diff --git a/WOS/wos_analysis/wos_analyses.ipynb b/WOS/wos_analysis/wos_analyses.ipynb index 227585b..8d592dc 100644 --- a/WOS/wos_analysis/wos_analyses.ipynb +++ b/WOS/wos_analysis/wos_analyses.ipynb @@ -42,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "outputs": [], "source": [ "os.makedirs('plot_html',exist_ok=True)\n", @@ -54,8 +54,15 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 3, "outputs": [ + { + "data": { + "text/html": " \n " + }, + "metadata": {}, + "output_type": "display_data" + }, { "data": { "application/vnd.plotly.v1+json": { @@ -238087,7 +238094,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -238122,18 +238129,8 @@ }, { "cell_type": "code", - "execution_count": 55, - "outputs": [ - { - "data": { - "text/plain": " Publication Years Record Count Scope\n0 2022 137341 CH\n1 2021 115165 CH\n2 2020 93883 CH\n3 2019 84000 CH\n4 2018 66036 CH\n.. ... ... ...\n79 2015 37300 USA\n80 2014 33877 USA\n81 2013 29787 USA\n82 2012 27217 USA\n83 2011 24728 USA\n\n[84 rows x 3 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
Publication YearsRecord CountScope
02022137341CH
12021115165CH
2202093883CH
3201984000CH
4201866036CH
............
79201537300USA
80201433877USA
81201329787USA
82201227217USA
83201124728USA
\n

84 rows × 3 columns

\n
" - }, - "execution_count": 55, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": 5, + "outputs": [], "source": [ "aggr_dict = dict()\n", "file_end = '_analyze.txt'\n", @@ -238146,8 +238143,8 @@ " minidf = minidf[minidf[\"Publication Years\"]<2023].copy()\n", " minidf[\"Scope\"] = d.replace(file_end,'').replace('_',\"+\")\n", " aggr_analys = pd.concat([aggr_analys,minidf], ignore_index=True)\n", - "aggr_analys\n", - "\n" + "aggr_analys[\"Scope\"] = aggr_analys[\"Scope\"].str.replace(\"USA\",\"US\")\n", + "aggr_analys[\"Scope\"] = aggr_analys[\"Scope\"].str.replace(\"CH\",\"CN\")\n" ], "metadata": { "collapsed": false @@ -238155,20 +238152,20 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 7, "outputs": [ { "data": { - "text/plain": " Publication Years Record Count Scope\n0 2022 137341 CH\n1 2021 115165 CH\n2 2020 93883 CH\n3 2019 84000 CH\n4 2018 66036 CH\n5 2017 51529 CH\n6 2016 44347 CH\n7 2015 37593 CH\n8 2014 33470 CH\n9 2013 27578 CH\n10 2012 24429 CH\n11 2011 20404 CH\n24 2021 106565 EU\n25 2022 101584 EU\n26 2020 91410 EU\n27 2019 86682 EU\n28 2018 74242 EU\n29 2017 62142 EU\n30 2016 57011 EU\n31 2015 52705 EU\n32 2014 47238 EU\n33 2013 41915 EU\n34 2012 36755 EU\n35 2011 34544 EU\n60 2022 382731 global\n61 2021 372598 global\n62 2020 319809 global\n63 2019 300113 global\n64 2018 254106 global\n65 2017 206298 global\n66 2016 184780 global\n67 2015 166257 global\n68 2014 148467 global\n69 2013 128943 global\n70 2012 114565 global\n71 2011 100984 global\n72 2021 81152 USA\n73 2022 72447 USA\n74 2020 71191 USA\n75 2019 68441 USA\n76 2018 57237 USA\n77 2017 47038 USA\n78 2016 41587 USA\n79 2015 37300 USA\n80 2014 33877 USA\n81 2013 29787 USA\n82 2012 27217 USA\n83 2011 24728 USA", - "text/html": "
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Publication YearsRecord CountScope
02022137341CH
12021115165CH
2202093883CH
3201984000CH
4201866036CH
5201751529CH
6201644347CH
7201537593CH
8201433470CH
9201327578CH
10201224429CH
11201120404CH
242021106565EU
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26202091410EU
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31201552705EU
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33201341915EU
34201236755EU
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602022382731global
612021372598global
622020319809global
632019300113global
642018254106global
652017206298global
662016184780global
672015166257global
682014148467global
692013128943global
702012114565global
712011100984global
72202181152USA
73202272447USA
74202071191USA
75201968441USA
76201857237USA
77201747038USA
78201641587USA
79201537300USA
80201433877USA
81201329787USA
82201227217USA
83201124728USA
\n
" + "text/plain": " Publication Years Record Count Scope\n12 2021 11677 CN+US\n13 2020 10774 CN+US\n14 2022 10551 CN+US\n15 2019 10098 CN+US\n16 2018 7907 CN+US\n17 2017 6064 CN+US\n18 2016 4827 CN+US\n19 2015 4051 CN+US\n20 2014 3321 CN+US\n21 2013 2541 CN+US\n22 2012 2002 CN+US\n23 2011 1629 CN+US\n36 2022 11079 EU+CN\n37 2021 10573 EU+CN\n38 2020 8492 EU+CN\n39 2019 7007 EU+CN\n40 2018 5243 EU+CN\n41 2017 3802 EU+CN\n42 2016 3046 EU+CN\n43 2015 2487 EU+CN\n44 2014 2123 EU+CN\n45 2013 1558 EU+CN\n46 2012 1226 EU+CN\n47 2011 1000 EU+CN\n48 2021 13659 EU+US\n49 2022 12705 EU+US\n50 2020 11524 EU+US\n51 2019 10610 EU+US\n52 2018 9044 EU+US\n53 2017 7587 EU+US\n54 2016 6714 EU+US\n55 2015 5911 EU+US\n56 2014 5177 EU+US\n57 2013 4525 EU+US\n58 2012 3982 EU+US\n59 2011 3663 EU+US", + "text/html": "
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Publication YearsRecord CountScope
12202111677CN+US
13202010774CN+US
14202210551CN+US
15201910098CN+US
1620187907CN+US
1720176064CN+US
1820164827CN+US
1920154051CN+US
2020143321CN+US
2120132541CN+US
2220122002CN+US
2320111629CN+US
36202211079EU+CN
37202110573EU+CN
3820208492EU+CN
3920197007EU+CN
4020185243EU+CN
4120173802EU+CN
4220163046EU+CN
4320152487EU+CN
4420142123EU+CN
4520131558EU+CN
4620121226EU+CN
4720111000EU+CN
48202113659EU+US
49202212705EU+US
50202011524EU+US
51201910610EU+US
5220189044EU+US
5320177587EU+US
5420166714EU+US
5520155911EU+US
5620145177EU+US
5720134525EU+US
5820123982EU+US
5920113663EU+US
\n
" }, - "execution_count": 60, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "aggr_analys[(~aggr_analys[\"Scope\"].str.contains(\"+\", regex=False))]" + "aggr_analys[(aggr_analys[\"Scope\"].str.contains(\"+\", regex=False))]" ], "metadata": { "collapsed": false @@ -238176,7 +238173,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 8, "outputs": [ { "data": { @@ -238229,8 +238226,8 @@ "type": "scatter" }, { - "hovertemplate": "Scope=CH
Publication Years=%{x}
Record Count=%{y}", - "legendgroup": "CH", + "hovertemplate": "Scope=CN
Publication Years=%{x}
Record Count=%{y}", + "legendgroup": "CN", "line": { "color": "#EF553B", "dash": "solid" @@ -238239,7 +238236,7 @@ "symbol": "circle" }, "mode": "lines+markers", - "name": "CH", + "name": "CN", "orientation": "v", "showlegend": true, "x": [ @@ -238275,8 +238272,8 @@ "type": "scatter" }, { - "hovertemplate": "Scope=USA
Publication Years=%{x}
Record Count=%{y}", - "legendgroup": "USA", + "hovertemplate": "Scope=US
Publication Years=%{x}
Record Count=%{y}", + "legendgroup": "US", "line": { "color": "#00cc96", "dash": "solid" @@ -238285,7 +238282,7 @@ "symbol": "circle" }, "mode": "lines+markers", - "name": "USA", + "name": "US", "orientation": "v", "showlegend": true, "x": [ @@ -239239,7 +239236,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -239248,7 +239245,7 @@ "source": [ "aggr_analys\n", "fig = px.line(aggr_analys[(~aggr_analys[\"Scope\"].str.contains(\"+\",regex=False))].sort_values(by='Publication Years'),x='Publication Years', y='Record Count', color='Scope',markers=True,\n", - " category_orders={\"Scope\": [\"global\", \"CH\", \"USA\", \"EU\"]})\n", + " category_orders={\"Scope\": [\"global\", \"CN\", \"US\", \"EU\"]})\n", "fig.update_layout(xaxis_tickformat='d',font_family=\"Montserrat\", template=\"plotly\")\n", "fig.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", @@ -239271,15 +239268,15 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 9, "outputs": [ { "data": { "application/vnd.plotly.v1+json": { "data": [ { - "hovertemplate": "Scope=EU+USA
Publication Years=%{x}
Record Count=%{y}", - "legendgroup": "EU+USA", + "hovertemplate": "Scope=EU+US
Publication Years=%{x}
Record Count=%{y}", + "legendgroup": "EU+US", "line": { "color": "#FFA15A", "dash": "solid" @@ -239288,7 +239285,7 @@ "symbol": "circle" }, "mode": "lines+markers", - "name": "EU+USA", + "name": "EU+US", "orientation": "v", "showlegend": true, "x": [ @@ -239324,8 +239321,8 @@ "type": "scatter" }, { - "hovertemplate": "Scope=EU+CH
Publication Years=%{x}
Record Count=%{y}", - "legendgroup": "EU+CH", + "hovertemplate": "Scope=EU+CN
Publication Years=%{x}
Record Count=%{y}", + "legendgroup": "EU+CN", "line": { "color": "#19d3f3", "dash": "solid" @@ -239334,7 +239331,7 @@ "symbol": "circle" }, "mode": "lines+markers", - "name": "EU+CH", + "name": "EU+CN", "orientation": "v", "showlegend": true, "x": [ @@ -239370,8 +239367,8 @@ "type": "scatter" }, { - "hovertemplate": "Scope=CH+USA
Publication Years=%{x}
Record Count=%{y}", - "legendgroup": "CH+USA", + "hovertemplate": "Scope=CN+US
Publication Years=%{x}
Record Count=%{y}", + "legendgroup": "CN+US", "line": { "color": "#FF6692", "dash": "solid" @@ -239380,7 +239377,7 @@ "symbol": "circle" }, "mode": "lines+markers", - "name": "CH+USA", + "name": "CN+US", "orientation": "v", "showlegend": true, "x": [ @@ -240288,7 +240285,7 @@ "plotlyServerURL": "https://plotly.com" } }, - "text/html": "
" + "text/html": "
" }, "metadata": {}, "output_type": "display_data" @@ -240297,7 +240294,7 @@ "source": [ "aggr_analys\n", "fig = px.line(aggr_analys[(aggr_analys[\"Scope\"].str.contains(\"+\",regex=False))].sort_values(by='Publication Years'),x='Publication Years', y='Record Count', color='Scope',markers=True,\n", - " category_orders={\"Scope\": [\"global\", \"CH\", \"USA\", \"EU\"]})\n", + " category_orders={\"Scope\": [\"global\", \"CN\", \"US\", \"EU\"]})\n", "fig.update_layout(xaxis_tickformat='d',font_family=\"Montserrat\", template=\"plotly\")\n", "fig.update_yaxes(\n", " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", @@ -240321,6 +240318,15 @@ { "cell_type": "code", "execution_count": null, + "outputs": [], + "source": [], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 8, "id": "ea3629f5", "metadata": {}, "outputs": [], @@ -240332,7 +240338,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "fb7baf32", "metadata": {}, "outputs": [], @@ -240345,7 +240351,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "4dd8e081", "metadata": {}, "outputs": [], @@ -240364,7 +240370,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "eb933d66", "metadata": {}, "outputs": [], @@ -240375,7 +240381,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "cd0b0efa", "metadata": {}, "outputs": [], @@ -240386,7 +240392,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "1e737dbf", "metadata": {}, "outputs": [], @@ -240394,6 +240400,28 @@ "record_col = \"UT (Unique WOS ID)\"" ] }, + { + "cell_type": "code", + "execution_count": 18, + "outputs": [ + { + "data": { + "text/plain": "0.06419887103777681" + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "w_temp = wos_country.merge(wos_country_types, on='Country')\n", + "\n", + "w_temp[((w_temp['Eurovoc_Class']=='Eastern Europe')|(w_temp['Country'].isin([\"Estonia\",\"Latvia\",\"Lithuania\"])))][record_col].nunique()/wos_country[record_col].nunique()" + ], + "metadata": { + "collapsed": false + } + }, { "cell_type": "markdown", "id": "b1aa7f2d", @@ -240935,1794 +240963,1429 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "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", - " data[\"ISO3\"] = cc.pandas_convert(series=data[\"Country\"], to='ISO3')\n", - " # plt.figure(figsize=(9,12))\n", - " col_by=\"Country_Type\"\n", - " y_lab=\"ISO3\"\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,text_auto=True,\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", - "\n", - "figsuper['layout']['xaxis1'].update(tickformat=\".0f\")\n", - "figsuper['layout']['xaxis2'].update(tickformat=\".1%\")\n", - "figsuper['layout']['xaxis3'].update(tickformat=\".1%\")\n", - "figsuper['layout'][\"font\"][\"size\"]=12\n", - "for a in figsuper['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 14\n", - "figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\n", - "figsuper[\"layout\"][\"yaxis2\"][\"tickfont\"][\"size\"] = 10\n", - "figsuper[\"layout\"][\"yaxis3\"][\"tickfont\"][\"size\"] = 10\n", - "\n", - "figsuper.update_layout(uniformtext_minsize=10)\n", - "\n", - "figsuper.show(config= dict(displayModeBar = False, responsive = True))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "figsuper.write_html(f\"plot_html/europe_contribution_bar.html\",config= dict(displayModeBar = False, responsive = True))\n", - "figsuper.write_image(f\"plot_html/europe_contribution_bar.svg\",height=800,width=1600)\n", - "figsuper.write_image(f\"plot_html/europe_contribution_bar.png\",height=800,width=1600)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "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", - "EU_co_occur_columns = list(EU_co_occur.columns)\n", - "EU_co_occur_index = list(EU_co_occur.index)\n", - "EU_co_occur_columns[-1] = \"\"\n", - "EU_co_occur_index[0] = \"\"\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=EU_co_occur_columns,\n", - " y=EU_co_occur_index, title=\"Intraeuropean patterns
