diff --git a/code/README.md b/code/README.md index 9ddec4f..d13fa44 100644 --- a/code/README.md +++ b/code/README.md @@ -4,4 +4,5 @@ This folder contains notebooks to replicate analyses and figures (`_rep` denotes 2. `results(_rep).ipynb` - code to run all non-statistical analyses and generate all data figures 3. `r_code(_rep).ipynb` - code to run statistical tests in R 4. `stats_results(_rep).ipynb` - code to generate all statistical results -5. `meta-analysis.ipynb` - code to carry out the meta analysis +5. `meta-analysis.ipynb` - code to carry out the large scale analysis +6. `metafor.ipynb` - code to carry out the meta-analysis in R diff --git a/code/meta-analysis.ipynb b/code/meta-analysis.ipynb index 144c1b1..d6be9b3 100644 --- a/code/meta-analysis.ipynb +++ b/code/meta-analysis.ipynb @@ -50,6 +50,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -83,7 +84,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -214,11 +215,12 @@ " sentence_dfs[file_name] = df\n", "\n", " df_results = pd.DataFrame(results_dict).T\n", - "\n", + " \n", " return df_results, sentence_dfs" ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -235,7 +237,7 @@ "text/markdown": [ "**Past keywords and phrases:**\n", "\n", - "elapsed | yesterday | had | last semester | made | previously | did | last quarter | bygone | once upon a time | last year | heretofore | recently | terminated | used to be | then | earlier | since | final | hitherto | back when | last season | used to | before | historically | ago | thus far | antiquity | last week | concluded | formerly | last month | yesteryear | olden days | said | so far | were | to date | up to now | was | ceased | already | once | last time | wrote | in the past | long ago | expired | in those days | last night" + "final | so far | used to be | to date | had | made | last night | long ago | already | last season | concluded | were | once | previously | last month | ceased | earlier | in the past | before | said | up to now | heretofore | last year | wrote | terminated | last semester | yesteryear | was | antiquity | last time | since | in those days | did | thus far | back when | last quarter | ago | formerly | elapsed | olden days | yesterday | recently | once upon a time | then | expired | hitherto | used to | historically | last week | bygone" ], "text/plain": [ "" @@ -252,6 +254,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -268,7 +271,7 @@ "text/markdown": [ "**Future keywords and phrases:**\n", "\n", - "later | next season | next quarter | next week | forthcoming | eventually | succeeding | might | upcoming | in the works | predicted | imminently | prospective | to be | futuristic | next year | could | after | tomorrow | in the future | shall | next time | soon | intend to | in time | shortly | on the horizon | subsequent | next month | anticipated | going to | can | some day | may | looming | will | impending | later on | eventual | down the line | scheduled to | next semester | plan to | in the cards | hereafter" + "to be | prospective | futuristic | next time | tomorrow | on the horizon | imminently | next quarter | forthcoming | soon | next year | next season | could | subsequent | impending | can | down the line | in time | eventual | later on | going to | predicted | may | in the future | some day | might | succeeding | anticipated | shall | next week | looming | scheduled to | later | in the cards | intend to | eventually | hereafter | upcoming | after | will | next semester | shortly | in the works | next month | plan to" ], "text/plain": [ "" @@ -285,6 +288,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -592,6 +596,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": {}, "source": [ @@ -629,7 +634,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 115, "metadata": {}, "outputs": [ { @@ -654,11 +659,13 @@ "source": [ "datadir = Path.cwd().parent.joinpath('data')\n", "results = []\n", + "sentence = []\n", "\n", "# should we just download the already-completed results or compute them from scratch?\n", "force_rerun = False\n", "\n", "for i, row in data.iterrows():\n", + "# for i, row in data[9:10].iterrows():\n", " print('Processing dataset: ' + row['Dataset'])\n", " results_fname = datadir.joinpath(row['Short name'].lower() + '_results.pkl')\n", " if not results_fname.exists():\n", @@ -679,14 +686,15 @@ " f.write(x.content)\n", " \n", " with open(results_fname, 'rb') as f:\n", - " next_results, _ = pickle.load(f)\n", + " next_results, sentence_dfs = pickle.load(f)\n", " \n", - " next_results = next_results.reset_index().rename(columns={\"index\": \"filename\"}).melt(id_vars=[\"filename\"], var_name=\"tense\", value_name=\"count\")\n", + " next_results = next_results.reset_index().rename(columns={\"index\": \"filename\"}).melt(id_vars=[\"filename\"], \n", + " var_name=\"tense\", value_name=\"count\")\n", " next_results['proportion'] = next_results['count'] / next_results.groupby('filename')['count'].transform('sum')\n", " next_results['Dataset'] = row['Short name']\n", "\n", " results.append(next_results)\n", - "\n", + " sentence.append(sentence_dfs)\n", "results = pd.concat(results)" ] }, @@ -694,184 +702,247 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Load in manual counts for *The Chair*" + "## the Chair" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = []\n", + "d = 0\n", + "\n", + "for key in sentence[d].keys():\n", + " res = {}\n", + " # res['dataset'] = data['Short name'][d]\n", + " # res['n_files'] = len(sentence[d])\n", + " res['episode'] = key\n", + " res['n_sentences'] = len(sentence[d][key])\n", + " res['n_past_refs_c'] = sentence[d][key]['past'].astype(bool).sum()\n", + " res['n_future_refs_c'] = sentence[d][key]['future'].astype(bool).sum()\n", + " print(res)\n", + " results.append(res)\n", + " \n", + "# pd.DataFrame(results).to_csv(\"../data/the_chair/the_chair_auto_reference_counts.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## all datasets" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "results = []\n", + "\n", + "for d in range(len(sentence)):\n", + " res = {}\n", + " res['dataset'] = data_filter['Short name'][d]\n", + " res['n_files'] = len(sentence[d])\n", + " res['n_sentences'] = sum([len(sentence[d][key]) for key in sentence[d].keys()])\n", + " res['n_past_refs_c'] = sum([sentence[d][key]['past'].astype(bool).sum() for key in sentence[d].keys()])\n", + " res['n_future_refs_c'] = sum([sentence[d][key]['future'].astype(bool).sum() for key in sentence[d].keys()])\n", + " results.append(res)\n", + " \n", + "# pd.DataFrame(results).to_csv(\"../data/ref_counts_summary.csv\", index=False)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# figure S12" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from bokeh.palettes import Category20c" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "auto = pd.read_csv(\"../data/the_chair/the_chair_auto_reference_counts.csv\")\n", + "manual = pd.read_csv(\"../data/the_chair/the_chair_manual_reference_counts.csv\")\n", + "\n", + "auto['auto_ratio'] = auto['Past']/auto['Future']\n", + "manual['manual_ratio'] = manual['Past']/manual['Future']\n", + "auto_long = auto[['Episode','Past','Future']].melt(var_name='Direction', value_name='auto_count', id_vars=['Episode'])\n", + "manual_long = manual[['Episode','Past','Future']].melt(var_name='Direction', value_name='manual_count', id_vars=['Episode'])\n", + "auto_long['auto_proportion'] = auto_long['auto_count'] / auto_long.groupby('Episode')['auto_count'].transform('sum')\n", + "manual_long['manual_proportion'] = manual_long['manual_count'] / manual_long.groupby('Episode')['manual_count'].transform('sum')\n", + "\n", + "count_all_long = manual_long.merge(auto_long, on=['Episode','Direction'])\n", + "count_all = manual.merge(auto, on=['Episode'])\n", + "count_all['manual_prop'] = count_all['Past_x']/(count_all['Past_x']+count_all['Future_x'])\n", + "count_all['auto_prop'] = count_all['Past_y']/(count_all['Past_y']+count_all['Future_y'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [ { "data": { "text/html": [ - 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Episodetensecountproportion
