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
Trends_Visuals.ipynb
{
"cells": [
{
"cell_type": "markdown",
"id": "8f8dc678",
"metadata": {},
"source": [
"This notebook contains codes to produce plots in General Trends section. "
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "3e4cae91",
"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",
"import selenium"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "0971910b",
"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": "00569b4d",
"metadata": {},
"outputs": [
{
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"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
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"source": [
"colors = [\n",
" \"#1f77b4\",\n",
" \"#ff7f0e\",\n",
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"]\n",
"sns.color_palette(colors)"
]
},
{
"cell_type": "markdown",
"id": "c84ba6cd",
"metadata": {},
"source": [
"### Tracks"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "8c7f23b6",
"metadata": {},
"outputs": [],
"source": [
"dic = dict(Counter(paper[paper.Year < 2021].Conference))\n",
"conf_df = pd.DataFrame(\n",
" dic.items(), \n",
" columns = ['Conference track', 'Number of publications'])\n",
"conf_df.sort_values(by='Number of publications', inplace = True)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "b88a38e9",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Conference track</th>\n",
" <th>Number of publications</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>SciVis</td>\n",
" <td>291</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>VAST</td>\n",
" <td>572</td>\n",
" </tr>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>InfoVis</td>\n",
" <td>834</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Vis</td>\n",
" <td>1373</td>\n",
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],
"text/plain": [
" Conference track Number of publications\n",
"3 SciVis 291\n",
"2 VAST 572\n",
"0 InfoVis 834\n",
"1 Vis 1373"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conf_df"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "34c4893f",
"metadata": {},
"outputs": [
{
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sns.color_palette(colors)\n",
"sns.countplot(x = 'Conference', data = paper)\n",
"plt.grid(axis = 'y')"
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "7d99284e",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-74ba38b4fca44acbac8f1a6dd2d34e04\"></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-74ba38b4fca44acbac8f1a6dd2d34e04\") {\n",
" outputDiv = document.getElementById(\"altair-viz-74ba38b4fca44acbac8f1a6dd2d34e04\");\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: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-18cbb5ade9a8290bcfa8aeff8a667c69\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"Conference track\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Conference track\", \"sort\": [\"VIS\", \"SciVis\", \"VAST\", \"InfoVis\", \"Vis\"], \"type\": \"nominal\"}, \"y\": {\"field\": \"Number of publications\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-18cbb5ade9a8290bcfa8aeff8a667c69\": [{\"Conference track\": \"SciVis\", \"Number of publications\": 291}, {\"Conference track\": \"VAST\", \"Number of publications\": 572}, {\"Conference track\": \"InfoVis\", \"Number of publications\": 834}, {\"Conference track\": \"Vis\", \"Number of publications\": 1373}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alt.Chart(conf_df).mark_bar().encode(\n",
" x = alt.X(\n",
" 'Conference track',\n",
" sort = [\n",
" 'VIS',\n",
" 'SciVis',\n",
" 'VAST',\n",
" 'InfoVis',\n",
" 'Vis',\n",
" ]\n",
" ),\n",
" y = 'Number of publications',\n",
" color = 'Conference track'\n",
")"
]
},
{
"cell_type": "markdown",
"id": "42e9ac39",
"metadata": {},
"source": [
"### Number of publications by year"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "9fb3665c",
"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 Publications</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1990-01-01</td>\n",
" <td>52</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1991-01-01</td>\n",
" <td>50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1992-01-01</td>\n",
" <td>53</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1993-01-01</td>\n",
" <td>55</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1994-01-01</td>\n",
" <td>53</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Number of Publications\n",
"0 1990-01-01 52\n",
"1 1991-01-01 50\n",
"2 1992-01-01 53\n",
"3 1993-01-01 55\n",
"4 1994-01-01 53"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pub_num_df = paper.groupby(\n",
" 'Year').size().reset_index(name = 'Number of Publications')\n",
"pub_num_df['Year'] = pd.to_datetime(\n",
" pub_num_df['Year'], format=\"%Y\"\n",
")\n",
"pub_num_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "0ea279d9",
"metadata": {
"scrolled": true
},
"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 Publications</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2017-01-01</td>\n",
" <td>114</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2018-01-01</td>\n",
" <td>127</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2019-01-01</td>\n",
" <td>129</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>2020-01-01</td>\n",
" <td>157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>2021-01-01</td>\n",
" <td>170</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Number of Publications\n",
"27 2017-01-01 114\n",
"28 2018-01-01 127\n",
"29 2019-01-01 129\n",
"30 2020-01-01 157\n",
"31 2021-01-01 170"
]
},
"execution_count": 9,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pub_num_df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "c942eac6",
"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 = 'Number of Publications', data = pub_num_df)\n",
"g.set_ylim(0,)\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "e6a92d98",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-acd697a3987542e0aeba0109fc3ebc42\"></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-acd697a3987542e0aeba0109fc3ebc42\") {\n",
" outputDiv = document.getElementById(\"altair-viz-acd697a3987542e0aeba0109fc3ebc42\");\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: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-bcbb612dd2e1839e4b52a066ab50d933\"}, \"mark\": \"line\", \"encoding\": {\"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"Number of Publications\", \"title\": \"Number of publications\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-bcbb612dd2e1839e4b52a066ab50d933\": [{\"Year\": \"1990-01-01T00:00:00\", \"Number of Publications\": 52}, {\"Year\": \"1991-01-01T00:00:00\", \"Number of Publications\": 50}, {\"Year\": \"1992-01-01T00:00:00\", \"Number of Publications\": 53}, {\"Year\": \"1993-01-01T00:00:00\", \"Number of Publications\": 55}, {\"Year\": \"1994-01-01T00:00:00\", \"Number of Publications\": 53}, {\"Year\": \"1995-01-01T00:00:00\", \"Number of Publications\": 74}, {\"Year\": \"1996-01-01T00:00:00\", \"Number of Publications\": 77}, {\"Year\": \"1997-01-01T00:00:00\", \"Number of Publications\": 88}, {\"Year\": \"1998-01-01T00:00:00\", \"Number of Publications\": 90}, {\"Year\": \"1999-01-01T00:00:00\", \"Number of Publications\": 88}, {\"Year\": \"2000-01-01T00:00:00\", \"Number of Publications\": 92}, {\"Year\": \"2001-01-01T00:00:00\", \"Number of Publications\": 96}, {\"Year\": \"2002-01-01T00:00:00\", \"Number of Publications\": 101}, {\"Year\": \"2003-01-01T00:00:00\", \"Number of Publications\": 102}, {\"Year\": \"2004-01-01T00:00:00\", \"Number of Publications\": 97}, {\"Year\": \"2005-01-01T00:00:00\", \"Number of Publications\": 119}, {\"Year\": \"2006-01-01T00:00:00\", \"Number of Publications\": 113}, {\"Year\": \"2007-01-01T00:00:00\", \"Number of Publications\": 107}, {\"Year\": \"2008-01-01T00:00:00\", \"Number of Publications\": 99}, {\"Year\": \"2009-01-01T00:00:00\", \"Number of Publications\": 117}, {\"Year\": \"2010-01-01T00:00:00\", \"Number of Publications\": 109}, {\"Year\": \"2011-01-01T00:00:00\", \"Number of Publications\": 119}, {\"Year\": \"2012-01-01T00:00:00\", \"Number of Publications\": 116}, {\"Year\": \"2013-01-01T00:00:00\", \"Number of Publications\": 101}, {\"Year\": \"2014-01-01T00:00:00\", \"Number of Publications\": 133}, {\"Year\": \"2015-01-01T00:00:00\", \"Number of Publications\": 127}, {\"Year\": \"2016-01-01T00:00:00\", \"Number of Publications\": 115}, {\"Year\": \"2017-01-01T00:00:00\", \"Number of Publications\": 114}, {\"Year\": \"2018-01-01T00:00:00\", \"Number of Publications\": 127}, {\"Year\": \"2019-01-01T00:00:00\", \"Number of Publications\": 129}, {\"Year\": \"2020-01-01T00:00:00\", \"Number of Publications\": 157}, {\"Year\": \"2021-01-01T00:00:00\", \"Number of Publications\": 170}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"pub_num = alt.Chart(pub_num_df).mark_line().encode(\n",
" x = 'Year',\n",
" y = alt.Y(\n",
" 'Number of Publications',\n",
" title = 'Number of publications'\n",
" )\n",
")\n",
"\n",
"pub_num"
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "cf521a1c",
"metadata": {},
"outputs": [],
"source": [
"# (ggplot(pub_num_df, aes('Year', 'Number of Publications'))\n",
"# + p9.theme_bw()\n",
"# + p9.geom_line() \n",
"# )"
]
},
{
"cell_type": "markdown",
"id": "c9dcdf7e",
"metadata": {},
"source": [
"### Number of unique authors"
]
},
{
"cell_type": "code",
"execution_count": 13,
"id": "6a7720df",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"110"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# how many rows miss openalex author id\n",
"author[author['OpenAlex Author ID'].isnull()].shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 14,
"id": "d9e673ed",
"metadata": {},
"outputs": [],
"source": [
"author_with_author_id = author[\n",
" author['OpenAlex Author ID'].notnull()]"
]
},
{
"cell_type": "code",
"execution_count": 15,
"id": "4b7f4338",
"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 Unique Authors</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1990-01-01</td>\n",
" <td>118</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1991-01-01</td>\n",
" <td>126</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1992-01-01</td>\n",
" <td>121</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1993-01-01</td>\n",
" <td>117</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1994-01-01</td>\n",
" <td>130</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Number of Unique Authors\n",
"0 1990-01-01 118\n",
"1 1991-01-01 126\n",
"2 1992-01-01 121\n",
"3 1993-01-01 117\n",
"4 1994-01-01 130"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_author_num_df = author_with_author_id.groupby(\n",
" 'Year')['OpenAlex Author ID'].nunique().reset_index(\n",
" name = 'Number of Unique Authors')\n",
"unique_author_num_df['Year'] = pd.to_datetime(\n",
" unique_author_num_df['Year'], format=\"%Y\",\n",
")\n",
"unique_author_num_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "99ecb59d",
"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 Unique Authors</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2017-01-01</td>\n",
" <td>455</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2018-01-01</td>\n",
" <td>494</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2019-01-01</td>\n",
" <td>551</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>2020-01-01</td>\n",
" <td>640</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>2021-01-01</td>\n",
" <td>670</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Number of Unique Authors\n",
"27 2017-01-01 455\n",
"28 2018-01-01 494\n",
"29 2019-01-01 551\n",
"30 2020-01-01 640\n",
"31 2021-01-01 670"
]
},
"execution_count": 16,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_author_num_df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 17,
"id": "fc5291c5",
"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 Unique Authors', \n",
" data = unique_author_num_df\n",
" )\n",
"g.set_ylim(0,)\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "91f49774",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-07b1a65582ae47f396424ff840c27f2a\"></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-07b1a65582ae47f396424ff840c27f2a\") {\n",
" outputDiv = document.getElementById(\"altair-viz-07b1a65582ae47f396424ff840c27f2a\");\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: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-6ceec6ad84a14b2f28097442ba84d375\"}, \"mark\": \"line\", \"encoding\": {\"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"Number of Unique Authors\", \"title\": \"Number of unique authors\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-6ceec6ad84a14b2f28097442ba84d375\": [{\"Year\": \"1990-01-01T00:00:00\", \"Number of Unique Authors\": 118}, {\"Year\": \"1991-01-01T00:00:00\", \"Number of Unique Authors\": 126}, {\"Year\": \"1992-01-01T00:00:00\", \"Number of Unique Authors\": 121}, {\"Year\": \"1993-01-01T00:00:00\", \"Number of Unique Authors\": 117}, {\"Year\": \"1994-01-01T00:00:00\", \"Number of Unique Authors\": 130}, {\"Year\": \"1995-01-01T00:00:00\", \"Number of Unique Authors\": 184}, {\"Year\": \"1996-01-01T00:00:00\", \"Number of Unique Authors\": 229}, {\"Year\": \"1997-01-01T00:00:00\", \"Number of Unique Authors\": 224}, {\"Year\": \"1998-01-01T00:00:00\", \"Number of Unique Authors\": 232}, {\"Year\": \"1999-01-01T00:00:00\", \"Number of Unique Authors\": 245}, {\"Year\": \"2000-01-01T00:00:00\", \"Number of Unique Authors\": 276}, {\"Year\": \"2001-01-01T00:00:00\", \"Number of Unique Authors\": 285}, {\"Year\": \"2002-01-01T00:00:00\", \"Number of Unique Authors\": 270}, {\"Year\": \"2003-01-01T00:00:00\", \"Number of Unique Authors\": 283}, {\"Year\": \"2004-01-01T00:00:00\", \"Number of Unique Authors\": 287}, {\"Year\": \"2005-01-01T00:00:00\", \"Number of Unique Authors\": 374}, {\"Year\": \"2006-01-01T00:00:00\", \"Number of Unique Authors\": 326}, {\"Year\": \"2007-01-01T00:00:00\", \"Number of Unique Authors\": 342}, {\"Year\": \"2008-01-01T00:00:00\", \"Number of Unique Authors\": 335}, {\"Year\": \"2009-01-01T00:00:00\", \"Number of Unique Authors\": 375}, {\"Year\": \"2010-01-01T00:00:00\", \"Number of Unique Authors\": 369}, {\"Year\": \"2011-01-01T00:00:00\", \"Number of Unique Authors\": 412}, {\"Year\": \"2012-01-01T00:00:00\", \"Number of Unique Authors\": 422}, {\"Year\": \"2013-01-01T00:00:00\", \"Number of Unique Authors\": 351}, {\"Year\": \"2014-01-01T00:00:00\", \"Number of Unique Authors\": 506}, {\"Year\": \"2015-01-01T00:00:00\", \"Number of Unique Authors\": 495}, {\"Year\": \"2016-01-01T00:00:00\", \"Number of Unique Authors\": 445}, {\"Year\": \"2017-01-01T00:00:00\", \"Number of Unique Authors\": 455}, {\"Year\": \"2018-01-01T00:00:00\", \"Number of Unique Authors\": 494}, {\"Year\": \"2019-01-01T00:00:00\", \"Number of Unique Authors\": 551}, {\"Year\": \"2020-01-01T00:00:00\", \"Number of Unique Authors\": 640}, {\"Year\": \"2021-01-01T00:00:00\", \"Number of Unique Authors\": 670}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"unique_authors = alt.Chart(unique_author_num_df).mark_line().encode(\n",
" x = 'Year',\n",
" y = alt.Y(\n",
" 'Number of Unique Authors',\n",
" title = 'Number of unique authors'\n",
" )\n",
")\n",
"\n",
"unique_authors"
]
},
{
"cell_type": "code",
"execution_count": 19,
"id": "de88a859",
"metadata": {},
"outputs": [],
"source": [
"# alt.layer(\n",
"# pub_num,\n",
"# unique_authors\n",
"# )"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "120cd52c",
"metadata": {},
"outputs": [],
"source": [
"# (ggplot(unique_author_num_df, aes('Year', 'Number of Unique Authors'))\n",
"# + p9.theme_bw()\n",
"# + p9.geom_line() \n",
"# )"
]
},
{
"cell_type": "markdown",
"id": "7ccec819",
"metadata": {},
"source": [
"### Average number of authors"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "77852a1a",
"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 Authors</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>2017</td>\n",
" <td>4.728070</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>2018</td>\n",
" <td>4.629921</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>2019</td>\n",
" <td>5.255814</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>2020</td>\n",
" <td>5.050955</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>2021</td>\n",
" <td>4.970588</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Number of Authors\n",
"27 2017 4.728070\n",
"28 2018 4.629921\n",
"29 2019 5.255814\n",
"30 2020 5.050955\n",
"31 2021 4.970588"
]
},
"execution_count": 21,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"author_num_df = paper.groupby(\n",
" 'Year')['Number of Authors'].mean().to_frame().reset_index()\n",
"author_num_df.tail()"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "1ae1208a",
"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', \n",
" y='Number of Authors', \n",
" data = author_num_df)\n",
"g.set_ylim(0,)\n",
"g.set_ylabel('Average Number of Authors')\n",
"plt.grid()\n",
"plt.savefig('../results/supp/ave_author_num.pdf')"
]
},
{
"cell_type": "code",
"execution_count": 23,
"id": "b06620f4",
"metadata": {},
"outputs": [],
"source": [
"# (ggplot(author_num_df, aes('Year', 'Number of Authors'))\n",
"# + p9.geom_line() \n",
"# )"
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "2e60ba60",
"metadata": {},
"outputs": [],
"source": [
"def get_total_and_ratio_by_year(DF, BY, subgroup_par):\n",
" tuples = []\n",
" for group in DF.groupby(BY):\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": "markdown",
"id": "6c2ac73d",
"metadata": {},
"source": [
"### Cross-country collaboration"
]
},
{
"cell_type": "code",
"execution_count": 25,
"id": "41a24f62",
"metadata": {},
"outputs": [],
"source": [
"cross_country = get_total_and_ratio_by_year(\n",
" paper, \n",
" 'Year',\n",
" 'Cross-country Collaboration'\n",
")\n",
"cross_country['year'] = pd.to_datetime(\n",
