gchung05

[gchung05]

Best & Worst California Hospitals for Surgery

If you had to pick a local hospital for a major surgery, how would you know which one? What if you had a map of hospitals with their grades by surgical procedure?

This is (almost) it, with the help of the California Health & Human Services Open Data Portal. While the following map gives a pretty interesting idea, it mainly illustrates the potential of government-run open data portals. Inspired by the great folks over at Hack for LA and Code for America.

https://gchung.shinyapps.io/CAhospitals

The R code is quite simple as I elected to run only the most basic statistics to get an illustrative output. If there is enough interest, I will continue to develop it further.

[gchung05]

Intuitive Motorist GPS Trip Visualization

How might we visualize a motorist’s GPS-tracked trips efficiently?

This post will show how to use R to create intuitive plots from a set of GPS coordinates. The immediate purpose is to guide us in our understanding of the various types of trips a motorist may take. The larger purpose is the study of driver telematics, or the learning of motorist behavior.

The Data

Data comes from the Driver Telematics Analysis Kaggle competition. Because the data cannot be open sourced per competition rules, I will briefly describe it.

The GPS within a motor vehicle tracks a vehicle-trip as a series of X-Y coordinates, such as follows:

X Y0.0 0.017.6 -10.135.1 -23.954.7 -33.671.1 -40.888.5 -51.6

It is fair to assume that successive rows are approximately equally spaced in time, which will be useful in the estimation of velocity and acceleration. We are provided 200 vehicle-trips per vehicle. The number of observations per vehicle-trip varies according to the duration of each trip. Furthermore, to protect privacy, the beginning and ends of each trip were trimmed, and the trajectories of each trip were randomly rotated in Cartesian space. We have a large number of vehicles available (with 200 trips each).

For each vehicle, I created a R list object containing 200 data frames, each data frame representing a vehicle-trip. This blog post will focus on 3 vehicles, which I saved as the list objects:

vehicle1 vehicle2 vehicle3

An Aside: The Purpose of the Kaggle Competition

The data provider, in this case a car insurance company, substituted a small proportion of the 200 vehicle-trips with trips not belonging to the primary driver of the vehicle. Our objective was to predict which of the 200 trips belonged to the primary driver of the vehicle.

Plotting One Vehicle-Trip

First, let’s plot one trip, such as the following:

Our code will:

Use linear regression and trigonometry to rotate the trip so that it starts from the left and heads right.

Approximate instantaneous speed using the Cartesian distance formula.

Plot the trip using ggplot2 and geom_path

First Run Some Required Code

# Required Libraries library(ggplot2) library(dplyr) # ggplot2 options # Blank background blankBG %` before, review the [dplyr package introduction](http://cran.rstudio.com/web/packages/dplyr/vignettes/introduction.html). # Select the vehicle-trip currentTrip % # Predict ŷ for the last recorded x %>% # Take the arc-tangent of ŷ and x theta % predict(lastPoint) %>% atan2(lastPoint$x) # Rotate the vehicle-trip so the orientation is left to right currentTrip$xN

[gchung05]

Micro-Stories on AIDS in the U.S. from 1982-2002

What can cause a 250% increase in the reported prevalence of AIDS in the U.S.? Turns out it’s not a wave of sexual emancipation, but a change in the medical definition of AIDS.

This and another micro-stories can be teased out with some patience from 20+ years of AIDS data. The kind folks from the Polaris Project provided me with 20 years of AIDS prevalence data (sourced from the CDC) to play with. A few hours of playtime uncovered the following tidbits…

Exploring & Understanding the Data

Describing the Data

By each year and major U.S. city, the CDC dataset provides the number of reported AIDS cases, an age category, and a country of birth. Here’s a detailed explanation:

Year. Years range from prior to 1982 (as a single category, denoted as 1981), to 2002, by year. Number of reported cases rise dramatically between 1982 and 1988. The cause may be increased reporting, increased disease incidence, or another factor.

Location. There are 110 locations available, typically major cities. Examples include San Jose, CA and Atlanta, GA.

Age. There are 14 age categories, examples include 1-12 years, 20-24 years, 40-44 years, and 65+ years. Reported cases increase dramatically between 12 and 20 years.

Country of Birth. Only two categories, born in the U.S./U.S. territories, or not.

Seeing Patterns Between Predictors

Relationships between predictors are very important. We explore them here.

Does the % of foreign born AIDS cases vary by age group?

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There’s a suggestive trend of increased foreign-borns among the early 20s and the 55+. Perhaps a generational relationship? Note: A good thing to do is to continue exploring predictor relationships. In the interest of brevity, I will move on.

Modeling and Visualizing the Data

AIDS Cases Over Time

How many AIDS cases were reported from 1981-2002, categorized by age group?

