Create app.py

app.py

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+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 Comaprison"], 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()