01Past600.769231
11Future180.230769
22Past300.681818
32Future140.318182
43Past430.565789
53Future330.434211
64Past310.596154
74Future210.403846
85Past360.765957
95Future110.234043
106Past270.692308
116Future120.307692
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0.7872340425531915, \"auto_count\": 116, \"auto_proportion\": 0.6373626373626373}, {\"Episode\": 6, \"Direction\": \"Past\", \"manual_count\": 27, \"manual_proportion\": 0.6923076923076923, \"auto_count\": 121, \"auto_proportion\": 0.55}, {\"Episode\": 1, \"Direction\": \"Future\", \"manual_count\": 19, \"manual_proportion\": 0.24050632911392406, \"auto_count\": 74, \"auto_proportion\": 0.3978494623655914}, {\"Episode\": 2, \"Direction\": \"Future\", \"manual_count\": 14, \"manual_proportion\": 0.32558139534883723, \"auto_count\": 64, \"auto_proportion\": 0.4444444444444444}, {\"Episode\": 3, \"Direction\": \"Future\", \"manual_count\": 33, \"manual_proportion\": 0.4342105263157895, \"auto_count\": 82, \"auto_proportion\": 0.4}, {\"Episode\": 4, \"Direction\": \"Future\", \"manual_count\": 20, \"manual_proportion\": 0.4, \"auto_count\": 75, \"auto_proportion\": 0.4098360655737705}, {\"Episode\": 5, \"Direction\": \"Future\", \"manual_count\": 10, \"manual_proportion\": 0.2127659574468085, \"auto_count\": 66, \"auto_proportion\": 0.3626373626373626}, {\"Episode\": 6, \"Direction\": \"Future\", \"manual_count\": 12, \"manual_proportion\": 0.3076923076923077, \"auto_count\": 99, \"auto_proportion\": 0.45}], \"data-723fe09044526ebf24326bdcf66425ba\": [{\"Episode\": 1, \"Past_x\": 60, \"Future_x\": 19, \"manual_ratio\": 3.1578947368421053, \"Total\": 457, \"Past_y\": 112, \"Future_y\": 74, \"auto_ratio\": 1.5135135135135136, \"manual_prop\": 0.759493670886076, \"auto_prop\": 0.6021505376344086}, {\"Episode\": 2, \"Past_x\": 29, \"Future_x\": 14, \"manual_ratio\": 2.0714285714285716, \"Total\": 501, \"Past_y\": 80, \"Future_y\": 64, \"auto_ratio\": 1.25, \"manual_prop\": 0.6744186046511628, \"auto_prop\": 0.5555555555555556}, {\"Episode\": 3, \"Past_x\": 43, \"Future_x\": 33, \"manual_ratio\": 1.303030303030303, \"Total\": 518, \"Past_y\": 123, \"Future_y\": 82, \"auto_ratio\": 1.5, \"manual_prop\": 0.5657894736842105, \"auto_prop\": 0.6}, {\"Episode\": 4, \"Past_x\": 30, \"Future_x\": 20, \"manual_ratio\": 1.5, \"Total\": 442, \"Past_y\": 108, \"Future_y\": 75, \"auto_ratio\": 1.44, \"manual_prop\": 0.6, \"auto_prop\": 0.5901639344262295}, {\"Episode\": 5, \"Past_x\": 37, \"Future_x\": 10, \"manual_ratio\": 3.7, \"Total\": 508, \"Past_y\": 116, \"Future_y\": 66, \"auto_ratio\": 1.7575757575757576, \"manual_prop\": 0.7872340425531915, \"auto_prop\": 0.6373626373626373}, {\"Episode\": 6, \"Past_x\": 27, \"Future_x\": 12, \"manual_ratio\": 2.25, \"Total\": 474, \"Past_y\": 121, \"Future_y\": 99, \"auto_ratio\": 1.2222222222222223, \"manual_prop\": 0.6923076923076923, \"auto_prop\": 0.55}]}}, {\"mode\": \"vega-lite\"});\n", + "" ], "text/plain": [ - " Episode tense count proportion\n", - "0 1 Past 60 0.769231\n", - "1 1 Future 18 0.230769\n", - "2 2 Past 30 0.681818\n", - "3 2 Future 14 0.318182\n", - "4 3 Past 43 0.565789\n", - "5 3 Future 33 0.434211\n", - "6 4 Past 31 0.596154\n", - "7 4 Future 21 0.403846\n", - "8 5 Past 36 0.765957\n", - "9 5 Future 11 0.234043\n", - "10 6 Past 27 0.692308\n", - "11 6 Future 12 0.307692" + "alt.HConcatChart(...)" ] }, - "execution_count": 9, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "# make sure the chair dataset has been downloaded\n", - "chair_url = data.query('Dataset == \"The Chair\"')['Data URL'].values[0]\n", - "chair_datadir = datadir.joinpath(get_folder_name(chair_url))\n", - "if not (chair_datadir.exists() and len(lsdir(str(chair_datadir.joinpath('*.txt')))) >= 5):\n", - " download_dataset(chair_url, datadir)\n", - "\n", - "# fill in proportions for manual reference counts\n", - "ref_fname = str(Path.cwd().parent.joinpath('data', 'the_chair', 'the_chair_manual_reference_counts.csv'))\n", - "manual = pd.read_csv(ref_fname)\n", - "manual['Total'] = manual['Past'] + manual['Future']\n", - "\n", - "manual.reset_index(inplace=True)\n", - "manual['Episode'] = manual['index'] + 1\n", - "manual.drop(['index', 'Total'], axis=1, inplace=True)\n", - "\n", - "manual = manual.melt(var_name='tense', value_name='count', id_vars=['Episode'])\n", - "manual.sort_values(['Episode'], inplace=True)\n", - "manual.reset_index(inplace=True, drop=True)\n", - "manual['proportion'] = manual['count'] / manual.groupby('Episode')['count'].transform('sum')\n", - "manual" + "bar_count = alt.Chart().mark_bar(color=Category20c[20][18]).encode(\n", + " x='Episode:O',\n", + " y=alt.Y('auto_count', title=\"Number of references\"),\n", + " # column='direction'\n", + ").properties(\n", + " width=alt.Step(30) # controls width of bar.\n", + ")\n", + "tick_count = alt.Chart().mark_tick(color='#8C6238', thickness=2,).encode(\n", + " x='Episode:O',\n", + " y='manual_count',\n", + " # column='Direction',\n", + ")\n", + "count_plot = alt.layer(bar, tick, data=count_all_long).facet(column=alt.Column('Direction', sort=\"descending\", title='', header=alt.Header(labelOrient='top', titleFontSize=14, labelFontSize=14, titleFontWeight='normal', titlePadding=0))).properties(title='A')\n", + "\n", + "bar_prop = alt.Chart(count_all).mark_bar(color=Category20c[20][18]).encode(\n", + " x='Episode:O',\n", + " y=alt.Y('auto_prop', scale=alt.Scale(domain=[0,1]), axis=alt.Axis(format='%'), title=\"Past / (Past + Future) %\"),\n", + ").properties(\n", + " width=alt.Step(30) # controls width of bar.\n", + ")\n", + "\n", + "tick_prop = alt.Chart(count_all).mark_tick(color='#8C6238', thickness=2,).encode(\n", + " x='Episode:O',\n", + " y=alt.Y('manual_prop', scale=alt.Scale(domain=[0,1]), axis=alt.Axis(format='%')),\n", + ")\n", + "prop_plot = (bar_prop+tick_prop).properties(title='B')\n", + "bar_ratio = alt.Chart(count_all).mark_bar(color=Category20c[20][18]).encode(\n", + " x='Episode:O',\n", + " y=alt.Y('auto_ratio', scale=alt.Scale(domain=[1,6], type=\"log\", base=2), title=\"Past / Future Ratio (log scale)\"),\n", + ").properties(\n", + " width=alt.Step(30) # controls width of bar.