" cross_country['year'], format=\"%Y\"\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 26,
"id": "474137de",
"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',\n",
" y='ratio',\n",
" hue='type', \n",
" data = cross_country\n",
" )\n",
"g.set_title('changes in ratio of cross-country collaboration over 32 years')\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 27,
"id": "c5a966c1",
"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',\n",
" y='count',\n",
" hue='type', \n",
" data = cross_country\n",
" )\n",
"g.set_title('changes in number of cross-country collaboration over 32 years')\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 28,
"id": "edb58f1b",
"metadata": {},
"outputs": [],
"source": [
"# (ggplot(cross_country, aes('year', 'count', color='type'))\n",
"# + p9.theme_bw()\n",
"# + p9.geom_line() \n",
"# )"
]
},
{
"cell_type": "code",
"execution_count": 29,
"id": "16e00a45",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-8e3533f4bcd949e0994e78ab5e8c3d9f\"></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-8e3533f4bcd949e0994e78ab5e8c3d9f\") {\n",
" outputDiv = document.getElementById(\"altair-viz-8e3533f4bcd949e0994e78ab5e8c3d9f\");\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: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-13dc27e4ecc591ee8fd381db4be435d5\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"type\", \"scale\": {\"domain\": [\"Yes\", \"No\"], \"range\": [\"steelblue\", \"orange\"]}, \"sort\": [\"Yes\", \"No\"], \"title\": \"Cross-country collab.\", \"type\": \"nominal\"}, \"x\": {\"field\": \"year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"ratio\", \"title\": \"Ratio\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-13dc27e4ecc591ee8fd381db4be435d5\": [{\"year\": \"1990-01-01T00:00:00\", \"type\": \"No\", \"count\": 50, \"ratio\": 0.9615384615384616}, {\"year\": \"1990-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 2, \"ratio\": 0.038461538461538464}, {\"year\": \"1991-01-01T00:00:00\", \"type\": \"No\", \"count\": 49, \"ratio\": 0.98}, {\"year\": \"1991-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 1, \"ratio\": 0.02}, {\"year\": \"1992-01-01T00:00:00\", \"type\": \"No\", \"count\": 47, \"ratio\": 0.8867924528301887}, {\"year\": \"1992-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 6, \"ratio\": 0.11320754716981132}, {\"year\": \"1993-01-01T00:00:00\", \"type\": \"No\", \"count\": 51, \"ratio\": 0.9272727272727272}, {\"year\": \"1993-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 4, \"ratio\": 0.07272727272727272}, {\"year\": \"1994-01-01T00:00:00\", \"type\": \"No\", \"count\": 49, \"ratio\": 0.9245283018867925}, {\"year\": \"1994-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 4, \"ratio\": 0.07547169811320754}, {\"year\": \"1995-01-01T00:00:00\", \"type\": \"No\", \"count\": 69, \"ratio\": 0.9324324324324325}, {\"year\": \"1995-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 5, \"ratio\": 0.06756756756756757}, {\"year\": \"1996-01-01T00:00:00\", \"type\": \"No\", \"count\": 69, \"ratio\": 0.8961038961038961}, {\"year\": \"1996-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 8, \"ratio\": 0.1038961038961039}, {\"year\": \"1997-01-01T00:00:00\", \"type\": \"No\", \"count\": 82, \"ratio\": 0.9318181818181818}, {\"year\": \"1997-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 6, \"ratio\": 0.06818181818181818}, {\"year\": \"1998-01-01T00:00:00\", \"type\": \"No\", \"count\": 83, \"ratio\": 0.9222222222222223}, {\"year\": \"1998-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 7, \"ratio\": 0.07777777777777778}, {\"year\": \"1999-01-01T00:00:00\", \"type\": \"No\", \"count\": 83, \"ratio\": 0.9431818181818182}, {\"year\": \"1999-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 5, \"ratio\": 0.056818181818181816}, {\"year\": \"2000-01-01T00:00:00\", \"type\": \"No\", \"count\": 81, \"ratio\": 0.8804347826086957}, {\"year\": \"2000-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 11, \"ratio\": 0.11956521739130435}, {\"year\": \"2001-01-01T00:00:00\", \"type\": \"No\", \"count\": 83, \"ratio\": 0.8645833333333334}, {\"year\": \"2001-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 13, \"ratio\": 0.13541666666666666}, {\"year\": \"2002-01-01T00:00:00\", \"type\": \"No\", \"count\": 86, \"ratio\": 0.8514851485148515}, {\"year\": \"2002-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 15, \"ratio\": 0.1485148514851485}, {\"year\": \"2003-01-01T00:00:00\", \"type\": \"No\", \"count\": 91, \"ratio\": 0.8921568627450981}, {\"year\": \"2003-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 11, \"ratio\": 0.10784313725490197}, {\"year\": \"2004-01-01T00:00:00\", \"type\": \"No\", \"count\": 79, \"ratio\": 0.8144329896907216}, {\"year\": \"2004-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 18, \"ratio\": 0.18556701030927836}, {\"year\": \"2005-01-01T00:00:00\", \"type\": \"No\", \"count\": 98, \"ratio\": 0.8235294117647058}, {\"year\": \"2005-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 21, \"ratio\": 0.17647058823529413}, {\"year\": \"2006-01-01T00:00:00\", \"type\": \"No\", \"count\": 90, \"ratio\": 0.7964601769911505}, {\"year\": \"2006-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 23, \"ratio\": 0.20353982300884957}, {\"year\": \"2007-01-01T00:00:00\", \"type\": \"No\", \"count\": 75, \"ratio\": 0.7009345794392523}, {\"year\": \"2007-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 32, \"ratio\": 0.29906542056074764}, {\"year\": \"2008-01-01T00:00:00\", \"type\": \"No\", \"count\": 75, \"ratio\": 0.7575757575757576}, {\"year\": \"2008-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 24, \"ratio\": 0.24242424242424243}, {\"year\": \"2009-01-01T00:00:00\", \"type\": \"No\", \"count\": 90, \"ratio\": 0.7692307692307693}, {\"year\": \"2009-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 27, \"ratio\": 0.23076923076923078}, {\"year\": \"2010-01-01T00:00:00\", \"type\": \"No\", \"count\": 79, \"ratio\": 0.7247706422018348}, {\"year\": \"2010-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 30, \"ratio\": 0.27522935779816515}, {\"year\": \"2011-01-01T00:00:00\", \"type\": \"No\", \"count\": 91, \"ratio\": 0.7647058823529411}, {\"year\": \"2011-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 28, \"ratio\": 0.23529411764705882}, {\"year\": \"2012-01-01T00:00:00\", \"type\": \"No\", \"count\": 74, \"ratio\": 0.6379310344827587}, {\"year\": \"2012-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 42, \"ratio\": 0.3620689655172414}, {\"year\": \"2013-01-01T00:00:00\", \"type\": \"No\", \"count\": 75, \"ratio\": 0.7425742574257426}, {\"year\": \"2013-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 26, \"ratio\": 0.25742574257425743}, {\"year\": \"2014-01-01T00:00:00\", \"type\": \"No\", \"count\": 85, \"ratio\": 0.6390977443609023}, {\"year\": \"2014-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 48, \"ratio\": 0.3609022556390977}, {\"year\": \"2015-01-01T00:00:00\", \"type\": \"No\", \"count\": 80, \"ratio\": 0.6299212598425197}, {\"year\": \"2015-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 47, \"ratio\": 0.3700787401574803}, {\"year\": \"2016-01-01T00:00:00\", \"type\": \"No\", \"count\": 66, \"ratio\": 0.5739130434782609}, {\"year\": \"2016-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 49, \"ratio\": 0.4260869565217391}, {\"year\": \"2017-01-01T00:00:00\", \"type\": \"No\", \"count\": 65, \"ratio\": 0.5701754385964912}, {\"year\": \"2017-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 49, \"ratio\": 0.4298245614035088}, {\"year\": \"2018-01-01T00:00:00\", \"type\": \"No\", \"count\": 82, \"ratio\": 0.6456692913385826}, {\"year\": \"2018-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 45, \"ratio\": 0.3543307086614173}, {\"year\": \"2019-01-01T00:00:00\", \"type\": \"No\", \"count\": 81, \"ratio\": 0.627906976744186}, {\"year\": \"2019-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 48, \"ratio\": 0.37209302325581395}, {\"year\": \"2020-01-01T00:00:00\", \"type\": \"No\", \"count\": 93, \"ratio\": 0.5923566878980892}, {\"year\": \"2020-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 64, \"ratio\": 0.40764331210191085}, {\"year\": \"2021-01-01T00:00:00\", \"type\": \"No\", \"count\": 94, \"ratio\": 0.5529411764705883}, {\"year\": \"2021-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 76, \"ratio\": 0.4470588235294118}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cross_country_ratio = alt.Chart(cross_country).mark_line().encode(\n",
" x = 'year',\n",
" y = alt.Y(\n",
" 'ratio',\n",
" title = 'Ratio'\n",
" ),\n",
" color = alt.Color(\n",
" 'type',\n",
" title = 'Cross-country collab.',\n",
" sort = ['Yes', 'No'],\n",
" scale=alt.Scale(domain=['Yes', 'No'],\n",
" range=['steelblue', 'orange'])\n",
" )\n",
")\n",
"\n",
"cross_country_ratio"
]
},
{
"cell_type": "code",
"execution_count": 30,
"id": "9c6e4073",
"metadata": {},
"outputs": [
{
"data": {
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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",
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" throw err;\n",
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" 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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" 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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0.1038961038961039}, {\"year\": \"1997-01-01T00:00:00\", \"type\": \"No\", \"count\": 82, \"ratio\": 0.9318181818181818}, {\"year\": \"1997-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 6, \"ratio\": 0.06818181818181818}, {\"year\": \"1998-01-01T00:00:00\", \"type\": \"No\", \"count\": 83, \"ratio\": 0.9222222222222223}, {\"year\": \"1998-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 7, \"ratio\": 0.07777777777777778}, {\"year\": \"1999-01-01T00:00:00\", \"type\": \"No\", \"count\": 83, \"ratio\": 0.9431818181818182}, {\"year\": \"1999-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 5, \"ratio\": 0.056818181818181816}, {\"year\": \"2000-01-01T00:00:00\", \"type\": \"No\", \"count\": 81, \"ratio\": 0.8804347826086957}, {\"year\": \"2000-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 11, \"ratio\": 0.11956521739130435}, {\"year\": \"2001-01-01T00:00:00\", \"type\": \"No\", \"count\": 83, \"ratio\": 0.8645833333333334}, {\"year\": \"2001-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 13, \"ratio\": 0.13541666666666666}, {\"year\": \"2002-01-01T00:00:00\", \"type\": \"No\", \"count\": 86, \"ratio\": 0.8514851485148515}, {\"year\": \"2002-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 15, \"ratio\": 0.1485148514851485}, {\"year\": \"2003-01-01T00:00:00\", \"type\": \"No\", \"count\": 91, \"ratio\": 0.8921568627450981}, {\"year\": \"2003-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 11, \"ratio\": 0.10784313725490197}, {\"year\": \"2004-01-01T00:00:00\", \"type\": \"No\", \"count\": 79, \"ratio\": 0.8144329896907216}, {\"year\": \"2004-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 18, \"ratio\": 0.18556701030927836}, {\"year\": \"2005-01-01T00:00:00\", \"type\": \"No\", \"count\": 98, \"ratio\": 0.8235294117647058}, {\"year\": \"2005-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 21, \"ratio\": 0.17647058823529413}, {\"year\": \"2006-01-01T00:00:00\", \"type\": \"No\", \"count\": 90, \"ratio\": 0.7964601769911505}, {\"year\": \"2006-01-01T00:00:00\", 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0.5739130434782609}, {\"year\": \"2016-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 49, \"ratio\": 0.4260869565217391}, {\"year\": \"2017-01-01T00:00:00\", \"type\": \"No\", \"count\": 65, \"ratio\": 0.5701754385964912}, {\"year\": \"2017-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 49, \"ratio\": 0.4298245614035088}, {\"year\": \"2018-01-01T00:00:00\", \"type\": \"No\", \"count\": 82, \"ratio\": 0.6456692913385826}, {\"year\": \"2018-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 45, \"ratio\": 0.3543307086614173}, {\"year\": \"2019-01-01T00:00:00\", \"type\": \"No\", \"count\": 81, \"ratio\": 0.627906976744186}, {\"year\": \"2019-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 48, \"ratio\": 0.37209302325581395}, {\"year\": \"2020-01-01T00:00:00\", \"type\": \"No\", \"count\": 93, \"ratio\": 0.5923566878980892}, {\"year\": \"2020-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 64, \"ratio\": 0.40764331210191085}, {\"year\": \"2021-01-01T00:00:00\", \"type\": \"No\", \"count\": 94, \"ratio\": 0.5529411764705883}, {\"year\": \"2021-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 76, \"ratio\": 0.4470588235294118}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cross_country_count = alt.Chart(cross_country).mark_line().encode(\n",
" x = 'year',\n",
" y = alt.Y(\n",
" 'count',\n",
" title = 'Number of publications'\n",
" ),\n",
" color = alt.Color(\n",
" 'type',\n",
" title = 'Cross-country collab.',\n",
" sort = ['Yes', 'No']\n",
" )\n",
")\n",
"\n",
"cross_country_count "
]
},
{
"cell_type": "markdown",
"id": "894215ed",
"metadata": {},
"source": [
"### Cross-type collaboration"
]
},
{
"cell_type": "code",
"execution_count": 31,
"id": "2a961172",
"metadata": {},
"outputs": [
{
"data": {
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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",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1990-01-01</td>\n",
" <td>No</td>\n",
" <td>47</td>\n",
" <td>0.903846</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1990-01-01</td>\n",
" <td>Yes</td>\n",
" <td>5</td>\n",
" <td>0.096154</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1991-01-01</td>\n",
" <td>No</td>\n",
" <td>43</td>\n",
" <td>0.860000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1991-01-01</td>\n",
" <td>Yes</td>\n",
" <td>7</td>\n",
" <td>0.140000</td>\n",
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" <th>4</th>\n",
" <td>1992-01-01</td>\n",
" <td>No</td>\n",
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" <td>0.811321</td>\n",
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],
"text/plain": [
" year type count ratio\n",
"0 1990-01-01 No 47 0.903846\n",
"1 1990-01-01 Yes 5 0.096154\n",
"2 1991-01-01 No 43 0.860000\n",
"3 1991-01-01 Yes 7 0.140000\n",
"4 1992-01-01 No 43 0.811321"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cross_type = get_total_and_ratio_by_year(\n",
" paper, \n",
" 'Year',\n",
" 'Cross-type Collaboration'\n",
")\n",
"cross_type['year'] = pd.to_datetime(\n",
" cross_type['year'], format=\"%Y\"\n",
")\n",
"cross_type.head()"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "eca165eb",
"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',\n",
" y='ratio',\n",
" hue='type', \n",
" data = cross_type\n",
" )\n",
"g.set_title('changes in ratio of cross-type collaboration over 32 years')\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 33,
"id": "c8a87f18",
"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',\n",
" y='count',\n",
" hue='type', \n",
" data = cross_type\n",
" )\n",
"g.set_title('changes in number of cross-type collaboration over 32 years')\n",
"plt.grid()"
]
},
{
"cell_type": "code",
"execution_count": 34,
"id": "6b51e516",
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-37e2e43e21194b99ade05f35bd162c89\"></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-37e2e43e21194b99ade05f35bd162c89\") {\n",
