{ "cells": [ { "cell_type": "code", "execution_count": 2, "id": "40038234", "metadata": {}, "outputs": [ { "data": { "text/html": " \n " }, "metadata": {}, "output_type": "display_data" } ], "source": [ "import os\n", "\n", "import pandas as pd\n", "import janitor\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from matplotlib.ticker import MaxNLocator\n", "import math\n", "import plotly.express as px\n", "import plotly.graph_objects as go\n", "import plotly.offline as pyo\n", "from plotly.subplots import make_subplots\n", "import plotly.graph_objects as go\n", "pyo.init_notebook_mode()\n", "\n", "import plotly.io as pio\n", "pio.renderers.default = \"plotly_mimetype+notebook\"\n", "\n", "import country_converter as coco\n", "cc = coco.CountryConverter()\n", "\n", "\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 3, "outputs": [], "source": [ "os.makedirs('plot_html',exist_ok=True)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 4, "outputs": [ { "data": { "text/plain": "'https://plotly.com/~radvanyimome/5/'" }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import chart_studio.plotly as py\n", "import plotly.graph_objects as go\n", "\n", "trace0 = go.Scatter(\n", " x=[1, 2, 3, 4],\n", " y=[10, 15, 13, 17]\n", ")\n", "trace1 = go.Scatter(\n", " x=[1, 2, 3, 4],\n", " y=[16, 5, 11, 9]\n", ")\n", "data = [trace0, trace1]\n", "\n", "py.plot(data, filename = 'basic-line', auto_open=True)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 5, "id": "ea3629f5", "metadata": {}, "outputs": [], "source": [ "# Seaborn palette\n", "# sns.set_theme(context='notebook', style='ticks', palette='colorblind', font='sans-serif', font_scale=1, color_codes=True, rc=None)\n", "# sns.palplot(sns.color_palette())" ] }, { "cell_type": "code", "execution_count": 6, "id": "fb7baf32", "metadata": {}, "outputs": [], "source": [ "outdir=\"wos_processed_data\"\n", "\n", "wos = pd.read_excel(f\"../{outdir}/wos_processed.xlsx\")\n", "wos_univ = pd.read_excel(f\"../{outdir}/wos_institution_locations_harmonized.xlsx\")" ] }, { "cell_type": "code", "execution_count": 7, "id": "4dd8e081", "metadata": {}, "outputs": [], "source": [ "def eurovoc_classer(x):\n", " eurovoc_classification = {\"Eastern Europe\":[\"Bulgaria\",\"Czech Republic\",\"Croatia\",\"Hungary\",\"Poland\",\"Romania\",\"Slovakia\",\"Slovenia\"],\n", " \"Northern Europe\":[\"Denmark\",\"Estonia\",\"Finland\",\"Latvia\",\"Lithuania\",\"Sweden\",\"Norway\",\"Iceland\"],\n", " \"Southern Europe\":[\"Cyprus\",\"Greece\",\"Italy\",\"Portugal\",\"Spain\",\"Malta\"],\n", " \"Western Europe\":[\"Austria\",\"Belgium\",\"France\",\"Germany\",\"Luxembourg\",\"Netherlands\",\"Switzerland\",\"United Kingdom\",\"Ireland\"]}\n", " if x == 'China':\n", " return x\n", " for k in eurovoc_classification.keys():\n", " if x in eurovoc_classification[k]:\n", " return k" ] }, { "cell_type": "code", "execution_count": 8, "id": "eb933d66", "metadata": {}, "outputs": [], "source": [ "wos_country = pd.read_excel(f\"../{outdir}/wos_countries.xlsx\")\n", "wos_country_types = pd.read_excel(f\"../{outdir}/wos_country_types.xlsx\")" ] }, { "cell_type": "code", "execution_count": 9, "id": "cd0b0efa", "metadata": {}, "outputs": [ { "data": { "text/plain": " Country Country_Type Eurovoc_Class\n0 Belgium EU Western Europe\n1 China China China\n2 Luxembourg EU Western Europe\n3 Netherlands EU Western Europe\n4 Norway Non-EU associate Northern Europe\n5 United Kingdom Non-EU associate Western Europe\n6 France EU Western Europe\n7 Sweden EU Northern Europe\n8 Italy EU Southern Europe\n9 Denmark EU Northern Europe\n10 Germany EU Western Europe\n11 Slovenia EU Eastern Europe\n12 Estonia EU Northern Europe\n13 Finland EU Northern Europe\n14 Bulgaria EU Eastern Europe\n15 Slovakia EU Eastern Europe\n16 Spain EU Southern Europe\n17 Poland EU Eastern Europe\n18 Czech Republic EU Eastern Europe\n19 Greece EU Southern Europe\n20 Malta EU Southern Europe\n21 Austria EU Western Europe\n22 Switzerland Non-EU associate Western Europe\n23 Ireland EU Western Europe\n24 Portugal EU Southern Europe\n25 Romania EU Eastern Europe\n26 Hungary EU Eastern Europe\n27 Cyprus EU Southern Europe\n28 Croatia EU Eastern Europe\n29 Lithuania EU Northern Europe\n30 Latvia EU Northern Europe", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
CountryCountry_TypeEurovoc_Class
0BelgiumEUWestern Europe
1ChinaChinaChina
2LuxembourgEUWestern Europe
3NetherlandsEUWestern Europe
4NorwayNon-EU associateNorthern Europe
5United KingdomNon-EU associateWestern Europe
6FranceEUWestern Europe
7SwedenEUNorthern Europe
8ItalyEUSouthern Europe
9DenmarkEUNorthern Europe
10GermanyEUWestern Europe
11SloveniaEUEastern Europe
12EstoniaEUNorthern Europe
13FinlandEUNorthern Europe
14BulgariaEUEastern Europe
15SlovakiaEUEastern Europe
16SpainEUSouthern Europe
17PolandEUEastern Europe
18Czech RepublicEUEastern Europe
19GreeceEUSouthern Europe
20MaltaEUSouthern Europe
21AustriaEUWestern Europe
22SwitzerlandNon-EU associateWestern Europe
23IrelandEUWestern Europe
24PortugalEUSouthern Europe
25RomaniaEUEastern Europe
26HungaryEUEastern Europe
27CyprusEUSouthern Europe
28CroatiaEUEastern Europe
29LithuaniaEUNorthern Europe
30LatviaEUNorthern Europe
\n
" }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wos_country_types[\"Eurovoc_Class\"] = wos_country_types[\"Country\"].map(eurovoc_classer)\n", "wos_country_types" ] }, { "cell_type": "code", "execution_count": 10, "id": "1e737dbf", "metadata": {}, "outputs": [], "source": [ "record_col = \"UT (Unique WOS ID)\"" ] }, { "cell_type": "markdown", "id": "b1aa7f2d", "metadata": {}, "source": [ "# Analysis by METRIX classification" ] }, { "cell_type": "markdown", "id": "a97f1cbb", "metadata": {}, "source": [ "## Distribution of topics via the METRIX classification" ] }, { "cell_type": "code", "execution_count": 11, "id": "f39cb21d", "metadata": {}, "outputs": [], "source": [ "def replace_nth(s, sub=\" \", repl=\"
\", n=2):\n", " chunks = s.split(sub)\n", " size = len(chunks)\n", " rows = size // n + (0 if size % n == 0 else 1)\n", " return (repl.join([\n", " sub.join([chunks[i * n + j] for j in range(n if (i + 1) * n < size else size - i * n)])\n", " for i in range(rows)\n", " ])).replace(\"
&\",\" &
\")\n", "\n", "\n", "groups = ['Domain_English',\"Field_English\",'SubField_English']\n", "data = wos.groupby(groups, as_index=False)[record_col].nunique().sort_values(ascending=False, by=record_col)\n", "data[\"percent\"] = data[record_col]/data[record_col].sum()*100\n", "\n", "data[groups] = data[groups].applymap(replace_nth)\n", "# for c in [\"Domain_English\",\"Field_English\",\"SubField_English\"]:\n", "# data[c] = data[c]+\"
(\"+(pd.DataFrame(data[c],columns=[c]).merge(data.groupby(c,as_index=False)[record_col].sum(), on=c)[record_col]).astype(str)+\")\"\n", "# data" ] }, { "cell_type": "code", "execution_count": 12, "id": "2c9d6d5a", "metadata": {}, "outputs": [], "source": [ "fig = px.sunburst(data, path=groups, values=record_col,\n", " color='Domain_English',title=\"Distribution of topics
(METRIX taxonomy)\", template='plotly')\n", "# fig.update_traces(hovertemplate='%{label}
%{value:.2f}%')\n", "fig.update_traces(textinfo=\"label+value+percent root\")\n", "fig.update_traces(hovertemplate='%{id}
%{value}')\n", "metrix_distr = go.Figure(fig)\n", "# metrix_distr.show()" ] }, { "cell_type": "code", "execution_count": 13, "outputs": [], "source": [ "# metrix_distr.show(config= dict(displayModeBar = False))\n", "data = (wos.groupby(['Publication Year'])[record_col].nunique(dropna=False)\n", " .reset_index()\n", " .rename(columns={0:record_col}))\n", "data[record_col+\"_relative_growth\"] = data[data[record_col]>0].sort_values(by=[\"Publication Year\"], ascending=True)[record_col][0]\n", "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n", "\n", "data = data.sort_values(by =[\"Publication Year\"], ascending=[True])\n", "data[record_col+\"_cumsum\"] = (data[record_col].cumsum())\n", "\n", "year_output = px.line(data,x=\"Publication Year\", y=record_col, markers=True)\n", "year_output.update_traces(hovertemplate='Year:%{x:d}
Number of co-publications:%{y:d}')\n", "\n", "year_rel_output = px.line(data,x=\"Publication Year\", y=record_col+\"_relative_growth\", markers=True)\n", "year_rel_output.update_traces(hovertemplate='Year:%{x:d}
Rel.growth in co-publications:%{y:.0%}')\n", "\n", "year_rel_cumsum = px.area(data,x=\"Publication Year\", y=record_col+\"_cumsum\")\n", "year_rel_cumsum.update_traces(hovertemplate='Year:%{x:d}
Cumulative number co-publications:%{y:d}')\n", "\n", "\n", "figsuper = make_subplots(rows=3, cols=2, subplot_titles=[\"Distribution of topics\",\n", " \"Co-publications per year\",\"Relative growth of co-publications\",\n", " \"Cumulative sum of co-publications\",],\n", " specs=[\n", " [{\"type\": \"domain\", \"rowspan\":3}, {\"type\": \"xy\"}],\n", " [None,{\"type\": \"xy\"}],\n", " [None, {\"type\": \"xy\"}]\n", " ])\n", "\n", "\n", "for trace in list(metrix_distr.select_traces()):\n", " # trace.barmode\n", " figsuper.add_trace(trace,\n", " row=[1,2,3], col=1\n", " )\n", "\n", "for trace in list(year_output.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=1, col=2\n", " )\n", "\n", "for trace in list(year_rel_output.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=2, col=2\n", " )\n", "\n", "for trace in list(year_rel_cumsum.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=3, col=2\n", " )\n", "\n", "# figsuper.update_layout(hovermode='x unified')\n", "figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative')\n", "figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", "figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", "figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", "figsuper['layout']['yaxis2'].update(zerolinecolor='grey',tickformat=\".0%\")\n", "# figsuper.layout.annotations[0].update(x=0.1)\n", "# figsuper.layout.annotations[2].update(x=0.105)\n", "# figsuper.layout.annotations[1].update(x=0.7)\n", "# figsuper.layout.annotations[3].update(x=0.7)\n", "\n", "# figsuper.show(config= dict(displayModeBar = False, responsive = True))\n", "figsuper.write_html(f\"plot_html/Overall_distr&trends.html\",config= dict(displayModeBar = False, responsive = True))\n", "\n", "\n", "# py.plot(figsuper, filename = 'ZSI_ReConnect_overall_distr&trend', auto_open=True,config= dict(displayModeBar = False, responsive = True))" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 14, "outputs": [], "source": [ "# data\n" ], "metadata": { "collapsed": false } }, { "cell_type": "markdown", "id": "66fca444", "metadata": {}, "source": [ "## Domains, distribution, yearly trends" ] }, { "cell_type": "code", "execution_count": 15, "id": "14e82a73", "metadata": {}, "outputs": [], "source": [ "group = 'Domain_English'\n", "data = wos.groupby(group, as_index=False)[record_col].nunique().sort_values(ascending=False, by=record_col)\n", "\n", "fig = px.bar(data.sort_values(by=group), x=record_col, y=group, color=group,barmode='relative',\n", " labels={\n", " record_col: 'Number of co-publications',\n", " group: \"\",\n", " },\n", " title=\"Distribution of Domains\", template='plotly')\n", "fig.update_layout(showlegend=False, xaxis_tickformat='d',font_family=\"Montserrat\")\n", "fig.update_traces(hovertemplate='%{x:d}')\n", "fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", "fig.update_layout(yaxis={'categoryorder':'total ascending'})\n", "fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "dom_distr = go.Figure(fig)\n", "# dom_distr.show(config= dict(displayModeBar = False, responsive = True))" ] }, { "cell_type": "code", "execution_count": 16, "id": "8cbe20ab", "metadata": {}, "outputs": [], "source": [ "group = ['Publication Year','Domain_English']\n", "data = (wos.groupby(['Publication Year','Domain_English'])[record_col].nunique(dropna=False).unstack()\n", " .fillna(0)\n", " .stack()\n", " .reset_index()\n", " .rename(columns={0:record_col}))\n", "data = data.merge(data[data[record_col]>0].sort_values(by=[\"Publication Year\"], ascending=True).drop_duplicates(subset='Domain_English'),\n", " on='Domain_English', suffixes=[None,\"_relative_growth\"])\n", "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n", "\n", "data = data.sort_values(by =[\"Domain_English\",\"Publication Year\"], ascending=[True,True])\n", "data[record_col+\"_cumsum\"] = (data.groupby('Domain_English',as_index=False)[record_col].cumsum())\n", "\n", "# data" ] }, { "cell_type": "code", "execution_count": 17, "id": "05d0922a", "metadata": {}, "outputs": [], "source": [ "fig = px.line(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col,x=group[0], color=group[-1], markers=True, labels={\n", " record_col: 'Number of co-publications',\n", " group[-1]: \"Domain\",\n", " },\n", " title=\"Yearly output of co-publications\", template='plotly')\n", "fig.update_traces(hovertemplate='%{y:d}')\n", "fig.update_layout(hovermode='x unified')\n", "fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", "fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "\n", "year_output_by_domain = go.Figure(fig)\n", "\n", "fig = px.line(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col+\"_relative_growth\",x=group[0], color=group[-1], markers=True, labels={\n", " record_col+\"_relative_growth\": 'Rel. growth
