Collector_R_WebDriver.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "The data in the file is downloaded automatically by **WebDriver** from **https://infoshare.stats.govt.nz**.\n",
    "\n",
    "The data includes:\n",
    "- Labour force status data (unemployment rate)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "#install.packages(c(\"RSelenium\", \"writexl\"))\n",
    "#install.packages(\"here\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "here() starts at /Users/zhoujinze/Documents/DataScience/ds-assessment/DATA-422/group project/data422-group-project\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# install.packages(c(\"RSelenium\", \"writexl\", \"here\"))\n",
    "\n",
    "# CPI data\n",
    "library(magrittr)\n",
    "library(polite)\n",
    "library(rvest)\n",
    "library(here)\n",
    "library(RSelenium)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "data_dir = paste(here(), \"data\", sep = \"/\")\n",
    "\n",
    "# session <- bow(\n",
    "#     \"https://www.rbnz.govt.nz/-/media/e3420ae9ec24421bac870e70660d20d0.ashx\",\n",
    "#     user_agent = \"polite R package\",\n",
    "#     delay = 5,\n",
    "#     times = 3,\n",
    "#     force = FALSE,\n",
    "#     verbose = FALSE\n",
    "# )\n",
    "\n",
    "# rip(session, \n",
    "#    destfile = \"CPI.xlsx\",\n",
    "#    path = data_dir\n",
    "#)\n",
    "\n",
    "eCaps <- list(\n",
    "  chromeOptions = \n",
    "    list(prefs = list(\n",
    "        \"profile.default_content_settings.popups\" = 0L,\n",
    "        \"download.prompt_for_download\" = FALSE,\n",
    "        \"directory_upgrade\" = TRUE,\n",
    "        \"download.default_directory\" = data_dir),\n",
    "      args = list(\n",
    "        \"--disable-blink-features=AutomationControlled\"\n",
    "      )),\n",
    "      useAutomationExtension = FALSE,\n",
    "      excludeSwitches = list(\n",
    "        \"enable-automation\"\n",
    "      )\n",
    ")\n",
    "\n",
    "rD <- rsDriver(verbose = FALSE, browser = \"chrome\", chromever = \"106.0.5249.61\",\n",
    "                extraCapabilities = eCaps)\n",
    "remDr <- rD$client\n",
    "remDr$maxWindowSize()\n",
    "remDr$navigate(\"https://infoshare.stats.govt.nz/infoshare/Default.aspx\")\n",
    "# remDr$screenshot(display = TRUE)\n",
    "\n",
    "# webElem <- remDr$findElement(using = \"css\", \"[data-ga-action='hm1.xlsx'] button\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# Wrapper of remoteDriver$findElement, enable wait and check for 3 times\n",
    "# rmd:  remote driver client\n",
    "# p_using: `using` param of findElement\n",
    "# p_value: `value` param of findElement\n",
    "get_tree_element <- function(rmd, p_using, p_value) {\n",
    "    sleep_secs = 2\n",
    "    vis_check = 0\n",
    "\n",
    "    # wait for progress valid\n",
    "    Sys.sleep(sleep_secs)\n",
    "    while (sleep_secs < 10 & vis_check < 30) {\n",
    "        vis = remDr$executeScript(\"return document.getElementById(arguments[0]).style.visibility\",\n",
    "                            args = list(\"ctl00_MainContent_goProgress\", \"\"))\n",
    "        # wait for progress finish\n",
    "        if (vis == \"visible\") {\n",
    "            # in progress, keep wait\n",
    "            vis_check = vis_check + 1\n",
    "            sleep_secs = 2\n",
    "            Sys.sleep(sleep_secs)\n",
    "            next\n",
    "        } else {\n",
    "            # wait for element load\n",
    "           Sys.sleep(sleep_secs) \n",
    "        }\n",
    "        \n",
    "        try(suppressMessages(rmd$findElement(using = p_using, value = p_value)), silent = TRUE)\n",
    "        if (rmd$status == 0) {\n",