\n", + ")\n", + "\n", + "tick_ratio = alt.Chart(count_all).mark_tick(color='#8C6238', thickness=2,).encode(\n", + " x='Episode:O',\n", + " y=alt.Y('manual_ratio', scale=alt.Scale(domain=[1,6], type=\"log\", base=2)),\n", + ")\n", + "\n", + "ratio_plot = (bar_ratio+tick_ratio).properties(title='C')\n", + "\n", + "(count_plot | prop_plot | ratio_plot\n", + ").configure_legend(\n", + "\n", + ").configure_axis(\n", + " titleFontSize=14,\n", + " labelFontSize=14,\n", + " titleFontWeight='normal',\n", + " labelFontWeight='normal',\n", + ").configure_concat(\n", + " spacing=50\n", + ").configure_title(\n", + " fontSize=20,\n", + " anchor='start',\n", + "# offset=20\n", + ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "Load automatically identified counts from *The Chair*" + "# stats" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from scipy.stats import chi2_contingency" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": {}, "outputs": [ { @@ -895,602 +966,462 @@ " \n", " \n", " \n", - " tense\n", - " count\n", - " Episode\n", - " proportion\n", + " dataset\n", + " type\n", + " source\n", + " full\n", + " non-empty\n", + " is_equal\n", + " past\n", + " future\n", + " total\n", + " corrected_past\n", + " corrected_future\n", + " past_prop\n", + " future_prop\n", + " RR\n", + " non_past\n", + " non_future\n", " \n", " \n", " \n", " \n", " 0\n", - " Past\n", - " 152\n", - " 1\n", - " 0.575758\n", + " IMSDb\n", + " Scripted\n", + " NaN\n", + " 1091\n", + " 1091\n", + " True\n", + " 833026\n", + " 472519\n", + " 3080674\n", + " 657475\n", + " 316525\n", + " 0.213419\n", + " 0.102745\n", + " 2.077166\n", + " 2423199\n", + " 2764149\n", " \n", " \n", " 1\n", - " Future\n", - " 112\n", - " 1\n", - " 0.424242\n", + " Movies\n", + " Scripted\n", + " ConvoKit\n", + " 304713\n", + " 304446\n", + " False\n", + " 179729\n", + " 129622\n", + " 516163\n", + " 127744\n", + " 85937\n", + " 0.247488\n", + " 0.166492\n", + " 1.486484\n", + " 388419\n", + " 430226\n", " \n", " \n", " 2\n", - " Past\n", - " 108\n", - " 2\n", - " 0.529412\n", + " Switchboard\n", + " Spontaneous\n", + " ConvoKit\n", + " 122646\n", + " 122646\n", + " True\n", + " 62464\n", + " 32372\n", + " 245461\n", + " 41488\n", + " 22079\n", + " 0.169021\n", + " 0.089949\n", + " 1.879071\n", + " 203973\n", + " 223382\n", " \n", " \n", " 3\n", - " Future\n", - " 96\n", - " 2\n", - " 0.470588\n", + " SCOTUS\n", + " Constrained\n", + " ConvoKit\n", + " 1700789\n", + " 1700789\n", + " True\n", + " 3089509\n", + " 1802239\n", + " 3880259\n", + " 1963578\n", + " 1207377\n", + " 0.506043\n", + " 0.311159\n", + " 1.626317\n", + " 1916681\n", + " 2672882\n", " \n", " \n", " 4\n", - " Past\n", - " 177\n", - " 3\n", - " 0.608247\n", + " Tennis\n", + " Constrained\n", + " ConvoKit\n", + " 163948\n", + " 163948\n", + " True\n", + " 448444\n", + " 193802\n", + " 599172\n", + " 281669\n", + " 134638\n", + " 0.470097\n", + " 0.224707\n", + " 2.092047\n", + " 317503\n", + " 464534\n", " \n", " \n", " 5\n", - " Future\n", - " 114\n", - " 3\n", - " 0.391753\n", + " PfG\n", + " Constrained\n", + " ConvoKit\n", + " 20932\n", + " 20932\n", + " True\n", + " 9695\n", + " 15520\n", + " 37184\n", + " 7408\n", + " 9771\n", + " 0.199225\n", + " 0.262774\n", + " 0.758162\n", + " 29776\n", + " 27413\n", " \n", " \n", " 6\n", - " Past\n", - " 148\n", - " 4\n", - " 0.594378\n", + " IQ2\n", + " Constrained\n", + " ConvoKit\n", + " 26562\n", + " 26317\n", + " False\n", + " 67626\n", + " 51780\n", + " 122925\n", + " 46630\n", + " 34811\n", + " 0.379337\n", + " 0.283189\n", + " 1.339519\n", + " 76295\n", + " 88114\n", " \n", " \n", " 7\n", - " Future\n", - " 101\n", - " 4\n", - " 0.405622\n", + " GAP\n", + " Constrained\n", + " ConvoKit\n", + " 8009\n", + " 8009\n", + " True\n", + " 2739\n", + " 1958\n", + " 8009\n", + " 1800\n", + " 1338\n", + " 0.224747\n", + " 0.167062\n", + " 1.345291\n", + " 6209\n", + " 6671\n", " \n", " \n", " 8\n", - " Past\n", - " 164\n", - " 5\n", - " 0.616541\n", + " Chair\n", + " Scripted\n", + " NaN\n", + " 6\n", + " 6\n", + " True\n", + " 909\n", + " 663\n", + " 2900\n", + " 660\n", + " 460\n", + " 0.227586\n", + " 0.158621\n", + " 1.434783\n", + " 2240\n", + " 2440\n", " \n", " \n", " 9\n", - " Future\n", - " 102\n", - " 5\n", - " 0.383459\n", + " Friends\n", + " Scripted\n", + " ConvoKit\n", + " 67373\n", + " 61310\n", + " False\n", + " 32105\n", + " 23931\n", + " 107082\n", + " 22067\n", + " 16356\n", + " 0.206076\n", + " 0.152743\n", + " 1.349169\n", + " 85015\n", + " 90726\n", " \n", " \n", " 10\n", - " Past\n", - " 160\n", - " 6\n", - " 0.536913\n", + " Gutenberg\n", + " Scripted\n", + " NaN\n", + " 14773741\n", + " 14773741\n", + " True\n", + " 14617983\n", + " 13714226\n", + " 29119393\n", + " 10234952\n", + " 8672030\n", + " 0.351482\n", + " 0.297809\n", + " 1.180226\n", + " 18884441\n", + " 20447363\n", " \n", " \n", " 11\n", - " Future\n", - " 138\n", - " 6\n", - " 0.463087\n", + " Reddit\n", + " Constrained\n", + " ConvoKit\n", + " 74468\n", + " 72985\n", + " False\n", + " 120512\n", + " 105127\n", + " 217924\n", + " 86513\n", + " 66700\n", + " 0.396987\n", + " 0.306070\n", + " 1.297046\n", + " 131411\n", + " 151224\n", " \n", " \n", "\n", "" ], "text/plain": [ - " tense count Episode proportion\n", - "0 Past 152 1 0.575758\n", - "1 Future 112 1 0.424242\n", - "2 Past 108 2 0.529412\n", - "3 Future 96 2 0.470588\n", - "4 Past 177 3 0.608247\n", - "5 Future 114 3 0.391753\n", - "6 Past 148 4 0.594378\n", - "7 Future 101 4 0.405622\n", - "8 Past 164 5 0.616541\n", - "9 Future 102 5 0.383459\n", - "10 Past 160 6 0.536913\n", - "11 Future 138 6 0.463087" + " dataset type source full non-empty is_equal \\\n", + "0 IMSDb Scripted NaN 1091 1091 True \n", + "1 Movies Scripted ConvoKit 304713 304446 False \n", + "2 Switchboard Spontaneous ConvoKit 122646 122646 True \n", + "3 SCOTUS Constrained ConvoKit 1700789 1700789 True \n", + "4 Tennis Constrained ConvoKit 163948 163948 True \n", + "5 PfG Constrained ConvoKit 20932 20932 True \n", + "6 IQ2 Constrained ConvoKit 26562 26317 False \n", + "7 GAP Constrained ConvoKit 8009 8009 True \n", + "8 Chair Scripted NaN 6 6 True \n", + "9 Friends Scripted ConvoKit 67373 61310 False \n", + "10 Gutenberg Scripted NaN 14773741 14773741 True \n", + "11 Reddit Constrained ConvoKit 74468 72985 False \n", + "\n", + " past future total corrected_past corrected_future past_prop \\\n", + "0 833026 472519 3080674 657475 316525 0.213419 \n", + "1 179729 129622 516163 127744 85937 0.247488 \n", + "2 62464 32372 245461 41488 22079 0.169021 \n", + "3 3089509 1802239 3880259 1963578 1207377 0.506043 \n", + "4 448444 193802 599172 281669 134638 0.470097 \n", + "5 9695 15520 37184 7408 9771 0.199225 \n", + "6 67626 51780 122925 46630 34811 0.379337 \n", + "7 2739 1958 8009 1800 1338 0.224747 \n", + "8 909 663 2900 660 460 0.227586 \n", + "9 32105 23931 107082 22067 16356 0.206076 \n", + "10 14617983 13714226 29119393 10234952 8672030 0.351482 \n", + "11 120512 105127 217924 86513 66700 0.396987 \n", + "\n", + " future_prop RR non_past non_future \n", + "0 0.102745 2.077166 2423199 2764149 \n", + "1 0.166492 1.486484 388419 430226 \n", + "2 0.089949 1.879071 203973 223382 \n", + "3 0.311159 1.626317 1916681 2672882 \n", + "4 0.224707 2.092047 317503 464534 \n", + "5 0.262774 0.758162 29776 27413 \n", + "6 0.283189 1.339519 76295 88114 \n", + "7 0.167062 1.345291 6209 6671 \n", + "8 0.158621 1.434783 2240 2440 \n", + "9 0.152743 1.349169 85015 90726 \n", + "10 0.297809 1.180226 18884441 20447363 \n", + "11 0.306070 1.297046 131411 151224 " ] }, - "execution_count": 10, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "chair_fname = str(Path.cwd().parent.joinpath('data', 'chair_results.pkl'))\n", - "with open(chair_fname, 'rb') as f:\n", - " chair_results, _ = pickle.load(f)\n", - "\n", - "auto = chair_results.reset_index().rename(columns={\"index\": \"filename\"}).melt(id_vars=[\"filename\"], var_name=\"tense\", value_name=\"count\")\n", - "auto['Episode'] = auto['filename'].apply(lambda filename: int(filename.split('_')[2][3]))\n", - "auto['proportion'] = auto['count'] / auto.groupby('Episode')['count'].transform('sum')\n", - "auto.sort_values(by=['Episode'], inplace=True)\n", - "auto.drop(columns=['filename'], inplace=True)\n", - "auto.reset_index(drop=True, inplace=True)\n", - "auto" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Create meta-analysis figure\n", - "\n", - "- Panel A: Numbers of (manually and automatically detected) past and future events from each episode of *The Chair*, season 1.\n", - "- Panel B: Proportions of (manually and automatically detected) past and future events from each episode of *The Chair*, season 1.\n", - "- Panel C: Proportions of automatically detected past and future events from each dataset" + "df = pd.read_csv(\"ref_counts_summary.csv\")\n", + "df" ] }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ - "
" + "24040006" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "fig, axes = plt.subplots(figsize=(16, 4), ncols=3)\n", - "\n", - "# panel A\n", - "sns.barplot(data=manual, x='tense', y='count', hue='Episode', palette='viridis', ax=axes[0])\n", - "sns.barplot(data=auto, x='tense', y='count', hue='Episode', palette='viridis', alpha=0.5, ax=axes[0])\n", - "sns.barplot(data=auto, x='tense', y='count', hue='Episode', fill=False, edgecolor='k', linewidth=1.5, ax=axes[0])\n", - "axes[0].get_legend().remove()\n", - "\n", - "axes[0].set_xlabel('Tense', fontsize=14)\n", - "axes[0].set_ylabel('Number of events', fontsize=14)\n", - "sns.despine(top=True, right=True)\n", - "\n", - "\n", - "# panel B\n", - "sns.barplot(data=manual, x='tense', y='proportion', hue='Episode', palette='viridis', ax=axes[1])\n", - "sns.barplot(data=auto, x='tense', y='proportion', hue='Episode', palette='viridis', alpha=0.5, ax=axes[1])\n", - "sns.barplot(data=auto, x='tense', y='proportion', hue='Episode', fill=False, edgecolor='k', linewidth=1.5, ax=axes[1])\n", - "handles, labels = axes[1].get_legend_handles_labels()\n", - "axes[1].legend(loc='upper right', title='Episode', handles=handles[:6], labels=labels[:6], frameon=True, framealpha=0.75, ncol=2, fontsize=8)\n", - "\n", - "axes[1].set_xlabel('Tense', fontsize=14)\n", - "axes[1].set_ylabel('Proportion of events', fontsize=14)\n", - "sns.despine(top=True, right=True)\n", - "\n", - "\n", - "# panel C\n", - "sns.barplot(results, x='tense', y='proportion', hue='Dataset', palette='Spectral', ax=axes[2])\n", - "axes[2].set_xlabel('Tense', fontsize=14)\n", - "axes[2].set_ylabel('Proportion of events', fontsize=14)\n", - "axes[2].legend(loc='lower left', title='Dataset', frameon=True, ncol=3, fontsize=8, facecolor='white', framealpha=0.75)\n", - "axes[2].set_ylim(axes[1].get_ylim())\n", - "sns.despine(top=True, right=True)\n", - "\n", - "\n", - "plt.tight_layout()\n", - "plt.savefig('meta-analysis.pdf', bbox_inches='tight')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Include some stats (to report in the main text)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Total number of observations and words across all datasets" + "total = df['corrected_past'].sum() + df['corrected_future'].sum()\n", + "total" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "There are a total of 17264278 observations across all datasets.\n", - "There are a total of 443756731 words across all datasets.\n" - ] + "data": { + "text/plain": [ + "13471984" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "x = data.sum()\n", - "print(f\"There are a total of {x['Number of observations']} observations across all datasets.\")\n", - "print(f\"There are a total of {x['Number of words']} words across all datasets.\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Proportion of past versus future events, across all documents (and total number of past + future events)" + "df['corrected_past'].sum()" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Past events: 19464741 (54.06%)\n", - "Future events: 16543759 (45.94%)\n", - "Total events: 36008500\n" - ] + "data": { + "text/plain": [ + "0.5603985290186699" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# print the numbers and proportions of past and future events, across all documents\n", - "\n", - "n_past = results.query('tense == \"Past\"')['count'].sum()\n", - "n_future = results.query('tense == \"Future\"')['count'].sum()\n", - "n_total = n_past + n_future\n", - "\n", - "print(f\"Past events: {n_past} ({n_past / n_total:.2%})\")\n", - "print(f\"Future events: {n_future} ({n_future / n_total:.2%})\")\n", - "print(f\"Total events: {n_total}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "T-test comparing proportions of past vs. future events across datasets" + "df['corrected_past'].sum() / total" ] }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average proportion of past events: 58.99% ± 7.28%\n", - "t(11) = 4.28, p = 0.0013\n" - ] + "data": { + "text/plain": [ + "10568022" + ] + }, + "metadata": {}, + "output_type": "display_data" } ], "source": [ - "# average proportions of past events for each dataset\n", - "past_proportions = results.groupby('Dataset').apply(lambda x: x.query('tense == \"Past\"')['proportion'].mean()).sort_values(ascending=False)\n", - "x = ttest_1samp(past_proportions, 0.5)\n", - "print(f\"Average proportion of past events: {past_proportions.mean():.2%} ± {past_proportions.std():.2%}\")\n", - "print(f\"t({x.df}) = {x.statistic:.2f}, p = {x.pvalue:.4f}\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Effect sizes and confidence intervals (effect size = past refs / future refs)" + "df['corrected_future'].sum()" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Average effect size: 1.45 ± 0.40\n", - "Median effect size: 1.44\n", - "95% CI: [0.82, 2.16]\n", - "Range: [0.69, 2.18]\n" - ] - }, { "data": { - "text/html": [ - "
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tenseFuturePastEffect size
Dataset
Chair0.4231250.5768751.363366
Friends0.4169980.5830021.398092
GAP0.4153730.5846271.407473
Gutenberg0.4639620.5360381.155350
IMSDb0.3605410.6394591.773610
IQ20.4380840.5619161.282669
Movies0.4054140.5945861.466612
PfG0.5917780.4082220.689823
Reddit0.4036090.5963911.477646
SCOTUS0.3659240.6340761.732808
Switchboard0.3221300.6778702.104335
Tennis0.3145150.6854852.179502
\n", - "
