app.py

import streamlit as st
import streamlit_option_menu as som
import plotly.graph_objects as go
import pandas as pd
import csv
import json

st.set_page_config(page_title="MUP", page_icon="bar-chart", layout = "wide")

#hiding default elememnts
hide_meu = """<style> #MainMenu {visibility: hidden;}
                footer {visibility: hidden;} </style>"""
st.markdown(hide_meu, unsafe_allow_html=True)

main_bar_selected = som.option_menu(None, ["Home", "University View", "Institutional Comparison"], icons = ["house-fill", "building", "book-fill"], orientation = "horizontal")
st.write("####")

def display_home_page():
    st.markdown('<h1 style="text-align: center; color: black; font: serif">MUP: Measuring University Performance</h1>', unsafe_allow_html=True)
    st.write("##")
    
    col1, col2 = st.columns([2, 1])

    with col1:
        st.markdown('<h2 style=" color: black;">Blazing fast university analytics at your fingertips</h2>', unsafe_allow_html=True)
        st.markdown('<h3 style=" color: black;">Administrators looking for peer institution data?</h3>', unsafe_allow_html=True)
        st.markdown('<h3 style=" color: black;">Students finding the right institution for your research career?</h3>', unsafe_allow_html=True)
        st.markdown('<h3 style=" color: black;">Researchers looking for the right university for your careers?</h3>', unsafe_allow_html=True)
        st.markdown('<h2 style=" color: black;">You have come to the right place!</h2>', unsafe_allow_html=True)
   
    with col2:
        st.image('data/chart_image.jpeg')

def display_uni_view_page():

    #code to filter by type and select the institutions
    col1, col2 = st.columns(2)
    with col1:
        type_select = st.radio("Filter by Institution Type", ["All", "Private", "Public"], horizontal=True)
    with col2:
        view_type = st.radio("View Type", ["Latest Stats", "Chart View"], horizontal=True)

    institution_list = []
    if type_select == "Private":
        with open("data/private_institution_list.csv", "r") as f:
            reader = csv.reader(f)
            for row in reader:
                institution_list.append(row[0])
    elif type_select == "Public":
        with open("data/public_institution_list.csv", "r") as f:
            reader = csv.reader(f)
            for row in reader:
                institution_list.append(row[0])
    else:
        with open("data/institution_list.csv", "r") as f:
            reader = csv.reader(f)
            for row in reader:
                institution_list.append(row[0])

    institution_select = st.selectbox("Select colleges to view", options = institution_list)
    st.write("##")

    def load_data(input_file_path, institution_name):
        data = pd.read_excel(input_file_path)
        data = data[data["Institution"] == institution_name]
        return data

    aamc = load_data("data/aamc.xlsx", institution_select)
    doctorates = load_data("data/doctorates.xlsx", institution_select)
    endowment = load_data("data/endowment.xlsx", institution_select)
    faculty_awards = load_data("data/faculty_awards.xlsx", institution_select)
    federal_research = load_data("data/federal_research.xlsx", institution_select)
    giving = load_data("data/giving.xlsx", institution_select)
    headcount = load_data("data/headcount.xlsx", institution_select)
    national_academy = load_data("data/national_academy.xlsx", institution_select)
    non_federal_research = load_data("data/non_federal_research.xlsx", institution_select)
    postdocs = load_data("data/postdocs.xlsx", institution_select)
    rnd_federal = load_data("data/rnd_by_discipline_federal.xlsx", institution_select)
    rnd_total = load_data("data/rnd_by_discipline_total.xlsx", institution_select)
    total_research = load_data("data/total_research.xlsx", institution_select)

    def latest_stats(institution_select):
        display_dict = {}
        display_dict['Medical Research Spending (in USD)']= str(int(aamc['2018']) / 1000000) + " Million"
        display_dict["PhD's graduated"]=  int(doctorates['2018'])
        display_dict["Endowment (in USD)"]= str(int(endowment['2018']) / 1000000) + " Million"
        display_dict["Number of annual Faculty Awards"]= int(faculty_awards['2018'])
        display_dict["Federal Research Spending (in USD)"]= str(int(federal_research['2018']) / 1000000) + " Million"
        display_dict["Annual Giving (in USD)"]= str(int(giving['2018']) / 1000000) + " Million"
        display_dict["Student Headcount"]= int(headcount['2018'])
        display_dict["National Academy Members"]= int(national_academy['2018'])
        display_dict["Non-Federal Research Spending (in USD)"]= str(int(non_federal_research['2018']) / 1000000) + " Million"
        display_dict["Postdoctoral Fellows"]= int(postdocs['2018'])
        display_dict["Total Research Spending (in USD)"]= str(int(total_research['2018']) / 1000000) + " Million"
        df = pd.DataFrame.from_dict(display_dict, orient='index')
        df.rename(columns = {0:'Values'}, inplace = True)
        st.markdown('<h3 style="text-align: center; color: black; font: serif">Table View</h3>', unsafe_allow_html=True)
        st.table(df.astype(str))
        col1, col2 = st.columns(2)
        with col1:
            st.download_button("Download this data as CSV", data = df.to_csv(), file_name = str(institution_select) + "_at_a_glance.csv")
        with col2:
            st.download_button("Download this data as JSON", data = json.dumps(display_dict), file_name = str(institution_select) + "_at_a_glance.json")
    