in co-publications (%)',\n", " group[-1]: \"Domain\",\n", " },\n", " title=\"Relative growth in the output of co-publications\", template='plotly')\n", "# fig.update_traces(hovertemplate='%{y:.2f}%')\n", "\n", "fig.update_layout(hovermode='x unified',yaxis_tickformat='.0f%',font_family=\"Montserrat\")\n", "fig.update_traces(hovertemplate='%{y:.0f}00%')\n", "fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", "fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "# fig['layout']['yaxis4'].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey')\n", "# fig.update_yaxes(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey')\n", "\n", "rel_output_by_domain = go.Figure(fig)\n", "\n", "\n", "fig = px.area(data.sort_values(ascending=[True,True], by=[group[0],group[-1]]),y=record_col+\"_cumsum\",x=group[0], color=group[-1],line_group=group[-1],\n", " labels={\n", " record_col+\"_cumsum\": 'Cumulative number of co-publications',\n", " group[-1]: \"Domain\",\n", " },\n", " title=\"Cumulative number of co-publications\", template='plotly')\n", "fig.update_traces(hovertemplate='%{y:d}')\n", "fig.update_layout(hovermode='x unified')\n", "fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", "fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "\n", "cumsum_by_domain = go.Figure(fig)\n", "# cumsum_by_domain.show(config= dict(displayModeBar = False))" ] }, { "cell_type": "code", "execution_count": 18, "id": "3a07c24d", "metadata": {}, "outputs": [], "source": [ "from plotly.subplots import make_subplots\n", "import plotly.graph_objects as go\n", "\n", "# dom_distr\n", "# year_output_by_domain\n", "# rel_output_by_domain\n", "# cumsum_by_domain\n", "\n", "figsuper = make_subplots(rows=2, cols=2, subplot_titles=[\"Distribution of domains\",\"Cumulative sum of co-publications\",\n", " \"Co-publications per year\",\"Relative growth of co-publications\"])\n", "\n", "\n", "for trace in list(dom_distr.select_traces()):\n", " trace.showlegend=False\n", " # trace.barmode\n", " figsuper.add_trace(trace,\n", " row=1, col=1\n", " )\n", "\n", "for trace in list(cumsum_by_domain.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=1, col=2\n", " )\n", "\n", "for trace in list(year_output_by_domain.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=2, col=1\n", " )\n", "\n", "for trace in list(rel_output_by_domain.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=2, col=2\n", " )\n", "\n", "# figsuper.update_layout(hovermode='x unified')\n", "figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative')\n", "figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", "figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", "figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", "figsuper['layout']['yaxis4'].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", "# figsuper.layout.annotations[0].update(x=0.1)\n", "# figsuper.layout.annotations[2].update(x=0.105)\n", "# figsuper.layout.annotations[1].update(x=0.7)\n", "# figsuper.layout.annotations[3].update(x=0.7)\n", "\n", "# figsuper.show(config= dict(displayModeBar = False, responsive = True))\n", "figsuper.write_html(f\"plot_html/Domains_distr&trends.html\",config= dict(displayModeBar = False, responsive = True))" ] }, { "cell_type": "code", "execution_count": 19, "outputs": [], "source": [ "# figsuper['layout']" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 20, "id": "329b6889", "metadata": {}, "outputs": [ { "data": { "text/plain": "Publication Year 2011 2012 2013 2014 2015 2016 2017 2018 \nDomain_English \nApplied Sciences 490 593 738 1031 1201 1535 1920 2808 \\\nArts & Humanities 0 0 0 4 1 3 7 4 \nEconomic & Social Sciences 20 22 29 28 34 40 84 105 \nHealth Sciences 116 120 155 184 216 243 321 403 \nMultidisciplinary 15 21 43 52 57 64 75 76 \nNatural Sciences 181 223 298 318 380 437 568 753 \n\nPublication Year 2019 2020 2021 2022 \nDomain_English \nApplied Sciences 3729 4446 5295 6199 \nArts & Humanities 11 11 16 13 \nEconomic & Social Sciences 160 211 252 375 \nHealth Sciences 611 755 1035 1182 \nMultidisciplinary 83 97 115 149 \nNatural Sciences 999 1232 1403 1665 ", "text/html": "
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Publication Year201120122013201420152016201720182019202020212022
Domain_English
Applied Sciences490593738103112011535192028083729444652956199
Arts & Humanities0004137411111613
Economic & Social Sciences20222928344084105160211252375
Health Sciences11612015518421624332140361175510351182
Multidisciplinary15214352576475768397115149
Natural Sciences181223298318380437568753999123214031665
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" }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pivot_data = pd.pivot_table(data, values=record_col, index=['Domain_English'],\n", "\n", " columns=['Publication Year'], fill_value=0)\n", "pivot_data" ] }, { "cell_type": "code", "execution_count": 21, "id": "100f3002", "metadata": {}, "outputs": [], "source": [ "# f, ax = plt.subplots(figsize=(9, 6))\n", "# g = sns.heatmap(pivot_data, annot=True, fmt=\"d\", linewidths=.5, ax=ax)\n", "# g.set(xlabel=\"\", ylabel=\"\")" ] }, { "cell_type": "code", "execution_count": 22, "id": "a8d24046", "metadata": {}, "outputs": [ { "data": { "text/plain": "Publication Year 2011 2012 2013 2014 \nDomain_English \nApplied Sciences 59.610706 60.572012 58.432304 63.760049 \\\nArts & Humanities 0.000000 0.000000 0.000000 0.247372 \nEconomic & Social Sciences 2.433090 2.247191 2.296120 1.731602 \nHealth Sciences 14.111922 12.257406 12.272367 11.379097 \nMultidisciplinary 1.824818 2.145046 3.404592 3.215832 \nNatural Sciences 22.019465 22.778345 23.594616 19.666048 \n\nPublication Year 2015 2016 2017 2018 \nDomain_English \nApplied Sciences 63.578613 66.106804 64.537815 67.678959 \\\nArts & Humanities 0.052938 0.129199 0.235294 0.096409 \nEconomic & Social Sciences 1.799894 1.722653 2.823529 2.530730 \nHealth Sciences 11.434621 10.465116 10.789916 9.713184 \nMultidisciplinary 3.017470 2.756245 2.521008 1.831767 \nNatural Sciences 20.116464 18.819983 19.092437 18.148952 \n\nPublication Year 2019 2020 2021 2022 \nDomain_English \nApplied Sciences 66.672626 65.847156 65.241498 64.687467 \nArts & Humanities 0.196674 0.162915 0.197141 0.135657 \nEconomic & Social Sciences 2.860719 3.125000 3.104978 3.913180 \nHealth Sciences 10.924370 11.181872 12.752587 12.334342 \nMultidisciplinary 1.483998 1.436611 1.416954 1.554837 \nNatural Sciences 17.861613 18.246445 17.286841 17.374517 ", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Publication Year201120122013201420152016201720182019202020212022
Domain_English
Applied Sciences59.61070660.57201258.43230463.76004963.57861366.10680464.53781567.67895966.67262665.84715665.24149864.687467
Arts & Humanities0.0000000.0000000.0000000.2473720.0529380.1291990.2352940.0964090.1966740.1629150.1971410.135657
Economic & Social Sciences2.4330902.2471912.2961201.7316021.7998941.7226532.8235292.5307302.8607193.1250003.1049783.913180
Health Sciences14.11192212.25740612.27236711.37909711.43462110.46511610.7899169.71318410.92437011.18187212.75258712.334342
Multidisciplinary1.8248182.1450463.4045923.2158323.0174702.7562452.5210081.8317671.4839981.4366111.4169541.554837
Natural Sciences22.01946522.77834523.59461619.66604820.11646418.81998319.09243718.14895217.86161318.24644517.28684117.374517
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" }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import numpy as np\n", "percent_pivot = pd.crosstab(data['Domain_English'], data['Publication Year'], values=data[record_col], aggfunc=np.sum, normalize='columns')*100\n", "percent_pivot" ] }, { "cell_type": "code", "execution_count": 23, "id": "3bda79fb", "metadata": {}, "outputs": [], "source": [ " # f, ax = plt.subplots(figsize=(15, 6))\n", "# # g = sns.heatmap(percent_pivot, annot=True, fmt='.2f', linewidths=.5, ax=ax, cbar=False)\n", "# # for t in ax.texts: t.set_text(t.get_text() + \" %\")\n", "# g.set(xlabel=\"\", ylabel=\"\")" ] }, { "cell_type": "code", "execution_count": 24, "id": "01024cc0", "metadata": {}, "outputs": [], "source": [ "# percent_pivot.T.plot(kind='bar',\n", "# stacked=True,\n", "# figsize=(10, 6))" ] }, { "cell_type": "code", "execution_count": 25, "id": "4caa215d", "metadata": {}, "outputs": [], "source": [ "# percent_pivot.T.plot(kind='bar',\n", "# stacked=True,\n", "# figsize=(15, 8))\n", "#\n", "# plt.legend(loc=\"lower left\", ncol=2)\n", "# # plt.ylabel(\"Release Year\")\n", "# # plt.xlabel(\"Proportion\")\n", "#\n", "#\n", "# for n, x in enumerate([*pivot_data.T.index.values]):\n", "# for (proportion, count, y_loc) in zip(percent_pivot.T.loc[x],\n", "# pivot_data.T.loc[x],\n", "# percent_pivot.T.loc[x].cumsum()):\n", "#\n", "# plt.text(y=(y_loc - proportion) + (proportion / 2),\n", "# x=n - 0.11,\n", "# s=f'{count}',# ({np.round(proportion, 1)}%)',\n", "# color=\"black\",\n", "# fontsize=8,\n", "# fontweight=\"bold\")\n", "#\n", "# plt.show()" ] }, { "cell_type": "markdown", "id": "dcae04bd", "metadata": {}, "source": [ "## Field" ] }, { "cell_type": "code", "execution_count": 26, "id": "d3807072", "metadata": {}, "outputs": [], "source": [ "# group = ['Publication Year',\"Domain_English\",'Field_English']\n", "# # data = wos.groupby(['Publication Year',\"Domain_English\",'Field_English'], as_index=False)[record_col].nunique().sort_values(ascending=False, by=group+[record_col])\n", "#\n", "#\n", "# data = (wos.groupby(['Publication Year','Field_English'],)[record_col].nunique(dropna=False).unstack()\n", "# .fillna(0)\n", "# .stack()\n", "# .reset_index()\n", "# .rename(columns={0:record_col}))\n", "#\n", "# data = data.merge(wos[[\"Domain_English\",'Field_English']].drop_duplicates(),on=\"Field_English\")\n", "#\n", "# data = data.merge(data[data[record_col]>0].sort_values(by=[\"Publication Year\"], ascending=True).drop_duplicates(subset='Field_English'),\n", "# on='Field_English', suffixes=[None,\"_relative_growth\"])\n", "# data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]*100\n", "#\n", "# data = data.sort_values(by =[\"Field_English\",\"Publication Year\"], ascending=[True,True])\n", "# data[record_col+\"_cumsum\"] = (data.groupby('Domain_English',as_index=False)[record_col].cumsum())" ] }, { "cell_type": "code", "execution_count": 27, "id": "756513b5", "metadata": {}, "outputs": [], "source": [ "# data_complete = pd.DataFrame()\n", "#\n", "# for cat in sorted(data[group[-2]].unique()):\n", "# #data segment\n", "# sub_data = data[data[group[-2]]==cat]\n", "# sub_data = sub_data.complete({group[0]:range(int(data[group[0]].min()), int(data[group[0]].max()) + 1)}\n", "# ,group[-1],fill_value=0)\n", "# data_complete = pd.concat([data_complete,sub_data], ignore_index=True)\n", "\n", "\n", " # seaborn version plot\n", " # g=sns.lineplot(sub_data.sort_values(ascending=True, by=group[-1]),\n", " # y=record_col,x=group[0], hue=group[-1], marker=\"o\")\n", " # g.set(xticks=list(range(2012,2022+1,2)))\n", " # g.legend(title=None)\n", " # g.set_title(cat)\n", " # g.yaxis.set_major_locator(MaxNLocator(integer=True))\n", " # plt.show()" ] }, { "cell_type": "code", "execution_count": 28, "id": "d09c080a", "metadata": {}, "outputs": [], "source": [ "# data_complete = pd.DataFrame()\n", "#\n", "# # Creating subplot axes\n", "# fig, axes = plt.subplots(nrows=3,ncols=2,figsize=(15, 15))\n", "#\n", "# for cat,ax in zip(sorted(data[group[-2]].unique()),axes.flatten()):\n", "# #data segment\n", "# sub_data = data[data[group[-2]]==cat]\n", "# sub_data = sub_data.complete({group[0]:range(int(data[group[0]].min()), int(data[group[0]].max()) + 1)}\n", "# ,group[-1],fill_value=0)\n", "# data_complete = pd.concat([data_complete,sub_data], ignore_index=True)\n", "# #plot\n", "# g=sns.lineplot(sub_data.sort_values(ascending=True, by=group[-1]),\n", "# y=record_col,x=group[0], hue=group[-1], marker=\"o\", ax=ax)\n", "# g.set(xticks=list(range(2012,2022+1,2)))\n", "# g.legend(title=None)\n", "# g.set_title(cat)\n", "# g.set_xlabel(None)\n", "# g.set_ylabel(None)\n", "# g.yaxis.set_major_locator(MaxNLocator(integer=True))\n", "# fig.suptitle(\"Number of co-publications in domains and respective fields\", y=0.92)\n", "# plt.show()" ] }, { "cell_type": "markdown", "id": "09a6de71", "metadata": {}, "source": [ "## SubField" ] }, { "cell_type": "code", "execution_count": 29, "id": "0397eb85", "metadata": {}, "outputs": [], "source": [ "group = ['Publication Year',\"Domain_English\",'Field_English',\"SubField_English\"]\n", "data = wos.groupby(group, as_index=False)[record_col].nunique().sort_values(ascending=False, by=group+[record_col])\n", "# data" ] }, { "cell_type": "code", "execution_count": 30, "id": "846596cf", "metadata": {}, "outputs": [], "source": [ "for cat in sorted(data[group[-2]].unique()):\n", " sub_data = data[data[group[-2]]==cat]\n", " sub_data = sub_data.complete({group[0]:range(int(data[group[0]].min()), int(data[group[0]].max()) + 1)}\n", " ,group[-1],fill_value=0)\n", " # g=sns.lineplot(sub_data.sort_values(ascending=True, by=group[-1]),y=record_col,x=group[0],\n", " # hue=group[-1], marker=\"o\", errorbar=None)\n", " # g.set(xticks=list(range(2012,2022+1,2)))\n", " # g.legend(title=None,bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0., ncols=math.ceil(len(g.legend_.texts)/12))\n", " # g.set_title(f'Number or co-publications in {cat}')\n", " # g.set_ylabel(None)\n", " # plt.show()" ] }, { "cell_type": "code", "execution_count": 31, "id": "27c90aaf", "metadata": {}, "outputs": [], "source": [ "from matplotlib.ticker import FuncFormatter\n", "import math\n", "def orderOfMagnitude(number):\n", " return math.floor(math.log(number, 10))\n", "\n", "def roundToNearest(number):\n", " order = orderOfMagnitude(number)\n", " # if order!