" - ], "text/plain": [ - "tense Future Past Effect size\n", - "Dataset \n", - "Chair 0.423125 0.576875 1.363366\n", - "Friends 0.416998 0.583002 1.398092\n", - "GAP 0.415373 0.584627 1.407473\n", - "Gutenberg 0.463962 0.536038 1.155350\n", - "IMSDb 0.360541 0.639459 1.773610\n", - "IQ2 0.438084 0.561916 1.282669\n", - "Movies 0.405414 0.594586 1.466612\n", - "PfG 0.591778 0.408222 0.689823\n", - "Reddit 0.403609 0.596391 1.477646\n", - "SCOTUS 0.365924 0.634076 1.732808\n", - "Switchboard 0.322130 0.677870 2.104335\n", - "Tennis 0.314515 0.685485 2.179502" + "0.43960147098133" ] }, - "execution_count": 15, "metadata": {}, - "output_type": "execute_result" + "output_type": "display_data" } ], "source": [ - "# Overall effect size\n", - "\n", - "# compute average numbers of past and future references per dataset\n", - "df = results.groupby(['Dataset', 'tense'])['proportion'].mean().unstack()\n", - "df['Effect size'] = df['Past'] / df['Future']\n", - "\n", - "# compute average of effect sizes\n", - "def average_effect_size(x):\n", - " return np.exp(np.mean(np.log(x)))\n", - "\n", - "def standard_error_effect_size(x):\n", - " return np.exp(np.std(np.log(x))) / np.sqrt(len(x) - 1)\n", - "\n", - "\n", - "# report average effect size, standard deviation, and 95% confidence interval across datasets\n", - "x = df['Effect size']\n", - "print(f\"Average effect size: {average_effect_size(x):.2f} ± {standard_error_effect_size(x):.2f}\")\n", - "print(f\"Median effect size: {x.median():.2f}\")\n", - "print(f\"95% CI: [{np.quantile(x, 0.025):.2f}, {np.quantile(x, 0.975):.2f}]\")\n", - "print(f\"Range: [{x.min():.2f}, {x.max():.2f}]\")\n", - "\n", - "df" + "df['corrected_future'].sum() / total" ] }, { "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "def print_effect_size_stats(x, n_bootstraps=1000):\n", - " effect_sizes = []\n", - "\n", - " for _ in range(n_bootstraps):\n", - " # take a random sample from the dataset (with replacement)\n", - " sample = x.sample(n=len(x), replace=True)\n", - "\n", - " # compute the effect size for the sample\n", - " n_past = sample.query('tense == \"Past\"')['count'].sum()\n", - " n_future = sample.query('tense == \"Future\"')['count'].sum()\n", - " effect_size = n_past / n_future\n", - "\n", - " effect_sizes.append(effect_size)\n", - " \n", - " effect_sizes = np.array(effect_sizes)\n", - " print(f'Average effect size: {average_effect_size(effect_sizes):.2f} ± {standard_error_effect_size(effect_sizes):.2f}')\n", - " print(f'Effect size boostrap-estimated 95% CI: [{np.percentile(effect_sizes, 2.5):.2f}, {np.percentile(effect_sizes, 97.5):.2f}]')\n", - " print(f'Estimated p-value: {2 * np.min([np.mean(effect_sizes > 1), np.mean(effect_sizes < 1)]):.4f}')" - ] - }, - { - "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\n", - "IMSDb: \n", - "Average effect size: 1.78 ± 0.10\n", - "Effect size boostrap-estimated 95% CI: [1.64, 1.92]\n", - "Estimated p-value: 0.0000\n", - "\n", - "Movies: \n", - "Average effect size: 1.39 ± 0.10\n", - "Effect size boostrap-estimated 95% CI: [1.37, 1.40]\n", - "Estimated p-value: 0.0000\n", - "\n", - "Switchboard: \n", - "Average effect size: 1.93 ± 0.10\n", - "Effect size boostrap-estimated 95% CI: [1.88, 1.96]\n", - "Estimated p-value: 0.0000\n", - "\n", - "SCOTUS: \n", - "Average effect size: 1.71 ± 0.10\n", - "Effect size boostrap-estimated 95% CI: [1.70, 1.72]\n", - "Estimated p-value: 0.0000\n", - "\n", - "Tennis: \n", - "Average effect size: 2.32 ± 0.10\n", - "Effect size boostrap-estimated 95% CI: [2.29, 2.34]\n", - "Estimated p-value: 0.0000\n", - "\n", - "PfG: \n", - "Average effect size: 0.63 ± 0.10\n", - "Effect size boostrap-estimated 95% CI: [0.61, 0.65]\n", - "Estimated p-value: 0.0000\n", - "\n", - "IQ2: \n", - "Average effect size: 1.31 ± 0.10\n", - "Effect size boostrap-estimated 95% CI: [1.26, 1.37]\n", - "Estimated p-value: 0.0000\n", - "\n", - "GAP: \n", - "Average effect size: 1.40 ± 0.10\n", - "Effect size boostrap-estimated 95% CI: [1.29, 1.49]\n", - "Estimated p-value: 0.0000\n", - "\n", - "Chair: \n", - "Average effect size: 1.61 ± 0.18\n", - "Effect size boostrap-estimated 95% CI: [0.57, 4.21]\n", - "Estimated p-value: 0.4000\n", - "\n", - "Friends: \n", - "Average effect size: 1.34 ± 0.10\n", - "Effect size boostrap-estimated 95% CI: [1.31, 1.37]\n", - "Estimated p-value: 0.0000\n", - "\n", - "Gutenberg: \n", - "Average effect size: 1.07 ± 0.10\n", - "Effect size boostrap-estimated 95% CI: [1.06, 1.07]\n", - "Estimated p-value: 0.0000\n", - "\n", - "Reddit: \n", - "Average effect size: 1.15 ± 0.10\n", - "Effect size boostrap-estimated 95% CI: [1.12, 1.18]\n", - "Estimated p-value: 0.0000\n" + "IMSDb\n", + "0.0\n", + "Movies\n", + "0.0\n", + "Switchboard\n", + "0.0\n", + "SCOTUS\n", + "0.0\n", + "Tennis\n", + "0.0\n", + "PfG\n", + "7.651987244822489e-94\n", + "IQ2\n", + "0.0\n", + "GAP\n", + "4.415379561347529e-20\n", + "Chair\n", + "3.6004633077240275e-11\n", + "Friends\n", + "7.023120502011673e-227\n", + "Gutenberg\n", + "0.0\n", + "Reddit\n", + "0.0\n" ] } ], "source": [ - "debug = False # set to True to print out quicker estimates\n", - "if debug:\n", - " n_boostraps = 10\n", - "else:\n", - " n_boostraps = 100\n", - "\n", - "datasets = results['Dataset'].unique()\n", - "\n", - "for d in datasets:\n", - " past_proportions = results.query('Dataset == @d and tense == \"Past\"')['proportion']\n", - " future_proportions = results.query('Dataset == @d and tense == \"Future\"')['proportion']\n", - "\n", - " odds_ratios = past_proportions / future_proportions\n", - "\n", - " print(f\"\\n{d}: \")\n", - " print_effect_size_stats(results.query('Dataset == @d'), n_bootstraps=n_boostraps)" + "for i in df.iterrows():\n", + " print(i[1]['dataset'])\n", + " stat, p, dof, expected = chi2_contingency([[i[1]['corrected_past'], i[1]['non_past']], [i[1]['corrected_future'], i[1]['non_future']]])\n", + " print(p)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { - "display_name": "prediction-retrodiction", + "display_name": "pr", "language": "python", - 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A data.frame: 12 x 16
X...datasettypesourcefullnon.emptyis_equalpastfuturetotalcorrected_pastcorrected_futurepast_propfuture_propRRnon_pastnon_future
<chr><chr><chr><int><int><lgl><int><int><int><int><int><dbl><dbl><dbl><int><int>
IMSDb Scripted 1091 1091 TRUE 833026 472519 3080674 657475 3165250.21341920.102745372.0771661 2423199 2764149
Movies Scripted ConvoKit 304713 304446FALSE 179729 129622 516163 127744 859370.24748770.166491981.4864843 388419 430226
SwitchboardSpontaneousConvoKit 122646 122646 TRUE 62464 32372 245461 41488 220790.16902070.089949121.8790706 203973 223382
SCOTUS ConstrainedConvoKit 1700789 1700789 TRUE 3089509 1802239 3880259 196357812073770.50604300.311158871.6263172 1916681 2672882
Tennis ConstrainedConvoKit 163948 163948 TRUE 448444 193802 599172 281669 1346380.47009710.224706762.0920468 317503 464534
PfG ConstrainedConvoKit 20932 20932 TRUE 9695 15520 37184 7408 97710.19922550.262774310.7581619 29776 27413
IQ2 ConstrainedConvoKit 26562 26317FALSE 67626 51780 122925 46630 348110.37933700.283188941.3395191 76295 88114
GAP ConstrainedConvoKit 8009 8009 TRUE 2739 1958 8009 1800 13380.22474720.167062051.3452915 6209 6671
Chair Scripted 6 6 TRUE 909 663 2900 660 4600.22758620.158620691.4347826 2240 2440
Friends Scripted ConvoKit 67373 61310FALSE 32105 23931 107082 22067 163560.20607570.152742761.3491685 85015 90726
Gutenberg Scripted 1477374114773741 TRUE1461798313714226291193931023495286720300.35148230.297809441.18022561888444120447363
