PublicAwardSearch7363-ReferenceCode.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing \"7363\" Data (Reference Code)\n",
    "Author: Nicholas Goldsmith <br>\n",
    "Start Date: 20 December 2022 <br>\n",
    "Last Updated: 20 December 2022"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Purpose\n",
    "This is a Jupyter notebook with some basic analysis of public US government data. I am making this analysis as a means of keeping track of some common functions that I use in Python scripting. In other words, this is personal reference code and the analysis is intended for me to reference functions I am used to using. The point of this script is NOT to make a meaningful analysis of the utilized data. I would recommend https://beta.nsf.gov/about/about-nsf-by-the-numbers if you are interested in more informative analyses.\n",
    "<br><br> \n",
    "Disclaimer: The contents of this document do not necessarily represent the views of my employer nor the views of any organization I may be associated with. The contents of this document are meant to serve as a reference for me of Python functions and code chunks I want to remember, and potentially to be utilized as a way to add to my individual Python repetoire (eg. asking others how they might crunch a certain bit of data)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas # The pandas package allows me to read in a csv and manipulate it as a dataframe\n",
    "import matplotlib.pyplot as plt # Package to help with some of the graphing."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Data\n",
    "I am using publically available data. Using the public NSF Award Search (https://nsf.gov/awardsearch/advancedSearch.jsp), I've searched Element Code \"7363\" and included all active and expired awards. There are 2573 awards in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "Awards = pandas.read_excel(\"Awards-2022-12-20.xls\") # Reads in the data file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 2573 entries, 0 to 2572\n",
      "Data columns (total 25 columns):\n",
      " #   Column                   Non-Null Count  Dtype \n",
      "---  ------                   --------------  ----- \n",
      " 0   AwardNumber              2573 non-null   int64 \n",
      " 1   Title                    2573 non-null   object\n",
      " 2   NSFOrganization          2573 non-null   object\n",
      " 3   Program(s)               2573 non-null   object\n",
      " 4   StartDate                2573 non-null   object\n",
      " 5   LastAmendmentDate        2573 non-null   object\n",
      " 6   PrincipalInvestigator    2568 non-null   object\n",
      " 7   State                    2573 non-null   object\n",
      " 8   Organization             2573 non-null   object\n",
      " 9   AwardInstrument          2573 non-null   object\n",
      " 10  ProgramManager           2573 non-null   object\n",
      " 11  EndDate                  2573 non-null   object\n",
      " 12  AwardedAmountToDate      2573 non-null   object\n",
      " 13  Co-PIName(s)             773 non-null    object\n",
      " 14  PIEmailAddress           2568 non-null   object\n",
      " 15  OrganizationStreet       2573 non-null   object\n",
      " 16  OrganizationCity         2573 non-null   object\n",
      " 17  OrganizationState        2573 non-null   object\n",
      " 18  OrganizationZip          2573 non-null   int64 \n",
      " 19  OrganizationPhone        2573 non-null   int64 \n",
      " 20  NSFDirectorate           2573 non-null   object\n",
      " 21  ProgramElementCode(s)    2573 non-null   object\n",
      " 22  ProgramReferenceCode(s)  2538 non-null   object\n",
      " 23  ARRAAmount               2573 non-null   object\n",
      " 24  Abstract                 2570 non-null   object\n",
      "dtypes: int64(3), object(22)\n",
      "memory usage: 502.7+ KB\n"
     ]
    }
   ],
   "source": [
    "Awards.info() # Gives the structure of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Quality Control"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0             $0.00\n",
       "1             $0.00\n",
       "2             $0.00\n",
       "3             $0.00\n",
       "4             $0.00\n",
       "           ...     \n",
       "2568          $0.00\n",
       "2569          $0.00\n",
       "2570          $0.00\n",
       "2571          $0.00\n",
       "2572    $150,000.00\n",
       "Name: ARRAAmount, Length: 2573, dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ARRAAmount appears to be stored as text rather than a number\n",
    "Awards[\"ARRAAmount\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            0.00\n",
       "1            0.00\n",
       "2            0.00\n",
       "3            0.00\n",
