https://github.com/hongtaoh/32vis
Tip revision: 9960413711b0efb1f51ff7cce3548d259be8d8cb authored by Hongtao Hao on 24 May 2025, 20:13:11 UTC
Update README.md
Update README.md
Tip revision: 9960413
Authors_Visuals.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "628383c9",
"metadata": {},
"source": [
"This notebooks documents the process of generating Author visualizations."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "ec679238",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"from collections import Counter\n",
"import seaborn as sns\n",
"import matplotlib\n",
"font = {'family' : 'Arial',\n",
" 'weight' : 'normal',\n",
" 'size' : 14}\n",
"matplotlib.rc('font', **font)\n",
"# matplotlib.rcParams['figure.dpi']= 200\n",
"import altair as alt\n",
"pd.set_option('display.max_rows', 500)\n",
"import numpy as np\n",
"import plotnine as p9\n",
"from plotnine import ggplot, geom_point, aes, stat_smooth, facet_wrap\n",
"from altair_saver import save"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "73a84a99",
"metadata": {},
"outputs": [],
"source": [
"author = pd.read_csv('../data/ht_class/ht_cleaned_author_df.csv')\n",
"paper = pd.read_csv('../data/ht_class/ht_cleaned_paper_df.csv')"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "9cf4a711",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"12428"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"total_authors = author.shape[0]\n",
"total_authors"
]
},
{
"cell_type": "markdown",
"id": "37a01eb3",
"metadata": {},
"source": [
"### Number of participating countries by year"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "bad28c36",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Year</th>\n",
" <th>Number of participating countries</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1990-01-01</td>\n",
" <td>5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1991-01-01</td>\n",
" <td>8</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1992-01-01</td>\n",
" <td>11</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1993-01-01</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1994-01-01</td>\n",
" <td>9</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Number of participating countries\n",
"0 1990-01-01 5\n",
"1 1991-01-01 8\n",
"2 1992-01-01 11\n",
"3 1993-01-01 12\n",
"4 1994-01-01 9"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cntry_num_by_year = author.groupby('Year')[\n",
" 'Affiliation Country Code'].nunique().reset_index(\n",
" name='Number of participating countries')\n",
"# cntry_num_by_year.rename(columns = {'Year': 'year'}, inplace = True)\n",
"cntry_num_by_year['Year'] = pd.to_datetime(\n",
" cntry_num_by_year['Year'], format='%Y')\n",
"cntry_num_by_year.head()"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "c8aa3b4b",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"g = sns.lineplot(x = 'Year', \n",
" y = 'Number of participating countries', \n",
" data = cntry_num_by_year\n",
" )\n",
"g.set_ylim(0,30)\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "d5520f30",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-bfbe1eb0d2fc4d0aaf520c41a27563a1\"></div>\n",
"<script type=\"text/javascript\">\n",
" var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
" (function(spec, embedOpt){\n",
" let outputDiv = document.currentScript.previousElementSibling;\n",
" if (outputDiv.id !== \"altair-viz-bfbe1eb0d2fc4d0aaf520c41a27563a1\") {\n",
" outputDiv = document.getElementById(\"altair-viz-bfbe1eb0d2fc4d0aaf520c41a27563a1\");\n",
" }\n",
" const paths = {\n",
" \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
" \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
" \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
" };\n",
"\n",
" function maybeLoadScript(lib, version) {\n",
" var key = `${lib.replace(\"-\", \"\")}_version`;\n",
" return (VEGA_DEBUG[key] == version) ?\n",
" Promise.resolve(paths[lib]) :\n",
" new Promise(function(resolve, reject) {\n",
" var s = document.createElement('script');\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" s.async = true;\n",
" s.onload = () => {\n",
" VEGA_DEBUG[key] = version;\n",
" return resolve(paths[lib]);\n",
" };\n",
" s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
" s.src = paths[lib];\n",
" });\n",
" }\n",
"\n",
" function showError(err) {\n",
" outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
" throw err;\n",
" }\n",
"\n",
" function displayChart(vegaEmbed) {\n",
" vegaEmbed(outputDiv, spec, embedOpt)\n",
" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
" }\n",
"\n",
" if(typeof define === \"function\" && define.amd) {\n",
" requirejs.config({paths});\n",
" require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"title\": {\"anchor\": \"start\"}}, \"data\": {\"name\": \"data-081a37db6bfaf91777c7255d4ac745af\"}, \"mark\": \"line\", \"encoding\": {\"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"Number of participating countries\", \"type\": \"quantitative\"}}, \"title\": \"A\", \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-081a37db6bfaf91777c7255d4ac745af\": [{\"Year\": \"1990-01-01T00:00:00\", \"Number of participating countries\": 5}, {\"Year\": \"1991-01-01T00:00:00\", \"Number of participating countries\": 8}, {\"Year\": \"1992-01-01T00:00:00\", \"Number of participating countries\": 11}, {\"Year\": \"1993-01-01T00:00:00\", \"Number of participating countries\": 12}, {\"Year\": \"1994-01-01T00:00:00\", \"Number of participating countries\": 9}, {\"Year\": \"1995-01-01T00:00:00\", \"Number of participating countries\": 11}, {\"Year\": \"1996-01-01T00:00:00\", \"Number of participating countries\": 13}, {\"Year\": \"1997-01-01T00:00:00\", \"Number of participating countries\": 13}, {\"Year\": \"1998-01-01T00:00:00\", \"Number of participating countries\": 14}, {\"Year\": \"1999-01-01T00:00:00\", \"Number of participating countries\": 11}, {\"Year\": \"2000-01-01T00:00:00\", \"Number of participating countries\": 14}, {\"Year\": \"2001-01-01T00:00:00\", \"Number of participating countries\": 12}, {\"Year\": \"2002-01-01T00:00:00\", \"Number of participating countries\": 15}, {\"Year\": \"2003-01-01T00:00:00\", \"Number of participating countries\": 15}, {\"Year\": \"2004-01-01T00:00:00\", \"Number of participating countries\": 16}, {\"Year\": \"2005-01-01T00:00:00\", \"Number of participating countries\": 19}, {\"Year\": \"2006-01-01T00:00:00\", \"Number of participating countries\": 18}, {\"Year\": \"2007-01-01T00:00:00\", \"Number of participating countries\": 19}, {\"Year\": \"2008-01-01T00:00:00\", \"Number of participating countries\": 19}, {\"Year\": \"2009-01-01T00:00:00\", \"Number of participating countries\": 19}, {\"Year\": \"2010-01-01T00:00:00\", \"Number of participating countries\": 19}, {\"Year\": \"2011-01-01T00:00:00\", \"Number of participating countries\": 20}, {\"Year\": \"2012-01-01T00:00:00\", \"Number of participating countries\": 21}, {\"Year\": \"2013-01-01T00:00:00\", \"Number of participating countries\": 17}, {\"Year\": \"2014-01-01T00:00:00\", \"Number of participating countries\": 21}, {\"Year\": \"2015-01-01T00:00:00\", \"Number of participating countries\": 21}, {\"Year\": \"2016-01-01T00:00:00\", \"Number of participating countries\": 20}, {\"Year\": \"2017-01-01T00:00:00\", \"Number of participating countries\": 21}, {\"Year\": \"2018-01-01T00:00:00\", \"Number of participating countries\": 18}, {\"Year\": \"2019-01-01T00:00:00\", \"Number of participating countries\": 17}, {\"Year\": \"2020-01-01T00:00:00\", \"Number of participating countries\": 24}, {\"Year\": \"2021-01-01T00:00:00\", \"Number of participating countries\": 26}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"source = cntry_num_by_year\n",
"\n",
"alt.Chart(source).mark_line().encode(\n",
" x='Year:T',\n",
" y=alt.Y(\n",
" 'Number of participating countries',\n",
" )\n",
").properties(\n",
" title = 'A'\n",
").configure_title(\n",
" anchor='start', \n",
")"
]
},
{
"cell_type": "markdown",
"id": "d923d097",
"metadata": {},
"source": [
"### By continent"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "c8723cb5",
"metadata": {},
"outputs": [],
"source": [
"from pycountry_convert import country_alpha2_to_continent_code\n",
"import pycountry"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "6ee1cc97",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>code</th>\n",
" <th>count</th>\n",
" <th>continent</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>US</td>\n",
" <td>6576</td>\n",
" <td>NA</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>NL</td>\n",
" <td>308</td>\n",
" <td>EU</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>DE</td>\n",
" <td>1656</td>\n",
" <td>EU</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>CH</td>\n",
" <td>161</td>\n",
" <td>EU</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>IL</td>\n",
" <td>72</td>\n",
" <td>AS</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" code count continent\n",
"0 US 6576 NA\n",
"1 NL 308 EU\n",
"2 DE 1656 EU\n",
"3 CH 161 EU\n",
"4 IL 72 AS"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dic = dict(Counter(author['Affiliation Country Code']))\n",
"continent_df = pd.DataFrame(dic.items(), columns = ['code', 'count'])\n",
"continent_df['continent'] = continent_df['code'].apply(\n",
" lambda x: country_alpha2_to_continent_code(x))\n",
"continent_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "9e035ff2",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>continent</th>\n",
" <th>count</th>\n",
" <th>proportion</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>Africa</td>\n",
" <td>3</td>\n",
" <td>0.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Asia</td>\n",
" <td>1383</td>\n",
" <td>0.111</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Europe</td>\n",
" <td>3802</td>\n",
" <td>0.306</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>N. America</td>\n",
" <td>7005</td>\n",
" <td>0.564</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Oceania</td>\n",
" <td>151</td>\n",
" <td>0.012</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>S. America</td>\n",
" <td>84</td>\n",
" <td>0.007</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" continent count proportion\n",
"0 Africa 3 0.000\n",
"1 Asia 1383 0.111\n",
"2 Europe 3802 0.306\n",
"3 N. America 7005 0.564\n",
"4 Oceania 151 0.012\n",
"5 S. America 84 0.007"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"continent_count = continent_df.groupby(\n",
" 'continent')['count'].sum().reset_index()\n",
"continent_count['proportion'] = round(\n",
" continent_count['count'] / continent_count['count'].sum(),\n",
" 3\n",
")\n",
"continent_count.replace({'AF': 'Africa',\n",
" 'AS': 'Asia',\n",
" 'EU': 'Europe',\n",
" 'NA': 'N. America',\n",
" 'SA': 'S. America',\n",
" 'OC': 'Oceania',\n",
" }, inplace = True)\n",
"continent_count"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "45918ffc",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.barplot(x = 'continent', y = 'count', data = continent_count)\n",
"plt.yscale('log')"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "248ffe16",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
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" }\n",
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" \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
" \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
" \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
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"\n",
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" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
" }\n",
"\n",
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"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"source = continent_count\n",
"\n",
"chart = alt.Chart(source).mark_bar().encode(\n",
" x='continent',\n",
" y=alt.Y(\n",
" 'count',\n",
" scale=alt.Scale(type=\"log\") # Here the scale is applied\n",
" ),\n",
" color = 'continent',\n",
").properties(\n",
" title = 'A'\n",
")\n",
"\n",
"chart"
]
},
{
"cell_type": "markdown",
"id": "e8051dfe",
"metadata": {},
"source": [
"### Top countries (Stack area charts)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "56a2dea9",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>country</th>\n",
" <th>count</th>\n",
" <th>ratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>US</td>\n",
" <td>6576</td>\n",
" <td>0.529</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>DE</td>\n",
" <td>1656</td>\n",
" <td>0.133</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>CN</td>\n",
" <td>1013</td>\n",
" <td>0.082</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>AT</td>\n",
" <td>523</td>\n",
" <td>0.042</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>CA</td>\n",
" <td>429</td>\n",
" <td>0.035</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" country count ratio\n",
"0 US 6576 0.529\n",
"1 DE 1656 0.133\n",
"2 CN 1013 0.082\n",
"3 AT 523 0.042\n",
"4 CA 429 0.035"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dic = dict(Counter(author['Affiliation Country Code']).most_common(5))\n",
"top_five = list(dic.keys())\n",
"df = pd.DataFrame(dic.items(), columns = ['country', 'count'])\n",
"df['ratio'] = round(df['count'] / total_authors, 3)\n",
"df"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "c78ab5f9",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0.8210000000000001"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df.ratio.sum()"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "e013d420",
"metadata": {},
"outputs": [],
"source": [
"# prepare for timeseries plot\n",
"cntry_ts_prop = []\n",
"for group in author.groupby('Year'):\n",
" year = group[0]\n",
" year_total = group[1].shape[0]\n",
" group[1]['cntry new'] = group[1]['Affiliation Country Code'].apply(\n",
" lambda x: x if x in top_five else 'Other'\n",
" )\n",
" for cntry_group in group[1].groupby('cntry new'):\n",
" cntry_code = cntry_group[0]\n",
" cntry_year_total = cntry_group[1].shape[0]\n",
" cntry_prop = cntry_year_total / year_total\n",
" cntry_ts_prop.append((year, cntry_code, cntry_year_total, cntry_prop))"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "556ea5dd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[(1990, 'DE', 4, 0.03361344537815126),\n",
" (1990, 'Other', 5, 0.04201680672268908),\n",
" (1990, 'US', 109, 0.9159663865546218),\n",
" (1991, 'DE', 3, 0.0234375),\n",
" (1991, 'Other', 13, 0.1015625),\n",
" (1991, 'US', 112, 0.875),\n",
" (1992, 'CA', 1, 0.007518796992481203),\n",
" (1992, 'CN', 2, 0.015037593984962405),\n",
" (1992, 'DE', 7, 0.05263157894736842)]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cntry_ts_prop[1:10]"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "aa032da7",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>country</th>\n",
" <th>year total</th>\n",
" <th>ratio</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1990</td>\n",
" <td>Canada</td>\n",
" <td>1</td>\n",
" <td>0.008403</td>\n",
" <td>1990-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1990</td>\n",
" <td>Germany</td>\n",
" <td>4</td>\n",
" <td>0.033613</td>\n",
" <td>1990-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1990</td>\n",
" <td>Other</td>\n",
" <td>5</td>\n",
" <td>0.042017</td>\n",
" <td>1990-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1990</td>\n",
" <td>US</td>\n",
" <td>109</td>\n",
" <td>0.915966</td>\n",
" <td>1990-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1991</td>\n",
" <td>Germany</td>\n",
" <td>3</td>\n",
" <td>0.023438</td>\n",
" <td>1991-01-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year country year total ratio Year\n",
"0 1990 Canada 1 0.008403 1990-01-01\n",
"1 1990 Germany 4 0.033613 1990-01-01\n",
"2 1990 Other 5 0.042017 1990-01-01\n",
"3 1990 US 109 0.915966 1990-01-01\n",
"4 1991 Germany 3 0.023438 1991-01-01"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cntry_ts_prop_df = pd.DataFrame(\n",
" list(cntry_ts_prop), columns=['year', 'country', 'year total', 'ratio']) \n",
"cntry_ts_prop_df['Year'] = pd.to_datetime(\n",
" cntry_ts_prop_df['year'], format='%Y')\n",
"cntry_ts_prop_df.replace({'CA': 'Canada',\n",
" 'DE': 'Germany',\n",
" 'CN': 'China',\n",
" 'AT': 'Austria',\n",
" }, inplace = True)\n",
"cntry_ts_prop_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "547db32a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['Germany', 'China', 'Canada', 'Other', 'US', 'Austria']"
]
},
"execution_count": 17,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"list(set(cntry_ts_prop_df.country.tolist()))"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "54ea29fd",
"metadata": {},
"outputs": [
{
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"\n",