Co-occurences of countries in chinese co-publications\",\n", - " text_auto=True\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',yaxis_title=None,xaxis_title=None)\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))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "fig.write_html(f\"plot_html/intraeurope_collabs.html\",config= dict(displayModeBar = False, responsive = True))\n", - "fig.write_image(f\"plot_html/intraeurope_collabs.svg\",height=800,width=1600)\n", - "fig.write_image(f\"plot_html/intraeurope_collabs.png\",height=800,width=1600)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "figsuper_ppt = go.Figure(fig)\n", - "\n", - "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - "for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - "\n", - "s=16\n", - "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", - "figsuper_ppt.write_image(f\"plot_html/PPT_plots/intraeurope_collabs.png\",height=900,width=1600,scale = 4)\n", - "figsuper_ppt.show()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "id": "122d0260", - "metadata": {}, - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "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\"]\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", - "\n", - "yearsum = collab_year.groupby(\"Publication Year\")[record_col].nunique().reset_index().rename(columns={record_col:\"year_unique\"})\n", - "data = data.merge(yearsum, on=\"Publication Year\")\n", - "data[\"pub_output_percent\"] = data[record_col]/data[\"year_unique\"]\n", - "\n", - "\n", - "data[\"ISO3\"] = cc.pandas_convert(series=data[\"Country\"], to='ISO3')\n", - "data" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "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", - "\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", - "\n", - "figsuper['layout']['yaxis3'].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", - "\n", - "for i in[\"xaxis\",\"xaxis2\",\"xaxis3\"]:\n", - " figsuper['layout'][f'{i}'][\"range\"] = [2010.8,2022.2]\n", - "figsuper.show(config= dict(displayModeBar = False, responsive = True))" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "figsuper_ppt = go.Figure(figsuper)\n", - "\n", - "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - "for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - "figsuper_ppt.show()\n", - "figsuper_ppt.update_yaxes(tickfont=dict(size=18))\n", - "figsuper_ppt.write_image(f\"plot_html/PPT_plots/country_trends_overall.png\",height=900,width=1600,scale = 4)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "figsuper.write_html(f\"plot_html/country_trends_overall.html\",config= dict(displayModeBar = False, responsive = True))\n", - "figsuper.write_image(f\"plot_html/country_trends_overall.svg\",height=800,width=1600)\n", - "figsuper.write_image(f\"plot_html/country_trends_overall.png\",height=800,width=1600)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "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\",facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\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", - "fig.update_yaxes(\n", - " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", - " ticks=\"outside\")\n", - "fig.update_xaxes(\n", - " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", - " ticks=\"outside\")\n", - "fig.update_layout({'template':\"plotly\"})\n", - "fig.update_yaxes(title='')\n", - "fig.update_xaxes(title='')\n", - "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", - "fig.show(config= dict(displayModeBar = False, responsive = True))\n", - "figsuper_ppt = go.Figure(fig)\n", - "\n", - "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - "for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - "\n", - "s=16\n", - "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", - "figsuper_ppt.write_image(f\"plot_html/PPT_plots/country_year_trends.png\",height=900,width=1600,scale = 4)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "fig.write_html(f\"plot_html/country_year_trends.html\",config= dict(displayModeBar = False, responsive = True))\n", - "fig.write_image(f\"plot_html/country_year_trends.svg\",height=800,width=1600)\n", - "fig.write_image(f\"plot_html/country_year_trends.png\",height=800,width=1600)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "\n", - "figsuper_ppt.show()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "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,facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", - " labels={\n", - " record_col+\"_relative_growth\": 'Relative growth of co-publications (%)',\"Eurovoc_Class\": \"Region\"\n", - " },\n", - " title=\"Relative growth of co-publication output\", template='plotly',hover_name= \"Country\")\n", - "fig.update_traces(hovertemplate='%{hovertext}
%{x}
Relative growth: %{y}')\n", - "\n", - "\n", - "fig.update_yaxes(\n", - " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", - " ticks=\"outside\")\n", - "fig.update_xaxes(\n", - " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", - " ticks=\"outside\")\n", - "# for candidate in fig[\"layout\"].keys():\n", - "# if \"yaxis\" in candidate:\n", - "# fig[\"layout\"][candidate].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", - "fig.update_layout({'template':\"plotly\"})\n", - "fig.update_yaxes(title='',zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", - "fig.update_xaxes(title='')\n", - "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", - "figsuper_ppt = go.Figure(fig)\n", - "\n", - "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - "for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - "\n", - "s=16\n", - "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", - "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", - "figsuper_ppt.write_image(f\"plot_html/PPT_plots/country_year_rel_trends.png\",height=900,width=1600,scale = 4)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "figsuper_ppt = go.Figure(fig)\n", - "\n", - "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - "for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - "\n", - "s=16\n", - "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", - "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", - "figsuper_ppt.write_image(f\"plot_html/PPT_plots/country_year_rel_trends.png\",height=900,width=1600,scale = 4)\n", - "figsuper_ppt.show()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "fig.write_html(f\"plot_html/country_year_rel_trends.html\",config= dict(displayModeBar = False, responsive = True))\n", - "fig.write_image(f\"plot_html/country_year_rel_trends.svg\",height=800,width=1600)\n", - "fig.write_image(f\"plot_html/country_year_rel_trends.png\",height=800,width=1600)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "fig = px.line(data.sort_values(ascending=True, by='Publication Year'),\n", - " y=\"pub_output_percent\",\n", - " x='Publication Year',\n", - " color=\"Eurovoc_Class\",line_group=\"Country\",markers=True,facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", - " labels={\n", - " record_col+\"_relative_growth\": 'Relative growth of co-publications (%)',\"Eurovoc_Class\": \"Region\"\n", - " },\n", - " title=\"Relative changes in co-publication focus of China\", template='plotly',hover_name= \"Country\")\n", - "fig.update_traces(hovertemplate='%{hovertext}
%{x}
Relative growth: %{y}')\n", - "\n", - "\n", - "fig.update_yaxes(\n", - " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", - " ticks=\"outside\")\n", - "fig.update_xaxes(\n", - " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", - " ticks=\"outside\")\n", - "# for candidate in fig[\"layout\"].keys():\n", - "# if \"yaxis\" in candidate:\n", - "# fig[\"layout\"][candidate].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", - "fig.update_layout({'template':\"plotly\"})\n", - "fig.update_yaxes(title='',zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", - "fig.update_xaxes(title='')\n", - "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", - "figsuper_ppt = go.Figure(fig)\n", - "\n", - "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - "for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - "\n", - "s=16\n", - "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", - "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", - "figsuper_ppt.write_image(f\"plot_html/PPT_plots/country_year_rel_focus_trend.png\",height=900,width=1600,scale = 4)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "figsuper_ppt = go.Figure(fig)\n", - "\n", - "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - "for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - "\n", - "s=16\n", - "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", - "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", - "figsuper_ppt.write_image(f\"plot_html/PPT_plots/country_year_rel_focus_trend.png\",height=900,width=1600,scale = 4)\n", - "figsuper_ppt.show()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e4c50e14", - "metadata": {}, - "outputs": [], - "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": null, - "id": "e4e82db7", - "metadata": {}, - "outputs": [], - "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": null, - "id": "78bb0b4e", - "metadata": {}, - "outputs": [], - "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": null, - "id": "42dc8be7", - "metadata": {}, - "outputs": [], - "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": null, - "id": "e7b754ea", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "48f2898f", - "metadata": {}, - "outputs": [], - "source": [ - "# Institutional collab" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a9538e1", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6bb0e68d", - "metadata": {}, - "outputs": [], - "source": [ - "color_discrete_map= {'China': '#EF553B',\n", - " 'EU': '#636EFA',\n", - " 'Non-EU associate': '#00CC96'}" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "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,text_auto=True,\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.update_traces(textposition='outside')\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, responsive=True))\n", - " fig.write_html(f\"plot_html/overall_inst_collab_bar_{c_scope}.html\",config= dict(displayModeBar = False, responsive = True))\n", - " fig.write_image(f\"plot_html/overall_inst_collab_bar_{c_scope}.svg\",height=800,width=1600)\n", - " fig.write_image(f\"plot_html/overall_inst_collab_bar_{c_scope}.png\",height=800,width=1600)\n", - " figsuper_ppt = go.Figure(fig)\n", - " figsuper_ppt.update_traces(textposition='inside')\n", - " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - "\n", - " s=16\n", - " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - " figsuper_ppt.update_xaxes(tickfont=dict(size=s))\n", - " figsuper_ppt.write_image(f\"plot_html/PPT_plots/overall_inst_collab_bar_{c_scope}.png\",height=900,width=1000,scale = 4)\n", - " figsuper_ppt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "figsuper_ppt['layout']" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "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": null, - "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),text_auto=True,\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.show(config= dict(displayModeBar = False, responsive = True))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "fig.write_html(f\"plot_html/overall_inst_collab_europe.html\",config= dict(displayModeBar = False, responsive = True))\n", - "fig.write_image(f\"plot_html/overall_inst_collab_europe.svg\",height=800,width=1600)\n", - "fig.write_image(f\"plot_html/overall_inst_collab_europe.png\",height=800,width=1600)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "figsuper_ppt = go.Figure(fig)\n", - "\n", - "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - "for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - "\n", - "s=16\n", - "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", - "figsuper_ppt.write_image(f\"plot_html/PPT_plots/overall_inst_collab_europe.png\",height=900,width=1600,scale = 4)\n", - "figsuper_ppt.show()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "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\"),text_auto=True,\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, responsive = True))" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "fig.write_html(f\"plot_html/overall_inst_collab_eu28.html\",config= dict(displayModeBar = False, responsive = True))\n", - "fig.write_image(f\"plot_html/overall_inst_collab_eu28.svg\",height=800,width=1600)\n", - "fig.write_image(f\"plot_html/overall_inst_collab_eu28.png\",height=800,width=1600)" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], - "source": [ - "figsuper_ppt = go.Figure(fig)\n", - "\n", - "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - "for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - "\n", - "s=16\n", - "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", - "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", - "figsuper_ppt.write_image(f\"plot_html/PPT_plots/overall_inst_collab_eu28.png\",height=900,width=1600,scale = 4)\n", - "figsuper_ppt.show()" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "markdown", - "source": [ - "# Drilldown to field" - ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "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": null, - "outputs": [], - "source": [ - "wos[wos[\"Domain_English\"]==\"Applied Sciences\"].groupby(\"Field_English\", as_index=False)[record_col].nunique().sort_values(ascending=False, by=record_col)" + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_9908\\1606125869.py:24: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_9908\\1606125869.py:24: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n", + "C:\\Users\\radvanyi\\AppData\\Local\\Temp\\ipykernel_9908\\1606125869.py:24: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n", + "\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "%{y}