// jsData function gvisDataLineChartID571a3fe2c2a1 () { var data = new google.visualization.DataTable(); var datajson = [ [ "1981", 0, 0, 6, 29, 42, 38, 22, 9, 10, 2, 0, 0, 0 ], [ "1982", 5, 1, 30, 122, 164, 111, 75, 51, 26, 13, 4, 2, 6 ], [ "1983", 9, 8, 81, 361, 526, 397, 273, 155, 102, 60, 17, 10, 18 ], [ "1984", 15, 25, 181, 768, 1160, 935, 587, 321, 208, 98, 52, 29, 31 ], [ "1985", 78, 35, 365, 1334, 2092, 1784, 1079, 610, 377, 247, 102, 96, 56 ], [ "1986", 106, 53, 642, 2162, 3224, 2892, 1675, 991, 565, 393, 216, 164, 89 ], [ "1987", 177, 77, 969, 3475, 5260, 4610, 2893, 1727, 969, 614, 371, 350, 160 ], [ "1988", 368, 127, 1498, 5150, 7810, 6836, 4308, 2352, 1301, 866, 496, 539, 221 ], [ "1989", 373, 138, 1487, 5544, 8522, 7575, 4844, 2694, 1502, 971, 532, 496, 255 ], [ "1990", 457, 181, 1673, 6765, 9932, 9598, 6382, 3540, 1854, 1154, 661, 619, 307 ], [ "1991", 426, 162, 1503, 6641, 10500, 10093, 7017, 3839, 2109, 1223, 757, 638, 254 ], [ "1992", 446, 153, 1504, 6695, 10600, 10651, 7687, 4222, 2284, 1229, 774, 717, 322 ], [ "1993", 572, 573, 3860, 15057, 23974, 23551, 17349, 9381, 4850, 2575, 1423, 1199, 349 ], [ "1994", 685, 413, 2655, 10360, 17893, 17857, 13332, 7367, 3839, 2121, 1146, 962, 350 ], [ "1995", 521, 395, 2428, 9029, 16312, 16459, 12709, 7178, 3811, 1917, 1099, 883, 275 ], [ "1996", 459, 397, 2170, 7876, 15081, 15452, 12034, 7119, 3479, 1934, 1000, 923, 208 ], [ "1997", 334, 370, 1845, 6603, 12827, 13544, 10715, 6527, 3318, 1748, 951, 851, 129 ], [ "1998", 284, 298, 1488, 4914, 9631, 10672, 8564, 5604, 2943, 1477, 794, 753, 95 ], [ "1999", 174, 310, 1493, 4527, 8841, 10355, 8489, 5494, 2992, 1545, 832, 735, 87 ], [ "2000", 129, 334, 1341, 3900, 7461, 9602, 7703, 5254, 2954, 1449, 782, 701, 62 ], [ "2001", 123, 361, 1449, 3690, 7089, 9581, 8204, 5648, 3198, 1611, 876, 859, 47 ], [ "2002", 108, 408, 1575, 3815, 6975, 9554, 8546, 5882, 3480, 1788, 929, 840, 50 ] ]; data.addColumn('string','Year.Reported.Code'); data.addColumn('number','1 - 12 Years'); data.addColumn('number','13 - 19 Years'); data.addColumn('number','20 - 24 Years'); data.addColumn('number','25 - 29 Years'); data.addColumn('number','30 - 34 Years'); data.addColumn('number','35 - 39 Years or age is missing'); data.addColumn('number','40 - 44 Years'); data.addColumn('number','45 - 49 Years'); data.addColumn('number','50 - 54 Years'); data.addColumn('number','55 - 59 Years'); data.addColumn('number','60 - 64 Years'); data.addColumn('number','65+ Years'); data.addColumn('number','Less than 1 Year'); data.addRows(datajson); return(data); } // jsDrawChart function drawChartLineChartID571a3fe2c2a1() { var data = gvisDataLineChartID571a3fe2c2a1(); var options = {}; options["allowHtml"] = true; options["vAxis"] = {title:'Reported AIDS Cases'}; options["height"] = 300; options["curveType"] = "function"; var chart = new google.visualization.LineChart( document.getElementById('LineChartID571a3fe2c2a1') ); chart.draw(data,options); } // jsDisplayChart (function() { var pkgs = window.__gvisPackages = window.__gvisPackages || []; var callbacks = window.__gvisCallbacks = window.__gvisCallbacks || []; var chartid = "corechart"; // Manually see if chartid is in pkgs (not all browsers support Array.indexOf) var i, newPackage = true; for (i = 0; newPackage && i < pkgs.length; i++) { if (pkgs[i] === chartid) newPackage = false; } if (newPackage) pkgs.push(chartid); // Add the drawChart function to the global list of callbacks callbacks.push(drawChartLineChartID571a3fe2c2a1); })(); function displayChartLineChartID571a3fe2c2a1() { var pkgs = window.__gvisPackages = window.__gvisPackages || []; var callbacks = window.__gvisCallbacks = window.__gvisCallbacks || []; window.clearTimeout(window.__gvisLoad); // The timeout is set to 100 because otherwise the container div we are // targeting might not be part of the document yet window.__gvisLoad = setTimeout(function() { var pkgCount = pkgs.length; google.load("visualization", "1", { packages:pkgs, callback: function() { if (pkgCount != pkgs.length) { // Race condition where another setTimeout call snuck in after us; if // that call added a package, we must not shift its callback return; } while (callbacks.length > 0) callbacks.shift()(); } }); }, 100); } // jsFooter

Two primary points:

Clearly the 30s age group has the highest reporting rate.

A huge spike in reporting levels occurred in 1993, indicative of a shock in the system. UCSF indicates this is due to a change in the AIDS case definition.