    "            elem = rmd$findElement(using = p_using, value = p_value)\n",
    "            return(elem)\n",
    "        } else if (remDr$status == 7) {\n",
    "            # wait for 2 seconds\n",
    "            Sys.sleep(sleep_secs)\n",
    "        }\n",
    "        sleep_secs = sleep_secs * 2\n",
    "    }\n",
    "    return(NULL)\n",
    "}\n",
    "\n",
    "get_element <- function(rmd, p_using, p_value) {\n",
    "    sleep_secs = 2\n",
    "\n",
    "    # wait for progress valid\n",
    "    Sys.sleep(sleep_secs)\n",
    "    while (sleep_secs < 10) {\n",
    "        try(suppressMessages(rmd$findElement(using = p_using, value = p_value)), silent = TRUE)\n",
    "        if (rmd$status == 0) {\n",
    "            elem = rmd$findElement(using = p_using, value = p_value)\n",
    "            return(elem)\n",
    "        } else if (remDr$status == 7) {\n",
    "            # wait for 2 seconds\n",
    "            Sys.sleep(sleep_secs)\n",
    "        }\n",
    "        sleep_secs = sleep_secs * 2\n",
    "    }\n",
    "    return(NULL)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# navigate into the labour data select panel\n",
    "elem_lv_one = get_tree_element(remDr, \"link text\", \"Work income and spending\")\n",
    "elem_lv_one$clickElement()\n",
    "\n",
    "# wait for loading\n",
    "elem_lv_two = get_tree_element(remDr, \"link text\",  \"Household Labour Force Survey - HLF\")\n",
    "elem_lv_two$clickElement()\n",
    "\n",
    "elem_lv_three = get_tree_element(remDr, \"link text\", \"Labour Force Status for people aged 15 to 64 years: Seasonally Adjusted (Qrtly-Mar/Jun/Sep/Dec)\")\n",
    "elem_lv_three$clickElement()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# setup options of labour data for extract data\n",
    "season_adj  = get_element(remDr, \"css selector\", \"span#ctl00_MainContent_ctl02_lblSelectAll\")\n",
    "time_range  = get_element(remDr, \"css selector\", \"span#ctl00_MainContent_ctl07_lblSelectAll\")\n",
    "obs_range  = get_element(remDr, \"css selector\", \"span#ctl00_MainContent_ctl04_lblSelectAll\")\n",
    "submit  = get_element(remDr, \"css selector\", \"input#ctl00_MainContent_btnGo\")\n",
    "\n",
    "season_adj$clickElement()\n",
    "time_range$clickElement()\n",
    "obs_range$clickElement()\n",
    "submit$clickElement()\n",
    "# get footnotes for finishing page loading\n",
    "foot = get_element(remDr, \"link text\", \"Footnotes\")\n",
    "\n",
    "page_source = remDr$getPageSource()[[1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "# parse the page source\n",
    "doc = xml2::read_html(page_source)\n",
    "labour_table = doc %>% html_table()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data Wrangling & Save"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"dataframe\">\n",
       "<caption>A tibble: 10 × 9</caption>\n",
       "<thead>\n",
       "\t<tr><th scope=col>date</th><th scope=col>Persons Employed in Labour Force</th><th scope=col>Persons Unemployed in Labour Force</th><th scope=col>Not in Labour Force</th><th scope=col>Working Age Population</th><th scope=col>Labour Force Participation Rate</th><th scope=col>Unemployment Rate</th><th scope=col>Employment Rate</th><th scope=col>Total Labour Force</th></tr>\n",
       "\t<tr><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;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;chr&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><td>1986Q1</td><td>1,594</td><td>70</td><td>536</td><td>2,200</td><td>75.6</td><td>4.2</td><td>72.4</td><td>1,664</td></tr>\n",
       "\t<tr><td>1986Q2</td><td>1,592</td><td>69</td><td>541</td><td>2,202</td><td>75.4</td><td>4.2</td><td>72.3</td><td>1,661</td></tr>\n",