=0:\n", " # order+=1\n", " near = math.ceil(number/10**order)*10**order\n", " return near" ] }, { "cell_type": "markdown", "id": "91d2cc8a", "metadata": {}, "source": [ "## Country contributions" ] }, { "cell_type": "code", "execution_count": 32, "id": "b3adb06a", "metadata": {}, "outputs": [], "source": [ "wos_univ_locations = wos_univ.merge(wos_country_types, on=\"Country\")\n", "wos_collabs = wos_univ_locations[wos_univ_locations[\"Country_Type\"]!=\"Other\"][[record_col,\"Country\"]].drop_duplicates()\n", "\n", "collab_desc = wos_collabs[wos_collabs[\"Country\"]!=\"China\"][\"Country\"].value_counts().reset_index()\n", "collab_desc[\"percent_of_copubs\"] = collab_desc[\"count\"]/wos_collabs[record_col].nunique()#*100\n", "collab_desc[\"percent_contrib_in_copubs\"] = collab_desc[\"count\"]/wos_collabs[record_col].size#*100\n", "collab_desc = collab_desc.merge(wos_country_types, on=\"Country\")\n", "# collab_desc\n", "\n", "c_dict = {\"count\":\"Number of co-publications\",\n", " \"percent_of_copubs\":\"Percent of co-publications\",\n", " \"percent_contrib_in_copubs\":\"Contribution to co-publications\"}\n", "\n", "color_discrete_map= {'China': '#EF553B',\n", " 'EU': '#636EFA',\n", " 'Non-EU associate': '#00CC96'}\n", "\n", "fig_dict = dict()\n", "# Creating subplot axes\n", "# fig, axes = plt.subplots(ncols=3,figsize=(15, 15))\n", "# for c,ax in zip(c_dict.keys(),axes.flatten()):\n", "for c in c_dict.keys():\n", " data = collab_desc[[\"Country\",c,\"Country_Type\"]]\n", " # plt.figure(figsize=(9,12))\n", " col_by=\"Country_Type\"\n", " y_lab=\"Country\"\n", " # g = sns.barplot(data, x=c, y=\"Country\", hue=\"Country_Type\", dodge=False)\n", " fig = px.bar(data, x=c, y=y_lab, color=col_by, color_discrete_map=color_discrete_map,\n", " labels=dict({\n", " record_col: 'Number of co-publications',\n", " \"Institution_harm\": \"Institution\",\n", " \"Institution_harm_label\": \"Institution\",\n", " \"Country_Type\":\"Country type\",\n", " \"Eurovoc_Class\":\"Region\"\n", " },**c_dict),\n", " title=c_dict[c], template='plotly')\n", " fig.update_layout(xaxis_tickformat='d',font_family=\"Montserrat\",\n", " yaxis={'categoryorder':'total ascending'},\n", " width=1000, height=1000,)\n", " if \"percent\" in c:\n", " fig.update_traces(hovertemplate='%{y}
%{x}')\n", " fig.update_xaxes(tickformat=\".1%\")\n", " else:\n", " fig.update_traces(hovertemplate='%{y}
%{x:d}')\n", " fig_dict[c] = go.Figure(fig)\n", " # fig.show(config= dict(displayModeBar = False, responsive = True))\n", " # g.set_xlim(0,roundToNearest(data[c].max()))\n", " # g.set_ylabel(None)\n", " # g.set_xlabel(c_dict.get(c))\n", " # g.set_title(c_dict.get(c))\n", " # g.legend(title=None, loc=\"right\")\n", " # for i in g.containers:\n", " # g.bar_label(i,fontsize=10, fmt='%.1f%%' if 'percent' in c else '%.0f')\n", " # if 'percent' in c:\n", " # g.xaxis.set_major_locator(MaxNLocator(integer=True))\n", " # vals = g.get_xticks()\n", " # g.set_xticklabels([str(int(val))+'%' for val in vals])\n", " # plt.show()\n", "figsuper = make_subplots(rows=1, cols=3, subplot_titles =list(c_dict.values()))\n", "for i,f in enumerate(fig_dict.keys()):\n", " sfig = fig_dict[f]\n", " for trace in list(sfig.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=1, col=i+1)\n", "\n", "figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n", "figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", "figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", "figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", "# figsuper.show(config= dict(displayModeBar = False, responsive = True))\n", "figsuper.write_html(f\"plot_html/europe_contribution_bar.html\",config= dict(displayModeBar = False, responsive = True))" ] }, { "cell_type": "code", "execution_count": 33, "id": "140395ac", "metadata": {}, "outputs": [], "source": [ "# wos_collabs_EU = wos_univ_locations[~wos_univ_locations[\"Country_Type\"].isin([\"Other\",\"China\"])][[record_col,\"Country\"]].drop_duplicates()\n", "# wos_collabs_EU = wos_collabs_EU.merge(wos_collabs_EU, on=record_col)\n", "# EU_co_occur = pd.crosstab(wos_collabs_EU['Country_x'], wos_collabs_EU['Country_y'], values=wos_collabs_EU[record_col], aggfunc='nunique', normalize='all').fillna(0)\n", "#\n", "# # Generate a mask for the upper triangle\n", "# mask = np.triu(np.ones_like(EU_co_occur, dtype=bool))\n", "#\n", "# # Set up the matplotlib figure\n", "# f, ax = plt.subplots(figsize=(11, 9))\n", "#\n", "# # Draw the heatmap with the mask and correct aspect ratio\n", "# g = sns.heatmap(EU_co_occur, mask=mask,\n", "# square=True, linewidths=.5)\n", "#\n", "# g.set_ylabel(None)\n", "# g.set_xlabel(None)" ] }, { "cell_type": "code", "execution_count": 34, "id": "c959287e", "metadata": {}, "outputs": [], "source": [ "wos_collabs_EU = wos_univ_locations[~wos_univ_locations[\"Country_Type\"].isin([\"Other\",\"China\"])][[record_col,\"Country\"]].drop_duplicates()\n", "wos_collabs_EU = wos_collabs_EU.merge(wos_collabs_EU, on=record_col)\n", "EU_co_occur = pd.crosstab(wos_collabs_EU['Country_x'], wos_collabs_EU['Country_y'], values=wos_collabs_EU[record_col], aggfunc='nunique').fillna(0).astype(int)\n", "\n", "\n", "eu_list = wos_collabs_EU.groupby(['Country_x'])[record_col].count().sort_values(ascending=False).index\n", "# pre_fig = sns.clustermap(EU_co_occur)\n", "# re_index = [i.get_text() for i in pre_fig.ax_heatmap.yaxis.get_majorticklabels()]\n", "# re_column = [i.get_text() for i in pre_fig.ax_heatmap.xaxis.get_majorticklabels()]\n", "\n", "EU_co_occur = EU_co_occur.reindex(index = eu_list, columns=eu_list)\n", "\n", "# Generate a mask for the upper triangle\n", "mask = np.triu(np.ones_like(EU_co_occur, dtype=bool))\n", "data = np.where(mask,None,EU_co_occur)\n", "\n", "fig = px.imshow(data,\n", " labels=dict(x=\"Country\", y=\"Country\", color=\"Co-publication with China\"),\n", " x=list(EU_co_occur.columns),\n", " y=list(EU_co_occur.index), title=\"Intraeuropean patterns
Co-occurences of countries in chinese co-publications\"\n", " )\n", "fig.update_layout(title_x=0.5,\n", " width=1000, height=1000,\n", " xaxis_showgrid=False,\n", " yaxis_showgrid=False,\n", " yaxis_autorange='reversed', template='plotly_white')\n", "# fig.update_traces(hovertemplate='%{y}
%{x}
Co-publications: %{hovertext}')\n", "fig.update_xaxes(tickangle= -90)\n", "fig.update_yaxes(\n", " ticks=\"outside\")\n", "fig.update_xaxes(\n", " ticks=\"outside\")\n", "# fig.show(config= dict(displayModeBar = False,responsive=True))\n", "fig.write_html(f\"plot_html/intraeurope_collabs.html\",config= dict(displayModeBar = False, responsive = True))\n" ] }, { "cell_type": "code", "execution_count": 35, "id": "df1f03ea", "metadata": {}, "outputs": [], "source": [ "# collab_year = wos_collabs[wos_collabs[\"Country\"]!=\"China\"].copy()\n", "# collab_year = collab_year.merge(wos_country_types, on=\"Country\").merge(wos[[record_col,\"Publication Year\"]],on=record_col).drop_duplicates()\n", "# data = collab_year.groupby([\"Publication Year\",'Country_Type'],as_index=False)[record_col].nunique()\n", "#\n", "#\n", "# g=sns.lineplot(data,y=record_col,x=\"Publication Year\", hue=\"Country_Type\", marker=\"o\")\n", "# g.set(xticks=list(range(2012,2022+1,2)))\n", "# g.legend(title=None)\n", "# g.set_xlabel(None)\n", "# g.set_ylabel(None)\n", "# g.set_title(\"Yearly output of co-publications with China\")" ] }, { "cell_type": "markdown", "id": "122d0260", "metadata": {}, "source": [] }, { "cell_type": "code", "execution_count": 36, "id": "f19501a9", "metadata": {}, "outputs": [], "source": [ "collab_year = wos_collabs[wos_collabs[\"Country\"]!=\"China\"].copy()\n", "collab_year = collab_year.merge(wos_country_types, on=\"Country\").merge(wos[[record_col,\"Publication Year\"]],on=record_col).drop_duplicates()\n", "\n", "data = (collab_year.groupby(['Publication Year',\"Country\"])[record_col]\n", " .nunique(dropna=False).unstack()\n", " .fillna(0)\n", " .stack()\n", " .reset_index()\n", " .rename(columns={0:record_col}))\n", "data = data.merge(data[data[record_col]>0].sort_values(by=[\"Publication Year\"], ascending=True).drop_duplicates(subset=\"Country\"),\n", " on=[\"Country\"], suffixes=[None,\"_relative_growth\"])\n", "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]*100\n", "data = data.sort_values(by =[\"Country\",\"Publication Year\"], ascending=[True,True])\n", "data[record_col+\"_cumsum\"] = (data.groupby('Country',as_index=False)[record_col].cumsum())\n", "data = data.merge(wos_country_types, on='Country')\n", "# data\n", "\n", "data[\"ISO3\"] = cc.pandas_convert(series=data[\"Country\"], to='ISO3')\n", "fig = px.choropleth(data[data[\"Publication Year\"] == 2022], locations=\"ISO3\", color=record_col+\"_cumsum\", hover_name=\"Country\",\n", " scope=\"europe\", template='plotly',\n", " range_color=[data[record_col+\"_cumsum\"].min(),data[record_col+\"_cumsum\"].max()],hover_data=[\"Eurovoc_Class\"])\n", "# original: '%{hovertext}

ISO3=%{location}
Eurovoc_Class=%{customdata[0]}
UT (Unique WOS ID)_cumsum=%{z}'\n", "\n", "fig.update_traces(hovertemplate='%{hovertext}'\n", " '
Region: %{customdata[0]}
'\n", " 'Co-pubications: %{z:d}')\n", "\n", "cumsum_country = go.Figure(fig)" ] }, { "cell_type": "code", "execution_count": 37, "id": "ae3cb8e1", "metadata": {}, "outputs": [], "source": [ "# fig = px.line(data.sort_values(ascending=True, by='Publication Year'),y=record_col,x='Publication Year', color=\"Eurovoc_Class\",line_group=\"Country\", markers=True,\n", "# labels={\n", "# record_col: 'Number of co-publications',\n", "# \"Eurovoc_Class\": \"Region\"\n", "# },\n", "# title=\"Yearly output of co-publications\", template='plotly',hover_name= \"Country\")\n", "# fig.update_traces(hovertemplate='%{hovertext}
%{x}
Co-publications: %{y}')\n", "# # fig.update_layout(hovermode='x unified')\n", "# fig.add_shape(\n", "# # Rectangle with reference to the plot\n", "# type=\"rect\",\n", "# xref=\"paper\",\n", "# yref=\"paper\",\n", "# x0=0,\n", "# y0=0,\n", "# x1=1.0,\n", "# y1=1.0,\n", "# line=dict(\n", "# color=\"black\",\n", "# width=0.5,\n", "# )\n", "# )\n", "# fig.update_yaxes(\n", "# showgrid=True,\n", "# ticks=\"outside\")\n", "# fig.update_xaxes(\n", "# showgrid=True,\n", "# ticks=\"outside\")\n", "# fig.show(config= dict(displayModeBar = False))" ] }, { "cell_type": "code", "execution_count": 38, "id": "dd72ad3f", "metadata": {}, "outputs": [], "source": [ "# fig.data[0].hovertemplate" ] }, { "cell_type": "code", "execution_count": 39, "id": "600d7459", "metadata": {}, "outputs": [], "source": [ "# fig = px.line(data.sort_values(ascending=True, by='Publication Year'),\n", "# y=record_col+\"_relative_growth\",\n", "# x='Publication Year',\n", "# color=\"Eurovoc_Class\",line_group=\"Country\",markers=True,\n", "# labels={\n", "# record_col+\"_relative_growth\": 'Relative growth of co-publications (%)',\"Eurovoc_Class\": \"Region\"\n", "# },\n", "# title=\"Relative growth of co-publications
(baseline: 2011)\", template='plotly',hover_name= \"Country\")\n", "# fig.update_traces(hovertemplate='%{hovertext}
%{x}
Relative growth: %{y}%')\n", "# fig.add_shape(\n", "# # Rectangle with reference to the plot\n", "# type=\"rect\",\n", "# xref=\"paper\",\n", "# yref=\"paper\",\n", "# x0=0,\n", "# y0=0,\n", "# x1=1.0,\n", "# y1=1.0,\n", "# line=dict(\n", "# color=\"black\",\n", "# width=0.5,\n", "# )\n", "# )\n", "# fig.update_yaxes(\n", "# showgrid=True,\n", "# ticks=\"outside\")\n", "# fig.update_xaxes(\n", "# showgrid=True,\n", "# ticks=\"outside\")\n", "# fig.show(config= dict(displayModeBar = False))" ] }, { "cell_type": "code", "execution_count": 40, "id": "0ee76d32", "metadata": {}, "outputs": [], "source": [ "from plotly.subplots import make_subplots\n", "import plotly.graph_objects as go\n", "\n", "figsuper = make_subplots(rows=3, cols=2, subplot_titles=[\"Number of publications (2022)\",\"Cumulative number of co-publications\",\n", " \"Yearly output of co-publications\",\"Relative growth of co-publications\"],\n", " specs=[\n", " [{\"type\": \"geo\", \"rowspan\":3}, {\"type\": \"xy\"}],\n", " [None,{\"type\": \"xy\"}],\n", " [None, {\"type\": \"xy\"}]\n", " ])\n", "\n", "for trace in list(cumsum_country.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=1, col=1\n", " )\n", "\n", "fig = px.area(data.sort_values(ascending=True, by='Publication Year'), y=record_col+\"_cumsum\",\n", " x='Publication Year',\n", " color=\"Eurovoc_Class\",\n", " line_group=\"Country\",\n", " labels={\n", " record_col: 'Number of co-publications',\n", " \"Eurovoc_Class\": \"Region\"\n", " },\n", " title=\"Cumulative number of co-publications\",\n", " hover_name= \"Country\")\n", "fig.update_traces(hovertemplate='%{hovertext}
%{x}