The Top 7 AIDS Locations

Case Patterns by Location, Time, and Age Group Hint: Hit the Play button

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69, 165, 258, 201, 149, 73, 33, 17, 17, 12, 1085 ], [ "Northeast, pediatric or not in MSA", 2002, 32, 10, 20, 50, 136, 196, 193, 129, 88, 37, 18, 15, 8, 932 ], [ "San Francisco, CA", 1981, 0, 0, 1, 2, 1, 2, 1, 3, 0, 0, 0, 0, 0, 10 ], [ "San Francisco, CA", 1982, 0, 0, 1, 4, 4, 5, 2, 1, 0, 0, 0, 0, 0, 17 ], [ "San Francisco, CA", 1983, 0, 0, 2, 26, 47, 39, 23, 7, 7, 3, 1, 2, 0, 157 ], [ "San Francisco, CA", 1984, 0, 2, 13, 59, 125, 112, 77, 36, 24, 12, 7, 1, 0, 468 ], [ "San Francisco, CA", 1985, 0, 2, 17, 80, 190, 197, 104, 61, 38, 27, 7, 11, 0, 734 ], [ "San Francisco, CA", 1986, 0, 0, 11, 91, 184, 230, 146, 78, 53, 28, 9, 2, 0, 832 ], [ "San Francisco, CA", 1987, 0, 0, 31, 157, 359, 402, 272, 158, 89, 44, 19, 14, 0, 1545 ], [ "San Francisco, CA", 1988, 0, 1, 42, 180, 369, 427, 307, 181, 84, 44, 22, 30, 0, 1687 ], [ "San Francisco, CA", 1989, 0, 2, 29, 186, 377, 422, 331, 212, 92, 57, 30, 17, 0, 1755 ], [ "San Francisco, CA", 1990, 0, 1, 43, 191, 427, 514, 432, 229, 88, 78, 41, 32, 0, 2076 ], [ "San Francisco, CA", 1991, 0, 4, 22, 197, 409, 471, 391, 196, 122, 62, 41, 28, 0, 1943 ], [ "San Francisco, CA", 1992, 0, 0, 35, 171, 439, 496, 425, 246, 128, 66, 37, 29, 0, 2072 ], [ "San Francisco, CA", 1993, 0, 3, 107, 499, 962, 1032, 901, 526, 271, 115, 70, 42, 0, 4528 ], [ "San Francisco, CA", 1994, 0, 6, 46, 287, 553, 562, 485, 309, 145, 82, 35, 31, 0, 2541 ], [ "San Francisco, CA", 1995, 0, 2, 47, 198, 459, 488, 413, 258, 113, 55, 24, 16, 0, 2073 ], [ "San Francisco, CA", 1996, 0, 1, 21, 139, 324, 376, 282, 186, 104, 47, 19, 22, 0, 1521 ], [ "San Francisco, CA", 1997, 0, 2, 31, 136, 265, 313, 221, 151, 80, 38, 16, 17, 0, 1270 ], [ "San Francisco, CA", 1998, 0, 3, 12, 81, 205, 185, 207, 141, 65, 39, 13, 4, 0, 955 ], [ "San Francisco, CA", 1999, 0, 0, 17, 70, 174, 210, 160, 112, 59, 28, 11, 3, 0, 844 ], [ "San Francisco, CA", 2000, 0, 2, 14, 54, 137, 184, 131, 113, 59, 33, 12, 13, 0, 752 ], [ "San Francisco, CA", 2001, 0, 3, 11, 38, 108, 135, 111, 94, 51, 24, 9, 11, 0, 595 ], [ "San Francisco, CA", 2002, 0, 1, 12, 39, 96, 118, 101, 84, 57, 31, 10, 10, 0, 559 ], [ "South, pediatric or not in MSA", 1981, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1 ], [ "South, pediatric or not in MSA", 1982, 0, 0, 0, 2, 5, 2, 0, 0, 1, 0, 0, 0, 1, 11 ], [ "South, pediatric or not in MSA", 1983, 1, 2, 2, 9, 17, 5, 4, 6, 2, 2, 0, 1, 8, 59 ], [ "South, pediatric or not in MSA", 1984, 3, 0, 9, 19, 23, 23, 18, 10, 9, 2, 3, 1, 3, 123 ], [ "South, pediatric or not in MSA", 1985, 23, 4, 25, 56, 78, 54, 32, 20, 10, 7, 6, 8, 17, 340 ], [ "South, pediatric or not in MSA", 1986, 31, 5, 48, 115, 126, 99, 36, 25, 17, 10, 18, 11, 27, 568 ], [ "South, pediatric or not in MSA", 1987, 47, 9, 81, 174, 280, 211, 120, 68, 43, 27, 38, 47, 45, 1190 ], [ "South, pediatric or not in MSA", 1988, 101, 12, 142, 344, 392, 338, 184, 97, 61, 40, 37, 56, 71, 1875 ], [ "South, pediatric or not in MSA", 1989, 123, 25, 142, 474, 551, 416, 249, 141, 77, 56, 46, 43, 88, 2431 ], [ "South, pediatric or not in MSA", 1990, 150, 33, 205, 613, 697, 569, 384, 196, 96, 87, 59, 85, 130, 3304 ], [ "South, pediatric or not in MSA", 1991, 158, 25, 159, 594, 763, 596, 424, 224, 144, 80, 53, 71, 121, 3412 ], [ "South, pediatric or not in MSA", 1992, 171, 34, 190, 670, 890, 796, 502, 269, 161, 81, 61, 72, 153, 4050 ], [ "South, pediatric or not in MSA", 1993, 192, 89, 456, 1430, 1906, 1578, 1052, 552, 295, 177, 112, 103, 155, 8097 ], [ "South, pediatric or not in MSA", 1994, 245, 58, 344, 1041, 1495, 1345, 884, 479, 257, 149, 88, 104, 143, 6632 ], [ "South, pediatric or not in MSA", 1995, 182, 50, 362, 981, 1457, 1325, 933, 482, 318, 157, 95, 96, 119, 6557 ], [ "South, pediatric or not in MSA", 1996, 188, 54, 293, 916, 1484, 1338, 1001, 532, 270, 180, 86, 99, 80, 6521 ], [ "South, pediatric or not in MSA", 1997, 114, 53, 238, 736, 1218, 1254, 875, 503, 257, 160, 73, 82, 56, 5619 ], [ "South, pediatric or not in MSA", 1998, 88, 39, 185, 574, 1020, 1014, 810, 492, 265, 144, 92, 96, 45, 4864 ], [ "South, pediatric or not in MSA", 1999, 64, 42, 233, 583, 995, 1139, 905, 560, 291, 141, 101, 104, 34, 5192 ], [ "South, pediatric or not in MSA", 2000, 47, 40, 196, 499, 866, 1050, 786, 456, 305, 160, 84, 78, 31, 4598 ], [ "South, pediatric or not in MSA", 2001, 44, 40, 210, 453, 889, 1084, 887, 618, 327, 160, 112, 98, 23, 4945 ], [ "South, pediatric or not in MSA", 2002, 53, 54, 207, 447, 792, 1049, 958, 648, 382, 189, 107, 97, 23, 5006 ], [ "Washington, DC", 1982, 0, 0, 1, 0, 0, 2, 2, 0, 0, 0, 0, 0, 0, 5 ], [ "Washington, DC", 1983, 0, 0, 1, 9, 11, 7, 6, 3, 0, 0, 0, 1, 0, 38 ], [ "Washington, DC", 1984, 0, 0, 6, 24, 37, 35, 15, 14, 8, 1, 2, 1, 0, 143 ], [ "Washington, DC", 1985, 0, 3, 18, 47, 69, 76, 42, 25, 13, 6, 6, 3, 0, 308 ], [ "Washington, DC", 1986, 0, 0, 23, 54, 73, 88, 54, 37, 17, 12, 10, 7, 0, 375 ], [ "Washington, DC", 1987, 0, 3, 41, 136, 188, 178, 95, 50, 29, 23, 8, 12, 0, 763 ], [ "Washington, DC", 1988, 0, 0, 36, 132, 216, 174, 117, 65, 33, 18, 5, 16, 0, 812 ], [ "Washington, DC", 1989, 0, 1, 46, 157, 201, 179, 121, 61, 42, 25, 13, 10, 0, 856 ], [ "Washington, DC", 1990, 0, 4, 60, 213, 303, 285, 204, 102, 53, 32, 12, 17, 0, 1285 ], [ "Washington, DC", 1991, 0, 3, 51, 206, 309, 298, 199, 126, 52, 33, 18, 14, 0, 1309 ], [ "Washington, DC", 1992, 0, 3, 47, 200, 293, 310, 246, 120, 65, 42, 17, 17, 0, 1360 ], [ "Washington, DC", 1993, 0, 12, 85, 361, 652, 587, 485, 290, 140, 75, 31, 19, 0, 2737 ], [ "Washington, DC", 1994, 0, 6, 83, 278, 503, 556, 440, 234, 109, 55, 31, 26, 0, 2321 ], [ "Washington, DC", 1995, 0, 16, 69, 260, 439, 483, 376, 221, 105, 55, 43, 20, 0, 2087 ], [ "Washington, DC", 1996, 0, 20, 73, 245, 456, 438, 431, 253, 97, 61, 20, 18, 0, 2112 ], [ "Washington, DC", 1997, 0, 10, 49, 182, 378, 369, 351, 202, 113, 46, 35, 22, 0, 1757 ], [ "Washington, DC", 1998, 0, 14, 69, 181, 299, 365, 276, 170, 105, 52, 18, 23, 0, 1572 ], [ "Washington, DC", 1999, 0, 7, 36, 142, 315, 330, 277, 199, 116, 51, 22, 24, 0, 1519 ], [ "Washington, DC", 2000, 0, 11, 47, 166, 308, 323, 292, 209, 91, 35, 26, 26, 0, 1534 ], [ "Washington, DC", 2001, 0, 6, 61, 150, 295, 372, 300, 209, 134, 63, 31, 20, 0, 1641 ], [ "Washington, DC", 2002, 0, 14, 67, 173, 293, 410, 339, 280, 133, 59, 36, 21, 0, 1825 ] ]; data.addColumn('string','Location'); data.addColumn('number','Year'); data.addColumn('number','Cases | Age 1-12'); data.addColumn('number','Cases | Age 13-19'); data.addColumn('number','Cases | Age 20-24'); data.addColumn('number','Cases | Age 25-29'); data.addColumn('number','Cases | Age 30-34'); data.addColumn('number','Cases | Age 35-39 or missing'); data.addColumn('number','Cases | Age 40-44'); data.addColumn('number','Cases | Age 45-49'); data.addColumn('number','Cases | Age 50-54'); data.addColumn('number','Cases | Age 55-59'); data.addColumn('number','Cases | Age 60-64'); data.addColumn('number','Cases | Age 65+'); data.addColumn('number','Cases | Age <1'); data.addColumn('number','Cases | All Ages'); data.addRows(datajson); return(data); } // jsDrawChart function drawChartMotionChartID571a4057f8cb() { var data = gvisDataMotionChartID571a4057f8cb(); var options = {}; options["width"] = 700; options["height"] = 500; options["state"] = "\n {\"orderedByY\":false,\"yLambda\":1,\"yZoomedDataMax\":456,\"time\":\"2002\",\"showTrails\":true,\"xZoomedDataMin\":0,\"iconKeySettings\":[{\"trailStart\":\"1981\",\"key\":{\"dim0\":\"Los Angeles, CA\"}},{\"trailStart\":\"1981\",\"key\":{\"dim0\":\"New York, NY\"}}],\"xAxisOption\":\"6\",\"playDuration\":15000,\"sizeOption\":\"15\",\"xZoomedIn\":false,\"xLambda\":1,\"colorOption\":\"_UNIQUE_COLOR\",\"yZoomedIn\":false,\"nonSelectedAlpha\":0.4,\"orderedByX\":false,\"iconType\":\"BUBBLE\",\"duration\":{\"timeUnit\":\"Y\",\"multiplier\":1},\"uniColorForNonSelected\":false,\"yZoomedDataMin\":0,\"xZoomedDataMax\":2588,\"dimensions\":{\"iconDimensions\":[\"dim0\"]},\"yAxisOption\":\"4\"}\n"; var chart = new google.visualization.MotionChart( document.getElementById('MotionChartID571a4057f8cb') ); chart.draw(data,options); } // jsDisplayChart (function() { var pkgs = window.__gvisPackages = window.__gvisPackages || []; var callbacks = window.__gvisCallbacks = window.__gvisCallbacks || []; var chartid = "motionchart"; // Manually see if chartid is in pkgs (not all browsers support Array.indexOf) var i, newPackage = true; for (i = 0; newPackage && i < pkgs.length; i++) { if (pkgs[i] === chartid) newPackage = false; } if (newPackage) pkgs.push(chartid); // Add the drawChart function to the global list of callbacks callbacks.push(drawChartMotionChartID571a4057f8cb); })(); function displayChartMotionChartID571a4057f8cb() { var pkgs = window.__gvisPackages = window.__gvisPackages || []; var callbacks = window.__gvisCallbacks = window.__gvisCallbacks || []; window.clearTimeout(window.__gvisLoad); // The timeout is set to 100 because otherwise the container div we are // targeting might not be part of the document yet window.__gvisLoad = setTimeout(function() { var pkgCount = pkgs.length; google.load("visualization", "1", { packages:pkgs, callback: function() { if (pkgCount != pkgs.length) { // Race condition where another setTimeout call snuck in after us; if // that call added a package, we must not shift its callback return; } while (callbacks.length > 0) callbacks.shift()(); } }); }, 100); } // jsFooter

