integration-tests/examples/test_templates/jupyter/template_visualization_sparkr.ipynb

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 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "sparkR.session()\n",
    "\n",
    "full_path <- function(file_path) {\n",
    "    working_storage <- \"WORKING_STORAGE\"\n",
    "    output_directory <- \"jupyter/r\"\n",
    "    protocol_name <- \"PROTOCOL_NAME://%s/%s/%s\"   \n",
    "    sprintf(protocol_name, working_storage, output_directory, file_path)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Carriers data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "carriers <- read.df(full_path(\"carriers\"), \"parquet\")\n",
    "createOrReplaceTempView(carriers, \"carriers\")\n",
    "printSchema(carriers)\n",
    "head(carriers, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Airports data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "airports <- read.df(full_path(\"airports\"), \"parquet\")\n",
    "createOrReplaceTempView(airports, \"airports\")\n",
    "printSchema(airports)\n",
    "head(airports, 20)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load Flights data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "flights <- read.df(full_path(\"flights\"), \"parquet\")\n",
    "createOrReplaceTempView(flights, \"flights\")\n",
    "printSchema(flights)\n",
    "head(flights, 10)[c(\"ArrDelay\",\"CarrierDelay\",\"WeatherDelay\",\"Distance\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Taxonomy for ArrDelay, CarrierDelay, and Distance colums"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "head(summary(limit(flights,1000)))[c(\"summary\", \"ArrDelay\",\"CarrierDelay\",\"Distance\")]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Let's find the top 10 of the most unpunctual airlines"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(ggplot2)\n",
    "library(reshape2)\n",
    "\n",
    "delay_sql <- sql(\"\n",
    "SELECT SUBSTR(c.description, 0, 15) as Carrier, WorkDayDelay, WeekendDelay \n",
    "FROM\n",
    "       (SELECT CEIL( AVG(f.ArrDelay + f.DepDelay) ) as WorkDayDelay, f.UniqueCarrier\n",
    "        FROM flights f\n",
    "        WHERE f.DayOfWeek < 6\n",
    "        GROUP BY f.UniqueCarrier \n",
    "        ORDER BY WorkDayDelay desc \n",
    "        LIMIT 10) t\n",
    "    JOIN\n",
    "       (SELECT CEIL( AVG(f.ArrDelay + f.DepDelay) ) as WeekendDelay, f.UniqueCarrier\n",
    "        FROM flights f\n",
    "        WHERE f.DayOfWeek > 5\n",
    "        GROUP BY f.UniqueCarrier) t1\n",
    "      ON t.UniqueCarrier = t1.UniqueCarrier\n",
    "    JOIN carriers c \n",
    "      ON t.UniqueCarrier = c.code \n",
    "ORDER BY WeekendDelay DESC, WorkDayDelay DESC\n",
    "\")\n",
    "\n",
    "delay <- collect(delay_sql)\n",
    "delay_melt <- melt(delay[c('Carrier', 'WorkDayDelay', 'WeekendDelay')])\n",
    "\n",
    "color_range_days <- c(\"#2966FF\", \"#61F2FF\")\n",
    "\n",
    "ggplot(data=delay_melt, aes(x=Carrier, y=value, fill=variable)) +\n",
    "    geom_bar(stat=\"identity\", width=.7, position=\"dodge\") +\n",
    "    stat_summary(fun.y=mean, geom = \"line\", mapping = aes(group = 1), color=\"red\") +\n",
    "    stat_summary(fun.y=mean, geom = \"point\", mapping = aes(group = 1), color=\"red\") +\n",
    "    theme(legend.position=\"right\", axis.text.x=element_text(angle=90)) +\n",
    "    labs(x=\"Carrier\", y=\"Minutes\", fill=\"Day Type\") +\n",
    "    coord_fixed(ratio = .2) +\n",
    "    scale_fill_manual(values=color_range_days) +\n",
    "    scale_y_continuous(breaks=seq(0, 30, 5))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Number of flight performed by top companies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "top_flights_sql <- sql(\"\n",
    "SELECT t.cnt as FlightsAmt, carriers.description as Carrier \n",
    "FROM (\n",
    "    SELECT count(*) as cnt, flights.UniqueCarrier as carrier_code \n",
    "    FROM flights \n",
    "    GROUP BY flights.UniqueCarrier LIMIT 6) t \n",
    "LEFT JOIN carriers \n",
    "  ON t.carrier_code = carriers.code\n",
    "\")\n",
    "\n",
    "top_flights <- collect(top_flights_sql)\n",
    "\n",
    "ggplot(transform(transform(top_flights, value=FlightsAmt/sum(FlightsAmt)), labPos=cumsum(FlightsAmt)-FlightsAmt/2), \n",
    "       aes(x=\"\", y = FlightsAmt, fill = Carrier)) +\n",
    "    geom_bar(width = 1, stat = \"identity\") +\n",
    "    coord_polar(\"y\", start=0) +\n",
    "    scale_fill_brewer(palette=\"Dark2\") +\n",
    "    theme_bw() +\n",
    "    theme(axis.text.x=element_blank() ,panel.grid.major=element_blank(),panel.grid.minor = element_blank(),panel.border = element_blank()) +\n",
    "    geom_text(size=4, aes(y=labPos, label=scales::percent(value))) + \n",
    "    geom_text(size=3, aes(x=1.8, y=labPos, label=top_flights$Carrier)) + \n",
    "    theme(legend.position=\"none\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The average Flight Distance per Company"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "distance_sql = sql(\"\n",
    "SELECT SUBSTR(c.description, 0, 15) as Carrier, COUNT(Distance) AS Distance \n",
    "FROM flights f \n",
    "JOIN carriers c \n",
    "  ON f.UniqueCarrier = c.code \n",
    "GROUP BY c.description \n",
    "ORDER BY distance DESC \n",
    "LIMIT 10\n",
    "\")\n",
    "\n",
    "distance <- collect(distance_sql)\n",
    "\n",
    "distance$Carrier <- factor(distance$Carrier, levels = distance$Carrier[order(-distance$Distance)])\n",
    "\n",
    "color_range <-  c(\"#2966FF\", \"#2E73FF\",\"#3380FF\", \"#388CFF\", \"#3D99FF\", \"#42A6FF\", \"#47B2FF\", \"#4CBFFF\", \"#52CCFF\", \n",
    "                  \"#57D9FF\", \"#5CE6FF\", \"#61F2FF\", \"#66FFFF\")\n",
    "\n",
    "ggplot(data=distance, aes(x=Carrier, y=Distance, fill=Carrier)) +\n",
    "    geom_bar(stat=\"identity\", width=.7, position=\"dodge\") +\n",
    "    theme(axis.text.x=element_text(angle=90)) +\n",
    "    scale_fill_manual(values=color_range) +\n",
    "    theme(legend.position=\"none\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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