{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
"<img src=\"http://www.nserc-crsng.gc.ca/_gui/wmms.gif\" alt=\"Canada logo\" align=\"right\">\n",
"\n",
"<br>\n",
"\n",
"<img src=\"http://www.triumf.ca/sites/default/files/styles/gallery_large/public/images/nserc_crsng.gif?itok=H7AhTN_F\" alt=\"NSERC logo\" align=\"right\" width = 90>\n",
"\n",
"\n",
"\n",
"# Exploring NSERC Awards Data\n",
"\n",
"\n",
"Canada's [Open Government Portal](http://open.canada.ca/en) includes [NSERC Awards Data](http://open.canada.ca/data/en/dataset/c1b0f627-8c29-427c-ab73-33968ad9176e) from 1991 through 2016.\n",
"\n",
"The awards data (in .csv format) were copied to an [Amazon Web Services S3 bucket](http://docs.aws.amazon.com/AmazonS3/latest/dev/UsingBucket.html). This open Jupyter notebook starts an exploration of the NSERC investment portfolio during the 1995 -- 2016 epoch. If you'd like access to the data hosted on S3, please contact [James Colliander](http://colliand.com).)\n",
"\n",
"> **Acknowledgement:** I thank [Ian Allison](https://github.com/ianabc) and [James Colliander](http://colliand.com) of the [Pacific Institute for the Mathematical Sciences](http://www.pims.math.ca/) for building the [JupyterHub service](https://pims.jupyter.ca) and for help with this notebook. -- I. Heisz"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: plotly in /home/iheisz/.local/lib/python3.6/site-packages\n",
"Requirement already satisfied: six in /opt/conda/lib/python3.6/site-packages (from plotly)\n",
"Requirement already satisfied: requests in /opt/conda/lib/python3.6/site-packages (from plotly)\n",
"Requirement already satisfied: nbformat>=4.2 in /opt/conda/lib/python3.6/site-packages (from plotly)\n",
"Requirement already satisfied: pytz in /opt/conda/lib/python3.6/site-packages (from plotly)\n",
"Requirement already satisfied: decorator>=4.0.6 in /opt/conda/lib/python3.6/site-packages (from plotly)\n",
"Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/lib/python3.6/site-packages (from requests->plotly)\n",
"Requirement already satisfied: idna<2.7,>=2.5 in /opt/conda/lib/python3.6/site-packages (from requests->plotly)\n",
"Requirement already satisfied: urllib3<1.23,>=1.21.1 in /opt/conda/lib/python3.6/site-packages (from requests->plotly)\n",
"Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.6/site-packages (from requests->plotly)\n"
]
}
],
"source": [
"# For Plotting\n",
"import matplotlib.pyplot as plt\n",
"import matplotlib.ticker as mtick\n",
"!pip3 install plotly --user; #Plotly for graphing\n",
"import plotly.plotly as py\n",
"import plotly.graph_objs as go"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2009\n",
"2010\n",
"2011\n",
"2012\n",
"2013\n",
"2014\n",
"2015\n",
"2016\n"
]
}
],
"source": [
"# Note: haven't yet uploaded this to github or shared it\n",
"import numpy as np\n",
"import pandas as pd\n",
"import sys\n",
"\n",
"## Bring in a selection of the NSERC awards data starting with 1995 and ending with 2016.\n",
"## Throw away as much as you can to keep the DataFrame small enough to manipulate using a laptop.\n",
"\n",
"df = pd.DataFrame()\n",
"\n",
"startYear = 2009\n",
"endYear = 2017 ## The last year is not included, so if it was 2017 it means we include the 2016 collection but not 2017.\n",
"\n",
"## Reads and processes the raw csv datafiles.\n",
"for year in range(startYear, endYear):\n",
" file = 'https://s3.ca-central-1.amazonaws.com/open-data-ro/NSERC/NSERC_GRT_FYR' + str(year) + '_AWARD.csv.gz'\n",
" df = df.append(pd.read_csv(file, compression='gzip', usecols = [9, 11, 17], encoding='latin-1'))\n",
" \n",
" print(year)\n",
" \n",
"## Rename columns for better readability.\n",
"df.columns = ['FiscalYear', 'AwardAmount', 'Committee'] "
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>FiscalYear</th>\n",
" <th>AwardAmount</th>\n",
" <th>Committee</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1753</th>\n",
" <td>2009</td>\n",
" <td>34728</td>\n",
" <td>1508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3805</th>\n",
" <td>2009</td>\n",
" <td>46190</td>\n",
" <td>1508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5722</th>\n",
" <td>2009</td>\n",
" <td>48530</td>\n",
" <td>1508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5780</th>\n",
" <td>2009</td>\n",
