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<!DOCTYPE html>
<!--[if lt IE 7]> <html class="no-js lt-ie9 lt-ie8 lt-ie7"> <![endif]-->
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<meta charset="utf-8">
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<title>Potentially Preventable Deaths in US</title>
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<h2>Introduction</h2>
<p>The most recent <em>Morbidity and Mortality Weekly Report</em>, dated May 2, 2014, from the Centers for Disease Control and Prevention had <a href="http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6317a1.htm?s_cid=mm6317a1_w">a report by Yoon et al. (2014) on potentially preventable deaths from 5 leading causes of death for people under the age of 80.</a> In this post, I use interactive bar charts and choropleths to help visualize state-wise statistics. For these charts, I use googleVis and RStudio's shiny server platform. This post was generated using slidify and the <a href="https://github.com/patilv/preventabledeaths">code necessary to recreate it can be found on github.</a> The <a href="https://github.com/patilv/preventableshiny">code for the accompanying shiny app can also be found on github.</a> </p>
<h2>Data</h2>
<p>The report mentions that in 2010, the top 5 causes of death - diseases of the heart, cancer, chronic lower respiratory disease, cerebrovascular diseases (stroke), and unintentional injuries accounted for approximately 63% of all deaths. For the purposes of their report, they used mortality data from the National Vital Statistics System for 2008-2010. Please <a href="http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6317a1.htm?s_cid=mm6317a1_w">read their report</a> for caveats associated with the data as well as the assumptions underlying the procedures used. Implications are also discussed in the report and the discussion section of the report is really worth a read. </p>
<h3>Retrieve Data</h3>
<p>This section of the R code retrieves data from CDC's report. </p>
<pre><code class="r">library(XML)
URL = "http://www.cdc.gov/mmwr/preview/mmwrhtml/mm6317a1.htm?s_cid=mm6317a1_w"
table = readHTMLTable(URL)
statewise = table[[1]] # first of two tables on that page
</code></pre>
<h3>Data Cleaning and Manipulations</h3>
<p>Let's clean the dataset by doing the following.</p>
<ol>
<li>Changing column names</li>
<li>Removing the top 3 rows and the bottom two rows</li>
</ol>
<p>Let's also check the structure of the data.</p>
<pre><code class="r">colnames(statewise) = c("State", "HeartDiseasesObserved", "HeartDiseasesExpected",
"HeartDiseasesPreventable", "CancerDiseasesObserved", "CancerDiseasesExpected",
"CancerDiseasesPreventable", "ChroniclowerrespiratoryDiseasesObserved",
"ChroniclowerrespiratoryDiseasesExpected", "ChroniclowerrespiratoryDiseasesPreventable",
"CerebrovascularDiseasesObserved", "CerebrovascularDiseasesExpected", "CerebrovascularDiseasesPreventable",
"UnintentionalinjuriesObserved", "UnintentionalinjuriesExpected", "UnintentionalinjuriesPreventable")
statewise = statewise[-(1:3), ]
statewise = statewise[-(52:53), ]
str(statewise)
</code></pre>
<pre><code>## 'data.frame': 51 obs. of 16 variables:
## $ State : Factor w/ 56 levels "Abbreviation: DC = District of Columbia.\r\n\t\t\t\t\t\t\t\t*\tExpected deaths are the lowest three-state average age-specific "| __truncated__,..: 2 3 4 5 6 7 8 11 9 12 ...
## $ HeartDiseasesObserved : Factor w/ 54 levels "1,007","1,080",..: 43 31 29 24 22 20 17 49 46 12 ...
## $ HeartDiseasesExpected : Factor w/ 54 levels "1,063","1,194",..: 22 33 29 8 14 20 15 42 32 12 ...
## $ HeartDiseasesPreventable : Factor w/ 54 levels "0","1,089","1,092",..: 29 10 49 6 39 9 32 26 40 35 ...
## $ CancerDiseasesObserved : Factor w/ 54 levels "1,054","1,304",..: 44 45 41 30 27 32 29 3 46 23 ...
## $ CancerDiseasesExpected : Factor w/ 54 levels "1,006","1,112",..: 33 39 43 22 27 31 24 1 37 20 ...
## $ CancerDiseasesPreventable : Factor w/ 51 levels "0","1,059","1,126",..: 21 14 44 7 30 16 41 35 18 40 ...
## $ ChroniclowerrespiratoryDiseasesObserved : Factor w/ 53 levels "1,016","1,035",..: 15 17 12 3 45 7 42 29 47 41 ...
## $ ChroniclowerrespiratoryDiseasesExpected : Factor w/ 53 levels "1,004","1,148",..: 42 43 1 32 28 38 34 12 44 24 ...
## $ ChroniclowerrespiratoryDiseasesPreventable: Factor w/ 51 levels "0","1,013","1,117",..: 2 30 39 42 4 35 1 47 1 12 ...