My presets compare New York and Los Angeles over time. The axes compare cases between the 20-24 and 30-34 year old cohorts. Additionally, the bubble size indicates cases for all ages. There are many interpretations to make, but here is one: We can visually verify the assumption that reporting rates between the 20-24 and 30-34 cohorts remain the same over time. If they do, all the bubbles should lie on the same line. It appears, however, that in 2001-2002 in New York, 20-24 year olds were more likely than 30-34 year olds to report an AIDS infection. Play around with the graph to see what you can find out.

Credits & Copyright

Data: All data were provided by the Polaris Project and the CDC. Visualization: googleVis-0.5.5 • R version 3.1.1 (2014-07-10) • Google Terms of Use • Documentation and Data Policy Copyright: All rights reserved.

[gchung05]

Visualizing Geo-Temporal Patterns in Clinical Trials, by Health Condition

As an organization engaging in clinical research, knowing where to run your trials is critical to success. As a person interested in a health condition, you may like to know the current volume of research in your home county, as it compares to other counties across the U.S.

Fortunately, the ClinicalTrials.gov database is a rich source of data spanning back more than 10 years. Plotting this data geographically and temporally may expose some useful insights.

As we explore the following hypertension example, keep in mind that you’ll be able to choose other health conditions via the application link at the end of this post.

Bubble Chart of U.S. Hypertension Clinical Trials

By County Population, People/Trial, and Volatility

// jsData function gvisDataBubbleChartID258f6e7fd3f7 () { var data = new google.visualization.DataTable(); var datajson = [ [ "Allegheny PA", 1231527, 51.58, "<100k people/trial", 4.47 ], [ "Baltimore City MD", 823015, 48.17, "<100k people/trial", 3.38 ], [ "Broward FL", 1838844, 27, "<100k people/trial", 10.88 ], [ "Cook IL", 5240700, 65.42, "<100k people/trial", 9.75 ], [ "Cuyahoga OH", 1263154, 49.17, "<100k people/trial", 3.34 ], [ "Fulton GA", 984293, 42, "<100k people/trial", 3.36 ], [ "Hamilton OH", 804520, 45.67, "<100k people/trial", 3.73 ], [ "Harris TX", 4336853, 55.83, "<100k people/trial", 11.72 ], [ "Kings NY", 2592149, 86.92, "<100k people/trial", 5.41 ], [ "Miami-dade FL", 2617176, 47.75, "<100k people/trial", 18.47 ], [ "Philadelphia PA", 1553165, 53.67, "<100k people/trial", 7.75 ], [ "San Diego CA", 3211252, 39.92, "<100k people/trial", 8.22 ], [ "St Louis City MO", 318416, 41.83, "<100k people/trial", 4.67 ], [ "Suffolk MA", 755503, 88.92, "<100k people/trial", 8.32 ], [ "Los Angeles CA", 10017068, 88, "100k-200k people/trial", 21.35 ], [ "Maricopa AZ", 4009412, 27.92, "100k-200k people/trial", 10.1 ], [ "Orange CA", 3114363, 28, "100k-200k people/trial", 10.34 ] ]; data.addColumn('string','CountyState'); data.addColumn('number','Population'); data.addColumn('number','Trials'); data.addColumn('string','PPT'); data.addColumn('number','Volatility'); data.addRows(datajson); return(data); } // jsDrawChart function drawChartBubbleChartID258f6e7fd3f7() { var data = gvisDataBubbleChartID258f6e7fd3f7(); var options = {}; options["vAxis"] = {minValue:0, title:'Average Number of Trials in 2013'}; options["hAxis"] = {title: 'County Population in 2013'}; options["bubble"] = {textStyle: {fontSize: 11}}; options["colors"] = ['#FFCC00','#ff9900','#ff6600','#FF3300']; options["height"] = 500; var chart = new google.visualization.BubbleChart( document.getElementById('BubbleChartID258f6e7fd3f7') ); chart.draw(data,options); } // jsDisplayChart (function() { var pkgs = window.__gvisPackages = window.__gvisPackages || []; var callbacks = window.__gvisCallbacks = window.__gvisCallbacks || []; var chartid = "corechart"; // Manually see if chartid is in pkgs (not all browsers support Array.indexOf) var i, newPackage = true; for (i = 0; newPackage && i < pkgs.length; i++) { if (pkgs[i] === chartid) newPackage = false; } if (newPackage) pkgs.push(chartid); // Add the drawChart function to the global list of callbacks callbacks.push(drawChartBubbleChartID258f6e7fd3f7); })(); function displayChartBubbleChartID258f6e7fd3f7() { var pkgs = window.__gvisPackages = window.__gvisPackages || []; var callbacks = window.__gvisCallbacks = window.__gvisCallbacks || []; window.clearTimeout(window.__gvisLoad); // The timeout is set to 100 because otherwise the container div we are // targeting might not be part of the document yet window.__gvisLoad = setTimeout(function() { var pkgCount = pkgs.length; google.load("visualization", "1", { packages:pkgs, callback: function() { if (pkgCount != pkgs.length) { // Race condition where another setTimeout call snuck in after us; if // that call added a package, we must not shift its callback return; } while (callbacks.length > 0) callbacks.shift()(); } }); }, 100); } // jsFooter