" outputDiv = document.getElementById(\"altair-viz-37e2e43e21194b99ade05f35bd162c89\");\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: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-9828840f8f199db131f1eea3ca3d2229\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"type\", \"scale\": {\"domain\": [\"No\", \"Yes\"], \"range\": [\"purple\", \"green\"]}, \"sort\": [\"Yes\", \"No\"], \"title\": \"Cross-type collab.\", \"type\": \"nominal\"}, \"x\": {\"field\": \"year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"ratio\", \"title\": \"Ratio\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-9828840f8f199db131f1eea3ca3d2229\": [{\"year\": \"1990-01-01T00:00:00\", \"type\": \"No\", \"count\": 47, \"ratio\": 0.9038461538461539}, {\"year\": \"1990-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 5, \"ratio\": 0.09615384615384616}, {\"year\": \"1991-01-01T00:00:00\", \"type\": \"No\", \"count\": 43, \"ratio\": 0.86}, {\"year\": \"1991-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 7, \"ratio\": 0.14}, {\"year\": \"1992-01-01T00:00:00\", \"type\": \"No\", \"count\": 43, \"ratio\": 0.8113207547169812}, {\"year\": \"1992-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 10, \"ratio\": 0.18867924528301888}, {\"year\": \"1993-01-01T00:00:00\", \"type\": \"No\", \"count\": 46, \"ratio\": 0.8363636363636363}, {\"year\": \"1993-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 9, \"ratio\": 0.16363636363636364}, {\"year\": \"1994-01-01T00:00:00\", \"type\": \"No\", \"count\": 48, \"ratio\": 0.9056603773584906}, {\"year\": \"1994-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 5, \"ratio\": 0.09433962264150944}, {\"year\": \"1995-01-01T00:00:00\", \"type\": \"No\", \"count\": 64, \"ratio\": 0.8648648648648649}, {\"year\": \"1995-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 10, \"ratio\": 0.13513513513513514}, {\"year\": \"1996-01-01T00:00:00\", \"type\": \"No\", \"count\": 59, \"ratio\": 0.7662337662337663}, {\"year\": \"1996-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 18, \"ratio\": 0.23376623376623376}, {\"year\": \"1997-01-01T00:00:00\", \"type\": \"No\", \"count\": 78, \"ratio\": 0.8863636363636364}, {\"year\": \"1997-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 10, \"ratio\": 0.11363636363636363}, {\"year\": \"1998-01-01T00:00:00\", \"type\": \"No\", \"count\": 77, \"ratio\": 0.8555555555555555}, {\"year\": \"1998-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 13, \"ratio\": 0.14444444444444443}, {\"year\": \"1999-01-01T00:00:00\", \"type\": \"No\", \"count\": 72, \"ratio\": 0.8181818181818182}, {\"year\": \"1999-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 16, \"ratio\": 0.18181818181818182}, {\"year\": \"2000-01-01T00:00:00\", \"type\": \"No\", \"count\": 71, \"ratio\": 0.7717391304347826}, {\"year\": \"2000-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 21, \"ratio\": 0.22826086956521738}, {\"year\": \"2001-01-01T00:00:00\", \"type\": \"No\", \"count\": 68, \"ratio\": 0.7083333333333334}, {\"year\": \"2001-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 28, \"ratio\": 0.2916666666666667}, {\"year\": \"2002-01-01T00:00:00\", \"type\": \"No\", \"count\": 84, \"ratio\": 0.8316831683168316}, {\"year\": \"2002-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 17, \"ratio\": 0.16831683168316833}, {\"year\": \"2003-01-01T00:00:00\", \"type\": \"No\", \"count\": 82, \"ratio\": 0.803921568627451}, {\"year\": \"2003-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 20, \"ratio\": 0.19607843137254902}, {\"year\": \"2004-01-01T00:00:00\", \"type\": \"No\", \"count\": 73, \"ratio\": 0.7525773195876289}, {\"year\": \"2004-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 24, \"ratio\": 0.24742268041237114}, {\"year\": \"2005-01-01T00:00:00\", \"type\": \"No\", \"count\": 97, \"ratio\": 0.8151260504201681}, {\"year\": \"2005-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 22, \"ratio\": 0.18487394957983194}, {\"year\": \"2006-01-01T00:00:00\", \"type\": \"No\", \"count\": 90, \"ratio\": 0.7964601769911505}, {\"year\": \"2006-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 23, \"ratio\": 0.20353982300884957}, {\"year\": \"2007-01-01T00:00:00\", \"type\": \"No\", \"count\": 69, \"ratio\": 0.6448598130841121}, {\"year\": \"2007-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 38, \"ratio\": 0.35514018691588783}, {\"year\": \"2008-01-01T00:00:00\", \"type\": \"No\", \"count\": 70, \"ratio\": 0.7070707070707071}, {\"year\": \"2008-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 29, \"ratio\": 0.29292929292929293}, {\"year\": \"2009-01-01T00:00:00\", \"type\": \"No\", \"count\": 84, \"ratio\": 0.717948717948718}, {\"year\": \"2009-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 33, \"ratio\": 0.28205128205128205}, {\"year\": \"2010-01-01T00:00:00\", \"type\": \"No\", \"count\": 82, \"ratio\": 0.7522935779816514}, {\"year\": \"2010-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 27, \"ratio\": 0.24770642201834864}, {\"year\": \"2011-01-01T00:00:00\", \"type\": \"No\", \"count\": 91, \"ratio\": 0.7647058823529411}, {\"year\": \"2011-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 28, \"ratio\": 0.23529411764705882}, {\"year\": \"2012-01-01T00:00:00\", \"type\": \"No\", \"count\": 80, \"ratio\": 0.6896551724137931}, {\"year\": \"2012-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 36, \"ratio\": 0.3103448275862069}, {\"year\": \"2013-01-01T00:00:00\", \"type\": \"No\", \"count\": 74, \"ratio\": 0.7326732673267327}, {\"year\": \"2013-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 27, \"ratio\": 0.26732673267326734}, {\"year\": \"2014-01-01T00:00:00\", \"type\": \"No\", \"count\": 87, \"ratio\": 0.6541353383458647}, {\"year\": \"2014-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 46, \"ratio\": 0.3458646616541353}, {\"year\": \"2015-01-01T00:00:00\", \"type\": \"No\", \"count\": 79, \"ratio\": 0.6220472440944882}, {\"year\": \"2015-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 48, \"ratio\": 0.3779527559055118}, {\"year\": \"2016-01-01T00:00:00\", \"type\": \"No\", \"count\": 75, \"ratio\": 0.6521739130434783}, {\"year\": \"2016-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 40, \"ratio\": 0.34782608695652173}, {\"year\": \"2017-01-01T00:00:00\", \"type\": \"No\", \"count\": 71, \"ratio\": 0.6228070175438597}, {\"year\": \"2017-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 43, \"ratio\": 0.37719298245614036}, {\"year\": \"2018-01-01T00:00:00\", \"type\": \"No\", \"count\": 69, \"ratio\": 0.5433070866141733}, {\"year\": \"2018-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 58, \"ratio\": 0.4566929133858268}, {\"year\": \"2019-01-01T00:00:00\", \"type\": \"No\", \"count\": 86, \"ratio\": 0.6666666666666666}, {\"year\": \"2019-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 43, \"ratio\": 0.3333333333333333}, {\"year\": \"2020-01-01T00:00:00\", \"type\": \"No\", \"count\": 108, \"ratio\": 0.6878980891719745}, {\"year\": \"2020-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 49, \"ratio\": 0.31210191082802546}, {\"year\": \"2021-01-01T00:00:00\", \"type\": \"No\", \"count\": 116, \"ratio\": 0.6823529411764706}, {\"year\": \"2021-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 54, \"ratio\": 0.3176470588235294}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# scale=alt.Scale(scheme='set1')\n",
"cross_type_ratio = alt.Chart(cross_type).mark_line().encode(\n",
" x = 'year',\n",
" y = alt.Y(\n",
" 'ratio',\n",
" title = 'Ratio'\n",
" ),\n",
" color = alt.Color(\n",
" 'type',\n",
" title = 'Cross-type collab.',\n",
" sort = ['Yes', 'No'],\n",
" scale=alt.Scale(domain=['No', 'Yes'],\n",
" range=['purple', 'green'])\n",
" ),\n",
")\n",
"\n",
"cross_type_ratio"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "172a5f94",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-d67352c7ffea460e8310a8b4a004c587\"></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-d67352c7ffea460e8310a8b4a004c587\") {\n",
" outputDiv = document.getElementById(\"altair-viz-d67352c7ffea460e8310a8b4a004c587\");\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: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"data\": {\"name\": \"data-9828840f8f199db131f1eea3ca3d2229\"}, \"mark\": {\"type\": \"line\", \"point\": {\"filled\": false, \"fill\": \"white\"}, \"strokeDash\": [1, 2]}, \"encoding\": {\"color\": {\"field\": \"type\", \"scale\": {\"domain\": [\"No\", \"Yes\"], \"range\": [\"purple\", \"green\"]}, \"sort\": [\"Yes\", \"No\"], \"title\": \"Cross-type collab.\", \"type\": \"nominal\"}, \"x\": {\"field\": \"year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"count\", \"title\": \"Number of authors\", \"type\": \"quantitative\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-9828840f8f199db131f1eea3ca3d2229\": [{\"year\": \"1990-01-01T00:00:00\", \"type\": \"No\", \"count\": 47, \"ratio\": 0.9038461538461539}, {\"year\": \"1990-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 5, \"ratio\": 0.09615384615384616}, {\"year\": \"1991-01-01T00:00:00\", \"type\": \"No\", \"count\": 43, \"ratio\": 0.86}, {\"year\": \"1991-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 7, \"ratio\": 0.14}, {\"year\": \"1992-01-01T00:00:00\", \"type\": \"No\", \"count\": 43, \"ratio\": 0.8113207547169812}, {\"year\": \"1992-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 10, \"ratio\": 0.18867924528301888}, {\"year\": \"1993-01-01T00:00:00\", \"type\": \"No\", \"count\": 46, \"ratio\": 0.8363636363636363}, {\"year\": \"1993-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 9, \"ratio\": 0.16363636363636364}, {\"year\": \"1994-01-01T00:00:00\", \"type\": \"No\", \"count\": 48, \"ratio\": 0.9056603773584906}, {\"year\": \"1994-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 5, \"ratio\": 0.09433962264150944}, {\"year\": \"1995-01-01T00:00:00\", \"type\": \"No\", \"count\": 64, \"ratio\": 0.8648648648648649}, {\"year\": \"1995-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 10, \"ratio\": 0.13513513513513514}, {\"year\": \"1996-01-01T00:00:00\", \"type\": \"No\", \"count\": 59, \"ratio\": 0.7662337662337663}, {\"year\": \"1996-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 18, \"ratio\": 0.23376623376623376}, {\"year\": \"1997-01-01T00:00:00\", \"type\": \"No\", \"count\": 78, \"ratio\": 0.8863636363636364}, {\"year\": \"1997-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 10, \"ratio\": 0.11363636363636363}, {\"year\": \"1998-01-01T00:00:00\", \"type\": \"No\", \"count\": 77, \"ratio\": 0.8555555555555555}, {\"year\": \"1998-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 13, \"ratio\": 0.14444444444444443}, {\"year\": \"1999-01-01T00:00:00\", \"type\": \"No\", \"count\": 72, \"ratio\": 0.8181818181818182}, {\"year\": \"1999-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 16, \"ratio\": 0.18181818181818182}, {\"year\": \"2000-01-01T00:00:00\", \"type\": \"No\", \"count\": 71, \"ratio\": 0.7717391304347826}, {\"year\": \"2000-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 21, \"ratio\": 0.22826086956521738}, {\"year\": \"2001-01-01T00:00:00\", \"type\": \"No\", \"count\": 68, \"ratio\": 0.7083333333333334}, {\"year\": \"2001-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 28, \"ratio\": 0.2916666666666667}, {\"year\": \"2002-01-01T00:00:00\", \"type\": \"No\", \"count\": 84, \"ratio\": 0.8316831683168316}, {\"year\": \"2002-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 17, \"ratio\": 0.16831683168316833}, {\"year\": \"2003-01-01T00:00:00\", \"type\": \"No\", \"count\": 82, \"ratio\": 0.803921568627451}, {\"year\": \"2003-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 20, \"ratio\": 0.19607843137254902}, {\"year\": \"2004-01-01T00:00:00\", \"type\": \"No\", \"count\": 73, \"ratio\": 0.7525773195876289}, {\"year\": \"2004-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 24, \"ratio\": 0.24742268041237114}, {\"year\": \"2005-01-01T00:00:00\", \"type\": \"No\", \"count\": 97, \"ratio\": 0.8151260504201681}, {\"year\": \"2005-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 22, \"ratio\": 0.18487394957983194}, {\"year\": \"2006-01-01T00:00:00\", \"type\": \"No\", \"count\": 90, \"ratio\": 0.7964601769911505}, {\"year\": \"2006-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 23, \"ratio\": 0.20353982300884957}, {\"year\": \"2007-01-01T00:00:00\", \"type\": \"No\", \"count\": 69, \"ratio\": 0.6448598130841121}, {\"year\": \"2007-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 38, \"ratio\": 0.35514018691588783}, {\"year\": \"2008-01-01T00:00:00\", \"type\": \"No\", \"count\": 70, \"ratio\": 0.7070707070707071}, {\"year\": \"2008-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 29, \"ratio\": 0.29292929292929293}, {\"year\": \"2009-01-01T00:00:00\", \"type\": \"No\", \"count\": 84, \"ratio\": 0.717948717948718}, {\"year\": \"2009-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 33, \"ratio\": 0.28205128205128205}, {\"year\": \"2010-01-01T00:00:00\", \"type\": \"No\", \"count\": 82, \"ratio\": 0.7522935779816514}, {\"year\": \"2010-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 27, \"ratio\": 0.24770642201834864}, {\"year\": \"2011-01-01T00:00:00\", \"type\": \"No\", \"count\": 91, \"ratio\": 0.7647058823529411}, {\"year\": \"2011-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 28, \"ratio\": 0.23529411764705882}, {\"year\": \"2012-01-01T00:00:00\", \"type\": \"No\", \"count\": 80, \"ratio\": 0.6896551724137931}, {\"year\": \"2012-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 36, \"ratio\": 0.3103448275862069}, {\"year\": \"2013-01-01T00:00:00\", \"type\": \"No\", \"count\": 74, \"ratio\": 0.7326732673267327}, {\"year\": \"2013-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 27, \"ratio\": 0.26732673267326734}, {\"year\": \"2014-01-01T00:00:00\", \"type\": \"No\", \"count\": 87, \"ratio\": 0.6541353383458647}, {\"year\": \"2014-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 46, \"ratio\": 0.3458646616541353}, {\"year\": \"2015-01-01T00:00:00\", \"type\": \"No\", \"count\": 79, \"ratio\": 0.6220472440944882}, {\"year\": \"2015-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 48, \"ratio\": 0.3779527559055118}, {\"year\": \"2016-01-01T00:00:00\", \"type\": \"No\", \"count\": 75, \"ratio\": 0.6521739130434783}, {\"year\": \"2016-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 40, \"ratio\": 0.34782608695652173}, {\"year\": \"2017-01-01T00:00:00\", \"type\": \"No\", \"count\": 71, \"ratio\": 0.6228070175438597}, {\"year\": \"2017-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 43, \"ratio\": 0.37719298245614036}, {\"year\": \"2018-01-01T00:00:00\", \"type\": \"No\", \"count\": 69, \"ratio\": 0.5433070866141733}, {\"year\": \"2018-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 58, \"ratio\": 0.4566929133858268}, {\"year\": \"2019-01-01T00:00:00\", \"type\": \"No\", \"count\": 86, \"ratio\": 0.6666666666666666}, {\"year\": \"2019-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 43, \"ratio\": 0.3333333333333333}, {\"year\": \"2020-01-01T00:00:00\", \"type\": \"No\", \"count\": 108, \"ratio\": 0.6878980891719745}, {\"year\": \"2020-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 49, \"ratio\": 0.31210191082802546}, {\"year\": \"2021-01-01T00:00:00\", \"type\": \"No\", \"count\": 116, \"ratio\": 0.6823529411764706}, {\"year\": \"2021-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 54, \"ratio\": 0.3176470588235294}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.Chart(...)"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# scale=alt.Scale(scheme='set1')\n",
"cross_type_count = alt.Chart(cross_type).mark_line(\n",
" point={\n",
" \"filled\": False,\n",
" \"fill\": \"white\"\n",
" },\n",
" strokeDash=[1,2]\n",
").encode(\n",
" x = 'year',\n",
" y = alt.Y(\n",
" 'count',\n",
" title = 'Number of authors'\n",
" ),\n",
" color = alt.Color(\n",
" 'type',\n",
" title = 'Cross-type collab.',\n",
" sort = ['Yes', 'No'],\n",
" scale=alt.Scale(domain=['No', 'Yes'],\n",
" range=['purple', 'green'])\n",
" ),\n",
")\n",
"\n",
"cross_type_count"
]
},
{
"cell_type": "code",
"execution_count": 36,
"id": "114a1992",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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"text/plain": [
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},
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"alt.layer(\n",
" cross_type_count,\n",
" cross_country_count\n",
").resolve_scale(\n",
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")"
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{
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"id": "191b818c",
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\"type\": \"No\", \"count\": 48, \"ratio\": 0.9056603773584906}, {\"year\": \"1994-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 5, \"ratio\": 0.09433962264150944}, {\"year\": \"1995-01-01T00:00:00\", \"type\": \"No\", \"count\": 64, \"ratio\": 0.8648648648648649}, {\"year\": \"1995-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 10, \"ratio\": 0.13513513513513514}, {\"year\": \"1996-01-01T00:00:00\", \"type\": \"No\", \"count\": 59, \"ratio\": 0.7662337662337663}, {\"year\": \"1996-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 18, \"ratio\": 0.23376623376623376}, {\"year\": \"1997-01-01T00:00:00\", \"type\": \"No\", \"count\": 78, \"ratio\": 0.8863636363636364}, {\"year\": \"1997-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 10, \"ratio\": 0.11363636363636363}, {\"year\": \"1998-01-01T00:00:00\", \"type\": \"No\", \"count\": 77, \"ratio\": 0.8555555555555555}, {\"year\": \"1998-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 13, \"ratio\": 0.14444444444444443}, {\"year\": \"1999-01-01T00:00:00\", \"type\": \"No\", \"count\": 72, \"ratio\": 0.8181818181818182}, {\"year\": \"1999-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 16, \"ratio\": 0.18181818181818182}, {\"year\": \"2000-01-01T00:00:00\", \"type\": \"No\", \"count\": 71, \"ratio\": 0.7717391304347826}, {\"year\": \"2000-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 21, \"ratio\": 0.22826086956521738}, {\"year\": \"2001-01-01T00:00:00\", \"type\": \"No\", \"count\": 68, \"ratio\": 0.7083333333333334}, {\"year\": \"2001-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 28, \"ratio\": 0.2916666666666667}, {\"year\": \"2002-01-01T00:00:00\", \"type\": \"No\", \"count\": 84, \"ratio\": 0.8316831683168316}, {\"year\": \"2002-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 17, \"ratio\": 0.16831683168316833}, {\"year\": \"2003-01-01T00:00:00\", \"type\": \"No\", \"count\": 82, \"ratio\": 0.803921568627451}, {\"year\": \"2003-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 20, \"ratio\": 0.19607843137254902}, {\"year\": \"2004-01-01T00:00:00\", \"type\": \"No\", \"count\": 73, \"ratio\": 0.7525773195876289}, {\"year\": \"2004-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 24, \"ratio\": 0.24742268041237114}, {\"year\": \"2005-01-01T00:00:00\", \"type\": \"No\", \"count\": 97, \"ratio\": 0.8151260504201681}, {\"year\": \"2005-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 22, \"ratio\": 0.18487394957983194}, {\"year\": \"2006-01-01T00:00:00\", \"type\": \"No\", \"count\": 90, \"ratio\": 0.7964601769911505}, {\"year\": \"2006-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 23, \"ratio\": 0.20353982300884957}, {\"year\": \"2007-01-01T00:00:00\", \"type\": \"No\", \"count\": 69, \"ratio\": 0.6448598130841121}, {\"year\": \"2007-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 38, \"ratio\": 0.35514018691588783}, {\"year\": \"2008-01-01T00:00:00\", \"type\": \"No\", \"count\": 70, \"ratio\": 0.7070707070707071}, {\"year\": \"2008-01-01T00:00:00\", \"type\": \"Yes\", 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\"2018-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 58, \"ratio\": 0.4566929133858268}, {\"year\": \"2019-01-01T00:00:00\", \"type\": \"No\", \"count\": 86, \"ratio\": 0.6666666666666666}, {\"year\": \"2019-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 43, \"ratio\": 0.3333333333333333}, {\"year\": \"2020-01-01T00:00:00\", \"type\": \"No\", \"count\": 108, \"ratio\": 0.6878980891719745}, {\"year\": \"2020-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 49, \"ratio\": 0.31210191082802546}, {\"year\": \"2021-01-01T00:00:00\", \"type\": \"No\", \"count\": 116, \"ratio\": 0.6823529411764706}, {\"year\": \"2021-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 