" <td>23630</td>\n",
" <td>1508</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18980</th>\n",
" <td>2009</td>\n",
" <td>38941</td>\n",
" <td>1508</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" FiscalYear AwardAmount Committee\n",
"1753 2009 34728 1508\n",
"3805 2009 46190 1508\n",
"5722 2009 48530 1508\n",
"5780 2009 23630 1508\n",
"18980 2009 38941 1508"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Get the data for the Mathematics and Statistics Funding\n",
"mathFundingData = df.loc[(df['Committee'] == 1508)]\n",
"mathFundingData.head()\n",
"\n",
"# Make years the index so that data is easier to manipulate\n",
"# mathFundingData = mathFundingData.set_index('FiscalYear')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Total Awards Granted Per Year \n",
"Note: Committee 1508 for Mathematics and Statistics was created in 2009 so there is no pre-2009 data on it"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>FiscalYear</th>\n",
" <th>TotalAward</th>\n",
" <th>AwardCount</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>24129</th>\n",
" <td>2009</td>\n",
" <td>217360</td>\n",
" <td>6</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24222</th>\n",
" <td>2010</td>\n",
" <td>3813412</td>\n",
" <td>190</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23299</th>\n",
" <td>2011</td>\n",
" <td>6834781</td>\n",
" <td>356</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23611</th>\n",
" <td>2012</td>\n",
" <td>10187618</td>\n",
" <td>518</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13302</th>\n",
" <td>2013</td>\n",
" <td>13480881</td>\n",
" <td>691</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24247</th>\n",
" <td>2014</td>\n",
" <td>16235610</td>\n",
" <td>845</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24483</th>\n",
" <td>2015</td>\n",
" <td>16354650</td>\n",
" <td>863</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25084</th>\n",
" <td>2016</td>\n",
" <td>17231868</td>\n",
" <td>865</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" FiscalYear TotalAward AwardCount\n",
"24129 2009 217360 6\n",
"24222 2010 3813412 190\n",
"23299 2011 6834781 356\n",
"23611 2012 10187618 518\n",
"13302 2013 13480881 691\n",
"24247 2014 16235610 845\n",
"24483 2015 16354650 863\n",
"25084 2016 17231868 865"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Make a new column that is the sum of all Awards for each individual year\n",
"awardTotalsByYear = mathFundingData.assign(TotalAward=mathFundingData.groupby(['FiscalYear'])['AwardAmount'].transform('sum'))\n",
"\n",
"# Count the number of awards given in each year and make a new column with them called 'AwardCount'\n",
"awardTotalsByYear = awardTotalsByYear.assign(AwardCount=awardTotalsByYear.groupby('FiscalYear').cumcount() + 1)\n",
"#awardTotalsByYear = \n",
"# Make a new column that is the sum of all Awards that each individual has received \n",
"#year2016 = year2016.assign(TotalAward=year2016.groupby(['Name'])['AwardAmount'].transform('sum'))\n",
"\n",
"# Drop all but the last of duplicate years, now that we have the award amount info from them. \n",
"# We keep the last one because that is where the final value of cumCount is.\n",
"deduplicatedData = awardTotalsByYear.drop_duplicates(subset = 'FiscalYear', keep = 'last')\n",
"\n",
"deduplicatedData[['FiscalYear','TotalAward', 'AwardCount']]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Plot of the Data"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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eCEyNiE2AqfmxUa16doAN1x66zLSdOWvjVClv1ToZNmsok1JVUpI+QhpoUMA/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\nMyvl/wOc8/F6rNYacQAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1ad07b91d0>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot Settings\n",
"data = mathFundingData\n",
"\n",
"xAxis = 'FiscalYear'\n",
"yAxis = 'AwardAmount'\n",
"\n",
"yAxisRange = [0,120]\n",
"yScalingFactor = 10**3\n",
"yScalingFactorString = ' In Thousands'\n",
"\n",
"plotPointSizes = 3 \n",
"\n",
"title = 'Graph of Awards Given by Committee 1508'\n",
"\n",
"# Make the Plot\n",
"axes = plt.gca()\n",
"x = data[xAxis]\n",
"plt.xlabel(xAxis, fontsize=16)\n",
"\n",
"y = data[yAxis]\n",
"plt.ylabel(yAxis+yScalingFactorString, fontsize=14)\n",
