Association rules (R).ipynb

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    "## create new data matrix for decision tree analysis\n",
    "all_data = read.csv( 'data/wvs.csv')\n",
    "\n",
    "## ugly hack to create a correct size empty data frame, where we can add selected variables.\n",
    "## this is to avoid needing to modify all variables in all_data to binary variables.\n",
    "data = data.frame( matrix(NA, nrow=nrow(all_data), ncol=1) )\n",
    "\n",
    "variables = c( 'V10', 'V4', 'V5', 'V6', 'V7', 'V8', 'V9')\n",
    "\n",
    "for( variable in variables ){\n",
    "    data[ , variable ] = all_data[ , variable ] >= 3\n",
    "    \n",
    "}    \n",
    "head( data )"
   ]
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   "cell_type": "code",
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   "source": [
    "library(arules)\n",
    "\n",
    "rules <- apriori( data , parameter = list(support = 0.0000001 ) )"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "inspect( rules ) "
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tasks\n",
    "\n",
    "* Analyse also lift and support. Do you find any rules which might be interesting for further investigation?\n",
    "* Try adding more variables. Note that these can only be True/False variables."
   ]
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