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" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-7fddc5325b6b90c852650b84e5d0f0a7\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"country\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"year total\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-7fddc5325b6b90c852650b84e5d0f0a7\": [{\"year\": 1990, \"country\": \"Canada\", \"year total\": 1, \"ratio\": 0.008403361344537815, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1990, \"country\": \"Germany\", \"year total\": 4, \"ratio\": 0.03361344537815126, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1990, \"country\": \"Other\", \"year total\": 5, \"ratio\": 0.04201680672268908, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1990, \"country\": \"US\", \"year total\": 109, \"ratio\": 0.9159663865546218, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1991, \"country\": \"Germany\", \"year total\": 3, \"ratio\": 0.0234375, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1991, \"country\": \"Other\", \"year total\": 13, \"ratio\": 0.1015625, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1991, \"country\": \"US\", \"year total\": 112, \"ratio\": 0.875, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1992, \"country\": \"Canada\", \"year total\": 1, \"ratio\": 0.007518796992481203, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1992, \"country\": \"China\", \"year total\": 2, \"ratio\": 0.015037593984962405, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1992, \"country\": \"Germany\", \"year total\": 7, \"ratio\": 0.05263157894736842, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1992, \"country\": \"Other\", \"year total\": 10, \"ratio\": 0.07518796992481203, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1992, \"country\": \"US\", \"year total\": 113, \"ratio\": 0.849624060150376, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1993, \"country\": \"Austria\", \"year total\": 3, \"ratio\": 0.023255813953488372, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1993, \"country\": \"Germany\", \"year total\": 9, \"ratio\": 0.06976744186046512, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1993, \"country\": \"Other\", \"year total\": 34, \"ratio\": 0.26356589147286824, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1993, \"country\": \"US\", \"year total\": 83, \"ratio\": 0.6434108527131783, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1994, \"country\": \"Canada\", \"year total\": 3, \"ratio\": 0.021897810218978103, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1994, \"country\": \"Germany\", \"year total\": 9, \"ratio\": 0.06569343065693431, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1994, \"country\": \"Other\", \"year total\": 11, \"ratio\": 0.08029197080291971, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1994, \"country\": \"US\", \"year total\": 114, \"ratio\": 0.8321167883211679, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1995, \"country\": \"Austria\", \"year total\": 1, \"ratio\": 0.005128205128205128, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1995, \"country\": \"Canada\", \"year total\": 21, \"ratio\": 0.1076923076923077, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1995, \"country\": \"Germany\", \"year total\": 13, \"ratio\": 0.06666666666666667, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1995, \"country\": \"Other\", \"year total\": 42, \"ratio\": 0.2153846153846154, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1995, \"country\": \"US\", \"year total\": 118, \"ratio\": 0.6051282051282051, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1996, \"country\": \"Canada\", \"year total\": 15, \"ratio\": 0.05928853754940711, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1996, \"country\": \"China\", \"year total\": 2, \"ratio\": 0.007905138339920948, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1996, \"country\": 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},
"execution_count": 18,
"metadata": {},
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}
],
"source": [
"source = cntry_ts_prop_df\n",
"\n",
"alt.Chart(source).mark_line().encode(\n",
" x=\"Year:T\",\n",
" y=\"year total:Q\",\n",
" color=\"country:N\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "723bdda0",
"metadata": {},
"outputs": [
{
"data": {
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\"ratio\": 0.17857142857142858, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"country\": \"Germany\", \"year total\": 82, \"ratio\": 0.13945578231292516, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"country\": \"Other\", \"year total\": 94, \"ratio\": 0.1598639455782313, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"country\": \"US\", \"year total\": 283, \"ratio\": 0.4812925170068027, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019, \"country\": \"Austria\", \"year total\": 14, \"ratio\": 0.02064896755162242, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"country\": \"Canada\", \"year total\": 26, \"ratio\": 0.038348082595870206, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"country\": \"China\", \"year total\": 146, \"ratio\": 0.2153392330383481, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"country\": \"Germany\", \"year total\": 67, \"ratio\": 0.09882005899705015, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, 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"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"source = cntry_ts_prop_df\n",
"\n",
"chart1 = alt.Chart(source).mark_area().encode(\n",
" x=\"Year:T\",\n",
" y=\"year total:Q\",\n",
" color=\"country:N\"\n",
")\n",
"\n",
"chart1"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "3b33f5d2",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
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" \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
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" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
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"\n",
" if(typeof define === \"function\" && define.amd) {\n",
" requirejs.config({paths});\n",
" require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
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\"ratio\": 0.14035087719298245, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2007, \"country\": \"US\", \"year total\": 239, \"ratio\": 0.5989974937343359, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2008, \"country\": \"Austria\", \"year total\": 15, \"ratio\": 0.0395778364116095, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008, \"country\": \"Canada\", \"year total\": 12, \"ratio\": 0.0316622691292876, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008, \"country\": \"China\", \"year total\": 11, \"ratio\": 0.029023746701846966, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008, \"country\": \"Germany\", \"year total\": 64, \"ratio\": 0.16886543535620052, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008, \"country\": \"Other\", \"year total\": 49, \"ratio\": 0.12928759894459102, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008, \"country\": \"US\", \"year total\": 228, \"ratio\": 0.6015831134564644, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2009, \"country\": \"Austria\", \"year total\": 22, \"ratio\": 0.04899777282850779, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009, \"country\": \"Canada\", \"year total\": 30, \"ratio\": 0.066815144766147, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009, \"country\": \"China\", \"year total\": 31, \"ratio\": 0.06904231625835189, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009, \"country\": \"Germany\", \"year total\": 66, \"ratio\": 0.14699331848552338, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009, \"country\": \"Other\", \"year total\": 69, \"ratio\": 0.15367483296213807, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009, \"country\": \"US\", \"year total\": 231, \"ratio\": 0.5144766146993318, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2010, \"country\": \"Austria\", \"year total\": 19, \"ratio\": 0.04513064133016627, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010, \"country\": \"Canada\", \"year total\": 35, \"ratio\": 0.0831353919239905, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010, \"country\": \"China\", \"year total\": 14, \"ratio\": 0.0332541567695962, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010, \"country\": \"Germany\", \"year total\": 65, \"ratio\": 0.1543942992874109, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010, \"country\": \"Other\", \"year total\": 80, \"ratio\": 0.19002375296912113, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010, \"country\": \"US\", \"year total\": 208, \"ratio\": 0.49406175771971494, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2011, \"country\": \"Austria\", \"year total\": 35, \"ratio\": 0.07352941176470588, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011, \"country\": \"Canada\", \"year total\": 15, \"ratio\": 0.031512605042016806, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011, \"country\": \"China\", \"year total\": 21, \"ratio\": 0.04411764705882353, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011, \"country\": \"Germany\", \"year total\": 84, \"ratio\": 0.17647058823529413, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011, \"country\": \"Other\", \"year total\": 122, \"ratio\": 0.25630252100840334, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011, \"country\": \"US\", \"year total\": 199, \"ratio\": 0.4180672268907563, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2012, \"country\": \"Austria\", \"year total\": 31, \"ratio\": 0.062, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012, \"country\": \"Canada\", \"year total\": 18, \"ratio\": 0.036, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012, \"country\": \"China\", \"year total\": 16, \"ratio\": 0.032, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012, \"country\": \"Germany\", \"year total\": 122, \"ratio\": 0.244, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012, \"country\": \"Other\", \"year total\": 102, \"ratio\": 0.204, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012, \"country\": \"US\", \"year total\": 211, \"ratio\": 0.422, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2013, \"country\": \"Austria\", \"year total\": 47, \"ratio\": 0.11270983213429256, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013, \"country\": \"Canada\", \"year total\": 26, \"ratio\": 0.06235011990407674, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013, \"country\": \"China\", \"year total\": 22, \"ratio\": 0.05275779376498801, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013, \"country\": \"Germany\", \"year total\": 53, \"ratio\": 0.12709832134292565, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013, \"country\": \"Other\", \"year total\": 81, \"ratio\": 0.19424460431654678, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013, \"country\": \"US\", \"year total\": 188, \"ratio\": 0.45083932853717024, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2014, \"country\": \"Austria\", \"year total\": 52, \"ratio\": 0.08482871125611746, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014, \"country\": \"Canada\", \"year total\": 11, \"ratio\": 0.01794453507340946, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014, \"country\": \"China\", \"year total\": 65, \"ratio\": 0.10603588907014681, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014, \"country\": \"Germany\", \"year total\": 98, \"ratio\": 0.1598694942903752, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014, \"country\": \"Other\", \"year total\": 111, \"ratio\": 0.18107667210440456, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014, \"country\": \"US\", \"year total\": 276, \"ratio\": 0.45024469820554647, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2015, \"country\": \"Austria\", \"year total\": 21, \"ratio\": 0.036585365853658534, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"country\": \"Canada\", \"year total\": 18, \"ratio\": 0.0313588850174216, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"country\": \"China\", \"year total\": 42, \"ratio\": 0.07317073170731707, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"country\": \"Germany\", \"year total\": 106, \"ratio\": 0.18466898954703834, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"country\": \"Other\", \"year total\": 113, \"ratio\": 0.19686411149825783, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"country\": \"US\", \"year total\": 274, \"ratio\": 0.47735191637630664, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2016, \"country\": \"Austria\", \"year total\": 23, \"ratio\": 0.04267161410018553, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"country\": \"Canada\", \"year total\": 19, \"ratio\": 0.03525046382189239, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"country\": \"China\", \"year total\": 55, \"ratio\": 0.10204081632653061, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"country\": \"Germany\", \"year total\": 71, \"ratio\": 0.13172541743970315, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"country\": \"Other\", \"year total\": 131, \"ratio\": 0.24304267161410018, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"country\": \"US\", \"year total\": 240, \"ratio\": 0.4452690166975881, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2017, \"country\": \"Austria\", \"year total\": 44, \"ratio\": 0.08163265306122448, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"country\": \"Canada\", \"year total\": 17, \"ratio\": 0.03153988868274583, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"country\": \"China\", \"year total\": 74, \"ratio\": 0.137291280148423, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"country\": \"Germany\", \"year total\": 70, \"ratio\": 0.12987012987012986, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"country\": \"Other\", \"year total\": 99, \"ratio\": 0.1836734693877551, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"country\": \"US\", \"year total\": 235, \"ratio\": 0.4359925788497217, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018, \"country\": \"Austria\", \"year total\": 14, \"ratio\": 0.023809523809523808, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"country\": \"Canada\", \"year total\": 10, \"ratio\": 0.017006802721088437, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"country\": \"China\", \"year total\": 105, \"ratio\": 0.17857142857142858, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"country\": \"Germany\", \"year total\": 82, \"ratio\": 0.13945578231292516, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"country\": \"Other\", \"year total\": 94, \"ratio\": 0.1598639455782313, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"country\": \"US\", \"year total\": 283, \"ratio\": 0.4812925170068027, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019, \"country\": \"Austria\", \"year total\": 14, \"ratio\": 0.02064896755162242, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"country\": \"Canada\", \"year total\": 26, \"ratio\": 0.038348082595870206, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"country\": \"China\", \"year total\": 146, \"ratio\": 0.2153392330383481, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"country\": \"Germany\", \"year total\": 67, \"ratio\": 0.09882005899705015, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"country\": \"Other\", \"year total\": 91, \"ratio\": 0.13421828908554573, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"country\": \"US\", \"year total\": 334, \"ratio\": 0.49262536873156343, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020, \"country\": \"Austria\", \"year total\": 10, \"ratio\": 0.012610340479192938, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"country\": \"Canada\", \"year total\": 16, \"ratio\": 0.0201765447667087, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"country\": \"China\", \"year total\": 201, \"ratio\": 0.25346784363177804, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"country\": \"Germany\", \"year total\": 87, \"ratio\": 0.10970996216897856, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"country\": \"Other\", \"year total\": 167, \"ratio\": 0.21059268600252207, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"country\": \"US\", \"year total\": 312, \"ratio\": 0.39344262295081966, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021, \"country\": \"Austria\", \"year total\": 19, \"ratio\": 0.022485207100591716, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"country\": \"Canada\", \"year total\": 25, \"ratio\": 0.029585798816568046, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"country\": \"China\", \"year total\": 190, \"ratio\": 0.22485207100591717, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"country\": \"Germany\", \"year total\": 81, \"ratio\": 0.09585798816568047, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"country\": \"Other\", \"year total\": 170, \"ratio\": 0.20118343195266272, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"country\": \"US\", \"year total\": 360, \"ratio\": 0.4260355029585799, \"Year\": \"2021-01-01T00:00:00\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"source = cntry_ts_prop_df\n",
"\n",
"chart2 = alt.Chart(source).mark_area().encode(\n",
" x=\"Year:T\",\n",
" y=alt.Y(\n",
" \"ratio:Q\",\n",
" title = 'Proportion'\n",
" ),\n",
" color=alt.Color(\n",
" \"country:N\",\n",
" title = 'Country/Region',\n",
" )\n",
").configure_axis(\n",
" labelFontSize=15,\n",
" titleFontSize=20\n",
").configure_legend(\n",
" titleFontSize=16,\n",
" labelFontSize=16\n",
").configure_title(\n",
" anchor='start',\n",
" fontSize=20\n",
").properties(\n",
" width=420\n",
")\n",
"\n",
"chart2"
]
},
{
"cell_type": "markdown",
"id": "60abc41d",
"metadata": {},
"source": [
"### Papers involving US authors"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "3703b4ca",
"metadata": {},
"outputs": [],
"source": [
"def get_total_and_ratio_by_year(DF, subgroup_par):\n",
" tuples = []\n",
" for group in DF.groupby('Year'):\n",
" year = group[0]\n",
" total = group[1].shape[0]\n",
" for subgroup in group[1].groupby(subgroup_par):\n",
" subtype = 'Yes' if subgroup[0] else 'No'\n",
" count = subgroup[1].shape[0]\n",
" ratio = count / total\n",
" tuples.append((year, subtype, count, ratio))\n",
" dff = pd.DataFrame(\n",
" list(tuples), columns=['year', 'type', 'count', 'ratio']) \n",
" return dff"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "859db304",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>With US Authors</th>\n",
" <th>count</th>\n",
" <th>Ratio</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1990</td>\n",
" <td>No</td>\n",
" <td>6</td>\n",
" <td>0.115385</td>\n",
" <td>1990-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1990</td>\n",
" <td>Yes</td>\n",
" <td>46</td>\n",
" <td>0.884615</td>\n",
" <td>1990-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1991</td>\n",
" <td>No</td>\n",
" <td>6</td>\n",
" <td>0.120000</td>\n",
" <td>1991-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1991</td>\n",
" <td>Yes</td>\n",
" <td>44</td>\n",
" <td>0.880000</td>\n",
" <td>1991-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1992</td>\n",
" <td>No</td>\n",
" <td>10</td>\n",
" <td>0.188679</td>\n",
" <td>1992-01-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year With US Authors count Ratio Year\n",
"0 1990 No 6 0.115385 1990-01-01\n",
"1 1990 Yes 46 0.884615 1990-01-01\n",
"2 1991 No 6 0.120000 1991-01-01\n",
"3 1991 Yes 44 0.880000 1991-01-01\n",
"4 1992 No 10 0.188679 1992-01-01"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"withus_authors = get_total_and_ratio_by_year(paper, 'With US Authors')\n",