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" + }, + "metadata": {}, + "output_type": "display_data" + } ], - "metadata": { - "collapsed": false - } - }, - { - "cell_type": "code", - "execution_count": null, - "outputs": [], "source": [ - "%%capture\n", - "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", + "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", - "# wos[\"Field_English\"] = wos[\"Field_English\"].str.replace(\"&\",\"&
\")\n", - "# wos[\"Field_English\"] = wos[\"Field_English\"].str.replace(\"
\",\"\")\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", - "for cat in sorted(wos[\"Domain_English\"].unique()):\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", - " os.makedirs(rf'plot_html/{cat}',exist_ok=True)\n", - " os.makedirs(rf'plot_html/PPT_plots/{cat}',exist_ok=True)\n", - " id_subset = wos[wos[\"Domain_English\"]==cat][record_col].unique()\n", + "color_discrete_map= {'China': '#EF553B',\n", + " 'EU': '#636EFA',\n", + " 'Non-EU associate': '#00CC96'}\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", - " 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',text_auto=True,\n", - " labels={\n", - " record_col: 'Number of co-publications',\n", - " },\n", - " title=f\"Distribution of topics ({cat})\", 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", - " 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", - " trace.showlegend=False #\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(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", - " for i in range(2,5):\n", - " figsuper['layout'][f'xaxis{i}'][\"range\"] = [2010.8,2022.2]\n", - " figsuper['layout'][\"font\"][\"size\"]=12\n", - " for a in figsuper['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 14\n", - "\n", - " figsuper.update_layout(title_text=f\"{cat}\")\n", - " figsuper.write_html(f\"plot_html/{cat}/{cat}_distr&trends.html\",config= dict(displayModeBar = False, responsive = True))\n", - "\n", - "\n", - " figsuper_ppt = go.Figure(figsuper)\n", - " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - " for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - " figsuper_ppt.show()\n", - " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_distr&trends.png\",height=900,width=1600,scale = 4)\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", - " data[\"ISO3\"] = cc.pandas_convert(series=data[\"Country\"], to='ISO3')\n", - " col_by=\"Country_Type\"\n", - " y_lab=\"ISO3\"\n", - " fig = px.bar(data, x=c, y=y_lab, color=col_by, color_discrete_map=color_discrete_map,text_auto=True,\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['layout']['xaxis1'].update(tickformat=\".0f\")\n", - " figsuper['layout']['xaxis2'].update(tickformat=\".1%\")\n", - " figsuper['layout']['xaxis3'].update(tickformat=\".1%\")\n", - " figsuper['layout'][\"font\"][\"size\"]=12\n", - " for a in figsuper['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 14\n", - " figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\n", - " figsuper[\"layout\"][\"yaxis2\"][\"tickfont\"][\"size\"] = 10\n", - " figsuper[\"layout\"][\"yaxis3\"][\"tickfont\"][\"size\"] = 10\n", - "\n", - " figsuper.update_layout(uniformtext_minsize=10)\n", - " figsuper.update_layout(title=f\"Contribution of european countries ({cat})\")\n", - " figsuper.write_html(f\"plot_html/{cat}/{cat}_europe_contribution_bar.html\",config= dict(displayModeBar = False, responsive = True))\n", - "\n", - " figsuper_ppt = go.Figure(figsuper)\n", - "\n", - " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - " for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - " figsuper_ppt.show()\n", - " figsuper_ppt.update_yaxes(tickfont=dict(size=18))\n", - " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_europe_contribution_bar.png\",height=900,width=1600,scale = 4)\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=f\"Intraeuropean patterns: Co-occurences of countries in chinese co-publications ({cat})\"\n", - " )\n", - " fig.update_layout(\n", - " width=1000, height=1000,\n", - " xaxis_showgrid=False,\n", - " yaxis_showgrid=False,\n", - " yaxis_autorange='reversed', template='plotly_white',font_family=\"Montserrat\",)\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.write_html(f\"plot_html/{cat}/{cat}_intraeurope_collabs.html\",config= dict(displayModeBar = False, responsive = True))\n", - " figsuper_ppt = go.Figure(fig)\n", - "\n", - " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - " for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - "\n", - " s=16\n", - " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - " figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", - " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_intraeurope_collabs.png\",height=900,width=1600,scale = 4)\n", - "\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\"]\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", - "\n", - " yearsum = collab_year.groupby(\"Publication Year\")[record_col].nunique().reset_index().rename(columns={record_col:\"year_unique\"})\n", - " data = data.merge(yearsum, on=\"Publication Year\")\n", - " data[\"pub_output_percent\"] = data[record_col]/data[\"year_unique\"]\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", " data[\"ISO3\"] = cc.pandas_convert(series=data[\"Country\"], to='ISO3')\n", + " # plt.figure(figsize=(9,12))\n", + " col_by=\"Country_Type\"\n", + " y_lab=\"ISO3\"\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,text_auto=True,\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", - "\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", - "\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", + "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=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.update_layout(font_family=\"Montserrat\")\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", - " for i in[\"xaxis\",\"xaxis2\",\"xaxis3\"]:\n", - " figsuper['layout'][f'{i}'][\"range\"] = [2010.8,2022.2]\n", - " figsuper.write_html(f\"plot_html/{cat}/{cat}_country_trends_overall.html\",config= dict(displayModeBar = False, responsive = True))\n", - "\n", - " figsuper_ppt = go.Figure(figsuper)\n", - "\n", - " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - " for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - " figsuper_ppt.show()\n", - " figsuper_ppt.update_yaxes(tickfont=dict(size=18))\n", - " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_country_trends_overall.png\",height=900,width=1600,scale = 4)\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\",facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", - " markers=True,\n", - " labels={\n", - " record_col: 'Number of co-publications',\n", - " \"Eurovoc_Class\": \"Region\"\n", - " },\n", - " title=f\"Yearly output of co-publications ({cat})\",hover_name= \"Country\")\n", - " fig.update_traces(hovertemplate='%{hovertext}
%{x}
Co-publications: %{y}')\n", - " fig.update_yaxes(\n", - " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", - " ticks=\"outside\")\n", - " fig.update_xaxes(\n", - " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", - " ticks=\"outside\")\n", - " fig.update_layout({'template':\"plotly\"})\n", - " fig.update_layout(font_family=\"Montserrat\")\n", - " fig.update_yaxes(title='')\n", - " fig.update_xaxes(title='')\n", - " fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", - " fig.show(config= dict(displayModeBar = False, responsive = True))\n", - " figsuper_ppt = go.Figure(fig)\n", - "\n", - " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - " for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - "\n", - " s=16\n", - " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - " figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", - " figsuper_ppt.write_image(f\"plot_html//PPT_plots/{cat}/{cat}_country_year_trends.png\",height=900,width=1600,scale = 4)\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,facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", - " labels={\n", - " record_col+\"_relative_growth\": 'Relative growth of co-publications (%)',\"Eurovoc_Class\": \"Region\"\n", - " },\n", - " title=f\"Relative growth of co-publication output ({cat})\", template='plotly',hover_name= \"Country\")\n", - " fig.update_traces(hovertemplate='%{hovertext}
%{x}
Relative growth: %{y}')\n", - "\n", - "\n", - " fig.update_yaxes(\n", - " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", - " ticks=\"outside\")\n", - " fig.update_xaxes(\n", - " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", - " ticks=\"outside\")\n", - " # for candidate in fig[\"layout\"].keys():\n", - " # if \"yaxis\" in candidate:\n", - " # fig[\"layout\"][candidate].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", - " fig.update_layout({'template':\"plotly\"})\n", - " fig.update_layout(font_family=\"Montserrat\")\n", - " fig.update_yaxes(title='',zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", - " fig.update_xaxes(title='')\n", - " fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", - " figsuper_ppt = go.Figure(fig)\n", - "\n", - " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - " for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - "\n", - " s=16\n", - " figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", - " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - " figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", - " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_country_year_rel_trends.png\",height=900,width=1600,scale = 4)\n", - "\n", - " fig = px.line(data.sort_values(ascending=True, by='Publication Year'),\n", - " y=\"pub_output_percent\",\n", - " x='Publication Year',\n", - " color=\"Eurovoc_Class\",line_group=\"Country\",markers=True,facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", - " labels={\n", - " record_col+\"_relative_growth\": 'Relative growth of co-publications (%)',\"Eurovoc_Class\": \"Region\"\n", - " },\n", - " title=f\"Relative changes in co-publication focus of China ({cat})\", template='plotly',hover_name= \"Country\")\n", - " fig.update_traces(hovertemplate='%{hovertext}
%{x}
Relative growth: %{y}')\n", - "\n", - "\n", - " fig.update_yaxes(\n", - " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", - " ticks=\"outside\")\n", - " fig.update_xaxes(\n", - " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", - " ticks=\"outside\")\n", - " # for candidate in fig[\"layout\"].keys():\n", - " # if \"yaxis\" in candidate:\n", - " # fig[\"layout\"][candidate].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", - " fig.update_layout({'template':\"plotly\"})\n", - " fig.update_layout(font_family=\"Montserrat\")\n", - " fig.update_yaxes(title='',zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", - " fig.update_xaxes(title='')\n", - " fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", - " figsuper_ppt = go.Figure(fig)\n", - "\n", - " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - " for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - "\n", - " s=16\n", - " figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", - " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - " figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", - " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_country_year_rel_focus_trend.png\",height=900,width=1600,scale = 4)\n", - "\n", - "\n", - "\n", - " TOPN = 15\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", - " [\"Europe\",\"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,text_auto=True,\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\"Top {TOPN} institutes within {c_scope}