Co-publications: %{y}')\n", "\n", "for trace in list(fig.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=1, col=2\n", " )\n", "\n", "\n", "fig = px.line(data.sort_values(ascending=True, by='Publication Year'),\n", " y=record_col,\n", " x='Publication Year',\n", " color=\"Eurovoc_Class\",\n", " line_group=\"Country\",\n", " markers=True,\n", " labels={\n", " record_col: 'Number of co-publications',\n", " \"Eurovoc_Class\": \"Region\"\n", " },\n", " title=\"Yearly output of co-publications\",hover_name= \"Country\")\n", "fig.update_traces(hovertemplate='%{hovertext}
%{x}
Co-publications: %{y}')\n", "\n", "for trace in list(fig.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=2, col=2\n", " )\n", "\n", "fig = px.line(data.sort_values(ascending=True, by='Publication Year'),\n", " y=record_col+\"_relative_growth\",\n", " x='Publication Year',\n", " color=\"Eurovoc_Class\",line_group=\"Country\",markers=True,\n", " labels={\n", " record_col+\"_relative_growth\": 'Relative growth of co-publications (%)',\"Eurovoc_Class\": \"Region\"\n", " },\n", " title=\"Relative growth of co-publications\", template='plotly',hover_name= \"Country\")\n", "fig.update_traces(hovertemplate='%{hovertext}
%{x}
Relative growth: %{y}%')\n", "fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", "\n", "for trace in list(fig.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=3, col=2\n", " )\n", "\n", "figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", "figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", "figsuper.update_layout({'template':\"plotly\"})\n", "figsuper.layout[\"geo\"][\"scope\"] = 'europe'\n", "figsuper.update_coloraxes(colorbar=dict(lenmode='fraction',len=0.55, orientation=\"v\",yanchor='top', title=\"Co-publications\",\n", " ticks=\"outside\", ticksuffix=\" \",outlinewidth=0.5))\n", "# figsuper.show(config= dict(displayModeBar = False, responsive = True))\n", "figsuper.write_html(f\"plot_html/country_trends_overall.html\",config= dict(displayModeBar = False, responsive = True))" ] }, { "cell_type": "code", "execution_count": 41, "id": "e4c50e14", "metadata": {}, "outputs": [ { "data": { "text/plain": "Publication Year 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 \nCountry \nAustria 22 24 26 39 50 57 72 89 138 137 \\\nBelgium 34 38 40 65 71 81 90 133 179 213 \nBulgaria 4 5 8 9 7 19 21 18 10 25 \nCroatia 1 2 6 8 10 7 10 19 27 29 \nCyprus 2 1 5 5 5 5 8 7 15 28 \nCzech Republic 13 15 16 21 20 36 37 56 64 81 \nDenmark 35 33 40 59 68 74 101 195 234 245 \nEstonia 3 3 7 10 12 10 15 15 16 38 \nFinland 31 35 44 82 100 125 126 198 241 256 \nFrance 117 130 174 231 269 325 348 491 648 691 \nGermany 123 172 192 273 310 365 456 604 801 907 \nGreece 15 18 19 32 35 50 47 81 114 122 \nHungary 11 11 21 16 20 38 34 47 61 61 \nIreland 13 16 22 31 27 45 66 72 84 116 \nItaly 51 70 84 116 178 187 247 325 441 571 \nLatvia 0 0 1 0 1 8 10 15 10 9 \nLithuania 1 2 10 4 4 13 12 23 38 36 \nLuxembourg 2 3 3 1 8 9 13 15 18 22 \nMalta 1 0 0 0 1 1 0 0 6 2 \nNetherlands 72 64 77 103 139 166 220 297 408 470 \nNorway 30 42 60 76 67 88 104 134 222 253 \nPoland 17 31 37 57 73 82 98 110 138 181 \nPortugal 16 23 35 41 45 58 79 119 136 147 \nRomania 7 15 13 16 25 26 37 57 64 55 \nSlovakia 9 6 6 10 12 22 18 27 27 34 \nSlovenia 7 7 10 12 17 27 22 47 54 31 \nSpain 50 49 69 112 138 185 232 273 356 386 \nSweden 34 50 59 83 113 170 233 232 385 359 \nSwitzerland 37 50 54 74 74 95 155 195 233 263 \nUnited Kingdom 363 417 531 660 781 979 1350 1837 2430 3108 \n\nPublication Year 2021 2022 \nCountry \nAustria 185 205 \nBelgium 242 292 \nBulgaria 32 19 \nCroatia 33 35 \nCyprus 36 43 \nCzech Republic 93 123 \nDenmark 293 343 \nEstonia 45 39 \nFinland 289 380 \nFrance 807 858 \nGermany 1210 1386 \nGreece 139 181 \nHungary 83 90 \nIreland 167 187 \nItaly 641 811 \nLatvia 13 18 \nLithuania 38 38 \nLuxembourg 35 51 \nMalta 7 10 \nNetherlands 529 655 \nNorway 304 311 \nPoland 276 353 \nPortugal 204 212 \nRomania 48 62 \nSlovakia 36 45 \nSlovenia 48 40 \nSpain 473 640 \nSweden 428 510 \nSwitzerland 349 447 \nUnited Kingdom 3718 4245 ", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Publication Year201120122013201420152016201720182019202020212022
Country
Austria2224263950577289138137185205
Belgium34384065718190133179213242292
Bulgaria4589719211810253219
Croatia1268107101927293335
Cyprus2155558715283643
Czech Republic1315162120363756648193123
Denmark353340596874101195234245293343
Estonia337101210151516384539
Finland31354482100125126198241256289380
France117130174231269325348491648691807858
Germany12317219227331036545660480190712101386
Greece1518193235504781114122139181
Hungary111121162038344761618390
Ireland131622312745667284116167187
Italy517084116178187247325441571641811
Latvia00101810151091318
Lithuania12104413122338363838
Luxembourg233189131518223551
Malta1000110062710
Netherlands726477103139166220297408470529655
Norway304260766788104134222253304311
Poland17313757738298110138181276353
Portugal16233541455879119136147204212
Romania71513162526375764554862
Slovakia966101222182727343645
Slovenia7710121727224754314840
Spain504969112138185232273356386473640
Sweden34505983113170233232385359428510
Switzerland375054747495155195233263349447
United Kingdom363417531660781979135018372430310837184245
\n
" }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "year_pivot = pd.crosstab(collab_year['Country'], collab_year['Publication Year'], values=collab_year[record_col], aggfunc='nunique').fillna(0).astype(int)\n", "year_pivot" ] }, { "cell_type": "code", "execution_count": 42, "id": "e4e82db7", "metadata": {}, "outputs": [ { "data": { "text/plain": "
", "image/png": 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}, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(figsize=(15, 15))\n", "g = sns.heatmap(year_pivot, annot=True, fmt=\"d\", linewidths=.5, ax=ax)\n", "g.set(xlabel=\"\", ylabel=\"\")\n", "for i in range(year_pivot.shape[0]+1):\n", " ax.axhline(i, color='white', lw=10)" ] }, { "cell_type": "code", "execution_count": 43, "id": "78bb0b4e", "metadata": {}, "outputs": [ { "data": { "text/plain": "Publication Year 2011 2012 2013 2014 2015 \nCountry \nAustria 1.962533 1.801802 1.557819 1.736420 1.865672 \\\nBelgium 3.033006 2.852853 2.396645 2.894034 2.649254 \nBulgaria 0.356824 0.375375 0.479329 0.400712 0.261194 \nCroatia 0.089206 0.150150 0.359497 0.356189 0.373134 \nCyprus 0.178412 0.075075 0.299581 0.222618 0.186567 \nCzech Republic 1.159679 1.126126 0.958658 0.934996 0.746269 \nDenmark 3.122212 2.477477 2.396645 2.626892 2.537313 \nEstonia 0.267618 0.225225 0.419413 0.445236 0.447761 \nFinland 2.765388 2.627628 2.636309 3.650935 3.731343 \nFrance 10.437110 9.759760 10.425404 10.284951 10.037313 \nGermany 10.972346 12.912913 11.503895 12.154942 11.567164 \nGreece 1.338091 1.351351 1.138406 1.424755 1.305970 \nHungary 0.981267 0.825826 1.258238 0.712378 0.746269 \nIreland 1.159679 1.201201 1.318155 1.380232 1.007463 \nItaly 4.549509 5.255255 5.032954 5.164737 6.641791 \nLatvia 0.000000 0.000000 0.059916 0.000000 0.037313 \nLithuania 0.089206 0.150150 0.599161 0.178094 0.149254 \nLuxembourg 0.178412 0.225225 0.179748 0.044524 0.298507 \nMalta 0.089206 0.000000 0.000000 0.000000 0.037313 \nNetherlands 6.422837 4.804805 4.613541 4.585931 5.186567 \nNorway 2.676182 3.153153 3.594967 3.383793 2.500000 \nPoland 1.516503 2.327327 2.216896 2.537845 2.723881 \nPortugal 1.427297 1.726727 2.097064 1.825467 1.679104 \nRomania 0.624442 1.126126 0.778910 0.712378 0.932836 \nSlovakia 0.802855 0.450450 0.359497 0.445236 0.447761 \nSlovenia 0.624442 0.525526 0.599161 0.534283 0.634328 \nSpain 4.460303 3.678679 4.134212 4.986643 5.149254 \nSweden 3.033006 3.753754 3.535051 3.695459 4.216418 \nSwitzerland 3.300624 3.753754 3.235470 3.294746 2.761194 \nUnited Kingdom 32.381802 31.306306 31.815458 29.385574 29.141791 \n\nPublication Year 2016 2017 2018 2019 2020 \nCountry \nAustria 1.699970 1.689744 1.552958 1.816267 1.543488 \\\nBelgium 2.415747 2.112180 2.320712 2.355883 2.399730 \nBulgaria 0.566657 0.492842 0.314081 0.131614 0.281658 \nCroatia 0.208768 0.234687 0.331530 0.355357 0.326724 \nCyprus 0.149120 0.187749 0.122143 0.197420 0.315457 \nCzech Republic 1.073665 0.868341 0.977142 0.842327 0.912573 \nDenmark 2.206979 2.370336 3.402548 3.079758 2.760252 \nEstonia 0.298240 0.352030 0.261734 0.210582 0.428121 \nFinland 3.728005 2.957052 3.454894 3.171887 2.884182 \nFrance 9.692812 8.167097 8.567440 8.528560 7.785038 \nGermany 10.885774 10.701713 10.539173 10.542248 10.218567 \nGreece 1.491202 1.103027 1.413366 1.500395 1.374493 \nHungary 1.133313 0.797935 0.820101 0.802843 0.687247 \nIreland 1.342082 1.548932 1.256325 1.105554 1.306895 \nItaly 5.577095 5.796761 5.670913 5.804159 6.433078 \nLatvia 0.238592 0.234687 0.261734 0.131614 0.101397 \nLithuania 0.387712 0.281624 0.401326 0.500132 0.405588 \nLuxembourg 0.268416 0.305093 0.261734 0.236904 0.247859 \nMalta 0.029824 0.000000 0.000000 0.078968 0.022533 \nNetherlands 4.950790 5.163107 5.182342 5.369834 5.295178 \nNorway 2.624515 2.440742 2.338161 2.921822 2.850383 \nPoland 2.445571 2.299930 1.919386 1.816267 2.039207 \nPortugal 1.729794 1.854025 2.076426 1.789945 1.656151 \nRomania 0.775425 0.868341 0.994591 0.842327 0.619648 \nSlovakia 0.656129 0.422436 0.471122 0.355357 0.383055 \nSlovenia 0.805249 0.516311 0.820101 0.710713 0.349256 \nSpain 5.517447 5.444731 4.763567 4.685444 4.348806 \nSweden 5.070086 5.468200 4.048159 5.067123 4.044615 \nSwitzerland 2.833284 3.637644 3.402548 3.066596 2.963046 \nUnited Kingdom 29.197733 31.682704 32.053743 31.982101 35.015773 \n\nPublication Year 2021 2022 \nCountry \nAustria 1.712804 1.623248 \nBelgium 2.240533 2.312139 \nBulgaria 0.296269 0.150447 \nCroatia 0.305527 0.277140 \nCyprus 0.333302 0.340486 \nCzech Republic 0.861031 0.973949 \nDenmark 2.712712 2.715971 \nEstonia 0.416628 0.308813 \nFinland 2.675678 3.008948 \nFrance 7.471530 6.793887 \nGermany 11.202666 10.974741 \nGreece 1.286918 1.433209 \nHungary 0.768447 0.712645 \nIreland 1.546153 1.480719 \nItaly 5.934636 6.421728 \nLatvia 0.120359 0.142529 \nLithuania 0.351819 0.300895 \nLuxembourg 0.324044 0.403832 \nMalta 0.064809 0.079183 \nNetherlands 4.897695 5.186476 \nNorway 2.814554 2.462586 \nPoland 2.555319 2.795154 \nPortugal 1.888714 1.678676 \nRomania 0.444403 0.490934 \nSlovakia 0.333302 0.356323 \nSlovenia 0.444403 0.316731 \nSpain 4.379224 5.067701 \nSweden 3.962596 4.038324 \nSwitzerland 3.231182 3.539473 \nUnited Kingdom 34.422739 33.613113 ", "text/html": "
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Publication Year201120122013201420152016201720182019202020212022
Country
Austria1.9625331.8018021.5578191.7364201.8656721.6999701.6897441.5529581.8162671.5434881.7128041.623248
Belgium3.0330062.8528532.3966452.8940342.6492542.4157472.1121802.3207122.3558832.3997302.2405332.312139
Bulgaria0.3568240.3753750.4793290.4007120.2611940.5666570.4928420.3140810.1316140.2816580.2962690.150447
Croatia0.0892060.1501500.3594970.3561890.3731340.2087680.2346870.3315300.3553570.3267240.3055270.277140
Cyprus0.1784120.0750750.2995810.2226180.1865670.1491200.1877490.1221430.1974200.3154570.3333020.340486
Czech Republic1.1596791.1261260.9586580.9349960.7462691.0736650.8683410.9771420.8423270.9125730.8610310.973949
Denmark3.1222122.4774772.3966452.6268922.5373132.2069792.3703363.4025483.0797582.7602522.7127122.715971
Estonia0.2676180.2252250.4194130.4452360.4477610.2982400.3520300.2617340.2105820.4281210.4166280.308813
Finland2.7653882.6276282.6363093.6509353.7313433.7280052.9570523.4548943.1718872.8841822.6756783.008948
France10.4371109.75976010.42540410.28495110.0373139.6928128.1670978.5674408.5285607.7850387.4715306.793887
Germany10.97234612.91291311.50389512.15494211.56716410.88577410.70171310.53917310.54224810.21856711.20266610.974741
Greece1.3380911.3513511.1384061.4247551.3059701.4912021.1030271.4133661.5003951.3744931.2869181.433209
Hungary0.9812670.8258261.2582380.7123780.7462691.1333130.7979350.8201010.8028430.6872470.7684470.712645
Ireland1.1596791.2012011.3181551.3802321.0074631.3420821.5489321.2563251.1055541.3068951.5461531.480719
Italy4.5495095.2552555.0329545.1647376.6417915.5770955.7967615.6709135.8041596.4330785.9346366.421728
Latvia0.0000000.0000000.0599160.0000000.0373130.2385920.2346870.2617340.1316140.1013970.1203590.142529
Lithuania0.0892060.1501500.5991610.1780940.1492540.3877120.2816240.4013260.5001320.4055880.3518190.300895
Luxembourg0.1784120.2252250.1797480.0445240.2985070.2684160.3050930.2617340.2369040.2478590.3240440.403832
Malta0.0892060.0000000.0000000.0000000.0373130.0298240.0000000.0000000.0789680.0225330.0648090.079183
Netherlands6.4228374.8048054.6135414.5859315.1865674.9507905.1631075.1823425.3698345.2951784.8976955.186476
Norway2.6761823.1531533.5949673.3837932.5000002.6245152.4407422.3381612.9218222.8503832.8145542.462586
Poland1.5165032.3273272.2168962.5378452.7238812.4455712.2999301.9193861.8162672.0392072.5553192.795154
Portugal1.4272971.7267272.0970641.8254671.6791041.7297941.8540252.0764261.7899451.6561511.8887141.678676
Romania0.6244421.1261260.7789100.7123780.9328360.7754250.8683410.9945910.8423270.6196480.4444030.490934
Slovakia0.8028550.4504500.3594970.4452360.4477610.6561290.4224360.4711220.3553570.3830550.3333020.356323
Slovenia0.6244420.5255260.5991610.5342830.6343280.8052490.5163110.8201010.7107130.3492560.4444030.316731
Spain4.4603033.6786794.1342124.9866435.1492545.5174475.4447314.7635674.6854444.3488064.3792245.067701
Sweden3.0330063.7537543.5350513.6954594.2164185.0700865.4682004.0481595.0671234.0446153.9625964.038324