54, \"ratio\": 0.3176470588235294}], \"data-13dc27e4ecc591ee8fd381db4be435d5\": [{\"year\": \"1990-01-01T00:00:00\", \"type\": \"No\", \"count\": 50, \"ratio\": 0.9615384615384616}, {\"year\": \"1990-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 2, \"ratio\": 0.038461538461538464}, {\"year\": \"1991-01-01T00:00:00\", \"type\": \"No\", \"count\": 49, \"ratio\": 0.98}, {\"year\": \"1991-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 1, \"ratio\": 0.02}, {\"year\": \"1992-01-01T00:00:00\", \"type\": \"No\", \"count\": 47, \"ratio\": 0.8867924528301887}, {\"year\": \"1992-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 6, \"ratio\": 0.11320754716981132}, {\"year\": \"1993-01-01T00:00:00\", \"type\": \"No\", \"count\": 51, \"ratio\": 0.9272727272727272}, {\"year\": \"1993-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 4, \"ratio\": 0.07272727272727272}, {\"year\": \"1994-01-01T00:00:00\", \"type\": \"No\", \"count\": 49, \"ratio\": 0.9245283018867925}, {\"year\": \"1994-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 4, \"ratio\": 0.07547169811320754}, {\"year\": \"1995-01-01T00:00:00\", \"type\": \"No\", \"count\": 69, \"ratio\": 0.9324324324324325}, {\"year\": \"1995-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 5, \"ratio\": 0.06756756756756757}, {\"year\": \"1996-01-01T00:00:00\", \"type\": \"No\", 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\"2006-01-01T00:00:00\", \"type\": \"No\", \"count\": 90, \"ratio\": 0.7964601769911505}, {\"year\": \"2006-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 23, \"ratio\": 0.20353982300884957}, {\"year\": \"2007-01-01T00:00:00\", \"type\": \"No\", \"count\": 75, \"ratio\": 0.7009345794392523}, {\"year\": \"2007-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 32, \"ratio\": 0.29906542056074764}, {\"year\": \"2008-01-01T00:00:00\", \"type\": \"No\", \"count\": 75, \"ratio\": 0.7575757575757576}, {\"year\": \"2008-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 24, \"ratio\": 0.24242424242424243}, {\"year\": \"2009-01-01T00:00:00\", \"type\": \"No\", \"count\": 90, \"ratio\": 0.7692307692307693}, {\"year\": \"2009-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 27, \"ratio\": 0.23076923076923078}, {\"year\": \"2010-01-01T00:00:00\", \"type\": \"No\", \"count\": 79, \"ratio\": 0.7247706422018348}, {\"year\": \"2010-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 30, \"ratio\": 0.27522935779816515}, {\"year\": \"2011-01-01T00:00:00\", \"type\": \"No\", \"count\": 91, \"ratio\": 0.7647058823529411}, {\"year\": \"2011-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 28, \"ratio\": 0.23529411764705882}, {\"year\": \"2012-01-01T00:00:00\", \"type\": \"No\", \"count\": 74, \"ratio\": 0.6379310344827587}, {\"year\": \"2012-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 42, \"ratio\": 0.3620689655172414}, {\"year\": \"2013-01-01T00:00:00\", \"type\": \"No\", \"count\": 75, \"ratio\": 0.7425742574257426}, {\"year\": \"2013-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 26, \"ratio\": 0.25742574257425743}, {\"year\": \"2014-01-01T00:00:00\", \"type\": \"No\", \"count\": 85, \"ratio\": 0.6390977443609023}, {\"year\": \"2014-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 48, \"ratio\": 0.3609022556390977}, {\"year\": \"2015-01-01T00:00:00\", \"type\": \"No\", \"count\": 80, \"ratio\": 0.6299212598425197}, {\"year\": \"2015-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 47, \"ratio\": 0.3700787401574803}, {\"year\": \"2016-01-01T00:00:00\", \"type\": \"No\", \"count\": 66, \"ratio\": 0.5739130434782609}, {\"year\": \"2016-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 49, \"ratio\": 0.4260869565217391}, {\"year\": \"2017-01-01T00:00:00\", \"type\": \"No\", \"count\": 65, \"ratio\": 0.5701754385964912}, {\"year\": \"2017-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 49, \"ratio\": 0.4298245614035088}, {\"year\": \"2018-01-01T00:00:00\", \"type\": \"No\", \"count\": 82, \"ratio\": 0.6456692913385826}, {\"year\": \"2018-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 45, \"ratio\": 0.3543307086614173}, {\"year\": \"2019-01-01T00:00:00\", \"type\": \"No\", \"count\": 81, \"ratio\": 0.627906976744186}, {\"year\": \"2019-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 48, \"ratio\": 0.37209302325581395}, {\"year\": \"2020-01-01T00:00:00\", \"type\": \"No\", \"count\": 93, \"ratio\": 0.5923566878980892}, {\"year\": \"2020-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 64, \"ratio\": 0.40764331210191085}, {\"year\": \"2021-01-01T00:00:00\", \"type\": \"No\", \"count\": 94, \"ratio\": 0.5529411764705883}, {\"year\": \"2021-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 76, \"ratio\": 0.4470588235294118}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 37,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alt.layer(\n",
" cross_type_ratio,\n",
" cross_country_ratio\n",
").resolve_scale(\n",
" color='independent'\n",
")"
]
},
{
"cell_type": "markdown",
"id": "7ee320f1",
"metadata": {},
"source": [
"### Non-VIS VS VIS citations"
]
},
{
"cell_type": "code",
"execution_count": 38,
"id": "8703debb",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 38,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# VIS PAPERS' OPENALEX IDS\n",
"ids = paper['OpenAlex ID'].tolist()\n",
"len(list(set(ids))) == paper.shape[0]"
]
},
{
"cell_type": "code",
"execution_count": 39,
"id": "cdfb32a1",
"metadata": {},
"outputs": [],
"source": [
"ref = pd.read_csv('../data/processed/openalex_reference_paper_df.csv')\n",
"ref = ref[ref['OpenAlex ID'].notnull()]"
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "018e85e5",
"metadata": {},
"outputs": [],
"source": [
"cit = pd.read_csv('../data/processed/openalex_citation_paper_df.csv')\n",
"cit = cit[cit['Citation Paper OpenAlex ID'].notnull()]"
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "6eafa09e",
"metadata": {},
"outputs": [],
"source": [
"ref['VIS'] = ref['OpenAlex ID'].apply(\n",
" lambda x: 'Yes' if x in ids else 'No'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 42,
"id": "254f342a",
"metadata": {},
"outputs": [],
"source": [
"cit['VIS'] = cit['Citation Paper OpenAlex ID'].apply(\n",
" lambda x: 'Yes' if x in ids else 'No'\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 43,
"id": "b46ace38",
"metadata": {},
"outputs": [],
"source": [
"tuples = []\n",
"for group in cit.groupby('OpenAlex Year'):\n",
" year = group[0]\n",
" total = group[1].shape[0]\n",
" for subgroup in group[1].groupby('VIS'):\n",
" type_of_paper = subgroup[0] # true or false\n",
" count = subgroup[1].shape[0]\n",
" ratio = count / total\n",
" tuples.append((year, type_of_paper, count, ratio))"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "6b9f35c8",
"metadata": {},
"outputs": [],
"source": [
"cit_df = pd.DataFrame(list(tuples), columns=['year', 'type', 'count', 'ratio']) "
]
},
{
"cell_type": "code",
"execution_count": 45,
"id": "de361c34",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
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" .dataframe thead th {\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>3</th>\n",
" <td>1990.0</td>\n",
" <td>Non-VIS</td>\n",
" <td>6</td>\n",
" <td>0.750000</td>\n",
" <td>1990-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1990.0</td>\n",
" <td>VIS</td>\n",
" <td>2</td>\n",
" <td>0.250000</td>\n",
" <td>1990-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1991.0</td>\n",
" <td>Non-VIS</td>\n",
" <td>56</td>\n",
" <td>0.736842</td>\n",
" <td>1991-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>1991.0</td>\n",
" <td>VIS</td>\n",
" <td>20</td>\n",
" <td>0.263158</td>\n",
" <td>1991-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>1992.0</td>\n",
" <td>Non-VIS</td>\n",
" <td>113</td>\n",
" <td>0.710692</td>\n",
" <td>1992-01-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year type count ratio Year\n",
"3 1990.0 Non-VIS 6 0.750000 1990-01-01\n",
"4 1990.0 VIS 2 0.250000 1990-01-01\n",
"5 1991.0 Non-VIS 56 0.736842 1991-01-01\n",
"6 1991.0 VIS 20 0.263158 1991-01-01\n",
"7 1992.0 Non-VIS 113 0.710692 1992-01-01"
]
},
"execution_count": 45,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cit_df = cit_df[(cit_df.year >= 1990) & (cit_df.year <= 2021)]\n",
"cit_df['Year'] = pd.to_datetime(cit_df['year'], format='%Y')\n",
"cit_df.replace({'Yes': 'VIS', 'No': 'Non-VIS'}, inplace=True)\n",
"cit_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 46,
"id": "2e95b823",
"metadata": {},
"outputs": [
{
"data": {
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" <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>45</th>\n",
" <td>2011.0</td>\n",
" <td>Non-VIS</td>\n",
" <td>6091</td>\n",
" <td>0.888938</td>\n",
" <td>2011-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>2011.0</td>\n",
" <td>VIS</td>\n",
" <td>761</td>\n",
" <td>0.111062</td>\n",
" <td>2011-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>65</th>\n",
" <td>2021.0</td>\n",
" <td>Non-VIS</td>\n",
" <td>11309</td>\n",
" <td>0.843703</td>\n",
" <td>2021-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>66</th>\n",
" <td>2021.0</td>\n",
" <td>VIS</td>\n",
" <td>2095</td>\n",
" <td>0.156297</td>\n",
" <td>2021-01-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year type count ratio Year\n",
"45 2011.0 Non-VIS 6091 0.888938 2011-01-01\n",
"46 2011.0 VIS 761 0.111062 2011-01-01\n",
"65 2021.0 Non-VIS 11309 0.843703 2021-01-01\n",
"66 2021.0 VIS 2095 0.156297 2021-01-01"
]
},
"execution_count": 46,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cit_df[cit_df.year.isin([2021, 2011])]"
]
},
{
"cell_type": "code",
"execution_count": 47,
"id": "7d91aa0a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Ratio')"
]
},
"execution_count": 47,
"metadata": {},
"output_type": "execute_result"
},
{
"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 = cit_df)\n",
"g.set_xlim(1990,2021)\n",
"g.legend(title = 'Papers citing VIS', labels = ['Non-VIS', 'VIS'])\n",
"g.set_xlabel('Year')\n",
"g.set_ylabel('Ratio')"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "f8fc1970",
"metadata": {},
"outputs": [],
"source": [
"to_plot_stackarea = []\n",
"for type_group in cit_df.groupby('type'):\n",
" a_list = type_group[1]['count'].tolist()\n",
" to_plot_stackarea.append(a_list)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "d18e44c3",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 49,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(to_plot_stackarea[0]) == len(to_plot_stackarea[1])"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "2ce550f6",
"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": [
"plt.stackplot(range(1990, 2022), to_plot_stackarea, \n",
" labels=['Non-VIS', 'VIS'])\n",
"plt.legend(title= 'Papers citing VIS', loc='upper left')\n",
"plt.xlabel('Year')\n",
"plt.ylabel('Number of Publications')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "5196338c",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-a240c6ffdbf040348611ad501a4caeb8\"></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-a240c6ffdbf040348611ad501a4caeb8\") {\n",
" outputDiv = document.getElementById(\"altair-viz-a240c6ffdbf040348611ad501a4caeb8\");\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: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"hconcat\": [{\"mark\": \"area\", \"encoding\": {\"color\": {\"field\": \"type\", \"scale\": {\"domain\": [\"VIS\", \"Non-VIS\"], \"range\": [\"skyblue\", \"pink\"]}, \"title\": \"Type\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"count\", \"type\": \"quantitative\"}}}, {\"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"type\", \"scale\": {\"domain\": [\"VIS\", \"Non-VIS\"], \"range\": [\"green\", \"purple\"]}, \"title\": \"Type\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"ratio\", \"type\": \"quantitative\"}}}], \"data\": {\"name\": \"data-caf6a8430b2373eb8cd48a7a77ac4916\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-caf6a8430b2373eb8cd48a7a77ac4916\": [{\"year\": 1990.0, \"type\": \"Non-VIS\", \"count\": 6, \"ratio\": 0.75, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1990.0, \"type\": \"VIS\", \"count\": 2, \"ratio\": 0.25, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1991.0, \"type\": \"Non-VIS\", \"count\": 56, \"ratio\": 0.7368421052631579, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1991.0, \"type\": \"VIS\", \"count\": 20, \"ratio\": 0.2631578947368421, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1992.0, \"type\": \"Non-VIS\", \"count\": 113, \"ratio\": 0.710691823899371, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1992.0, \"type\": \"VIS\", \"count\": 46, \"ratio\": 0.2893081761006289, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1993.0, \"type\": \"Non-VIS\", \"count\": 202, \"ratio\": 0.8015873015873016, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1993.0, \"type\": \"VIS\", \"count\": 50, \"ratio\": 0.1984126984126984, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1994.0, \"type\": \"Non-VIS\", \"count\": 285, \"ratio\": 0.7894736842105263, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1994.0, \"type\": \"VIS\", \"count\": 76, \"ratio\": 0.21052631578947367, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1995.0, \"type\": \"Non-VIS\", \"count\": 454, \"ratio\": 0.8092691622103387, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1995.0, \"type\": \"VIS\", \"count\": 107, \"ratio\": 0.19073083778966132, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1996.0, \"type\": \"Non-VIS\", \"count\": 485, \"ratio\": 0.8220338983050848, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1996.0, \"type\": \"VIS\", \"count\": 105, \"ratio\": 0.17796610169491525, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1997.0, \"type\": \"Non-VIS\", \"count\": 766, \"ratio\": 0.829004329004329, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1997.0, \"type\": \"VIS\", \"count\": 158, \"ratio\": 0.170995670995671, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1998.0, \"type\": \"Non-VIS\", \"count\": 809, \"ratio\": 0.7884990253411306, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1998.0, \"type\": \"VIS\", \"count\": 217, \"ratio\": 0.21150097465886938, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1999.0, \"type\": \"Non-VIS\", \"count\": 1079, \"ratio\": 0.8396887159533074, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 1999.0, \"type\": \"VIS\", \"count\": 206, \"ratio\": 0.16031128404669262, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 2000.0, \"type\": \"Non-VIS\", \"count\": 1355, \"ratio\": 0.8479349186483104, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2000.0, \"type\": \"VIS\", \"count\": 243, \"ratio\": 0.1520650813516896, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2001.0, \"type\": \"Non-VIS\", \"count\": 1664, \"ratio\": 0.8917470525187567, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2001.0, \"type\": \"VIS\", \"count\": 202, \"ratio\": 0.1082529474812433, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2002.0, \"type\": \"Non-VIS\", \"count\": 2159, \"ratio\": 0.8895756077461887, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2002.0, \"type\": \"VIS\", \"count\": 268, \"ratio\": 0.11042439225381129, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2003.0, \"type\": \"Non-VIS\", \"count\": 2746, \"ratio\": 0.8988543371522095, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2003.0, \"type\": \"VIS\", \"count\": 309, \"ratio\": 0.1011456628477905, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2004.0, \"type\": \"Non-VIS\", \"count\": 3354, \"ratio\": 0.8920212765957447, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2004.0, \"type\": \"VIS\", \"count\": 406, \"ratio\": 0.10797872340425532, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2005.0, \"type\": \"Non-VIS\", \"count\": 3862, \"ratio\": 0.9019149929939281, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2005.0, \"type\": \"VIS\", \"count\": 420, \"ratio\": 0.09808500700607192, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2006.0, \"type\": \"Non-VIS\", \"count\": 4075, \"ratio\": 0.8995584988962473, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2006.0, \"type\": \"VIS\", \"count\": 455, \"ratio\": 0.10044150110375276, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2007.0, \"type\": \"Non-VIS\", \"count\": 4321, \"ratio\": 0.9054903604358759, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2007.0, \"type\": \"VIS\", \"count\": 451, \"ratio\": 0.09450963956412406, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2008.0, \"type\": \"Non-VIS\", \"count\": 4708, \"ratio\": 0.9094069924666796, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008.0, \"type\": \"VIS\", \"count\": 469, \"ratio\": 0.09059300753332046, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2009.0, \"type\": \"Non-VIS\", \"count\": 4822, \"ratio\": 0.8829884636513459, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009.0, \"type\": \"VIS\", \"count\": 639, \"ratio\": 0.11701153634865409, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2010.0, \"type\": \"Non-VIS\", \"count\": 5093, \"ratio\": 0.8911636045494313, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010.0, \"type\": \"VIS\", \"count\": 622, \"ratio\": 0.10883639545056868, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2011.0, \"type\": \"Non-VIS\", \"count\": 6091, \"ratio\": 0.8889375364856976, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011.0, \"type\": \"VIS\", \"count\": 761, \"ratio\": 0.11106246351430239, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2012.0, \"type\": \"Non-VIS\", \"count\": 6427, \"ratio\": 0.9067437923250564, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012.0, \"type\": \"VIS\", \"count\": 661, \"ratio\": 0.09325620767494357, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2013.0, \"type\": \"Non-VIS\", \"count\": 6896, \"ratio\": 0.9103630363036304, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013.0, \"type\": \"VIS\", \"count\": 679, \"ratio\": 0.08963696369636964, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2014.0, \"type\": \"Non-VIS\", \"count\": 7458, \"ratio\": 0.8837539992890153, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014.0, \"type\": \"VIS\", \"count\": 981, \"ratio\": 0.11624600071098472, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2015.0, \"type\": \"Non-VIS\", \"count\": 7318, \"ratio\": 0.9834699637145545, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015.0, \"type\": \"VIS\", \"count\": 123, \"ratio\": 0.016530036285445505, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2016.0, \"type\": \"Non-VIS\", \"count\": 8313, \"ratio\": 0.893390650188071, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016.0, \"type\": \"VIS\", \"count\": 992, \"ratio\": 0.10660934981192907, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2017.0, \"type\": \"Non-VIS\", \"count\": 8338, \"ratio\": 0.8957885689729265, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017.0, \"type\": \"VIS\", \"count\": 970, \"ratio\": 0.10421143102707349, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018.0, \"type\": \"Non-VIS\", \"count\": 8628, \"ratio\": 0.8963224600041554, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018.0, \"type\": \"VIS\", \"count\": 998, \"ratio\": 0.10367753999584459, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019.0, \"type\": \"Non-VIS\", \"count\": 10013, \"ratio\": 0.8898071625344353, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019.0, \"type\": \"VIS\", \"count\": 1240, \"ratio\": 0.11019283746556474, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020.0, \"type\": \"Non-VIS\", \"count\": 11514, \"ratio\": 0.890762803651555, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020.0, \"type\": \"VIS\", \"count\": 1412, \"ratio\": 0.10923719634844499, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021.0, \"type\": \"Non-VIS\", \"count\": 11309, \"ratio\": 0.8437033721277231, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021.0, \"type\": \"VIS\", \"count\": 2095, \"ratio\": 0.15629662787227694, \"Year\": \"2021-01-01T00:00:00\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Using Altair\n",