       "4            0.00\n",
       "          ...    \n",
       "2568         0.00\n",
       "2569         0.00\n",
       "2570         0.00\n",
       "2571         0.00\n",
       "2572    150000.00\n",
       "Name: ARRAAmount, Length: 2573, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To fix this I will first need to remove the dollar signs\n",
    "Awards[\"ARRAAmount\"] = Awards[\"ARRAAmount\"].replace('\\$|,', '', regex=True)\n",
    "Awards[\"ARRAAmount\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0            0.0\n",
       "1            0.0\n",
       "2            0.0\n",
       "3            0.0\n",
       "4            0.0\n",
       "          ...   \n",
       "2568         0.0\n",
       "2569         0.0\n",
       "2570         0.0\n",
       "2571         0.0\n",
       "2572    150000.0\n",
       "Name: ARRAAmount, Length: 2573, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Now I can convert it to a float\n",
    "Awards[\"ARRAAmount\"] = pandas.to_numeric(Awards[\"ARRAAmount\"])\n",
    "Awards[\"ARRAAmount\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ARRA\n",
    "First I want to make a subset for only those awards with ARRA funding, and then I can look at it some more"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "ARRA = Awards[Awards[\"ARRAAmount\"] > 0] # Subsets all those where ARRA Amount is more than 0\n",
    "ARRA = ARRA.reset_index() # resets the index of the dataframe, which is useful if one wants to for-loop through it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "70"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(ARRA['ARRAAmount']) # number of ARRA Awards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "23983399.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARRA['ARRAAmount'].sum() # Total amount of ARRA Awards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "342619.9857142857"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ARRA['ARRAAmount'].mean() # Mean amount of ARRA funds used for ARRA awards"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "([], [])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Code to make a plot bigger\n",
    "plt.rcParams[\"figure.figsize\"] = 12, 9 # Makes the plot bigger\n",
    "plt.rcParams.update({'font.size' : 18}) # Makes the plot text bigger\n",
    "\n",
    "# Plot\n",
    "# Makes a bar plot of the ARRA amounts with the data sorted by size\n",
    "ARRA['ARRAAmount'].sort_values().plot.bar(use_index=False) \n",
    "plt.title(\"Distribution of ARRA Amounts for ARRA-Supported Awards\")\n",
    "plt.xlabel('Awards Ordered by ARRA Amount')\n",
    "plt.ylabel('ARRA Amount ($)')\n",
    "plt.xticks([],[])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## States"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "State\n",
       "AL     18\n",
       "AR      2\n",
       "AZ     58\n",
       "CA    390\n",
       "CO     48\n",
       "CT     36\n",
       "DC     33\n",
       "DE      8\n",
       "FL     79\n",
       "GA     75\n",
       "HI      3\n",
       "IA     23\n",
       "ID      1\n",
       "IL    118\n",
       "IN     63\n",
       "KS     20\n",
       "KY      9\n",
       "LA     10\n",
       "MA    239\n",
       "MD     39\n",
       "MI     70\n",
       "MN     30\n",
       "MO     38\n",
       "MS      5\n",
       "MT      8\n",
       "NC     80\n",
       "ND      2\n",
       "NE     19\n",
       "NH      7\n",
       "NJ    124\n",
       "NM     11\n",
       "NV      7\n",
       "NY    191\n",
       "OH     66\n",
       "OK      6\n",
       "OR     27\n",
       "PA    165\n",
       "PR      1\n",
       "RI     11\n",
       "SC     20\n",
       "SD      4\n",
       "TN     30\n",
       "TX    168\n",
       "UT     16\n",
       "VA     98\n",
       "VT      1\n",
       "WA     43\n",
       "WI     53\n",
       "Name: AwardNumber, dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Awards.groupby('State')['AwardNumber'].nunique() # Returns the number of unique Awards by State"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Directorate and Program Element Code(s)\n",
    "This one is mostly just to show grouping by multiple things at once"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NSFDirectorate  ProgramElementCode(s)                   \n",
       "CSE             005Y, 033Y, 7363                            1\n",
       "                018Y, 1640, 7363                            1\n",
       "                024F, 1688, 7363                            1\n",
       "                024F, 7363                                  2\n",
       "                024F, 7363, 7917                            1\n",
       "                                                           ..\n",
