-
Notifications
You must be signed in to change notification settings - Fork 0
/
draft.html
287 lines (206 loc) · 53.2 KB
/
draft.html
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
<!DOCTYPE html>
<!-- saved from url=(0014)about:internet -->
<html>
<head>
<meta http-equiv="Content-Type" content="text/html; charset=utf-8"/>
<meta http-equiv="x-ua-compatible" content="IE=9" >
<title>Shine Mosquito, shine !! (DRAFT)</title>
<style type="text/css">
body, td {
font-family: sans-serif;
background-color: white;
font-size: 12px;
margin: 8px;
}
tt, code, pre {
font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace;
}
h1 {
font-size:2.2em;
}
h2 {
font-size:1.8em;
}
h3 {
font-size:1.4em;
}
h4 {
font-size:1.0em;
}
h5 {
font-size:0.9em;
}
h6 {
font-size:0.8em;
}
a:visited {
color: rgb(50%, 0%, 50%);
}
pre {
margin-top: 0;
max-width: 95%;
border: 1px solid #ccc;
white-space: pre-wrap;
}
pre code {
display: block; padding: 0.5em;
}
code.r, code.cpp {
background-color: #F8F8F8;
}
table, td, th {
border: none;
}
blockquote {
color:#666666;
margin:0;
padding-left: 1em;
border-left: 0.5em #EEE solid;
}
hr {
height: 0px;
border-bottom: none;
border-top-width: thin;
border-top-style: dotted;
border-top-color: #999999;
}
@media print {
* {
background: transparent !important;
color: black !important;
filter:none !important;
-ms-filter: none !important;
}
body {
font-size:12pt;
max-width:100%;
}
a, a:visited {
text-decoration: underline;
}
hr {
visibility: hidden;
page-break-before: always;
}
pre, blockquote {
padding-right: 1em;
page-break-inside: avoid;
}
tr, img {
page-break-inside: avoid;
}
img {
max-width: 100% !important;
}
@page :left {
margin: 15mm 20mm 15mm 10mm;
}
@page :right {
margin: 15mm 10mm 15mm 20mm;
}
p, h2, h3 {
orphans: 3; widows: 3;
}
h2, h3 {
page-break-after: avoid;
}
}
</style>
<!-- Styles for R syntax highlighter -->
<style type="text/css">
pre .operator,
pre .paren {
color: rgb(104, 118, 135)
}
pre .literal {
color: rgb(88, 72, 246)
}
pre .number {
color: rgb(0, 0, 205);
}
pre .comment {
color: rgb(76, 136, 107);
}
pre .keyword {
color: rgb(0, 0, 255);
}
pre .identifier {
color: rgb(0, 0, 0);
}
pre .string {
color: rgb(3, 106, 7);
}
</style>
<!-- R syntax highlighter -->
<script type="text/javascript">
var hljs=new function(){function m(p){return p.replace(/&/gm,"&").replace(/</gm,"<")}function f(r,q,p){return RegExp(q,"m"+(r.cI?"i":"")+(p?"g":""))}function b(r){for(var p=0;p<r.childNodes.length;p++){var q=r.childNodes[p];if(q.nodeName=="CODE"){return q}if(!(q.nodeType==3&&q.nodeValue.match(/\s+/))){break}}}function h(t,s){var p="";for(var r=0;r<t.childNodes.length;r++){if(t.childNodes[r].nodeType==3){var q=t.childNodes[r].nodeValue;if(s){q=q.replace(/\n/g,"")}p+=q}else{if(t.childNodes[r].nodeName=="BR"){p+="\n"}else{p+=h(t.childNodes[r])}}}if(/MSIE [678]/.test(navigator.userAgent)){p=p.replace(/\r/g,"\n")}return p}function a(s){var r=s.className.split(/\s+/);r=r.concat(s.parentNode.className.split(/\s+/));for(var q=0;q<r.length;q++){var p=r[q].replace(/^language-/,"");if(e[p]){return p}}}function c(q){var p=[];(function(s,t){for(var r=0;r<s.childNodes.length;r++){if(s.childNodes[r].nodeType==3){t+=s.childNodes[r].nodeValue.length}else{if(s.childNodes[r].nodeName=="BR"){t+=1}else{if(s.childNodes[r].nodeType==1){p.push({event:"start",offset:t,node:s.childNodes[r]});t=arguments.callee(s.childNodes[r],t);p.push({event:"stop",offset:t,node:s.childNodes[r]})}}}}return t})(q,0);return p}function k(y,w,x){var q=0;var z="";var s=[];function u(){if(y.length&&w.length){if(y[0].offset!