"\n",
"source = cit_df\n",
"\n",
"chart1 = alt.Chart(source).mark_area().encode(\n",
" x=\"Year:T\",\n",
" y=\"count:Q\",\n",
" color=alt.Color(\n",
" \"type:N\",\n",
" title = 'Type',\n",
" scale=alt.Scale(\n",
" domain=['VIS', 'Non-VIS'],\n",
" range=['skyblue', 'pink']\n",
" )\n",
" \n",
" )\n",
")\n",
"\n",
"chart12 = alt.Chart(source).mark_line().encode(\n",
" x='Year',\n",
" y='ratio',\n",
" color=alt.Color(\n",
" \"type:N\",\n",
" title = 'Type',\n",
" scale=alt.Scale(\n",
" domain=['VIS', 'Non-VIS'],\n",
" range=['green', 'purple']\n",
" )\n",
" \n",
" )\n",
")\n",
"\n",
"alt.hconcat(chart1, chart12)\n"
]
},
{
"cell_type": "markdown",
"id": "ad1725f4",
"metadata": {},
"source": [
"### Non-VIS VS VIS References"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "6a690f94",
"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>DOI</th>\n",
" <th>Title</th>\n",
" <th>Number of References</th>\n",
" <th>Index of Reference</th>\n",
" <th>Reference</th>\n",
" <th>OpenAlex Year</th>\n",
" <th>OpenAlex Publication Date</th>\n",
" <th>OpenAlex ID</th>\n",
" <th>OpenAlex Title</th>\n",
" <th>...</th>\n",
" <th>Publication Type</th>\n",
" <th>OpenAlex First Page</th>\n",
" <th>OpenAlex Last Page</th>\n",
" <th>Number of References for Reference paper</th>\n",
" <th>Number of Citations</th>\n",
" <th>Number of Authors</th>\n",
" <th>Number of Concepts</th>\n",
" <th>Citation API URL</th>\n",
" <th>Number of Citation API URLs</th>\n",
" <th>VIS</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2011</td>\n",
" <td>10.1109/TVCG.2011.185</td>\n",
" <td>D³ Data-Driven Documents</td>\n",
" <td>27.0</td>\n",
" <td>1.0</td>\n",
" <td>https://openalex.org/W146699588</td>\n",
" <td>2005.0</td>\n",
" <td>2005-07-01</td>\n",
" <td>W146699588</td>\n",
" <td>The Grammar of Graphics (Statistics and Comput...</td>\n",
" <td>...</td>\n",
" <td>book</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>113.0</td>\n",
" <td>1.0</td>\n",
" <td>4.0</td>\n",
" <td>https://api.openalex.org/works?filter=cites:W1...</td>\n",
" <td>1.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2011</td>\n",
" <td>10.1109/TVCG.2011.185</td>\n",
" <td>D³ Data-Driven Documents</td>\n",
" <td>27.0</td>\n",
" <td>2.0</td>\n",
" <td>https://openalex.org/W1516293359</td>\n",
" <td>1999.0</td>\n",
" <td>1999-01-01</td>\n",
" <td>W1516293359</td>\n",
" <td>Readings in Information Visualization: Using V...</td>\n",
" <td>...</td>\n",
" <td>book</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>3082.0</td>\n",
" <td>3.0</td>\n",
" <td>13.0</td>\n",
" <td>https://api.openalex.org/works?filter=cites:W1...</td>\n",
" <td>1.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2011</td>\n",
" <td>10.1109/TVCG.2011.185</td>\n",
" <td>D³ Data-Driven Documents</td>\n",
" <td>27.0</td>\n",
" <td>3.0</td>\n",
" <td>https://openalex.org/W1587026990</td>\n",
" <td>2009.0</td>\n",
" <td>2009-08-13</td>\n",
" <td>W1587026990</td>\n",
" <td>ggplot2: Elegant Graphics for Data Analysis</td>\n",
" <td>...</td>\n",
" <td>book</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>0.0</td>\n",
" <td>18282.0</td>\n",
" <td>1.0</td>\n",
" <td>10.0</td>\n",
" <td>https://api.openalex.org/works?filter=cites:W1...</td>\n",
" <td>1.0</td>\n",
" <td>No</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>3 rows × 25 columns</p>\n",
"</div>"
],
"text/plain": [
" Year DOI Title \\\n",
"0 2011 10.1109/TVCG.2011.185 D³ Data-Driven Documents \n",
"1 2011 10.1109/TVCG.2011.185 D³ Data-Driven Documents \n",
"2 2011 10.1109/TVCG.2011.185 D³ Data-Driven Documents \n",
"\n",
" Number of References Index of Reference Reference \\\n",
"0 27.0 1.0 https://openalex.org/W146699588 \n",
"1 27.0 2.0 https://openalex.org/W1516293359 \n",
"2 27.0 3.0 https://openalex.org/W1587026990 \n",
"\n",
" OpenAlex Year OpenAlex Publication Date OpenAlex ID \\\n",
"0 2005.0 2005-07-01 W146699588 \n",
"1 1999.0 1999-01-01 W1516293359 \n",
"2 2009.0 2009-08-13 W1587026990 \n",
"\n",
" OpenAlex Title ... Publication Type \\\n",
"0 The Grammar of Graphics (Statistics and Comput... ... book \n",
"1 Readings in Information Visualization: Using V... ... book \n",
"2 ggplot2: Elegant Graphics for Data Analysis ... book \n",
"\n",
" OpenAlex First Page OpenAlex Last Page \\\n",
"0 NaN NaN \n",
"1 NaN NaN \n",
"2 NaN NaN \n",
"\n",
" Number of References for Reference paper Number of Citations \\\n",
"0 0.0 113.0 \n",
"1 0.0 3082.0 \n",
"2 0.0 18282.0 \n",
"\n",
" Number of Authors Number of Concepts \\\n",
"0 1.0 4.0 \n",
"1 3.0 13.0 \n",
"2 1.0 10.0 \n",
"\n",
" Citation API URL \\\n",
"0 https://api.openalex.org/works?filter=cites:W1... \n",
"1 https://api.openalex.org/works?filter=cites:W1... \n",
"2 https://api.openalex.org/works?filter=cites:W1... \n",
"\n",
" Number of Citation API URLs VIS \n",
"0 1.0 No \n",
"1 1.0 No \n",
"2 1.0 No \n",
"\n",
"[3 rows x 25 columns]"
]
},
"execution_count": 52,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ref.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "5d9df9e7",
"metadata": {},
"outputs": [],
"source": [
"tuples = []\n",
"for group in ref.groupby('Year'):\n",
" year = group[0]\n",
" total = group[1].shape[0]\n",
" for subgroup in group[1].groupby('VIS'):\n",
" type_of_paper = subgroup[0] \n",
" count = subgroup[1].shape[0]\n",
" ratio = count / total\n",
" tuples.append((year, type_of_paper, count, ratio))"
]
},
{
"cell_type": "code",
"execution_count": 54,
"id": "08b33717",
"metadata": {},
"outputs": [],
"source": [
"ref_df = pd.DataFrame(list(tuples), columns=['year', 'type', 'count', 'ratio']) "
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "cb1e9c79",
"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>0</th>\n",
" <td>1990</td>\n",
" <td>Non-VIS</td>\n",
" <td>590</td>\n",
" <td>0.996622</td>\n",
" <td>1990-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1990</td>\n",
" <td>VIS</td>\n",
" <td>2</td>\n",
" <td>0.003378</td>\n",
" <td>1990-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1991</td>\n",
" <td>Non-VIS</td>\n",
" <td>402</td>\n",
" <td>0.952607</td>\n",
" <td>1991-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1991</td>\n",
" <td>VIS</td>\n",
" <td>20</td>\n",
" <td>0.047393</td>\n",
" <td>1991-01-01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1992</td>\n",
" <td>Non-VIS</td>\n",
" <td>528</td>\n",
" <td>0.919861</td>\n",
" <td>1992-01-01</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" year type count ratio Year\n",
"0 1990 Non-VIS 590 0.996622 1990-01-01\n",
"1 1990 VIS 2 0.003378 1990-01-01\n",
"2 1991 Non-VIS 402 0.952607 1991-01-01\n",
"3 1991 VIS 20 0.047393 1991-01-01\n",
"4 1992 Non-VIS 528 0.919861 1992-01-01"
]
},
"execution_count": 55,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ref_df = ref_df[(ref_df.year >= 1990) & (ref_df.year <= 2021)]\n",
"ref_df['Year'] = pd.to_datetime(ref_df['year'], format='%Y')\n",
"ref_df.replace({'No': 'Non-VIS', 'Yes': 'VIS'}, inplace = True)\n",
"ref_df.head()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "d133e8df",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0, 0.5, 'Ratio')"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
},
{
"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 = ref_df)\n",
"# g.set_xlim(1990,2021)\n",
"g.legend(title = 'Papers cited in VIS', labels = ['Non-VIS', 'VIS'])\n",
"g.set_xlabel('Year')\n",
"g.set_ylabel('Ratio')"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "847c07a5",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-809751b498844441ada11f852691865e\"></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-809751b498844441ada11f852691865e\") {\n",
" outputDiv = document.getElementById(\"altair-viz-809751b498844441ada11f852691865e\");\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: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}, \"axis\": {\"labelFontSize\": 13, \"titleFontSize\": 14}}, \"hconcat\": [{\"mark\": \"area\", \"encoding\": {\"color\": {\"field\": \"type\", \"type\": \"nominal\"}, \"x\": {\"axis\": {\"title\": \"Year\"}, \"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"axis\": {\"title\": \"Raio\"}, \"field\": \"count\", \"type\": \"quantitative\"}}}, {\"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"type\", \"type\": \"nominal\"}, \"x\": {\"axis\": {\"title\": \"Year\"}, \"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"axis\": {\"title\": \"Raio\"}, \"field\": \"ratio\", \"type\": \"quantitative\"}}}], \"data\": {\"name\": \"data-e1eb1706bbda09e5b5767506b936f4b1\"}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-e1eb1706bbda09e5b5767506b936f4b1\": [{\"year\": 1990, \"type\": \"Non-VIS\", \"count\": 590, \"ratio\": 0.9966216216216216, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1990, \"type\": \"VIS\", \"count\": 2, \"ratio\": 0.0033783783783783786, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1991, \"type\": \"Non-VIS\", \"count\": 402, \"ratio\": 0.95260663507109, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1991, \"type\": \"VIS\", \"count\": 20, \"ratio\": 0.04739336492890995, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1992, \"type\": \"Non-VIS\", \"count\": 528, \"ratio\": 0.9198606271777003, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1992, \"type\": \"VIS\", \"count\": 46, \"ratio\": 0.08013937282229965, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1993, \"type\": \"Non-VIS\", \"count\": 510, \"ratio\": 0.9107142857142857, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1993, \"type\": \"VIS\", \"count\": 50, \"ratio\": 0.08928571428571429, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1994, \"type\": \"Non-VIS\", \"count\": 547, \"ratio\": 0.8752, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1994, \"type\": \"VIS\", \"count\": 78, \"ratio\": 0.1248, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1995, \"type\": \"Non-VIS\", \"count\": 826, \"ratio\": 0.8815368196371398, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1995, \"type\": \"VIS\", \"count\": 111, \"ratio\": 0.11846318036286019, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1996, \"type\": \"Non-VIS\", \"count\": 886, \"ratio\": 0.8931451612903226, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1996, \"type\": \"VIS\", \"count\": 106, \"ratio\": 0.10685483870967742, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"Non-VIS\", \"count\": 1142, \"ratio\": 0.8791377983063895, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"VIS\", \"count\": 157, \"ratio\": 0.12086220169361046, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"Non-VIS\", \"count\": 1287, \"ratio\": 0.8591455273698264, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"VIS\", \"count\": 211, \"ratio\": 0.14085447263017356, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"Non-VIS\", \"count\": 1110, \"ratio\": 0.8434650455927052, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"VIS\", \"count\": 206, \"ratio\": 0.15653495440729484, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"Non-VIS\", \"count\": 1403, \"ratio\": 0.8523693803159174, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"VIS\", \"count\": 243, \"ratio\": 0.14763061968408261, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"Non-VIS\", \"count\": 1485, \"ratio\": 0.8802608180201541, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"VIS\", \"count\": 202, \"ratio\": 0.11973918197984589, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"Non-VIS\", \"count\": 1697, \"ratio\": 0.8640529531568228, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"VIS\", \"count\": 267, \"ratio\": 0.13594704684317718, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"Non-VIS\", \"count\": 2143, \"ratio\": 0.8711382113821138, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"VIS\", \"count\": 317, \"ratio\": 0.1288617886178862, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"Non-VIS\", \"count\": 2034, \"ratio\": 0.8356614626129828, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"VIS\", \"count\": 400, \"ratio\": 0.16433853738701726, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"Non-VIS\", \"count\": 2345, \"ratio\": 0.8490224475018103, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"VIS\", \"count\": 417, \"ratio\": 0.15097755249818973, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"Non-VIS\", \"count\": 2472, \"ratio\": 0.8439740525776716, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"VIS\", \"count\": 457, \"ratio\": 0.15602594742232845, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"Non-VIS\", \"count\": 2616, \"ratio\": 0.8521172638436482, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"VIS\", \"count\": 454, \"ratio\": 0.14788273615635178, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"Non-VIS\", \"count\": 2120, \"ratio\": 0.8144448713023434, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"VIS\", \"count\": 483, \"ratio\": 0.18555512869765656, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"Non-VIS\", \"count\": 2660, \"ratio\": 0.811222933821287, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"VIS\", \"count\": 619, \"ratio\": 0.188777066178713, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"Non-VIS\", \"count\": 2738, \"ratio\": 0.8146385004462957, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"VIS\", \"count\": 623, \"ratio\": 0.18536149955370426, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"Non-VIS\", \"count\": 3266, \"ratio\": 0.8110255773528682, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"VIS\", \"count\": 761, \"ratio\": 0.18897442264713185, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"Non-VIS\", \"count\": 3175, \"ratio\": 0.8279009126466753, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"VIS\", \"count\": 660, \"ratio\": 0.17209908735332463, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"Non-VIS\", \"count\": 3100, \"ratio\": 0.8203228367292935, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"VIS\", \"count\": 679, \"ratio\": 0.17967716327070654, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"Non-VIS\", \"count\": 3987, \"ratio\": 0.802536231884058, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"VIS\", \"count\": 981, \"ratio\": 0.19746376811594202, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"Non-VIS\", \"count\": 4009, \"ratio\": 0.8005191693290735, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"VIS\", \"count\": 999, \"ratio\": 0.1994808306709265, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"Non-VIS\", \"count\": 3860, \"ratio\": 0.8034970857618651, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"VIS\", \"count\": 944, \"ratio\": 0.1965029142381349, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Non-VIS\", \"count\": 4564, \"ratio\": 0.8131124176019954, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"VIS\", \"count\": 1049, \"ratio\": 0.18688758239800463, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Non-VIS\", \"count\": 4998, \"ratio\": 0.80198973042362, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"VIS\", \"count\": 1234, \"ratio\": 0.19801026957638, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Non-VIS\", \"count\": 5453, \"ratio\": 0.8147318093530554, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"VIS\", \"count\": 1240, \"ratio\": 0.18526819064694458, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Non-VIS\", \"count\": 6176, \"ratio\": 0.8126315789473684, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"VIS\", \"count\": 1424, \"ratio\": 0.18736842105263157, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Non-VIS\", \"count\": 4109, \"ratio\": 0.8138245197068726, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"VIS\", \"count\": 940, \"ratio\": 0.18617548029312736, \"Year\": \"2021-01-01T00:00:00\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Using Altair\n",