       "MPS             7363, 7564, 7976                            1\n",
       "O/D             1253, 7299, 7363                            2\n",
       "                7299, 7363                                  1\n",
       "SBE             1372, 7363                                  1\n",
       "                1640, 1714, 7354, 7363, 7603, 8060, 8078    1\n",
       "Name: AwardNumber, Length: 159, dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Awards.groupby(['NSFDirectorate','ProgramElementCode(s)'])['AwardNumber'].nunique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CAREER vs Non-CAREER by Year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Adding a Year Column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "Awards['Year'] = 0 # Creating a year column with all 0's\n",
    "\n",
    "# The below uses a for loop to go through every row. It takes the last four characters of\n",
    "# Start Date and calls them the year\n",
    "for i in range(0, len(Awards['AwardNumber'])):\n",
    "    Awards['Year'].at[i] = int(Awards['StartDate'][i][len(Awards['StartDate'][i])-4:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Marking which awards are CAREERs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# NSF Awards often have program information in the title\n",
    "# CAREER Awards /should/ have CAREER in their title\n",
    "# So I will make a substring to check if it is in the title\n",
    "substring = 'CAREER'\n",
    "# I'll create an empty array to add the information to\n",
    "IsCAREER = []\n",
    "\n",
    "for row in Awards['Title']: # Going through every Title\n",
    "    rowAllCaps = row.upper() # Make it all caps to capitalization doesn't affect equality\n",
    "    placeholder = False\n",
    "    if substring in rowAllCaps: # Check if the substring is in the Title\n",
    "        placeholder = True\n",
    "    IsCAREER.append(placeholder) # Add the result to the array\n",
    "\n",
    "Awards['IsCAREER'] = IsCAREER # Make the array a column in the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotting CAREER vs non-CAREER by Year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Awards')"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# First we need the data of CAREERs and non-CAREERs by Year\n",
    "AwardsCAREER = Awards[Awards['IsCAREER']] # Subsetting the CAREERS\n",
    "CAREERAwardsByYear = AwardsCAREER.groupby('Year')['AwardNumber'].nunique()\n",
    "\n",
    "AwardsNotCAREER = Awards[Awards['IsCAREER']==False]\n",
    "NotCAREERAwardsByYear = AwardsNotCAREER.groupby('Year')['AwardNumber'].nunique()\n",
    "\n",
    "# Now make one dataset to plot from\n",
    "AwardsByYear = pandas.DataFrame({'CAREER' : CAREERAwardsByYear,\n",
    "                                'Non-CAREER' : NotCAREERAwardsByYear})\n",
    "\n",
    "# Code to make a plot bigger\n",
    "plt.rcParams[\"figure.figsize\"] = 12, 9 # Makes the plot bigger\n",
    "plt.rcParams.update({'font.size' : 18}) # Makes the plot text bigger\n",
    "\n",
    "# Plot\n",
    "AwardsByYear.plot.line(lw=4)\n",
    "plt.title(\"Awards by Year\")\n",
    "plt.xlabel(\"Year the Award Started\")\n",
    "plt.ylabel(\"Number of Awards\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking Institutions that got an award but not a CAREER"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['California State University-Dominguez Hills Foundation',\n",
       " 'Franklin W. Olin College of Engineering',\n",
       " 'University of Houston - Clear Lake',\n",
       " 'University of Minnesota Duluth',\n",
       " 'University of Massachusetts Boston',\n",
       " 'Bucknell University',\n",
       " 'University Enterprises, Incorporated',\n",
       " 'San Diego State University Foundation',\n",
       " 'Florida Atlantic University',\n",
       " 'BBN Systems & Technologies Corporation',\n",
       " 'Saint Louis University',\n",
       " 'Corporation for Education Network Initiatives in California',\n",
       " 'Chicago Zoological Society',\n",
       " 'Electrical and Computer Engineering Department Heads Association',\n",
       " 'Research Foundation Of The City University Of New York (Lehman)',\n",
       " 'University of Missouri-Kansas City',\n",
       " 'University of Mississippi',\n",
       " 'Texas Tech University',\n",
       " 'University of Colorado at Denver-Downtown Campus',\n",
       " 'Henry M Jackson Fdn for Advmt of Military Medicine',\n",
       " 'Institute of Electrical & Electronics Engineers, Inc.',\n",
       " 'South Dakota State University',\n",
       " 'College of William and Mary',\n",
       " 'Texas A&M University-Commerce',\n",
       " 'Rutgers, The State University of New Jersey-RBHS-Robert Wood',\n",
