total.Cash.Flows.From.Financing.Activities | Effect.Of.Exchange.Rate.Changes | Change.In.Cash.and.Cash.Equivalents |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| HOG       | 2018      |   5716875 |  3803576  |  1913299  |       NA  |  1090436  | NA        | NA        |  4894012  | ...       |  -10007   | -11598    |   -662269 |   -245810 | NA        |    618128 |      NA   |    -14763 |  -15351   |  513538   |\n",
       "| HOG       | 2017      |   5647224 |  3739199  |  1908025  |       NA  |  1016761  | NA        | NA        |  4755960  | ...       |    6916   |    547    |   -562468 |   -251862 | NA        |    155511 |    8458   |   -550261 |   26747   |  -80921   |\n",
       "| HOG       | 2016      |   5996458 |  3885527  |  2110931  |       NA  |  1061995  | NA        | NA        |  4947522  | ...       |   40014   |    411    |   -392731 |   -252321 | NA        |    -78256 |    2251   |   -777885 |   -9443   |   -5720   |\n",
       "| HOG       | 2015      |   5995402 |  3772841  |  2222561  |       NA  |  1040266  | NA        | NA        |  4813107  | ...       |   11507   |   7474    |   -915848 |   -249262 | NA        |   1397161 |   14878   |   -354064 |  -14677   | -184471   |\n",
       "| MSFT      | 2019      | 125843000 | 42910000  | 82933000  | 16876000  | 23098000  | NA        | NA        | 82884000  | ...       |  540000   |     NA    | -15773000 | -13811000 | NA        |  -4000000 | -675000   | -36887000 | -115000   | -590000   |\n",
       "| MSFT      | 2018      | 110360000 | 38353000  | 72007000  | 14726000  | 22223000  | NA        | NA        | 75302000  | ...       | 6557000   | -98000    |  -6061000 | -12699000 | NA        | -10201000 | -971000   | -33590000 |   50000   | 4283000   |\n",
       "\n"
      ],
      "text/plain": [
       "  symbol year Total.Revenue Cost.of.Revenue Gross.Profit Research.Development\n",
       "1 HOG    2018   5716875      3803576         1913299           NA            \n",
       "2 HOG    2017   5647224      3739199         1908025           NA            \n",
       "3 HOG    2016   5996458      3885527         2110931           NA            \n",
       "4 HOG    2015   5995402      3772841         2222561           NA            \n",
       "5 MSFT   2019 125843000     42910000        82933000     16876000            \n",
       "6 MSFT   2018 110360000     38353000        72007000     14726000            \n",
       "  Selling.General.and.Administrative Non.Recurring Others\n",
       "1  1090436                           NA            NA    \n",
       "2  1016761                           NA            NA    \n",
       "3  1061995                           NA            NA    \n",
       "4  1040266                           NA            NA    \n",
       "5 23098000                           NA            NA    \n",
       "6 22223000                           NA            NA    \n",
       "  Total.Operating.Expenses ... Investments\n",
       "1  4894012                 ...  -10007    \n",
       "2  4755960                 ...    6916    \n",
       "3  4947522                 ...   40014    \n",
       "4  4813107                 ...   11507    \n",
       "5 82884000                 ...  540000    \n",
       "6 75302000                 ... 6557000    \n",
       "  Other.Cash.flows.from.Investing.Activities\n",
       "1 -11598                                    \n",
       "2    547                                    \n",
       "3    411                                    \n",
       "4   7474                                    \n",
       "5     NA                                    \n",
       "6 -98000                                    \n",
       "  Total.Cash.Flows.From.Investing.Activities Dividends.Paid\n",
       "1   -662269                                    -245810     \n",
