https://github.com/hongtaoh/32vis
Tip revision: 9960413711b0efb1f51ff7cce3548d259be8d8cb authored by Hongtao Hao on 24 May 2025, 20:13:11 UTC
Update README.md
Update README.md
Tip revision: 9960413
Replicability.ipynb
{
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{
"cell_type": "code",
"execution_count": 47,
"id": "be762e59",
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"source": [
"import pandas as pd\n",
"import seaborn as sns\n",
"import numpy as np\n",
"import matplotlib\n",
"import matplotlib.pyplot as plt\n",
"plt.rcParams[\"figure.figsize\"] = (8,6)\n",
"from scipy.stats import ttest_ind\n",
"pd.set_option('display.max_columns', None)\n",
"import numpy as np\n",
"from collections import Counter\n",
"font = {'family' : 'Arial',\n",
" 'weight' : 'normal',\n",
" 'size' : 6.5}\n",
"matplotlib.rc('font', **font)\n",
"import string\n",
"from scipy.stats import mannwhitneyu"
]
},
{
"cell_type": "code",
"execution_count": 48,
"id": "a6be4c67",
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv('../data/ht_class/ht_cleaned_paper_df.csv')\n",
"# Replace True/False to Yes/No\n",
"# Snippet: https://stackoverflow.com/a/45196807\n",
"bool_cols = df.columns[df.dtypes == 'bool']\n",
"df[bool_cols] = df[bool_cols].replace({True: 'Yes', False: 'No'})\n",
"df['PaperType'] = df['PaperType'].replace({'J': 'Jor.', 'C': 'Con.'})\n",
"\n",
"# cutoff\n",
"cutoff_year = 2020\n",
"df = df[df['Year'] <= cutoff_year]"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "1040921a",
"metadata": {},
"outputs": [
{
"data": {
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"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Conference</th>\n",
" <th>Year</th>\n",
" <th>Title</th>\n",
" <th>DOI</th>\n",
" <th>FirstPage</th>\n",
" <th>LastPage</th>\n",
" <th>PaperType</th>\n",
" <th>OpenAlex Year</th>\n",
" <th>OpenAlex Publication Date</th>\n",
" <th>OpenAlex ID</th>\n",
" <th>OpenAlex Venue Name</th>\n",
" <th>OpenAlex First Page</th>\n",
" <th>OpenAlex Last Page</th>\n",
" <th>Number of Pages</th>\n",
" <th>Number of References</th>\n",
" <th>Number of Concepts</th>\n",
" <th>Number of Citations</th>\n",
" <th>Number of Authors</th>\n",
" <th>Cross-type Collaboration</th>\n",
" <th>Cross-country Collaboration</th>\n",
" <th>With US Authors</th>\n",
" <th>Both Cross-type and Cross-country Collaboration</th>\n",
" <th>IEEE Title</th>\n",
" <th>Citation Counts on Google Scholar</th>\n",
" <th>Award</th>\n",
" <th>Award Name</th>\n",
" <th>Award Track</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>InfoVis</td>\n",
" <td>2011</td>\n",
" <td>D³ Data-Driven Documents</td>\n",
" <td>10.1109/TVCG.2011.185</td>\n",
" <td>2301.0</td>\n",
" <td>2309</td>\n",
" <td>Jor.</td>\n",
" <td>2011</td>\n",
" <td>2011-12-01</td>\n",
" <td>W2135415614</td>\n",
" <td>IEEE Transactions on Visualization and Compute...</td>\n",
" <td>2301.0</td>\n",
" <td>2309.0</td>\n",
" <td>9.0</td>\n",
" <td>27</td>\n",
" <td>15</td>\n",
" <td>2239</td>\n",
" <td>3.0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>D³ Data-Driven Documents</td>\n",
" <td>3597</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Vis</td>\n",
" <td>1991</td>\n",
" <td>Tree-maps: a space-filling approach to the vis...</td>\n",
" <td>10.1109/VISUAL.1991.175815</td>\n",
" <td>284.0</td>\n",
" <td>291</td>\n",
" <td>Con.</td>\n",
" <td>1991</td>\n",
" <td>1991-10-22</td>\n",
" <td>W2146872957</td>\n",
" <td>ieee visualization</td>\n",
" <td>284.0</td>\n",
" <td>291.0</td>\n",
" <td>8.0</td>\n",
" <td>21</td>\n",
" <td>13</td>\n",
" <td>1116</td>\n",
" <td>2.0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Tree-maps: a space-filling approach to the vis...</td>\n",
" <td>2193</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Vis</td>\n",
" <td>1990</td>\n",