({cat})\", 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.write_html(f\"plot_html/{cat}/{cat}_overall_inst_collab_bar_{c_scope}.html\",config= dict(displayModeBar = False, responsive = True))\n", - " fig.write_image(f\"plot_html/overall_inst_collab_bar_{c_scope}.svg\",height=800,width=1600)\n", - " fig.write_image(f\"plot_html/overall_inst_collab_bar_{c_scope}.png\",height=800,width=1600)\n", - " figsuper_ppt = go.Figure(fig)\n", - " figsuper_ppt.update_traces(textposition='inside')\n", - " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - "\n", - " s=16\n", - " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - " figsuper_ppt.update_xaxes(tickfont=dict(size=s))\n", - " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_overall_inst_collab_bar_{c_scope}.png\",height=900,width=1000,scale = 4)\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_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\"Top {TOPN} institutes within Europe ({cat})\"\n", - " )\n", - " fig.update_layout(\n", - " width=1000, height=1000,\n", - " xaxis_showgrid=False,\n", - " yaxis_showgrid=False,\n", - " yaxis_autorange='reversed',\n", - " template='plotly_white',font_family=\"Montserrat\",\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", - " figsuper_ppt = go.Figure(fig)\n", - "\n", - " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - " for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", - "\n", - " s=16\n", - " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - " figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", - " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_overall_inst_collab_europe.png\",height=900,width=1600,scale = 4)\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\"Top {TOPN} institutes within EU-28 ({cat})\"\n", - " )\n", - " fig.update_layout(\n", - " width=1000, height=1000,\n", - " xaxis_showgrid=False,\n", - " yaxis_showgrid=False,\n", - " yaxis_autorange='reversed',\n", - " template='plotly_white',font_family=\"Montserrat\",\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", + " row=1, col=i+1)\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))\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", "\n", - " figsuper_ppt = go.Figure(fig)\n", + "figsuper['layout']['xaxis1'].update(tickformat=\".0f\")\n", + "figsuper['layout']['xaxis2'].update(tickformat=\".1%\")\n", + "figsuper['layout']['xaxis3'].update(tickformat=\".1%\")\n", + "figsuper['layout'][\"font\"][\"size\"]=12\n", + "for a in figsuper['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 14\n", + "figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\n", + "figsuper[\"layout\"][\"yaxis2\"][\"tickfont\"][\"size\"] = 10\n", + "figsuper[\"layout\"][\"yaxis3\"][\"tickfont\"][\"size\"] = 10\n", "\n", - " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", - " for a in figsuper_ppt['layout'][\"annotations\"]:\n", - " a[\"font\"][\"size\"] = 22\n", + "figsuper.update_layout(uniformtext_minsize=10)\n", "\n", - " s=16\n", - " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", - " figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", - " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_overall_inst_collab_eu28.png\",height=900,width=1600,scale = 4)" - ], - "metadata": { - "collapsed": false - } + "figsuper.show(config= dict(displayModeBar = False, responsive = True))" + ] }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [ - "# Drill down to subfield" + "figsuper.write_html(f\"plot_html/europe_contribution_bar.html\",config= dict(displayModeBar = False, responsive = True))\n", + "figsuper.write_image(f\"plot_html/europe_contribution_bar.svg\",height=800,width=1600)\n", + "figsuper.write_image(f\"plot_html/europe_contribution_bar.png\",height=800,width=1600)" ], "metadata": { "collapsed": false @@ -242730,8 +242393,287884 @@ }, { "cell_type": "code", - "execution_count": null, - "outputs": [], + "execution_count": 21, + "id": "c959287e", + "metadata": {}, + 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"shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "baxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + }, + "bgcolor": "white", + "caxis": { + "gridcolor": "#DFE8F3", + "linecolor": "#A2B1C6", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "#EBF0F8", + "linecolor": "#EBF0F8", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "#EBF0F8", + "zerolinewidth": 2 + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0.0, + 1.0 + ], + "scaleanchor": "y", + "constrain": "domain", + "title": {}, + "showgrid": false, + "tickangle": -90, + "ticks": "outside" + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0.0, + 1.0 + ], + "autorange": "reversed", + "constrain": "domain", + "title": {}, + "showgrid": false, + "ticks": "outside" + }, + "coloraxis": { + "colorbar": { + "title": { + "text": "Co-publication with China" + } + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + }, + "title": { + "text": "Intraeuropean patterns
Co-occurences of countries in chinese co-publications", + "x": 0.5 + }, + "width": 1000, + "height": 1000 + }, + "config": { + "displayModeBar": false, + "responsive": true, + "plotlyServerURL": "https://plotly.com" + } + }, + "text/html": "
" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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", + "EU_co_occur_columns = list(EU_co_occur.columns)\n", + "EU_co_occur_index = list(EU_co_occur.index)\n", + "EU_co_occur_columns[-1] = \"\"\n", + "EU_co_occur_index[0] = \"\"\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=EU_co_occur_columns,\n", + " y=EU_co_occur_index, title=\"Intraeuropean patterns
Co-occurences of countries in chinese co-publications\",\n", + " text_auto=True\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',yaxis_title=None,xaxis_title=None)\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))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "fig.write_html(f\"plot_html/intraeurope_collabs.html\",config= dict(displayModeBar = False, responsive = True))\n", + "fig.write_image(f\"plot_html/intraeurope_collabs.svg\",height=800,width=1600)\n", + "fig.write_image(f\"plot_html/intraeurope_collabs.png\",height=800,width=1600)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "figsuper_ppt = go.Figure(fig)\n", + "\n", + "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + "for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + "\n", + "s=16\n", + "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", + "figsuper_ppt.write_image(f\"plot_html/PPT_plots/intraeurope_collabs.png\",height=900,width=1600,scale = 4)\n", + "figsuper_ppt.show()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "id": "122d0260", + "metadata": {}, + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "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\"]\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", + "\n", + "yearsum = collab_year.groupby(\"Publication Year\")[record_col].nunique().reset_index().rename(columns={record_col:\"year_unique\"})\n", + "data = data.merge(yearsum, on=\"Publication Year\")\n", + "data[\"pub_output_percent\"] = data[record_col]/data[\"year_unique\"]\n", + "\n", + "\n", + "data[\"ISO3\"] = cc.pandas_convert(series=data[\"Country\"], to='ISO3')\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "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", + "\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", + "\n", + "figsuper['layout']['yaxis3'].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", + "\n", + "for i in[\"xaxis\",\"xaxis2\",\"xaxis3\"]:\n", + " figsuper['layout'][f'{i}'][\"range\"] = [2010.8,2022.2]\n", + "figsuper.show(config= dict(displayModeBar = False, responsive = True))" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "figsuper_ppt = go.Figure(figsuper)\n", + "\n", + "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + "for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + "figsuper_ppt.show()\n", + "figsuper_ppt.update_yaxes(tickfont=dict(size=18))\n", + "figsuper_ppt.write_image(f\"plot_html/PPT_plots/country_trends_overall.png\",height=900,width=1600,scale = 4)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "figsuper.write_html(f\"plot_html/country_trends_overall.html\",config= dict(displayModeBar = False, responsive = True))\n", + "figsuper.write_image(f\"plot_html/country_trends_overall.svg\",height=800,width=1600)\n", + "figsuper.write_image(f\"plot_html/country_trends_overall.png\",height=800,width=1600)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "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\",facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\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", + "fig.update_yaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "fig.update_xaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "fig.update_layout({'template':\"plotly\"})\n", + "fig.update_yaxes(title='')\n", + "fig.update_xaxes(title='')\n", + "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", + "fig.show(config= dict(displayModeBar = False, responsive = True))\n", + "figsuper_ppt = go.Figure(fig)\n", + "\n", + "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + "for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + "\n", + "s=16\n", + "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", + "figsuper_ppt.write_image(f\"plot_html/PPT_plots/country_year_trends.png\",height=900,width=1600,scale = 4)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "fig.write_html(f\"plot_html/country_year_trends.html\",config= dict(displayModeBar = False, responsive = True))\n", + "fig.write_image(f\"plot_html/country_year_trends.svg\",height=800,width=1600)\n", + "fig.write_image(f\"plot_html/country_year_trends.png\",height=800,width=1600)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "\n", + "figsuper_ppt.show()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "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,facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", + " labels={\n", + " record_col+\"_relative_growth\": 'Relative growth of co-publications (%)',\"Eurovoc_Class\": \"Region\"\n", + " },\n", + " title=\"Relative growth of co-publication output\", template='plotly',hover_name= \"Country\")\n", + "fig.update_traces(hovertemplate='%{hovertext}
%{x}
Relative growth: %{y}')\n", + "\n", + "\n", + "fig.update_yaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "fig.update_xaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "# for candidate in fig[\"layout\"].keys():\n", + "# if \"yaxis\" in candidate:\n", + "# fig[\"layout\"][candidate].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", + "fig.update_layout({'template':\"plotly\"})\n", + "fig.update_yaxes(title='',zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", + "fig.update_xaxes(title='')\n", + "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", + "figsuper_ppt = go.Figure(fig)\n", + "\n", + "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + "for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + "\n", + "s=16\n", + "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", + "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", + "figsuper_ppt.write_image(f\"plot_html/PPT_plots/country_year_rel_trends.png\",height=900,width=1600,scale = 4)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "figsuper_ppt = go.Figure(fig)\n", + "\n", + "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + "for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + "\n", + "s=16\n", + "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", + "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", + "figsuper_ppt.write_image(f\"plot_html/PPT_plots/country_year_rel_trends.png\",height=900,width=1600,scale = 4)\n", + "figsuper_ppt.show()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "fig.write_html(f\"plot_html/country_year_rel_trends.html\",config= dict(displayModeBar = False, responsive = True))\n", + "fig.write_image(f\"plot_html/country_year_rel_trends.svg\",height=800,width=1600)\n", + "fig.write_image(f\"plot_html/country_year_rel_trends.png\",height=800,width=1600)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "fig = px.line(data.sort_values(ascending=True, by='Publication Year'),\n", + " y=\"pub_output_percent\",\n", + " x='Publication Year',\n", + " color=\"Eurovoc_Class\",line_group=\"Country\",markers=True,facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", + " labels={\n", + " record_col+\"_relative_growth\": 'Relative growth of co-publications (%)',\"Eurovoc_Class\": \"Region\"\n", + " },\n", + " title=\"Relative changes in co-publication focus of China\", template='plotly',hover_name= \"Country\")\n", + "fig.update_traces(hovertemplate='%{hovertext}