Data: Bubbles • Chart ID: BubbleChartID258f6e7fd3f7

Definitions of each statistic are as follows:

Number of Trials - # of active clinical trials in 2013, averaged by month

County Population - Predicted county population in 2013, based on U.S. Census data

People/Trial (PPT) - County population divided by # of trials (potential indicators of trial saturation and/or disease prevalence)

Volatility - Standard deviation of active trials from 2009-2013

// jsData function gvisDataTableID258fa3535ce () { var data = new google.visualization.DataTable(); var datajson = [ [ "Allegheny County, Pennsylvania", 1231527, 51.58, 23876, 4.47, "Pittsburgh" ], [ "Baltimore City County, Maryland", 823015, 48.17, 17086, 3.38, "Baltimore" ], [ "Broward County, Florida", 1838844, 27, 68105, 10.88, "Fort Lauderdale" ], [ "Cook County, Illinois", 5240700, 65.42, 80109, 9.75, "Chicago" ], [ "Cuyahoga County, Ohio", 1263154, 49.17, 25690, 3.34, "Cleveland" ], [ "Fulton County, Georgia", 984293, 42, 23436, 3.36, "Atlanta" ], [ "Hamilton County, Ohio", 804520, 45.67, 17616, 3.73, "Cincinnati" ], [ "Harris County, Texas", 4336853, 55.83, 77680, 11.72, "Houston" ], [ "Kings County, New York", 2592149, 86.92, 29822, 5.41, "New York" ], [ "Miami-dade County, Florida", 2617176, 47.75, 54810, 18.47, "Miami" ], [ "Philadelphia County, Pennsylvania", 1553165, 53.67, 28939, 7.75, "Philadelphia" ], [ "San Diego County, California", 3211252, 39.92, 80442, 8.22, "San Diego" ], [ "St Louis City County, Missouri", 318416, 41.83, 7612, 4.67, "St. Louis" ], [ "Suffolk County, Massachusetts", 755503, 88.92, 8496, 8.32, "Boston" ], [ "Los Angeles County, California", 10017068, 88, 113830, 21.35, "Los Angeles" ], [ "Maricopa County, Arizona", 4009412, 27.92, 143604, 10.1, "Phoenix" ], [ "Orange County, California", 3114363, 28, 111227, 10.34, "Santa Ana" ] ]; data.addColumn('string','County, State'); data.addColumn('number','2013 Population'); data.addColumn('number','2013 Average Trials/Month'); data.addColumn('number','People Per Trial'); data.addColumn('number','Volatility'); data.addColumn('string','Largest City in County'); data.addRows(datajson); return(data); } // jsDrawChart function drawChartTableID258fa3535ce() { var data = gvisDataTableID258fa3535ce(); var options = {}; options["allowHtml"] = true; var dataFormat1 = new google.visualization.NumberFormat({pattern:"#,###"}); dataFormat1.format(data, 1); var dataFormat2 = new google.visualization.NumberFormat({pattern:"#,###"}); dataFormat2.format(data, 3); var chart = new google.visualization.Table( document.getElementById('TableID258fa3535ce') ); chart.draw(data,options); } // jsDisplayChart (function() { var pkgs = window.__gvisPackages = window.__gvisPackages || []; var callbacks = window.__gvisCallbacks = window.__gvisCallbacks || []; var chartid = "table"; // Manually see if chartid is in pkgs (not all browsers support Array.indexOf) var i, newPackage = true; for (i = 0; newPackage && i < pkgs.length; i++) { if (pkgs[i] === chartid) newPackage = false; } if (newPackage) pkgs.push(chartid); // Add the drawChart function to the global list of callbacks callbacks.push(drawChartTableID258fa3535ce); })(); function displayChartTableID258fa3535ce() { var pkgs = window.__gvisPackages = window.__gvisPackages || []; var callbacks = window.__gvisCallbacks = window.__gvisCallbacks || []; window.clearTimeout(window.__gvisLoad); // The timeout is set to 100 because otherwise the container div we are // targeting might not be part of the document yet window.__gvisLoad = setTimeout(function() { var pkgCount = pkgs.length; google.load("visualization", "1", { packages:pkgs, callback: function() { if (pkgCount != pkgs.length) { // Race condition where another setTimeout call snuck in after us; if // that call added a package, we must not shift its callback return; } while (callbacks.length > 0) callbacks.shift()(); } }); }, 100); } // jsFooter