Switzerland3.3006243.7537543.2354703.2947462.7611942.8332843.6376443.4025483.0665962.9630463.2311823.539473
United Kingdom32.38180231.30630631.81545829.38557429.14179129.19773331.68270432.05374331.98210135.01577334.42273933.613113
\n
" }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "year_percent_pivot = pd.crosstab(collab_year['Country'], collab_year['Publication Year'], values=collab_year[record_col], aggfunc='nunique', normalize='columns').fillna(0)*100\n", "year_percent_pivot" ] }, { "cell_type": "code", "execution_count": 44, "id": "42dc8be7", "metadata": {}, "outputs": [ { "data": { "text/plain": "
", "image/png": 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\n" }, "metadata": {}, "output_type": "display_data" } ], "source": [ "f, ax = plt.subplots(figsize=(15, 15))\n", "g = sns.heatmap(year_percent_pivot, annot=True, fmt='.1f', linewidths=(.5), ax=ax, cbar=False)\n", "for t in ax.texts: t.set_text(t.get_text() + \" %\")\n", "g.set(xlabel=\"\", ylabel=\"\")\n", "for i in range(year_percent_pivot.shape[1]+1):\n", " ax.axvline(i, color='white', lw=10)" ] }, { "cell_type": "code", "execution_count": 44, "id": "e7b754ea", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 45, "id": "48f2898f", "metadata": {}, "outputs": [], "source": [ "# Institutional collab" ] }, { "cell_type": "code", "execution_count": 45, "id": "3a9538e1", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 46, "id": "6bb0e68d", "metadata": {}, "outputs": [], "source": [ "color_discrete_map= {'China': '#EF553B',\n", " 'EU': '#636EFA',\n", " 'Non-EU associate': '#00CC96'}" ] }, { "cell_type": "code", "execution_count": 47, "id": "df8701eb", "metadata": {}, "outputs": [], "source": [ "TOPN = 25\n", "\n", "\n", "wos_univ_locations = wos_univ.merge(wos_country_types, on=\"Country\")\n", "wos_univ_collabs = wos_univ_locations[wos_univ_locations[\"Country_Type\"]!=\"Other\"][[record_col,\"Country\",\"Institution_harm\",\"Country_Type\",\"Eurovoc_Class\"]].drop_duplicates()\n", "wos_univ_collabs[\"ISO3\"] = cc.pandas_convert(series=wos_univ_collabs[\"Country\"], to='ISO3')\n", "wos_univ_collabs[\"Institution_harm_label\"] = wos_univ_collabs[\"Institution_harm\"] + \" (\"+wos_univ_collabs[\"ISO3\"]+ \")\"\n", "\n", "\n", "wos_univ_ch = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]==\"China\"]\n", "wos_univ_eu = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]!=\"China\"]\n", "\n", "wos_univ_eu_strict = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]==\"EU\"]\n", "\n", "data_eu = (wos_univ_eu.groupby([\"Country\",\"Institution_harm_label\",\"Country_Type\"], as_index=False)[record_col].nunique()\n", " .sort_values(by=record_col,ascending=False).head(TOPN).copy()).sort_values(by=\"Country_Type\")\n", "\n", "data_eu_strict = (wos_univ_eu_strict.groupby([\"Country\",\"Institution_harm_label\",\"Eurovoc_Class\"], as_index=False)[record_col].nunique()\n", " .sort_values(by=record_col,ascending=False).head(TOPN).copy())\n", "\n", "data_ch = (wos_univ_ch.groupby([\"Country\",\"Institution_harm\",\"Country_Type\"], as_index=False)[record_col].nunique()\n", " .sort_values(by=record_col,ascending=False).head(TOPN).copy())\n", "\n", "\n", "for data,c_scope, y_lab, col_by, pat in zip([data_eu,data_eu_strict,data_ch],\n", " [\"European countries in scope\",\"EU-28 only\",\"China\"],\n", " [\"Institution_harm_label\",\"Institution_harm_label\",\"Institution_harm\"],\n", " [\"Country\",\"Eurovoc_Class\",\"Country_Type\"],\n", " [\"Country_Type\",None,None]):\n", " fig = px.bar(data, x=record_col, y=y_lab, color=col_by, color_discrete_map=color_discrete_map,pattern_shape=pat,\n", " labels={\n", " record_col: 'Number of co-publications',\n", " \"Institution_harm\": \"Institution\",\n", " \"Institution_harm_label\": \"Institution\",\n", " \"Country_Type\":\"Country type\",\n", " \"Eurovoc_Class\":\"Region\"\n", " },\n", " title=f\"Most visible institutions (top {TOPN} within {c_scope})\", template='plotly')\n", " fig.update_layout(xaxis_tickformat='d',font_family=\"Montserrat\",yaxis={'categoryorder':'total ascending'},\n", " width=1000, height=1000,)\n", " fig.update_traces(hovertemplate='%{x:d}')\n", " fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", " fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " # fig.show(config= dict(displayModeBar = False))\n", " fig.write_html(f\"plot_html/overall_inst_collab_bar_{c_scope}.html\",config= dict(displayModeBar = False, responsive = True))" ] }, { "cell_type": "code", "execution_count": 48, "id": "31a0769d", "metadata": {}, "outputs": [], "source": [ "wos_univ_ch = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]==\"China\"]\n", "wos_univ_eu = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]!=\"China\"]\n", "\n", "wos_univ_dipol = wos_univ_eu.merge(wos_univ_ch, on=record_col, suffixes=('_eu', '_ch')).merge(wos[[record_col,\"Domain_English\",\"Field_English\",\"SubField_English\"]], on =record_col)" ] }, { "cell_type": "code", "execution_count": 49, "id": "606e1af0", "metadata": {}, "outputs": [], "source": [ "fig = px.parallel_categories(wos_univ_dipol[[\"Country_eu\",\"Domain_English\",\"Country_ch\"]])" ] }, { "cell_type": "code", "execution_count": 50, "id": "ea0951e9", "metadata": {}, "outputs": [ { "data": { "text/plain": "Index(['Country', 'Institution_harm', 'Country_Type', 'UT (Unique WOS ID)'], dtype='object')" }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data_ch.columns" ] }, { "cell_type": "code", "execution_count": 51, "id": "dd4210b3", "metadata": {}, "outputs": [], "source": [ "subfilter = ((wos_univ_dipol[\"Institution_harm_label_eu\"].isin(data_eu[\"Institution_harm_label\"]))&\n", " (wos_univ_dipol[\"Institution_harm_ch\"].isin(data_ch[\"Institution_harm\"])))\n", "\n", "fig = px.parallel_categories(wos_univ_dipol[subfilter][[\"Country_eu\",\"Domain_English\",\"Country_ch\"]])\n", "# fig.show()" ] }, { "cell_type": "code", "execution_count": 52, "id": "2c5d1d94", "metadata": {}, "outputs": [], "source": [ "subfilter = ((wos_univ_dipol[\"Institution_harm_label_eu\"].isin(data_eu[\"Institution_harm_label\"]))&\n", " (wos_univ_dipol[\"Institution_harm_ch\"].isin(data_ch[\"Institution_harm\"])))\n", "\n", "fig = px.parallel_categories(wos_univ_dipol[subfilter][[\"Country_eu\",\"Institution_harm_eu\",\"Domain_English\",\"Institution_harm_ch\"]])\n", "# fig.show()\n", "sub_df =wos_univ_dipol[subfilter]\n", "\n", "inst_co_occur = pd.crosstab(sub_df['Institution_harm_label_eu'], sub_df['Institution_harm_ch'],\n", " values=sub_df[record_col], aggfunc='nunique').fillna(0).astype(int)\n", "\n", "eu_list = sub_df.groupby(['Institution_harm_label_eu'])[record_col].count().sort_values(ascending=False).index\n", "ch_list = sub_df.groupby(['Institution_harm_ch'])[record_col].count().sort_values(ascending=False).index\n", "\n", "inst_co_occur = inst_co_occur.reindex(index = eu_list, columns=ch_list)\n", "\n", "mask = np.triu(np.ones_like(inst_co_occur, dtype=bool))\n", "data = np.where(mask,inst_co_occur,inst_co_occur)\n", "\n", "fig = px.imshow(data,\n", " labels=dict(x=\"Institute (CH)\", y=\"Institute (EU)\", color=\"Co-publication\"),\n", " x=list(inst_co_occur.columns),\n", " y=list(inst_co_occur.index), title=f\"Most visible institutions (top {TOPN} within Europe)\"\n", " )\n", "fig.update_layout(title_x=0.5,\n", " width=1000, height=1000,\n", " xaxis_showgrid=False,\n", " yaxis_showgrid=False,\n", " yaxis_autorange='reversed',\n", " template='plotly_white',\n", " coloraxis_colorbar=dict(\n", " thicknessmode=\"pixels\", thickness=25,\n", " ticks=\"outside\", ticksuffix=\" \",\n", " dtick=20,outlinewidth=1,\n", " ))\n", "fig.update_xaxes(tickangle= -45)\n", "fig.update_yaxes(\n", " ticks=\"outside\")\n", "fig.update_xaxes(\n", " ticks=\"outside\")\n", "\n", "fig.write_html(f\"plot_html/overall_inst_collab_europe.html\",config= dict(displayModeBar = False, responsive = True))" ] }, { "cell_type": "code", "execution_count": 53, "id": "7bd7d149", "metadata": {}, "outputs": [], "source": [ "subfilter = ((wos_univ_dipol[\"Institution_harm_label_eu\"].isin(data_eu_strict[\"Institution_harm_label\"]))&\n", " (wos_univ_dipol[\"Institution_harm_ch\"].isin(data_ch[\"Institution_harm\"])))\n", "\n", "fig = px.parallel_categories(wos_univ_dipol[subfilter][[\"Country_eu\",\"Institution_harm_eu\",\"Domain_English\",\"Institution_harm_ch\"]])\n", "# fig.show()\n", "sub_df =wos_univ_dipol[subfilter]\n", "\n", "inst_co_occur = pd.crosstab(sub_df['Institution_harm_label_eu'], sub_df['Institution_harm_ch'],\n", " values=sub_df[record_col], aggfunc='nunique').fillna(0).astype(int)\n", "\n", "eu_list = sub_df.groupby(['Institution_harm_label_eu'])[record_col].count().sort_values(ascending=False).index\n", "ch_list = sub_df.groupby(['Institution_harm_ch'])[record_col].count().sort_values(ascending=False).index\n", "\n", "inst_co_occur = inst_co_occur.reindex(index = eu_list, columns=ch_list)\n", "\n", "mask = np.triu(np.ones_like(inst_co_occur, dtype=bool))\n", "data = np.where(mask,inst_co_occur,inst_co_occur)\n", "fig = px.imshow(data,\n", " labels=dict(x=\"Institute (CH)\", y=\"Institute (EU)\", color=\"Co-publication\"),\n", " x=list(inst_co_occur.columns),\n", " y=list(inst_co_occur.index), title=f\"Most visible institutions (top {TOPN} within EU-28)\"\n", " )\n", "fig.update_layout(title_x=0.5,\n", " width=1000, height=1000,\n", " xaxis_showgrid=False,\n", " yaxis_showgrid=False,\n", " yaxis_autorange='reversed',\n", " template='plotly_white',\n", " coloraxis_colorbar=dict(\n", " thicknessmode=\"pixels\", thickness=25,\n", " ticks=\"outside\", ticksuffix=\" \",\n", " dtick=20,outlinewidth=1,\n", " ))\n", "fig.update_xaxes(tickangle= -45)\n", "fig.update_yaxes(\n", " ticks=\"outside\")\n", "fig.update_xaxes(\n", " ticks=\"outside\")\n", "\n", "# fig.show(config= dict(displayModeBar = False))\n", "fig.write_html(f\"plot_html/overall_inst_collab_eu28.html\",config= dict(displayModeBar = False, responsive = True))" ] }, { "cell_type": "markdown", "source": [ "# Drilldown to field" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 54, "outputs": [], "source": [ "group = ['Publication Year',\"Domain_English\",'Field_English']\n", "# data = wos.groupby(['Publication Year',\"Domain_English\",'Field_English'], as_index=False)[record_col].nunique().sort_values(ascending=False, by=group+[record_col])\n", "\n", "\n", "data = (wos.groupby(['Publication Year','Field_English'],)[record_col].nunique(dropna=False).unstack()\n", " .fillna(0)\n", " .stack()\n", " .reset_index()\n", " .rename(columns={0:record_col}))\n", "\n", "data = data.merge(wos[[\"Domain_English\",'Field_English']].drop_duplicates(),on=\"Field_English\")\n", "\n", "data = data.merge(data[data[record_col]>0].sort_values(by=[\"Publication Year\"], ascending=True).drop_duplicates(subset='Field_English'),\n", " on='Field_English', suffixes=[None,\"_relative_growth\"])\n", "data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n", "\n", "data = data.sort_values(by =[\"Field_English\",\"Publication Year\"], ascending=[True,True])\n", "data[record_col+\"_cumsum\"] = (data.groupby('Field_English',as_index=False)[record_col].cumsum())" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 55, "outputs": [ { "data": { "text/plain": " Publication Year Field_English UT (Unique WOS ID) \n0 2011 Agriculture, Fisheries & Forestry 9.0 \\\n1 2012 Agriculture, Fisheries & Forestry 18.0 \n2 2013 Agriculture, Fisheries & Forestry 15.0 \n3 2014 Agriculture, Fisheries & Forestry 26.0 \n4 2015 Agriculture, Fisheries & Forestry 12.0 \n.. ... ... ... \n255 2018 Social Sciences 25.0 \n257 2019 Social Sciences 37.0 \n259 2020 Social Sciences 57.0 \n261 2021 Social Sciences 65.0 \n263 2022 Social Sciences 60.0 \n\n Domain_English Publication Year_relative_growth \n0 Applied Sciences 2011 \\\n1 Applied Sciences 2011 \n2 Applied Sciences 2011 \n3 Applied Sciences 2011 \n4 Applied Sciences 2011 \n.. ... ... \n255 Applied Sciences 2011 \n257 Applied Sciences 2011 \n259 Applied Sciences 2011 \n261 Applied Sciences 2011 \n263 Applied Sciences 2011 \n\n UT (Unique WOS ID)_relative_growth Domain_English_relative_growth \n0 0.000000 Applied Sciences \\\n1 1.000000 Applied Sciences \n2 0.666667 Applied Sciences \n3 1.888889 Applied Sciences \n4 0.333333 Applied Sciences \n.. ... ... \n255 1.272727 Applied Sciences \n257 2.363636 Applied Sciences \n259 4.181818 Applied Sciences \n261 4.909091 Applied Sciences \n263 4.454545 Applied Sciences \n\n UT (Unique WOS ID)_cumsum \n0 9.0 \n1 27.0 \n2 42.0 \n3 68.0 \n4 80.0 \n.. ... \n255 216.0 \n257 290.0 \n259 404.0 \n261 534.0 \n263 654.0 \n\n[84 rows x 8 columns]", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Publication YearField_EnglishUT (Unique WOS ID)Domain_EnglishPublication Year_relative_growthUT (Unique WOS ID)_relative_growthDomain_English_relative_growthUT (Unique WOS ID)_cumsum
02011Agriculture, Fisheries & Forestry9.0Applied Sciences20110.000000Applied Sciences9.0
12012Agriculture, Fisheries & Forestry18.0Applied Sciences20111.000000Applied Sciences27.0
22013Agriculture, Fisheries & Forestry15.0Applied Sciences20110.666667Applied Sciences42.0
32014Agriculture, Fisheries & Forestry26.0Applied Sciences20111.888889Applied Sciences68.0
42015Agriculture, Fisheries & Forestry12.0Applied Sciences20110.333333Applied Sciences80.0
...........................