"\n",
"source = ref_df\n",
"\n",
"chart1 = alt.Chart(source).mark_area().encode(\n",
"# x=\"Year:T\",\n",
" alt.X('Year:T', axis=alt.Axis(title=\"Year\")),\n",
" alt.Y('count:Q', axis=alt.Axis(title=\"Raio\")),\n",
" color=\"type:N\"\n",
")\n",
"\n",
"chart22 = alt.Chart(source).mark_line().encode(\n",
" alt.X('Year:T', axis=alt.Axis(title=\"Year\")),\n",
" alt.Y('ratio:Q', axis=alt.Axis(title=\"Raio\")),\n",
" color='type',\n",
")\n",
"\n",
"alt.hconcat(chart1, chart22).configure_axis(\n",
" labelFontSize=13,\n",
" titleFontSize=14,\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 58,
"id": "c629ae27",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-9222395bcee14ce7a9bc8eb0f5679b8f\"></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-9222395bcee14ce7a9bc8eb0f5679b8f\") {\n",
" outputDiv = document.getElementById(\"altair-viz-9222395bcee14ce7a9bc8eb0f5679b8f\");\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: ${err.message}`));\n",
" } else {\n",
" maybeLoadScript(\"vega\", \"5\")\n",
" .then(() => maybeLoadScript(\"vega-lite\", \"4.17.0\"))\n",
" .then(() => maybeLoadScript(\"vega-embed\", \"6\"))\n",
" .catch(showError)\n",
" .then(() => displayChart(vegaEmbed));\n",
" }\n",
" })({\"config\": {\"view\": {\"continuousWidth\": 400, \"continuousHeight\": 300}}, \"layer\": [{\"data\": {\"name\": \"data-caf6a8430b2373eb8cd48a7a77ac4916\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"type\", \"scale\": {\"domain\": [\"VIS\", \"Non-VIS\"], \"range\": [\"green\", \"purple\"]}, \"title\": \"Type\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"ratio\", \"type\": \"quantitative\"}}}, {\"data\": {\"name\": \"data-e1eb1706bbda09e5b5767506b936f4b1\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"type\", \"type\": \"nominal\"}, \"x\": {\"axis\": {\"title\": \"Year\"}, \"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"axis\": {\"title\": \"Raio\"}, \"field\": \"ratio\", \"type\": \"quantitative\"}}}], \"resolve\": {\"scale\": {\"color\": \"independent\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-caf6a8430b2373eb8cd48a7a77ac4916\": [{\"year\": 1990.0, \"type\": \"Non-VIS\", \"count\": 6, \"ratio\": 0.75, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1990.0, \"type\": \"VIS\", \"count\": 2, \"ratio\": 0.25, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1991.0, \"type\": \"Non-VIS\", \"count\": 56, \"ratio\": 0.7368421052631579, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1991.0, \"type\": \"VIS\", \"count\": 20, \"ratio\": 0.2631578947368421, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1992.0, \"type\": \"Non-VIS\", \"count\": 113, \"ratio\": 0.710691823899371, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1992.0, \"type\": \"VIS\", \"count\": 46, \"ratio\": 0.2893081761006289, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1993.0, \"type\": \"Non-VIS\", \"count\": 202, \"ratio\": 0.8015873015873016, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1993.0, \"type\": \"VIS\", \"count\": 50, \"ratio\": 0.1984126984126984, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1994.0, \"type\": \"Non-VIS\", \"count\": 285, \"ratio\": 0.7894736842105263, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1994.0, \"type\": \"VIS\", \"count\": 76, \"ratio\": 0.21052631578947367, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1995.0, \"type\": \"Non-VIS\", \"count\": 454, \"ratio\": 0.8092691622103387, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1995.0, \"type\": \"VIS\", \"count\": 107, \"ratio\": 0.19073083778966132, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1996.0, \"type\": \"Non-VIS\", \"count\": 485, \"ratio\": 0.8220338983050848, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1996.0, \"type\": \"VIS\", \"count\": 105, \"ratio\": 0.17796610169491525, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1997.0, \"type\": \"Non-VIS\", \"count\": 766, \"ratio\": 0.829004329004329, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1997.0, \"type\": \"VIS\", \"count\": 158, \"ratio\": 0.170995670995671, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1998.0, \"type\": \"Non-VIS\", \"count\": 809, \"ratio\": 0.7884990253411306, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1998.0, \"type\": \"VIS\", \"count\": 217, \"ratio\": 0.21150097465886938, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1999.0, \"type\": \"Non-VIS\", \"count\": 1079, \"ratio\": 0.8396887159533074, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 1999.0, \"type\": \"VIS\", \"count\": 206, \"ratio\": 0.16031128404669262, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 2000.0, \"type\": \"Non-VIS\", \"count\": 1355, \"ratio\": 0.8479349186483104, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2000.0, \"type\": \"VIS\", \"count\": 243, \"ratio\": 0.1520650813516896, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2001.0, \"type\": \"Non-VIS\", \"count\": 1664, \"ratio\": 0.8917470525187567, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2001.0, \"type\": \"VIS\", \"count\": 202, \"ratio\": 0.1082529474812433, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2002.0, \"type\": \"Non-VIS\", \"count\": 2159, \"ratio\": 0.8895756077461887, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2002.0, \"type\": \"VIS\", \"count\": 268, \"ratio\": 0.11042439225381129, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2003.0, \"type\": \"Non-VIS\", \"count\": 2746, \"ratio\": 0.8988543371522095, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2003.0, \"type\": \"VIS\", \"count\": 309, \"ratio\": 0.1011456628477905, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2004.0, \"type\": \"Non-VIS\", \"count\": 3354, \"ratio\": 0.8920212765957447, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2004.0, \"type\": \"VIS\", \"count\": 406, \"ratio\": 0.10797872340425532, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2005.0, \"type\": \"Non-VIS\", \"count\": 3862, \"ratio\": 0.9019149929939281, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2005.0, \"type\": \"VIS\", \"count\": 420, \"ratio\": 0.09808500700607192, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2006.0, \"type\": \"Non-VIS\", \"count\": 4075, \"ratio\": 0.8995584988962473, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2006.0, \"type\": \"VIS\", \"count\": 455, \"ratio\": 0.10044150110375276, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2007.0, \"type\": \"Non-VIS\", \"count\": 4321, \"ratio\": 0.9054903604358759, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2007.0, \"type\": \"VIS\", \"count\": 451, \"ratio\": 0.09450963956412406, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2008.0, \"type\": \"Non-VIS\", \"count\": 4708, \"ratio\": 0.9094069924666796, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008.0, \"type\": \"VIS\", \"count\": 469, \"ratio\": 0.09059300753332046, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2009.0, \"type\": \"Non-VIS\", \"count\": 4822, \"ratio\": 0.8829884636513459, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009.0, \"type\": \"VIS\", \"count\": 639, \"ratio\": 0.11701153634865409, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2010.0, \"type\": \"Non-VIS\", \"count\": 5093, \"ratio\": 0.8911636045494313, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010.0, \"type\": \"VIS\", \"count\": 622, \"ratio\": 0.10883639545056868, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2011.0, \"type\": \"Non-VIS\", \"count\": 6091, \"ratio\": 0.8889375364856976, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011.0, \"type\": \"VIS\", \"count\": 761, \"ratio\": 0.11106246351430239, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2012.0, \"type\": \"Non-VIS\", \"count\": 6427, \"ratio\": 0.9067437923250564, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012.0, \"type\": \"VIS\", \"count\": 661, \"ratio\": 0.09325620767494357, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2013.0, \"type\": \"Non-VIS\", \"count\": 6896, \"ratio\": 0.9103630363036304, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013.0, \"type\": \"VIS\", \"count\": 679, \"ratio\": 0.08963696369636964, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2014.0, \"type\": \"Non-VIS\", \"count\": 7458, \"ratio\": 0.8837539992890153, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014.0, \"type\": \"VIS\", \"count\": 981, \"ratio\": 0.11624600071098472, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2015.0, \"type\": \"Non-VIS\", \"count\": 7318, \"ratio\": 0.9834699637145545, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015.0, \"type\": \"VIS\", \"count\": 123, \"ratio\": 0.016530036285445505, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2016.0, \"type\": \"Non-VIS\", \"count\": 8313, \"ratio\": 0.893390650188071, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016.0, \"type\": \"VIS\", \"count\": 992, \"ratio\": 0.10660934981192907, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2017.0, \"type\": \"Non-VIS\", \"count\": 8338, \"ratio\": 0.8957885689729265, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017.0, \"type\": \"VIS\", \"count\": 970, \"ratio\": 0.10421143102707349, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018.0, \"type\": \"Non-VIS\", \"count\": 8628, \"ratio\": 0.8963224600041554, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018.0, \"type\": \"VIS\", \"count\": 998, \"ratio\": 0.10367753999584459, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019.0, \"type\": \"Non-VIS\", \"count\": 10013, \"ratio\": 0.8898071625344353, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019.0, \"type\": \"VIS\", \"count\": 1240, \"ratio\": 0.11019283746556474, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020.0, \"type\": \"Non-VIS\", \"count\": 11514, \"ratio\": 0.890762803651555, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020.0, \"type\": \"VIS\", \"count\": 1412, \"ratio\": 0.10923719634844499, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021.0, \"type\": \"Non-VIS\", \"count\": 11309, \"ratio\": 0.8437033721277231, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021.0, \"type\": \"VIS\", \"count\": 2095, \"ratio\": 0.15629662787227694, \"Year\": \"2021-01-01T00:00:00\"}], \"data-e1eb1706bbda09e5b5767506b936f4b1\": [{\"year\": 1990, \"type\": \"Non-VIS\", \"count\": 590, \"ratio\": 0.9966216216216216, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1990, \"type\": \"VIS\", \"count\": 2, \"ratio\": 0.0033783783783783786, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1991, \"type\": \"Non-VIS\", \"count\": 402, \"ratio\": 0.95260663507109, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1991, \"type\": \"VIS\", \"count\": 20, \"ratio\": 0.04739336492890995, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1992, \"type\": \"Non-VIS\", \"count\": 528, \"ratio\": 0.9198606271777003, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1992, \"type\": \"VIS\", \"count\": 46, \"ratio\": 0.08013937282229965, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1993, \"type\": \"Non-VIS\", \"count\": 510, \"ratio\": 0.9107142857142857, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1993, \"type\": \"VIS\", \"count\": 50, \"ratio\": 0.08928571428571429, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1994, \"type\": \"Non-VIS\", \"count\": 547, \"ratio\": 0.8752, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1994, \"type\": \"VIS\", \"count\": 78, \"ratio\": 0.1248, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1995, \"type\": \"Non-VIS\", \"count\": 826, \"ratio\": 0.8815368196371398, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1995, \"type\": \"VIS\", \"count\": 111, \"ratio\": 0.11846318036286019, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1996, \"type\": \"Non-VIS\", \"count\": 886, \"ratio\": 0.8931451612903226, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1996, \"type\": \"VIS\", \"count\": 106, \"ratio\": 0.10685483870967742, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"Non-VIS\", \"count\": 1142, \"ratio\": 0.8791377983063895, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1997, \"type\": \"VIS\", \"count\": 157, \"ratio\": 0.12086220169361046, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"Non-VIS\", \"count\": 1287, \"ratio\": 0.8591455273698264, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1998, \"type\": \"VIS\", \"count\": 211, \"ratio\": 0.14085447263017356, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"Non-VIS\", \"count\": 1110, \"ratio\": 0.8434650455927052, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 1999, \"type\": \"VIS\", \"count\": 206, \"ratio\": 0.15653495440729484, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"Non-VIS\", \"count\": 1403, \"ratio\": 0.8523693803159174, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2000, \"type\": \"VIS\", \"count\": 243, \"ratio\": 0.14763061968408261, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"Non-VIS\", \"count\": 1485, \"ratio\": 0.8802608180201541, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2001, \"type\": \"VIS\", \"count\": 202, \"ratio\": 0.11973918197984589, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"Non-VIS\", \"count\": 1697, \"ratio\": 0.8640529531568228, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2002, \"type\": \"VIS\", \"count\": 267, \"ratio\": 0.13594704684317718, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"Non-VIS\", \"count\": 2143, \"ratio\": 0.8711382113821138, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2003, \"type\": \"VIS\", \"count\": 317, \"ratio\": 0.1288617886178862, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"Non-VIS\", \"count\": 2034, \"ratio\": 0.8356614626129828, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2004, \"type\": \"VIS\", \"count\": 400, \"ratio\": 0.16433853738701726, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"Non-VIS\", \"count\": 2345, \"ratio\": 0.8490224475018103, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2005, \"type\": \"VIS\", \"count\": 417, \"ratio\": 0.15097755249818973, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"Non-VIS\", \"count\": 2472, \"ratio\": 0.8439740525776716, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2006, \"type\": \"VIS\", \"count\": 457, \"ratio\": 0.15602594742232845, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"Non-VIS\", \"count\": 2616, \"ratio\": 0.8521172638436482, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2007, \"type\": \"VIS\", \"count\": 454, \"ratio\": 0.14788273615635178, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"Non-VIS\", \"count\": 2120, \"ratio\": 0.8144448713023434, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008, \"type\": \"VIS\", \"count\": 483, \"ratio\": 0.18555512869765656, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"Non-VIS\", \"count\": 2660, \"ratio\": 0.811222933821287, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009, \"type\": \"VIS\", \"count\": 619, \"ratio\": 0.188777066178713, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"Non-VIS\", \"count\": 2738, \"ratio\": 0.8146385004462957, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010, \"type\": \"VIS\", \"count\": 623, \"ratio\": 0.18536149955370426, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"Non-VIS\", \"count\": 3266, \"ratio\": 0.8110255773528682, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011, \"type\": \"VIS\", \"count\": 761, \"ratio\": 0.18897442264713185, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"Non-VIS\", \"count\": 3175, \"ratio\": 0.8279009126466753, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012, \"type\": \"VIS\", \"count\": 660, \"ratio\": 0.17209908735332463, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"Non-VIS\", \"count\": 3100, \"ratio\": 0.8203228367292935, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013, \"type\": \"VIS\", \"count\": 679, \"ratio\": 0.17967716327070654, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"Non-VIS\", \"count\": 3987, \"ratio\": 0.802536231884058, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014, \"type\": \"VIS\", \"count\": 981, \"ratio\": 0.19746376811594202, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"Non-VIS\", \"count\": 4009, \"ratio\": 0.8005191693290735, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015, \"type\": \"VIS\", \"count\": 999, \"ratio\": 0.1994808306709265, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"Non-VIS\", \"count\": 3860, \"ratio\": 0.8034970857618651, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016, \"type\": \"VIS\", \"count\": 944, \"ratio\": 0.1965029142381349, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"Non-VIS\", \"count\": 4564, \"ratio\": 0.8131124176019954, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017, \"type\": \"VIS\", \"count\": 1049, \"ratio\": 0.18688758239800463, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"Non-VIS\", \"count\": 4998, \"ratio\": 0.80198973042362, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018, \"type\": \"VIS\", \"count\": 1234, \"ratio\": 0.19801026957638, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"Non-VIS\", \"count\": 5453, \"ratio\": 0.8147318093530554, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019, \"type\": \"VIS\", \"count\": 1240, \"ratio\": 0.18526819064694458, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"Non-VIS\", \"count\": 6176, \"ratio\": 0.8126315789473684, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020, \"type\": \"VIS\", \"count\": 1424, \"ratio\": 0.18736842105263157, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"Non-VIS\", \"count\": 4109, \"ratio\": 0.8138245197068726, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021, \"type\": \"VIS\", \"count\": 940, \"ratio\": 0.18617548029312736, \"Year\": \"2021-01-01T00:00:00\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.LayerChart(...)"