Data: theTable • Chart ID: TableID258fa3535ce

Counties chosen for the bubble plot are based on these criteria:

Top 5 in population

Top 5 in # of trials

Top 5 in volatility

Top 10 most atypical counties based on PPT

Top 25 U.S. Counties for Hypertension Trials

// jsData function gvisDataGeoChartID258f2b5f6c9e () { var data = new google.visualization.DataTable(); var datajson = [ [ "Allegheny County, Pennsylvania", "Allegheny County Pop: 1231527, Largest City: Pittsburgh", 51.58 ], [ "Arapahoe County, Colorado", "Arapahoe County Pop: 607070, Largest City: Aurora", 30.75 ], [ "Baltimore City County, Maryland", "Baltimore City County Pop: 823015, Largest City: Baltimore", 48.17 ], [ "Broward County, Florida", "Broward County Pop: 1838844, Largest City: Fort Lauderdale", 27 ], [ "Cook County, Illinois", "Cook County Pop: 5240700, Largest City: Chicago", 65.42 ], [ "Cuyahoga County, Ohio", "Cuyahoga County Pop: 1263154, Largest City: Cleveland", 49.17 ], [ "Dallas County, Texas", "Dallas County Pop: 2480331, Largest City: Dallas", 47.42 ], [ "Durham County, North Carolina", "Durham County Pop: 288133, Largest City: Durham", 31.25 ], [ "Franklin County, Ohio", "Franklin County Pop: 1212263, Largest City: Columbus", 39.75 ], [ "Fulton County, Georgia", "Fulton County Pop: 984293, Largest City: Atlanta", 42 ], [ "Hamilton County, Ohio", "Hamilton County Pop: 804520, Largest City: Cincinnati", 45.67 ], [ "Harris County, Texas", "Harris County Pop: 4336853, Largest City: Houston", 55.83 ], [ "Jefferson County, Alabama", "Jefferson County Pop: 659479, Largest City: Birmingham", 35.58 ], [ "Jefferson County, Kentucky", "Jefferson County Pop: 756832, Largest City: Louisville", 35.17 ], [ "Kings County, New York", "Kings County Pop: 2592149, Largest City: New York", 86.92 ], [ "Los Angeles County, California", "Los Angeles County Pop: 10017068, Largest City: Los Angeles", 88 ], [ "Maricopa County, Arizona", "Maricopa County Pop: 4009412, Largest City: Phoenix", 27.92 ], [ "Miami-dade County, Florida", "Miami-dade County Pop: 2617176, Largest City: Miami", 47.75 ], [ "Milwaukee County, Wisconsin", "Milwaukee County Pop: 956023, Largest City: Milwaukee", 31.83 ], [ "Multnomah County, Oregon", "Multnomah County Pop: 766135, Largest City: Portland", 30.08 ], [ "Orange County, California", "Orange County Pop: 3114363, Largest City: Santa Ana", 28 ], [ "Philadelphia County, Pennsylvania", "Philadelphia County Pop: 1553165, Largest City: Philadelphia", 53.67 ], [ "San Diego County, California", "San Diego County Pop: 3211252, Largest City: San Diego", 39.92 ], [ "St Louis City County, Missouri", "St Louis City County Pop: 318416, Largest City: St. Louis", 41.83 ], [ "Suffolk County, Massachusetts", "Suffolk County Pop: 755503, Largest City: Boston", 88.92 ] ]; data.addColumn('string','County..State'); data.addColumn('string','hover'); data.addColumn('number','Trials'); data.addRows(datajson); return(data); } // jsDrawChart function drawChartGeoChartID258f2b5f6c9e() { var data = gvisDataGeoChartID258f2b5f6c9e(); var options = {}; options["width"] = 600; options["height"] = 400; options["region"] = "US"; options["displayMode"] = "markers"; options["resolution"] = "provinces"; var chart = new google.visualization.GeoChart( document.getElementById('GeoChartID258f2b5f6c9e') ); chart.draw(data,options); } // jsDisplayChart (function() { var pkgs = window.__gvisPackages = window.__gvisPackages || []; var callbacks = window.__gvisCallbacks = window.__gvisCallbacks || []; var chartid = "geochart"; // Manually see if chartid is in pkgs (not all browsers support Array.indexOf) var i, newPackage = true; for (i = 0; newPackage && i < pkgs.length; i++) { if (pkgs[i] === chartid) newPackage = false; } if (newPackage) pkgs.push(chartid); // Add the drawChart function to the global list of callbacks callbacks.push(drawChartGeoChartID258f2b5f6c9e); })(); function displayChartGeoChartID258f2b5f6c9e() { var pkgs = window.__gvisPackages = window.__gvisPackages || []; var callbacks = window.__gvisCallbacks = window.__gvisCallbacks || []; window.clearTimeout(window.__gvisLoad); // The timeout is set to 100 because otherwise the container div we are // targeting might not be part of the document yet window.__gvisLoad = setTimeout(function() { var pkgCount = pkgs.length; google.load("visualization", "1", { packages:pkgs, callback: function() { if (pkgCount != pkgs.length) { // Race condition where another setTimeout call snuck in after us; if // that call added a package, we must not shift its callback return; } while (callbacks.length > 0) callbacks.shift()(); } }); }, 100); } // jsFooter

Data: top25 • Chart ID: GeoChartID258f2b5f6c9e

The following tables will show that there are clearly other factors besides population that influence where trials are run.

