From 70a1e1348c2ae75ae98cb76a1a5b462abede0e8c Mon Sep 17 00:00:00 2001 From: Jill Naiman Date: Sat, 4 Nov 2023 15:03:00 -0500 Subject: [PATCH] Auto-commit: Sat Nov 4 15:02:53 CDT 2023 --- Gemfile.lock | 3 + _site/Gemfile.lock | 3 + _site/week11/inClass_week11.ipynb | 2709 ++++++++++++++++- _site/week11/prep_notebook_week11.ipynb | 1393 +++++++-- _site/week12/prep_notebook_week13_part1.ipynb | 2 +- _site/week12/prep_notebook_week13_part2.ipynb | 2 +- week11/inClass/_projects/2_inclass_week10.md | 14 - .../json/altair_mobility_dashboard.json | 2 +- .../json/altair_mobility_data_dashboard.json | 2 +- week11/inClass/assets/json/chart1.json | 2 +- .../assets/json/dashboard_mobility.json | 2 +- .../assets/json/population_scatter.json | 2 +- .../assets/json/side_by_side_from_dict.json | 2 +- .../inClass/assets/json/static_mobility.json | 2 +- 14 files changed, 3920 insertions(+), 220 deletions(-) diff --git a/Gemfile.lock b/Gemfile.lock index 61f2b74..dae4e07 100644 --- a/Gemfile.lock +++ b/Gemfile.lock @@ -213,6 +213,8 @@ GEM minitest (5.17.0) nokogiri (1.13.10-arm64-darwin) racc (~> 1.4) + nokogiri (1.13.10-x86_64-darwin) + racc (~> 1.4) octokit (4.25.1) faraday (>= 1, < 3) sawyer (~> 0.9) @@ -254,6 +256,7 @@ GEM PLATFORMS arm64-darwin-21 + x86_64-darwin-23 DEPENDENCIES github-pages diff --git a/_site/Gemfile.lock b/_site/Gemfile.lock index 61f2b74..dae4e07 100644 --- a/_site/Gemfile.lock +++ b/_site/Gemfile.lock @@ -213,6 +213,8 @@ GEM minitest (5.17.0) nokogiri (1.13.10-arm64-darwin) racc (~> 1.4) + nokogiri (1.13.10-x86_64-darwin) + racc (~> 1.4) octokit (4.25.1) faraday (>= 1, < 3) sawyer (~> 0.9) @@ -254,6 +256,7 @@ GEM PLATFORMS arm64-darwin-21 + x86_64-darwin-23 DEPENDENCIES github-pages diff --git a/_site/week11/inClass_week11.ipynb b/_site/week11/inClass_week11.ipynb index 4b1000c..96312f3 100644 --- a/_site/week11/inClass_week11.ipynb +++ b/_site/week11/inClass_week11.ipynb @@ -22,32 +22,2723 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "0684505e", "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n", + "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n" + ] + } + ], + "source": [ + "import altair as alt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f56a6f9f", + "metadata": {}, "outputs": [], - "source": [] + "source": [ + "brush = alt.selection_interval(encodings=['x','y'])" + ] }, { "cell_type": "code", - "execution_count": null, - "id": "ef333782", + "execution_count": 3, + "id": "0def2e74", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jnaiman/opt/anaconda3/envs/DataVizPL/lib/python3.8/site-packages/altair/utils/deprecation.py:65: AltairDeprecationWarning: 'add_selection' is deprecated. Use 'add_params' instead.\n", + " warnings.warn(message, AltairDeprecationWarning, stacklevel=1)\n" + ] + } + ], + "source": [ + "chart1 = alt.Chart.from_dict({\n", + " \"data\":{\"url\":\"https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/mobility.csv\"},\n", + " \"mark\":\"rect\", #// equivalent to gridheatmap in bqplot\n", + " \"height\":400,\n", + " \"encoding\":{\n", + " #//\"x\":{\"field\": \"Student_teacher_ratio\", \"type\":\"quantitative\"},\n", + " \"x\":{\"bin\":{\"maxbins\":10}, \"field\":\"Student_teacher_ratio\", \"type\":\"quantitative\"},\n", + " \"y\":{\"field\":\"State\", \"type\":\"ordinal\"},\n", + " \"color\":{\"aggregate\":\"count\", \"type\":\"quantitative\"}\n", + " } #// end of encoding\n", + "}).add_selection(\n", + " brush\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "de515017", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "chart2 = alt.Chart.from_dict({\n", + " \"data\":{\"url\":\"https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/mobility.csv\"},\n", + " \"mark\":\"bar\",\n", + " \"encoding\":{\n", + " \"x\":{\"bin\":True, \"field\":\"Mobility\", \"type\":\"quantitative\", \"title\":\"Mobility Score\"},\n", + " \"y\":{\"aggregate\":\"count\", \"type\":\"quantitative\"}\n", + " }#// end encoding\n", + "}).transform_filter(\n", + " brush\n", + ")" + ] }, { "cell_type": "code", - "execution_count": null, - "id": "199dbb9a", + "execution_count": 5, + "id": "e30d017a", "metadata": {}, "outputs": [], - "source": [] + "source": [ + "chart = chart1 | chart2" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8180dd4f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.HConcatChart(...)" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chart" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "26aca538", + "metadata": {}, + "outputs": [], + "source": [ + "# if on workspace:\n", + "#myJekyllDir = './' # save in same folder as notebook\n", + "myJekyllDir = '/Users/jnaiman/jnaiman.github.io/assets/json/' # make sure to include \"/\" at the end" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "70a1a160", + "metadata": {}, + "outputs": [], + "source": [ + "chart.save(myJekyllDir + 'side_by_side_from_dict.json')" + ] + }, + { + "cell_type": "markdown", + "id": "625252df", + "metadata": {}, + "source": [ + "## Use Altair to make the chart" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "6e1420a1", + "metadata": {}, + "outputs": [], + "source": [ + "mobility_url = 'https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/mobility.csv'" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "f89e2800", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter1 = alt.Chart(mobility_url).mark_point().encode(\n", + " x='Mobility:Q', # Quantitative\n", + " y='Population:Q'\n", + ")\n", + "scatter1" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "b3c06763", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter2 = alt.Chart(mobility_url).mark_point().encode(\n", + " x='Mobility:Q', # Quantitative\n", + " y='Population:Q',\n", + " color = alt.Color('Income:Q')\n", + ")\n", + "scatter2" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "5f31df12", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter3 = alt.Chart(mobility_url).mark_point().encode(\n", + " x='Mobility:Q', # Quantitative\n", + " y='Population:Q',\n", + " color = alt.Color('Income:Q', scale=alt.Scale(scheme='viridis'))\n", + ")\n", + "scatter3" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a798386d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter4 = alt.Chart(mobility_url).mark_point().encode(\n", + " x='Mobility:Q', # Quantitative\n", + " y='Population:Q',\n", + " color = alt.Color('Income:Q', scale=alt.Scale(scheme='viridis'),\n", + " bin=alt.Bin(maxbins=5))\n", + ")\n", + "scatter4" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "3048cb98", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter4 = alt.Chart(mobility_url).mark_point().encode(\n", + " x='Mobility:Q', # Quantitative\n", + " y='Population:Q',\n", + " color = alt.Color('Income:Q', scale=alt.Scale(scheme='sinebow'),\n", + " bin=alt.Bin(maxbins=5))\n", + ")\n", + "scatter4" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "ee4bac38", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "scatter4 = alt.Chart(mobility_url).mark_point().encode(\n", + " x='Mobility:Q', # Quantitative\n", + " #y='Population:Q',\n", + " y=alt.Y('Population:Q', scale=alt.Scale(type='log')),\n", + " color = alt.Color('Income:Q', scale=alt.Scale(scheme='sinebow'),\n", + " bin=alt.Bin(maxbins=5))\n", + ")\n", + "scatter4" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "590c0e8c", + "metadata": {}, + "outputs": [], + "source": [ + "scatter4.save(myJekyllDir + 'pop_scatter.json')" + ] + }, + { + "cell_type": "markdown", + "id": "bfb50a53", + "metadata": {}, + "source": [ + "```javascript\n", + "chart1 = alt.Chart.from_dict({\n", + " \"data\":{\"url\":\"https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/mobility.csv\"},\n", + " \"mark\":\"rect\", #// equivalent to gridheatmap in bqplot\n", + " \"height\":400,\n", + " \"encoding\":{\n", + " #//\"x\":{\"field\": \"Student_teacher_ratio\", \"type\":\"quantitative\"},\n", + " \"x\":{\"bin\":{\"maxbins\":10}, \"field\":\"Student_teacher_ratio\", \"type\":\"quantitative\"},\n", + " \"y\":{\"field\":\"State\", \"type\":\"ordinal\"},\n", + " \"color\":{\"aggregate\":\"count\", \"type\":\"quantitative\"}\n", + " } #// end of encoding\n", + "}).add_selection(\n", + " brush\n", + ")\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "077097f6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chart1 = alt.Chart(mobility_url).mark_rect().encode(\n", + " alt.X(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " alt.Y(\"State:O\"),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=400\n", + ")\n", + "chart1" + ] + }, + { + "cell_type": "markdown", + "id": "9a703ba4", + "metadata": {}, + "source": [ + "```javascript\n", + "chart2 = alt.Chart.from_dict({\n", + " \"data\":{\"url\":\"https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/mobility.csv\"},\n", + " \"mark\":\"bar\",\n", + " \"encoding\":{\n", + " \"x\":{\"bin\":True, \"field\":\"Mobility\", \"type\":\"quantitative\", \"title\":\"Mobility Score\"},\n", + " \"y\":{\"aggregate\":\"count\", \"type\":\"quantitative\"}\n", + " }#// end encoding\n", + "}).transform_filter(\n", + " brush\n", + ")\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "f2736941", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chart2 = alt.Chart(mobility_url).mark_bar().encode(\n", + " alt.X(\"Mobility:Q\", bin=True, axis=alt.Axis(title='Mobility Score')),\n", + " alt.Y('count()')\n", + ")\n", + "chart2" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "bb47d94d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.HConcatChart(...)" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chart = chart1 | chart2\n", + "chart" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "664f9973", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.HConcatChart(...)" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brush = alt.selection_interval(encodings=['x','y'])\n", + "\n", + "chart1 = alt.Chart(mobility_url).mark_rect().encode(\n", + " alt.X(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " alt.Y(\"State:O\"),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=400\n", + ").add_selection(\n", + " brush\n", + ")\n", + "\n", + "chart2 = alt.Chart(mobility_url).mark_bar().encode(\n", + " alt.X(\"Mobility:Q\", bin=True, axis=alt.Axis(title='Mobility Score')),\n", + " alt.Y('count()')\n", + ").transform_filter(\n", + " brush\n", + ")\n", + "\n", + "chart = chart1 | chart2\n", + "chart" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "0bf725bc", + "metadata": {}, + "outputs": [], + "source": [ + "chart.save(myJekyllDir + 'side_by_side_mobility_url.