]
},
"execution_count": 58,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"alt.layer(\n",
" chart12,\n",
" chart22\n",
").resolve_scale(\n",
" color='independent'\n",
")"
]
},
{
"cell_type": "markdown",
"id": "46d758cf",
"metadata": {},
"source": [
"## Subplots"
]
},
{
"cell_type": "markdown",
"id": "89537551",
"metadata": {},
"source": [
"To include eight charts:\n",
"\n",
"- conference track\n",
"- number of pubs\n",
"- number of unique authors\n",
"- vis vs non-vis citations\n",
"- citation & reference ratio changes\n",
"- cross-country collab., ratio\n",
"- cross-type collab., ratio\n",
"- cross-country & cross-type, absolute count"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "1fe77673",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-f7d1167345694d54a16d4b6bcd8d12e3\"></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-f7d1167345694d54a16d4b6bcd8d12e3\") {\n",
" outputDiv = document.getElementById(\"altair-viz-f7d1167345694d54a16d4b6bcd8d12e3\");\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}, \"axis\": {\"labelFontSize\": 15, \"titleFontSize\": 20}, \"legend\": {\"labelFontSize\": 16, \"titleFontSize\": 14}, \"title\": {\"anchor\": \"start\", \"fontSize\": 22}}, \"hconcat\": [{\"data\": {\"name\": \"data-18cbb5ade9a8290bcfa8aeff8a667c69\"}, \"mark\": \"bar\", \"encoding\": {\"color\": {\"field\": \"Conference track\", \"legend\": null, \"type\": \"nominal\"}, \"x\": {\"axis\": {\"labelAngle\": -20}, \"field\": \"Conference track\", \"sort\": [\"VIS\", \"SciVis\", \"VAST\", \"InfoVis\", \"Vis\"], \"type\": \"nominal\"}, \"y\": {\"field\": \"Number of publications\", \"type\": \"quantitative\"}}, \"title\": \"a\", \"width\": 200}, {\"data\": {\"name\": \"data-bcbb612dd2e1839e4b52a066ab50d933\"}, \"mark\": \"line\", \"encoding\": {\"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"Number of Publications\", \"title\": \"Number of publications\", \"type\": \"quantitative\"}}, \"title\": \"b\", \"width\": 300}, {\"data\": {\"name\": \"data-6ceec6ad84a14b2f28097442ba84d375\"}, \"mark\": \"line\", \"encoding\": {\"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"Number of Unique Authors\", \"title\": \"Number of unique authors\", \"type\": \"quantitative\"}}, \"title\": \"c\", \"width\": 300}, {\"data\": {\"name\": \"data-caf6a8430b2373eb8cd48a7a77ac4916\"}, \"mark\": \"area\", \"encoding\": {\"color\": {\"field\": \"type\", \"legend\": {\"orient\": \"top-left\"}, \"scale\": {\"domain\": [\"Non-VIS\", \"VIS\"], \"range\": [\"hotpink\", \"skyblue\"]}, \"title\": \"Who cited VIS\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"count\", \"title\": \"Number of citations\", \"type\": \"quantitative\"}}, \"title\": \"d\", \"width\": 300}], \"resolve\": {\"scale\": {\"color\": \"independent\"}}, \"$schema\": \"https://vega.github.io/schema/vega-lite/v4.17.0.json\", \"datasets\": {\"data-18cbb5ade9a8290bcfa8aeff8a667c69\": [{\"Conference track\": \"SciVis\", \"Number of publications\": 291}, {\"Conference track\": \"VAST\", \"Number of publications\": 572}, {\"Conference track\": \"InfoVis\", \"Number of publications\": 834}, {\"Conference track\": \"Vis\", \"Number of publications\": 1373}], \"data-bcbb612dd2e1839e4b52a066ab50d933\": [{\"Year\": \"1990-01-01T00:00:00\", \"Number of Publications\": 52}, {\"Year\": \"1991-01-01T00:00:00\", \"Number of Publications\": 50}, {\"Year\": \"1992-01-01T00:00:00\", \"Number of Publications\": 53}, {\"Year\": \"1993-01-01T00:00:00\", \"Number of Publications\": 55}, {\"Year\": \"1994-01-01T00:00:00\", \"Number of Publications\": 53}, {\"Year\": \"1995-01-01T00:00:00\", \"Number of Publications\": 74}, {\"Year\": \"1996-01-01T00:00:00\", \"Number of Publications\": 77}, {\"Year\": \"1997-01-01T00:00:00\", \"Number of Publications\": 88}, {\"Year\": \"1998-01-01T00:00:00\", \"Number of Publications\": 90}, {\"Year\": \"1999-01-01T00:00:00\", \"Number of Publications\": 88}, {\"Year\": \"2000-01-01T00:00:00\", \"Number of Publications\": 92}, {\"Year\": \"2001-01-01T00:00:00\", \"Number of Publications\": 96}, {\"Year\": \"2002-01-01T00:00:00\", \"Number of Publications\": 101}, {\"Year\": \"2003-01-01T00:00:00\", \"Number of Publications\": 102}, {\"Year\": \"2004-01-01T00:00:00\", \"Number of Publications\": 97}, {\"Year\": \"2005-01-01T00:00:00\", \"Number of Publications\": 119}, {\"Year\": \"2006-01-01T00:00:00\", \"Number of Publications\": 113}, {\"Year\": \"2007-01-01T00:00:00\", \"Number of Publications\": 107}, {\"Year\": \"2008-01-01T00:00:00\", \"Number of Publications\": 99}, {\"Year\": \"2009-01-01T00:00:00\", \"Number of Publications\": 117}, {\"Year\": \"2010-01-01T00:00:00\", \"Number of Publications\": 109}, {\"Year\": \"2011-01-01T00:00:00\", \"Number of Publications\": 119}, {\"Year\": \"2012-01-01T00:00:00\", \"Number of Publications\": 116}, {\"Year\": \"2013-01-01T00:00:00\", \"Number of Publications\": 101}, {\"Year\": \"2014-01-01T00:00:00\", \"Number of Publications\": 133}, {\"Year\": \"2015-01-01T00:00:00\", \"Number of Publications\": 127}, {\"Year\": \"2016-01-01T00:00:00\", \"Number of Publications\": 115}, {\"Year\": \"2017-01-01T00:00:00\", \"Number of Publications\": 114}, {\"Year\": \"2018-01-01T00:00:00\", \"Number of Publications\": 127}, {\"Year\": \"2019-01-01T00:00:00\", \"Number of Publications\": 129}, {\"Year\": \"2020-01-01T00:00:00\", \"Number of Publications\": 157}, {\"Year\": \"2021-01-01T00:00:00\", \"Number of Publications\": 170}], \"data-6ceec6ad84a14b2f28097442ba84d375\": [{\"Year\": \"1990-01-01T00:00:00\", \"Number of Unique Authors\": 118}, {\"Year\": \"1991-01-01T00:00:00\", \"Number of Unique Authors\": 126}, {\"Year\": \"1992-01-01T00:00:00\", \"Number of Unique Authors\": 121}, {\"Year\": \"1993-01-01T00:00:00\", \"Number of Unique Authors\": 117}, {\"Year\": \"1994-01-01T00:00:00\", \"Number of Unique Authors\": 130}, {\"Year\": \"1995-01-01T00:00:00\", \"Number of Unique Authors\": 184}, {\"Year\": \"1996-01-01T00:00:00\", \"Number of Unique Authors\": 229}, {\"Year\": \"1997-01-01T00:00:00\", \"Number of Unique Authors\": 224}, {\"Year\": \"1998-01-01T00:00:00\", \"Number of Unique Authors\": 232}, {\"Year\": \"1999-01-01T00:00:00\", \"Number of Unique Authors\": 245}, {\"Year\": \"2000-01-01T00:00:00\", \"Number of Unique Authors\": 276}, {\"Year\": \"2001-01-01T00:00:00\", \"Number of Unique Authors\": 285}, {\"Year\": \"2002-01-01T00:00:00\", \"Number of Unique Authors\": 270}, {\"Year\": \"2003-01-01T00:00:00\", \"Number of Unique Authors\": 283}, {\"Year\": \"2004-01-01T00:00:00\", \"Number of Unique Authors\": 287}, {\"Year\": \"2005-01-01T00:00:00\", \"Number of Unique Authors\": 374}, {\"Year\": \"2006-01-01T00:00:00\", \"Number of Unique Authors\": 326}, {\"Year\": \"2007-01-01T00:00:00\", \"Number of Unique Authors\": 342}, {\"Year\": \"2008-01-01T00:00:00\", \"Number of Unique Authors\": 335}, {\"Year\": \"2009-01-01T00:00:00\", \"Number of Unique Authors\": 375}, {\"Year\": \"2010-01-01T00:00:00\", \"Number of Unique Authors\": 369}, {\"Year\": \"2011-01-01T00:00:00\", \"Number of Unique Authors\": 412}, {\"Year\": \"2012-01-01T00:00:00\", \"Number of Unique Authors\": 422}, {\"Year\": \"2013-01-01T00:00:00\", \"Number of Unique Authors\": 351}, {\"Year\": \"2014-01-01T00:00:00\", \"Number of Unique Authors\": 506}, {\"Year\": \"2015-01-01T00:00:00\", \"Number of Unique Authors\": 495}, {\"Year\": \"2016-01-01T00:00:00\", \"Number of Unique Authors\": 445}, {\"Year\": \"2017-01-01T00:00:00\", \"Number of Unique Authors\": 455}, {\"Year\": \"2018-01-01T00:00:00\", \"Number of Unique Authors\": 494}, {\"Year\": \"2019-01-01T00:00:00\", \"Number of Unique Authors\": 551}, {\"Year\": \"2020-01-01T00:00:00\", \"Number of Unique Authors\": 640}, {\"Year\": \"2021-01-01T00:00:00\", \"Number of Unique Authors\": 670}], \"data-caf6a8430b2373eb8cd48a7a77ac4916\": [{\"year\": 1990.0, \"type\": \"Non-VIS\", \"count\": 6, \"ratio\": 0.75, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1990.0, \"type\": \"VIS\", \"count\": 2, \"ratio\": 0.25, \"Year\": \"1990-01-01T00:00:00\"}, {\"year\": 1991.0, \"type\": \"Non-VIS\", \"count\": 56, \"ratio\": 0.7368421052631579, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1991.0, \"type\": \"VIS\", \"count\": 20, \"ratio\": 0.2631578947368421, \"Year\": \"1991-01-01T00:00:00\"}, {\"year\": 1992.0, \"type\": \"Non-VIS\", \"count\": 113, \"ratio\": 0.710691823899371, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1992.0, \"type\": \"VIS\", \"count\": 46, \"ratio\": 0.2893081761006289, \"Year\": \"1992-01-01T00:00:00\"}, {\"year\": 1993.0, \"type\": \"Non-VIS\", \"count\": 202, \"ratio\": 0.8015873015873016, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1993.0, \"type\": \"VIS\", \"count\": 50, \"ratio\": 0.1984126984126984, \"Year\": \"1993-01-01T00:00:00\"}, {\"year\": 1994.0, \"type\": \"Non-VIS\", \"count\": 285, \"ratio\": 0.7894736842105263, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1994.0, \"type\": \"VIS\", \"count\": 76, \"ratio\": 0.21052631578947367, \"Year\": \"1994-01-01T00:00:00\"}, {\"year\": 1995.0, \"type\": \"Non-VIS\", \"count\": 454, \"ratio\": 0.8092691622103387, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1995.0, \"type\": \"VIS\", \"count\": 107, \"ratio\": 0.19073083778966132, \"Year\": \"1995-01-01T00:00:00\"}, {\"year\": 1996.0, \"type\": \"Non-VIS\", \"count\": 485, \"ratio\": 0.8220338983050848, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1996.0, \"type\": \"VIS\", \"count\": 105, \"ratio\": 0.17796610169491525, \"Year\": \"1996-01-01T00:00:00\"}, {\"year\": 1997.0, \"type\": \"Non-VIS\", \"count\": 766, \"ratio\": 0.829004329004329, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1997.0, \"type\": \"VIS\", \"count\": 158, \"ratio\": 0.170995670995671, \"Year\": \"1997-01-01T00:00:00\"}, {\"year\": 1998.0, \"type\": \"Non-VIS\", \"count\": 809, \"ratio\": 0.7884990253411306, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1998.0, \"type\": \"VIS\", \"count\": 217, \"ratio\": 0.21150097465886938, \"Year\": \"1998-01-01T00:00:00\"}, {\"year\": 1999.0, \"type\": \"Non-VIS\", \"count\": 1079, \"ratio\": 0.8396887159533074, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 1999.0, \"type\": \"VIS\", \"count\": 206, \"ratio\": 0.16031128404669262, \"Year\": \"1999-01-01T00:00:00\"}, {\"year\": 2000.0, \"type\": \"Non-VIS\", \"count\": 1355, \"ratio\": 0.8479349186483104, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2000.0, \"type\": \"VIS\", \"count\": 243, \"ratio\": 0.1520650813516896, \"Year\": \"2000-01-01T00:00:00\"}, {\"year\": 2001.0, \"type\": \"Non-VIS\", \"count\": 1664, \"ratio\": 0.8917470525187567, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2001.0, \"type\": \"VIS\", \"count\": 202, \"ratio\": 0.1082529474812433, \"Year\": \"2001-01-01T00:00:00\"}, {\"year\": 2002.0, \"type\": \"Non-VIS\", \"count\": 2159, \"ratio\": 0.8895756077461887, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2002.0, \"type\": \"VIS\", \"count\": 268, \"ratio\": 0.11042439225381129, \"Year\": \"2002-01-01T00:00:00\"}, {\"year\": 2003.0, \"type\": \"Non-VIS\", \"count\": 2746, \"ratio\": 0.8988543371522095, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2003.0, \"type\": \"VIS\", \"count\": 309, \"ratio\": 0.1011456628477905, \"Year\": \"2003-01-01T00:00:00\"}, {\"year\": 2004.0, \"type\": \"Non-VIS\", \"count\": 3354, \"ratio\": 0.8920212765957447, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2004.0, \"type\": \"VIS\", \"count\": 406, \"ratio\": 0.10797872340425532, \"Year\": \"2004-01-01T00:00:00\"}, {\"year\": 2005.0, \"type\": \"Non-VIS\", \"count\": 3862, \"ratio\": 0.9019149929939281, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2005.0, \"type\": \"VIS\", \"count\": 420, \"ratio\": 0.09808500700607192, \"Year\": \"2005-01-01T00:00:00\"}, {\"year\": 2006.0, \"type\": \"Non-VIS\", \"count\": 4075, \"ratio\": 0.8995584988962473, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2006.0, \"type\": \"VIS\", \"count\": 455, \"ratio\": 0.10044150110375276, \"Year\": \"2006-01-01T00:00:00\"}, {\"year\": 2007.0, \"type\": \"Non-VIS\", \"count\": 4321, \"ratio\": 0.9054903604358759, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2007.0, \"type\": \"VIS\", \"count\": 451, \"ratio\": 0.09450963956412406, \"Year\": \"2007-01-01T00:00:00\"}, {\"year\": 2008.0, \"type\": \"Non-VIS\", \"count\": 4708, \"ratio\": 0.9094069924666796, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2008.0, \"type\": \"VIS\", \"count\": 469, \"ratio\": 0.09059300753332046, \"Year\": \"2008-01-01T00:00:00\"}, {\"year\": 2009.0, \"type\": \"Non-VIS\", \"count\": 4822, \"ratio\": 0.8829884636513459, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2009.0, \"type\": \"VIS\", \"count\": 639, \"ratio\": 0.11701153634865409, \"Year\": \"2009-01-01T00:00:00\"}, {\"year\": 2010.0, \"type\": \"Non-VIS\", \"count\": 5093, \"ratio\": 0.8911636045494313, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2010.0, \"type\": \"VIS\", \"count\": 622, \"ratio\": 0.10883639545056868, \"Year\": \"2010-01-01T00:00:00\"}, {\"year\": 2011.0, \"type\": \"Non-VIS\", \"count\": 6091, \"ratio\": 0.8889375364856976, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2011.0, \"type\": \"VIS\", \"count\": 761, \"ratio\": 0.11106246351430239, \"Year\": \"2011-01-01T00:00:00\"}, {\"year\": 2012.0, \"type\": \"Non-VIS\", \"count\": 6427, \"ratio\": 0.9067437923250564, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2012.0, \"type\": \"VIS\", \"count\": 661, \"ratio\": 0.09325620767494357, \"Year\": \"2012-01-01T00:00:00\"}, {\"year\": 2013.0, \"type\": \"Non-VIS\", \"count\": 6896, \"ratio\": 0.9103630363036304, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2013.0, \"type\": \"VIS\", \"count\": 679, \"ratio\": 0.08963696369636964, \"Year\": \"2013-01-01T00:00:00\"}, {\"year\": 2014.0, \"type\": \"Non-VIS\", \"count\": 7458, \"ratio\": 0.8837539992890153, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2014.0, \"type\": \"VIS\", \"count\": 981, \"ratio\": 0.11624600071098472, \"Year\": \"2014-01-01T00:00:00\"}, {\"year\": 2015.0, \"type\": \"Non-VIS\", \"count\": 7318, \"ratio\": 0.9834699637145545, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2015.0, \"type\": \"VIS\", \"count\": 123, \"ratio\": 0.016530036285445505, \"Year\": \"2015-01-01T00:00:00\"}, {\"year\": 2016.0, \"type\": \"Non-VIS\", \"count\": 8313, \"ratio\": 0.893390650188071, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2016.0, \"type\": \"VIS\", \"count\": 992, \"ratio\": 0.10660934981192907, \"Year\": \"2016-01-01T00:00:00\"}, {\"year\": 2017.0, \"type\": \"Non-VIS\", \"count\": 8338, \"ratio\": 0.8957885689729265, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2017.0, \"type\": \"VIS\", \"count\": 970, \"ratio\": 0.10421143102707349, \"Year\": \"2017-01-01T00:00:00\"}, {\"year\": 2018.0, \"type\": \"Non-VIS\", \"count\": 8628, \"ratio\": 0.8963224600041554, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2018.0, \"type\": \"VIS\", \"count\": 998, \"ratio\": 0.10367753999584459, \"Year\": \"2018-01-01T00:00:00\"}, {\"year\": 2019.0, \"type\": \"Non-VIS\", \"count\": 10013, \"ratio\": 0.8898071625344353, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2019.0, \"type\": \"VIS\", \"count\": 1240, \"ratio\": 0.11019283746556474, \"Year\": \"2019-01-01T00:00:00\"}, {\"year\": 2020.0, \"type\": \"Non-VIS\", \"count\": 11514, \"ratio\": 0.890762803651555, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2020.0, \"type\": \"VIS\", \"count\": 1412, \"ratio\": 0.10923719634844499, \"Year\": \"2020-01-01T00:00:00\"}, {\"year\": 2021.0, \"type\": \"Non-VIS\", \"count\": 11309, \"ratio\": 0.8437033721277231, \"Year\": \"2021-01-01T00:00:00\"}, {\"year\": 2021.0, \"type\": \"VIS\", \"count\": 2095, \"ratio\": 0.15629662787227694, \"Year\": \"2021-01-01T00:00:00\"}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 59,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"conf = alt.Chart(conf_df).mark_bar().encode(\n",
" x = alt.X(\n",
" 'Conference track',\n",
" sort = [\n",
" 'VIS',\n",
" 'SciVis',\n",
" 'VAST',\n",
" 'InfoVis',\n",
" 'Vis',\n",
" ],\n",
" axis=alt.Axis(labelAngle=-20)\n",
" ),\n",
" y = 'Number of publications',\n",
" color = alt.Color(\n",
" 'Conference track',\n",
" legend=None,\n",
" )\n",
").properties(\n",
" title = 'a',\n",
" width=200\n",
")\n",
"\n",
"conf\n",
"\n",
"pub_num = alt.Chart(pub_num_df).mark_line().encode(\n",
" x = 'Year',\n",
" y = alt.Y(\n",
" 'Number of Publications',\n",
" title = 'Number of publications'\n",
" )\n",
").properties(\n",
" title = 'b',\n",
" width=300\n",
")\n",
"\n",
"pub_num\n",
"\n",
"unique_authors = alt.Chart(unique_author_num_df).mark_line().encode(\n",
" x = 'Year',\n",
" y = alt.Y(\n",
" 'Number of Unique Authors',\n",
" title = 'Number of unique authors'\n",
" )\n",
").properties(\n",
" title = 'c',\n",
" width=300,\n",
")\n",
"\n",
"unique_authors\n",
"\n",
"vis_cit = alt.Chart(cit_df).mark_area().encode(\n",
" x=\"Year:T\",\n",
" y = alt.Y(\n",
" \"count:Q\",\n",
" title = 'Number of citations'\n",
" ),\n",
" color=alt.Color(\n",
" \"type:N\",\n",
" title = 'Who cited VIS',\n",
" scale=alt.Scale(\n",
" domain=['Non-VIS', 'VIS'],\n",
" range=['hotpink', 'skyblue']\n",
" ),\n",
" legend=alt.Legend(\n",
" orient='top-left',\n",
"# legendX=60, legendY=-40,\n",
"# direction='horizontal',\n",
"# titleAnchor='middle'\n",
" )\n",
" \n",
" )\n",
").properties(\n",
" title = 'd',\n",
" width=300,\n",
")\n",
"\n",
"vis_cit\n",
"\n",
"trends1 = alt.hconcat(\n",
" conf,\n",
" pub_num,\n",
" unique_authors,\n",
" vis_cit\n",
").resolve_scale(\n",
" color = 'independent'\n",
").configure_axis(\n",
" labelFontSize=15,\n",
" titleFontSize=20\n",
").configure_legend(\n",