"withus_authors.rename(columns = {\n",
"# 'year': 'Year',\n",
" 'type': 'With US Authors', \n",
" 'ratio': 'Ratio'}, inplace=True)\n",
"withus_authors['Year'] = pd.to_datetime(withus_authors['year'], format='%Y')\n",
"withus_authors.head()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "a48bc4b6",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"g = sns.lineplot(x='Year', y='Ratio', hue='With US Authors', \n",
" data = withus_authors)\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "ac058fb3",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"g = sns.lineplot(x='Year', y='count', hue='With US Authors', \n",
" data = withus_authors)\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "fd2aebfb",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-f70bb757cf0f4b52b847e3b7978b7328\"></div>\n",
"<script type=\"text/javascript\">\n",
" var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
" (function(spec, embedOpt){\n",
" let outputDiv = document.currentScript.previousElementSibling;\n",
" if (outputDiv.id !== \"altair-viz-f70bb757cf0f4b52b847e3b7978b7328\") {\n",
" outputDiv = document.getElementById(\"altair-viz-f70bb757cf0f4b52b847e3b7978b7328\");\n",
" }\n",
" const paths = {\n",
" \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
" \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
" \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
" };\n",
"\n",
" function maybeLoadScript(lib, version) {\n",
" var key = `${lib.replace(\"-\", \"\")}_version`;\n",
" return (VEGA_DEBUG[key] == version) ?\n",
" Promise.resolve(paths[lib]) :\n",
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" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
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\"No\", \"count\": 42, \"Ratio\": 0.33070866141732286, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"With US Authors\": \"Yes\", \"count\": 85, \"Ratio\": 0.6692913385826772, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019, \"With US Authors\": \"No\", \"count\": 42, \"Ratio\": 0.32558139534883723, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"With US Authors\": \"Yes\", \"count\": 87, \"Ratio\": 0.6744186046511628, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020, \"With US Authors\": \"No\", \"count\": 66, \"Ratio\": 0.42038216560509556, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"With US Authors\": \"Yes\", \"count\": 91, \"Ratio\": 0.5796178343949044, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021, \"With US Authors\": \"No\", \"count\": 70, \"Ratio\": 0.4117647058823529, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"With US Authors\": \"Yes\", \"count\": 100, \"Ratio\": 0.5882352941176471, \"Year\": \"2021-01-01T00:00:00\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"source = withus_authors\n",
"\n",
"chart1 = alt.Chart(source).mark_line().encode(\n",
" x=\"Year:T\",\n",
" y=\"Ratio:Q\",\n",
" color=\"With US Authors:N\"\n",
").properties(\n",
" width=300\n",
")\n",
"\n",
"chart2 = alt.Chart(source).mark_line().encode(\n",
" x=\"Year:T\",\n",
" y=\"count:Q\",\n",
" color=\"With US Authors:N\"\n",
").properties(\n",
" width=300\n",
")\n",
"\n",
"alt.hconcat(chart1, chart2)"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "170eb4d1",
"metadata": {},
"outputs": [
{
"data": {
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Authors\": \"Yes\", \"count\": 73, \"Ratio\": 0.6460176991150443, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2007, \"With US Authors\": \"No\", \"count\": 31, \"Ratio\": 0.2897196261682243, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2007, \"With US Authors\": \"Yes\", \"count\": 76, \"Ratio\": 0.7102803738317757, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2008, \"With US Authors\": \"No\", \"count\": 32, \"Ratio\": 0.32323232323232326, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008, \"With US Authors\": \"Yes\", \"count\": 67, \"Ratio\": 0.6767676767676768, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2009, \"With US Authors\": \"No\", \"count\": 47, \"Ratio\": 0.4017094017094017, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009, \"With US Authors\": \"Yes\", \"count\": 70, \"Ratio\": 0.5982905982905983, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2010, \"With US Authors\": \"No\", \"count\": 40, \"Ratio\": 0.3669724770642202, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010, \"With US Authors\": \"Yes\", \"count\": 69, \"Ratio\": 0.6330275229357798, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2011, \"With US Authors\": \"No\", \"count\": 63, \"Ratio\": 0.5294117647058824, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011, \"With US Authors\": \"Yes\", \"count\": 56, \"Ratio\": 0.47058823529411764, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2012, \"With US Authors\": \"No\", \"count\": 49, \"Ratio\": 0.4224137931034483, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012, \"With US Authors\": \"Yes\", \"count\": 67, \"Ratio\": 0.5775862068965517, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2013, \"With US Authors\": \"No\", \"count\": 43, \"Ratio\": 0.42574257425742573, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013, \"With US Authors\": \"Yes\", \"count\": 58, \"Ratio\": 0.5742574257425742, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2014, \"With US Authors\": \"No\", \"count\": 51, \"Ratio\": 0.38345864661654133, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014, \"With US Authors\": \"Yes\", \"count\": 82, \"Ratio\": 0.6165413533834586, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2015, \"With US Authors\": \"No\", \"count\": 49, \"Ratio\": 0.3858267716535433, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"With US Authors\": \"Yes\", \"count\": 78, \"Ratio\": 0.6141732283464567, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2016, \"With US Authors\": \"No\", \"count\": 41, \"Ratio\": 0.3565217391304348, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"With US Authors\": \"Yes\", \"count\": 74, \"Ratio\": 0.6434782608695652, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2017, \"With US Authors\": \"No\", \"count\": 43, \"Ratio\": 0.37719298245614036, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"With US Authors\": \"Yes\", \"count\": 71, \"Ratio\": 0.6228070175438597, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018, \"With US Authors\": \"No\", \"count\": 42, \"Ratio\": 0.33070866141732286, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"With US Authors\": \"Yes\", \"count\": 85, \"Ratio\": 0.6692913385826772, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019, \"With US Authors\": \"No\", \"count\": 42, \"Ratio\": 0.32558139534883723, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"With US Authors\": \"Yes\", \"count\": 87, \"Ratio\": 0.6744186046511628, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020, \"With US Authors\": \"No\", \"count\": 66, \"Ratio\": 0.42038216560509556, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"With US Authors\": \"Yes\", \"count\": 91, \"Ratio\": 0.5796178343949044, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021, \"With US Authors\": \"No\", \"count\": 70, \"Ratio\": 0.4117647058823529, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"With US Authors\": \"Yes\", \"count\": 100, \"Ratio\": 0.5882352941176471, \"Year\": \"2021-01-01T00:00:00\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"source = withus_authors\n",
"\n",
"chart1 = alt.Chart(source).mark_area().encode(\n",
" x=\"Year:T\",\n",
" y=\"Ratio:Q\",\n",
" color=\"With US Authors:N\"\n",
").properties(\n",
" width=300\n",
")\n",
"\n",
"chart2 = alt.Chart(source).mark_area().encode(\n",
" x=\"Year:T\",\n",
" y=\"count:Q\",\n",
" color=\"With US Authors:N\"\n",
").properties(\n",
" width=300\n",
")\n",
"\n",
"alt.hconcat(chart1, chart2)"
]
},
{
"cell_type": "markdown",
"id": "553c7e62",
"metadata": {},
"source": [
"### Affiliation types, global"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "034e7bf7",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>type</th>\n",
" <th>count</th>\n",
" <th>ratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>education</td>\n",
" <td>9583</td>\n",
" <td>0.771</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>company</td>\n",
" <td>1292</td>\n",
" <td>0.104</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>facility</td>\n",
" <td>1085</td>\n",
" <td>0.087</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>government</td>\n",
" <td>241</td>\n",
" <td>0.019</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>nonprofit</td>\n",
" <td>65</td>\n",
" <td>0.005</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>other</td>\n",
" <td>9</td>\n",
" <td>0.001</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>healthcare</td>\n",
" <td>134</td>\n",
" <td>0.011</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>archive</td>\n",
" <td>19</td>\n",
" <td>0.002</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" type count ratio\n",
"0 education 9583 0.771\n",
"1 company 1292 0.104\n",
"2 facility 1085 0.087\n",
"3 government 241 0.019\n",
"4 nonprofit 65 0.005\n",
"5 other 9 0.001\n",
"6 healthcare 134 0.011\n",
"7 archive 19 0.002"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dic = dict(Counter(author['Affiliation Type']))\n",
"author_aff_count = pd.DataFrame(dic.items(), columns = ['type', 'count'])\n",
"author_aff_count['ratio'] = round(\n",
" author_aff_count['count'] / author_aff_count['count'].sum(),\n",
" 3)\n",
"author_aff_count"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "5ce5beac",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-73cbc3f6eb2447ffa1f69a2f45cdd81e\"></div>\n",
"<script type=\"text/javascript\">\n",
" var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
" (function(spec, embedOpt){\n",
" let outputDiv = document.currentScript.previousElementSibling;\n",
" if (outputDiv.id !== \"altair-viz-73cbc3f6eb2447ffa1f69a2f45cdd81e\") {\n",
" outputDiv = document.getElementById(\"altair-viz-73cbc3f6eb2447ffa1f69a2f45cdd81e\");\n",
" }\n",
" const paths = {\n",
" \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
" \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
" \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
" };\n",
"\n",
" function maybeLoadScript(lib, version) {\n",
" var key = `${lib.replace(\"-\", \"\")}_version`;\n",
" return (VEGA_DEBUG[key] == version) ?\n",
" Promise.resolve(paths[lib]) :\n",
" new Promise(function(resolve, reject) {\n",
" var s = document.createElement('script');\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" s.async = true;\n",
" s.onload = () => {\n",
" VEGA_DEBUG[key] = version;\n",
" return resolve(paths[lib]);\n",
" };\n",
" s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
" s.src = paths[lib];\n",
" });\n",
" }\n",
"\n",
" function showError(err) {\n",
" outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
" throw err;\n",
" }\n",
"\n",
" function displayChart(vegaEmbed) {\n",
" vegaEmbed(outputDiv, spec, embedOpt)\n",
" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
" }\n",
"\n",
" if(typeof define === \"function\" && define.amd) {\n",
" requirejs.config({paths});\n",
" require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-9dad40c1eec6f848d828f9fcd4905632\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"type\", \"type\": \"nominal\"}, \"x\": {\"field\": \"type\", \"type\": \"nominal\"}, \"y\": {\"field\": \"count\", \"scale\": {\"type\": \"log\"}, \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-9dad40c1eec6f848d828f9fcd4905632\": [{\"type\": \"education\", \"count\": 9583, \"ratio\": 0.771}, {\"type\": \"company\", \"count\": 1292, \"ratio\": 0.104}, {\"type\": \"facility\", \"count\": 1085, \"ratio\": 0.087}, {\"type\": \"government\", \"count\": 241, \"ratio\": 0.019}, {\"type\": \"nonprofit\", \"count\": 65, \"ratio\": 0.005}, {\"type\": \"other\", \"count\": 9, \"ratio\": 0.001}, {\"type\": \"healthcare\", \"count\": 134, \"ratio\": 0.011}, {\"type\": \"archive\", \"count\": 19, \"ratio\": 0.002}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"source = author_aff_count\n",
"\n",
"chart = alt.Chart(source).mark_bar().encode(\n",
" x='type',\n",
" y=alt.Y(\n",
" 'count',\n",
" scale=alt.Scale(type=\"log\") # Here the scale is applied\n",
" ),\n",
" color = 'type',\n",
")\n",
"\n",
"chart"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "8241ee7f",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 792x576 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"a4_dims = (11, 8)\n",
"fig, ax = plt.subplots(figsize=a4_dims)\n",
"sns.countplot(x = 'Affiliation Type', data = author, ax = ax)\n",
"plt.yscale('log')"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "7068c973",
"metadata": {},
"outputs": [],
"source": [
"tuples = []\n",
"for group in author.groupby('Year'):\n",
" year = group[0]\n",
" total = group[1].shape[0]\n",
" for subgroup in group[1].groupby('Binary Type'):\n",
" aff_type = subgroup[0]\n",
" count = subgroup[1].shape[0]\n",
" ratio = count / total\n",
" tuples.append((year, aff_type, count, ratio))"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "534dbec1",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>type</th>\n",
" <th>count</th>\n",
" <th>ratio</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>2017</td>\n",
" <td>Education</td>\n",
" <td>423</td>\n",
" <td>0.784787</td>\n",
" <td>2017-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>2017</td>\n",
" <td>Non-Education</td>\n",
" <td>116</td>\n",
" <td>0.215213</td>\n",
" <td>2017-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>2018</td>\n",
" <td>Education</td>\n",
" <td>466</td>\n",
" <td>0.792517</td>\n",
" <td>2018-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>2018</td>\n",
" <td>Non-Education</td>\n",
" <td>122</td>\n",
" <td>0.207483</td>\n",
" <td>2018-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>2019</td>\n",
" <td>Education</td>\n",
" <td>566</td>\n",
" <td>0.834808</td>\n",
" <td>2019-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>2019</td>\n",
" <td>Non-Education</td>\n",
" <td>112</td>\n",
" <td>0.165192</td>\n",
" <td>2019-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>2020</td>\n",
" <td>Education</td>\n",
" <td>665</td>\n",
" <td>0.838588</td>\n",
" <td>2020-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>2020</td>\n",
" <td>Non-Education</td>\n",
" <td>128</td>\n",
" <td>0.161412</td>\n",
" <td>2020-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>2021</td>\n",
" <td>Education</td>\n",
" <td>706</td>\n",
" <td>0.835503</td>\n",
" <td>2021-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>2021</td>\n",
" <td>Non-Education</td>\n",
" <td>139</td>\n",
" <td>0.164497</td>\n",
" <td>2021-01-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year type count ratio Year\n",
"54 2017 Education 423 0.784787 2017-01-01\n",
"55 2017 Non-Education 116 0.215213 2017-01-01\n",
"56 2018 Education 466 0.792517 2018-01-01\n",
"57 2018 Non-Education 122 0.207483 2018-01-01\n",
"58 2019 Education 566 0.834808 2019-01-01\n",
"59 2019 Non-Education 112 0.165192 2019-01-01\n",
"60 2020 Education 665 0.838588 2020-01-01\n",
"61 2020 Non-Education 128 0.161412 2020-01-01\n",
"62 2021 Education 706 0.835503 2021-01-01\n",
"63 2021 Non-Education 139 0.164497 2021-01-01"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"binary_type_df = pd.DataFrame(list(tuples), columns=['year', 'type', 'count', 'ratio']) \n",
"binary_type_df['Year'] = pd.to_datetime(binary_type_df['year'], format='%Y')\n",
"binary_type_df.replace({\n",
" 'education': 'Education',\n",
" 'non-education': 'Non-Education'\n",
"}, inplace= True)\n",
"binary_type_df.tail(10)"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "0e6686ba",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"g = sns.lineplot(x='year', y='ratio', hue='type', data = binary_type_df)\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "193234bf",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"g = sns.lineplot(x='year', y='count', hue='type', data = binary_type_df)\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "3ed7941a",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-f24d8f95a54445dfac6e17cb1ba85dc7\"></div>\n",
"<script type=\"text/javascript\">\n",