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "86e3e125", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "'https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/mobility.csv'" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mobility_url" + ] + }, + { + "cell_type": "markdown", + "id": "dadcd97d", + "metadata": {}, + "source": [ + "### Mobility dashboard with local dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "5c9d8dc7", + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "be0a5b03", + "metadata": {}, + "outputs": [], + "source": [ + "mobility = pd.read_csv(mobility_url)" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "11288218", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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IDNameMobilityStatePopulationUrbanBlackSeg_racialSeg_incomeSeg_poverty...Migration_outForeign_bornSocial_capitalReligiousViolent_crimeSingle_mothersDivorcedMarriedLongitudeLatitude
0100Johnson City0.062199TN57608110.0210.0900.0350.030...0.0050.012-0.2980.5140.0010.1900.1100.601-82.43638636.470371
1200Morristown0.053652TN22781610.0200.0930.0260.028...0.0140.023-0.7670.5440.0020.1850.1160.613-83.40724936.096539
2301Middlesborough0.072635TN6670800.0150.0640.0240.015...0.0120.007-1.2700.6680.0010.2110.1130.590-83.53533236.551540
3302Knoxville0.056281TN72760010.0560.2100.0920.084...0.0140.020-0.2220.6020.0010.2060.1140.575-84.24279035.952259
4401Winston-Salem0.044801NC49318010.1740.2620.0720.061...0.0190.053-0.0180.4880.0030.2200.0920.586-80.50533336.081276
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5 rows × 43 columns

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" + ], + "text/plain": [ + " ID Name Mobility State Population Urban Black Seg_racial \\\n", + "0 100 Johnson City 0.062199 TN 576081 1 0.021 0.090 \n", + "1 200 Morristown 0.053652 TN 227816 1 0.020 0.093 \n", + "2 301 Middlesborough 0.072635 TN 66708 0 0.015 0.064 \n", + "3 302 Knoxville 0.056281 TN 727600 1 0.056 0.210 \n", + "4 401 Winston-Salem 0.044801 NC 493180 1 0.174 0.262 \n", + "\n", + " Seg_income Seg_poverty ... Migration_out Foreign_born Social_capital \\\n", + "0 0.035 0.030 ... 0.005 0.012 -0.298 \n", + "1 0.026 0.028 ... 0.014 0.023 -0.767 \n", + "2 0.024 0.015 ... 0.012 0.007 -1.270 \n", + "3 0.092 0.084 ... 0.014 0.020 -0.222 \n", + "4 0.072 0.061 ... 0.019 0.053 -0.018 \n", + "\n", + " Religious Violent_crime Single_mothers Divorced Married Longitude \\\n", + "0 0.514 0.001 0.190 0.110 0.601 -82.436386 \n", + "1 0.544 0.002 0.185 0.116 0.613 -83.407249 \n", + "2 0.668 0.001 0.211 0.113 0.590 -83.535332 \n", + "3 0.602 0.001 0.206 0.114 0.575 -84.242790 \n", + "4 0.488 0.003 0.220 0.092 0.586 -80.505333 \n", + "\n", + " Latitude \n", + "0 36.470371 \n", + "1 36.096539 \n", + "2 36.551540 \n", + "3 35.952259 \n", + "4 36.081276 \n", + "\n", + "[5 rows x 43 columns]" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mobility.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "89311e19", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jnaiman/opt/anaconda3/envs/DataVizPL/lib/python3.8/site-packages/altair/utils/deprecation.py:65: AltairDeprecationWarning: 'add_selection' is deprecated. Use 'add_params' instead.\n", + " warnings.warn(message, AltairDeprecationWarning, stacklevel=1)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.HConcatChart(...)" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brush = alt.selection_interval(encodings=['x','y'])\n", + "\n", + "chart1 = alt.Chart(mobility).mark_rect().encode(\n", + " alt.X(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " alt.Y(\"State:O\"),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=400\n", + ").add_selection(\n", + " brush\n", + ")\n", + "\n", + "chart2 = alt.Chart(mobility).mark_bar().encode(\n", + " alt.X(\"Mobility:Q\", bin=True, axis=alt.Axis(title='Mobility Score')),\n", + " alt.Y('count()')\n", + ").transform_filter(\n", + " brush\n", + ")\n", + "\n", + "chart = chart1 | chart2\n", + "chart" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "f7f21293", + "metadata": {}, + "outputs": [], + "source": [ + "chart.save(myJekyllDir + 'side_by_side_with_localdata.json')" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "836db1fb", + "metadata": {}, + "outputs": [], + "source": [ + "import os" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "b2ed5ec5", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "875" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.stat(myJekyllDir+'side_by_side_mobility_url.json').st_size # size in bytes ~0.875 kb" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "0a99f722", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "699416" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.stat(myJekyllDir + 'side_by_side_with_localdata.json').st_size # ~7Mb" + ] + }, + { + "cell_type": "markdown", + "id": "de830923", + "metadata": {}, + "source": [ + "## Mobility dataset with dropdown selection for state" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "9a2290e7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chart1 = alt.Chart(mobility_url).mark_rect().encode(\n", + " alt.X(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " alt.Y(\"Population:Q\", bin=alt.Bin(maxbins=50), scale=alt.Scale(type='log')),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=300\n", + ")\n", + "chart1" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "7d7df191", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chart1 = alt.Chart(mobility_url).transform_calculate(\n", + " log_pop = 'log(datum.Population)/log(10)'\n", + ").mark_rect().encode(\n", + " alt.X(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " #alt.Y(\"Population:Q\", bin=alt.Bin(maxbins=50), scale=alt.Scale(type='log')),\n", + " alt.Y(\"log_pop:Q\", bin=alt.Bin(maxbins=50), title='log(Population)'),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=300\n", + ")\n", + "chart1" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "8b0d6b02", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jnaiman/opt/anaconda3/envs/DataVizPL/lib/python3.8/site-packages/altair/utils/deprecation.py:65: AltairDeprecationWarning: 'add_selection' is deprecated. Use 'add_params' instead.\n", + " warnings.warn(message, AltairDeprecationWarning, stacklevel=1)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.HConcatChart(...)" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brush = alt.selection_interval(encodings=['x','y'])\n", + "\n", + "\n", + "chart1 = alt.Chart(mobility_url).transform_calculate(\n", + " log_pop = 'log(datum.Population)/log(10)'\n", + ").mark_rect().encode(\n", + " alt.X(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " #alt.Y(\"Population:Q\", bin=alt.Bin(maxbins=50), scale=alt.Scale(type='log')),\n", + " alt.Y(\"log_pop:Q\", bin=alt.Bin(maxbins=50), title='log(Population)'),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=300\n", + ").add_selection(\n", + " brush\n", + ")\n", + "\n", + "chart2 = alt.Chart(mobility).transform_calculate(\n", + " log_pop = 'log(datum.Population)/log(10)'\n", + ").mark_bar().encode(\n", + " alt.X(\"Mobility:Q\", bin=True, axis=alt.Axis(title='Mobility Score')),\n", + " alt.Y('count()')\n", + ").transform_filter(\n", + " brush\n", + ")\n", + "\n", + "chart = chart1 | chart2\n", + "chart" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "bc48f289", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['TN', 'NC', 'VA', 'SC', 'MS', 'LA', 'AR', 'KY', 'AL', 'GA', 'FL',\n", + " 'WV', 'MD', 'DC', 'MI', 'OH', 'IN', 'IL', 'PA', 'NY', 'NJ', 'DE',\n", + " 'ME', 'VT', 'RI', 'MA', 'NH', 'CT', 'WI', 'MN', 'IA', 'MO', 'ND',\n", + " 'SD', 'MT', 'NE', 'WY', 'CO', 'KS', 'OK', 'TX', 'NM', 'AK', 'ID',\n", + " 'HI', 'AZ', 'OR', 'UT', 'CA', 'NV', 'WA'], dtype=object)" + ] + }, + "execution_count": 47, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mobility['State'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "694c1c19", + "metadata": {}, + "outputs": [], + "source": [ + "alt.binding_select?" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "8290aba2", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jnaiman/opt/anaconda3/envs/DataVizPL/lib/python3.8/site-packages/altair/utils/deprecation.py:65: AltairDeprecationWarning: 'selection_single' is deprecated. Use 'selection_point'\n", + " warnings.warn(message, AltairDeprecationWarning, stacklevel=1)\n", + "/Users/jnaiman/opt/anaconda3/envs/DataVizPL/lib/python3.8/site-packages/altair/utils/deprecation.py:65: AltairDeprecationWarning: 'add_selection' is deprecated. Use 'add_params' instead.\n", + " warnings.warn(message, AltairDeprecationWarning, stacklevel=1)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.HConcatChart(...)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brush = alt.selection_interval(encodings=['x','y'])\n", + "\n", + "dropdown = alt.binding_select(options=mobility['State'].unique())\n", + "selection_state = alt.selection_single(fields=['State'], bind=dropdown)\n", + "\n", + "\n", + "chart1 = alt.Chart(mobility_url).transform_calculate(\n", + " log_pop = 'log(datum.Population)/log(10)'\n", + ").mark_rect().encode(\n", + " alt.X(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " #alt.Y(\"Population:Q\", bin=alt.Bin(maxbins=50), scale=alt.Scale(type='log')),\n", + " alt.Y(\"log_pop:Q\", bin=alt.Bin(maxbins=50), title='log(Population)'),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=300\n", + ").transform_filter(\n", + " selection_state\n", + ").add_selection(\n", + " brush, selection_state\n", + ")\n", + "\n", + "chart2 = alt.Chart(mobility).transform_calculate(\n", + " log_pop = 'log(datum.Population)/log(10)'\n", + ").mark_bar().encode(\n", + " alt.X(\"Mobility:Q\", bin=True, axis=alt.Axis(title='Mobility Score')),\n", + " alt.Y('count()')\n", + ").transform_filter(\n", + " brush\n", + ").transform_filter(\n", + " selection_state\n", + ")\n", + "\n", + "chart = chart1 | chart2\n", + "chart" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "4cd397ea", + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/Users/jnaiman/opt/anaconda3/envs/DataVizPL/lib/python3.8/site-packages/altair/utils/deprecation.py:65: AltairDeprecationWarning: 'selection_single' is deprecated. Use 'selection_point'\n", + " warnings.warn(message, AltairDeprecationWarning, stacklevel=1)\n", + "/Users/jnaiman/opt/anaconda3/envs/DataVizPL/lib/python3.8/site-packages/altair/utils/deprecation.py:65: AltairDeprecationWarning: 'add_selection' is deprecated. Use 'add_params' instead.