    def chart_view(institution_select):
        #figure details should have x-axis title and y-axis title, in that order
        def plot_helper(df, figure_details):
            data = df.copy()
            series = data.T[3:][::-1]
            series.reset_index(inplace=True)
            series.columns = ["Year", "Value"]
            figure = go.Figure()
            figure.add_trace(go.Scatter(x=series["Year"], y=series["Value"], name=list(data['Institution'])[0]))
            del data
            figure.update_layout(height = 600, width = 900, legend_orientation = 'h', xaxis_title = figure_details[0],
                yaxis_title = figure_details[1], font = dict(family = 'Serif'))
            figure.update_xaxes(nticks = 5)
            figure.update_yaxes(rangemode="tozero")
            return figure

        line_charts = []
        line_charts.append(plot_helper(federal_research, ["Year", "Spending"])) #0
        line_charts.append(plot_helper(total_research, ["Year", "Spending"])) #1
        line_charts.append(plot_helper(aamc, ["Year", "Spending"])) #2
        line_charts.append(plot_helper(endowment, ["Year", "Fund size"])) #3
        line_charts.append(plot_helper(giving, ["Year", "Giving"])) #4
        line_charts.append(plot_helper(doctorates, ["Year", "Number of PhD's"])) #5
        line_charts.append(plot_helper(postdocs, ["Year", "Number of Fellows"])) #6
        line_charts.append(plot_helper(headcount, ["Year", "Headcount"])) #7
        line_charts.append(plot_helper(national_academy, ["Year", "Number of Members"])) #8
        line_charts.append(plot_helper(faculty_awards, ["Year", "Number of Awards"]))#9

        rnd_fed_subjects = ["Fed_Life_Sci", "Fed_Phy_Sci", "Fed_Envir_Sci", "Fed_Eng","Fed_Comp_Sci", "Fed_Math","Fed_Psych","Fed_Social_Sci","Fed_Other_Sci"]    
        rnd_total_subjects = ["Tot_Life_Sci", "Tot_Phy_Sci", "Tot_Envir_Sci", "Tot_Eng","Tot_Comp_Sci", "Tot_Math","Tot_Psych","Tot_Social_Sci","Tot_Other_Sci"]    
        
        rnd_fed_bar = rnd_federal[rnd_fed_subjects]
        rnd_fed_bar.reset_index(inplace=True)
        rnd_fed_bar = rnd_fed_bar.T[1:]    
        fed_xlist = list(rnd_fed_bar.index)
        fed_ylist = list(rnd_fed_bar[0])
        rnd_fed_figure = go.Figure()
        rnd_fed_figure.add_trace(go.Bar(x=fed_xlist, y=fed_ylist)) 
        rnd_fed_figure.update_layout(xaxis_title = "Discipline", yaxis_title = "R&D (in USD)", font = dict(family = 'Serif'))

        rnd_total_bar = rnd_total[rnd_total_subjects]
        rnd_total_bar.reset_index(inplace=True)
        rnd_total_bar = rnd_total_bar.T[1:]
        total_xlist = list(rnd_total_bar.index)
        total_ylist = list(rnd_total_bar[0])
        rnd_total_figure = go.Figure()
        rnd_total_figure.add_trace(go.Bar(x=total_xlist, y=total_ylist))
        rnd_total_figure.update_layout(xaxis_title = "Discipline", yaxis_title = "R&D (in USD)", font = dict(family = 'Serif'))

        col1, col2 = st.columns(2)
        with col1:
            st.write("<h3 style='text-align: center; color: black;'>" + "Federal Research Spending (in USD)" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(line_charts[0], use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "Medical Research Spending (in USD)" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(line_charts[2], use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "Annual Giving (in USD)" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(line_charts[4], use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "Number of Postdoctoral Fellows" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(line_charts[6], use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "Number of National Academy Members" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(line_charts[8], use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "R&D Breakup (Federal Dollars)" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(rnd_fed_figure, use_container_width=True)
        with col2:
            st.write("<h3 style='text-align: center; color: black;'>" + "Total Research Spending (in USD)" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(line_charts[1], use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "Endowment Size (in USD)" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(line_charts[3], use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "Number of PhD's graduated" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(line_charts[5], use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "Total Student Headcount (all levels)" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(line_charts[7], use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "Annual Faculty Awards achieved" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(line_charts[9], use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "R&D Breakup (All Dollars)" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(rnd_total_figure, use_container_width=True)

    if view_type == "Latest Stats":
        latest_stats(institution_select)
    elif view_type == "Chart View":
        chart_view(institution_select)   

