{"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\nsystem(\"ls ../input\")\n\n# Any results you write to the current directory are saved as output.\n\ndataset <- read.csv(\"../input/train.csv\")\ntestset <- read.csv(\"../input/test.csv\")\n#converting timestamp to hours\ndataset$hour <- round((dataset$time %% 86400) / 3600)\n\n#getting 0 hours data\nhour0 <- dataset[dataset[,\"hour\"] == 0, c(\"x\", \"y\", \"place_id\")]\nsummary(hour0)\n\nplot(hour0$x, hour0$y, col= as.factor(hour0$place_id))\n\nhist(hour0$place_id)"
 }
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