\n", + " warnings.warn(message, AltairDeprecationWarning, stacklevel=1)\n" + ] + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brush = alt.selection_interval(encodings=['x','y'])\n", + "\n", + "dropdown = alt.binding_select(options=mobility['State'].unique())\n", + "selection_state = alt.selection_single(fields=['State'], bind=dropdown)\n", + "\n", + "\n", + "chart1 = alt.Chart(mobility_url).transform_calculate(\n", + " log_pop = 'log(datum.Population)/log(10)'\n", + ").mark_rect().encode(\n", + " alt.X(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " #alt.Y(\"Population:Q\", bin=alt.Bin(maxbins=50), scale=alt.Scale(type='log')),\n", + " alt.Y(\"log_pop:Q\", bin=alt.Bin(maxbins=50), title='log(Population)'),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=300\n", + ").transform_filter(\n", + " selection_state\n", + ").add_selection(\n", + " brush, selection_state\n", + ")\n", + "\n", + "chart1" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "59bd7e7d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 55, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brush = alt.selection_interval(encodings=['x','y'])\n", + "\n", + "dropdown = alt.binding_select(options=mobility['State'].unique())\n", + "selection_state = alt.selection_single(fields=['State'], bind=dropdown)\n", + "\n", + "\n", + "chart1 = alt.Chart(mobility_url).transform_calculate(\n", + "# log_pop = 'log(datum.Population)/log(10)'\n", + " log_pop = \"log(datum['Population'])/log(10)\"\n", + ").mark_rect().encode(\n", + " alt.X(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " #alt.Y(\"Population:Q\", bin=alt.Bin(maxbins=50), scale=alt.Scale(type='log')),\n", + " alt.Y(\"log_pop:Q\", bin=alt.Bin(maxbins=50), title='log(Population)'),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=300\n", + ").transform_filter(\n", + " selection_state\n", + ").add_selection(\n", + " brush, selection_state\n", + ")\n", + "\n", + "chart1" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "1352367c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chart1 = alt.Chart(mobility_url).transform_calculate(\n", + "# log_pop = 'log(datum.Population)/log(10)'\n", + " log_pop = 'log(datum[\"Population\"])/log(10)'\n", + ").mark_rect().encode(\n", + " alt.Y(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " #alt.Y(\"Population:Q\", bin=alt.Bin(maxbins=50), scale=alt.Scale(type='log')),\n", + " alt.X(\"log_pop:Q\", bin=alt.Bin(maxbins=50), title='log(Population)'),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=300\n", + ")\n", + "chart1" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "b0d60428", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brush = alt.selection_interval(encodings=['x','y'])\n", + "\n", + "dropdown = alt.binding_select(options=[None] + mobility['State'].unique().tolist(), \n", + " labels = ['All']+ mobility['State'].unique().tolist())\n", + "selection_state = alt.selection_single(fields=['State'], bind=dropdown)\n", + "\n", + "\n", + "chart1 = alt.Chart(mobility_url).transform_calculate(\n", + " log_pop = 'log(datum.Population)/log(10)'\n", + ").mark_rect().encode(\n", + " alt.X(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " #alt.Y(\"Population:Q\", bin=alt.Bin(maxbins=50), scale=alt.Scale(type='log')),\n", + " alt.Y(\"log_pop:Q\", bin=alt.Bin(maxbins=50), title='log(Population)'),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=300\n", + ").transform_filter(\n", + " selection_state\n", + ").add_selection(\n", + " brush, selection_state\n", + ")\n", + "\n", + "chart1" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "0deb7583", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chart1 = alt.Chart(mobility_url).transform_calculate(\n", + "# log_pop = 'log(datum.Population)/log(10)'\n", + " log_pop = 'log(datum[\"Population\"])/log(10)'\n", + ").mark_rect().encode(\n", + " alt.Y(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " #alt.Y(\"Population:Q\", bin=alt.Bin(maxbins=50), scale=alt.Scale(type='log')),\n", + " alt.X(\"log_pop:Q\", bin=alt.Bin(maxbins=50), title='log(Population)'),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=300\n", + ").interactive()\n", + "chart1" + ] }, { "cell_type": "code", "execution_count": null, - "id": "941f4e67", + "id": "59b5d4dd", "metadata": {}, "outputs": [], "source": [] diff --git a/_site/week11/prep_notebook_week11.ipynb b/_site/week11/prep_notebook_week11.ipynb index e7ca847..ff96837 100644 --- a/_site/week11/prep_notebook_week11.ipynb +++ b/_site/week11/prep_notebook_week11.ipynb @@ -20,7 +20,8 @@ "1. Copying vega-lite code from other sources (in our case Starboard) and using Altair to export it as JSON\n", "1. Building dashboards with by combining vega-lite code from other sources with Altair layouts\n", "1. Using Altair to make the plot with Python, with the data stored online\n", - "1. Using Python for the data cleaning/transformation and Altair just for plotting and saving as vega-lite JSON" + "1. Using Python for the data cleaning/transformation and Altair just for plotting and saving as vega-lite JSON\n", + "1. Quick drop-down interactivity (if time)" ] }, { @@ -82,7 +83,16 @@ "execution_count": 1, "id": "9d432fe5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n", + "Intel MKL WARNING: Support of Intel(R) Streaming SIMD Extensions 4.2 (Intel(R) SSE4.2) enabled only processors has been deprecated. Intel oneAPI Math Kernel Library 2025.0 will require Intel(R) Advanced Vector Extensions (Intel(R) AVX) instructions.\n" + ] + } + ], "source": [ "import altair as alt" ] @@ -97,19 +107,30 @@ "data": { "text/html": [ "\n", - "