2552018Social Sciences25.0Applied Sciences20111.272727Applied Sciences216.0
2572019Social Sciences37.0Applied Sciences20112.363636Applied Sciences290.0
2592020Social Sciences57.0Applied Sciences20114.181818Applied Sciences404.0
2612021Social Sciences65.0Applied Sciences20114.909091Applied Sciences534.0
2632022Social Sciences60.0Applied Sciences20114.454545Applied Sciences654.0
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84 rows × 8 columns

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" }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "data[data[\"Domain_English\"]==\"Applied Sciences\"]" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 56, "outputs": [ { "data": { "text/plain": " Field_English UT (Unique WOS ID)\n5 Information & Communication Technologies 15648\n4 Engineering 9232\n3 Enabling & Strategic Technologies 3940\n0 Agriculture, Fisheries & Forestry 612\n1 Built Environment & Design 537\n2 Economics & Business 15\n6 Social Sciences 1", "text/html": "
\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Field_EnglishUT (Unique WOS ID)
5Information & Communication Technologies15648
4Engineering9232
3Enabling & Strategic Technologies3940
0Agriculture, Fisheries & Forestry612
1Built Environment & Design537
2Economics & Business15
6Social Sciences1
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" }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "wos[wos[\"Domain_English\"]==\"Applied Sciences\"].groupby(\"Field_English\", as_index=False)[record_col].nunique().sort_values(ascending=False, by=record_col)" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 59, "outputs": [], "source": [ "group = ['Publication Year',\"Domain_English\",'Field_English']\n", "# data = wos.groupby(['Publication Year',\"Domain_English\",'Field_English'], as_index=False)[record_col].nunique().sort_values(ascending=False, by=group+[record_col])\n", "data_complete = pd.DataFrame()\n", "\n", "for cat in sorted(wos[\"Domain_English\"].unique()):\n", "\n", " os.makedirs(rf'plot_html/{cat}',exist_ok=True)\n", " id_subset = wos[wos[\"Domain_English\"]==cat][record_col].unique()\n", "\n", " data = (wos.groupby(['Publication Year','Field_English'],)[record_col].nunique(dropna=False).unstack()\n", " .fillna(0)\n", " .stack()\n", " .reset_index()\n", " .rename(columns={0:record_col}))\n", "\n", " data = data.merge(wos[[\"Domain_English\",'Field_English']].drop_duplicates(),on=\"Field_English\")\n", "\n", " data = data.merge(data[data[record_col]>0].sort_values(by=[\"Publication Year\"], ascending=True).drop_duplicates(subset='Field_English'),\n", " on='Field_English', suffixes=[None,\"_relative_growth\"])\n", " data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n", "\n", " data = data.sort_values(by =[\"Field_English\",\"Publication Year\"], ascending=[True,True])\n", " data[record_col+\"_cumsum\"] = (data.groupby('Field_English',as_index=False)[record_col].cumsum())\n", "\n", "\n", "\n", " bar_data = wos[wos[\"Domain_English\"]==cat].groupby(\"Field_English\", as_index=False)[record_col].nunique().sort_values(ascending=False, by=record_col)\n", "\n", " fig = px.bar(bar_data.sort_values(by=\"Field_English\"), x=record_col, y=\"Field_English\", color=\"Field_English\",barmode='relative',\n", " labels={\n", " record_col: 'Number of co-publications',\n", " },\n", " title=\"Distribution of Domains\", template='plotly')\n", " fig.update_layout(showlegend=False, xaxis_tickformat='d',font_family=\"Montserrat\")\n", " fig.update_traces(hovertemplate='%{x:d}')\n", " fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", " fig.update_layout(yaxis={'categoryorder':'total ascending'})\n", " fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " dom_distr = go.Figure(fig)\n", "\n", "\n", " #data segment\n", " sub_data = data[data[\"Domain_English\"]==cat]\n", " # data_complete = pd.concat([data_complete,sub_data], ignore_index=True)\n", " fig = px.line(sub_data.sort_values(ascending=[True,True], by=[\"Publication Year\",\"Field_English\"]),y=record_col,x=\"Publication Year\", color=\"Field_English\", markers=True,\n", " labels={\n", " record_col: 'Number of co-publications',\n", " group[-1]: \"Domain\",\n", " },\n", " title=\"Yearly output of co-publications\", template='plotly')\n", " fig.update_traces(hovertemplate='%{y:d}')\n", " fig.update_layout(hovermode='x unified')\n", " fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", " fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "\n", " year_output_by_domain = go.Figure(fig)\n", "\n", " fig = px.line(sub_data.sort_values(ascending=[True,True], by=[\"Publication Year\",\"Field_English\"]), y=record_col+\"_relative_growth\",x=\"Publication Year\", color=\"Field_English\",\n", " markers=True,labels={\n", " record_col+\"_relative_growth\": 'Rel. growth
in co-publications (%)',\n", " group[-1]: \"Domain\",\n", " },\n", " title=\"Relative growth in the output of co-publications\", template='plotly')\n", " # fig.update_traces(hovertemplate='%{y:.2f}%')\n", "\n", " fig.update_layout(hovermode='x unified',yaxis_tickformat='.0f%',font_family=\"Montserrat\")\n", " fig.update_traces(hovertemplate='%{y:.0f}00%')\n", " fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", " fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " # fig['layout']['yaxis4'].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey')\n", " # fig.update_yaxes(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey')\n", "\n", " rel_output_by_domain = go.Figure(fig)\n", "\n", " fig = px.area(sub_data.sort_values(ascending=[True,True], by=[\"Publication Year\",\"Field_English\"]),y=record_col+\"_cumsum\",x=\"Publication Year\", color=\"Field_English\",line_group=\"Field_English\",\n", " labels={\n", " record_col+\"_cumsum\": 'Cumulative number of co-publications',\n", " },\n", " title=\"Cumulative number of co-publications\", template='plotly')\n", " fig.update_traces(hovertemplate='%{y:d}')\n", " fig.update_layout(hovermode='x unified')\n", " fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", " fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "\n", " cumsum_by_domain = go.Figure(fig)\n", " # cumsum_by_domain.show(config= dict(displayModeBar = False))\n", "\n", " # dom_distr\n", " # year_output_by_domain\n", " # rel_output_by_domain\n", " # cumsum_by_domain\n", "\n", " figsuper = make_subplots(rows=2, cols=2, subplot_titles=[\"Distribution of domains\",\"Cumulative sum of co-publications\",\n", " \"Co-publications per year\",\"Relative growth of co-publications\"])\n", "\n", "\n", " for trace in list(dom_distr.select_traces()):\n", " trace.showlegend=False\n", " # trace.barmode\n", " figsuper.add_trace(trace,\n", " row=1, col=1\n", " )\n", "\n", " for trace in list(cumsum_by_domain.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=1, col=2\n", " )\n", "\n", " for trace in list(year_output_by_domain.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=2, col=1\n", " )\n", "\n", " for trace in list(rel_output_by_domain.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=2, col=2\n", " )\n", "\n", " # figsuper.update_layout(hovermode='x unified')\n", " figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative')\n", " figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", " figsuper['layout']['yaxis4'].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", " # figsuper.layout.annotations[0].update(x=0.1)\n", " # figsuper.layout.annotations[2].update(x=0.105)\n", " # figsuper.layout.annotations[1].update(x=0.7)\n", " # figsuper.layout.annotations[3].update(x=0.7)\n", " figsuper.update_layout(title_text=f\"{cat}\")\n", "\n", " # figsuper.show(config= dict(displayModeBar = False, responsive = True))\n", " figsuper.write_html(f\"plot_html/{cat}/{cat}_distr&trends.html\",config= dict(displayModeBar = False, responsive = True))\n", "\n", "\n", " # country contributions\n", " wos_univ_locations = wos_univ[wos_univ[record_col].isin(id_subset)].merge(wos_country_types, on=\"Country\")\n", " wos_collabs = wos_univ_locations[wos_univ_locations[\"Country_Type\"]!=\"Other\"][[record_col,\"Country\"]].drop_duplicates()\n", "\n", " collab_desc = wos_collabs[wos_collabs[\"Country\"]!=\"China\"][\"Country\"].value_counts().reset_index()\n", " collab_desc[\"percent_of_copubs\"] = collab_desc[\"count\"]/wos_collabs[record_col].nunique()#*100\n", " collab_desc[\"percent_contrib_in_copubs\"] = collab_desc[\"count\"]/wos_collabs[record_col].size#*100\n", " collab_desc = collab_desc.merge(wos_country_types, on=\"Country\")\n", " # collab_desc\n", "\n", " c_dict = {\"count\":\"Number of co-publications\",\n", " \"percent_of_copubs\":\"Percent of co-publications\",\n", " \"percent_contrib_in_copubs\":\"Contribution to co-publications\"}\n", "\n", " color_discrete_map= {'China': '#EF553B',\n", " 'EU': '#636EFA',\n", " 'Non-EU associate': '#00CC96'}\n", "\n", " fig_dict = dict()\n", " for c in c_dict.keys():\n", " data = collab_desc[[\"Country\",c,\"Country_Type\"]]\n", " # plt.figure(figsize=(9,12))\n", " col_by=\"Country_Type\"\n", " y_lab=\"Country\"\n", " fig = px.bar(data, x=c, y=y_lab, color=col_by, color_discrete_map=color_discrete_map,\n", " labels=dict({\n", " record_col: 'Number of co-publications',\n", " \"Institution_harm\": \"Institution\",\n", " \"Institution_harm_label\": \"Institution\",\n", " \"Country_Type\":\"Country type\",\n", " \"Eurovoc_Class\":\"Region\"\n", " },**c_dict),\n", " title=c_dict[c], template='plotly')\n", " fig.update_layout(xaxis_tickformat='d',font_family=\"Montserrat\",\n", " yaxis={'categoryorder':'total ascending'},\n", " width=1000, height=1000,)\n", " if \"percent\" in c:\n", " fig.update_traces(hovertemplate='%{y}
%{x}')\n", " fig.update_xaxes(tickformat=\".1%\")\n", " else:\n", " fig.update_traces(hovertemplate='%{y}
%{x:d}')\n", " fig_dict[c] = go.Figure(fig)\n", "\n", " figsuper = make_subplots(rows=1, cols=3, subplot_titles =list(c_dict.values()))\n", " for i,f in enumerate(fig_dict.keys()):\n", " sfig = fig_dict[f]\n", " for trace in list(sfig.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=1, col=i+1)\n", "\n", " figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n", " figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", " # figsuper.show(config= dict(displayModeBar = False, responsive = True))\n", " figsuper.write_html(f\"plot_html/{cat}/{cat}_europe_contribution_bar.html\",config= dict(displayModeBar = False, responsive = True))\n", "\n", "\n", " # intraeurope collabs\n", " wos_collabs_EU = wos_univ_locations[~wos_univ_locations[\"Country_Type\"].isin([\"Other\",\"China\"])][[record_col,\"Country\"]].drop_duplicates()\n", " wos_collabs_EU = wos_collabs_EU.merge(wos_collabs_EU, on=record_col)\n", " EU_co_occur = pd.crosstab(wos_collabs_EU['Country_x'], wos_collabs_EU['Country_y'], values=wos_collabs_EU[record_col], aggfunc='nunique').fillna(0).astype(int)\n", "\n", "\n", " eu_list = wos_collabs_EU.groupby(['Country_x'])[record_col].count().sort_values(ascending=False).index\n", "\n", " EU_co_occur = EU_co_occur.reindex(index = eu_list, columns=eu_list)\n", "\n", " # Generate a mask for the upper triangle\n", " mask = np.triu(np.ones_like(EU_co_occur, dtype=bool))\n", " data = np.where(mask,None,EU_co_occur)\n", "\n", " fig = px.imshow(data,\n", " labels=dict(x=\"Country\", y=\"Country\", color=\"Co-publication with China\"),\n", " x=list(EU_co_occur.columns),\n", " y=list(EU_co_occur.index), title=\"Intraeuropean patterns
Co-occurences of countries in chinese co-publications\"\n", " )\n", " fig.update_layout(title_x=0.5,\n", " width=1000, height=1000,\n", " xaxis_showgrid=False,\n", " yaxis_showgrid=False,\n", " yaxis_autorange='reversed', template='plotly_white')\n", " # fig.update_traces(hovertemplate='%{y}
%{x}
Co-publications: %{hovertext}')\n", " fig.update_xaxes(tickangle= -90)\n", " fig.update_yaxes(\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " ticks=\"outside\")\n", " # fig.show(config= dict(displayModeBar = False,responsive=True))\n", " fig.write_html(f\"plot_html/{cat}/{cat}_intraeurope_collabs.html\",config= dict(displayModeBar = False, responsive = True))\n", "\n", " # country trends\n", " collab_year = wos_collabs[wos_collabs[\"Country\"]!=\"China\"].copy()\n", " collab_year = collab_year.merge(wos_country_types, on=\"Country\").merge(wos[[record_col,\"Publication Year\"]],on=record_col).drop_duplicates()\n", "\n", " data = (collab_year.groupby(['Publication Year',\"Country\"])[record_col]\n", " .nunique(dropna=False).unstack()\n", " .fillna(0)\n", " .stack()\n", " .reset_index()\n", " .rename(columns={0:record_col}))\n", " data = data.merge(data[data[record_col]>0].sort_values(by=[\"Publication Year\"], ascending=True).drop_duplicates(subset=\"Country\"),\n", " on=[\"Country\"], suffixes=[None,\"_relative_growth\"])\n", " data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]*100\n", " data = data.sort_values(by =[\"Country\",\"Publication Year\"], ascending=[True,True])\n", " data[record_col+\"_cumsum\"] = (data.groupby('Country',as_index=False)[record_col].cumsum())\n", " data = data.merge(wos_country_types, on='Country')\n", " # data\n", "\n", " data[\"ISO3\"] = cc.pandas_convert(series=data[\"Country\"], to='ISO3')\n", " fig = px.choropleth(data[data[\"Publication Year\"] == 2022], locations=\"ISO3\", color=record_col+\"_cumsum\", hover_name=\"Country\",\n", " scope=\"europe\", template='plotly',\n", " range_color=[data[record_col+\"_cumsum\"].min(),data[record_col+\"_cumsum\"].max()],hover_data=[\"Eurovoc_Class\"])\n", " # original: '%{hovertext}

ISO3=%{location}
Eurovoc_Class=%{customdata[0]}
UT (Unique WOS ID)_cumsum=%{z}'\n", "\n", " fig.update_traces(hovertemplate='%{hovertext}'\n", " '
Region: %{customdata[0]}
'\n", " 'Co-pubications: %{z:d}')\n", "\n", " cumsum_country = go.Figure(fig)\n", "\n", " figsuper = make_subplots(rows=3, cols=2, subplot_titles=[\"Number of publications (2022)\",\"Cumulative number of co-publications\",\n", " \"Yearly output of co-publications\",\"Relative growth of co-publications\"],\n", " specs=[\n", " [{\"type\": \"geo\", \"rowspan\":3}, {\"type\": \"xy\"}],\n", " [None,{\"type\": \"xy\"}],\n", " [None, {\"type\": \"xy\"}]\n", " ])\n", "\n", " for trace in list(cumsum_country.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=1, col=1\n", " )\n", "\n", " fig = px.area(data.sort_values(ascending=True, by='Publication Year'), y=record_col+\"_cumsum\",\n", " x='Publication Year',\n", " color=\"Eurovoc_Class\",\n", " line_group=\"Country\",\n", " labels={\n", " record_col: 'Number of co-publications',\n", " \"Eurovoc_Class\": \"Region\"\n", " },\n", " title=\"Cumulative number of co-publications\",\n", " hover_name= \"Country\")\n", " fig.update_traces(hovertemplate='%{hovertext}
%{x}
Co-publications: %{y}')\n", "\n", " for trace in list(fig.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=1, col=2\n", " )\n", "\n", "\n", " fig = px.line(data.sort_values(ascending=True, by='Publication Year'),\n", " y=record_col,\n", " x='Publication Year',\n", " color=\"Eurovoc_Class\",\n", " line_group=\"Country\",\n", " markers=True,\n", " labels={\n", " record_col: 'Number of co-publications',\n", " \"Eurovoc_Class\": \"Region\"\n", " },\n", " title=\"Yearly output of co-publications\",hover_name= \"Country\")\n", " fig.update_traces(hovertemplate='%{hovertext}
%{x}
Co-publications: %{y}')\n", "\n", " for trace in list(fig.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=2, col=2\n", " )\n", "\n", " fig = px.line(data.sort_values(ascending=True, by='Publication Year'),\n", " y=record_col+\"_relative_growth\",\n", " x='Publication Year',\n", " color=\"Eurovoc_Class\",line_group=\"Country\",markers=True,\n", " labels={\n", " record_col+\"_relative_growth\": 'Relative growth of co-publications (%)',\"Eurovoc_Class\": \"Region\"\n", " },\n", " title=\"Relative growth of co-publications\", template='plotly',hover_name= \"Country\")\n", " fig.update_traces(hovertemplate='%{hovertext}