%{x}
Relative growth: %{y}')\n", + "\n", + "\n", + "fig.update_yaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "fig.update_xaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + "# for candidate in fig[\"layout\"].keys():\n", + "# if \"yaxis\" in candidate:\n", + "# fig[\"layout\"][candidate].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", + "fig.update_layout({'template':\"plotly\"})\n", + "fig.update_yaxes(title='',zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", + "fig.update_xaxes(title='')\n", + "fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", + "figsuper_ppt = go.Figure(fig)\n", + "\n", + "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + "for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + "\n", + "s=16\n", + "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", + "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", + "figsuper_ppt.write_image(f\"plot_html/PPT_plots/country_year_rel_focus_trend.png\",height=900,width=1600,scale = 4)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "figsuper_ppt = go.Figure(fig)\n", + "\n", + "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + "for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + "\n", + "s=16\n", + "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", + "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", + "figsuper_ppt.write_image(f\"plot_html/PPT_plots/country_year_rel_focus_trend.png\",height=900,width=1600,scale = 4)\n", + "figsuper_ppt.show()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4c50e14", + "metadata": {}, + "outputs": [], + "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": null, + "id": "e4e82db7", + "metadata": {}, + "outputs": [], + "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": null, + "id": "78bb0b4e", + "metadata": {}, + "outputs": [], + "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": null, + "id": "42dc8be7", + "metadata": {}, + "outputs": [], + "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": null, + "id": "e7b754ea", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48f2898f", + "metadata": {}, + "outputs": [], + "source": [ + "# Institutional collab" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3a9538e1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6bb0e68d", + "metadata": {}, + "outputs": [], + "source": [ + "color_discrete_map= {'China': '#EF553B',\n", + " 'EU': '#636EFA',\n", + " 'Non-EU associate': '#00CC96'}" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "df8701eb", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "%{x:d}", + "legendgroup": "Denmark, EU", + "marker": { + "color": "#ab63fa", + "pattern": { + "shape": "" + } + }, + "name": "Denmark, EU", + "offsetgroup": "Denmark, EU", + "orientation": "h", + "showlegend": true, + "textposition": "outside", + "texttemplate": "%{x}", + "x": [ + 504 + ], + "xaxis": "x", + "y": [ + "Aalborg Univ (DNK)" + ], + "yaxis": "y", + "type": "bar" + }, + { + "alignmentgroup": "True", 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(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,text_auto=True,\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.update_traces(textposition='outside')\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, responsive=True))\n", + " fig.write_html(f\"plot_html/overall_inst_collab_bar_{c_scope}.html\",config= dict(displayModeBar = False, responsive = True))\n", + " fig.write_image(f\"plot_html/overall_inst_collab_bar_{c_scope}.svg\",height=800,width=1600)\n", + " fig.write_image(f\"plot_html/overall_inst_collab_bar_{c_scope}.png\",height=800,width=1600)\n", + " figsuper_ppt = go.Figure(fig)\n", + " figsuper_ppt.update_traces(textposition='inside')\n", + " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + "\n", + " s=16\n", + " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + " figsuper_ppt.update_xaxes(tickfont=dict(size=s))\n", + " figsuper_ppt.write_image(f\"plot_html/PPT_plots/overall_inst_collab_bar_{c_scope}.png\",height=900,width=1000,scale = 4)\n", + " # figsuper_ppt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "figsuper_ppt['layout']" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 25, + "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": 26, + "id": "2c5d1d94", + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + 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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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),text_auto=True,\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.show(config= dict(displayModeBar = False, responsive = True))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "fig.write_html(f\"plot_html/overall_inst_collab_europe.html\",config= dict(displayModeBar = False, responsive = True))\n", + "fig.write_image(f\"plot_html/overall_inst_collab_europe.svg\",height=800,width=1600)\n", + "fig.write_image(f\"plot_html/overall_inst_collab_europe.png\",height=800,width=1600)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "figsuper_ppt = go.Figure(fig)\n", + "\n", + "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + "for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + "\n", + "s=16\n", + "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", + 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dtype: int64" + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sub_df.groupby(['Institution_harm_label_eu'])[record_col].count()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 31, + "outputs": [ + { + "data": { + "text/plain": " UT (Unique WOS ID) Country_eu Institution_harm_eu Country_Type_eu \n2 WOS:000209339300005 Belgium Univ Ghent EU \\\n9 WOS:000285920700035 Belgium Univ Ghent EU \n41 WOS:000290883000018 Belgium Univ Ghent EU \n85 WOS:000295004100006 Belgium Univ Ghent EU \n86 WOS:000295004100006 Belgium Univ Ghent EU \n... ... ... ... ... \n334438 WOS:000799112900001 Portugal Univ Lisbon EU \n334485 WOS:000822117100001 Portugal Univ Lisbon EU \n334492 WOS:000837179400001 Portugal Univ Lisbon EU \n334493 WOS:000837179400001 Portugal Univ Lisbon EU \n334529 WOS:000863320100004 Portugal Univ Lisbon EU \n\n Eurovoc_Class_eu ISO3_eu Institution_harm_label_eu Country_ch \n2 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UT (Unique WOS ID)Country_euInstitution_harm_euCountry_Type_euEurovoc_Class_euISO3_euInstitution_harm_label_euCountry_chInstitution_harm_chCountry_Type_chEurovoc_Class_chISO3_chInstitution_harm_label_chDomain_EnglishField_EnglishSubField_English
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86WOS:000295004100006BelgiumUniv GhentEUWestern EuropeBELUniv Ghent (BEL)ChinaXi An Jiao Tong UnivChinaChinaCHNXi An Jiao Tong Univ (CHN)Health SciencesClinical MedicineObstetrics & Reproductive Medicine
...................................................
334438WOS:000799112900001PortugalUniv LisbonEUSouthern EuropePRTUniv Lisbon (PRT)ChinaChinese Univ Hong KongChinaChinaCHNChinese Univ Hong Kong (CHN)Health SciencesPsychology & Cognitive SciencesExperimental Psychology
334485WOS:000822117100001PortugalUniv LisbonEUSouthern EuropePRTUniv Lisbon (PRT)ChinaUniv Hong KongChinaChinaCHNUniv Hong Kong (CHN)Applied SciencesEngineeringGeological & Geomatics Engineering
334492WOS:000837179400001PortugalUniv LisbonEUSouthern EuropePRTUniv Lisbon (PRT)ChinaSun Yat Sen UnivChinaChinaCHNSun Yat Sen Univ (CHN)Applied SciencesEngineeringCivil Engineering
334493WOS:000837179400001PortugalUniv LisbonEUSouthern EuropePRTUniv Lisbon (PRT)ChinaUniv Elect Sci & Technol ChinaChinaChinaCHNUniv Elect Sci & Technol China (CHN)Applied SciencesEngineeringCivil Engineering
334529WOS:000863320100004PortugalUniv LisbonEUSouthern EuropePRTUniv Lisbon (PRT)ChinaUniv Elect Sci & Technol ChinaChinaChinaCHNUniv Elect Sci & Technol China (CHN)Applied SciencesEnabling & Strategic TechnologiesStrategic, Defence & Security Studies
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" + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "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\"),text_auto=True,\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, responsive = True))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "fig.write_html(f\"plot_html/overall_inst_collab_eu28.html\",config= dict(displayModeBar = False, responsive = True))\n", + "fig.write_image(f\"plot_html/overall_inst_collab_eu28.svg\",height=800,width=1600)\n", + "fig.write_image(f\"plot_html/overall_inst_collab_eu28.png\",height=800,width=1600)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "figsuper_ppt = go.Figure(fig)\n", + "\n", + "figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + "for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + "\n", + "s=16\n", + "figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", + "figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + "figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", + "figsuper_ppt.write_image(f\"plot_html/PPT_plots/overall_inst_collab_eu28.png\",height=900,width=1600,scale = 4)\n", + "figsuper_ppt.show()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "markdown", + "source": [ + "# Drilldown to field" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 47, + "outputs": [], + "source": [ + "id_subset = wos[wos[\"Domain_English\"]=='Applied Sciences'][record_col].unique()\n", + "\n", + "data = (wos[wos[record_col].isin(id_subset)].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[wos[record_col].isin(id_subset)][[\"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", + "bar_data = 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": 48, + "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.. ... ... ... \n79 2018 Social Sciences 0.0 \n80 2019 Social Sciences 0.0 \n81 2020 Social Sciences 0.0 \n82 2021 Social Sciences 0.0 \n83 2022 Social Sciences 0.0 \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.. ... ... \n79 Applied Sciences 2017 \n80 Applied Sciences 2017 \n81 Applied Sciences 2017 \n82 Applied Sciences 2017 \n83 Applied Sciences 2017 \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.. ... ... \n79 -1.000000 Applied Sciences \n80 -1.000000 Applied Sciences \n81 -1.000000 Applied Sciences \n82 -1.000000 Applied Sciences \n83 -1.000000 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.. ... \n79 1.0 \n80 1.0 \n81 1.0 \n82 1.0 \n83 1.0 \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
...........................
792018Social Sciences0.0Applied Sciences2017-1.000000Applied Sciences1.0
802019Social Sciences0.0Applied Sciences2017-1.000000Applied Sciences1.0
812020Social Sciences0.0Applied Sciences2017-1.000000Applied Sciences1.0
822021Social Sciences0.0Applied Sciences2017-1.000000Applied Sciences1.0
832022Social Sciences0.0Applied Sciences2017-1.000000Applied Sciences1.0
\n

84 rows × 8 columns

\n
" + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "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": 44, + "outputs": [ + { + "data": { + "text/plain": " Publication Year Domain_English Field_English \n0 2011 Applied Sciences Agriculture, Fisheries & Forestry \\\n1 2011 Applied Sciences Biology \n2 2011 Applied Sciences Biomedical Research \n3 2011 Applied Sciences Built Environment & Design \n4 2011 Applied Sciences Chemistry \n... ... ... ... \n1375 2022 Natural Sciences Philosophy & Theology \n1376 2022 Natural Sciences Physics & Astronomy \n1377 2022 Natural Sciences Psychology & Cognitive Sciences \n1378 2022 Natural Sciences Public Health & Health Services \n1379 2022 Natural Sciences Social Sciences \n\n UT (Unique WOS ID) \n0 9.0 \n1 0.0 \n2 0.0 \n3 6.0 \n4 0.0 \n... ... \n1375 0.0 \n1376 596.0 \n1377 0.0 \n1378 0.0 \n1379 0.0 \n\n[1380 rows x 4 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
Publication YearDomain_EnglishField_EnglishUT (Unique WOS ID)
02011Applied SciencesAgriculture, Fisheries & Forestry9.0
12011Applied SciencesBiology0.0
22011Applied SciencesBiomedical Research0.0
32011Applied SciencesBuilt Environment & Design6.0
42011Applied SciencesChemistry0.0
...............