def display_institution_comparison():
    col1, col2 = st.columns(2)
    with col1:
        type_select = st.radio("Filter by Institution Type", ["All", "Private", "Public"], horizontal=True)
    with col2:
        view = st.selectbox("Choose a view", ["Researcher View", "Recruiter View"])
    institution_list = []
    if type_select == "Private":
        with open("data/private_institution_list.csv", "r") as f:
            reader = csv.reader(f)
            for row in reader:
                institution_list.append(row[0])
    elif type_select == "Public":
        with open("data/public_institution_list.csv", "r") as f:
            reader = csv.reader(f)
            for row in reader:
                institution_list.append(row[0])
    else:
        with open("data/institution_list.csv", "r") as f:
            reader = csv.reader(f)
            for row in reader:
                institution_list.append(row[0])
    institution_mselect = st.multiselect("Select Institutions to Compare", 
    options = institution_list, help = "For best results, select 2-3 institutions")

    def load_data(input_file_path, institution_list):
        data = pd.read_excel(input_file_path)
        data = data[data["Institution"].isin(institution_list)]
        return data

    def plot_helper(df, figure_details):
        data = df.copy()
        series = (data.drop(columns = ["UnitID", "Control"]).T)
        series.columns = series.iloc[0]
        series.reset_index(inplace=True)
        series = series.iloc[1:, :][::-1]
        series.rename(columns = {"index": "Year"}, inplace = True)
        figure = go.Figure()
        for i in institution_mselect:
            #add a line chart for each institution
            figure.add_trace(go.Scatter(x = series["Year"], y = series[i], name = i))
        figure.update_layout(height = 600, width = 900, legend_orientation = 'h', xaxis_title = figure_details[0],
         yaxis_title = figure_details[1], legend_title = "Institution Key", font = dict(family = 'Serif'))
        figure.update_xaxes(nticks = 5)
        figure.update_yaxes(rangemode = "tozero")
        del data
        return figure

    doctorates = load_data("data/doctorates.xlsx", institution_mselect)
    faculty_awards = load_data("data/faculty_awards.xlsx", institution_mselect)
    federal_research = load_data("data/federal_research.xlsx", institution_mselect)
    national_academy = load_data("data/national_academy.xlsx", institution_mselect)
    postdocs = load_data("data/postdocs.xlsx", institution_mselect)
    total_research = load_data("data/total_research.xlsx", institution_mselect)
    giving = load_data("data/giving.xlsx", institution_mselect)
    headcount = load_data("data/headcount.xlsx", institution_mselect)
    rnd_fed = load_data("data/rnd_by_discipline_federal.xlsx", institution_mselect)
    rnd_total = load_data("data/rnd_by_discipline_total.xlsx", institution_mselect)

    def researcher_content_writer():
        st.write("##")
        st.write("<h3 style='text-align: center; color: black;'>" + "Researcher View" + "</h3>", unsafe_allow_html=True)
        st.write("##")
        col1, col2 = st.columns(2)
        with col1:
            st.write("<h3 style='text-align: center; color: black;'>" + "Number of PhD's graduated" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(plot_helper(doctorates, ["Year", "Number of PhD's graduated"]), use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "Federal Research Spending" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(plot_helper(federal_research, ["Year", "Spending (in USD)"]), use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "National Academy Members" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(plot_helper(national_academy, ["Year", "Number of Academy Members"]), use_container_width=True)
        with col2:
            st.write("<h3 style='text-align: center; color: black;'>" + "Number of Postdoctoral Fellows" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(plot_helper(postdocs, ["Year", "Number of Postdocs"]), use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "Total Research Spending" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(plot_helper(total_research, ["Year", "Spending (in USD)"]), use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "Annual Faculty Awards achieved" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(plot_helper(faculty_awards, ["Year", "Number of Awards"]), use_container_width=True)
    

    def recruiter_content_writer():
        st.write("##")
        st.write("<h3 style='text-align: center; color: black;'>" + "Recruiter View" + "</h3>", unsafe_allow_html=True)
        st.write("##")
        col1, col2 = st.columns(2)
        with col1:
            st.write("<h3 style='text-align: center; color: black;'>" + "Total Research Spending" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(plot_helper(total_research, ["Year", "Spending (in USD)"]), use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "Number of PhD's graduated" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(plot_helper(doctorates, ["Year", "Number of PhD's graduated"]), use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "National Academy Members" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(plot_helper(national_academy, ["Year", "Number of Academy Members"]), use_container_width=True)
        with col2:
            st.write("<h3 style='text-align: center; color: black;'>" + "Annual Giving" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(plot_helper(giving, ["Year", "Annual Giving (in USD)"]), use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "Total Student Headcount" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(plot_helper(headcount, ["Year", "Headcount"]), use_container_width=True)
            st.write("<h3 style='text-align: center; color: black;'>" + "Annual Faculty Awards achieved" + "</h3>", unsafe_allow_html=True)
            st.plotly_chart(plot_helper(faculty_awards, ["Year", "Number of Awards"]), use_container_width=True)

    if view == "Researcher View":
        researcher_content_writer()
    elif view == "Recruiter View":
        recruiter_content_writer()

if main_bar_selected == "Home":
    display_home_page()
elif main_bar_selected == "University View":
    display_uni_view_page()
elif main_bar_selected == "Institutional Comaprison":
    display_institution_comparison()