       "\t<tr><td>1986Q3</td><td>1,590</td><td>70</td><td>544</td><td>2,204</td><td>75.3</td><td>4.2</td><td>72.1</td><td>1,660</td></tr>\n",
       "\t<tr><td>1986Q4</td><td>1,581</td><td>71</td><td>552</td><td>2,204</td><td>75.0</td><td>4.3</td><td>71.7</td><td>1,652</td></tr>\n",
       "\t<tr><td>1987Q1</td><td>1,597</td><td>69</td><td>546</td><td>2,212</td><td>75.3</td><td>4.1</td><td>72.2</td><td>1,666</td></tr>\n",
       "\t<tr><td>1987Q2</td><td>1,598</td><td>70</td><td>548</td><td>2,216</td><td>75.3</td><td>4.2</td><td>72.1</td><td>1,668</td></tr>\n",
       "\t<tr><td>1987Q3</td><td>1,599</td><td>70</td><td>551</td><td>2,221</td><td>75.2</td><td>4.2</td><td>72.0</td><td>1,670</td></tr>\n",
       "\t<tr><td>1987Q4</td><td>1,590</td><td>74</td><td>556</td><td>2,221</td><td>74.9</td><td>4.5</td><td>71.6</td><td>1,665</td></tr>\n",
       "\t<tr><td>1988Q1</td><td>1,570</td><td>83</td><td>573</td><td>2,226</td><td>74.3</td><td>5.0</td><td>70.5</td><td>1,653</td></tr>\n",
       "\t<tr><td>1988Q2</td><td>1,557</td><td>91</td><td>580</td><td>2,228</td><td>74.0</td><td>5.5</td><td>69.9</td><td>1,648</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A tibble: 10 × 9\n",
       "\\begin{tabular}{lllllllll}\n",
       " date & Persons Employed in Labour Force & Persons Unemployed in Labour Force & Not in Labour Force & Working Age Population & Labour Force Participation Rate & Unemployment Rate & Employment Rate & Total Labour Force\\\\\n",
       " <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr> & <chr>\\\\\n",
       "\\hline\n",
       "\t 1986Q1 & 1,594 & 70 & 536 & 2,200 & 75.6 & 4.2 & 72.4 & 1,664\\\\\n",
       "\t 1986Q2 & 1,592 & 69 & 541 & 2,202 & 75.4 & 4.2 & 72.3 & 1,661\\\\\n",
       "\t 1986Q3 & 1,590 & 70 & 544 & 2,204 & 75.3 & 4.2 & 72.1 & 1,660\\\\\n",
       "\t 1986Q4 & 1,581 & 71 & 552 & 2,204 & 75.0 & 4.3 & 71.7 & 1,652\\\\\n",
       "\t 1987Q1 & 1,597 & 69 & 546 & 2,212 & 75.3 & 4.1 & 72.2 & 1,666\\\\\n",
       "\t 1987Q2 & 1,598 & 70 & 548 & 2,216 & 75.3 & 4.2 & 72.1 & 1,668\\\\\n",
       "\t 1987Q3 & 1,599 & 70 & 551 & 2,221 & 75.2 & 4.2 & 72.0 & 1,670\\\\\n",
       "\t 1987Q4 & 1,590 & 74 & 556 & 2,221 & 74.9 & 4.5 & 71.6 & 1,665\\\\\n",
       "\t 1988Q1 & 1,570 & 83 & 573 & 2,226 & 74.3 & 5.0 & 70.5 & 1,653\\\\\n",
       "\t 1988Q2 & 1,557 & 91 & 580 & 2,228 & 74.0 & 5.5 & 69.9 & 1,648\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A tibble: 10 × 9\n",
       "\n",
       "| date &lt;chr&gt; | Persons Employed in Labour Force &lt;chr&gt; | Persons Unemployed in Labour Force &lt;chr&gt; | Not in Labour Force &lt;chr&gt; | Working Age Population &lt;chr&gt; | Labour Force Participation Rate &lt;chr&gt; | Unemployment Rate &lt;chr&gt; | Employment Rate &lt;chr&gt; | Total Labour Force &lt;chr&gt; |\n",
       "|---|---|---|---|---|---|---|---|---|\n",
       "| 1986Q1 | 1,594 | 70 | 536 | 2,200 | 75.6 | 4.2 | 72.4 | 1,664 |\n",
       "| 1986Q2 | 1,592 | 69 | 541 | 2,202 | 75.4 | 4.2 | 72.3 | 1,661 |\n",
       "| 1986Q3 | 1,590 | 70 | 544 | 2,204 | 75.3 | 4.2 | 72.1 | 1,660 |\n",
       "| 1986Q4 | 1,581 | 71 | 552 | 2,204 | 75.0 | 4.3 | 71.7 | 1,652 |\n",
       "| 1987Q1 | 1,597 | 69 | 546 | 2,212 | 75.3 | 4.1 | 72.2 | 1,666 |\n",
       "| 1987Q2 | 1,598 | 70 | 548 | 2,216 | 75.3 | 4.2 | 72.1 | 1,668 |\n",
       "| 1987Q3 | 1,599 | 70 | 551 | 2,221 | 75.2 | 4.2 | 72.0 | 1,670 |\n",
       "| 1987Q4 | 1,590 | 74 | 556 | 2,221 | 74.9 | 4.5 | 71.6 | 1,665 |\n",
       "| 1988Q1 | 1,570 | 83 | 573 | 2,226 | 74.3 | 5.0 | 70.5 | 1,653 |\n",
       "| 1988Q2 | 1,557 | 91 | 580 | 2,228 | 74.0 | 5.5 | 69.9 | 1,648 |\n",
       "\n"