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\n", "" ], "text/plain": [ "alt.HConcatChart(...)" ] }, - "execution_count": 39, + "execution_count": 37, "metadata": {}, "output_type": "execute_result" } @@ -2490,12 +2714,12 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 38, "id": "f177492d", "metadata": {}, "outputs": [], "source": [ - "chart.save(myJekyllDir+\"assets/json/altair_mobility_data_dashboard.json\")" + "chart.save(myJekyllDir+\"altair_mobility_data_dashboard.json\")" ] }, { @@ -2508,7 +2732,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 40, "id": "db88e5af", "metadata": {}, "outputs": [], @@ -2518,44 +2742,44 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 41, "id": "d4cfdaee", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "890" + "953" ] }, - "execution_count": 42, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "os.stat(myJekyllDir+\"assets/json/altair_mobility_dashboard.json\").st_size # size in bytes - so ~1kb" + "os.stat(myJekyllDir+\"altair_mobility_dashboard.json\").st_size # size in bytes - so ~1kb" ] }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 42, "id": "c68d8f0b", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "699535" + "699598" ] }, - "execution_count": 43, + "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "os.stat(myJekyllDir+\"assets/json/altair_mobility_data_dashboard.json\").st_size # so ~7Mb" + "os.stat(myJekyllDir+\"altair_mobility_data_dashboard.json\").st_size # so ~7Mb" ] }, { @@ -2566,6 +2790,799 @@ "So, right now, there is no reason to really use this last method -- the data isn't really being manipulated at all so we don't really need to save it with our JSON output. Next time we'll go through a few more examples where we can do data cleaning/analysis in Python and then save our plots with Altair." ] }, + { + "cell_type": "markdown", + "id": "0d61f0ef", + "metadata": {}, + "source": [ + "## 6. Quick drop-down interactivity (if time)" + ] + }, + { + "cell_type": "markdown", + "id": "a838e6df", + "metadata": {}, + "source": [ + "Let's make this rectangle plot again, but a little differently, with the Population on the y-axis instead of the State.\n", + "\n", + "We'll use the mobility_url instead of the dataframe to save some space:" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "102dc6be", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chart1 = alt.Chart(mobility_url).mark_rect().encode(\n", + " alt.X(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " alt.Y(\"Population:Q\", bin=alt.Bin(maxbins=50)),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=400\n", + ")\n", + "chart1" + ] + }, + { + "cell_type": "markdown", + "id": "6b54c12d", + "metadata": {}, + "source": [ + "So, once again, we probably want to take the log of the Population:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "cdce7e8d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chart1 = alt.Chart(mobility_url).mark_rect().encode(\n", + " alt.X(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " alt.Y(\"Population:Q\", bin=alt.Bin(maxbins=50), scale=alt.Scale(type='log')),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=400\n", + ")\n", + "chart1" + ] + }, + { + "cell_type": "markdown", + "id": "4829f4d3", + "metadata": {}, + "source": [ + "But wait! We get an error -- this is a thing that can come up when taking the log and then binning and is currently [only partially supported in Altair](https://github.com/altair-viz/altair/issues/1074).\n", + "\n", + "But we can do a quick data transformation and create a new variable for plotting:" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "965179f2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.Chart(...)" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "chart1 = alt.Chart(mobility_url).transform_calculate(\n", + " log_pop = 'log(datum.Population)/log(10)'\n", + ").mark_rect().encode(\n", + " alt.X(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " #alt.Y(\"Population:Q\", bin=alt.Bin(maxbins=50)),\n", + " alt.Y(\"log_pop:Q\", bin=alt.Bin(maxbins=50), title='log(Population)'),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=400\n", + ")\n", + "chart1" + ] + }, + { + "cell_type": "markdown", + "id": "e85ac078", + "metadata": {}, + "source": [ + "Great! Now let's remake our interactive plot with these changes:" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "9e7050d2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.HConcatChart(...)" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brush = alt.selection_interval(encodings=['x','y']) # to select bins of student-teacher ratio and state\n", + "\n", + "chart1 = alt.Chart(mobility_url).transform_calculate(\n", + " log_pop = 'log(datum.Population)/log(10)'\n", + ").mark_rect().encode(\n", + " alt.X(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " alt.Y(\"log_pop:Q\", bin=alt.Bin(maxbins=50), title='log(Population)'),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=400\n", + ").add_selection(\n", + " brush\n", + ")\n", + "\n", + "chart2 = alt.Chart(mobility_url).mark_bar().encode(\n", + " alt.X(\"Mobility:Q\", bin=True,axis=alt.Axis(title='Mobility Score')),\n", + " alt.Y('count()', axis=alt.Axis(title='Mobility Score Distribution'))\n", + ").transform_filter(\n", + " brush\n", + ")\n", + "\n", + "chart = (chart1.properties(width=300) | chart2.properties(width=300))\n", + "\n", + "chart" + ] + }, + { + "cell_type": "markdown", + "id": "4dabd28e", + "metadata": {}, + "source": [ + "But wait! When we select -- our Mobility Score disappears? Why??\n", + "\n", + "This is because while the \"log_pop\" selection variable is created in the first chart, it is not there in the second plot, so let's add it!" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "id": "5036076a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.HConcatChart(...)" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brush = alt.selection_interval(encodings=['x','y']) # to select bins of student-teacher ratio and state\n", + "\n", + "chart1 = alt.Chart(mobility_url).transform_calculate(\n", + " log_pop = 'log(datum.Population)/log(10)'\n", + ").mark_rect().encode(\n", + " alt.X(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " alt.Y(\"log_pop:Q\", bin=alt.Bin(maxbins=50), title='log(Population)'),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=400\n", + ").add_selection(\n", + " brush\n", + ")\n", + "\n", + "chart2 = alt.Chart(mobility_url).transform_calculate(\n", + " log_pop = 'log(datum.Population)/log(10)'\n", + ").mark_bar().encode(\n", + " alt.X(\"Mobility:Q\", bin=True,axis=alt.Axis(title='Mobility Score')),\n", + " alt.Y('count()', axis=alt.Axis(title='Mobility Score Distribution'))\n", + ").transform_filter(\n", + " brush\n", + ")\n", + "\n", + "chart = (chart1.properties(width=300) | chart2.properties(width=300))\n", + "\n", + "chart" + ] + }, + { + "cell_type": "markdown", + "id": "3554fe9c", + "metadata": {}, + "source": [ + "Great! Now, let's say we want to look at this on a state-by-state basis -- how can we add this feature? Let's add a dropdown and \"bind\" it to the unique states listed in the \"State\" column:" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "id": "54270355", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array(['TN', 'NC', 'VA', 'SC', 'MS', 'LA', 'AR', 'KY', 'AL', 'GA', 'FL',\n", + " 'WV', 'MD', 'DC', 'MI', 'OH', 'IN', 'IL', 'PA', 'NY', 'NJ', 'DE',\n", + " 'ME', 'VT', 'RI', 'MA', 'NH', 'CT', 'WI', 'MN', 'IA', 'MO', 'ND',\n", + " 'SD', 'MT', 'NE', 'WY', 'CO', 'KS', 'OK', 'TX', 'NM', 'AK', 'ID',\n", + " 'HI', 'AZ', 'OR', 'UT', 'CA', 'NV', 'WA'], dtype=object)" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "mobility['State'].unique() # note, we have to use the actual mobility dataset, not URL here!" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "id": "f5cbbac9", + "metadata": {}, + "outputs": [], + "source": [ + "alt.binding_select?" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "id": "d7675514", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "
\n", + "" + ], + "text/plain": [ + "alt.HConcatChart(...)" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "brush = alt.selection_interval(encodings=['x','y']) # to select bins of student-teacher ratio and state\n", + "\n", + "dropdown = alt.binding_select(options=mobility['State'].unique(), name='State: ') # using mobility data!\n", + "selection_state = alt.selection_single(fields=['State'], bind=dropdown)\n", + "\n", + "chart1 = alt.Chart(mobility_url).transform_calculate(\n", + " log_pop = 'log(datum.Population)/log(10)'\n", + ").mark_rect().encode(\n", + " alt.X(\"Student_teacher_ratio:Q\", bin=alt.Bin(maxbins=10)),\n", + " alt.Y(\"log_pop:Q\", bin=alt.Bin(maxbins=50), title='log(Population)'),\n", + " alt.Color(\"count()\")\n", + ").properties(\n", + " height=400\n", + ").transform_filter(\n", + " selection_state\n", + ").add_selection(\n", + " brush, selection_state # be sure to add both!\n", + ")\n", + "\n", + "chart2 = alt.Chart(mobility_url).transform_calculate(\n", + " log_pop = 'log(datum.Population)/log(10)'\n", + ").mark_bar().encode(\n", + " alt.X(\"Mobility:Q\", bin=True,axis=alt.Axis(title='Mobility Score')),\n", + " alt.Y('count()', axis=alt.Axis(title='Mobility Score Distribution'))\n", + ").transform_filter(\n", + " brush\n", + ").transform_filter(\n", + " selection_state\n", + ")\n", + "\n", + "chart = (chart1.properties(width=300) | chart2.properties(width=300))\n", + "\n", + "chart" + ] + }, + { + "cell_type": "markdown", + "id": "b3d3cf3d", + "metadata": {}, + "source": [ + "But what if we want to be able to select not just unique values of the columns, but column names themselves (i.e. like plotting Population OR State on the y-axis)?\n", + "\n", + "That gets a little tricker and is something we'll look at next week (but feel free to look ahead at those prep materials if you are curious!)