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" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"hconcat\": [{\"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"type\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"count\", \"type\": \"quantitative\"}}, \"height\": 300, \"width\": 300}, {\"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"type\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"ratio\", \"type\": \"quantitative\"}}, \"height\": 300, \"width\": 250}], \"data\": {\"name\": \"data-d7c58f944dff75313d6d54b1fbf23c9c\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-d7c58f944dff75313d6d54b1fbf23c9c\": [{\"year\": 1990, \"type\": \"Education\", \"count\": 72, \"ratio\": 0.6050420168067226, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1990, \"type\": \"Non-Education\", \"count\": 47, \"ratio\": 0.3949579831932773, 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\"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1995, \"type\": \"Education\", \"count\": 131, \"ratio\": 0.6717948717948717, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1995, \"type\": \"Non-Education\", \"count\": 64, \"ratio\": 0.3282051282051282, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1996, \"type\": \"Education\", \"count\": 165, \"ratio\": 0.6521739130434783, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1996, \"type\": \"Non-Education\", \"count\": 88, \"ratio\": 0.34782608695652173, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"Education\", \"count\": 199, \"ratio\": 0.805668016194332, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"Non-Education\", \"count\": 48, \"ratio\": 0.19433198380566802, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"Education\", \"count\": 154, \"ratio\": 0.6311475409836066, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"Non-Education\", \"count\": 90, \"ratio\": 0.36885245901639346, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"Education\", \"count\": 177, \"ratio\": 0.6704545454545454, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"Non-Education\", \"count\": 87, \"ratio\": 0.32954545454545453, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"Education\", \"count\": 204, \"ratio\": 0.6891891891891891, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"Non-Education\", \"count\": 92, \"ratio\": 0.3108108108108108, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"Education\", \"count\": 221, \"ratio\": 0.6949685534591195, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"Non-Education\", \"count\": 97, \"ratio\": 0.3050314465408805, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"Education\", \"count\": 224, \"ratio\": 0.7441860465116279, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"Non-Education\", \"count\": 77, \"ratio\": 0.2558139534883721, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"Education\", \"count\": 237, \"ratio\": 0.7337461300309598, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"Non-Education\", \"count\": 86, \"ratio\": 0.26625386996904027, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"Education\", \"count\": 242, \"ratio\": 0.75625, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"Non-Education\", \"count\": 78, \"ratio\": 0.24375, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"Education\", \"count\": 316, \"ratio\": 0.7365967365967366, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"Non-Education\", \"count\": 113, \"ratio\": 0.2634032634032634, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"Education\", \"count\": 286, \"ratio\": 0.7486910994764397, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"Non-Education\", \"count\": 96, \"ratio\": 0.2513089005235602, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"Education\", \"count\": 291, \"ratio\": 0.7293233082706767, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"Non-Education\", \"count\": 108, \"ratio\": 0.2706766917293233, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"Education\", \"count\": 286, \"ratio\": 0.7546174142480211, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"Non-Education\", \"count\": 93, \"ratio\": 0.24538258575197888, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"Education\", \"count\": 329, \"ratio\": 0.732739420935412, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"Non-Education\", \"count\": 120, \"ratio\": 0.267260579064588, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"Education\", \"count\": 342, \"ratio\": 0.8123515439429929, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"Non-Education\", \"count\": 79, \"ratio\": 0.1876484560570071, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"Education\", \"count\": 373, \"ratio\": 0.7836134453781513, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"Non-Education\", \"count\": 103, \"ratio\": 0.21638655462184875, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"Education\", \"count\": 384, \"ratio\": 0.768, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"Non-Education\", \"count\": 116, \"ratio\": 0.232, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"Education\", \"count\": 343, \"ratio\": 0.8225419664268585, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"Non-Education\", \"count\": 74, \"ratio\": 0.1774580335731415, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"Education\", \"count\": 486, \"ratio\": 0.7928221859706363, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"Non-Education\", \"count\": 127, \"ratio\": 0.20717781402936378, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"Education\", \"count\": 443, \"ratio\": 0.7717770034843205, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"Non-Education\", \"count\": 131, \"ratio\": 0.22822299651567945, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"Education\", \"count\": 419, \"ratio\": 0.7773654916512059, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"Non-Education\", \"count\": 120, \"ratio\": 0.22263450834879406, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Education\", \"count\": 423, \"ratio\": 0.7847866419294991, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Non-Education\", \"count\": 116, \"ratio\": 0.21521335807050093, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Education\", \"count\": 466, \"ratio\": 0.7925170068027211, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Non-Education\", \"count\": 122, \"ratio\": 0.20748299319727892, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Education\", \"count\": 566, \"ratio\": 0.8348082595870207, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Non-Education\", \"count\": 112, \"ratio\": 0.16519174041297935, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Education\", \"count\": 665, \"ratio\": 0.8385876418663304, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Non-Education\", \"count\": 128, \"ratio\": 0.1614123581336696, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Education\", \"count\": 706, \"ratio\": 0.8355029585798817, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Non-Education\", \"count\": 139, \"ratio\": 0.16449704142011834, \"Year\": \"2021-01-01T00:00:00\"}]}}, {\"mode\": \"vega-lite\"});\n",
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"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"source = binary_type_df\n",
"\n",
"chart1 = alt.Chart(source).mark_line().encode(\n",
" x=\"Year:T\",\n",
" y=\"count:Q\",\n",
" color=\"type:N\"\n",
").properties(\n",
" height=300,\n",
" width=300\n",
")\n",
"\n",
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" y=\"ratio:Q\",\n",
" color=\"type:N\"\n",
").properties(\n",
" height=300,\n",
" width=250\n",
")\n",
"\n",
"alt.hconcat(chart1, chart2)"
]
},
{
"cell_type": "markdown",
"id": "efb6558a",
"metadata": {},
"source": [
"## Affiliation type without the US\n",
"\n",
"This is to answer Reviewer 3's question"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "309d3577",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
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"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
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" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Year</th>\n",
" <th>Binary Type</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2006</td>\n",
" <td>education</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2003</td>\n",
" <td>education</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Binary Type\n",
"0 2006 education\n",
"1 2003 education"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"non_us_authors = author[author['Affiliation Country Code'] != 'US'][[\n",
" 'Year', 'Binary Type']].reset_index(drop=True)\n",
"non_us_authors.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "4ce8a38f",
"metadata": {},
"outputs": [],
"source": [
"tuples = []\n",
"for group in non_us_authors.groupby('Year'):\n",
" year = group[0]\n",
" total = group[1].shape[0]\n",
" for subgroup in group[1].groupby('Binary Type'):\n",
" aff_type = subgroup[0]\n",
" count = subgroup[1].shape[0]\n",
" ratio = count / total\n",
" tuples.append((year, aff_type, count, ratio))"
]
},
{
"cell_type": "code",
"execution_count": 37,
"id": "2acec70b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
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"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>type</th>\n",
" <th>count</th>\n",
" <th>ratio</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>2017</td>\n",
" <td>Education (non-US)</td>\n",
" <td>267</td>\n",
" <td>0.878289</td>\n",
" <td>2017-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>2017</td>\n",
" <td>Non-Education (non-US)</td>\n",
" <td>37</td>\n",
" <td>0.121711</td>\n",
" <td>2017-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>2018</td>\n",
" <td>Education (non-US)</td>\n",
" <td>257</td>\n",
" <td>0.842623</td>\n",
" <td>2018-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>2018</td>\n",
" <td>Non-Education (non-US)</td>\n",
" <td>48</td>\n",
" <td>0.157377</td>\n",
" <td>2018-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>2019</td>\n",
" <td>Education (non-US)</td>\n",
" <td>313</td>\n",
" <td>0.909884</td>\n",
" <td>2019-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>2019</td>\n",
" <td>Non-Education (non-US)</td>\n",
" <td>31</td>\n",
" <td>0.090116</td>\n",
" <td>2019-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>2020</td>\n",
" <td>Education (non-US)</td>\n",
" <td>409</td>\n",
" <td>0.850312</td>\n",
" <td>2020-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>2020</td>\n",
" <td>Non-Education (non-US)</td>\n",
" <td>72</td>\n",
" <td>0.149688</td>\n",
" <td>2020-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>2021</td>\n",
" <td>Education (non-US)</td>\n",
" <td>432</td>\n",
" <td>0.890722</td>\n",
" <td>2021-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>2021</td>\n",
" <td>Non-Education (non-US)</td>\n",
" <td>53</td>\n",
" <td>0.109278</td>\n",
" <td>2021-01-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year type count ratio Year\n",
"54 2017 Education (non-US) 267 0.878289 2017-01-01\n",
"55 2017 Non-Education (non-US) 37 0.121711 2017-01-01\n",
"56 2018 Education (non-US) 257 0.842623 2018-01-01\n",
"57 2018 Non-Education (non-US) 48 0.157377 2018-01-01\n",
"58 2019 Education (non-US) 313 0.909884 2019-01-01\n",
"59 2019 Non-Education (non-US) 31 0.090116 2019-01-01\n",
"60 2020 Education (non-US) 409 0.850312 2020-01-01\n",
"61 2020 Non-Education (non-US) 72 0.149688 2020-01-01\n",
"62 2021 Education (non-US) 432 0.890722 2021-01-01\n",
"63 2021 Non-Education (non-US) 53 0.109278 2021-01-01"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"non_us_author_df = pd.DataFrame(list(tuples), columns=['year', 'type', 'count', 'ratio']) \n",
"non_us_author_df['Year'] = pd.to_datetime(non_us_author_df['year'], format='%Y')\n",
"non_us_author_df.replace({\n",
" 'education': 'Education (non-US)',\n",
" 'non-education': 'Non-Education (non-US)'\n",
"}, inplace= True)\n",
"non_us_author_df.tail(10)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "1b82cdc6",
"metadata": {},
"outputs": [
{
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" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"hconcat\": [{\"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"type\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"count\", \"type\": \"quantitative\"}}, \"height\": 300, \"width\": 300}, {\"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"type\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"ratio\", \"type\": \"quantitative\"}}, \"height\": 300, \"width\": 300}], \"data\": {\"name\": \"data-fbd1368e06e4da52c109b7ad43a401d8\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-fbd1368e06e4da52c109b7ad43a401d8\": [{\"year\": 1990, \"type\": \"Education (non-US)\", \"count\": 6, \"ratio\": 0.6, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1990, \"type\": \"Non-Education (non-US)\", \"count\": 4, \"ratio\": 0.4, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1991, \"type\": \"Education (non-US)\", \"count\": 7, \"ratio\": 0.4375, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1991, \"type\": \"Non-Education (non-US)\", \"count\": 9, \"ratio\": 0.5625, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1992, \"type\": \"Education (non-US)\", \"count\": 11, \"ratio\": 0.55, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1992, \"type\": \"Non-Education (non-US)\", \"count\": 9, \"ratio\": 0.45, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1993, \"type\": \"Education (non-US)\", \"count\": 22, \"ratio\": 0.4782608695652174, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1993, \"type\": \"Non-Education (non-US)\", \"count\": 24, \"ratio\": 0.5217391304347826, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1994, \"type\": \"Education (non-US)\", \"count\": 11, \"ratio\": 0.4782608695652174, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1994, \"type\": \"Non-Education (non-US)\", \"count\": 12, \"ratio\": 0.5217391304347826, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1995, \"type\": \"Education (non-US)\", \"count\": 65, \"ratio\": 0.8441558441558441, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1995, \"type\": \"Non-Education (non-US)\", \"count\": 12, \"ratio\": 0.15584415584415584, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1996, \"type\": \"Education (non-US)\", \"count\": 46, \"ratio\": 0.647887323943662, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1996, \"type\": \"Non-Education (non-US)\", \"count\": 25, \"ratio\": 0.352112676056338, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"Education (non-US)\", \"count\": 59, \"ratio\": 0.8676470588235294, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"Non-Education (non-US)\", \"count\": 9, \"ratio\": 0.1323529411764706, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"Education (non-US)\", \"count\": 46, \"ratio\": 0.5897435897435898, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"Non-Education (non-US)\", \"count\": 32, \"ratio\": 0.41025641025641024, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"Education (non-US)\", \"count\": 59, \"ratio\": 0.6483516483516484, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"Non-Education (non-US)\", \"count\": 32, \"ratio\": 0.3516483516483517, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"Education (non-US)\", \"count\": 83, \"ratio\": 0.6974789915966386, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"Non-Education (non-US)\", \"count\": 36, \"ratio\": 0.3025210084033613, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"Education (non-US)\", \"count\": 84, \"ratio\": 0.7567567567567568, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"Non-Education (non-US)\", \"count\": 27, \"ratio\": 0.24324324324324326, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"Education (non-US)\", \"count\": 97, \"ratio\": 0.7348484848484849, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"Non-Education (non-US)\", \"count\": 35, \"ratio\": 0.26515151515151514, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"Education (non-US)\", \"count\": 74, \"ratio\": 0.6548672566371682, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"Non-Education (non-US)\", \"count\": 39, \"ratio\": 0.34513274336283184, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"Education (non-US)\", \"count\": 79, \"ratio\": 0.7117117117117117, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"Non-Education (non-US)\", \"count\": 32, \"ratio\": 0.2882882882882883, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"Education (non-US)\", \"count\": 163, \"ratio\": 0.7546296296296297, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"Non-Education (non-US)\", \"count\": 53, \"ratio\": 0.24537037037037038, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"Education (non-US)\", \"count\": 123, \"ratio\": 0.7784810126582279, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"Non-Education (non-US)\", \"count\": 35, \"ratio\": 0.22151898734177214, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"Education (non-US)\", \"count\": 124, \"ratio\": 0.775, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"Non-Education (non-US)\", \"count\": 36, \"ratio\": 0.225, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"Education (non-US)\", \"count\": 113, \"ratio\": 0.7483443708609272, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"Non-Education (non-US)\", \"count\": 38, \"ratio\": 0.25165562913907286, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"Education (non-US)\", \"count\": 179, \"ratio\": 0.8211009174311926, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"Non-Education (non-US)\", \"count\": 39, \"ratio\": 0.17889908256880735, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"Education (non-US)\", \"count\": 176, \"ratio\": 0.8262910798122066, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"Non-Education (non-US)\", \"count\": 37, \"ratio\": 0.17370892018779344, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"Education (non-US)\", \"count\": 220, \"ratio\": 0.7942238267148014, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"Non-Education (non-US)\", \"count\": 57, \"ratio\": 0.20577617328519857, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"Education (non-US)\", \"count\": 230, \"ratio\": 0.7958477508650519, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"Non-Education (non-US)\", \"count\": 59, \"ratio\": 0.2041522491349481, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"Education (non-US)\", \"count\": 188, \"ratio\": 0.8209606986899564, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"Non-Education (non-US)\", \"count\": 41, \"ratio\": 0.17903930131004367, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"Education (non-US)\", \"count\": 268, \"ratio\": 0.7952522255192879, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"Non-Education (non-US)\", \"count\": 69, \"ratio\": 0.20474777448071216, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"Education (non-US)\", \"count\": 261, \"ratio\": 0.87, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"Non-Education (non-US)\", \"count\": 39, \"ratio\": 0.13, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"Education (non-US)\", \"count\": 260, \"ratio\": 0.8695652173913043, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"Non-Education (non-US)\", \"count\": 39, \"ratio\": 0.13043478260869565, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Education (non-US)\", \"count\": 267, \"ratio\": 0.8782894736842105, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Non-Education (non-US)\", \"count\": 37, \"ratio\": 0.12171052631578948, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Education (non-US)\", \"count\": 257, \"ratio\": 0.8426229508196721, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Non-Education (non-US)\", \"count\": 48, \"ratio\": 0.15737704918032788, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Education (non-US)\", \"count\": 313, \"ratio\": 0.9098837209302325, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Non-Education (non-US)\", \"count\": 31, \"ratio\": 0.09011627906976744, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Education (non-US)\", \"count\": 409, \"ratio\": 0.8503118503118503, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Non-Education (non-US)\", \"count\": 72, \"ratio\": 0.1496881496881497, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Education (non-US)\", \"count\": 432, \"ratio\": 0.8907216494845361, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Non-Education (non-US)\", \"count\": 53, \"ratio\": 0.10927835051546392, \"Year\": \"2021-01-01T00:00:00\"}]}}, {\"mode\": \"vega-lite\"});\n",