" <td>Parallel coordinates: a tool for visualizing m...</td>\n",
" <td>10.1109/VISUAL.1990.146402</td>\n",
" <td>361.0</td>\n",
" <td>378</td>\n",
" <td>Con.</td>\n",
" <td>1990</td>\n",
" <td>1990-10-23</td>\n",
" <td>W2034694694</td>\n",
" <td>ieee visualization</td>\n",
" <td>361.0</td>\n",
" <td>378.0</td>\n",
" <td>18.0</td>\n",
" <td>20</td>\n",
" <td>16</td>\n",
" <td>990</td>\n",
" <td>2.0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Parallel coordinates: a tool for visualizing m...</td>\n",
" <td>1704</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>InfoVis</td>\n",
" <td>2006</td>\n",
" <td>Hierarchical Edge Bundles: Visualization of Ad...</td>\n",
" <td>10.1109/TVCG.2006.147</td>\n",
" <td>741.0</td>\n",
" <td>748</td>\n",
" <td>Jor.</td>\n",
" <td>2006</td>\n",
" <td>2006-09-01</td>\n",
" <td>W2145640629</td>\n",
" <td>IEEE Transactions on Visualization and Compute...</td>\n",
" <td>741.0</td>\n",
" <td>748.0</td>\n",
" <td>8.0</td>\n",
" <td>32</td>\n",
" <td>11</td>\n",
" <td>807</td>\n",
" <td>1.0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Hierarchical Edge Bundles: Visualization of Ad...</td>\n",
" <td>1366</td>\n",
" <td>Yes</td>\n",
" <td>BP</td>\n",
" <td>InfoVis</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Vis</td>\n",
" <td>1997</td>\n",
" <td>ROAMing terrain: Real-time Optimally Adapting ...</td>\n",
" <td>10.1109/VISUAL.1997.663860</td>\n",
" <td>81.0</td>\n",
" <td>88</td>\n",
" <td>Con.</td>\n",
" <td>1997</td>\n",
" <td>1997-10-01</td>\n",
" <td>W2532506824</td>\n",
" <td>ieee visualization</td>\n",
" <td>81.0</td>\n",
" <td>88.0</td>\n",
" <td>8.0</td>\n",
" <td>20</td>\n",
" <td>12</td>\n",
" <td>585</td>\n",
" <td>6.0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>ROAMing terrain: Real-time Optimally Adapting ...</td>\n",
" <td>1408</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
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"</div>"
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"text/plain": [
" Conference Year Title \\\n",
"0 InfoVis 2011 D³ Data-Driven Documents \n",
"1 Vis 1991 Tree-maps: a space-filling approach to the vis... \n",
"2 Vis 1990 Parallel coordinates: a tool for visualizing m... \n",
"3 InfoVis 2006 Hierarchical Edge Bundles: Visualization of Ad... \n",
"4 Vis 1997 ROAMing terrain: Real-time Optimally Adapting ... \n",
"\n",
" DOI FirstPage LastPage PaperType OpenAlex Year \\\n",
"0 10.1109/TVCG.2011.185 2301.0 2309 Jor. 2011 \n",
"1 10.1109/VISUAL.1991.175815 284.0 291 Con. 1991 \n",
"2 10.1109/VISUAL.1990.146402 361.0 378 Con. 1990 \n",
"3 10.1109/TVCG.2006.147 741.0 748 Jor. 2006 \n",
"4 10.1109/VISUAL.1997.663860 81.0 88 Con. 1997 \n",
"\n",
" OpenAlex Publication Date OpenAlex ID \\\n",
"0 2011-12-01 W2135415614 \n",
"1 1991-10-22 W2146872957 \n",
"2 1990-10-23 W2034694694 \n",
"3 2006-09-01 W2145640629 \n",
"4 1997-10-01 W2532506824 \n",
"\n",
" OpenAlex Venue Name OpenAlex First Page \\\n",
"0 IEEE Transactions on Visualization and Compute... 2301.0 \n",
"1 ieee visualization 284.0 \n",
"2 ieee visualization 361.0 \n",
"3 IEEE Transactions on Visualization and Compute... 741.0 \n",
"4 ieee visualization 81.0 \n",
"\n",
" OpenAlex Last Page Number of Pages Number of References \\\n",
"0 2309.0 9.0 27 \n",
"1 291.0 8.0 21 \n",
"2 378.0 18.0 20 \n",
"3 748.0 8.0 32 \n",
"4 88.0 8.0 20 \n",
"\n",
" Number of Concepts Number of Citations Number of Authors \\\n",
"0 15 2239 3.0 \n",
"1 13 1116 2.0 \n",
"2 16 990 2.0 \n",
"3 11 807 1.0 \n",
"4 12 585 6.0 \n",
"\n",
" Cross-type Collaboration Cross-country Collaboration With US Authors \\\n",
"0 No No Yes \n",
"1 No No Yes \n",
"2 No No Yes \n",
"3 No No No \n",
"4 No No Yes \n",
"\n",
" Both Cross-type and Cross-country Collaboration \\\n",
"0 No \n",
"1 No \n",
"2 No \n",
"3 No \n",
"4 No \n",
"\n",
" IEEE Title \\\n",