       "2   -562468                                    -251862     \n",
       "3   -392731                                    -252321     \n",
       "4   -915848                                    -249262     \n",
       "5 -15773000                                  -13811000     \n",
       "6  -6061000                                  -12699000     \n",
       "  Sale.Purchase.of.Stock Net.Borrowings\n",
       "1 NA                        618128     \n",
       "2 NA                        155511     \n",
       "3 NA                        -78256     \n",
       "4 NA                       1397161     \n",
       "5 NA                      -4000000     \n",
       "6 NA                     -10201000     \n",
       "  Other.Cash.Flows.from.Financing.Activities\n",
       "1      NA                                   \n",
       "2    8458                                   \n",
       "3    2251                                   \n",
       "4   14878                                   \n",
       "5 -675000                                   \n",
       "6 -971000                                   \n",
       "  Total.Cash.Flows.From.Financing.Activities Effect.Of.Exchange.Rate.Changes\n",
       "1    -14763                                   -15351                        \n",
       "2   -550261                                    26747                        \n",
       "3   -777885                                    -9443                        \n",
       "4   -354064                                   -14677                        \n",
       "5 -36887000                                  -115000                        \n",
       "6 -33590000                                    50000                        \n",
       "  Change.In.Cash.and.Cash.Equivalents\n",
       "1  513538                            \n",
       "2  -80921                            \n",
       "3   -5720                            \n",
       "4 -184471                            \n",
       "5 -590000                            \n",
       "6 4283000                            "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Clean the data up a little bit\n",
    "ISBSCF <- rbind(IS, BS, CF)\n",
    "\n",
    "library(tibble)\n",
    "library(tidyr)\n",
    "\n",
    "ISBSCF <- ISBSCF %>%\n",
    "  t() %>%\n",
    "  data.frame() %>%\n",
    "  rownames_to_column('rn') %>%\n",
    "  separate(rn, into = c(\"symbol\", \"year\"),\n",
    "           sep = -4, convert = TRUE)\n",
    "\n",
    "\n",
    "ISBSCF$symbol <- gsub(\"(f).*\", \"\", ISBSCF$symbol)\n",
    "ISBSCF$symbol <- gsub('.$', '', ISBSCF$symbol)\n",
    "ISBSCF$symbol <- gsub('^X', '', ISBSCF$symbol)\n",
    "\n",
    "head(ISBSCF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>symbol</th><th scope=col>Trade.Time</th><th scope=col>Name</th><th scope=col>Volume</th><th scope=col>P.E.Ratio</th><th scope=col>Dividend.Yield</th><th scope=col>Shares.Outstanding</th><th scope=col>Price.Book</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>GOOG                 </td><td>2019-08-23 16:00:01  </td><td>Alphabet Inc.        </td><td> 1688271             </td><td>23.24195             </td><td>        NA           </td><td> 348264000           </td><td>4.157572             </td></tr>\n",
       "\t<tr><td>MSFT                 </td><td>2019-08-23 16:00:01  </td><td>Microsoft Corporation</td><td>32523998             </td><td>26.36166             </td><td>0.01335462           </td><td>7662819840           </td><td>9.962656             </td></tr>\n",
       "\t<tr><td>HOG                  </td><td>2019-08-23 16:02:58  </td><td>Harley-Davidson, Inc.</td><td> 1849495             </td><td>11.37356             </td><td>0.04660619           </td><td> 159072992           </td><td>2.489465             </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllll}\n",