13752022Natural SciencesPhilosophy & Theology0.0
13762022Natural SciencesPhysics & Astronomy596.0
13772022Natural SciencesPsychology & Cognitive Sciences0.0
13782022Natural SciencesPublic Health & Health Services0.0
13792022Natural SciencesSocial Sciences0.0
\n

1380 rows × 4 columns

\n
" + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "id_subset = wos[wos[\"Domain_English\"]=='Applied Sciences'][record_col].unique()\n", + "\n", + "data = (wos.groupby(['Publication Year',\"Domain_English\",'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" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 42, + "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": "
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Field_EnglishUT (Unique WOS ID)
5Information & Communication Technologies15648
4Engineering9232
3Enabling & Strategic Technologies3940
0Agriculture, Fisheries & Forestry612
1Built Environment & Design537
2Economics & Business15
6Social Sciences1
\n
" + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "\n", + "data = data.merge(wos[[\"Domain_English\",'Field_English']].drop_duplicates(),on=[\"Domain_English\",'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=[\"Domain_English\",'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", + "bar_data = wos[wos[\"Domain_English\"]=='Applied Sciences'].groupby(\"Field_English\", as_index=False)[record_col].nunique().sort_values(ascending=False, by=record_col)\n", + "bar_data" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 43, + "outputs": [ + { + "data": { + "text/plain": " Publication Year Domain_English \n0 2011 Applied Sciences \\\n1 2012 Applied Sciences \n2 2013 Applied Sciences \n3 2014 Applied Sciences \n4 2015 Applied Sciences \n5 2016 Applied Sciences \n6 2017 Applied Sciences \n7 2018 Applied Sciences \n8 2019 Applied Sciences \n9 2020 Applied Sciences \n10 2021 Applied Sciences \n11 2022 Applied Sciences \n12 2011 Applied Sciences \n13 2012 Applied Sciences \n14 2013 Applied Sciences \n15 2014 Applied Sciences \n16 2015 Applied Sciences \n17 2016 Applied Sciences \n18 2017 Applied Sciences \n19 2018 Applied Sciences \n20 2019 Applied Sciences \n21 2020 Applied Sciences \n22 2021 Applied Sciences \n23 2022 Applied Sciences \n24 2011 Applied Sciences \n25 2012 Applied Sciences \n26 2013 Applied Sciences \n27 2014 Applied Sciences \n28 2015 Applied Sciences \n29 2016 Applied Sciences \n30 2017 Applied Sciences \n31 2018 Applied Sciences \n32 2019 Applied Sciences \n33 2020 Applied Sciences \n34 2021 Applied Sciences \n35 2022 Applied Sciences \n36 2011 Applied Sciences \n37 2012 Applied Sciences \n38 2013 Applied Sciences \n39 2014 Applied Sciences \n40 2015 Applied Sciences \n41 2016 Applied Sciences \n42 2017 Applied Sciences \n43 2018 Applied Sciences \n44 2019 Applied Sciences \n45 2020 Applied Sciences \n46 2021 Applied Sciences \n47 2022 Applied Sciences \n48 2011 Applied Sciences \n49 2012 Applied Sciences \n50 2013 Applied Sciences \n51 2014 Applied Sciences \n52 2015 Applied Sciences \n53 2016 Applied Sciences \n54 2017 Applied Sciences \n55 2018 Applied Sciences \n56 2019 Applied Sciences \n57 2020 Applied Sciences \n58 2021 Applied Sciences \n59 2022 Applied Sciences \n\n Field_English UT (Unique WOS ID) \n0 Agriculture, Fisheries & Forestry 9.0 \\\n1 Agriculture, Fisheries & Forestry 18.0 \n2 Agriculture, Fisheries & Forestry 15.0 \n3 Agriculture, Fisheries & Forestry 26.0 \n4 Agriculture, Fisheries & Forestry 12.0 \n5 Agriculture, Fisheries & Forestry 24.0 \n6 Agriculture, Fisheries & Forestry 39.0 \n7 Agriculture, Fisheries & Forestry 46.0 \n8 Agriculture, Fisheries & Forestry 73.0 \n9 Agriculture, Fisheries & Forestry 105.0 \n10 Agriculture, Fisheries & Forestry 118.0 \n11 Agriculture, Fisheries & Forestry 127.0 \n12 Built Environment & Design 6.0 \n13 Built Environment & Design 8.0 \n14 Built Environment & Design 1.0 \n15 Built Environment & Design 17.0 \n16 Built Environment & Design 18.0 \n17 Built Environment & Design 13.0 \n18 Built Environment & Design 24.0 \n19 Built Environment & Design 41.0 \n20 Built Environment & Design 59.0 \n21 Built Environment & Design 84.0 \n22 Built Environment & Design 116.0 \n23 Built Environment & Design 150.0 \n24 Enabling & Strategic Technologies 53.0 \n25 Enabling & Strategic Technologies 61.0 \n26 Enabling & Strategic Technologies 80.0 \n27 Enabling & Strategic Technologies 115.0 \n28 Enabling & Strategic Technologies 117.0 \n29 Enabling & Strategic Technologies 161.0 \n30 Enabling & Strategic Technologies 213.0 \n31 Enabling & Strategic Technologies 330.0 \n32 Enabling & Strategic Technologies 468.0 \n33 Enabling & Strategic Technologies 577.0 \n34 Enabling & Strategic Technologies 815.0 \n35 Enabling & Strategic Technologies 950.0 \n36 Engineering 166.0 \n37 Engineering 181.0 \n38 Engineering 238.0 \n39 Engineering 318.0 \n40 Engineering 381.0 \n41 Engineering 481.0 \n42 Engineering 587.0 \n43 Engineering 767.0 \n44 Engineering 1059.0 \n45 Engineering 1379.0 \n46 Engineering 1626.0 \n47 Engineering 2049.0 \n48 Information & Communication Technologies 256.0 \n49 Information & Communication Technologies 325.0 \n50 Information & Communication Technologies 404.0 \n51 Information & Communication Technologies 554.0 \n52 Information & Communication Technologies 673.0 \n53 Information & Communication Technologies 855.0 \n54 Information & Communication Technologies 1055.0 \n55 Information & Communication Technologies 1622.0 \n56 Information & Communication Technologies 2068.0 \n57 Information & Communication Technologies 2296.0 \n58 Information & Communication Technologies 2617.0 \n59 Information & Communication Technologies 2923.0 \n\n Publication Year_relative_growth UT (Unique WOS ID)_relative_growth \n0 2011 0.000000 \\\n1 2011 1.000000 \n2 2011 0.666667 \n3 2011 1.888889 \n4 2011 0.333333 \n5 2011 1.666667 \n6 2011 3.333333 \n7 2011 4.111111 \n8 2011 7.111111 \n9 2011 10.666667 \n10 2011 12.111111 \n11 2011 13.111111 \n12 2011 0.000000 \n13 2011 0.333333 \n14 2011 -0.833333 \n15 2011 1.833333 \n16 2011 2.000000 \n17 2011 1.166667 \n18 2011 3.000000 \n19 2011 5.833333 \n20 2011 8.833333 \n21 2011 13.000000 \n22 2011 18.333333 \n23 2011 24.000000 \n24 2011 0.000000 \n25 2011 0.150943 \n26 2011 0.509434 \n27 2011 1.169811 \n28 2011 1.207547 \n29 2011 2.037736 \n30 2011 3.018868 \n31 2011 5.226415 \n32 2011 7.830189 \n33 2011 9.886792 \n34 2011 14.377358 \n35 2011 16.924528 \n36 2011 0.000000 \n37 2011 0.090361 \n38 2011 0.433735 \n39 2011 0.915663 \n40 2011 1.295181 \n41 2011 1.897590 \n42 2011 2.536145 \n43 2011 3.620482 \n44 2011 5.379518 \n45 2011 7.307229 \n46 2011 8.795181 \n47 2011 11.343373 \n48 2011 0.000000 \n49 2011 0.269531 \n50 2011 0.578125 \n51 2011 1.164062 \n52 2011 1.628906 \n53 2011 2.339844 \n54 2011 3.121094 \n55 2011 5.335938 \n56 2011 7.078125 \n57 2011 7.968750 \n58 2011 9.222656 \n59 2011 10.417969 \n\n UT (Unique WOS ID)_cumsum \n0 9.0 \n1 27.0 \n2 42.0 \n3 68.0 \n4 80.0 \n5 104.0 \n6 143.0 \n7 189.0 \n8 262.0 \n9 367.0 \n10 485.0 \n11 612.0 \n12 6.0 \n13 14.0 \n14 15.0 \n15 32.0 \n16 50.0 \n17 63.0 \n18 87.0 \n19 128.0 \n20 187.0 \n21 271.0 \n22 387.0 \n23 537.0 \n24 53.0 \n25 114.0 \n26 194.0 \n27 309.0 \n28 426.0 \n29 587.0 \n30 800.0 \n31 1130.0 \n32 1598.0 \n33 2175.0 \n34 2990.0 \n35 3940.0 \n36 166.0 \n37 347.0 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\n\n\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \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 YearDomain_EnglishField_EnglishUT (Unique WOS ID)Publication Year_relative_growthUT (Unique WOS ID)_relative_growthUT (Unique WOS ID)_cumsum
02011Applied SciencesAgriculture, Fisheries & Forestry9.020110.0000009.0
12012Applied SciencesAgriculture, Fisheries & Forestry18.020111.00000027.0
22013Applied SciencesAgriculture, Fisheries & Forestry15.020110.66666742.0
32014Applied SciencesAgriculture, Fisheries & Forestry26.020111.88888968.0
42015Applied SciencesAgriculture, Fisheries & Forestry12.020110.33333380.0
52016Applied SciencesAgriculture, Fisheries & Forestry24.020111.666667104.0
62017Applied SciencesAgriculture, Fisheries & Forestry39.020113.333333143.0
72018Applied SciencesAgriculture, Fisheries & Forestry46.020114.111111189.0
82019Applied SciencesAgriculture, Fisheries & Forestry73.020117.111111262.0
92020Applied SciencesAgriculture, Fisheries & Forestry105.0201110.666667367.0
102021Applied SciencesAgriculture, Fisheries & Forestry118.0201112.111111485.0
112022Applied SciencesAgriculture, Fisheries & Forestry127.0201113.111111612.0
122011Applied SciencesBuilt Environment & Design6.020110.0000006.0
132012Applied SciencesBuilt Environment & Design8.020110.33333314.0
142013Applied SciencesBuilt Environment & Design1.02011-0.83333315.0
152014Applied SciencesBuilt Environment & Design17.020111.83333332.0
162015Applied SciencesBuilt Environment & Design18.020112.00000050.0
172016Applied SciencesBuilt Environment & Design13.020111.16666763.0
182017Applied SciencesBuilt Environment & Design24.020113.00000087.0
192018Applied SciencesBuilt Environment & Design41.020115.833333128.0
202019Applied SciencesBuilt Environment & Design59.020118.833333187.0
212020Applied SciencesBuilt Environment & Design84.0201113.000000271.0
222021Applied SciencesBuilt Environment & Design116.0201118.333333387.0
232022Applied SciencesBuilt Environment & Design150.0201124.000000537.0
242011Applied SciencesEnabling & Strategic Technologies53.020110.00000053.0
252012Applied SciencesEnabling & Strategic Technologies61.020110.150943114.0
262013Applied SciencesEnabling & Strategic Technologies80.020110.509434194.0
272014Applied SciencesEnabling & Strategic Technologies115.020111.169811309.0
282015Applied SciencesEnabling & Strategic Technologies117.020111.207547426.0
292016Applied SciencesEnabling & Strategic Technologies161.020112.037736587.0
302017Applied SciencesEnabling & Strategic Technologies213.020113.018868800.0
312018Applied SciencesEnabling & Strategic Technologies330.020115.2264151130.0
322019Applied SciencesEnabling & Strategic Technologies468.020117.8301891598.0
332020Applied SciencesEnabling & Strategic Technologies577.020119.8867922175.0
342021Applied SciencesEnabling & Strategic Technologies815.0201114.3773582990.0
352022Applied SciencesEnabling & Strategic Technologies950.0201116.9245283940.0
362011Applied SciencesEngineering166.020110.000000166.0
372012Applied SciencesEngineering181.020110.090361347.0
382013Applied SciencesEngineering238.020110.433735585.0
392014Applied SciencesEngineering318.020110.915663903.0
402015Applied SciencesEngineering381.020111.2951811284.0
412016Applied SciencesEngineering481.020111.8975901765.0
422017Applied SciencesEngineering587.020112.5361452352.0
432018Applied SciencesEngineering767.020113.6204823119.0
442019Applied SciencesEngineering1059.020115.3795184178.0
452020Applied SciencesEngineering1379.020117.3072295557.0
462021Applied SciencesEngineering1626.020118.7951817183.0
472022Applied SciencesEngineering2049.0201111.3433739232.0
482011Applied SciencesInformation & Communication Technologies256.020110.000000256.0
492012Applied SciencesInformation & Communication Technologies325.020110.269531581.0
502013Applied SciencesInformation & Communication Technologies404.020110.578125985.0
512014Applied SciencesInformation & Communication Technologies554.020111.1640621539.0
522015Applied SciencesInformation & Communication Technologies673.020111.6289062212.0
532016Applied SciencesInformation & Communication Technologies855.020112.3398443067.0
542017Applied SciencesInformation & Communication Technologies1055.020113.1210944122.0
552018Applied SciencesInformation & Communication Technologies1622.020115.3359385744.0
562019Applied SciencesInformation & Communication Technologies2068.020117.0781257812.0
572020Applied SciencesInformation & Communication Technologies2296.020117.96875010108.0
582021Applied SciencesInformation & Communication Technologies2617.020119.22265612725.0
592022Applied SciencesInformation & Communication Technologies2923.0201110.41796915648.0
\n
" + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data[data[\"Domain_English\"]==\"Applied Sciences\"]" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 36, + "outputs": [ + { + "data": { + "text/plain": " Field_English UT (Unique WOS ID)\n0 Agriculture, Fisheries & Forestry 612\n1 Built Environment & Design 537\n2 Economics & Business 15\n3 Enabling & Strategic Technologies 3940\n4 Engineering 9232\n5 Information & Communication Technologies 15648\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)