      ],
      "text/plain": [
       "   date   Persons Employed in Labour Force Persons Unemployed in Labour Force\n",
       "1  1986Q1 1,594                            70                                \n",
       "2  1986Q2 1,592                            69                                \n",
       "3  1986Q3 1,590                            70                                \n",
       "4  1986Q4 1,581                            71                                \n",
       "5  1987Q1 1,597                            69                                \n",
       "6  1987Q2 1,598                            70                                \n",
       "7  1987Q3 1,599                            70                                \n",
       "8  1987Q4 1,590                            74                                \n",
       "9  1988Q1 1,570                            83                                \n",
       "10 1988Q2 1,557                            91                                \n",
       "   Not in Labour Force Working Age Population Labour Force Participation Rate\n",
       "1  536                 2,200                  75.6                           \n",
       "2  541                 2,202                  75.4                           \n",
       "3  544                 2,204                  75.3                           \n",
       "4  552                 2,204                  75.0                           \n",
       "5  546                 2,212                  75.3                           \n",
       "6  548                 2,216                  75.3                           \n",
       "7  551                 2,221                  75.2                           \n",
       "8  556                 2,221                  74.9                           \n",
       "9  573                 2,226                  74.3                           \n",
       "10 580                 2,228                  74.0                           \n",
       "   Unemployment Rate Employment Rate Total Labour Force\n",
       "1  4.2               72.4            1,664             \n",
       "2  4.2               72.3            1,661             \n",
       "3  4.2               72.1            1,660             \n",
       "4  4.3               71.7            1,652             \n",
       "5  4.1               72.2            1,666             \n",
       "6  4.2               72.1            1,668             \n",
       "7  4.2               72.0            1,670             \n",
       "8  4.5               71.6            1,665             \n",
       "9  5.0               70.5            1,653             \n",
       "10 5.5               69.9            1,648             "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "\"`gather_()` was deprecated in tidyr 1.2.0.\n",
      "Please use `gather()` instead.\"\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 420,
       "width": 420
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(visdat)\n",
    "library(\"writexl\")\n",
    "\n",
    "# labour data\n",
    "labour <- labour_table[[8]]\n",
    "labour[2,1] = \"date\"\n",
    "column_name = labour[2,]\n",
    "colnames(labour) = column_name\n",
    "labour <- labour[-c(1,2),]\n",
    "labour %>% head(10)\n",
    "\n",
    "vis_dat(labour)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "vscode": {
     "languageId": "r"
    }
   },
   "outputs": [],
   "source": [
    "\n",
    "write_xlsx(labour, paste(data_dir, \"labour_force_status.xlsx\", sep = \"/\"))"
   ]
  }
 ],
 "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": "4.2.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}