Reddit ConstrainedConvoKit 74468 72985FALSE 120512 105127 217924 86513 667000.39698700.306070011.2970465 131411 151224
\n" + ], + "text/latex": [ + "A data.frame: 12 x 16\n", + "\\begin{tabular}{llllllllllllllll}\n", + " X...dataset & type & source & full & non.empty & is\\_equal & past & future & total & corrected\\_past & corrected\\_future & past\\_prop & future\\_prop & RR & non\\_past & non\\_future\\\\\n", + " & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t IMSDb & Scripted & & 1091 & 1091 & TRUE & 833026 & 472519 & 3080674 & 657475 & 316525 & 0.2134192 & 0.10274537 & 2.0771661 & 2423199 & 2764149\\\\\n", + "\t Movies & Scripted & ConvoKit & 304713 & 304446 & FALSE & 179729 & 129622 & 516163 & 127744 & 85937 & 0.2474877 & 0.16649198 & 1.4864843 & 388419 & 430226\\\\\n", + "\t Switchboard & Spontaneous & ConvoKit & 122646 & 122646 & TRUE & 62464 & 32372 & 245461 & 41488 & 22079 & 0.1690207 & 0.08994912 & 1.8790706 & 203973 & 223382\\\\\n", + "\t SCOTUS & Constrained & ConvoKit & 1700789 & 1700789 & TRUE & 3089509 & 1802239 & 3880259 & 1963578 & 1207377 & 0.5060430 & 0.31115887 & 1.6263172 & 1916681 & 2672882\\\\\n", + "\t Tennis & Constrained & ConvoKit & 163948 & 163948 & TRUE & 448444 & 193802 & 599172 & 281669 & 134638 & 0.4700971 & 0.22470676 & 2.0920468 & 317503 & 464534\\\\\n", + "\t PfG & Constrained & ConvoKit & 20932 & 20932 & TRUE & 9695 & 15520 & 37184 & 7408 & 9771 & 0.1992255 & 0.26277431 & 0.7581619 & 29776 & 27413\\\\\n", + "\t IQ2 & Constrained & ConvoKit & 26562 & 26317 & FALSE & 67626 & 51780 & 122925 & 46630 & 34811 & 0.3793370 & 0.28318894 & 1.3395191 & 76295 & 88114\\\\\n", + "\t GAP & Constrained & ConvoKit & 8009 & 8009 & TRUE & 2739 & 1958 & 8009 & 1800 & 1338 & 0.2247472 & 0.16706205 & 1.3452915 & 6209 & 6671\\\\\n", + "\t Chair & Scripted & & 6 & 6 & TRUE & 909 & 663 & 2900 & 660 & 460 & 0.2275862 & 0.15862069 & 1.4347826 & 2240 & 2440\\\\\n", + "\t Friends & Scripted & ConvoKit & 67373 & 61310 & FALSE & 32105 & 23931 & 107082 & 22067 & 16356 & 0.2060757 & 0.15274276 & 1.3491685 & 85015 & 90726\\\\\n", + "\t Gutenberg & Scripted & & 14773741 & 14773741 & TRUE & 14617983 & 13714226 & 29119393 & 10234952 & 8672030 & 0.3514823 & 0.29780944 & 1.1802256 & 18884441 & 20447363\\\\\n", + "\t Reddit & Constrained & ConvoKit & 74468 & 72985 & FALSE & 120512 & 105127 & 217924 & 86513 & 66700 & 0.3969870 & 0.30607001 & 1.2970465 & 131411 & 151224\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A data.frame: 12 x 16\n", + "\n", + "| X...dataset <chr> | type <chr> | source <chr> | full <int> | non.empty <int> | is_equal <lgl> | past <int> | future <int> | total <int> | corrected_past <int> | corrected_future <int> | past_prop <dbl> | future_prop <dbl> | RR <dbl> | non_past <int> | non_future <int> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| IMSDb | Scripted | | 1091 | 1091 | TRUE | 833026 | 472519 | 3080674 | 657475 | 316525 | 0.2134192 | 0.10274537 | 2.0771661 | 2423199 | 2764149 |\n", + "| Movies | Scripted | ConvoKit | 304713 | 304446 | FALSE | 179729 | 129622 | 516163 | 127744 | 85937 | 0.2474877 | 0.16649198 | 1.4864843 | 388419 | 430226 |\n", + "| Switchboard | Spontaneous | ConvoKit | 122646 | 122646 | TRUE | 62464 | 32372 | 245461 | 41488 | 22079 | 0.1690207 | 0.08994912 | 1.8790706 | 203973 | 223382 |\n", + "| SCOTUS | Constrained | ConvoKit | 1700789 | 1700789 | TRUE | 3089509 | 1802239 | 3880259 | 1963578 | 1207377 | 0.5060430 | 0.31115887 | 1.6263172 | 1916681 | 2672882 |\n", + "| Tennis | Constrained | ConvoKit | 163948 | 163948 | TRUE | 448444 | 193802 | 599172 | 281669 | 134638 | 0.4700971 | 0.22470676 | 2.0920468 | 317503 | 464534 |\n", + "| PfG | Constrained | ConvoKit | 20932 | 20932 | TRUE | 9695 | 15520 | 37184 | 7408 | 9771 | 0.1992255 | 0.26277431 | 0.7581619 | 29776 | 27413 |\n", + "| IQ2 | Constrained | ConvoKit | 26562 | 26317 | FALSE | 67626 | 51780 | 122925 | 46630 | 34811 | 0.3793370 | 0.28318894 | 1.3395191 | 76295 | 88114 |\n", + "| GAP | Constrained | ConvoKit | 8009 | 8009 | TRUE | 2739 | 1958 | 8009 | 1800 | 1338 | 0.2247472 | 0.16706205 | 1.3452915 | 6209 | 6671 |\n", + "| Chair | Scripted | | 6 | 6 | TRUE | 909 | 663 | 2900 | 660 | 460 | 0.2275862 | 0.15862069 | 1.4347826 | 2240 | 2440 |\n", + "| Friends | Scripted | ConvoKit | 67373 | 61310 | FALSE | 32105 | 23931 | 107082 | 22067 | 16356 | 0.2060757 | 0.15274276 | 1.3491685 | 85015 | 90726 |\n", + "| Gutenberg | Scripted | | 14773741 | 14773741 | TRUE | 14617983 | 13714226 | 29119393 | 10234952 | 8672030 | 0.3514823 | 0.29780944 | 1.1802256 | 18884441 | 20447363 |\n", + "| Reddit | Constrained | ConvoKit | 74468 | 72985 | FALSE | 120512 | 105127 | 217924 | 86513 | 66700 | 0.3969870 | 0.30607001 | 1.2970465 | 131411 | 151224 |\n", + "\n" + ], + "text/plain": [ + " X...dataset type source full non.empty is_equal past \n", + "1 IMSDb Scripted 1091 1091 TRUE 833026\n", + "2 Movies Scripted ConvoKit 304713 304446 FALSE 179729\n", + "3 Switchboard Spontaneous ConvoKit 122646 122646 TRUE 62464\n", + "4 SCOTUS Constrained ConvoKit 1700789 1700789 TRUE 3089509\n", + "5 Tennis Constrained ConvoKit 163948 163948 TRUE 448444\n", + "6 PfG Constrained ConvoKit 20932 20932 TRUE 9695\n", + "7 IQ2 Constrained ConvoKit 26562 26317 FALSE 67626\n", + "8 GAP Constrained ConvoKit 8009 8009 TRUE 2739\n", + "9 Chair Scripted 6 6 TRUE 909\n", + "10 Friends Scripted ConvoKit 67373 61310 FALSE 32105\n", + "11 Gutenberg Scripted 14773741 14773741 TRUE 14617983\n", + "12 Reddit Constrained ConvoKit 74468 72985 FALSE 120512\n", + " future total corrected_past corrected_future past_prop future_prop\n", + "1 472519 3080674 657475 316525 0.2134192 0.10274537 \n", + "2 129622 516163 127744 85937 0.2474877 0.16649198 \n", + "3 32372 245461 41488 22079 0.1690207 0.08994912 \n", + "4 1802239 3880259 1963578 1207377 0.5060430 0.31115887 \n", + "5 193802 599172 281669 134638 0.4700971 0.22470676 \n", + "6 15520 37184 7408 9771 0.1992255 0.26277431 \n", + "7 51780 122925 46630 34811 0.3793370 0.28318894 \n", + "8 1958 8009 1800 1338 0.2247472 0.16706205 \n", + "9 663 2900 660 460 0.2275862 0.15862069 \n", + "10 23931 107082 22067 16356 0.2060757 0.15274276 \n", + "11 13714226 29119393 10234952 8672030 0.3514823 0.29780944 \n", + "12 105127 217924 86513 66700 0.3969870 0.30607001 \n", + " RR non_past non_future\n", + "1 2.0771661 2423199 2764149 \n", + "2 1.4864843 388419 430226 \n", + "3 1.8790706 203973 223382 \n", + "4 1.6263172 1916681 2672882 \n", + "5 2.0920468 317503 464534 \n", + "6 0.7581619 29776 27413 \n", + "7 1.3395191 76295 88114 \n", + "8 1.3452915 6209 6671 \n", + "9 1.4347826 2240 2440 \n", + "10 1.3491685 85015 90726 \n", + "11 1.1802256 18884441 20447363 \n", + "12 1.2970465 131411 151224 " + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df <- read.csv(file = 'ref_counts_summary.csv')\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\n", + "\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\t\n", + "\n", + "
A escalc: 12 x 18
X...datasettypesourcefullnon.emptyis_equalpastfuturetotalcorrected_pastcorrected_futurepast_propfuture_propRRnon_pastnon_futureyivi
<chr><chr><chr><int><int><lgl><int><int><int><int><int><dbl><dbl><dbl><int><int><dbl><dbl>