       " symbol & Trade.Time & Name & Volume & P.E.Ratio & Dividend.Yield & Shares.Outstanding & Price.Book\\\\\n",
       "\\hline\n",
       "\t GOOG                  & 2019-08-23 16:00:01   & Alphabet Inc.         &  1688271              & 23.24195              &         NA            &  348264000            & 4.157572             \\\\\n",
       "\t MSFT                  & 2019-08-23 16:00:01   & Microsoft Corporation & 32523998              & 26.36166              & 0.01335462            & 7662819840            & 9.962656             \\\\\n",
       "\t HOG                   & 2019-08-23 16:02:58   & Harley-Davidson, Inc. &  1849495              & 11.37356              & 0.04660619            &  159072992            & 2.489465             \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| symbol | Trade.Time | Name | Volume | P.E.Ratio | Dividend.Yield | Shares.Outstanding | Price.Book |\n",
       "|---|---|---|---|---|---|---|---|\n",
       "| GOOG                  | 2019-08-23 16:00:01   | Alphabet Inc.         |  1688271              | 23.24195              |         NA            |  348264000            | 4.157572              |\n",
       "| MSFT                  | 2019-08-23 16:00:01   | Microsoft Corporation | 32523998              | 26.36166              | 0.01335462            | 7662819840            | 9.962656              |\n",
       "| HOG                   | 2019-08-23 16:02:58   | Harley-Davidson, Inc. |  1849495              | 11.37356              | 0.04660619            |  159072992            | 2.489465              |\n",
       "\n"
      ],
      "text/plain": [
       "  symbol Trade.Time          Name                  Volume   P.E.Ratio\n",
       "1 GOOG   2019-08-23 16:00:01 Alphabet Inc.          1688271 23.24195 \n",
       "2 MSFT   2019-08-23 16:00:01 Microsoft Corporation 32523998 26.36166 \n",
       "3 HOG    2019-08-23 16:02:58 Harley-Davidson, Inc.  1849495 11.37356 \n",
       "  Dividend.Yield Shares.Outstanding Price.Book\n",
       "1         NA      348264000         4.157572  \n",
       "2 0.01335462     7662819840         9.962656  \n",
       "3 0.04660619      159072992         2.489465  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############################## Get stats data ##################################\n",
    "library(quantmod)\n",
    "metrics <- yahooQF(c(\"Name\", \"Enterprise Value\", \"Enterprise Value/Revenue\", \"Enterprise Value/EBITDA\",\n",
    "                     \"Total Cash Per Share (mrq)\", \"EBITDA\", \"Volume\", \"P/E Ratio\", \"Dividend Yield\", \"Shares Outstanding\", \"Price/Book\"))\n",
    "\n",
    "stats <- getQuote(symbols, what = metrics) %>%\n",
    "  rownames_to_column(\"symbol\")\n",
    "\n",
    "colnames(stats) <- gsub(\" \", \".\", colnames(stats))\n",
    "colnames(stats) <- gsub(\"/\", \".\", colnames(stats))\n",
    "\n",
    "head(stats)\n",
    "#################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>symbol</th><th scope=col>date</th><th scope=col>adjusted</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>GOOG      </td><td>2019-08-23</td><td>1151.29   </td></tr>\n",
       "\t<tr><td>MSFT      </td><td>2019-08-23</td><td> 133.39   </td></tr>\n",
       "\t<tr><td>HOG       </td><td>2019-08-23</td><td>  30.72   </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lll}\n",
       " symbol & date & adjusted\\\\\n",
       "\\hline\n",
       "\t GOOG       & 2019-08-23 & 1151.29   \\\\\n",
       "\t MSFT       & 2019-08-23 &  133.39   \\\\\n",
       "\t HOG        & 2019-08-23 &   30.72   \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| symbol | date | adjusted |\n",
       "|---|---|---|\n",
       "| GOOG       | 2019-08-23 | 1151.29    |\n",
       "| MSFT       | 2019-08-23 |  133.39    |\n",
       "| HOG        | 2019-08-23 |   30.72    |\n",
       "\n"
      ],
      "text/plain": [