" titleFontSize=14,\n",
" labelFontSize=16\n",
").configure_title(\n",
" anchor='start',\n",
" fontSize=22)\n",
"\n",
"trends1"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "73a84207",
"metadata": {},
"outputs": [],
"source": [
"# trends1.save('chart.png')"
]
},
{
"cell_type": "code",
"execution_count": 61,
"id": "1ea1bbf6",
"metadata": {},
"outputs": [],
"source": [
"ratio_wording = 'Proportion'"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "ab34de7f",
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"\n",
"<div id=\"altair-viz-ab8c07b840604b1a826514e27e137226\"></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-ab8c07b840604b1a826514e27e137226\") {\n",
" outputDiv = document.getElementById(\"altair-viz-ab8c07b840604b1a826514e27e137226\");\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}, \"axis\": {\"labelFontSize\": 15, \"titleFontSize\": 20}, \"legend\": {\"labelFontSize\": 16, \"titleFontSize\": 16}, \"title\": {\"anchor\": \"start\", \"fontSize\": 22}}, \"hconcat\": [{\"layer\": [{\"data\": {\"name\": \"data-caf6a8430b2373eb8cd48a7a77ac4916\"}, \"mark\": {\"type\": \"line\", \"point\": {\"filled\": false, \"fill\": \"white\"}, \"strokeDash\": [1, 2]}, \"encoding\": {\"color\": {\"field\": \"type\", \"legend\": {\"direction\": \"horizontal\", \"legendX\": 0, \"legendY\": -40, \"orient\": \"none\", \"titleAnchor\": \"middle\"}, \"scale\": {\"domain\": [\"Non-VIS\", \"VIS\"], \"range\": [\"purple\", \"green\"]}, \"title\": \"Who cited VIS\", \"type\": \"nominal\"}, \"x\": {\"field\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"ratio\", \"title\": \"Proportion\", \"type\": \"quantitative\"}}}, {\"data\": {\"name\": \"data-e1eb1706bbda09e5b5767506b936f4b1\"}, \"mark\": 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\"title\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"ratio\", \"title\": \"Proportion\", \"type\": \"quantitative\"}}, \"title\": \"f\", \"width\": 280}, {\"data\": {\"name\": \"data-9828840f8f199db131f1eea3ca3d2229\"}, \"mark\": \"line\", \"encoding\": {\"color\": {\"field\": \"type\", \"legend\": {\"direction\": \"horizontal\", \"legendX\": 90, \"legendY\": -40, \"orient\": \"none\", \"titleAnchor\": \"middle\"}, \"scale\": {\"domain\": [\"No\", \"Yes\"], \"range\": [\"purple\", \"green\"]}, \"title\": \"Cross-type\", \"type\": \"nominal\"}, \"x\": {\"field\": \"year\", \"title\": \"Year\", \"type\": \"temporal\"}, \"y\": {\"field\": \"ratio\", \"title\": \"Proportion\", \"type\": \"quantitative\"}}, \"title\": \"g\", \"width\": 280}, {\"layer\": [{\"data\": {\"name\": \"data-9828840f8f199db131f1eea3ca3d2229\"}, \"mark\": {\"type\": \"line\", \"point\": {\"filled\": false, \"fill\": \"white\"}, \"strokeDash\": [1, 2]}, \"encoding\": {\"color\": {\"field\": \"type\", 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\"2013-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 26, \"ratio\": 0.25742574257425743}, {\"year\": \"2014-01-01T00:00:00\", \"type\": \"No\", \"count\": 85, \"ratio\": 0.6390977443609023}, {\"year\": \"2014-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 48, \"ratio\": 0.3609022556390977}, {\"year\": \"2015-01-01T00:00:00\", \"type\": \"No\", \"count\": 80, \"ratio\": 0.6299212598425197}, {\"year\": \"2015-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 47, \"ratio\": 0.3700787401574803}, {\"year\": \"2016-01-01T00:00:00\", \"type\": \"No\", \"count\": 66, \"ratio\": 0.5739130434782609}, {\"year\": \"2016-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 49, \"ratio\": 0.4260869565217391}, {\"year\": \"2017-01-01T00:00:00\", \"type\": \"No\", \"count\": 65, \"ratio\": 0.5701754385964912}, {\"year\": \"2017-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 49, \"ratio\": 0.4298245614035088}, {\"year\": \"2018-01-01T00:00:00\", \"type\": \"No\", \"count\": 82, \"ratio\": 0.6456692913385826}, {\"year\": \"2018-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 45, \"ratio\": 0.3543307086614173}, {\"year\": \"2019-01-01T00:00:00\", \"type\": \"No\", \"count\": 81, \"ratio\": 0.627906976744186}, {\"year\": \"2019-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 48, \"ratio\": 0.37209302325581395}, {\"year\": \"2020-01-01T00:00:00\", \"type\": \"No\", \"count\": 93, \"ratio\": 0.5923566878980892}, {\"year\": \"2020-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 64, \"ratio\": 0.40764331210191085}, {\"year\": \"2021-01-01T00:00:00\", \"type\": \"No\", \"count\": 94, \"ratio\": 0.5529411764705883}, {\"year\": \"2021-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 76, \"ratio\": 0.4470588235294118}], \"data-9828840f8f199db131f1eea3ca3d2229\": [{\"year\": \"1990-01-01T00:00:00\", \"type\": \"No\", \"count\": 47, \"ratio\": 0.9038461538461539}, {\"year\": \"1990-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 5, \"ratio\": 0.09615384615384616}, {\"year\": \"1991-01-01T00:00:00\", \"type\": \"No\", \"count\": 43, \"ratio\": 0.86}, {\"year\": \"1991-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 7, \"ratio\": 0.14}, {\"year\": \"1992-01-01T00:00:00\", \"type\": \"No\", \"count\": 43, \"ratio\": 0.8113207547169812}, {\"year\": \"1992-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 10, \"ratio\": 0.18867924528301888}, {\"year\": \"1993-01-01T00:00:00\", \"type\": \"No\", \"count\": 46, \"ratio\": 0.8363636363636363}, {\"year\": \"1993-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 9, \"ratio\": 0.16363636363636364}, {\"year\": \"1994-01-01T00:00:00\", \"type\": \"No\", \"count\": 48, \"ratio\": 0.9056603773584906}, {\"year\": \"1994-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 5, \"ratio\": 0.09433962264150944}, {\"year\": \"1995-01-01T00:00:00\", \"type\": \"No\", \"count\": 64, \"ratio\": 0.8648648648648649}, {\"year\": \"1995-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 10, \"ratio\": 0.13513513513513514}, {\"year\": \"1996-01-01T00:00:00\", \"type\": \"No\", \"count\": 59, \"ratio\": 0.7662337662337663}, {\"year\": \"1996-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 18, \"ratio\": 0.23376623376623376}, {\"year\": \"1997-01-01T00:00:00\", \"type\": \"No\", \"count\": 78, \"ratio\": 0.8863636363636364}, {\"year\": \"1997-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 10, \"ratio\": 0.11363636363636363}, {\"year\": \"1998-01-01T00:00:00\", \"type\": \"No\", \"count\": 77, \"ratio\": 0.8555555555555555}, {\"year\": \"1998-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 13, \"ratio\": 0.14444444444444443}, {\"year\": \"1999-01-01T00:00:00\", \"type\": \"No\", \"count\": 72, \"ratio\": 0.8181818181818182}, {\"year\": \"1999-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 16, \"ratio\": 0.18181818181818182}, {\"year\": \"2000-01-01T00:00:00\", \"type\": \"No\", \"count\": 71, \"ratio\": 0.7717391304347826}, {\"year\": \"2000-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 21, \"ratio\": 0.22826086956521738}, {\"year\": \"2001-01-01T00:00:00\", \"type\": \"No\", \"count\": 68, \"ratio\": 0.7083333333333334}, {\"year\": \"2001-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 28, \"ratio\": 0.2916666666666667}, {\"year\": \"2002-01-01T00:00:00\", \"type\": \"No\", \"count\": 84, \"ratio\": 0.8316831683168316}, {\"year\": \"2002-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 17, \"ratio\": 0.16831683168316833}, {\"year\": \"2003-01-01T00:00:00\", \"type\": \"No\", \"count\": 82, \"ratio\": 0.803921568627451}, {\"year\": \"2003-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 20, \"ratio\": 0.19607843137254902}, {\"year\": \"2004-01-01T00:00:00\", \"type\": \"No\", \"count\": 73, \"ratio\": 0.7525773195876289}, {\"year\": \"2004-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 24, \"ratio\": 0.24742268041237114}, {\"year\": \"2005-01-01T00:00:00\", \"type\": \"No\", \"count\": 97, \"ratio\": 0.8151260504201681}, {\"year\": \"2005-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 22, \"ratio\": 0.18487394957983194}, {\"year\": \"2006-01-01T00:00:00\", \"type\": \"No\", \"count\": 90, \"ratio\": 0.7964601769911505}, {\"year\": \"2006-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 23, \"ratio\": 0.20353982300884957}, {\"year\": \"2007-01-01T00:00:00\", \"type\": \"No\", \"count\": 69, \"ratio\": 0.6448598130841121}, {\"year\": \"2007-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 38, \"ratio\": 0.35514018691588783}, {\"year\": \"2008-01-01T00:00:00\", \"type\": \"No\", \"count\": 70, \"ratio\": 0.7070707070707071}, {\"year\": \"2008-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 29, \"ratio\": 0.29292929292929293}, {\"year\": \"2009-01-01T00:00:00\", \"type\": \"No\", \"count\": 84, \"ratio\": 0.717948717948718}, {\"year\": \"2009-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 33, \"ratio\": 0.28205128205128205}, {\"year\": \"2010-01-01T00:00:00\", \"type\": \"No\", \"count\": 82, \"ratio\": 0.7522935779816514}, {\"year\": \"2010-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 27, \"ratio\": 0.24770642201834864}, {\"year\": \"2011-01-01T00:00:00\", \"type\": \"No\", \"count\": 91, \"ratio\": 0.7647058823529411}, {\"year\": \"2011-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 28, \"ratio\": 0.23529411764705882}, {\"year\": \"2012-01-01T00:00:00\", \"type\": \"No\", \"count\": 80, \"ratio\": 0.6896551724137931}, {\"year\": \"2012-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 36, \"ratio\": 0.3103448275862069}, {\"year\": \"2013-01-01T00:00:00\", \"type\": \"No\", \"count\": 74, \"ratio\": 0.7326732673267327}, {\"year\": \"2013-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 27, \"ratio\": 0.26732673267326734}, {\"year\": \"2014-01-01T00:00:00\", \"type\": \"No\", \"count\": 87, \"ratio\": 0.6541353383458647}, {\"year\": \"2014-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 46, \"ratio\": 0.3458646616541353}, {\"year\": \"2015-01-01T00:00:00\", \"type\": \"No\", \"count\": 79, \"ratio\": 0.6220472440944882}, {\"year\": \"2015-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 48, \"ratio\": 0.3779527559055118}, {\"year\": \"2016-01-01T00:00:00\", \"type\": \"No\", \"count\": 75, \"ratio\": 0.6521739130434783}, {\"year\": \"2016-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 40, \"ratio\": 0.34782608695652173}, {\"year\": \"2017-01-01T00:00:00\", \"type\": \"No\", \"count\": 71, \"ratio\": 0.6228070175438597}, {\"year\": \"2017-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 43, \"ratio\": 0.37719298245614036}, {\"year\": \"2018-01-01T00:00:00\", \"type\": \"No\", \"count\": 69, \"ratio\": 0.5433070866141733}, {\"year\": \"2018-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 58, \"ratio\": 0.4566929133858268}, {\"year\": \"2019-01-01T00:00:00\", \"type\": \"No\", \"count\": 86, \"ratio\": 0.6666666666666666}, {\"year\": \"2019-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 43, \"ratio\": 0.3333333333333333}, {\"year\": \"2020-01-01T00:00:00\", \"type\": \"No\", \"count\": 108, \"ratio\": 0.6878980891719745}, {\"year\": \"2020-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 49, \"ratio\": 0.31210191082802546}, {\"year\": \"2021-01-01T00:00:00\", \"type\": \"No\", \"count\": 116, \"ratio\": 0.6823529411764706}, {\"year\": \"2021-01-01T00:00:00\", \"type\": \"Yes\", \"count\": 54, \"ratio\": 0.3176470588235294}]}}, {\"mode\": \"vega-lite\"});\n",
"</script>"
],
"text/plain": [
"alt.HConcatChart(...)"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"cit_ratio = alt.Chart(cit_df).mark_line(\n",
" point={\n",
" \"filled\": False,\n",
" \"fill\": \"white\"\n",
" },\n",
" strokeDash=[1,2]\n",
").encode(\n",
" x='Year',\n",
" y=alt.Y(\n",
" 'ratio',\n",
" title=ratio_wording\n",
" ),\n",
" color=alt.Color(\n",
" \"type:N\",\n",
" title = 'Who cited VIS',\n",
" scale=alt.Scale(\n",
" domain=['Non-VIS', 'VIS'],\n",
" range=['purple', 'green']\n",
" ),\n",
" legend=alt.Legend(\n",
" orient='none',\n",
" legendX=0, legendY=-40,\n",
" direction='horizontal',\n",
" titleAnchor='middle'\n",
" )\n",
" )\n",
")\n",
"\n",
"cit_ratio\n",
"\n",
"ref_ratio = alt.Chart(ref_df).mark_line(\n",
").encode(\n",
" x='Year',\n",
" y=alt.Y(\n",
" 'ratio',\n",
" title = ratio_wording\n",
" ),\n",
" color=alt.Color(\n",
" \"type:N\",\n",
" title = 'Whom VIS cited',\n",
" scale=alt.Scale(\n",
" domain=['Non-VIS', 'VIS'],\n",
" range=['orange', 'steelblue']\n",
" ),\n",
" legend=alt.Legend(\n",
" orient='none',\n",
" legendX=150, legendY=-40,\n",
" direction='horizontal',\n",
" titleAnchor='middle'\n",
" ),\n",
" )\n",
")\n",
"\n",
"ref_ratio\n",
"\n",
"citation_ratio_compare = alt.layer(\n",
" cit_ratio,\n",
" ref_ratio,\n",
").resolve_scale(\n",
" color='independent'\n",
").properties(\n",
" title = 'e',\n",
" width=280\n",
")\n",
"\n",
"citation_ratio_compare\n",
"\n",
"cross_country_ratio = alt.Chart(cross_country).mark_line().encode(\n",
" x = alt.X('year', title='Year'),\n",
" y = alt.Y(\n",
" 'ratio',\n",
" title = ratio_wording\n",
" ),\n",
" color = alt.Color(\n",
" 'type',\n",
" title = 'Cross-country',\n",
" scale=alt.Scale(domain=['No', 'Yes'],\n",
" range=['orange', 'steelblue']),\n",
" legend=alt.Legend(\n",
" orient='none',\n",
" legendX=80, legendY=-40,\n",
" direction='horizontal',\n",
" titleAnchor='middle'\n",
" )\n",
" )\n",
").properties(\n",
" title = 'f',\n",
" width=280\n",
")\n",
"\n",
"cross_country_ratio\n",
"\n",
"cross_type_ratio = alt.Chart(cross_type).mark_line().encode(\n",
" x = alt.X('year', title='Year'),\n",
" y = alt.Y(\n",
" 'ratio',\n",
" title = ratio_wording\n",
" ),\n",
" color = alt.Color(\n",
" 'type',\n",
" title = 'Cross-type',\n",
"# sort = ['Yes', 'No'],\n",
" scale=alt.Scale(domain=['No', 'Yes'],\n",
" range=['purple', 'green']),\n",
" legend=alt.Legend(\n",
" orient='none',\n",
" legendX=90, legendY=-40,\n",
" direction='horizontal',\n",
" titleAnchor='middle'\n",
" )\n",
" )\n",
").properties(\n",
" title = 'g',\n",
" width=280\n",
")\n",
"\n",
"cross_type_ratio\n",
"\n",
"cross_country_count = alt.Chart(cross_country).mark_line(\n",
").encode(\n",
" x = alt.X('year', title='Year'),\n",
" y = alt.Y(\n",
" 'count',\n",
" title = 'Number of publications'\n",
" ),\n",
" color = alt.Color(\n",
" 'type',\n",
" title = 'Cross-country',\n",
" scale=alt.Scale(domain=['No', 'Yes'],\n",
" range=['orange', 'steelblue']),\n",
" legend=alt.Legend(\n",
" orient='none',\n",
" legendX=0, legendY=-40,\n",
" direction='horizontal',\n",
" titleAnchor='middle'\n",
" )\n",
"))\n",
"\n",
"cross_country_count \n",
"\n",
"cross_type_count = alt.Chart(cross_type).mark_line(\n",
" point={\n",
" \"filled\": False,\n",
" \"fill\": \"white\"\n",
" },\n",
" strokeDash=[1,2]\n",
").encode(\n",
" x = alt.X('year', title='Year'),\n",
" y = alt.Y(\n",
" 'count',\n",
" title = 'Number of publications'\n",
" ),\n",
" color = alt.Color(\n",
" 'type',\n",
" title = 'Cross-type',\n",
" sort = ['Yes', 'No'],\n",
" scale=alt.Scale(domain=['No', 'Yes'],\n",
" range=['purple', 'green']),\n",
" legend=alt.Legend(\n",
" orient='none',\n",
" legendX=180, legendY=-40,\n",
" direction='horizontal',\n",
" titleAnchor='middle'\n",
" )),\n",
")\n",
"\n",
"cross_type_count\n",
"\n",
"count_compare = alt.layer(\n",
" cross_type_count,\n",
" cross_country_count\n",
").resolve_scale(\n",
" color='independent'\n",
").properties(\n",
" title = 'h',\n",
" width=280\n",
")\n",
"\n",
"count_compare\n",
"\n",
"trends2 = alt.hconcat(\n",
" citation_ratio_compare,\n",
" cross_country_ratio,\n",
" cross_type_ratio,\n",
" count_compare\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",
"trends2"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "ffd053a2",
"metadata": {},
"outputs": [],
"source": [
"# f, axs = plt.subplots(1,4,\n",
"# figsize=(20, 5),\n",
"# sharex=False,\n",
"# sharey=False,\n",
"# gridspec_kw=dict(width_ratios=[1,1,1,1]))\n",
"# sns.set_style(\"whitegrid\")\n",
"# sns.set_palette('Set1')\n",
"# # sns.set_palette('bright')\n",
"\n",
"# # sns.set(font_scale=1.5)\n",
"\n",
"# ## Number of publications by year\n",
"# g0 = sns.lineplot(\n",
"# x = 'Year', \n",
"# y = 'Number of Publications', \n",
"# data = pub_num_df,\n",
"# ax = axs[0]\n",
"# )\n",
"# g0.set_ylim(0,)\n",
"# g0.text(-0.1, 1.1, 'A', transform=g0.transAxes, \n",
"# size=20, weight='bold')\n",
"\n",
"\n",
"# # Number of unique authors by year\n",
"# g1 = sns.lineplot(x = 'Year', \n",
"# y= 'Number of Unique Authors', \n",
"# data = unique_author_num_df,\n",
"# ax = axs[1]\n",
"# )\n",
"# g1.set_ylim(0,)\n",
"# g1.text(-0.1, 1.1, 'B', transform=g1.transAxes, \n",
"# size=20, weight='bold')\n",
"\n",
"# # cross-country\n",
"# g2 = sns.lineplot(x='year',\n",
"# y='ratio',\n",
"# hue='type', \n",
"# data = cross_country,\n",
"# ax = axs[2],\n",
"# )\n",
"# g2.legend(title = 'Cross-country')\n",
"# g2.set_xlabel('Year')\n",
"# g2.set_ylabel('Ratio')\n",
"# g2.text(-0.1, 1.1, 'C', transform=g2.transAxes, \n",
"# size=20, weight='bold')\n",
"\n",
"# # cross-type\n",
"# g3 = sns.lineplot(x='year',\n",
"# y='ratio',\n",
"# hue='type', \n",
"# data = cross_type,\n",
"# ax = axs[3]\n",
"# )\n",
"# g3.legend(title = 'Cross-type')\n",
"# g3.set_xlabel('Year')\n",
"# g3.set_ylabel('Ratio')\n",
"# g3.text(-0.1, 1.1, 'D', transform=g3.transAxes, \n",
"# size=20, weight='bold')\n",
"\n",
"# # # Citations from vis and non-vis papers\n",
"# # plt.subplot(1,5,5)\n",
"# # plt.stackplot(range(1990, 2022), \n",
"# # to_plot_stackarea, \n",
"# # labels=['Non-VIS', 'VIS'],\n",
"# # )\n",
"# # plt.legend(title= 'Papers citing VIS', loc='upper left')\n",
"# # plt.xlabel('Year')\n",
"# # plt.ylabel('Number of Publications')\n",
"\n",
"# # plt.show()\n",
"# f.tight_layout()\n",
"# f.savefig('../results/figures/trends.png', dpi = 200)"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
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"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
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"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
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