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"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"source = non_us_author_df\n",
"\n",
"chart1 = alt.Chart(source).mark_line().encode(\n",
" x=\"Year:T\",\n",
" y=\"count:Q\",\n",
" color=\"type:N\"\n",
").properties(\n",
" height=300,\n",
" width=300\n",
")\n",
"\n",
"chart2 = alt.Chart(source).mark_line().encode(\n",
" x=\"Year:T\",\n",
" y=\"ratio:Q\",\n",
" color=\"type:N\"\n",
").properties(\n",
" height=300,\n",
" width=300\n",
")\n",
"\n",
"alt.hconcat(chart1, chart2)"
]
},
{
"cell_type": "markdown",
"id": "9ff714a7",
"metadata": {},
"source": [
"### Afffiliation type within the US"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "98473d35",
"metadata": {},
"outputs": [],
"source": [
"us_authors = author[author['Affiliation Country Code'] == 'US'][[\n",
" 'Year', 'Binary Type']].reset_index(drop=True)"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "8dd8f27b",
"metadata": {},
"outputs": [],
"source": [
"tuples = []\n",
"for group in us_authors.groupby('Year'):\n",
" year = group[0]\n",
" total = group[1].shape[0]\n",
" for subgroup in group[1].groupby('Binary Type'):\n",
" aff_type = subgroup[0]\n",
" count = subgroup[1].shape[0]\n",
" ratio = count / total\n",
" tuples.append((year, aff_type, count, ratio))"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "d5854d4a",
"metadata": {},
"outputs": [
{
"data": {
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"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>year</th>\n",
" <th>type</th>\n",
" <th>count</th>\n",
" <th>ratio</th>\n",
" <th>Year</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>54</th>\n",
" <td>2017</td>\n",
" <td>Education (US)</td>\n",
" <td>156</td>\n",
" <td>0.663830</td>\n",
" <td>2017-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td>2017</td>\n",
" <td>Non-Education (US)</td>\n",
" <td>79</td>\n",
" <td>0.336170</td>\n",
" <td>2017-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td>2018</td>\n",
" <td>Education (US)</td>\n",
" <td>209</td>\n",
" <td>0.738516</td>\n",
" <td>2018-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>57</th>\n",
" <td>2018</td>\n",
" <td>Non-Education (US)</td>\n",
" <td>74</td>\n",
" <td>0.261484</td>\n",
" <td>2018-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>58</th>\n",
" <td>2019</td>\n",
" <td>Education (US)</td>\n",
" <td>253</td>\n",
" <td>0.757485</td>\n",
" <td>2019-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>59</th>\n",
" <td>2019</td>\n",
" <td>Non-Education (US)</td>\n",
" <td>81</td>\n",
" <td>0.242515</td>\n",
" <td>2019-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>60</th>\n",
" <td>2020</td>\n",
" <td>Education (US)</td>\n",
" <td>256</td>\n",
" <td>0.820513</td>\n",
" <td>2020-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>61</th>\n",
" <td>2020</td>\n",
" <td>Non-Education (US)</td>\n",
" <td>56</td>\n",
" <td>0.179487</td>\n",
" <td>2020-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>62</th>\n",
" <td>2021</td>\n",
" <td>Education (US)</td>\n",
" <td>274</td>\n",
" <td>0.761111</td>\n",
" <td>2021-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>63</th>\n",
" <td>2021</td>\n",
" <td>Non-Education (US)</td>\n",
" <td>86</td>\n",
" <td>0.238889</td>\n",
" <td>2021-01-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year type count ratio Year\n",
"54 2017 Education (US) 156 0.663830 2017-01-01\n",
"55 2017 Non-Education (US) 79 0.336170 2017-01-01\n",
"56 2018 Education (US) 209 0.738516 2018-01-01\n",
"57 2018 Non-Education (US) 74 0.261484 2018-01-01\n",
"58 2019 Education (US) 253 0.757485 2019-01-01\n",
"59 2019 Non-Education (US) 81 0.242515 2019-01-01\n",
"60 2020 Education (US) 256 0.820513 2020-01-01\n",
"61 2020 Non-Education (US) 56 0.179487 2020-01-01\n",
"62 2021 Education (US) 274 0.761111 2021-01-01\n",
"63 2021 Non-Education (US) 86 0.238889 2021-01-01"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"us_author_df = pd.DataFrame(list(tuples), columns=['year', 'type', 'count', 'ratio']) \n",
"us_author_df['Year'] = pd.to_datetime(us_author_df['year'], format='%Y')\n",
"us_author_df.replace({\n",
" 'education': 'Education (US)',\n",
" 'non-education': 'Non-Education (US)'\n",
"}, inplace= True)\n",
"us_author_df.tail(10)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "80451526",
"metadata": {},
"outputs": [
{
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\"type\": \"Education (US)\", \"count\": 274, \"ratio\": 0.7611111111111111, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Non-Education (US)\", \"count\": 86, \"ratio\": 0.2388888888888889, \"Year\": \"2021-01-01T00:00:00\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"source = us_author_df\n",
"\n",
"chart1 = alt.Chart(source).mark_line().encode(\n",
" x=\"Year:T\",\n",
" y=\"count:Q\",\n",
" color=\"type:N\"\n",
").properties(\n",
" height=300,\n",
" width=300\n",
")\n",
"\n",
"chart2 = alt.Chart(source).mark_line().encode(\n",
" x=\"Year:T\",\n",
" y=\"ratio:Q\",\n",
" color=\"type:N\"\n",
").properties(\n",
" height=300,\n",
" width=300\n",
")\n",
"\n",
"alt.hconcat(chart1, chart2)"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "33f3b302",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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\"Education\", \"count\": 423, \"ratio\": 0.7847866419294991, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Non-Education\", \"count\": 116, \"ratio\": 0.21521335807050093, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Education\", \"count\": 466, \"ratio\": 0.7925170068027211, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Non-Education\", \"count\": 122, \"ratio\": 0.20748299319727892, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Education\", \"count\": 566, \"ratio\": 0.8348082595870207, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Non-Education\", \"count\": 112, \"ratio\": 0.16519174041297935, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Education\", \"count\": 665, \"ratio\": 0.8385876418663304, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Non-Education\", \"count\": 128, \"ratio\": 0.1614123581336696, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Education\", \"count\": 706, \"ratio\": 0.8355029585798817, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Non-Education\", \"count\": 139, \"ratio\": 0.16449704142011834, \"Year\": \"2021-01-01T00:00:00\"}], \"data-7ec3b6808d361595453b472948c0fff9\": [{\"year\": 1990, \"type\": \"Education (US)\", \"count\": 66, \"ratio\": 0.6055045871559633, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1990, \"type\": \"Non-Education (US)\", \"count\": 43, \"ratio\": 0.3944954128440367, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1991, \"type\": \"Education (US)\", \"count\": 58, \"ratio\": 0.5178571428571429, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1991, \"type\": \"Non-Education (US)\", \"count\": 54, \"ratio\": 0.48214285714285715, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1992, \"type\": \"Education (US)\", \"count\": 44, \"ratio\": 0.3893805309734513, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1992, \"type\": \"Non-Education (US)\", \"count\": 69, \"ratio\": 0.6106194690265486, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1993, \"type\": \"Education (US)\", \"count\": 42, \"ratio\": 0.5060240963855421, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1993, \"type\": \"Non-Education (US)\", \"count\": 41, \"ratio\": 0.4939759036144578, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1994, \"type\": \"Education (US)\", \"count\": 75, \"ratio\": 0.6578947368421053, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1994, \"type\": \"Non-Education (US)\", \"count\": 39, \"ratio\": 0.34210526315789475, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1995, \"type\": \"Education (US)\", \"count\": 66, \"ratio\": 0.559322033898305, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1995, \"type\": \"Non-Education (US)\", \"count\": 52, \"ratio\": 0.4406779661016949, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1996, \"type\": \"Education (US)\", \"count\": 119, \"ratio\": 0.6538461538461539, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1996, \"type\": \"Non-Education (US)\", \"count\": 63, \"ratio\": 0.34615384615384615, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"Education (US)\", \"count\": 140, \"ratio\": 0.7821229050279329, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"Non-Education (US)\", \"count\": 39, \"ratio\": 0.21787709497206703, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"Education (US)\", \"count\": 108, \"ratio\": 0.6506024096385542, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"Non-Education (US)\", \"count\": 58, \"ratio\": 0.3493975903614458, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"Education (US)\", \"count\": 118, \"ratio\": 0.6820809248554913, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"Non-Education (US)\", \"count\": 55, \"ratio\": 0.3179190751445087, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"Education (US)\", \"count\": 121, \"ratio\": 0.6836158192090396, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"Non-Education (US)\", \"count\": 56, \"ratio\": 0.3163841807909605, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"Education (US)\", \"count\": 137, \"ratio\": 0.6618357487922706, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"Non-Education (US)\", \"count\": 70, \"ratio\": 0.33816425120772947, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"Education (US)\", \"count\": 127, \"ratio\": 0.7514792899408284, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"Non-Education (US)\", \"count\": 42, \"ratio\": 0.2485207100591716, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"Education (US)\", \"count\": 163, \"ratio\": 0.7761904761904762, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"Non-Education (US)\", \"count\": 47, \"ratio\": 0.22380952380952382, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"Education (US)\", \"count\": 163, \"ratio\": 0.7799043062200957, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"Non-Education (US)\", \"count\": 46, \"ratio\": 0.22009569377990432, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"Education (US)\", \"count\": 153, \"ratio\": 0.7183098591549296, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"Non-Education (US)\", \"count\": 60, \"ratio\": 0.28169014084507044, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"Education (US)\", \"count\": 163, \"ratio\": 0.7276785714285714, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"Non-Education (US)\", \"count\": 61, \"ratio\": 0.27232142857142855, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"Education (US)\", \"count\": 167, \"ratio\": 0.698744769874477, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"Non-Education (US)\", \"count\": 72, \"ratio\": 0.301255230125523, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"Education (US)\", \"count\": 173, \"ratio\": 0.7587719298245614, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"Non-Education (US)\", \"count\": 55, \"ratio\": 0.2412280701754386, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"Education (US)\", \"count\": 150, \"ratio\": 0.6493506493506493, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"Non-Education (US)\", \"count\": 81, \"ratio\": 0.35064935064935066, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"Education (US)\", \"count\": 166, \"ratio\": 0.7980769230769231, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"Non-Education (US)\", \"count\": 42, \"ratio\": 0.20192307692307693, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"Education (US)\", \"count\": 153, \"ratio\": 0.7688442211055276, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"Non-Education (US)\", \"count\": 46, \"ratio\": 0.23115577889447236, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"Education (US)\", \"count\": 154, \"ratio\": 0.7298578199052133, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"Non-Education (US)\", \"count\": 57, \"ratio\": 0.27014218009478674, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"Education (US)\", \"count\": 155, \"ratio\": 0.824468085106383, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"Non-Education (US)\", \"count\": 33, \"ratio\": 0.17553191489361702, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"Education (US)\", \"count\": 218, \"ratio\": 0.7898550724637681, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"Non-Education (US)\", \"count\": 58, \"ratio\": 0.21014492753623187, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"Education (US)\", \"count\": 182, \"ratio\": 0.6642335766423357, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"Non-Education (US)\", \"count\": 92, \"ratio\": 0.3357664233576642, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"Education (US)\", \"count\": 159, \"ratio\": 0.6625, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"Non-Education (US)\", \"count\": 81, \"ratio\": 0.3375, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Education (US)\", \"count\": 156, \"ratio\": 0.6638297872340425, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Non-Education (US)\", \"count\": 79, \"ratio\": 0.33617021276595743, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Education (US)\", \"count\": 209, \"ratio\": 0.7385159010600707, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Non-Education (US)\", \"count\": 74, \"ratio\": 0.26148409893992935, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Education (US)\", \"count\": 253, \"ratio\": 0.7574850299401198, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Non-Education (US)\", \"count\": 81, \"ratio\": 0.24251497005988024, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Education (US)\", \"count\": 256, \"ratio\": 0.8205128205128205, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Non-Education (US)\", \"count\": 56, \"ratio\": 0.1794871794871795, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Education (US)\", \"count\": 274, \"ratio\": 0.7611111111111111, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Non-Education (US)\", \"count\": 86, \"ratio\": 0.2388888888888889, \"Year\": \"2021-01-01T00:00:00\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 43,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"source1 = binary_type_df\n",
"\n",
"chart1 = alt.Chart(source1).mark_line().encode(\n",
" x=\"Year:T\",\n",
" y=\"ratio:Q\",\n",
" color=alt.Color(\"type:N\",\n",
" title = 'Affiliation types',\n",
" scale=alt.Scale(domain=['Education', 'Non-Education'],\n",
" range=['steelblue', 'orange']))\n",
").properties(\n",
" height=300,\n",
" width=250\n",
")\n",
"\n",
"source2 = us_author_df\n",
"\n",
"chart2 = alt.Chart(source2).mark_line().encode(\n",
" x=\"Year:T\",\n",
" y=\"ratio:Q\",\n",
" color=alt.Color(\"type:N\",\n",
" title = '',\n",
" scale=alt.Scale(domain=['Education (US)', 'Non-Education (US)'],\n",
" range=['purple', 'green']))\n",
").properties(\n",
" height=300,\n",
" width=300\n",
")\n",
"\n",
"alt.layer(\n",
" chart1,\n",
" chart2\n",
").resolve_scale(color='independent')"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "b114796b",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-4a5c7e2e4b2c4f60ae7f0a0fe2a3ec46\"></div>\n",
"<script type=\"text/javascript\">\n",
" var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
" (function(spec, embedOpt){\n",
" let outputDiv = document.currentScript.previousElementSibling;\n",
" if (outputDiv.id !== \"altair-viz-4a5c7e2e4b2c4f60ae7f0a0fe2a3ec46\") {\n",
" outputDiv = document.getElementById(\"altair-viz-4a5c7e2e4b2c4f60ae7f0a0fe2a3ec46\");\n",
" }\n",
" const paths = {\n",
" \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
" \"vega-lite\": \"https://cdn.jsdelivr.net/npm//vega-lite@4.17.0?noext\",\n",
" \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
" };\n",
"\n",
" function maybeLoadScript(lib, version) {\n",
" var key = `${lib.replace(\"-\", \"\")}_version`;\n",
" return (VEGA_DEBUG[key] == version) ?\n",
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" new Promise(function(resolve, reject) {\n",
" var s = document.createElement('script');\n",
" document.getElementsByTagName(\"head\")[0].appendChild(s);\n",
" s.async = true;\n",
" s.onload = () => {\n",
" VEGA_DEBUG[key] = version;\n",
" return resolve(paths[lib]);\n",
" };\n",
" s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
" s.src = paths[lib];\n",
" });\n",
" }\n",
"\n",
" function showError(err) {\n",
" outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
" throw err;\n",
" }\n",
"\n",
" function displayChart(vegaEmbed) {\n",
" vegaEmbed(outputDiv, spec, embedOpt)\n",
" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
" }\n",
"\n",
" if(typeof define === \"function\" && define.amd) {\n",
" requirejs.config({paths});\n",
" require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
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" .then(() => displayChart(vegaEmbed));\n",