       "  symbol date       adjusted\n",
       "1 GOOG   2019-08-23 1151.29 \n",
       "2 MSFT   2019-08-23  133.39 \n",
       "3 HOG    2019-08-23   30.72 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############################## Get latest stock price data ######################\n",
    "shhh(library(tidyquant))\n",
    "stocks <- symbols %>%\n",
    "  tq_get(get = \"stock.prices\",\n",
    "         from =  Sys.Date(),\n",
    "         to =  Sys.Date()) %>%\n",
    "  select(symbol, date, adjusted)\n",
    "\n",
    "head(stocks)\n",
    "#################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>symbol</th><th scope=col>year</th><th scope=col>Total.Revenue</th><th scope=col>Cost.of.Revenue</th><th scope=col>Gross.Profit</th><th scope=col>Research.Development</th><th scope=col>Selling.General.and.Administrative</th><th scope=col>Non.Recurring</th><th scope=col>Others</th><th scope=col>Total.Operating.Expenses</th><th scope=col>...</th><th scope=col>Change.In.Cash.and.Cash.Equivalents</th><th scope=col>Trade.Time</th><th scope=col>Name</th><th scope=col>Volume</th><th scope=col>P.E.Ratio</th><th scope=col>Dividend.Yield</th><th scope=col>Shares.Outstanding</th><th scope=col>Price.Book</th><th scope=col>date</th><th scope=col>adjusted</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>HOG                  </td><td>2018                 </td><td>  5716875            </td><td> 3803576             </td><td> 1913299             </td><td>      NA             </td><td> 1090436             </td><td>NA                   </td><td>NA                   </td><td>  4894012            </td><td>...                  </td><td> 513538              </td><td>2019-08-23 16:02:58  </td><td>Harley-Davidson, Inc.</td><td> 1849495             </td><td>11.37356             </td><td>0.04660619           </td><td> 159072992           </td><td>2.489465             </td><td>2019-08-23           </td><td>  30.72              </td></tr>\n",
       "\t<tr><td>MSFT                 </td><td>2018                 </td><td>110360000            </td><td>38353000             </td><td>72007000             </td><td>14726000             </td><td>22223000             </td><td>NA                   </td><td>NA                   </td><td> 75302000            </td><td>...                  </td><td>4283000              </td><td>2019-08-23 16:00:01  </td><td>Microsoft Corporation</td><td>32523998             </td><td>26.36166             </td><td>0.01335462           </td><td>7662819840           </td><td>9.962656             </td><td>2019-08-23           </td><td> 133.39              </td></tr>\n",
       "\t<tr><td>GOOG                 </td><td>2018                 </td><td>136819000            </td><td>59549000             </td><td>77270000             </td><td>21419000             </td><td>24459000             </td><td>NA                   </td><td>NA                   </td><td>105427000            </td><td>...                  </td><td>5986000              </td><td>2019-08-23 16:00:01  </td><td>Alphabet Inc.        </td><td> 1688271             </td><td>23.24195             </td><td>        NA           </td><td> 348264000           </td><td>4.157572             </td><td>2019-08-23           </td><td>1151.29              </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllll}\n",
       " symbol & year & Total.Revenue & Cost.of.Revenue & Gross.Profit & Research.Development & Selling.General.and.Administrative & Non.Recurring & Others & Total.Operating.Expenses & ... & Change.In.Cash.and.Cash.Equivalents & Trade.Time & Name & Volume & P.E.Ratio & Dividend.Yield & Shares.Outstanding & Price.Book & date & adjusted\\\\\n",
       "\\hline\n",
