diff --git a/consumptionGraph.jpg b/consumptionGraph.jpg new file mode 100644 index 0000000..2410836 Binary files /dev/null and b/consumptionGraph.jpg differ diff --git a/graph.ipynb b/graph.ipynb new file mode 100644 index 0000000..5eed5fe --- /dev/null +++ b/graph.ipynb @@ -0,0 +1,118 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 93, + "id": "b0cf1209", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 94, + "id": "d2e78945", + "metadata": {}, + "outputs": [], + "source": [ + "data = pd.read_csv(\"istherecorrelation.csv\", delimiter = \";\")" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "id": "464ab8b5", + "metadata": {}, + "outputs": [], + "source": [ + "data = data.rename(columns={\"Year\":\"year\",\"WO [x1000]\":\"WO\", \"NL Beer consumption [x1000 hectoliter]\":\"Consumption\"})" + ] + }, + { + "cell_type": "code", + "execution_count": 96, + "id": "93a90dac", + "metadata": {}, + "outputs": [], + "source": [ + "data[\"WO\"] = data['WO'].str.replace(',','.')\n", + "data[\"WO\"] = data[\"WO\"].astype(float)" + ] + }, + { + "cell_type": "code", + "execution_count": 101, + "id": "c254ae39", + "metadata": {}, + "outputs": [], + "source": [ + "R=np.corrcoef(data.WO, data.Consumption)[0][1]\n" + ] + }, + { + "cell_type": "code", + "execution_count": 113, + "id": "36e0b413", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "# create figure and axis objects with subplots()\n", + "fig,ax = plt.subplots()\n", + "# make a plot\n", + "ax.plot(data.year, data.WO, color=\"red\", marker=\"o\")\n", + "ax.set_title(label=f\"Correlation between students and beer consumption\\n R={round(R, 3)}\")\n", + "# set x-axis label\n", + "ax.set_xlabel(\"year\",fontsize=14)\n", + "# set y-axis label\n", + "ax.set_ylabel(\"University Students\",color=\"red\",fontsize=14)\n", + "# twin object for two different y-axis on the sample plot\n", + "ax2=ax.twinx()\n", + "# make a plot with different y-axis using second axis object\n", + "ax2.plot(data.year, data[\"Consumption\"],color=\"blue\",marker=\"o\")\n", + "ax2.set_ylabel(\"Consumption\",color=\"blue\",fontsize=14)\n", + "plt.savefig(\"consumptionGraph.jpg\", \n", + " format=\"jpeg\",\n", + " dpi=300,\n", + " bbox_inches='tight')\n" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.5" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/pictures/consumptionGraph.jpg b/pictures/consumptionGraph.jpg new file mode 100644 index 0000000..2410836 Binary files /dev/null and b/pictures/consumptionGraph.jpg differ diff --git a/solution_julius.md b/solution_julius.md new file mode 100644 index 0000000..2102907 --- /dev/null +++ b/solution_julius.md @@ -0,0 +1,26 @@ +

Solution


+

Titles of papers

+
    +
  1. MCC Van Dyke et al., 2019
  2. + +
  3. JT Harvey, Applied Ergonomics, 2002
  4. + +
  5. DW Ziegler et al., 2005
  6. + +
+

Correlation

+

+ +

+

There seems to be a correlation between the beer consumption and the amount
+of university students in the Netherlands. Whether this correlation also
+signals causality cannot be said from this dataset, but I certainly did my
+part to increase both numbers! +