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dcd69aa6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8121da82", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "71b6a7e4", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cd3bf1f7", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "193b15c0", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "089823e0", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "code", "execution_count": null, diff --git a/_site/week12/prep_notebook_week13_part1.ipynb b/_site/week12/prep_notebook_week13_part1.ipynb index 2a000ff..9cfb068 100644 --- a/_site/week12/prep_notebook_week13_part1.ipynb +++ b/_site/week12/prep_notebook_week13_part1.ipynb @@ -1601,7 +1601,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/_site/week12/prep_notebook_week13_part2.ipynb b/_site/week12/prep_notebook_week13_part2.ipynb index 02c7d9a..ac6fae1 100644 --- a/_site/week12/prep_notebook_week13_part2.ipynb +++ b/_site/week12/prep_notebook_week13_part2.ipynb @@ -15194,7 +15194,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.12" + "version": "3.8.12" } }, "nbformat": 4, diff --git a/week11/inClass/_projects/2_inclass_week10.md b/week11/inClass/_projects/2_inclass_week10.md index 03b5c2d..8f0715f 100644 --- a/week11/inClass/_projects/2_inclass_week10.md +++ b/week11/inClass/_projects/2_inclass_week10.md @@ -39,20 +39,6 @@ Something more complex with interactivity: -## Quick aside into Altair-only plots - -This was a plot that was made purely in Altair: - - - -## Side-by-side, interactive using Altair syntax - - - -## Side-by-side, interactive using Altair syntax, local data - - - diff --git a/week11/inClass/assets/json/altair_mobility_dashboard.json b/week11/inClass/assets/json/altair_mobility_dashboard.json index 584f387..89dfce9 100644 --- a/week11/inClass/assets/json/altair_mobility_dashboard.json +++ b/week11/inClass/assets/json/altair_mobility_dashboard.json @@ -1 +1 @@ -{"config": {"view": {"continuousWidth": 300, "continuousHeight": 300}}, "hconcat": [{"mark": {"type": "rect"}, "encoding": {"color": {"aggregate": "count", "type": "quantitative"}, "x": {"bin": {"maxbins": 10}, "field": "Student_teacher_ratio", "type": "quantitative"}, "y": {"field": "State", "type": "ordinal"}}, "height": 400, "name": "view_2", "width": 300}, {"mark": {"type": "bar"}, "encoding": {"x": {"axis": {"title": "Mobility Score"}, "bin": true, "field": "Mobility", "type": "quantitative"}, "y": {"aggregate": "count", "axis": {"title": "Mobility Score Distribution"}, "type": "quantitative"}}, "transform": [{"filter": {"param": "param_3"}}], "width": 300}], "data": {"url": "https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/mobility.csv"}, "params": [{"name": "param_3", "select": {"type": "interval", "encodings": ["x", "y"]}, "views": ["view_2"]}], "$schema": "https://vega.github.io/schema/vega-lite/v5.8.0.json"} \ No newline at end of file +{"config": {"view": {"continuousWidth": 400, "continuousHeight": 300}}, "hconcat": [{"mark": "rect", "encoding": {"color": {"aggregate": "count", "type": "quantitative"}, "x": {"bin": {"maxbins": 10}, "field": "Student_teacher_ratio", "type": "quantitative"}, "y": {"field": "State", "type": "ordinal"}}, "height": 400, "selection": {"selector002": {"type": "interval", "encodings": ["x", "y"]}}, "width": 300}, {"mark": "bar", "encoding": {"x": {"axis": {"title": "Mobility Score"}, "bin": true, "field": "Mobility", "type": "quantitative"}, "y": {"aggregate": "count", "axis": {"title": "Mobility Score Distribution"}, "type": "quantitative"}}, "transform": [{"filter": {"selection": "selector002"}}], "width": 300}], "data": {"url": "https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_bcubcg_fall2022/main/data/mobility.csv"}, "$schema": "https://vega.github.io/schema/vega-lite/v4.17.0.json"} \ No newline at end of file diff --git a/week11/inClass/assets/json/altair_mobility_data_dashboard.json b/week11/inClass/assets/json/altair_mobility_data_dashboard.json index 81587d0..0bde37d 100644 --- a/week11/inClass/assets/json/altair_mobility_data_dashboard.json +++ b/week11/inClass/assets/json/altair_mobility_data_dashboard.json @@ -1 +1 @@ -{"config": {"view": {"continuousWidth": 300, "continuousHeight": 300}}, "hconcat": [{"data": {"name": "data-b10526190cf48649f6e7312c7784ddd8"}, "mark": {"type": "rect"}, "encoding": {"color": {"aggregate": "count", "type": "quantitative"}, "x": {"bin": {"maxbins": 10}, "field": "Student_teacher_ratio", "type": "quantitative"}, "y": {"field": "State", "type": "ordinal"}}, "height": 400, "name": "view_3", "width": 300}, {"data": {"url": "https://raw.githubusercontent.com/UIUC-iSchool-DataViz/is445_data/main/mobility.csv"}, "mark": {"type": "bar"}, "encoding": {"x": {"axis": {"title": "Mobility Score"}, "bin": true, "field": "Mobility", "type": "quantitative"}, "y": {"aggregate": "count", "axis": {"title": "Mobility Score Distribution"}, "type": "quantitative"}}, "transform": [{"filter": {"param": "param_4"}}], "width": 300}], "params": [{"name": "param_4", "select": {"type": "interval", "encodings": ["x", "y"]}, "views": ["view_3"]}], "$schema": "https://vega.github.io/schema/vega-lite/v5.8.0.json", "datasets": {"data-b10526190cf48649f6e7312c7784ddd8": [{"ID": 100, "Name": "Johnson City", "Mobility": 0.0621988103, "State": "TN", "Population": 576081, "Urban": 1, "Black": 0.021, "Seg_racial": 0.09, "Seg_income": 0.035, "Seg_poverty": 0.03, "Seg_affluence": 0.038, "Commute": 0.325, "Income": 31560, "Gini": 0.468, "Share01": 13.459, "Gini_99": 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