IMSDb Scripted 1091 1091 TRUE 833026 472519 3080674 657475 3165250.21341920.102745372.0771661 2423199 2764149 0.73100454.031070e-06
Movies Scripted ConvoKit 304713 304446FALSE 179729 129622 516163 127744 859370.24748770.166491981.4864843 388419 430226 0.39641381.558984e-05
SwitchboardSpontaneousConvoKit 122646 122646 TRUE 62464 32372 245461 41488 220790.16902070.089949121.8790706 203973 223382 0.63077736.124733e-05
SCOTUS ConstrainedConvoKit 1700789 1700789 TRUE 3089509 1802239 3880259 196357812073770.50604300.311158871.6263172 1916681 2672882 0.48631818.220866e-07
Tennis ConstrainedConvoKit 163948 163948 TRUE 448444 193802 599172 281669 1346380.47009710.224706762.0920468 317503 464534 0.73814297.639650e-06
PfG ConstrainedConvoKit 20932 20932 TRUE 9695 15520 37184 7408 97710.19922550.262774310.7581619 29776 27413-0.27685831.835463e-04
IQ2 ConstrainedConvoKit 26562 26317FALSE 67626 51780 122925 46630 348110.37933700.283188941.3395191 76295 88114 0.29231073.390189e-05
GAP ConstrainedConvoKit 8009 8009 TRUE 2739 1958 8009 1800 13380.22474720.167062051.3452915 6209 6671 0.29661071.053221e-03
Chair Scripted 6 6 TRUE 909 663 2900 660 4600.22758620.158620691.4347826 2240 2440 0.36101332.999409e-03
Friends Scripted ConvoKit 67373 61310FALSE 32105 23931 107082 22067 163560.20607570.152742761.3491685 85015 90726 0.29948858.777890e-05
Gutenberg Scripted 1477374114773741 TRUE1461798313714226291193931023495286720300.35148230.297809441.18022561888444120447363 0.16570561.443349e-07
Reddit ConstrainedConvoKit 74468 72985FALSE 120512 105127 217924 86513 667000.39698700.306070011.2970465 131411 151224 0.26008971.737395e-05
\n" + ], + "text/latex": [ + "A escalc: 12 x 18\n", + "\\begin{tabular}{llllllllllllllllll}\n", + " X...dataset & type & source & full & non.empty & is\\_equal & past & future & total & corrected\\_past & corrected\\_future & past\\_prop & future\\_prop & RR & non\\_past & non\\_future & yi & vi\\\\\n", + " & & & & & & & & & & & & & & & & & \\\\\n", + "\\hline\n", + "\t IMSDb & Scripted & & 1091 & 1091 & TRUE & 833026 & 472519 & 3080674 & 657475 & 316525 & 0.2134192 & 0.10274537 & 2.0771661 & 2423199 & 2764149 & 0.7310045 & 4.031070e-06\\\\\n", + "\t Movies & Scripted & ConvoKit & 304713 & 304446 & FALSE & 179729 & 129622 & 516163 & 127744 & 85937 & 0.2474877 & 0.16649198 & 1.4864843 & 388419 & 430226 & 0.3964138 & 1.558984e-05\\\\\n", + "\t Switchboard & Spontaneous & ConvoKit & 122646 & 122646 & TRUE & 62464 & 32372 & 245461 & 41488 & 22079 & 0.1690207 & 0.08994912 & 1.8790706 & 203973 & 223382 & 0.6307773 & 6.124733e-05\\\\\n", + "\t SCOTUS & Constrained & ConvoKit & 1700789 & 1700789 & TRUE & 3089509 & 1802239 & 3880259 & 1963578 & 1207377 & 0.5060430 & 0.31115887 & 1.6263172 & 1916681 & 2672882 & 0.4863181 & 8.220866e-07\\\\\n", + "\t Tennis & Constrained & ConvoKit & 163948 & 163948 & TRUE & 448444 & 193802 & 599172 & 281669 & 134638 & 0.4700971 & 0.22470676 & 2.0920468 & 317503 & 464534 & 0.7381429 & 7.639650e-06\\\\\n", + "\t PfG & Constrained & ConvoKit & 20932 & 20932 & TRUE & 9695 & 15520 & 37184 & 7408 & 9771 & 0.1992255 & 0.26277431 & 0.7581619 & 29776 & 27413 & -0.2768583 & 1.835463e-04\\\\\n", + "\t IQ2 & Constrained & ConvoKit & 26562 & 26317 & FALSE & 67626 & 51780 & 122925 & 46630 & 34811 & 0.3793370 & 0.28318894 & 1.3395191 & 76295 & 88114 & 0.2923107 & 3.390189e-05\\\\\n", + "\t GAP & Constrained & ConvoKit & 8009 & 8009 & TRUE & 2739 & 1958 & 8009 & 1800 & 1338 & 0.2247472 & 0.16706205 & 1.3452915 & 6209 & 6671 & 0.2966107 & 1.053221e-03\\\\\n", + "\t Chair & Scripted & & 6 & 6 & TRUE & 909 & 663 & 2900 & 660 & 460 & 0.2275862 & 0.15862069 & 1.4347826 & 2240 & 2440 & 0.3610133 & 2.999409e-03\\\\\n", + "\t Friends & Scripted & ConvoKit & 67373 & 61310 & FALSE & 32105 & 23931 & 107082 & 22067 & 16356 & 0.2060757 & 0.15274276 & 1.3491685 & 85015 & 90726 & 0.2994885 & 8.777890e-05\\\\\n", + "\t Gutenberg & Scripted & & 14773741 & 14773741 & TRUE & 14617983 & 13714226 & 29119393 & 10234952 & 8672030 & 0.3514823 & 0.29780944 & 1.1802256 & 18884441 & 20447363 & 0.1657056 & 1.443349e-07\\\\\n", + "\t Reddit & Constrained & ConvoKit & 74468 & 72985 & FALSE & 120512 & 105127 & 217924 & 86513 & 66700 & 0.3969870 & 0.30607001 & 1.2970465 & 131411 & 151224 & 0.2600897 & 1.737395e-05\\\\\n", + "\\end{tabular}\n" + ], + "text/markdown": [ + "\n", + "A escalc: 12 x 18\n", + "\n", + "| X...dataset <chr> | type <chr> | source <chr> | full <int> | non.empty <int> | is_equal <lgl> | past <int> | future <int> | total <int> | corrected_past <int> | corrected_future <int> | past_prop <dbl> | future_prop <dbl> | RR <dbl> | non_past <int> | non_future <int> | yi <dbl> | vi <dbl> |\n", + "|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|\n", + "| IMSDb | Scripted | | 1091 | 1091 | TRUE | 833026 | 472519 | 3080674 | 657475 | 316525 | 0.2134192 | 0.10274537 | 2.0771661 | 2423199 | 2764149 | 0.7310045 | 4.031070e-06 |\n", + "| Movies | Scripted | ConvoKit | 304713 | 304446 | FALSE | 179729 | 129622 | 516163 | 127744 | 85937 | 0.2474877 | 0.16649198 | 1.4864843 | 388419 | 430226 | 0.3964138 | 1.558984e-05 |\n", + "| Switchboard | Spontaneous | ConvoKit | 122646 | 122646 | TRUE | 62464 | 32372 | 245461 | 41488 | 22079 | 0.1690207 | 0.08994912 | 1.8790706 | 203973 | 223382 | 0.6307773 | 6.124733e-05 |\n", + "| SCOTUS | Constrained | ConvoKit | 1700789 | 1700789 | TRUE | 3089509 | 1802239 | 3880259 | 1963578 | 1207377 | 0.5060430 | 0.31115887 | 1.6263172 | 1916681 | 2672882 | 0.4863181 | 8.220866e-07 |\n", + "| Tennis | Constrained | ConvoKit | 163948 | 163948 | TRUE | 448444 | 193802 | 599172 | 281669 | 134638 | 0.4700971 | 0.22470676 | 2.0920468 | 317503 | 464534 | 0.7381429 | 7.639650e-06 |\n", + "| PfG | Constrained | ConvoKit | 20932 | 20932 | TRUE | 9695 | 15520 | 37184 | 7408 | 9771 | 0.1992255 | 0.26277431 | 0.7581619 | 29776 | 27413 | -0.2768583 | 1.835463e-04 |\n", + "| IQ2 | Constrained | ConvoKit | 26562 | 26317 | FALSE | 67626 | 51780 | 122925 | 46630 | 34811 | 0.3793370 | 0.28318894 | 1.3395191 | 76295 | 88114 | 0.2923107 | 3.390189e-05 |\n", + "| GAP | Constrained | ConvoKit | 8009 | 8009 | TRUE | 2739 | 1958 | 8009 | 1800 | 1338 | 0.2247472 | 0.16706205 | 1.3452915 | 6209 | 6671 | 0.2966107 | 1.053221e-03 |\n", + "| Chair | Scripted | | 6 | 6 | TRUE | 909 | 663 | 2900 | 660 | 460 | 0.2275862 | 0.15862069 | 1.4347826 | 2240 | 2440 | 0.3610133 | 2.999409e-03 |\n", + "| Friends | Scripted | ConvoKit | 67373 | 61310 | FALSE | 32105 | 23931 | 107082 | 22067 | 16356 | 0.2060757 | 0.15274276 | 1.3491685 | 85015 | 90726 | 0.2994885 | 8.777890e-05 |\n", + "| Gutenberg | Scripted | | 14773741 | 14773741 | TRUE | 14617983 | 13714226 | 29119393 | 10234952 | 8672030 | 0.3514823 | 0.29780944 | 1.1802256 | 18884441 | 20447363 | 0.1657056 | 1.443349e-07 |\n", + "| Reddit | Constrained | ConvoKit | 74468 | 72985 | FALSE | 120512 | 105127 | 217924 | 86513 | 66700 | 0.3969870 | 0.30607001 | 1.2970465 | 131411 | 151224 | 0.2600897 | 1.737395e-05 |\n", + "\n" + ], + "text/plain": [ + " X...dataset type source full non.empty is_equal past \n", + "1 IMSDb Scripted 1091 1091 TRUE 833026\n", + "2 Movies Scripted ConvoKit 304713 304446 FALSE 179729\n", + "3 Switchboard Spontaneous ConvoKit 122646 122646 TRUE 