5 Most Trialed Counties in 2013 Based On Population

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Data: theTable • Chart ID: TableID258f5d40f51

At only 7,612 people per trial, the St. Louis area takes the top spot. Is this because of disease prevalence, availability of research centers, or something else?

5 Least Trialed Counties in 2013 Based On Population

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Data: theTable • Chart ID: TableID258f253991d4

This table may be most useful for shortlisting locations for future clinical trial sites. Why do these counties have atypically low numbers of hypertension trials?

5 Most Volatile Counties For Hypertension Clinical Trials

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Date(1993,3,1), null, null, 1, null, null ], [ new Date(1993,4,1), null, null, 1, null, null ], [ new Date(1993,5,1), null, null, 1, null, null ], [ new Date(1993,6,1), null, null, 1, null, null ], [ new Date(1993,7,1), null, null, 1, null, null ], [ new Date(1993,8,1), null, null, 1, null, null ], [ new Date(1993,9,1), null, null, 1, 1, null ], [ new Date(1993,10,1), null, null, 1, 1, null ], [ new Date(1993,11,1), null, null, 1, 1, null ], [ new Date(1994,0,1), null, null, 1, 1, null ], [ new Date(1994,1,1), null, null, 1, 1, null ], [ new Date(1997,2,1), null, null, null, 1, null ], [ new Date(1997,3,1), null, null, null, 1, null ], [ new Date(1997,4,1), null, null, null, 1, null ], [ new Date(1997,5,1), null, null, null, 1, null ], [ new Date(1994,2,1), null, null, null, 1, null ], [ new Date(1994,3,1), null, null, null, 1, null ], [ new Date(1994,4,1), null, null, null, 1, null ], [ new Date(1994,5,1), null, null, null, 1, null ], [ new Date(1994,6,1), null, null, null, 1, null ], [ new Date(1994,7,1), null, null, null, 1, null ], [ new Date(1994,8,1), null, null, null, 1, null ], [ new Date(1994,9,1), null, null, null, 1, null ], [ new Date(1994,10,1), null, null, null, 1, null ], [ new Date(1994,11,1), null, null, null, 1, null ], [ new Date(1996,11,1), null, null, null, 1, null ], [ new Date(1997,0,1), null, null, null, 1, null ], [ new Date(1997,1,1), null, null, null, 1, null ] ]; data.addColumn('date','yearMon'); data.addColumn('number','Broward County, Florida'); data.addColumn('number','Harris County, Texas'); data.addColumn('number','Los Angeles County, California'); data.addColumn('number','Miami-dade County, Florida'); data.addColumn('number','Orange County, California'); data.addRows(datajson); return(data); } // jsDrawChart function drawChartAnnotationChartID258f66950863() { var data = gvisDataAnnotationChartID258f66950863(); var options = {}; options["width"] = 600; options["height"] = 700; options["vAxis"] = {minValue:0, title:'Volatility (Standard Deviation of Trials 2009-2013)'}; options["title"] = "Top 5 Most Volatile Counties"; options["dateFormat"] = "MMMM yyyy"; options["displayZoomButtons"] = false; options["fill"] = 10; options["displayExactValues"] = true; var chart = new google.visualization.AnnotationChart( document.getElementById('AnnotationChartID258f66950863') ); chart.draw(data,options); } // jsDisplayChart (function() { var pkgs = window.__gvisPackages = window.__gvisPackages || []; var callbacks = window.__gvisCallbacks = window.__gvisCallbacks || []; var chartid = "annotationchart"; // Manually see if chartid is in pkgs (not all browsers support Array.indexOf) var i, newPackage = true; for (i = 0; newPackage && i < pkgs.length; i++) { if (pkgs[i] === chartid) newPackage = false; } if (newPackage) pkgs.push(chartid); // Add the drawChart function to the global list of callbacks callbacks.push(drawChartAnnotationChartID258f66950863); })(); function displayChartAnnotationChartID258f66950863() { var pkgs = window.__gvisPackages = window.__gvisPackages || []; var callbacks = window.__gvisCallbacks = window.__gvisCallbacks || []; window.clearTimeout(window.__gvisLoad); // The timeout is set to 100 because otherwise the container div we are // targeting might not be part of the document yet window.__gvisLoad = setTimeout(function() { var pkgCount = pkgs.length; google.load("visualization", "1", { packages:pkgs, callback: function() { if (pkgCount != pkgs.length) { // Race condition where another setTimeout call snuck in after us; if // that call added a package, we must not shift its callback return; } while (callbacks.length > 0) callbacks.shift()(); } }); }, 100); } // jsFooter

Data: top5 • Chart ID: AnnotationChartID258f66950863

Why are these counties particularly variable when it comes to trials run over time?

More Health Conditions

See plots for other conditions on my Shiny App.

Currently there are plots for 5 of the most prevalent health conditions in the U.S. today:

Hypertension

Arteriosclerosis

Prostate Cancer

Asthma

Alzheimer’s

Want plots produced for another health condition? Just let me know. Server load limits prohibit me from allowing the public to generate these plots.

Credits

ClinicalTrials.gov for all trial data Geonames.org for geocoding services Census.gov for U.S. county population data Live charting services: googleVis-0.5.5 • R version 3.1.1 (2014-07-10) • Google Terms of Use • Documentation and Data Policy Shinyapps.io for application hosting And, of course, R and RStudio for giving me the tools