       "\t HOG                   & 2018                  &   5716875             &  3803576              &  1913299              &       NA              &  1090436              & NA                    & NA                    &   4894012             & ...                   &  513538               & 2019-08-23 16:02:58   & Harley-Davidson, Inc. &  1849495              & 11.37356              & 0.04660619            &  159072992            & 2.489465              & 2019-08-23            &   30.72              \\\\\n",
       "\t MSFT                  & 2018                  & 110360000             & 38353000              & 72007000              & 14726000              & 22223000              & NA                    & NA                    &  75302000             & ...                   & 4283000               & 2019-08-23 16:00:01   & Microsoft Corporation & 32523998              & 26.36166              & 0.01335462            & 7662819840            & 9.962656              & 2019-08-23            &  133.39              \\\\\n",
       "\t GOOG                  & 2018                  & 136819000             & 59549000              & 77270000              & 21419000              & 24459000              & NA                    & NA                    & 105427000             & ...                   & 5986000               & 2019-08-23 16:00:01   & Alphabet Inc.         &  1688271              & 23.24195              &         NA            &  348264000            & 4.157572              & 2019-08-23            & 1151.29              \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| symbol | year | Total.Revenue | Cost.of.Revenue | Gross.Profit | Research.Development | Selling.General.and.Administrative | Non.Recurring | Others | Total.Operating.Expenses | ... | Change.In.Cash.and.Cash.Equivalents | Trade.Time | Name | Volume | P.E.Ratio | Dividend.Yield | Shares.Outstanding | Price.Book | date | adjusted |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| HOG                   | 2018                  |   5716875             |  3803576              |  1913299              |       NA              |  1090436              | NA                    | NA                    |   4894012             | ...                   |  513538               | 2019-08-23 16:02:58   | Harley-Davidson, Inc. |  1849495              | 11.37356              | 0.04660619            |  159072992            | 2.489465              | 2019-08-23            |   30.72               |\n",
       "| MSFT                  | 2018                  | 110360000             | 38353000              | 72007000              | 14726000              | 22223000              | NA                    | NA                    |  75302000             | ...                   | 4283000               | 2019-08-23 16:00:01   | Microsoft Corporation | 32523998              | 26.36166              | 0.01335462            | 7662819840            | 9.962656              | 2019-08-23            |  133.39               |\n",
       "| GOOG                  | 2018                  | 136819000             | 59549000              | 77270000              | 21419000              | 24459000              | NA                    | NA                    | 105427000             | ...                   | 5986000               | 2019-08-23 16:00:01   | Alphabet Inc.         |  1688271              | 23.24195              |         NA            |  348264000            | 4.157572              | 2019-08-23            | 1151.29               |\n",
       "\n"
      ],
      "text/plain": [
       "  symbol year Total.Revenue Cost.of.Revenue Gross.Profit Research.Development\n",
       "1 HOG    2018   5716875      3803576         1913299           NA            \n",
       "2 MSFT   2018 110360000     38353000        72007000     14726000            \n",
       "3 GOOG   2018 136819000     59549000        77270000     21419000            \n",
       "  Selling.General.and.Administrative Non.Recurring Others\n",
       "1  1090436                           NA            NA    \n",