62464\n", + "4 SCOTUS Constrained ConvoKit 1700789 1700789 TRUE 3089509\n", + "5 Tennis Constrained ConvoKit 163948 163948 TRUE 448444\n", + "6 PfG Constrained ConvoKit 20932 20932 TRUE 9695\n", + "7 IQ2 Constrained ConvoKit 26562 26317 FALSE 67626\n", + "8 GAP Constrained ConvoKit 8009 8009 TRUE 2739\n", + "9 Chair Scripted 6 6 TRUE 909\n", + "10 Friends Scripted ConvoKit 67373 61310 FALSE 32105\n", + "11 Gutenberg Scripted 14773741 14773741 TRUE 14617983\n", + "12 Reddit Constrained ConvoKit 74468 72985 FALSE 120512\n", + " future total corrected_past corrected_future past_prop future_prop\n", + "1 472519 3080674 657475 316525 0.2134192 0.10274537 \n", + "2 129622 516163 127744 85937 0.2474877 0.16649198 \n", + "3 32372 245461 41488 22079 0.1690207 0.08994912 \n", + "4 1802239 3880259 1963578 1207377 0.5060430 0.31115887 \n", + "5 193802 599172 281669 134638 0.4700971 0.22470676 \n", + "6 15520 37184 7408 9771 0.1992255 0.26277431 \n", + "7 51780 122925 46630 34811 0.3793370 0.28318894 \n", + "8 1958 8009 1800 1338 0.2247472 0.16706205 \n", + "9 663 2900 660 460 0.2275862 0.15862069 \n", + "10 23931 107082 22067 16356 0.2060757 0.15274276 \n", + "11 13714226 29119393 10234952 8672030 0.3514823 0.29780944 \n", + "12 105127 217924 86513 66700 0.3969870 0.30607001 \n", + " RR non_past non_future yi vi \n", + "1 2.0771661 2423199 2764149 0.7310045 4.031070e-06\n", + "2 1.4864843 388419 430226 0.3964138 1.558984e-05\n", + "3 1.8790706 203973 223382 0.6307773 6.124733e-05\n", + "4 1.6263172 1916681 2672882 0.4863181 8.220866e-07\n", + "5 2.0920468 317503 464534 0.7381429 7.639650e-06\n", + "6 0.7581619 29776 27413 -0.2768583 1.835463e-04\n", + "7 1.3395191 76295 88114 0.2923107 3.390189e-05\n", + "8 1.3452915 6209 6671 0.2966107 1.053221e-03\n", + "9 1.4347826 2240 2440 0.3610133 2.999409e-03\n", + "10 1.3491685 85015 90726 0.2994885 8.777890e-05\n", + "11 1.1802256 18884441 20447363 0.1657056 1.443349e-07\n", + "12 1.2970465 131411 151224 0.2600897 1.737395e-05" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "dat <- escalc(measure=\"RR\", ai=corrected_past, bi=non_past, ci=corrected_future, di=non_future, data=df,\n", + " slab=X...dataset)\n", + "dat" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Random-Effects Model (k = 12; tau^2 estimator: REML)\n", + "\n", + "tau^2 (estimated amount of total heterogeneity): 0.0762 (SE = 0.0326)\n", + "tau (square root of estimated tau^2 value): 0.2760\n", + "I^2 (total heterogeneity / total variability): 100.00%\n", + "H^2 (total variability / sampling variability): 20907.29\n", + "\n", + "Test for Heterogeneity:\n", + "Q(df = 11) = 209989.5995, p-val < .0001\n", + "\n", + "Model Results:\n", + "\n", + "estimate se zval pval ci.lb ci.ub \n", + " 0.3653 0.0799 4.5732 <.0001 0.2087 0.5218 *** \n", + "\n", + "---\n", + "Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "res1 <- rma(yi, vi, data=dat)\n", + "res1" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + " pred ci.lb ci.ub pi.lb pi.ub \n", + " 1.441 1.232 1.685 0.820 2.531 \n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "predict(res1, transf=exp, digits=3)" + ] + }, + { + "cell_type": "code", + "execution_count": 97, + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "plot without title" + ] + }, + "metadata": { + "image/png": { + "height": 420, + "width": 420 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "# pdf(file='forestplot.pdf')\n", + "\n", + "forest(res1, header=c(\"Dataset\",\"Ratio [95% CI]\"), \n", + "atransf=exp, \n", + "shade=TRUE,\n", + "at=log(c(0.67, 1, 1.5, 2.25)),\n", + "ilab=cbind(type, total, corrected_past, corrected_future),\n", + "ilab.xpos=c(-2.05,-1.5,-0.95,-0.4),\n", + "cex=0.75,\n", + "xlim=c(-3.6,1.8),\n", + "# alim=c(0.5, 4),\n", + "xlab=c(\"Ratio (log scale)\"),\n", + "ilab.pos=2)\n", + "\n", + "op <- par(cex=0.8, font=2)\n", + "text(c(-2.05,-1.5,-0.95,-0.4), pos=2, res1$k+2.05, c(\"Type\", \"Total\", \"Past\", \"Future\"))\n", + "par(op)\n", + "\n", + "# dev.off() # Turn the PDF device off" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.3.2" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/code/results_rep.ipynb b/code/results_rep.ipynb index 6e25039..2387605 100755 --- a/code/results_rep.ipynb +++ b/code/results_rep.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": { "execution": { "iopub.execute_input": "2021-11-05T18:10:25.867414Z", @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "execution": { "iopub.execute_input": "2021-11-05T18:10:35.374315Z", @@ -100,7 +100,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": { "execution": { "iopub.execute_input": "2021-11-05T18:10:35.388571Z", @@ -2107,6 +2107,25 @@ "### read reference files" ] }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "# references count\n", + "df_refer_full = pd.read_excel('../data/rep/TheChair.xlsx', sheet_name='references_full')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "df_refer_full.groupby(['episode','direction'])['time'].count().reset_index().to_csv(\"../data/the_chair/the_chair_manual_reference_counts.csv\", index=False)" + ] + }, { "cell_type": "code", "execution_count": 53, diff --git a/data/metaanalysis-datasets.xlsx b/data/metaanalysis-datasets.xlsx index 7c67d91..3e0a700 100644 Binary files a/data/metaanalysis-datasets.xlsx and b/data/metaanalysis-datasets.xlsx differ diff --git a/data/ref_counts_summary.csv b/data/ref_counts_summary.csv new file mode 100644 index 0000000..62199ea --- /dev/null +++ b/data/ref_counts_summary.csv @@ -0,0 +1,13 @@ +dataset,type,total,corrected_past,corrected_future,non_past,non_future +IMSDb,Scripted,3080674,657475,316525,2423199,2764149 +Movies,Scripted,516163,127744,85937,388419,430226 +Switchboard,Spontaneous,245461,41488,22079,203973,223382 +SCOTUS,Constrained,3880259,1963578,1207377,1916681,2672882 +Tennis,Constrained,599172,281669,134638,317503,464534 +PfG,Constrained,37184,7408,9771,29776,27413 +IQ2,Constrained,122925,46630,34811,76295,88114 +GAP,Constrained,8009,1800,1338,6209,6671 +Chair,Scripted,2900,660,460,2240,2440 +Friends,Scripted,107082,22067,16356,85015,90726 +Gutenberg,Scripted,29119393,10234952,8672030,18884441,20447363 +Reddit,Constrained,217924,86513,66700,131411,151224 \ No newline at end of file diff --git a/data/rep/TheChair.xlsx b/data/rep/TheChair.xlsx index c5f3039..bb478f1 100644 Binary files a/data/rep/TheChair.xlsx and b/data/rep/TheChair.xlsx differ diff --git a/data/the_chair/the_chair_auto_reference_counts.csv b/data/the_chair/the_chair_auto_reference_counts.csv new file mode 100644 index 0000000..f261e7a --- /dev/null +++ b/data/the_chair/the_chair_auto_reference_counts.csv @@ -0,0 +1,7 @@ +Episode,Total,Past,Future +1,457,112,74 +2,501,80,64 +3,518,123,82 +4,442,108,75 +5,508,116,66 +6,474,121,99 \ No newline at end of file diff --git a/data/the_chair/the_chair_manual_reference_counts.csv b/data/the_chair/the_chair_manual_reference_counts.csv index 76ef062..8427c43 100644 --- a/data/the_chair/the_chair_manual_reference_counts.csv +++ b/data/the_chair/the_chair_manual_reference_counts.csv @@ -1,7 +1,7 @@ -Past,Future -60,18 -30,14 -43,33 -31,21 -36,11 -27,12 +Episode,Past,Future +1,60,19 +2,29,14 +3,43,33 +4,30,20 +5,37,10 +6,27,12 \ No newline at end of file