## $ CerebrovascularDiseasesObserved : Factor w/ 52 levels "1,003","1,119",..: 5 49 45 41 36 38 31 18 12 28 ...
## $ CerebrovascularDiseasesExpected : Factor w/ 52 levels "1,015","1,108",..: 34 37 46 26 21 32 28 7 36 16 ...
## $ CerebrovascularDiseasesPreventable : Factor w/ 49 levels "1,527","1,783",..: 36 12 39 16 1 44 27 31 21 43 ...
## $ UnintentionalinjuriesObserved : Factor w/ 52 levels "1,010","1,013",..: 21 34 25 6 47 10 49 29 18 44 ...
## $ UnintentionalinjuriesExpected : Factor w/ 52 levels "1,074","1,093",..: 49 17 4 38 42 50 43 19 14 29 ...
## $ UnintentionalinjuriesPreventable : Factor w/ 51 levels "0","1,027","1,054",..: 4 23 5 46 12 39 25 16 38 30 ...
</code></pre>
<p>Let's change columns for numbers from factor variables to numeric variables and view the data using googleVis's table. <strong>Entries can be sorted in this table by clicking on the header for a column.</strong></p>
<pre><code class="r">for (i in 2:16){statewise[, i] = as.character(statewise[,i])}
for (i in 2:16){statewise[, i] = gsub(",","",statewise[,i])}
for (i in 2:16){statewise[, i] = as.numeric(statewise[,i])}
library(googleVis)
plot(gvisTable(statewise,options=list(height=400, width=800)))
</code></pre>
<!-- Table generated in R 3.0.3 by googleVis 0.5.1 package -->
<!-- Sat May 03 14:14:48 2014 -->
<!-- jsHeader -->
<script type="text/javascript">
// jsData
function gvisDataTableID1dc0551279ba () {
var data = new google.visualization.DataTable();
var datajson =
[
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2036,
910,
1126
],
[
"Alaska",
463,
331,
132,
703,
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115,
112,
77,
35,
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29,
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],
[
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[
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[
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[
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[
"Illinois",
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"Indiana",
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[
"Iowa",
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[
"Kansas",
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[
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[
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1253,
257,
2393,
2194,
199,
535,
320,
215,
310,
246,
64,
1013,
386,
627
],
[
"New York",
17371,
11522,
5849,
23787,
20112,
3675,
3358,
2906,
452,
2423,
2246,
177,
3804,
3692,
112
],
[
"North Carolina",
9021,
5679,
3342,
13297,
9931,
3366,
2698,
1436,
1262,
1894,
1108,
786,
3268,
1802,
1466
],
[
"North Dakota",
512,
406,
106,
780,
708,
72,
170,
104,
66,
127,
80,
47,
193,
127,
66
],
[
"Ohio",
11875,
7164,
4711,
17413,
12514,
4899,
3729,
1818,
1911,
2271,
1400,
871,
4016,
2184,
1832
],
[
"Oklahoma",
4857,
2267,
2590,
5787,
3957,
1830,
1736,
581,
1155,
889,
448,
441,
1870,
703,
1167
],
[
"Oregon",
2421,
2364,
57,
5212,
4153,
1059,
1110,
599,
511,
635,
461,
174,
1068,
730,
338
],
[
"Pennsylvania",
12668,
8221,
4447,
19114,
14340,
4774,
3051,
2101,
950,
2194,
1611,
583,
4319,
2435,
1884
],
[
"Rhode Island",
820,
636,
184,
1423,
1112,
311,
225,
160,
65,
148,
123,
25,
339,
200,
139
],
[
"South Carolina",
5413,
2896,
2517,
7063,
5079,
1984,
1391,
740,
651,
1119,
567,
552,
1910,
883,
1027
],
[
"South Dakota",
590,
491,
99,
1054,
856,
198,
226,
126,
100,
126,
97,
29,
284,
151,
133
],
[
"Tennessee",
7956,
3916,
4040,
10185,
6853,
3332,
2197,
995,
1202,
1463,
765,
698,
2895,
1209,
1686
],
[
"Texas",
19939,
12683,
7256,
27141,
22143,
4998,
5061,
3139,
1922,
4254,
2471,
1783,
7612,
4551,
3061
],
[
"Utah",
1229,
1194,
35,
1931,
2080,
0,
383,
298,
85,
282,
238,
44,
765,
470,
295
],
[
"Vermont",
482,
411,
71,
921,
723,
198,
167,
103,
64,
91,
79,
12,
181,
122,
59
],
[
"Virginia",
6588,
4609,
1979,
10162,
8073,
2089,
1647,
1148,
499,
1369,
891,
478,
1889,
1521,
368
],
[
"Washington",
4437,
3844,
593,
8193,
6754,
1439,
1451,
956,
495,
907,
743,
164,
1925,
1269,
656
],
[
"West Virginia",
2400,
1308,
1092,
3415,
2289,
1126,