       "2 22223000                           NA            NA    \n",
       "3 24459000                           NA            NA    \n",
       "  Total.Operating.Expenses ... Change.In.Cash.and.Cash.Equivalents\n",
       "1   4894012                ...  513538                            \n",
       "2  75302000                ... 4283000                            \n",
       "3 105427000                ... 5986000                            \n",
       "  Trade.Time          Name                  Volume   P.E.Ratio Dividend.Yield\n",
       "1 2019-08-23 16:02:58 Harley-Davidson, Inc.  1849495 11.37356  0.04660619    \n",
       "2 2019-08-23 16:00:01 Microsoft Corporation 32523998 26.36166  0.01335462    \n",
       "3 2019-08-23 16:00:01 Alphabet Inc.          1688271 23.24195          NA    \n",
       "  Shares.Outstanding Price.Book date       adjusted\n",
       "1  159072992         2.489465   2019-08-23   30.72 \n",
       "2 7662819840         9.962656   2019-08-23  133.39 \n",
       "3  348264000         4.157572   2019-08-23 1151.29 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############################## Join all 3 datasets together ######################\n",
    "financials <- ISBSCF %>%\n",
    "  filter(year == 2018) %>%\n",
    "  inner_join(stats, by = \"symbol\") %>%\n",
    "  inner_join(stocks, by = \"symbol\")\n",
    "\n",
    "head(financials)\n",
    "#################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th scope=col>symbol</th><th scope=col>date</th><th scope=col>BvE</th><th scope=col>BvE_per_share</th><th scope=col>EBITDA</th><th scope=col>Market_Cap</th><th scope=col>EV</th><th scope=col>Mkt_Book</th><th scope=col>Price_Book</th><th scope=col>Price_Sales</th><th scope=col>PE_Ratio</th><th scope=col>EV_EBITDA</th><th scope=col>NI_REV</th><th scope=col>Rev_TA</th><th scope=col>TA_BvE</th><th scope=col>ROE</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><td>HOG       </td><td>2019-08-23</td><td>  1773949 </td><td> 11.15179 </td><td> 1053072  </td><td>   4886722</td><td>  13233889</td><td>1.307552  </td><td> 2.754714 </td><td>0.8547891 </td><td> 9.195057 </td><td>12.56694  </td><td>0.0929618 </td><td>0.5360074 </td><td>6.012385  </td><td>29.95864  </td></tr>\n",
       "\t<tr><td>MSFT      </td><td>2019-08-23</td><td> 82718000 </td><td> 10.79472 </td><td>44958000  </td><td>1022143531</td><td>1186327531</td><td>4.804852  </td><td>12.356966 </td><td>9.2619022 </td><td>61.682670 </td><td>26.38746  </td><td>0.1501540 </td><td>0.4263506 </td><td>3.129283  </td><td>20.03312  </td></tr>\n",
       "\t<tr><td>GOOG      </td><td>2019-08-23</td><td>177628000 </td><td>510.03836 </td><td>40421000  </td><td> 400952874</td><td> 439415874</td><td>2.033476  </td><td> 2.257262 </td><td>2.9305350 </td><td>13.045057 </td><td>10.87098  </td><td>0.2246472 </td><td>0.5877307 </td><td>1.310559  </td><td>17.30358  </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllllllll}\n",
       " symbol & date & BvE & BvE\\_per\\_share & EBITDA & Market\\_Cap & EV & Mkt\\_Book & Price\\_Book & Price\\_Sales & PE\\_Ratio & EV\\_EBITDA & NI\\_REV & Rev\\_TA & TA\\_BvE & ROE\\\\\n",
       "\\hline\n",
       "\t HOG        & 2019-08-23 &   1773949  &  11.15179  &  1053072   &    4886722 &   13233889 & 1.307552   &  2.754714  & 0.8547891  &  9.195057  & 12.56694   & 0.0929618  & 0.5360074  & 6.012385   & 29.95864  \\\\\n",
       "\t MSFT       & 2019-08-23 &  82718000  &  10.79472  & 44958000   & 1022143531 & 1186327531 & 4.804852   & 12.356966  & 9.2619022  & 61.682670  & 26.38746   & 0.1501540  & 0.4263506  & 3.129283   & 20.03312  \\\\\n",
       "\t GOOG       & 2019-08-23 & 177628000  & 510.03836  & 40421000   &  400952874 &  439415874 & 2.033476   &  2.257262  & 2.9305350  & 13.045057  & 10.87098   & 0.2246472  & 0.5877307  & 1.310559   & 17.30358  \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| symbol | date | BvE | BvE_per_share | EBITDA | Market_Cap | EV | Mkt_Book | Price_Book | Price_Sales | PE_Ratio | EV_EBITDA | NI_REV | Rev_TA | TA_BvE | ROE |\n",