"axes.set_ylim(yAxisRange)\n",
"y = y/yScalingFactor\n",
"\n",
"plt.title(title)\n",
"\n",
"plot = plt.scatter(x,y,s=plotPointSizes)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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NzDLhQDYzy4QD2cwsEw5kM7NMOJDNzDLhQDYzy4QD2cwsEw5kM7NMOJDNzDLhQDYzy4QD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\nB7KZWSYcyGZmmfh/CroTT+nLF/0AAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1ac7cccba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot Settings\n",
"year = 2016\n",
"data = mathFundingData.loc[(mathFundingData['FiscalYear'] == year)]\n",
"\n",
"yAxis = 'FiscalYear'\n",
"xAxis = 'AwardAmount'\n",
"\n",
"xScalingFactor = 10**3\n",
"xScalingFactorString = ' In Thousands'\n",
"\n",
"yAxisRange = [1.99,2.1]\n",
"\n",
"plotPointSizes = 3 \n",
"\n",
"title = 'Graph of Awards Given by Committee 1508 in 2016'\n",
"\n",
"# Make the Plot\n",
"axes = plt.gca()\n",
"x = data[xAxis]\n",
"plt.xlabel(xAxis, fontsize=16)\n",
"\n",
"y = data[yAxis]\n",
"#plt.ylabel(yAxis+yScalingFactorString, fontsize=14)\n",
"axes.set_ylim(yAxisRange)\n",
"y = y/yScalingFactor\n",
"axes.set_yticklabels([]) # make it so the y axis is not numbered\n",
"\n",
"plt.title(title)\n",
"\n",
"plot = plt.scatter(x,y,s=plotPointSizes)\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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QTbgEAACgm3AJAABAN+ESAACAbsIlAAAA3YRLAAAAugmXAAAAdBMuAQAA6CZcAgAA0E24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\n8/8BwUOULfB+uIsAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7f1ac7cab438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"ax = data[[xAxis]].plot(kind='bar', title =\"V comp\", figsize=(15, 10), legend=True, fontsize=12)\n",
"ax.set_xlabel(yAxis, fontsize=12)\n",
"ax.set_ylabel(xAxis, fontsize=12)\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f1ac95cfc50>], dtype=object)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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"text/plain": [
"<matplotlib.figure.Figure at 0x7f1ac9672438>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Plot Settings\n",
"year = 2016\n",
"data = mathFundingData.loc[(mathFundingData['FiscalYear'] == year)]\n",
"\n",
"xAxis = 'FiscalYear'\n",
"#xAxis = 'AwardAmount'\n",
"\n",
"xScalingFactor = 10**3\n",
"xScalingFactorString = ' In Thousands'\n",
"\n",
"yAxisRange = [1.99,2.1]\n",
"\n",
"plotPointSizes = 3 \n",
"\n",
"title = 'Graph of Awards Given by Committee 1508 in 2016'\n",
"\n",
"# Make the Plot\n",
"\n",
"mathFundingData.hist(column='AwardAmount', ax=plt.gca())"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"application/vnd.plotly.v1+json": {
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"line": {
"color": "rgba (31, 119, 180, 1)",
"dash": "solid",
"width": 1.5
},
"mode": "lines",
"name": "FiscalYear",
"type": "scatter",
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],
"yaxis": "y1"
}
],
"layout": {
"autosize": false,
"height": 288,
"hovermode": "closest",
"margin": {
"b": 36,
"l": 54,
"pad": 0,
"r": 43,
"t": 34
},
"showlegend": false,
"width": 432,
"xaxis1": {
"anchor": "y1",
"domain": [
0,
1
],
"mirror": "ticks",
"nticks": 9,
"range": [
-2287.05,
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],
"showgrid": false,
"showline": true,
"side": "bottom",
"tickfont": {
"size": 10
},
"ticks": "inside",
"type": "linear",
"zeroline": false
},
"yaxis1": {
"anchor": "x1",
"domain": [
0,
1
],
"mirror": "ticks",
"nticks": 11,
"range": [
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],
"showgrid": false,
"showline": true,
"side": "left",
"tickfont": {
"size": 10
},
"ticks": "inside",
"type": "linear",
"zeroline": false
}
}
}
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# Make the Plot Interactive \n",
"import plotly.offline as py\n",
"from plotly.offline import init_notebook_mode, iplot\n",
"import plotly.tools as tls\n",
"import matplotlib.pylab as plt\n",
"from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\n",
"\n",
"fig = plt.Figure()\n",
"axes = fig.gca()\n",
"\n",
"axes.plot(x,y)\n",
"canvas = FigureCanvas(fig)\n",
"plotly_fig = tls.mpl_to_plotly(fig)\n",
"py.iplot(plotly_fig)"
]
}
],
"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": "3.6.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}