"0 D³ Data-Driven Documents \n",
"1 Tree-maps: a space-filling approach to the vis... \n",
"2 Parallel coordinates: a tool for visualizing m... \n",
"3 Hierarchical Edge Bundles: Visualization of Ad... \n",
"4 ROAMing terrain: Real-time Optimally Adapting ... \n",
"\n",
" Citation Counts on Google Scholar Award Award Name Award Track \n",
"0 3597 No NaN NaN \n",
"1 2193 No NaN NaN \n",
"2 1704 No NaN NaN \n",
"3 1366 Yes BP InfoVis \n",
"4 1408 No NaN NaN "
]
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"metadata": {},
"output_type": "execute_result"
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"source": [
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 50,
"id": "7eb13a98",
"metadata": {},
"outputs": [],
"source": [
"citenum = 'Number of Citations'\n",
"gcitenum = 'Citation Counts on Google Scholar'"
]
},
{
"cell_type": "code",
"execution_count": 51,
"id": "5bf695c8",
"metadata": {},
"outputs": [
{
"data": {
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" <th>Award Name</th>\n",
" <th>Award Track</th>\n",
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" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>InfoVis</td>\n",
" <td>2011</td>\n",
" <td>D³ Data-Driven Documents</td>\n",
" <td>10.1109/TVCG.2011.185</td>\n",
" <td>2301.0</td>\n",
" <td>2309</td>\n",
" <td>Jor.</td>\n",
" <td>2011</td>\n",
" <td>2011-12-01</td>\n",
" <td>W2135415614</td>\n",
" <td>IEEE Transactions on Visualization and Compute...</td>\n",
" <td>2301.0</td>\n",
" <td>2309.0</td>\n",
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" <td>27</td>\n",
" <td>15</td>\n",
" <td>2239</td>\n",
" <td>3.0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>D³ Data-Driven Documents</td>\n",
" <td>3597</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>Vis</td>\n",
" <td>1991</td>\n",
" <td>Tree-maps: a space-filling approach to the vis...</td>\n",
" <td>10.1109/VISUAL.1991.175815</td>\n",
" <td>284.0</td>\n",
" <td>291</td>\n",
" <td>Con.</td>\n",
" <td>1991</td>\n",
" <td>1991-10-22</td>\n",
" <td>W2146872957</td>\n",
" <td>ieee visualization</td>\n",
" <td>284.0</td>\n",
" <td>291.0</td>\n",
" <td>8.0</td>\n",
" <td>21</td>\n",
" <td>13</td>\n",
" <td>1116</td>\n",
" <td>2.0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Tree-maps: a space-filling approach to the vis...</td>\n",
" <td>2193</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>Vis</td>\n",
" <td>1990</td>\n",
" <td>Parallel coordinates: a tool for visualizing m...</td>\n",
" <td>10.1109/VISUAL.1990.146402</td>\n",
" <td>361.0</td>\n",
" <td>378</td>\n",
" <td>Con.</td>\n",
" <td>1990</td>\n",
" <td>1990-10-23</td>\n",
" <td>W2034694694</td>\n",
" <td>ieee visualization</td>\n",
" <td>361.0</td>\n",
" <td>378.0</td>\n",
" <td>18.0</td>\n",
" <td>20</td>\n",
" <td>16</td>\n",
" <td>990</td>\n",
" <td>2.0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>Parallel coordinates: a tool for visualizing m...</td>\n",
" <td>1704</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>Vis</td>\n",
" <td>1997</td>\n",
" <td>ROAMing terrain: Real-time Optimally Adapting ...</td>\n",
" <td>10.1109/VISUAL.1997.663860</td>\n",
" <td>81.0</td>\n",
" <td>88</td>\n",
" <td>Con.</td>\n",
" <td>1997</td>\n",
" <td>1997-10-01</td>\n",
" <td>W2532506824</td>\n",
" <td>ieee visualization</td>\n",
" <td>81.0</td>\n",
" <td>88.0</td>\n",
" <td>8.0</td>\n",
" <td>20</td>\n",
" <td>12</td>\n",
" <td>585</td>\n",
" <td>6.0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Yes</td>\n",
" <td>No</td>\n",
" <td>ROAMing terrain: Real-time Optimally Adapting ...</td>\n",
" <td>1408</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>Vis</td>\n",
" <td>2003</td>\n",
" <td>Acceleration techniques for GPU-based volume r...</td>\n",
" <td>10.1109/VISUAL.2003.1250384</td>\n",
" <td>287.0</td>\n",