0Agriculture, Fisheries & Forestry612
1Built Environment & Design537
2Economics & Business15
3Enabling & Strategic Technologies3940
4Engineering9232
5Information & Communication Technologies15648
6Social Sciences1
\n
" + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + " wos[wos[\"Domain_English\"]=='Applied Sciences'].groupby(\"Field_English\", as_index=False)[record_col].nunique()" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 50, + "outputs": [], + "source": [ + "%%capture\n", + "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", + "# wos[\"Field_English\"] = wos[\"Field_English\"].str.replace(\"&\",\"&
\")\n", + "# wos[\"Field_English\"] = wos[\"Field_English\"].str.replace(\"
\",\"\")\n", + "\n", + "for cat in sorted(wos[\"Domain_English\"].unique()):\n", + "\n", + " os.makedirs(rf'plot_html/{cat}',exist_ok=True)\n", + " os.makedirs(rf'plot_html/PPT_plots/{cat}',exist_ok=True)\n", + " id_subset = wos[wos[\"Domain_English\"]==cat][record_col].unique()\n", + "\n", + " data = (wos[wos[record_col].isin(id_subset)].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[wos[record_col].isin(id_subset)][[\"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", + " 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',text_auto=True,\n", + " labels={\n", + " record_col: 'Number of co-publications',\n", + " },\n", + " title=f\"Distribution of topics ({cat})\", 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", + " 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", + " trace.showlegend=False #\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(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", + " for i in range(2,5):\n", + " figsuper['layout'][f'xaxis{i}'][\"range\"] = [2010.8,2022.2]\n", + " figsuper['layout'][\"font\"][\"size\"]=12\n", + " for a in figsuper['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 14\n", + "\n", + " figsuper.update_layout(title_text=f\"{cat}\")\n", + " figsuper.write_html(f\"plot_html/{cat}/{cat}_distr&trends.html\",config= dict(displayModeBar = False, responsive = True))\n", + "\n", + "\n", + " figsuper_ppt = go.Figure(figsuper)\n", + " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + " for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + " figsuper_ppt.show()\n", + " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_distr&trends.png\",height=900,width=1600,scale = 4)\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", + " data[\"ISO3\"] = cc.pandas_convert(series=data[\"Country\"], to='ISO3')\n", + " col_by=\"Country_Type\"\n", + " y_lab=\"ISO3\"\n", + " fig = px.bar(data, x=c, y=y_lab, color=col_by, color_discrete_map=color_discrete_map,text_auto=True,\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['layout']['xaxis1'].update(tickformat=\".0f\")\n", + " figsuper['layout']['xaxis2'].update(tickformat=\".1%\")\n", + " figsuper['layout']['xaxis3'].update(tickformat=\".1%\")\n", + " figsuper['layout'][\"font\"][\"size\"]=12\n", + " for a in figsuper['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 14\n", + " figsuper[\"layout\"][\"yaxis\"][\"tickfont\"][\"size\"] = 10\n", + " figsuper[\"layout\"][\"yaxis2\"][\"tickfont\"][\"size\"] = 10\n", + " figsuper[\"layout\"][\"yaxis3\"][\"tickfont\"][\"size\"] = 10\n", + "\n", + " figsuper.update_layout(uniformtext_minsize=10)\n", + " figsuper.update_layout(title=f\"Contribution of european countries ({cat})\")\n", + " figsuper.write_html(f\"plot_html/{cat}/{cat}_europe_contribution_bar.html\",config= dict(displayModeBar = False, responsive = True))\n", + "\n", + " figsuper_ppt = go.Figure(figsuper)\n", + "\n", + " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + " for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + " figsuper_ppt.show()\n", + " figsuper_ppt.update_yaxes(tickfont=dict(size=18))\n", + " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_europe_contribution_bar.png\",height=900,width=1600,scale = 4)\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=f\"Intraeuropean patterns: Co-occurences of countries in chinese co-publications ({cat})\"\n", + " )\n", + " fig.update_layout(\n", + " width=1000, height=1000,\n", + " xaxis_showgrid=False,\n", + " yaxis_showgrid=False,\n", + " yaxis_autorange='reversed', template='plotly_white',font_family=\"Montserrat\",)\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.write_html(f\"plot_html/{cat}/{cat}_intraeurope_collabs.html\",config= dict(displayModeBar = False, responsive = True))\n", + " figsuper_ppt = go.Figure(fig)\n", + "\n", + " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + " for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + "\n", + " s=16\n", + " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + " figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", + " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_intraeurope_collabs.png\",height=900,width=1600,scale = 4)\n", + "\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\"]\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", + "\n", + " yearsum = collab_year.groupby(\"Publication Year\")[record_col].nunique().reset_index().rename(columns={record_col:\"year_unique\"})\n", + " data = data.merge(yearsum, on=\"Publication Year\")\n", + " data[\"pub_output_percent\"] = data[record_col]/data[\"year_unique\"]\n", + " data[\"ISO3\"] = cc.pandas_convert(series=data[\"Country\"], to='ISO3')\n", + "\n", + "\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", + "\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.update_layout(font_family=\"Montserrat\")\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", + " for i in[\"xaxis\",\"xaxis2\",\"xaxis3\"]:\n", + " figsuper['layout'][f'{i}'][\"range\"] = [2010.8,2022.2]\n", + " figsuper.write_html(f\"plot_html/{cat}/{cat}_country_trends_overall.html\",config= dict(displayModeBar = False, responsive = True))\n", + "\n", + " figsuper_ppt = go.Figure(figsuper)\n", + "\n", + " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + " for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + " figsuper_ppt.show()\n", + " figsuper_ppt.update_yaxes(tickfont=dict(size=18))\n", + " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_country_trends_overall.png\",height=900,width=1600,scale = 4)\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\",facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", + " markers=True,\n", + " labels={\n", + " record_col: 'Number of co-publications',\n", + " \"Eurovoc_Class\": \"Region\"\n", + " },\n", + " title=f\"Yearly output of co-publications ({cat})\",hover_name= \"Country\")\n", + " fig.update_traces(hovertemplate='%{hovertext}
%{x}
Co-publications: %{y}')\n", + " fig.update_yaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + " fig.update_xaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + " fig.update_layout({'template':\"plotly\"})\n", + " fig.update_layout(font_family=\"Montserrat\")\n", + " fig.update_yaxes(title='')\n", + " fig.update_xaxes(title='')\n", + " fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", + " fig.show(config= dict(displayModeBar = False, responsive = True))\n", + " figsuper_ppt = go.Figure(fig)\n", + "\n", + " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + " for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + "\n", + " s=16\n", + " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + " figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", + " figsuper_ppt.write_image(f\"plot_html//PPT_plots/{cat}/{cat}_country_year_trends.png\",height=900,width=1600,scale = 4)\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,facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", + " labels={\n", + " record_col+\"_relative_growth\": 'Relative growth of co-publications (%)',\"Eurovoc_Class\": \"Region\"\n", + " },\n", + " title=f\"Relative growth of co-publication output ({cat})\", template='plotly',hover_name= \"Country\")\n", + " fig.update_traces(hovertemplate='%{hovertext}
%{x}
Relative growth: %{y}')\n", + "\n", + "\n", + " fig.update_yaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + " fig.update_xaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + " # for candidate in fig[\"layout\"].keys():\n", + " # if \"yaxis\" in candidate:\n", + " # fig[\"layout\"][candidate].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", + " fig.update_layout({'template':\"plotly\"})\n", + " fig.update_layout(font_family=\"Montserrat\")\n", + " fig.update_yaxes(title='',zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", + " fig.update_xaxes(title='')\n", + " fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", + " figsuper_ppt = go.Figure(fig)\n", + "\n", + " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + " for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + "\n", + " s=16\n", + " figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", + " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + " figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", + " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_country_year_rel_trends.png\",height=900,width=1600,scale = 4)\n", + "\n", + " fig = px.line(data.sort_values(ascending=True, by='Publication Year'),\n", + " y=\"pub_output_percent\",\n", + " x='Publication Year',\n", + " color=\"Eurovoc_Class\",line_group=\"Country\",markers=True,facet_col=\"Country\",facet_col_wrap=6,category_orders={\"Country\": sorted(data[\"Country\"].unique())},\n", + " labels={\n", + " record_col+\"_relative_growth\": 'Relative growth of co-publications (%)',\"Eurovoc_Class\": \"Region\"\n", + " },\n", + " title=f\"Relative changes in co-publication focus of China ({cat})\", template='plotly',hover_name= \"Country\")\n", + " fig.update_traces(hovertemplate='%{hovertext}
%{x}
Relative growth: %{y}')\n", + "\n", + "\n", + " fig.update_yaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + " fig.update_xaxes(\n", + " showgrid=True,showline=True, linewidth=1, linecolor='black', mirror=True,\n", + " ticks=\"outside\")\n", + " # for candidate in fig[\"layout\"].keys():\n", + " # if \"yaxis\" in candidate:\n", + " # fig[\"layout\"][candidate].update(zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", + " fig.update_layout({'template':\"plotly\"})\n", + " fig.update_layout(font_family=\"Montserrat\")\n", + " fig.update_yaxes(title='',zeroline=True, zerolinewidth=0.5, zerolinecolor='grey',tickformat=\".0%\")\n", + " fig.update_xaxes(title='')\n", + " fig.for_each_annotation(lambda a: a.update(text=a.text.split(\"=\")[-1]))\n", + " figsuper_ppt = go.Figure(fig)\n", + "\n", + " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + " for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + "\n", + " s=16\n", + " figsuper_ppt.update_yaxes(title='',zeroline=True, zerolinewidth=2, zerolinecolor='grey',tickformat=\".0%\")\n", + " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + " figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", + " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_country_year_rel_focus_trend.png\",height=900,width=1600,scale = 4)\n", + "\n", + "\n", + "\n", + " TOPN = 15\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", + " [\"Europe\",\"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,text_auto=True,\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\"Top {TOPN} institutes within {c_scope}