%{x}
Relative growth: %{y}%')\n", " fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", "\n", " for trace in list(fig.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=3, col=2\n", " )\n", "\n", " figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_layout({'template':\"plotly\"})\n", " figsuper.layout[\"geo\"][\"scope\"] = 'europe'\n", " figsuper.update_coloraxes(colorbar=dict(lenmode='fraction',len=0.55, orientation=\"v\",yanchor='top', title=\"Co-publications\",\n", " ticks=\"outside\", ticksuffix=\" \",outlinewidth=0.5))\n", " # figsuper.show(config= dict(displayModeBar = False, responsive = True))\n", " figsuper.write_html(f\"plot_html/{cat}/{cat}_country_trends_overall.html\",config= dict(displayModeBar = False, responsive = True))\n", "\n", "\n", " TOPN = 25\n", " wos_univ_locations = wos_univ[wos_univ[record_col].isin(id_subset)].merge(wos_country_types, on=\"Country\")\n", " wos_univ_collabs = wos_univ_locations[wos_univ_locations[\"Country_Type\"]!=\"Other\"][[record_col,\"Country\",\"Institution_harm\",\"Country_Type\",\"Eurovoc_Class\"]].drop_duplicates()\n", " wos_univ_collabs[\"ISO3\"] = cc.pandas_convert(series=wos_univ_collabs[\"Country\"], to='ISO3')\n", " wos_univ_collabs[\"Institution_harm_label\"] = wos_univ_collabs[\"Institution_harm\"] + \" (\"+wos_univ_collabs[\"ISO3\"]+ \")\"\n", "\n", "\n", " wos_univ_ch = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]==\"China\"]\n", " wos_univ_eu = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]!=\"China\"]\n", "\n", " wos_univ_eu_strict = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]==\"EU\"]\n", "\n", " data_eu = (wos_univ_eu.groupby([\"Country\",\"Institution_harm_label\",\"Country_Type\"], as_index=False)[record_col].nunique()\n", " .sort_values(by=record_col,ascending=False).head(TOPN).copy()).sort_values(by=\"Country_Type\")\n", "\n", " data_eu_strict = (wos_univ_eu_strict.groupby([\"Country\",\"Institution_harm_label\",\"Eurovoc_Class\"], as_index=False)[record_col].nunique()\n", " .sort_values(by=record_col,ascending=False).head(TOPN).copy())\n", "\n", " data_ch = (wos_univ_ch.groupby([\"Country\",\"Institution_harm\",\"Country_Type\"], as_index=False)[record_col].nunique()\n", " .sort_values(by=record_col,ascending=False).head(TOPN).copy())\n", "\n", "\n", " for data,c_scope, y_lab, col_by, pat in zip([data_eu,data_eu_strict,data_ch],\n", " [\"European countries in scope\",\"EU-28 only\",\"China\"],\n", " [\"Institution_harm_label\",\"Institution_harm_label\",\"Institution_harm\"],\n", " [\"Country\",\"Eurovoc_Class\",\"Country_Type\"],\n", " [\"Country_Type\",None,None]):\n", " fig = px.bar(data, x=record_col, y=y_lab, color=col_by, color_discrete_map=color_discrete_map,pattern_shape=pat,\n", " labels={\n", " record_col: 'Number of co-publications',\n", " \"Institution_harm\": \"Institution\",\n", " \"Institution_harm_label\": \"Institution\",\n", " \"Country_Type\":\"Country type\",\n", " \"Eurovoc_Class\":\"Region\"\n", " },\n", " title=f\"Most visible institutions (top {TOPN} within {c_scope})\", template='plotly')\n", " fig.update_layout(xaxis_tickformat='d',font_family=\"Montserrat\",yaxis={'categoryorder':'total ascending'},\n", " width=1000, height=1000,)\n", " fig.update_traces(hovertemplate='%{x:d}')\n", " fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", " fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " # fig.show(config= dict(displayModeBar = False))\n", " fig.write_html(f\"plot_html/{cat}/{cat}_overall_inst_collab_bar_{c_scope}.html\",config= dict(displayModeBar = False, responsive = True))\n", " wos_univ_ch = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]==\"China\"]\n", " wos_univ_eu = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]!=\"China\"]\n", "\n", " wos_univ_dipol = wos_univ_eu.merge(wos_univ_ch, on=record_col, suffixes=('_eu', '_ch')).merge(wos[[record_col,\"Domain_English\",\"Field_English\",\"SubField_English\"]], on =record_col)\n", "\n", " subfilter = ((wos_univ_dipol[\"Institution_harm_label_eu\"].isin(data_eu[\"Institution_harm_label\"]))&\n", " (wos_univ_dipol[\"Institution_harm_ch\"].isin(data_ch[\"Institution_harm\"])))\n", "\n", " fig = px.parallel_categories(wos_univ_dipol[subfilter][[\"Country_eu\",\"Institution_harm_eu\",\"Domain_English\",\"Institution_harm_ch\"]])\n", " # fig.show()\n", " sub_df = wos_univ_dipol[subfilter]\n", "\n", " inst_co_occur = pd.crosstab(sub_df['Institution_harm_label_eu'], sub_df['Institution_harm_ch'],\n", " values=sub_df[record_col], aggfunc='nunique').fillna(0).astype(int)\n", "\n", " eu_list = sub_df.groupby(['Institution_harm_label_eu'])[record_col].count().sort_values(ascending=False).index\n", " ch_list = sub_df.groupby(['Institution_harm_ch'])[record_col].count().sort_values(ascending=False).index\n", "\n", " inst_co_occur = inst_co_occur.reindex(index = eu_list, columns=ch_list)\n", "\n", " mask = np.triu(np.ones_like(inst_co_occur, dtype=bool))\n", " data = np.where(mask,inst_co_occur,inst_co_occur)\n", "\n", " fig = px.imshow(data,\n", " labels=dict(x=\"Institute (CH)\", y=\"Institute (EU)\", color=\"Co-publication\"),\n", " x=list(inst_co_occur.columns),\n", " y=list(inst_co_occur.index), title=f\"Most visible institutions (top {TOPN} within Europe)\"\n", " )\n", " fig.update_layout(title_x=0.5,\n", " width=1000, height=1000,\n", " xaxis_showgrid=False,\n", " yaxis_showgrid=False,\n", " yaxis_autorange='reversed',\n", " template='plotly_white',\n", " coloraxis_colorbar=dict(\n", " thicknessmode=\"pixels\", thickness=25,\n", " ticks=\"outside\", ticksuffix=\" \",\n", " dtick=20,outlinewidth=1,\n", " ))\n", " fig.update_xaxes(tickangle= -45)\n", " fig.update_yaxes(\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " ticks=\"outside\")\n", "\n", " fig.write_html(f\"plot_html/{cat}/{cat}_overall_inst_collab_europe.html\",config= dict(displayModeBar = False, responsive = True))\n", "\n", "\n", "\n", " subfilter = ((wos_univ_dipol[\"Institution_harm_label_eu\"].isin(data_eu_strict[\"Institution_harm_label\"]))&\n", " (wos_univ_dipol[\"Institution_harm_ch\"].isin(data_ch[\"Institution_harm\"])))\n", "\n", " fig = px.parallel_categories(wos_univ_dipol[subfilter][[\"Country_eu\",\"Institution_harm_eu\",\"Domain_English\",\"Institution_harm_ch\"]])\n", " # fig.show()\n", " sub_df =wos_univ_dipol[subfilter]\n", "\n", " inst_co_occur = pd.crosstab(sub_df['Institution_harm_label_eu'], sub_df['Institution_harm_ch'],\n", " values=sub_df[record_col], aggfunc='nunique').fillna(0).astype(int)\n", "\n", " eu_list = sub_df.groupby(['Institution_harm_label_eu'])[record_col].count().sort_values(ascending=False).index\n", " ch_list = sub_df.groupby(['Institution_harm_ch'])[record_col].count().sort_values(ascending=False).index\n", "\n", " inst_co_occur = inst_co_occur.reindex(index = eu_list, columns=ch_list)\n", "\n", " mask = np.triu(np.ones_like(inst_co_occur, dtype=bool))\n", " data = np.where(mask,inst_co_occur,inst_co_occur)\n", " fig = px.imshow(data,\n", " labels=dict(x=\"Institute (CH)\", y=\"Institute (EU)\", color=\"Co-publication\"),\n", " x=list(inst_co_occur.columns),\n", " y=list(inst_co_occur.index), title=f\"Most visible institutions (top {TOPN} within EU-28)\"\n", " )\n", " fig.update_layout(title_x=0.5,\n", " width=1000, height=1000,\n", " xaxis_showgrid=False,\n", " yaxis_showgrid=False,\n", " yaxis_autorange='reversed',\n", " template='plotly_white',\n", " coloraxis_colorbar=dict(\n", " thicknessmode=\"pixels\", thickness=25,\n", " ticks=\"outside\", ticksuffix=\" \",\n", " dtick=20,outlinewidth=1,\n", " ))\n", " fig.update_xaxes(tickangle= -45)\n", " fig.update_yaxes(\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " ticks=\"outside\")\n", "\n", " # fig.show(config= dict(displayModeBar = False))\n", " fig.write_html(f\"plot_html/{cat}/{cat}_overall_inst_collab_eu28.html\",config= dict(displayModeBar = False, responsive = True))" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ "# Drill down to subfield" ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 63, "outputs": [], "source": [ "group = ['Publication Year',\"Domain_English\",'Field_English']\n", "# data = wos.groupby(['Publication Year',\"Domain_English\",'Field_English'], as_index=False)[record_col].nunique().sort_values(ascending=False, by=group+[record_col])\n", "data_complete = pd.DataFrame()\n", "\n", "colt=[\"Domain_English\",'Field_English','SubField_English']\n", "\n", "for c in colt:\n", " wos[c] = wos[c].str.strip()\n", "\n", "for cat in sorted(wos[\"Domain_English\"].unique()):\n", " os.makedirs(rf'plot_html/{cat}',exist_ok=True)\n", " wos_sub = wos[wos[\"Domain_English\"]==cat]\n", "\n", " for cat2 in sorted(wos_sub[\"Field_English\"].unique()):\n", " os.makedirs(rf'plot_html/{cat}/{cat2}',exist_ok=True)\n", "\n", " id_subset = wos[((wos[\"Domain_English\"]==cat)&\n", " (wos[\"Field_English\"]==cat2))][record_col].unique()\n", "\n", " data = (wos[wos[record_col].isin(id_subset)]\n", " .groupby(['Publication Year','SubField_English'],)[record_col].nunique(dropna=False).unstack()\n", " .fillna(0)\n", " .stack()\n", " .reset_index()\n", " .rename(columns={0:record_col}))\n", "\n", " data = data.merge(wos_sub[[\"Field_English\",'SubField_English']]\n", " .drop_duplicates(),on=\"SubField_English\")\n", "\n", " data = data.merge(data[data[record_col]>0].sort_values(by=[\"Publication Year\"], ascending=True).drop_duplicates(subset='SubField_English'),\n", " on='SubField_English', suffixes=[None,\"_relative_growth\"])\n", " data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]\n", "\n", " data = data.sort_values(by =[\"SubField_English\",\"Publication Year\"], ascending=[True,True])\n", " data[record_col+\"_cumsum\"] = (data.groupby('SubField_English',as_index=False)[record_col].cumsum())\n", "\n", "\n", "\n", " bar_data = (wos[((wos[\"Domain_English\"]==cat)&\n", " (wos[\"Field_English\"]==cat2))]\n", " .groupby(\"SubField_English\", as_index=False)[record_col]\n", " .nunique()\n", " .sort_values(ascending=False, by=record_col))\n", "\n", " fig = px.bar(bar_data.sort_values(by=\"SubField_English\"),\n", " x=record_col, y=\"SubField_English\", color=\"SubField_English\",barmode='relative',\n", " labels={\n", " record_col: 'Number of co-publications',\n", " },\n", " title=\"Distribution of Domains\", template='plotly')\n", " fig.update_layout(showlegend=False, xaxis_tickformat='d',font_family=\"Montserrat\")\n", " fig.update_traces(hovertemplate='%{x:d}')\n", " fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", " fig.update_layout(yaxis={'categoryorder':'total ascending'})\n", " fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " dom_distr = go.Figure(fig)\n", "\n", "\n", " #data segment\n", " sub_data = data[data[\"Field_English\"]==cat2]\n", " # data_complete = pd.concat([data_complete,sub_data], ignore_index=True)\n", " fig = px.line(sub_data.sort_values(ascending=[True,True], by=[\"Publication Year\",\"SubField_English\"]),y=record_col,x=\"Publication Year\", color=\"SubField_English\", markers=True,\n", " labels={\n", " record_col: 'Number of co-publications',\n", " group[-1]: \"Domain\",\n", " },\n", " title=\"Yearly output of co-publications\", template='plotly')\n", " fig.update_traces(hovertemplate='%{y:d}')\n", " fig.update_layout(hovermode='x unified')\n", " fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", " fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "\n", " year_output_by_domain = go.Figure(fig)\n", "\n", " fig = px.line(sub_data.sort_values(ascending=[True,True], by=[\"Publication Year\",\"SubField_English\"]), y=record_col+\"_relative_growth\",x=\"Publication Year\", color=\"SubField_English\",\n", " markers=True,labels={\n", " record_col+\"_relative_growth\": 'Rel. growth
in co-publications (%)',\n", " group[-1]: \"Domain\",\n", " },\n", " title=\"Relative growth in the output of co-publications\", template='plotly')\n", " # fig.update_traces(hovertemplate='%{y:.2f}%')\n", "\n", " fig.update_layout(hovermode='x unified',yaxis_tickformat='.0f%',font_family=\"Montserrat\")\n", " fig.update_traces(hovertemplate='%{y:.0f}00%')\n", " fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", " fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " # fig['layout']['yaxis4'].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey')\n", " # fig.update_yaxes(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey')\n", "\n", " rel_output_by_domain = go.Figure(fig)\n", "\n", " fig = px.area(sub_data.sort_values(ascending=[True,True], by=[\"Publication Year\",\"SubField_English\"]),y=record_col+\"_cumsum\",x=\"Publication Year\", color=\"SubField_English\",line_group=\"SubField_English\",\n", " labels={\n", " record_col+\"_cumsum\": 'Cumulative number of co-publications',\n", " },\n", " title=\"Cumulative number of co-publications\", template='plotly')\n", " fig.update_traces(hovertemplate='%{y:d}')\n", " fig.update_layout(hovermode='x unified')\n", " fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", " fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", "\n", " cumsum_by_domain = go.Figure(fig)\n", " # cumsum_by_domain.show(config= dict(displayModeBar = False))\n", "\n", " # dom_distr\n", " # year_output_by_domain\n", " # rel_output_by_domain\n", " # cumsum_by_domain\n", "\n", " figsuper = make_subplots(rows=2, cols=2, subplot_titles=[\"Distribution of domains\",\"Cumulative sum of co-publications\",\n", " \"Co-publications per year\",\"Relative growth of co-publications\"])\n", "\n", "\n", " for trace in list(dom_distr.select_traces()):\n", " trace.showlegend=False\n", " # trace.barmode\n", " figsuper.add_trace(trace,\n", " row=1, col=1\n", " )\n", "\n", " for trace in list(cumsum_by_domain.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=1, col=2\n", " )\n", "\n", " for trace in list(year_output_by_domain.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=2, col=1\n", " )\n", "\n", " for trace in list(rel_output_by_domain.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=2, col=2\n", " )\n", "\n", " # figsuper.update_layout(hovermode='x unified')\n", " figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative')\n", " figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", " figsuper['layout']['yaxis4'].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", " # figsuper.layout.annotations[0].update(x=0.1)\n", " # figsuper.layout.annotations[2].update(x=0.105)\n", " # figsuper.layout.annotations[1].update(x=0.7)\n", " # figsuper.layout.annotations[3].update(x=0.7)\n", " figsuper.update_layout(title_text=f\"{cat}: {cat2}\")\n", "\n", " # figsuper.show(config= dict(displayModeBar = False, responsive = True))\n", " figsuper.write_html(f\"plot_html/{cat}/{cat2}/{cat2}_distr&trends.html\",config= dict(displayModeBar = False, responsive = True))\n", "\n", "\n", " # country contributions\n", " wos_univ_locations = wos_univ[wos_univ[record_col].isin(id_subset)].merge(wos_country_types, on=\"Country\")\n", " wos_collabs = wos_univ_locations[wos_univ_locations[\"Country_Type\"]!=\"Other\"][[record_col,\"Country\"]].drop_duplicates()\n", "\n", " collab_desc = wos_collabs[wos_collabs[\"Country\"]!=\"China\"][\"Country\"].value_counts().reset_index()\n", " collab_desc[\"percent_of_copubs\"] = collab_desc[\"count\"]/wos_collabs[record_col].nunique()#*100\n", " collab_desc[\"percent_contrib_in_copubs\"] = collab_desc[\"count\"]/wos_collabs[record_col].size#*100\n", " collab_desc = collab_desc.merge(wos_country_types, on=\"Country\")\n", " # collab_desc\n", "\n", " c_dict = {\"count\":\"Number of co-publications\",\n", " \"percent_of_copubs\":\"Percent of co-publications\",\n", " \"percent_contrib_in_copubs\":\"Contribution to co-publications\"}\n", "\n", " color_discrete_map= {'China': '#EF553B',\n", " 'EU': '#636EFA',\n", " 'Non-EU associate': '#00CC96'}\n", "\n", " fig_dict = dict()\n", " for c in c_dict.keys():\n", " data = collab_desc[[\"Country\",c,\"Country_Type\"]]\n", " # plt.figure(figsize=(9,12))\n", " col_by=\"Country_Type\"\n", " y_lab=\"Country\"\n", " fig = px.bar(data, x=c, y=y_lab, color=col_by, color_discrete_map=color_discrete_map,\n", " labels=dict({\n", " record_col: 'Number of co-publications',\n", " \"Institution_harm\": \"Institution\",\n", " \"Institution_harm_label\": \"Institution\",\n", " \"Country_Type\":\"Country type\",\n", " \"Eurovoc_Class\":\"Region\"\n", " },**c_dict),\n", " title=c_dict[c], template='plotly')\n", " fig.update_layout(xaxis_tickformat='d',font_family=\"Montserrat\",\n", " yaxis={'categoryorder':'total ascending'},\n", " width=1000, height=1000,)\n", " if \"percent\" in c:\n", " fig.update_traces(hovertemplate='%{y}
