{"cells":[
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
  },
  "outputs": [],
  "source": "# This R environment comes with all of CRAN preinstalled, as well as many other helpful packages\n# The environment is defined by the kaggle/rstats docker image: https://github.com/kaggle/docker-rstats\n# For example, here's several helpful packages to load in \n\nlibrary(ggplot2) # Data visualization\nlibrary(readr) # CSV file I/O, e.g. the read_csv function\n\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\n#system(\"ls ../input\")\n\n# Any results you write to the current directory are saved as output.\ntestset <- read.csv(\"../input/test.csv\")\ndataset <- read.csv(\"../input/train.csv\")\n\ndataset$hour <- round((dataset$time %% 86400) / 3600)\n\ntemp <- dataset\n\ndataset$place_id <- as.factor(dataset$place_id)\n\nplace1 <- temp[temp[\"place_id\"] == 8772469670,]\nsummary(place1)\nfor(i in 1:24) {\n  plot(place1[place1[\"hour\"] == i, \"x\"], place1[place1[\"hour\"] == i, \"y\"])  \n}\n"
 },
 {
  "cell_type": "code",
  "execution_count": null,
  "metadata": {
   "collapsed": false
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  "outputs": [],
  "source": ""
 }
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