" <td>292</td>\n",
" <td>Con.</td>\n",
" <td>2003</td>\n",
" <td>2003-10-22</td>\n",
" <td>W2113001609</td>\n",
" <td>ieee visualization</td>\n",
" <td>38.0</td>\n",
" <td>38.0</td>\n",
" <td>1.0</td>\n",
" <td>17</td>\n",
" <td>16</td>\n",
" <td>607</td>\n",
" <td>2.0</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>No</td>\n",
" <td>Acceleration techniques for GPU-based volume r...</td>\n",
" <td>1289</td>\n",
" <td>No</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
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],
"text/plain": [
" Conference Year Title \\\n",
"0 InfoVis 2011 D³ Data-Driven Documents \n",
"1 Vis 1991 Tree-maps: a space-filling approach to the vis... \n",
"2 Vis 1990 Parallel coordinates: a tool for visualizing m... \n",
"4 Vis 1997 ROAMing terrain: Real-time Optimally Adapting ... \n",
"5 Vis 2003 Acceleration techniques for GPU-based volume r... \n",
"\n",
" DOI FirstPage LastPage PaperType OpenAlex Year \\\n",
"0 10.1109/TVCG.2011.185 2301.0 2309 Jor. 2011 \n",
"1 10.1109/VISUAL.1991.175815 284.0 291 Con. 1991 \n",
"2 10.1109/VISUAL.1990.146402 361.0 378 Con. 1990 \n",
"4 10.1109/VISUAL.1997.663860 81.0 88 Con. 1997 \n",
"5 10.1109/VISUAL.2003.1250384 287.0 292 Con. 2003 \n",
"\n",
" OpenAlex Publication Date OpenAlex ID \\\n",
"0 2011-12-01 W2135415614 \n",
"1 1991-10-22 W2146872957 \n",
"2 1990-10-23 W2034694694 \n",
"4 1997-10-01 W2532506824 \n",
"5 2003-10-22 W2113001609 \n",
"\n",
" OpenAlex Venue Name OpenAlex First Page \\\n",
"0 IEEE Transactions on Visualization and Compute... 2301.0 \n",
"1 ieee visualization 284.0 \n",
"2 ieee visualization 361.0 \n",
"4 ieee visualization 81.0 \n",
"5 ieee visualization 38.0 \n",
"\n",
" OpenAlex Last Page Number of Pages Number of References \\\n",
"0 2309.0 9.0 27 \n",
"1 291.0 8.0 21 \n",
"2 378.0 18.0 20 \n",
"4 88.0 8.0 20 \n",
"5 38.0 1.0 17 \n",
"\n",
" Number of Concepts Number of Citations Number of Authors \\\n",
"0 15 2239 3.0 \n",
"1 13 1116 2.0 \n",
"2 16 990 2.0 \n",
"4 12 585 6.0 \n",
"5 16 607 2.0 \n",
"\n",
" Cross-type Collaboration Cross-country Collaboration With US Authors \\\n",
"0 No No Yes \n",
"1 No No Yes \n",
"2 No No Yes \n",
"4 No No Yes \n",
"5 No No No \n",
"\n",
" Both Cross-type and Cross-country Collaboration \\\n",
"0 No \n",
"1 No \n",
"2 No \n",
"4 No \n",
"5 No \n",
"\n",
" IEEE Title \\\n",
"0 D³ Data-Driven Documents \n",
"1 Tree-maps: a space-filling approach to the vis... \n",
"2 Parallel coordinates: a tool for visualizing m... \n",
"4 ROAMing terrain: Real-time Optimally Adapting ... \n",
"5 Acceleration techniques for GPU-based volume r... \n",
"\n",
" Citation Counts on Google Scholar Award Award Name Award Track \n",
"0 3597 No NaN NaN \n",
"1 2193 No NaN NaN \n",
"2 1704 No NaN NaN \n",
"4 1408 No NaN NaN \n",
"5 1289 No NaN NaN "
]
},
"execution_count": 51,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"df[df['Award'].isin(['No', 'Con.'])].head()"
]
},
{
"cell_type": "markdown",
"id": "a9b7719c",
"metadata": {},
"source": [
"## Plot boxplots with stripplots"
]
},
{
"cell_type": "code",
"execution_count": 52,
"id": "45e76055",
"metadata": {},
"outputs": [],
"source": [
"vars = ['Cross-type Collaboration', 'Cross-country Collaboration',\n",
" 'With US Authors', 'Award', 'PaperType']\n",
"short_varname_dic = {\n",
" 'Cross-type Collaboration':'Cross-type',\n",
" 'Cross-country Collaboration':'Cross-country',\n",
" 'With US Authors':'W/ US Authors'\n",
"}"
]
},
{
"cell_type": "code",
"execution_count": 53,
"id": "8c120703",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 53,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"'Cross-type Collaboration' in short_varname_dic.keys()"
]
},
{
"cell_type": "code",
"execution_count": 56,
"id": "b4bc562c",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([252., 252.])"