       " 'University of Missouri-Columbia',\n",
       " 'Denison University',\n",
       " 'Palo Alto Research Center Incorporated',\n",
       " 'Florida Institute of Technology',\n",
       " 'Louisiana State University',\n",
       " 'Ball State University',\n",
       " 'Florida State University',\n",
       " 'Brigham Young University',\n",
       " 'Dakota State University',\n",
       " 'National Academy of Sciences',\n",
       " 'Educause',\n",
       " 'University of Denver',\n",
       " 'South Dakota School of Mines and Technology',\n",
       " 'Polytechnic University of New York',\n",
       " 'Franklin and Marshall College',\n",
       " 'Argon ST Network Systems',\n",
       " 'Kennesaw State University Research and Service Foundation',\n",
       " 'North Dakota State University Fargo',\n",
       " 'University of Wisconsin-Whitewater',\n",
       " 'University of Cincinnati Main Campus',\n",
       " 'Santa Clara University',\n",
       " 'Complex Communications Research, Inc',\n",
       " 'New Mexico Consortium',\n",
       " 'University of North Texas',\n",
       " 'Appalachian State University',\n",
       " 'Oklahoma State University',\n",
       " 'SRI International',\n",
       " 'Lehigh University',\n",
       " 'University of San Francisco',\n",
       " 'University of Kansas Center for Research Inc',\n",
       " 'Air Force Institute of Tech',\n",
       " 'Mozilla Foundation',\n",
       " 'Raytheon BBN Technologies Corp.',\n",
       " 'Georgetown University',\n",
       " 'University of Hawaii',\n",
       " 'Clarkson University',\n",
       " 'Fordham University',\n",
       " 'MCNC',\n",
       " 'University of Texas at Dallas',\n",
       " 'New York Institute of Technology',\n",
       " 'Corporation for National Research Initiatives (NRI)',\n",
       " 'Manhattan College',\n",
       " 'Northern Arizona University',\n",
       " 'The Software Defined Radio Forum Inc.',\n",
       " 'Virginia Commonwealth University',\n",
       " 'Wichita State University',\n",
       " 'Research Triangle Institute',\n",
       " 'Wellesley College',\n",
       " 'California Institute of Technology',\n",
       " 'Washington University',\n",
       " 'International Computer Science Institute',\n",
       " 'Marquette University',\n",
       " 'University of Delaware',\n",
       " 'Tufts University',\n",
       " 'University of Arkansas Little Rock',\n",
       " 'Texas Christian University',\n",
       " 'California State University-Fresno Foundation',\n",
       " 'University of Missouri-Saint Louis',\n",
       " 'Norfolk State University',\n",
       " 'Computing Research Association',\n",
       " 'New Mexico State University',\n",
       " 'Nokia of America Corporation',\n",
       " 'University of Southern Mississippi',\n",
       " 'CUNY Brooklyn College',\n",
       " 'University of Colorado at Colorado Springs',\n",
       " 'University of Oklahoma Norman Campus',\n",
       " 'University of Massachusetts, Dartmouth',\n",
       " 'Lamar University',\n",
       " 'University of Vermont & State Agricultural College',\n",
       " 'USENIX Association',\n",
       " 'Idaho State University',\n",
       " 'University of Puerto Rico Mayaguez',\n",
       " 'Oakland University',\n",
       " 'University of Georgia Research Foundation Inc',\n",
       " 'CUNY College of Staten Island',\n",
       " 'Wesleyan University',\n",
       " 'Towson University',\n",
       " 'Southern Illinois University at Carbondale',\n",
       " 'Clemson University',\n",
       " 'University of Arkansas',\n",
       " 'Wake Forest University',\n",
       " 'Naval Postgraduate School',\n",
       " 'University of Nebraska at Omaha',\n",
       " 'The University of Central Florida Board of Trustees',\n",
       " 'Azusa Pacific University',\n",
       " 'Trustees of Boston University',\n",
       " 'Kennesaw State University',\n",
       " 'Drexel University',\n",
       " 'Colorado School of Mines',\n",
       " 'University of Akron',\n",
       " 'Oral Roberts University',\n",
       " 'Tulane University',\n",
       " 'Claremont Graduate University',\n",
       " 'Texas State University - San Marcos',\n",
       " 'University of Wisconsin-Milwaukee',\n",
       " 'INTERNET2',\n",
       " 'Mitre Corporation Virginia',\n",
       " 'University of Memphis',\n",
       " 'University of Kentucky Research Foundation']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The code below looks at which organizations are represented in the list of all awards\n",
    "# but NOT in the list of just CAREER awards\n",
    "list(set(Awards['Organization']) - set(AwardsCAREER['Organization']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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