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\"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"Non-Education\", \"count\": 48, \"ratio\": 0.19433198380566802, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"Education\", \"count\": 154, \"ratio\": 0.6311475409836066, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"Non-Education\", \"count\": 90, \"ratio\": 0.36885245901639346, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"Education\", \"count\": 177, \"ratio\": 0.6704545454545454, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"Non-Education\", \"count\": 87, \"ratio\": 0.32954545454545453, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"Education\", \"count\": 204, \"ratio\": 0.6891891891891891, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"Non-Education\", \"count\": 92, \"ratio\": 0.3108108108108108, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"Education\", \"count\": 221, \"ratio\": 0.6949685534591195, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"Non-Education\", \"count\": 97, \"ratio\": 0.3050314465408805, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"Education\", \"count\": 224, \"ratio\": 0.7441860465116279, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"Non-Education\", \"count\": 77, \"ratio\": 0.2558139534883721, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"Education\", \"count\": 237, \"ratio\": 0.7337461300309598, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"Non-Education\", \"count\": 86, \"ratio\": 0.26625386996904027, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"Education\", \"count\": 242, \"ratio\": 0.75625, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"Non-Education\", \"count\": 78, \"ratio\": 0.24375, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"Education\", \"count\": 316, \"ratio\": 0.7365967365967366, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"Non-Education\", \"count\": 113, \"ratio\": 0.2634032634032634, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"Education\", \"count\": 286, \"ratio\": 0.7486910994764397, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"Non-Education\", \"count\": 96, \"ratio\": 0.2513089005235602, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"Education\", \"count\": 291, \"ratio\": 0.7293233082706767, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"Non-Education\", \"count\": 108, \"ratio\": 0.2706766917293233, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"Education\", \"count\": 286, \"ratio\": 0.7546174142480211, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"Non-Education\", \"count\": 93, \"ratio\": 0.24538258575197888, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"Education\", \"count\": 329, \"ratio\": 0.732739420935412, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"Non-Education\", \"count\": 120, \"ratio\": 0.267260579064588, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"Education\", \"count\": 342, \"ratio\": 0.8123515439429929, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"Non-Education\", \"count\": 79, \"ratio\": 0.1876484560570071, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"Education\", \"count\": 373, \"ratio\": 0.7836134453781513, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"Non-Education\", \"count\": 103, \"ratio\": 0.21638655462184875, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"Education\", \"count\": 384, \"ratio\": 0.768, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"Non-Education\", \"count\": 116, \"ratio\": 0.232, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"Education\", \"count\": 343, \"ratio\": 0.8225419664268585, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"Non-Education\", \"count\": 74, \"ratio\": 0.1774580335731415, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"Education\", \"count\": 486, \"ratio\": 0.7928221859706363, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"Non-Education\", \"count\": 127, \"ratio\": 0.20717781402936378, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"Education\", \"count\": 443, \"ratio\": 0.7717770034843205, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"Non-Education\", \"count\": 131, \"ratio\": 0.22822299651567945, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"Education\", \"count\": 419, \"ratio\": 0.7773654916512059, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"Non-Education\", \"count\": 120, \"ratio\": 0.22263450834879406, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Education\", \"count\": 423, \"ratio\": 0.7847866419294991, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Non-Education\", \"count\": 116, \"ratio\": 0.21521335807050093, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Education\", \"count\": 466, \"ratio\": 0.7925170068027211, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Non-Education\", \"count\": 122, \"ratio\": 0.20748299319727892, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Education\", \"count\": 566, \"ratio\": 0.8348082595870207, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Non-Education\", \"count\": 112, \"ratio\": 0.16519174041297935, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Education\", \"count\": 665, \"ratio\": 0.8385876418663304, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Non-Education\", \"count\": 128, \"ratio\": 0.1614123581336696, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Education\", \"count\": 706, \"ratio\": 0.8355029585798817, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Non-Education\", \"count\": 139, \"ratio\": 0.16449704142011834, \"Year\": \"2021-01-01T00:00:00\"}], \"data-7ec3b6808d361595453b472948c0fff9\": [{\"year\": 1990, \"type\": \"Education (US)\", \"count\": 66, \"ratio\": 0.6055045871559633, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1990, \"type\": \"Non-Education (US)\", \"count\": 43, \"ratio\": 0.3944954128440367, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1991, \"type\": \"Education (US)\", \"count\": 58, \"ratio\": 0.5178571428571429, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1991, \"type\": \"Non-Education (US)\", \"count\": 54, \"ratio\": 0.48214285714285715, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1992, \"type\": \"Education (US)\", \"count\": 44, \"ratio\": 0.3893805309734513, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1992, \"type\": \"Non-Education (US)\", \"count\": 69, \"ratio\": 0.6106194690265486, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1993, \"type\": \"Education (US)\", \"count\": 42, \"ratio\": 0.5060240963855421, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1993, \"type\": \"Non-Education (US)\", \"count\": 41, \"ratio\": 0.4939759036144578, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1994, \"type\": \"Education (US)\", \"count\": 75, \"ratio\": 0.6578947368421053, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1994, \"type\": \"Non-Education (US)\", \"count\": 39, \"ratio\": 0.34210526315789475, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1995, \"type\": \"Education (US)\", \"count\": 66, \"ratio\": 0.559322033898305, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1995, \"type\": \"Non-Education (US)\", \"count\": 52, \"ratio\": 0.4406779661016949, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1996, \"type\": \"Education (US)\", \"count\": 119, \"ratio\": 0.6538461538461539, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1996, \"type\": \"Non-Education (US)\", \"count\": 63, \"ratio\": 0.34615384615384615, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"Education (US)\", \"count\": 140, \"ratio\": 0.7821229050279329, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"Non-Education (US)\", \"count\": 39, \"ratio\": 0.21787709497206703, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"Education (US)\", \"count\": 108, \"ratio\": 0.6506024096385542, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"Non-Education (US)\", \"count\": 58, \"ratio\": 0.3493975903614458, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"Education (US)\", \"count\": 118, \"ratio\": 0.6820809248554913, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"Non-Education (US)\", \"count\": 55, \"ratio\": 0.3179190751445087, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"Education (US)\", \"count\": 121, \"ratio\": 0.6836158192090396, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"Non-Education (US)\", \"count\": 56, \"ratio\": 0.3163841807909605, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"Education (US)\", \"count\": 137, \"ratio\": 0.6618357487922706, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"Non-Education (US)\", \"count\": 70, \"ratio\": 0.33816425120772947, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"Education (US)\", \"count\": 127, \"ratio\": 0.7514792899408284, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"Non-Education (US)\", \"count\": 42, \"ratio\": 0.2485207100591716, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"Education (US)\", \"count\": 163, \"ratio\": 0.7761904761904762, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"Non-Education (US)\", \"count\": 47, \"ratio\": 0.22380952380952382, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"Education (US)\", \"count\": 163, \"ratio\": 0.7799043062200957, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"Non-Education (US)\", \"count\": 46, \"ratio\": 0.22009569377990432, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"Education (US)\", \"count\": 153, \"ratio\": 0.7183098591549296, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"Non-Education (US)\", \"count\": 60, \"ratio\": 0.28169014084507044, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"Education (US)\", \"count\": 163, \"ratio\": 0.7276785714285714, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"Non-Education (US)\", \"count\": 61, \"ratio\": 0.27232142857142855, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"Education (US)\", \"count\": 167, \"ratio\": 0.698744769874477, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"Non-Education (US)\", \"count\": 72, \"ratio\": 0.301255230125523, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"Education (US)\", \"count\": 173, \"ratio\": 0.7587719298245614, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"Non-Education (US)\", \"count\": 55, \"ratio\": 0.2412280701754386, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"Education (US)\", \"count\": 150, \"ratio\": 0.6493506493506493, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"Non-Education (US)\", \"count\": 81, \"ratio\": 0.35064935064935066, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"Education (US)\", \"count\": 166, \"ratio\": 0.7980769230769231, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"Non-Education (US)\", \"count\": 42, \"ratio\": 0.20192307692307693, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"Education (US)\", \"count\": 153, \"ratio\": 0.7688442211055276, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"Non-Education (US)\", \"count\": 46, \"ratio\": 0.23115577889447236, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"Education (US)\", \"count\": 154, \"ratio\": 0.7298578199052133, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"Non-Education (US)\", \"count\": 57, \"ratio\": 0.27014218009478674, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"Education (US)\", \"count\": 155, \"ratio\": 0.824468085106383, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"Non-Education (US)\", \"count\": 33, \"ratio\": 0.17553191489361702, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"Education (US)\", \"count\": 218, \"ratio\": 0.7898550724637681, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"Non-Education (US)\", \"count\": 58, \"ratio\": 0.21014492753623187, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"Education (US)\", \"count\": 182, \"ratio\": 0.6642335766423357, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"Non-Education (US)\", \"count\": 92, \"ratio\": 0.3357664233576642, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"Education (US)\", \"count\": 159, \"ratio\": 0.6625, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"Non-Education (US)\", \"count\": 81, \"ratio\": 0.3375, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Education (US)\", \"count\": 156, \"ratio\": 0.6638297872340425, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Non-Education (US)\", \"count\": 79, \"ratio\": 0.33617021276595743, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Education (US)\", \"count\": 209, \"ratio\": 0.7385159010600707, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Non-Education (US)\", \"count\": 74, \"ratio\": 0.26148409893992935, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Education (US)\", \"count\": 253, \"ratio\": 0.7574850299401198, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Non-Education (US)\", \"count\": 81, \"ratio\": 0.24251497005988024, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Education (US)\", \"count\": 256, \"ratio\": 0.8205128205128205, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Non-Education (US)\", \"count\": 56, \"ratio\": 0.1794871794871795, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Education (US)\", \"count\": 274, \"ratio\": 0.7611111111111111, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Non-Education (US)\", \"count\": 86, \"ratio\": 0.2388888888888889, \"Year\": \"2021-01-01T00:00:00\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 44,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"source1 = binary_type_df\n",
"\n",
"chart1 = alt.Chart(source1).mark_line().encode(\n",
" x=\"Year:T\",\n",
" y=\"count:Q\",\n",
" color=alt.Color(\"type:N\",\n",
" title = 'Affiliation types',\n",
" scale=alt.Scale(domain=['Education', 'Non-Education'],\n",
" range=['steelblue', 'orange']))\n",
").properties(\n",
" height=300,\n",
" width=250\n",
")\n",
"\n",
"source2 = us_author_df\n",
"\n",
"chart2 = alt.Chart(source2).mark_line().encode(\n",
" x=\"Year:T\",\n",
" y=\"count:Q\",\n",
" color=alt.Color(\"type:N\",\n",
" title = '',\n",
" scale=alt.Scale(domain=['Education (US)', 'Non-Education (US)'],\n",
" range=['purple', 'green']))\n",
").properties(\n",
" height=300,\n",
" width=300\n",
")\n",
"\n",
"alt.layer(\n",
" chart1,\n",
" chart2\n",
").resolve_scale(color='independent')"
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "92319737",
"metadata": {},
"outputs": [],
"source": [
"ratio_wording = 'Proportion' \n",
"# or 'Percentage', or 'Ratio'"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "93a9b795",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" var VEGA_DEBUG = (typeof VEGA_DEBUG == \"undefined\") ? {} : VEGA_DEBUG;\n",
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" outputDiv = document.getElementById(\"altair-viz-d358b585d2b8407ba2b9d3336e6aee7f\");\n",
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" const paths = {\n",
" \"vega\": \"https://cdn.jsdelivr.net/npm//vega@5?noext\",\n",
" \"vega-lib\": \"https://cdn.jsdelivr.net/npm//vega-lib?noext\",\n",
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" \"vega-embed\": \"https://cdn.jsdelivr.net/npm//vega-embed@6?noext\",\n",
" };\n",
"\n",
" function maybeLoadScript(lib, version) {\n",
" var key = `${lib.replace(\"-\", \"\")}_version`;\n",
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" s.async = true;\n",
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" s.onerror = () => reject(`Error loading script: ${paths[lib]}`);\n",
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"\n",
" function showError(err) {\n",
" outputDiv.innerHTML = `<div class=\"error\" style=\"color:red;\">${err}</div>`;\n",
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" function displayChart(vegaEmbed) {\n",
" vegaEmbed(outputDiv, spec, embedOpt)\n",
" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
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"\n",
" if(typeof define === \"function\" && define.amd) {\n",
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\"country\": \"Germany\", \"year total\": 106, \"ratio\": 0.18466898954703834, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"country\": \"Other\", \"year total\": 113, \"ratio\": 0.19686411149825783, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"country\": \"US\", \"year total\": 274, \"ratio\": 0.47735191637630664, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2016, \"country\": \"Austria\", \"year total\": 23, \"ratio\": 0.04267161410018553, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"country\": \"Canada\", \"year total\": 19, \"ratio\": 0.03525046382189239, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"country\": \"China\", \"year total\": 55, \"ratio\": 0.10204081632653061, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"country\": \"Germany\", \"year total\": 71, \"ratio\": 0.13172541743970315, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"country\": \"Other\", \"year total\": 131, \"ratio\": 0.24304267161410018, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"country\": \"US\", \"year total\": 240, \"ratio\": 0.4452690166975881, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2017, \"country\": \"Austria\", \"year total\": 44, \"ratio\": 0.08163265306122448, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"country\": \"Canada\", \"year total\": 17, \"ratio\": 0.03153988868274583, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"country\": \"China\", \"year total\": 74, \"ratio\": 0.137291280148423, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"country\": \"Germany\", \"year total\": 70, \"ratio\": 0.12987012987012986, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"country\": \"Other\", \"year total\": 99, \"ratio\": 0.1836734693877551, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"country\": \"US\", \"year total\": 235, \"ratio\": 0.4359925788497217, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018, \"country\": \"Austria\", \"year total\": 14, \"ratio\": 0.023809523809523808, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"country\": \"Canada\", \"year total\": 10, \"ratio\": 0.017006802721088437, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"country\": \"China\", \"year total\": 105, \"ratio\": 0.17857142857142858, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"country\": \"Germany\", \"year total\": 82, \"ratio\": 0.13945578231292516, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"country\": \"Other\", \"year total\": 94, \"ratio\": 0.1598639455782313, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"country\": \"US\", \"year total\": 283, \"ratio\": 0.4812925170068027, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019, \"country\": \"Austria\", \"year total\": 