       "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n",
       "| HOG        | 2019-08-23 |   1773949  |  11.15179  |  1053072   |    4886722 |   13233889 | 1.307552   |  2.754714  | 0.8547891  |  9.195057  | 12.56694   | 0.0929618  | 0.5360074  | 6.012385   | 29.95864   |\n",
       "| MSFT       | 2019-08-23 |  82718000  |  10.79472  | 44958000   | 1022143531 | 1186327531 | 4.804852   | 12.356966  | 9.2619022  | 61.682670  | 26.38746   | 0.1501540  | 0.4263506  | 3.129283   | 20.03312   |\n",
       "| GOOG       | 2019-08-23 | 177628000  | 510.03836  | 40421000   |  400952874 |  439415874 | 2.033476   |  2.257262  | 2.9305350  | 13.045057  | 10.87098   | 0.2246472  | 0.5877307  | 1.310559   | 17.30358   |\n",
       "\n"
      ],
      "text/plain": [
       "  symbol date       BvE       BvE_per_share EBITDA   Market_Cap EV        \n",
       "1 HOG    2019-08-23   1773949  11.15179      1053072    4886722   13233889\n",
       "2 MSFT   2019-08-23  82718000  10.79472     44958000 1022143531 1186327531\n",
       "3 GOOG   2019-08-23 177628000 510.03836     40421000  400952874  439415874\n",
       "  Mkt_Book Price_Book Price_Sales PE_Ratio  EV_EBITDA NI_REV    Rev_TA   \n",
       "1 1.307552  2.754714  0.8547891    9.195057 12.56694  0.0929618 0.5360074\n",
       "2 4.804852 12.356966  9.2619022   61.682670 26.38746  0.1501540 0.4263506\n",
       "3 2.033476  2.257262  2.9305350   13.045057 10.87098  0.2246472 0.5877307\n",
       "  TA_BvE   ROE     \n",
       "1 6.012385 29.95864\n",
       "2 3.129283 20.03312\n",
       "3 1.310559 17.30358"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "############################## Create some financial ratios ######################\n",
    "\n",
    "financials %>%\n",
    "  mutate(BvE = Total.Assets - Total.Liabilities,\n",
    "         BvE_per_share = BvE / (Shares.Outstanding / 1000),\n",
    "         EBITDA = Earnings.Before.Interest.and.Taxes + Depreciation,\n",
    "         Market_Cap = adjusted * (Shares.Outstanding / 1000),                 #¡Cuidado!\n",
    "         EV = (Market_Cap + Total.Liabilities) - Cash.And.Cash.Equivalents,\n",
    "         Mkt_Book = EV /(Total.Assets - Cash.And.Cash.Equivalents),\n",
    "         Price_Book = Market_Cap / Total.stockholders..equity,\n",
    "         Price_Sales = Market_Cap / Total.Revenue,\n",
    "         PE_Ratio = Market_Cap / Net.Income,\n",
    "         EV_EBITDA =  EV / EBITDA,\n",
    "         NI_REV = Net.Income / Total.Revenue,\n",
    "         Rev_TA = Total.Revenue / Total.Assets,\n",
    "         TA_BvE = Total.Assets / BvE,\n",
    "         ROE = NI_REV * Rev_TA * TA_BvE * 100) %>%\n",
    "  select(symbol, date, BvE, BvE_per_share, EBITDA, Market_Cap, EV, Mkt_Book, Price_Book, Price_Sales,\n",
    "         PE_Ratio, EV_EBITDA, NI_REV, Rev_TA, TA_BvE, ROE)\n",
    "#################################################################################"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EISIyQLQtIjpEBIeQjp+gIc2t2EkAjp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\nlAAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "financials %>%\n",
    "  ggplot(aes(x = symbol)) +\n",
    "  geom_col(aes(y = Total.Assets, color = \"blue\", fill = \"blue\", alpha = 0.5)) +\n",
    "  geom_col(aes(y = Total.Liabilities, color = \"red\", fill = \"red\", alpha = 0.5)) +\n",
    "  ggtitle(\"Total Assets & Total Liabilities\") +\n",
    "  xlab(\"Symbols\") +\n",
    "  ylab(\"Total Assets & Total Liabilities\") +\n",
    "  theme_tq() +\n",
    "  theme(legend.position = \"none\")"
   ]
  }
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