]
},
"execution_count": 56,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 252x252 with 5 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# THE FOLLOWING IS FOR THE REPLICABILITY STAMP\n",
"smallfontsize = 6\n",
"f, axs = plt.subplots(5,1,\n",
" figsize=(3.5,3.5),\n",
" sharex=True,\n",
" sharey=False,\n",
" gridspec_kw=dict(height_ratios=[1,1,1,1,1]))\n",
"g0 = sns.boxplot(x=df[citenum], y=df['Award'].astype(str), palette=\"Set2\", ax=axs[0])\n",
"g0 = sns.stripplot(x=df[citenum], y=df['Award'].astype(str), \n",
" size=2, color=\"orange\", linewidth=0, ax=axs[0], alpha=0.2)\n",
"g0.set_xlabel(\"\")\n",
"g0.set_ylabel(\"Awards\", fontsize = smallfontsize)\n",
"###########\n",
"g1 = sns.boxplot(x=df[citenum], \n",
" y=df['Cross-type Collaboration'].astype(str), palette=\"Paired\", ax=axs[1])\n",
"g1 = sns.stripplot(x=df[citenum], y=df['Cross-type Collaboration'].astype(str), \n",
" size=2, color=\"orange\", linewidth=0, ax=axs[1], alpha=0.2)\n",
"g1.set_xlabel(\"\")\n",
"g1.set_ylabel(\"Cross-Type\", fontsize = smallfontsize)\n",
"#############\n",
"g2 = sns.boxplot(x=df[citenum], \n",
" y=df['Cross-country Collaboration'].astype(str), palette=\"vlag\", ax=axs[2])\n",
"g2 = sns.stripplot(x=df[citenum], y=df['Cross-country Collaboration'].astype(str),\n",
" size=2, color=\"orange\", linewidth=0, ax=axs[2], alpha=.2)\n",
"g2.set_xlabel(\"\")\n",
"g2.set_ylabel(\"Cross-Country\", fontsize = smallfontsize)\n",
"################\n",
"g3 = sns.boxplot(x=df[citenum], \n",
" y=df['With US Authors'].astype(str), \n",
" palette=\"Paired\", \n",
" ax=axs[3],\n",
" order = ['No', 'Yes'], \n",
" )\n",
"g3 = sns.stripplot(x=df[citenum], y=df['With US Authors'].astype(str), \n",
" order = ['No', 'Yes'], \n",
" size=2, color=\"orange\", linewidth=0, ax=axs[3], alpha=0.2)\n",
"g3.set_xlabel(\"\")\n",
"g3.set_ylabel(\"US Authors\", fontsize = smallfontsize)\n",
"##################\n",
"g4 = sns.boxplot(x=citenum, y='PaperType', data=df, palette=\"Set2\", ax=axs[4])\n",
"g4 = sns.stripplot(x=citenum, y='PaperType', data=df,\n",
" size=2, color=\"orange\", linewidth=0, ax=axs[4], alpha=0.2)\n",
"g4.set_xlabel('Number of citations', fontsize = 8)\n",
"g4.set_xscale(\"log\")\n",
"g4.set_ylabel(\"Paper Type\", fontsize = smallfontsize)\n",
"f.text(-0.1, 1.05, 'c', transform=g0.transAxes, \n",
" size=10, weight='bold')\n",
"# f.tight_layout()\n",
"f.savefig('../results/figures/boxplots_replicability.png', dpi = 150)\n",
"f.get_size_inches()*f.dpi"
]
}
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