({cat})\", 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.write_html(f\"plot_html/{cat}/{cat}_overall_inst_collab_bar_{c_scope}.html\",config= dict(displayModeBar = False, responsive = True))\n", + " fig.write_image(f\"plot_html/overall_inst_collab_bar_{c_scope}.svg\",height=800,width=1600)\n", + " fig.write_image(f\"plot_html/overall_inst_collab_bar_{c_scope}.png\",height=800,width=1600)\n", + " figsuper_ppt = go.Figure(fig)\n", + " figsuper_ppt.update_traces(textposition='inside')\n", + " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + "\n", + " s=16\n", + " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + " figsuper_ppt.update_xaxes(tickfont=dict(size=s))\n", + " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_overall_inst_collab_bar_{c_scope}.png\",height=900,width=1000,scale = 4)\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_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\"Top {TOPN} institutes within Europe ({cat})\"\n", + " )\n", + " fig.update_layout(\n", + " width=1000, height=1000,\n", + " xaxis_showgrid=False,\n", + " yaxis_showgrid=False,\n", + " yaxis_autorange='reversed',\n", + " template='plotly_white',font_family=\"Montserrat\",\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", + " figsuper_ppt = go.Figure(fig)\n", + "\n", + " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + " for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + "\n", + " s=16\n", + " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + " figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", + " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_overall_inst_collab_europe.png\",height=900,width=1600,scale = 4)\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\"Top {TOPN} institutes within EU-28 ({cat})\"\n", + " )\n", + " fig.update_layout(\n", + " width=1000, height=1000,\n", + " xaxis_showgrid=False,\n", + " yaxis_showgrid=False,\n", + " yaxis_autorange='reversed',\n", + " template='plotly_white',font_family=\"Montserrat\",\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))\n", + "\n", + " figsuper_ppt = go.Figure(fig)\n", + "\n", + " figsuper_ppt['layout'][\"font\"][\"size\"]=22\n", + " for a in figsuper_ppt['layout'][\"annotations\"]:\n", + " a[\"font\"][\"size\"] = 22\n", + "\n", + " s=16\n", + " figsuper_ppt.update_yaxes(tickfont=dict(size=s))\n", + " figsuper_ppt.update_xaxes(tickfont=dict(size=s),tickangle=45)\n", + " figsuper_ppt.write_image(f\"plot_html/PPT_plots/{cat}/{cat}_overall_inst_collab_eu28.png\",height=900,width=1600,scale = 4)" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": null, + "outputs": [], + "source": [ + "# Drill down to subfield" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 51, + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "alignmentgroup": "True", + "hovertemplate": "%{x:d}", + "legendgroup": "Agronomy & Agriculture", + "marker": { + "color": "#636efa", + "pattern": { + "shape": "" + } + }, + "name": "Agronomy & Agriculture", + "offsetgroup": "Agronomy & Agriculture", + "orientation": "h", + "showlegend": false, + "textposition": "auto", + "texttemplate": "%{x}", + "x": [ + 291 + ], + "xaxis": "x", + "y": [ + "Agronomy & Agriculture" + ], + "yaxis": "y", + "type": "bar" + }, + { + "alignmentgroup": "True", + "hovertemplate": 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Relative growth: %{y}%", + "hovertext": [ + "Netherlands", + "Netherlands", + "Netherlands", + "Netherlands", + "Netherlands", + "Netherlands", + "Netherlands", + "Netherlands", + "Netherlands", + "Netherlands", + "Netherlands", + "Netherlands" + ], + "legendgroup": "Western Europe", + "line": { + "color": "#636efa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Western Europe", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + 2022 + ], + "xaxis": "x3", + "y": [ + 0.0, + 100.0, + -100.0, + 100.0, + 100.0, + 100.0, + 200.0, + 800.0, + 900.0, + 1500.0, + 1200.0, + 1000.0 + ], + "yaxis": "y3", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}
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Relative growth: %{y}%", + "hovertext": [ + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg", + "Luxembourg" + ], + "legendgroup": "Western Europe", + "line": { + "color": "#636efa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Western Europe", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + 2022 + ], + "xaxis": "x3", + "y": [ + -100.0, + -100.0, + -100.0, + -100.0, + -100.0, + -100.0, + -100.0, + 0.0, + 0.0, + 100.0, + 100.0, + -100.0 + ], + "yaxis": "y3", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}
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Relative growth: %{y}%", + "hovertext": [ + "Ireland", + "Ireland", + "Ireland", + "Ireland", + "Ireland", + "Ireland", + "Ireland", + "Ireland", + "Ireland", + "Ireland", + "Ireland", + "Ireland" + ], + "legendgroup": "Western Europe", + "line": { + "color": "#636efa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Western Europe", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + 2022 + ], + "xaxis": "x3", + "y": [ + -100.0, + -100.0, + 0.0, + -50.0, + -100.0, + -50.0, + 0.0, + 50.0, + 100.0, + 150.0, + 0.0, + 200.0 + ], + "yaxis": "y3", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}
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Relative growth: %{y}%", + "hovertext": [ + "France", + "France", + "France", + "France", + "France", + "France", + "France", + "France", + "France", + "France", + "France", + "France" + ], + "legendgroup": "Western Europe", + "line": { + "color": "#636efa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Western Europe", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + 2022 + ], + "xaxis": "x3", + "y": [ + 0.0, + -80.0, + -60.0, + 0.0, + 0.0, + -20.0, + 40.0, + 120.0, + 260.0, + 220.00000000000003, + 280.0, + 220.00000000000003 + ], + "yaxis": "y3", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}
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Relative growth: %{y}%", + "hovertext": [ + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany" + ], + "legendgroup": "Western Europe", + "line": { + "color": "#636efa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Western Europe", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + 2022 + ], + "xaxis": "x3", + "y": [ + 0.0, + 300.0, + 400.0, + 700.0, + 500.0, + 1300.0, + 600.0, + 2100.0, + 2100.0, + 2200.0, + 2800.0, + 2900.0 + ], + "yaxis": "y3", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}
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Relative growth: %{y}%", + "hovertext": [ + "Finland", + "Finland", + "Finland", + "Finland", + "Finland", + "Finland", + "Finland", + "Finland", + "Finland", + "Finland", + "Finland", + "Finland" + ], + "legendgroup": "Northern Europe", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Northern Europe", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + 2022 + ], + "xaxis": "x3", + "y": [ + -100.0, + -100.0, + -100.0, + -100.0, + 0.0, + -100.0, + -33.33333333333333, + 66.66666666666666, + -33.33333333333333, + 100.0, + 100.0, + 133.33333333333331 + ], + "yaxis": "y3", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}
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Relative growth: %{y}%", + "hovertext": [ + "Estonia", + "Estonia", + "Estonia", + "Estonia", + "Estonia", + "Estonia", + "Estonia", + "Estonia", + "Estonia", + "Estonia", + "Estonia", + "Estonia" + ], + "legendgroup": "Northern Europe", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Northern Europe", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + 2022 + ], + "xaxis": "x3", + "y": [ + -100.0, + -100.0, + -100.0, + 0.0, + 0.0, + -100.0, + -100.0, + -100.0, + -100.0, + -100.0, + 0.0, + -100.0 + ], + "yaxis": "y3", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}
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Relative growth: %{y}%", + "hovertext": [ + "Spain", + "Spain", + "Spain", + "Spain", + "Spain", + "Spain", + "Spain", + "Spain", + "Spain", + "Spain", + "Spain", + "Spain" + ], + "legendgroup": "Southern Europe", + "line": { + "color": "#00cc96", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Southern Europe", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + 2022 + ], + "xaxis": "x3", + "y": [ + 0.0, + -50.0, + 50.0, + -100.0, + 100.0, + 50.0, + 250.0, + 350.0, + 400.0, + 200.0, + 500.0, + 800.0 + ], + "yaxis": "y3", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}
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Relative growth: %{y}%", + "hovertext": [ + "Italy", + "Italy", + "Italy", + "Italy", + "Italy", + "Italy", + "Italy", + "Italy", + "Italy", + "Italy", + "Italy", + "Italy" + ], + "legendgroup": "Southern Europe", + "line": { + "color": "#00cc96", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Southern Europe", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + 2022 + ], + "xaxis": "x3", + "y": [ + 0.0, + -50.0, + -50.0, + -100.0, + -25.0, + -50.0, + 75.0, + 125.0, + 75.0, + 175.0, + 325.0, + 425.0 + ], + "yaxis": "y3", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}
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Relative growth: %{y}%", + "hovertext": [ + "Poland", + "Poland", + "Poland", + "Poland", + "Poland", + "Poland", + "Poland", + "Poland", + "Poland", + "Poland", + "Poland", + "Poland" + ], + "legendgroup": "Eastern Europe", + "line": { + "color": "#ab63fa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Eastern Europe", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + 2022 + ], + "xaxis": "x3", + "y": [ + -100.0, + -100.0, + -100.0, + 0.0, + -50.0, + -50.0, + 0.0, + -100.0, + 150.0, + 250.0, + 300.0, + 650.0 + ], + "yaxis": "y3", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}
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Relative growth: %{y}%", + "hovertext": [ + "France", + "France", + "France", + "France", + "France", + "France", + "France", + "France", + "France", + "France", + "France", + "France" + ], + "legendgroup": "Western Europe", + "line": { + "color": "#636efa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Western Europe", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + 2022 + ], + "xaxis": "x3", + "y": [ + 0.0, + 40.0, + 20.0, + 240.0, + 280.0, + 280.0, + 240.0, + 420.0, + 580.0, + 560.0, + 800.0, + 760.0 + ], + "yaxis": "y3", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}
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Relative growth: %{y}%", + "hovertext": [ + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany", + "Germany" + ], + "legendgroup": "Western Europe", + "line": { + "color": "#636efa", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Western Europe", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + 2022 + ], + "xaxis": "x3", + "y": [ + 0.0, + 100.0, + 100.0, + 200.0, + 362.5, + 212.5, + 512.5, + 375.0, + 475.0, + 650.0, + 850.0, + 1087.5 + ], + "yaxis": "y3", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}
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Relative growth: %{y}%", + "hovertext": [ + "Finland", + "Finland", + "Finland", + "Finland", + "Finland", + "Finland", + "Finland", + "Finland", + "Finland", + "Finland", + "Finland", + "Finland" + ], + "legendgroup": "Northern Europe", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Northern Europe", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + 2022 + ], + "xaxis": "x3", + "y": [ + 0.0, + 200.0, + 0.0, + 200.0, + 1000.0, + 600.0, + 900.0, + 900.0, + 900.0, + 1900.0, + 2400.0, + 1500.0 + ], + "yaxis": "y3", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}
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Relative growth: %{y}%", + "hovertext": [ + "Estonia", + "Estonia", + "Estonia", + "Estonia", + "Estonia", + "Estonia", + "Estonia", + "Estonia", + "Estonia", + "Estonia", + "Estonia", + "Estonia" + ], + "legendgroup": "Northern Europe", + "line": { + "color": "#EF553B", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Northern Europe", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + 2022 + ], + "xaxis": "x3", + "y": [ + -100.0, + 0.0, + -100.0, + 0.0, + -100.0, + -100.0, + 100.0, + 100.0, + -100.0, + -100.0, + 200.0, + 200.0 + ], + "yaxis": "y3", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}
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Relative growth: %{y}%", + "hovertext": [ + "Cyprus", + "Cyprus", + "Cyprus", + "Cyprus", + "Cyprus", + "Cyprus", + "Cyprus", + "Cyprus", + "Cyprus", + "Cyprus", + "Cyprus", + "Cyprus" + ], + "legendgroup": "Southern Europe", + "line": { + "color": "#00cc96", + "dash": "solid" + }, + "marker": { + "symbol": "circle" + }, + "mode": "lines+markers", + "name": "Southern Europe", + "orientation": "v", + "showlegend": false, + "x": [ + 2011, + 2012, + 2013, + 2014, + 2015, + 2016, + 2017, + 2018, + 2019, + 2020, + 2021, + 2022 + ], + "xaxis": "x3", + "y": [ + -100.0, + -100.0, + -100.0, + -100.0, + -100.0, + -100.0, + -100.0, + -100.0, + -100.0, + 0.0, + -50.0, + -50.0 + ], + "yaxis": "y3", + "type": "scatter" + }, + { + "hovertemplate": "%{hovertext}
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