%{x}')\n", " fig.update_xaxes(tickformat=\".1%\")\n", " else:\n", " fig.update_traces(hovertemplate='%{y}
%{x:d}')\n", " fig_dict[c] = go.Figure(fig)\n", "\n", " figsuper = make_subplots(rows=1, cols=3, subplot_titles =list(c_dict.values()))\n", " for i,f in enumerate(fig_dict.keys()):\n", " sfig = fig_dict[f]\n", " for trace in list(sfig.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=1, col=i+1)\n", "\n", " figsuper.update_layout(yaxis={'categoryorder':'total ascending'}, barmode='relative',yaxis2={'categoryorder':'total ascending'},yaxis3={'categoryorder':'total ascending'})\n", " figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_layout({'template':\"plotly\",\"font_family\":\"Montserrat\"})\n", " # figsuper.show(config= dict(displayModeBar = False, responsive = True))\n", " figsuper.write_html(f\"plot_html/{cat}/{cat2}/{cat2}_europe_contribution_bar.html\",config= dict(displayModeBar = False, responsive = True))\n", "\n", "\n", " # intraeurope collabs\n", " wos_collabs_EU = wos_univ_locations[~wos_univ_locations[\"Country_Type\"].isin([\"Other\",\"China\"])][[record_col,\"Country\"]].drop_duplicates()\n", " wos_collabs_EU = wos_collabs_EU.merge(wos_collabs_EU, on=record_col)\n", " EU_co_occur = pd.crosstab(wos_collabs_EU['Country_x'], wos_collabs_EU['Country_y'], values=wos_collabs_EU[record_col], aggfunc='nunique').fillna(0).astype(int)\n", "\n", "\n", " eu_list = wos_collabs_EU.groupby(['Country_x'])[record_col].count().sort_values(ascending=False).index\n", "\n", " EU_co_occur = EU_co_occur.reindex(index = eu_list, columns=eu_list)\n", "\n", " # Generate a mask for the upper triangle\n", " mask = np.triu(np.ones_like(EU_co_occur, dtype=bool))\n", " data = np.where(mask,None,EU_co_occur)\n", "\n", " fig = px.imshow(data,\n", " labels=dict(x=\"Country\", y=\"Country\", color=\"Co-publication with China\"),\n", " x=list(EU_co_occur.columns),\n", " y=list(EU_co_occur.index), title=\"Intraeuropean patterns
Co-occurences of countries in chinese co-publications\"\n", " )\n", " fig.update_layout(title_x=0.5,\n", " width=1000, height=1000,\n", " xaxis_showgrid=False,\n", " yaxis_showgrid=False,\n", " yaxis_autorange='reversed', template='plotly_white')\n", " # fig.update_traces(hovertemplate='%{y}
%{x}
Co-publications: %{hovertext}')\n", " fig.update_xaxes(tickangle= -90)\n", " fig.update_yaxes(\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " ticks=\"outside\")\n", " # fig.show(config= dict(displayModeBar = False,responsive=True))\n", " fig.write_html(f\"plot_html/{cat}/{cat2}/{cat2}_intraeurope_collabs.html\",config= dict(displayModeBar = False, responsive = True))\n", "\n", " # country trends\n", " collab_year = wos_collabs[wos_collabs[\"Country\"]!=\"China\"].copy()\n", " collab_year = collab_year.merge(wos_country_types, on=\"Country\").merge(wos[[record_col,\"Publication Year\"]],on=record_col).drop_duplicates()\n", "\n", " data = (collab_year.groupby(['Publication Year',\"Country\"])[record_col]\n", " .nunique(dropna=False).unstack()\n", " .fillna(0)\n", " .stack()\n", " .reset_index()\n", " .rename(columns={0:record_col}))\n", " data = data.merge(data[data[record_col]>0].sort_values(by=[\"Publication Year\"], ascending=True).drop_duplicates(subset=\"Country\"),\n", " on=[\"Country\"], suffixes=[None,\"_relative_growth\"])\n", " data[record_col+\"_relative_growth\"] = (data[record_col]-data[record_col+\"_relative_growth\"])/data[record_col+\"_relative_growth\"]*100\n", " data = data.sort_values(by =[\"Country\",\"Publication Year\"], ascending=[True,True])\n", " data[record_col+\"_cumsum\"] = (data.groupby('Country',as_index=False)[record_col].cumsum())\n", " data = data.merge(wos_country_types, on='Country')\n", " # data\n", "\n", " data[\"ISO3\"] = cc.pandas_convert(series=data[\"Country\"], to='ISO3')\n", " fig = px.choropleth(data[data[\"Publication Year\"] == 2022], locations=\"ISO3\", color=record_col+\"_cumsum\", hover_name=\"Country\",\n", " scope=\"europe\", template='plotly',\n", " range_color=[data[record_col+\"_cumsum\"].min(),data[record_col+\"_cumsum\"].max()],hover_data=[\"Eurovoc_Class\"])\n", " # original: '%{hovertext}

ISO3=%{location}
Eurovoc_Class=%{customdata[0]}
UT (Unique WOS ID)_cumsum=%{z}'\n", "\n", " fig.update_traces(hovertemplate='%{hovertext}'\n", " '
Region: %{customdata[0]}
'\n", " 'Co-pubications: %{z:d}')\n", "\n", " cumsum_country = go.Figure(fig)\n", "\n", " figsuper = make_subplots(rows=3, cols=2, subplot_titles=[\"Number of publications (2022)\",\"Cumulative number of co-publications\",\n", " \"Yearly output of co-publications\",\"Relative growth of co-publications\"],\n", " specs=[\n", " [{\"type\": \"geo\", \"rowspan\":3}, {\"type\": \"xy\"}],\n", " [None,{\"type\": \"xy\"}],\n", " [None, {\"type\": \"xy\"}]\n", " ])\n", "\n", " for trace in list(cumsum_country.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=1, col=1\n", " )\n", "\n", " fig = px.area(data.sort_values(ascending=True, by='Publication Year'), y=record_col+\"_cumsum\",\n", " x='Publication Year',\n", " color=\"Eurovoc_Class\",\n", " line_group=\"Country\",\n", " labels={\n", " record_col: 'Number of co-publications',\n", " \"Eurovoc_Class\": \"Region\"\n", " },\n", " title=\"Cumulative number of co-publications\",\n", " hover_name= \"Country\")\n", " fig.update_traces(hovertemplate='%{hovertext}
%{x}
Co-publications: %{y}')\n", "\n", " for trace in list(fig.select_traces()):\n", " figsuper.add_trace(trace,\n", " row=1, col=2\n", " )\n", "\n", "\n", " fig = px.line(data.sort_values(ascending=True, by='Publication Year'),\n", " y=record_col,\n", " x='Publication Year',\n", " color=\"Eurovoc_Class\",\n", " line_group=\"Country\",\n", " markers=True,\n", " labels={\n", " record_col: 'Number of co-publications',\n", " \"Eurovoc_Class\": \"Region\"\n", " },\n", " title=\"Yearly output of co-publications\",hover_name= \"Country\")\n", " fig.update_traces(hovertemplate='%{hovertext}
%{x}
Co-publications: %{y}')\n", "\n", " for trace in list(fig.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=2, col=2\n", " )\n", "\n", " fig = px.line(data.sort_values(ascending=True, by='Publication Year'),\n", " y=record_col+\"_relative_growth\",\n", " x='Publication Year',\n", " color=\"Eurovoc_Class\",line_group=\"Country\",markers=True,\n", " labels={\n", " record_col+\"_relative_growth\": 'Relative growth of co-publications (%)',\"Eurovoc_Class\": \"Region\"\n", " },\n", " title=\"Relative growth of co-publications\", template='plotly',hover_name= \"Country\")\n", " fig.update_traces(hovertemplate='%{hovertext}
%{x}
Relative growth: %{y}%')\n", " fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", "\n", " for trace in list(fig.select_traces()):\n", " trace.showlegend=False\n", " figsuper.add_trace(trace,\n", " row=3, col=2\n", " )\n", "\n", " figsuper.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_xaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", " ticks=\"outside\")\n", " figsuper.update_layout({'template':\"plotly\"})\n", " figsuper.layout[\"geo\"][\"scope\"] = 'europe'\n", " figsuper.update_coloraxes(colorbar=dict(lenmode='fraction',len=0.55, orientation=\"v\",yanchor='top', title=\"Co-publications\",\n", " ticks=\"outside\", ticksuffix=\" \",outlinewidth=0.5))\n", " # figsuper.show(config= dict(displayModeBar = False, responsive = True))\n", " figsuper.write_html(f\"plot_html/{cat}/{cat2}/{cat2}_country_trends_overall.html\",config= dict(displayModeBar = False, responsive = True))\n", "\n", "\n", " TOPN = 25\n", " wos_univ_locations = wos_univ[wos_univ[record_col].isin(id_subset)].merge(wos_country_types, on=\"Country\")\n", " wos_univ_collabs = wos_univ_locations[wos_univ_locations[\"Country_Type\"]!=\"Other\"][[record_col,\"Country\",\"Institution_harm\",\"Country_Type\",\"Eurovoc_Class\"]].drop_duplicates()\n", " wos_univ_collabs[\"ISO3\"] = cc.pandas_convert(series=wos_univ_collabs[\"Country\"], to='ISO3')\n", " wos_univ_collabs[\"Institution_harm_label\"] = wos_univ_collabs[\"Institution_harm\"] + \" (\"+wos_univ_collabs[\"ISO3\"]+ \")\"\n", "\n", "\n", " wos_univ_ch = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]==\"China\"]\n", " wos_univ_eu = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]!=\"China\"]\n", "\n", " wos_univ_eu_strict = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]==\"EU\"]\n", "\n", " data_eu = (wos_univ_eu.groupby([\"Country\",\"Institution_harm_label\",\"Country_Type\"], as_index=False)[record_col].nunique()\n", " .sort_values(by=record_col,ascending=False).head(TOPN).copy()).sort_values(by=\"Country_Type\")\n", "\n", " data_eu_strict = (wos_univ_eu_strict.groupby([\"Country\",\"Institution_harm_label\",\"Eurovoc_Class\"], as_index=False)[record_col].nunique()\n", " .sort_values(by=record_col,ascending=False).head(TOPN).copy())\n", "\n", " data_ch = (wos_univ_ch.groupby([\"Country\",\"Institution_harm\",\"Country_Type\"], as_index=False)[record_col].nunique()\n", " .sort_values(by=record_col,ascending=False).head(TOPN).copy())\n", "\n", "\n", " for data,c_scope, y_lab, col_by, pat in zip([data_eu,data_eu_strict,data_ch],\n", " [\"European countries in scope\",\"EU-28 only\",\"China\"],\n", " [\"Institution_harm_label\",\"Institution_harm_label\",\"Institution_harm\"],\n", " [\"Country\",\"Eurovoc_Class\",\"Country_Type\"],\n", " [\"Country_Type\",None,None]):\n", " fig = px.bar(data, x=record_col, y=y_lab, color=col_by, color_discrete_map=color_discrete_map,pattern_shape=pat,\n", " labels={\n", " record_col: 'Number of co-publications',\n", " \"Institution_harm\": \"Institution\",\n", " \"Institution_harm_label\": \"Institution\",\n", " \"Country_Type\":\"Country type\",\n", " \"Eurovoc_Class\":\"Region\"\n", " },\n", " title=f\"Most visible institutions (top {TOPN} within {c_scope})\", template='plotly')\n", " fig.update_layout(xaxis_tickformat='d',font_family=\"Montserrat\",yaxis={'categoryorder':'total ascending'},\n", " width=1000, height=1000,)\n", " fig.update_traces(hovertemplate='%{x:d}')\n", " fig.add_shape(\n", " # Rectangle with reference to the plot\n", " type=\"rect\",\n", " xref=\"paper\",\n", " yref=\"paper\",\n", " x0=0,\n", " y0=0,\n", " x1=1.0,\n", " y1=1.0,\n", " line=dict(\n", " color=\"black\",\n", " width=0.5,\n", " )\n", " )\n", " fig.update_yaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " showgrid=True,\n", " ticks=\"outside\")\n", " # fig.show(config= dict(displayModeBar = False))\n", " fig.write_html(f\"plot_html/{cat}/{cat2}/{cat2}_overall_inst_collab_bar_{c_scope}.html\",config= dict(displayModeBar = False, responsive = True))\n", " wos_univ_ch = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]==\"China\"]\n", " wos_univ_eu = wos_univ_collabs[wos_univ_collabs[\"Country_Type\"]!=\"China\"]\n", "\n", " wos_univ_dipol = wos_univ_eu.merge(wos_univ_ch, on=record_col, suffixes=('_eu', '_ch')).merge(wos[[record_col,\"Domain_English\",\"Field_English\",\"SubField_English\"]], on =record_col)\n", "\n", " subfilter = ((wos_univ_dipol[\"Institution_harm_label_eu\"].isin(data_eu[\"Institution_harm_label\"]))&\n", " (wos_univ_dipol[\"Institution_harm_ch\"].isin(data_ch[\"Institution_harm\"])))\n", "\n", " fig = px.parallel_categories(wos_univ_dipol[subfilter][[\"Country_eu\",\"Institution_harm_eu\",\"Domain_English\",\"Institution_harm_ch\"]])\n", " # fig.show()\n", " sub_df = wos_univ_dipol[subfilter]\n", "\n", " inst_co_occur = pd.crosstab(sub_df['Institution_harm_label_eu'], sub_df['Institution_harm_ch'],\n", " values=sub_df[record_col], aggfunc='nunique').fillna(0).astype(int)\n", "\n", " eu_list = sub_df.groupby(['Institution_harm_label_eu'])[record_col].count().sort_values(ascending=False).index\n", " ch_list = sub_df.groupby(['Institution_harm_ch'])[record_col].count().sort_values(ascending=False).index\n", "\n", " inst_co_occur = inst_co_occur.reindex(index = eu_list, columns=ch_list)\n", "\n", " mask = np.triu(np.ones_like(inst_co_occur, dtype=bool))\n", " data = np.where(mask,inst_co_occur,inst_co_occur)\n", "\n", " fig = px.imshow(data,\n", " labels=dict(x=\"Institute (CH)\", y=\"Institute (EU)\", color=\"Co-publication\"),\n", " x=list(inst_co_occur.columns),\n", " y=list(inst_co_occur.index), title=f\"Most visible institutions (top {TOPN} within Europe)\"\n", " )\n", " fig.update_layout(title_x=0.5,\n", " width=1000, height=1000,\n", " xaxis_showgrid=False,\n", " yaxis_showgrid=False,\n", " yaxis_autorange='reversed',\n", " template='plotly_white',\n", " coloraxis_colorbar=dict(\n", " thicknessmode=\"pixels\", thickness=25,\n", " ticks=\"outside\", ticksuffix=\" \",\n", " dtick=20,outlinewidth=1,\n", " ))\n", " fig.update_xaxes(tickangle= -45)\n", " fig.update_yaxes(\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " ticks=\"outside\")\n", "\n", " fig.write_html(f\"plot_html/{cat}/{cat2}/{cat2}_overall_inst_collab_europe.html\",config= dict(displayModeBar = False, responsive = True))\n", "\n", "\n", "\n", " subfilter = ((wos_univ_dipol[\"Institution_harm_label_eu\"].isin(data_eu_strict[\"Institution_harm_label\"]))&\n", " (wos_univ_dipol[\"Institution_harm_ch\"].isin(data_ch[\"Institution_harm\"])))\n", "\n", " fig = px.parallel_categories(wos_univ_dipol[subfilter][[\"Country_eu\",\"Institution_harm_eu\",\"Domain_English\",\"Institution_harm_ch\"]])\n", " # fig.show()\n", " sub_df =wos_univ_dipol[subfilter]\n", "\n", " inst_co_occur = pd.crosstab(sub_df['Institution_harm_label_eu'], sub_df['Institution_harm_ch'],\n", " values=sub_df[record_col], aggfunc='nunique').fillna(0).astype(int)\n", "\n", " eu_list = sub_df.groupby(['Institution_harm_label_eu'])[record_col].count().sort_values(ascending=False).index\n", " ch_list = sub_df.groupby(['Institution_harm_ch'])[record_col].count().sort_values(ascending=False).index\n", "\n", " inst_co_occur = inst_co_occur.reindex(index = eu_list, columns=ch_list)\n", "\n", " mask = np.triu(np.ones_like(inst_co_occur, dtype=bool))\n", " data = np.where(mask,inst_co_occur,inst_co_occur)\n", " fig = px.imshow(data,\n", " labels=dict(x=\"Institute (CH)\", y=\"Institute (EU)\", color=\"Co-publication\"),\n", " x=list(inst_co_occur.columns),\n", " y=list(inst_co_occur.index), title=f\"Most visible institutions (top {TOPN} within EU-28)\"\n", " )\n", " fig.update_layout(title_x=0.5,\n", " width=1000, height=1000,\n", " xaxis_showgrid=False,\n", " yaxis_showgrid=False,\n", " yaxis_autorange='reversed',\n", " template='plotly_white',\n", " coloraxis_colorbar=dict(\n", " thicknessmode=\"pixels\", thickness=25,\n", " ticks=\"outside\", ticksuffix=\" \",\n", " dtick=20,outlinewidth=1,\n", " ))\n", " fig.update_xaxes(tickangle= -45)\n", " fig.update_yaxes(\n", " ticks=\"outside\")\n", " fig.update_xaxes(\n", " ticks=\"outside\")\n", "\n", " # fig.show(config= dict(displayModeBar = False))\n", " fig.write_html(f\"plot_html/{cat}/{cat2}/{cat2}_overall_inst_collab_eu28.html\",config= dict(displayModeBar = False, responsive = True))" ], "metadata": { "collapsed": false } } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.9.16" } }, "nbformat": 4, "nbformat_minor": 5 }