14, \"ratio\": 0.02064896755162242, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"country\": \"Canada\", \"year total\": 26, \"ratio\": 0.038348082595870206, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"country\": \"China\", \"year total\": 146, \"ratio\": 0.2153392330383481, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"country\": \"Germany\", \"year total\": 67, \"ratio\": 0.09882005899705015, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"country\": \"Other\", \"year total\": 91, \"ratio\": 0.13421828908554573, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"country\": \"US\", \"year total\": 334, \"ratio\": 0.49262536873156343, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020, \"country\": \"Austria\", \"year total\": 10, \"ratio\": 0.012610340479192938, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"country\": \"Canada\", \"year total\": 16, \"ratio\": 0.0201765447667087, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"country\": \"China\", \"year total\": 201, \"ratio\": 0.25346784363177804, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"country\": \"Germany\", \"year total\": 87, \"ratio\": 0.10970996216897856, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"country\": \"Other\", \"year total\": 167, \"ratio\": 0.21059268600252207, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"country\": \"US\", \"year total\": 312, \"ratio\": 0.39344262295081966, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021, \"country\": \"Austria\", \"year total\": 19, \"ratio\": 0.022485207100591716, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"country\": \"Canada\", \"year total\": 25, \"ratio\": 0.029585798816568046, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"country\": \"China\", \"year total\": 190, \"ratio\": 0.22485207100591717, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"country\": \"Germany\", \"year total\": 81, \"ratio\": 0.09585798816568047, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"country\": \"Other\", \"year total\": 170, \"ratio\": 0.20118343195266272, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"country\": \"US\", \"year total\": 360, \"ratio\": 0.4260355029585799, \"Year\": \"2021-01-01T00:00:00\"}], \"data-af2934e4c4ce3bf8be85758754cbdf1e\": [{\"continent\": \"Africa\", \"count\": 3, \"proportion\": 0.0}, {\"continent\": \"Asia\", \"count\": 1383, \"proportion\": 0.111}, {\"continent\": \"Europe\", \"count\": 3802, \"proportion\": 0.306}, {\"continent\": \"N. America\", \"count\": 7005, \"proportion\": 0.564}, {\"continent\": \"Oceania\", \"count\": 151, \"proportion\": 0.012}, {\"continent\": \"S. America\", \"count\": 84, \"proportion\": 0.007}], \"data-bccc276b38e463949058b1a620b2953c\": [{\"year\": 1990, \"With US Authors\": \"No\", \"count\": 6, \"Ratio\": 0.11538461538461539, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1990, \"With US Authors\": \"Yes\", \"count\": 46, \"Ratio\": 0.8846153846153846, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1991, \"With US Authors\": \"No\", \"count\": 6, \"Ratio\": 0.12, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1991, \"With US Authors\": \"Yes\", \"count\": 44, \"Ratio\": 0.88, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1992, \"With US Authors\": \"No\", \"count\": 10, \"Ratio\": 0.18867924528301888, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1992, \"With US Authors\": \"Yes\", \"count\": 43, \"Ratio\": 0.8113207547169812, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1993, \"With US Authors\": \"No\", \"count\": 17, \"Ratio\": 0.3090909090909091, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1993, \"With US Authors\": \"Yes\", \"count\": 38, \"Ratio\": 0.6909090909090909, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1994, \"With US Authors\": \"No\", \"count\": 8, \"Ratio\": 0.1509433962264151, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1994, \"With US Authors\": \"Yes\", \"count\": 45, \"Ratio\": 0.8490566037735849, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1995, \"With US Authors\": \"No\", \"count\": 31, \"Ratio\": 0.4189189189189189, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1995, \"With US Authors\": \"Yes\", \"count\": 43, \"Ratio\": 0.581081081081081, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1996, \"With US Authors\": \"No\", \"count\": 21, \"Ratio\": 0.2727272727272727, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1996, \"With US Authors\": \"Yes\", \"count\": 56, \"Ratio\": 0.7272727272727273, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1997, \"With US Authors\": \"No\", \"count\": 23, \"Ratio\": 0.26136363636363635, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1997, \"With US Authors\": \"Yes\", \"count\": 65, \"Ratio\": 0.7386363636363636, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1998, \"With US Authors\": \"No\", \"count\": 26, \"Ratio\": 0.28888888888888886, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1998, \"With US Authors\": \"Yes\", \"count\": 64, \"Ratio\": 0.7111111111111111, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1999, \"With US Authors\": \"No\", \"count\": 28, \"Ratio\": 0.3181818181818182, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 1999, \"With US Authors\": \"Yes\", \"count\": 60, \"Ratio\": 0.6818181818181818, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 2000, \"With US Authors\": \"No\", \"count\": 34, \"Ratio\": 0.3695652173913043, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2000, \"With US Authors\": \"Yes\", \"count\": 58, \"Ratio\": 0.6304347826086957, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2001, \"With US Authors\": \"No\", \"count\": 30, \"Ratio\": 0.3125, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2001, \"With US Authors\": \"Yes\", \"count\": 66, \"Ratio\": 0.6875, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2002, \"With US Authors\": \"No\", \"count\": 32, \"Ratio\": 0.31683168316831684, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2002, \"With US Authors\": \"Yes\", \"count\": 69, \"Ratio\": 0.6831683168316832, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2003, \"With US Authors\": \"No\", \"count\": 38, \"Ratio\": 0.37254901960784315, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2003, \"With US Authors\": \"Yes\", \"count\": 64, \"Ratio\": 0.6274509803921569, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2004, \"With US Authors\": \"No\", \"count\": 27, \"Ratio\": 0.27835051546391754, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2004, \"With US Authors\": \"Yes\", \"count\": 70, \"Ratio\": 0.7216494845360825, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2005, \"With US Authors\": \"No\", \"count\": 50, \"Ratio\": 0.42016806722689076, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2005, \"With US Authors\": \"Yes\", \"count\": 69, \"Ratio\": 0.5798319327731093, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2006, \"With US Authors\": \"No\", \"count\": 40, \"Ratio\": 0.35398230088495575, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2006, \"With US Authors\": \"Yes\", \"count\": 73, \"Ratio\": 0.6460176991150443, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2007, \"With US Authors\": \"No\", \"count\": 31, \"Ratio\": 0.2897196261682243, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2007, \"With US Authors\": \"Yes\", \"count\": 76, \"Ratio\": 0.7102803738317757, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2008, \"With US Authors\": \"No\", \"count\": 32, \"Ratio\": 0.32323232323232326, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008, \"With US Authors\": \"Yes\", \"count\": 67, \"Ratio\": 0.6767676767676768, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2009, \"With US Authors\": \"No\", \"count\": 47, \"Ratio\": 0.4017094017094017, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009, \"With US Authors\": \"Yes\", \"count\": 70, \"Ratio\": 0.5982905982905983, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2010, \"With US Authors\": \"No\", \"count\": 40, \"Ratio\": 0.3669724770642202, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010, \"With US Authors\": \"Yes\", \"count\": 69, \"Ratio\": 0.6330275229357798, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2011, \"With US Authors\": \"No\", \"count\": 63, \"Ratio\": 0.5294117647058824, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011, \"With US Authors\": \"Yes\", \"count\": 56, \"Ratio\": 0.47058823529411764, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2012, \"With US Authors\": \"No\", \"count\": 49, \"Ratio\": 0.4224137931034483, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012, \"With US Authors\": \"Yes\", \"count\": 67, \"Ratio\": 0.5775862068965517, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2013, \"With US Authors\": \"No\", \"count\": 43, \"Ratio\": 0.42574257425742573, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013, \"With US Authors\": \"Yes\", \"count\": 58, \"Ratio\": 0.5742574257425742, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2014, \"With US Authors\": \"No\", \"count\": 51, \"Ratio\": 0.38345864661654133, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014, \"With US Authors\": \"Yes\", \"count\": 82, \"Ratio\": 0.6165413533834586, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2015, \"With US Authors\": \"No\", \"count\": 49, \"Ratio\": 0.3858267716535433, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"With US Authors\": \"Yes\", \"count\": 78, \"Ratio\": 0.6141732283464567, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2016, \"With US Authors\": \"No\", \"count\": 41, \"Ratio\": 0.3565217391304348, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"With US Authors\": \"Yes\", \"count\": 74, \"Ratio\": 0.6434782608695652, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2017, \"With US Authors\": \"No\", \"count\": 43, \"Ratio\": 0.37719298245614036, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"With US Authors\": \"Yes\", \"count\": 71, \"Ratio\": 0.6228070175438597, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018, \"With US Authors\": \"No\", \"count\": 42, \"Ratio\": 0.33070866141732286, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"With US Authors\": \"Yes\", \"count\": 85, \"Ratio\": 0.6692913385826772, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019, \"With US Authors\": \"No\", \"count\": 42, \"Ratio\": 0.32558139534883723, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"With US Authors\": \"Yes\", \"count\": 87, \"Ratio\": 0.6744186046511628, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020, \"With US Authors\": \"No\", \"count\": 66, \"Ratio\": 0.42038216560509556, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"With US Authors\": \"Yes\", \"count\": 91, \"Ratio\": 0.5796178343949044, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021, \"With US Authors\": \"No\", \"count\": 70, \"Ratio\": 0.4117647058823529, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"With US Authors\": \"Yes\", \"count\": 100, \"Ratio\": 0.5882352941176471, \"Year\": \"2021-01-01T00:00:00\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Num of countries by year\n",
"\n",
"source = cntry_num_by_year\n",
"\n",
"chart1 = alt.Chart(source).mark_line().encode(\n",
" x='Year:T',\n",
" y=alt.Y(\n",
" 'Number of participating countries',\n",
" title = 'Number of countries'\n",
" )\n",
").properties(\n",
" title = 'a',\n",
" width=240\n",
")\n",
"\n",
"## top countries\n",
"\n",
"source = cntry_ts_prop_df\n",
"\n",
"chart2 = alt.Chart(source).mark_area().encode(\n",
" x=\"Year:T\",\n",
" y=alt.Y(\"year total:Q\",title='Number of authors'),\n",
" color=alt.Color(\n",
" \"country:N\",\n",
" title = 'Country/Region',\n",
"# legend=alt.Legend(orient='top-left')\n",
" )\n",
").properties(\n",
" title = 'b',\n",
" width = 300,\n",
")\n",
"\n",
"## Continent\n",
"source = continent_count\n",
"\n",
"chart3 = alt.Chart(source).mark_bar().encode(\n",
" x=alt.X(\"continent:N\",\n",
" title='Continent',\n",
" axis=alt.Axis(labelAngle=-40)\n",
" ),\n",
" y=alt.Y(\n",
" 'count',\n",
" title = 'Number of authors',\n",
" scale=alt.Scale(type=\"log\") # Here the scale is applied\n",
" ),\n",
" color=alt.Color(\n",
" \"continent:N\",\n",
" title = 'Continent',\n",
" legend=None,\n",
" )\n",
").properties(\n",
" title = 'c',\n",
" width=200\n",
")\n",
"\n",
"## With US Authors\n",
"source = withus_authors\n",
"\n",
"chart4 = alt.Chart(source).mark_line().encode(\n",
" x=\"Year:T\",\n",
" y=alt.Y(\n",
" \"Ratio:Q\",\n",
" title = ratio_wording\n",
" ),\n",
" color=alt.Color(\n",
" \"With US Authors:N\",\n",
" title=\"With US authors\",\n",
" sort=['Yes', 'No'],\n",
" legend=alt.Legend(\n",
" orient='none',\n",
" legendX=90, legendY=15,\n",
" direction='horizontal',\n",
" titleAnchor='middle'\n",
" )\n",
" )\n",
").properties(\n",
" title = 'd',\n",
" width=240\n",
")\n",
"\n",
"author1 = alt.hconcat(chart1, chart2, chart3, chart4).configure_title(\n",
" anchor='start',\n",
" fontSize = 20\n",
").resolve_scale(\n",
" color='independent',\n",
"# scheme='category10'\n",
" \n",
").configure_axis(\n",
" labelFontSize=15,\n",
" titleFontSize=20\n",
").configure_legend(\n",
" titleFontSize=16,\n",
" labelFontSize=16\n",
").configure_title(\n",
" anchor='start',\n",
" fontSize=22)\n",
"\n",
"\n",
"# .configure_axis(\n",
"# labelFontSize=15,\n",
"# titleFontSize=16\n",
"# ).configure_legend(\n",
"# titleFontSize=14,\n",
"# labelFontSize=16\n",
"# ) \n",
"\n",
"author1"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "090ac76e",
"metadata": {},
"outputs": [],
"source": [
"# ## Affiliation types\n",
"# source = author_aff_count\n",
"\n",
"# chart5 = alt.Chart(source).mark_bar().encode(\n",
"# x='type',\n",
"# y=alt.Y(\n",
"# 'count',\n",
"# scale=alt.Scale(type=\"log\") # Here the scale is applied\n",
"# ),\n",
"# color = 'type',\n",
"# ).properties(\n",
"# title = 'D'\n",
"# )\n",
"# chart5"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "685cc150",
"metadata": {},
"outputs": [
{
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" function displayChart(vegaEmbed) {\n",
" vegaEmbed(outputDiv, spec, embedOpt)\n",
" .catch(err => showError(`Javascript Error: ${err.message}<br>This usually means there's a typo in your chart specification. See the javascript console for the full traceback.`));\n",
" }\n",
"\n",
" if(typeof define === \"function\" && define.amd) {\n",
" requirejs.config({paths});\n",
" require([\"vega-embed\"], displayChart, err => showError(`Error loading script: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"labelFontSize\": 15, \"titleFontSize\": 20}, \"legend\": {\"labelFontSize\": 16, \"titleFontSize\": 16}, \"title\": {\"anchor\": \"start\", \"fontSize\": 22}}, \"hconcat\": [{\"data\": {\"name\": \"data-9dad40c1eec6f848d828f9fcd4905632\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"type\", \"legend\": null, \"title\": \"Affiliation type\", \"type\": \"nominal\"}, \"x\": {\"axis\": {\"labelAngle\": -40}, \"field\": \"type\", \"title\": \"Author affiliation type\", \"type\": \"nominal\"}, \"y\": {\"field\": \"count\", \"scale\": {\"type\": \"log\"}, \"title\": \"Number of authors\", \"type\": \"quantitative\"}}, \"height\": 300, \"title\": \"e\", \"width\": 260}, {\"layer\": [{\"data\": {\"name\": \"data-d7c58f944dff75313d6d54b1fbf23c9c\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"type\", \"scale\": {\"domain\": [\"Education\", \"Non-Education\"], \"range\": [\"steelblue\", \"orange\"]}, \"title\": \"Affiliation type\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"ratio\", \"title\": \"Proportion\", \"type\": \"quantitative\"}}}, {\"data\": {\"name\": \"data-7ec3b6808d361595453b472948c0fff9\"}, \"mark\": {\"type\": \"line\", \"point\": {\"filled\": false, \"fill\": \"white\"}, \"strokeDash\": [1, 2]}, \"encoding\": {\"color\": {\"field\": \"type\", \"scale\": {\"domain\": [\"Education (US)\", \"Non-Education (US)\"], \"range\": [\"green\", \"purple\"]}, \"title\": \"\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"ratio\", \"title\": \"Proportion\", \"type\": \"quantitative\"}}}], \"height\": 300, \"resolve\": {\"scale\": {\"color\": \"independent\"}}, \"title\": \"f\", \"width\": 260}, {\"layer\": [{\"data\": {\"name\": \"data-d7c58f944dff75313d6d54b1fbf23c9c\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"type\", \"scale\": {\"domain\": [\"Education\", \"Non-Education\"], \"range\": [\"steelblue\", \"orange\"]}, \"title\": \"Affiliation type\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"count\", \"title\": \"Number of authors\", \"type\": \"quantitative\"}}}, {\"data\": {\"name\": \"data-7ec3b6808d361595453b472948c0fff9\"}, \"mark\": {\"type\": \"line\", \"point\": {\"filled\": false, \"fill\": \"white\"}, \"strokeDash\": [1, 2]}, \"encoding\": {\"color\": {\"field\": \"type\", \"scale\": {\"domain\": [\"Education (US)\", \"Non-Education (US)\"], \"range\": [\"green\", \"purple\"]}, \"title\": \"\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"count\", \"type\": \"quantitative\"}}}], \"height\": 300, \"resolve\": {\"scale\": {\"color\": \"independent\"}}, \"title\": \"g\", \"width\": 260}], \"resolve\": {\"scale\": {\"color\": \"independent\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-9dad40c1eec6f848d828f9fcd4905632\": [{\"type\": \"education\", \"count\": 9583, \"ratio\": 0.771}, {\"type\": \"company\", \"count\": 1292, \"ratio\": 0.104}, {\"type\": \"facility\", \"count\": 1085, \"ratio\": 0.087}, {\"type\": \"government\", \"count\": 241, \"ratio\": 0.019}, {\"type\": \"nonprofit\", \"count\": 65, \"ratio\": 0.005}, {\"type\": \"other\", \"count\": 9, \"ratio\": 0.001}, {\"type\": \"healthcare\", \"count\": 134, \"ratio\": 0.011}, {\"type\": \"archive\", \"count\": 19, \"ratio\": 0.002}], \"data-d7c58f944dff75313d6d54b1fbf23c9c\": [{\"year\": 1990, \"type\": \"Education\", \"count\": 72, \"ratio\": 0.6050420168067226, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1990, \"type\": \"Non-Education\", \"count\": 47, \"ratio\": 0.3949579831932773, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1991, \"type\": \"Education\", \"count\": 65, \"ratio\": 0.5078125, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1991, \"type\": \"Non-Education\", \"count\": 63, \"ratio\": 0.4921875, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1992, \"type\": \"Education\", \"count\": 55, \"ratio\": 0.41353383458646614, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1992, \"type\": \"Non-Education\", \"count\": 78, \"ratio\": 0.5864661654135338, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1993, \"type\": \"Education\", \"count\": 64, \"ratio\": 0.49612403100775193, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1993, \"type\": \"Non-Education\", \"count\": 65, \"ratio\": 0.5038759689922481, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1994, \"type\": \"Education\", \"count\": 86, \"ratio\": 0.6277372262773723, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1994, \"type\": \"Non-Education\", \"count\": 51, \"ratio\": 0.3722627737226277, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1995, \"type\": \"Education\", \"count\": 131, \"ratio\": 0.6717948717948717, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1995, \"type\": \"Non-Education\", \"count\": 64, \"ratio\": 0.3282051282051282, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1996, \"type\": \"Education\", \"count\": 165, \"ratio\": 0.6521739130434783, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1996, \"type\": \"Non-Education\", \"count\": 88, \"ratio\": 0.34782608695652173, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"Education\", \"count\": 199, \"ratio\": 0.805668016194332, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"Non-Education\", \"count\": 48, \"ratio\": 0.19433198380566802, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"Education\", \"count\": 154, \"ratio\": 0.6311475409836066, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"Non-Education\", \"count\": 90, \"ratio\": 0.36885245901639346, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"Education\", \"count\": 177, \"ratio\": 0.6704545454545454, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"Non-Education\", \"count\": 87, \"ratio\": 0.32954545454545453, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"Education\", \"count\": 204, \"ratio\": 0.6891891891891891, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"Non-Education\", \"count\": 92, \"ratio\": 0.3108108108108108, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"Education\", \"count\": 221, \"ratio\": 0.6949685534591195, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"Non-Education\", \"count\": 97, \"ratio\": 0.3050314465408805, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"Education\", \"count\": 224, \"ratio\": 0.7441860465116279, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"Non-Education\", \"count\": 77, \"ratio\": 0.2558139534883721, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"Education\", \"count\": 237, \"ratio\": 0.7337461300309598, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"Non-Education\", \"count\": 86, \"ratio\": 0.26625386996904027, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"Education\", \"count\": 242, \"ratio\": 0.75625, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"Non-Education\", \"count\": 78, \"ratio\": 0.24375, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"Education\", \"count\": 316, \"ratio\": 0.7365967365967366, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"Non-Education\", \"count\": 113, \"ratio\": 0.2634032634032634, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"Education\", \"count\": 286, \"ratio\": 0.7486910994764397, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"Non-Education\", \"count\": 96, \"ratio\": 0.2513089005235602, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"Education\", \"count\": 291, \"ratio\": 0.7293233082706767, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"Non-Education\", \"count\": 108, \"ratio\": 0.2706766917293233, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"Education\", \"count\": 286, \"ratio\": 0.7546174142480211, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"Non-Education\", \"count\": 93, \"ratio\": 0.24538258575197888, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"Education\", \"count\": 329, \"ratio\": 0.732739420935412, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"Non-Education\", \"count\": 120, \"ratio\": 0.267260579064588, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"Education\", \"count\": 342, \"ratio\": 0.8123515439429929, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"Non-Education\", \"count\": 79, \"ratio\": 0.1876484560570071, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"Education\", \"count\": 373, \"ratio\": 0.7836134453781513, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"Non-Education\", \"count\": 103, \"ratio\": 0.21638655462184875, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"Education\", \"count\": 384, \"ratio\": 0.768, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"Non-Education\", \"count\": 116, \"ratio\": 0.232, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"Education\", \"count\": 343, \"ratio\": 0.8225419664268585, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"Non-Education\", \"count\": 74, \"ratio\": 0.1774580335731415, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"Education\", \"count\": 486, \"ratio\": 0.7928221859706363, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"Non-Education\", \"count\": 127, \"ratio\": 0.20717781402936378, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"Education\", \"count\": 443, \"ratio\": 0.7717770034843205, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"Non-Education\", \"count\": 131, \"ratio\": 0.22822299651567945, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"Education\", \"count\": 419, \"ratio\": 0.7773654916512059, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"Non-Education\", \"count\": 120, \"ratio\": 0.22263450834879406, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Education\", \"count\": 423, \"ratio\": 0.7847866419294991, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Non-Education\", \"count\": 116, \"ratio\": 0.21521335807050093, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Education\", \"count\": 466, \"ratio\": 0.7925170068027211, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Non-Education\", \"count\": 122, \"ratio\": 0.20748299319727892, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Education\", \"count\": 566, \"ratio\": 0.8348082595870207, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Non-Education\", \"count\": 112, \"ratio\": 0.16519174041297935, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Education\", \"count\": 665, \"ratio\": 0.8385876418663304, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Non-Education\", \"count\": 128, \"ratio\": 0.1614123581336696, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Education\", \"count\": 706, \"ratio\": 0.8355029585798817, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Non-Education\", \"count\": 139, \"ratio\": 0.16449704142011834, \"Year\": \"2021-01-01T00:00:00\"}], \"data-7ec3b6808d361595453b472948c0fff9\": [{\"year\": 1990, \"type\": \"Education (US)\", \"count\": 66, \"ratio\": 0.6055045871559633, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1990, \"type\": \"Non-Education (US)\", \"count\": 43, \"ratio\": 0.3944954128440367, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1991, \"type\": \"Education (US)\", \"count\": 58, \"ratio\": 0.5178571428571429, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1991, \"type\": \"Non-Education (US)\", \"count\": 54, \"ratio\": 0.48214285714285715, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1992, \"type\": \"Education (US)\", \"count\": 44, \"ratio\": 0.3893805309734513, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1992, \"type\": \"Non-Education (US)\", \"count\": 69, \"ratio\": 0.6106194690265486, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1993, \"type\": \"Education (US)\", \"count\": 42, \"ratio\": 0.5060240963855421, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1993, \"type\": \"Non-Education (US)\", \"count\": 41, \"ratio\": 0.4939759036144578, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1994, \"type\": \"Education (US)\", \"count\": 75, \"ratio\": 0.6578947368421053, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1994, \"type\": \"Non-Education (US)\", \"count\": 39, \"ratio\": 0.34210526315789475, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1995, \"type\": \"Education (US)\", \"count\": 66, \"ratio\": 0.559322033898305, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1995, \"type\": \"Non-Education (US)\", \"count\": 52, \"ratio\": 0.4406779661016949, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1996, \"type\": \"Education (US)\", \"count\": 119, \"ratio\": 0.6538461538461539, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1996, \"type\": \"Non-Education (US)\", \"count\": 63, \"ratio\": 0.34615384615384615, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"Education (US)\", \"count\": 140, \"ratio\": 0.7821229050279329, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"Non-Education (US)\", \"count\": 39, \"ratio\": 0.21787709497206703, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"Education (US)\", \"count\": 108, \"ratio\": 0.6506024096385542, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"Non-Education (US)\", \"count\": 58, \"ratio\": 0.3493975903614458, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"Education (US)\", \"count\": 118, \"ratio\": 0.6820809248554913, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"Non-Education (US)\", \"count\": 55, \"ratio\": 0.3179190751445087, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"Education (US)\", \"count\": 121, \"ratio\": 0.6836158192090396, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"Non-Education (US)\", \"count\": 56, \"ratio\": 0.3163841807909605, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"Education (US)\", \"count\": 137, \"ratio\": 0.6618357487922706, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"Non-Education (US)\", \"count\": 70, \"ratio\": 0.33816425120772947, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"Education (US)\", \"count\": 127, \"ratio\": 0.7514792899408284, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"Non-Education (US)\", \"count\": 42, \"ratio\": 0.2485207100591716, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"Education (US)\", \"count\": 163, \"ratio\": 0.7761904761904762, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"Non-Education (US)\", \"count\": 47, \"ratio\": 0.22380952380952382, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"Education (US)\", \"count\": 163, \"ratio\": 0.7799043062200957, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"Non-Education (US)\", \"count\": 46, \"ratio\": 0.22009569377990432, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"Education (US)\", \"count\": 153, \"ratio\": 0.7183098591549296, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"Non-Education (US)\", \"count\": 60, \"ratio\": 0.28169014084507044, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"Education (US)\", \"count\": 163, \"ratio\": 0.7276785714285714, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"Non-Education (US)\", \"count\": 61, \"ratio\": 0.27232142857142855, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"Education (US)\", \"count\": 167, \"ratio\": 0.698744769874477, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"Non-Education (US)\", \"count\": 72, \"ratio\": 0.301255230125523, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"Education (US)\", \"count\": 173, \"ratio\": 0.7587719298245614, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"Non-Education (US)\", \"count\": 55, \"ratio\": 0.2412280701754386, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"Education (US)\", \"count\": 150, \"ratio\": 0.6493506493506493, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"Non-Education (US)\", \"count\": 81, \"ratio\": 0.35064935064935066, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"Education (US)\", \"count\": 166, \"ratio\": 0.7980769230769231, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"Non-Education (US)\", \"count\": 42, \"ratio\": 0.20192307692307693, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"Education (US)\", \"count\": 153, \"ratio\": 0.7688442211055276, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"Non-Education (US)\", \"count\": 46, \"ratio\": 0.23115577889447236, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"Education (US)\", \"count\": 154, \"ratio\": 0.7298578199052133, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"Non-Education (US)\", \"count\": 57, \"ratio\": 0.27014218009478674, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"Education (US)\", \"count\": 155, \"ratio\": 0.824468085106383, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"Non-Education (US)\", \"count\": 33, \"ratio\": 0.17553191489361702, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"Education (US)\", \"count\": 218, \"ratio\": 0.7898550724637681, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"Non-Education (US)\", \"count\": 58, \"ratio\": 0.21014492753623187, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"Education (US)\", \"count\": 182, \"ratio\": 0.6642335766423357, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"Non-Education (US)\", \"count\": 92, \"ratio\": 0.3357664233576642, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"Education (US)\", \"count\": 159, \"ratio\": 0.6625, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"Non-Education (US)\", \"count\": 81, \"ratio\": 0.3375, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Education (US)\", \"count\": 156, \"ratio\": 0.6638297872340425, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Non-Education (US)\", \"count\": 79, \"ratio\": 0.33617021276595743, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Education (US)\", \"count\": 209, \"ratio\": 0.7385159010600707, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Non-Education (US)\", \"count\": 74, \"ratio\": 0.26148409893992935, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Education (US)\", \"count\": 253, \"ratio\": 0.7574850299401198, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Non-Education (US)\", \"count\": 81, \"ratio\": 0.24251497005988024, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Education (US)\", \"count\": 256, \"ratio\": 0.8205128205128205, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Non-Education (US)\", \"count\": 56, \"ratio\": 0.1794871794871795, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Education (US)\", \"count\": 274, \"ratio\": 0.7611111111111111, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Non-Education (US)\", \"count\": 86, \"ratio\": 0.2388888888888889, \"Year\": \"2021-01-01T00:00:00\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## Affiliation types\n",
"source1 = author_aff_count\n",
"\n",
"chart1 = alt.Chart(source1).mark_bar().encode(\n",
" x=alt.X('type',\n",
" title = 'Author affiliation type',\n",
" axis=alt.Axis(labelAngle=-40),\n",
" ),\n",
" y=alt.Y(\n",
" 'count',\n",
" title = \"Number of authors\",\n",
" scale=alt.Scale(type=\"log\") # Here the scale is applied\n",
" ),\n",
" color=alt.Color(\n",
" \"type:N\",\n",
" title = 'Affiliation type',\n",
" legend=None,\n",
" )\n",
").properties(\n",
" height=300,\n",
" width=260,\n",
" title = 'e'\n",
")\n",
"\n",
"source2_1 = binary_type_df\n",
"\n",
"chart2_1 = alt.Chart(source2_1).mark_line().encode(\n",
" x=\"Year:T\",\n",
" y=alt.Y(\n",
" \"ratio:Q\",\n",
" title = ratio_wording,\n",
" ),\n",
" color=alt.Color(\n",
" \"type:N\",\n",
" title = 'Affiliation type',\n",
" scale=alt.Scale(\n",
" domain=['Education', 'Non-Education'],\n",
" range = ['steelblue', 'orange']\n",
"# range = ['#4daf4a','#984ea3'],\n",
" )))\n",
"\n",
"source2_2 = us_author_df\n",
"\n",
"chart2_2 = alt.Chart(source2_2).mark_line(\n",
" point={\n",
" \"filled\": False,\n",
" \"fill\": \"white\"\n",
" },\n",
" strokeDash=[1,2]\n",
").encode(\n",
" x=\"Year:T\",\n",
" y=alt.Y(\n",
" \"ratio:Q\",\n",
" title = ratio_wording,\n",
" ),\n",
" color=alt.Color(\"type:N\",\n",
" title = '',\n",
" scale=alt.Scale(domain=['Education (US)', 'Non-Education (US)'],\n",
" range=['green', 'purple']))\n",
")\n",
"\n",
"chart2 = alt.layer(\n",
" chart2_1,\n",
" chart2_2\n",
").resolve_scale(color='independent').properties(\n",
" height=300,\n",
" width=260,\n",
" title = 'f'\n",
")\n",
"\n",
"\n",
"### \n",
"source3_1 = binary_type_df\n",
"\n",
"chart3_1 = alt.Chart(source3_1).mark_line().encode(\n",
" x=\"Year:T\",\n",
" y=alt.Y(\n",
" \"count:Q\",\n",
" title = 'Number of authors',\n",
" ),\n",
" color=alt.Color(\n",
" \"type:N\",\n",
" title = 'Affiliation type',\n",
" scale=alt.Scale(domain=['Education', 'Non-Education'],\n",
" range=['steelblue', 'orange']),\n",
"# legend=alt.Legend(orient='top-left')\n",
" )\n",
")\n",
"\n",
"source3_2 = us_author_df\n",
"\n",
"chart3_2 = alt.Chart(source3_2).mark_line(\n",
" point={\n",
" \"filled\": False,\n",
" \"fill\": \"white\"\n",
" },\n",
" strokeDash=[1,2]\n",
").encode(\n",
" x=\"Year:T\",\n",
" y=\"count:Q\",\n",
" color=alt.Color(\n",
" \"type:N\",\n",
" title = '',\n",
" scale=alt.Scale(\n",
" domain=['Education (US)', 'Non-Education (US)'],\n",
" range=['green', 'purple']),\n",
" )\n",
")\n",
"\n",
"chart3 = alt.layer(\n",
" chart3_1,\n",
" chart3_2,\n",
").resolve_scale(color='independent').properties(\n",
" title = 'g',\n",
" height=300,\n",
" width=260\n",
")\n",
"\n",
"# chart_last = alt.hconcat()\n",
"\n",
"author2 = alt.hconcat(chart1, chart2, chart3).configure_title(\n",
" anchor='start',\n",
" fontSize = 16\n",
").resolve_scale(\n",
" color='independent',\n",
").configure_axis(\n",
" labelFontSize=15,\n",
" titleFontSize=20\n",
").configure_legend(\n",
" titleFontSize=16,\n",
" labelFontSize=16\n",
").configure_title(\n",
" anchor='start',\n",
" fontSize=22)\n",
"\n",
"author2"
]
},
{
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"id": "78b1e8f2",
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"source": []
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