=w[0].offset){return(y[0].offset<w[0].offset)?y:w}else{return w[0].event=="start"?y:w}}else{return y.length?y:w}}function t(D){var A="<"+D.nodeName.toLowerCase();for(var B=0;B<D.attributes.length;B++){var C=D.attributes[B];A+=" "+C.nodeName.toLowerCase();if(C.value!==undefined&&C.value!==false&&C.value!==null){A+='="'+m(C.value)+'"'}}return A+">"}while(y.length||w.length){var v=u().splice(0,1)[0];z+=m(x.substr(q,v.offset-q));q=v.offset;if(v.event=="start"){z+=t(v.node);s.push(v.node)}else{if(v.event=="stop"){var p,r=s.length;do{r--;p=s[r];z+=("</"+p.nodeName.toLowerCase()+">")}while(p!=v.node);s.splice(r,1);while(r<s.length){z+=t(s[r]);r++}}}}return z+m(x.substr(q))}function j(){function q(x,y,v){if(x.compiled){return}var u;var s=[];if(x.k){x.lR=f(y,x.l||hljs.IR,true);for(var w in x.k){if(!x.k.hasOwnProperty(w)){continue}if(x.k[w] instanceof Object){u=x.k[w]}else{u=x.k;w="keyword"}for(var r in u){if(!u.hasOwnProperty(r)){continue}x.k[r]=[w,u[r]];s.push(r)}}}if(!v){if(x.bWK){x.b="\\b("+s.join("|")+")\\s"}x.bR=f(y,x.b?x.b:"\\B|\\b");if(!x.e&&!x.eW){x.e="\\B|\\b"}if(x.e){x.eR=f(y,x.e)}}if(x.i){x.iR=f(y,x.i)}if(x.r===undefined){x.r=1}if(!x.c){x.c=[]}x.compiled=true;for(var t=0;t<x.c.length;t++){if(x.c[t]=="self"){x.c[t]=x}q(x.c[t],y,false)}if(x.starts){q(x.starts,y,false)}}for(var p in e){if(!e.hasOwnProperty(p)){continue}q(e[p].dM,e[p],true)}}function d(B,C){if(!j.called){j();j.called=true}function q(r,M){for(var L=0;L<M.c.length;L++){if((M.c[L].bR.exec(r)||[null])[0]==r){return M.c[L]}}}function v(L,r){if(D[L].e&&D[L].eR.test(r)){return 1}if(D[L].eW){var M=v(L-1,r);return M?M+1:0}return 0}function w(r,L){return L.i&&L.iR.test(r)}function K(N,O){var M=[];for(var L=0;L<N.c.length;L++){M.push(N.c[L].b)}var r=D.length-1;do{if(D[r].e){M.push(D[r].e)}r--}while(D[r+1].eW);if(N.i){M.push(N.i)}return f(O,M.join("|"),true)}function p(M,L){var N=D[D.length-1];if(!N.t){N.t=K(N,E)}N.t.lastIndex=L;var r=N.t.exec(M);return r?[M.substr(L,r.index-L),r[0],false]:[M.substr(L),"",true]}function z(N,r){var L=E.cI?r[0].toLowerCase():r[0];var M=N.k[L];if(M&&M instanceof Array){return M}return false}function F(L,P){L=m(L);if(!P.k){return L}var r="";var O=0;P.lR.lastIndex=0;var M=P.lR.exec(L);while(M){r+=L.substr(O,M.index-O);var N=z(P,M);if(N){x+=N[1];r+='<span class="'+N[0]+'">'+M[0]+"</span>"}else{r+=M[0]}O=P.lR.lastIndex;M=P.lR.exec(L)}return r+L.substr(O,L.length-O)}function J(L,M){if(M.sL&&e[M.sL]){var r=d(M.sL,L);x+=r.keyword_count;return r.value}else{return F(L,M)}}function I(M,r){var L=M.cN?'<span class="'+M.cN+'">':"";if(M.rB){y+=L;M.buffer=""}else{if(M.eB){y+=m(r)+L;M.buffer=""}else{y+=L;M.buffer=r}}D.push(M);A+=M.r}function G(N,M,Q){var R=D[D.length-1];if(Q){y+=J(R.buffer+N,R);return false}var P=q(M,R);if(P){y+=J(R.buffer+N,R);I(P,M);return P.rB}var L=v(D.length-1,M);if(L){var O=R.cN?"</span>":"";if(R.rE){y+=J(R.buffer+N,R)+O}else{if(R.eE){y+=J(R.buffer+N,R)+O+m(M)}else{y+=J(R.buffer+N+M,R)+O}}while(L>1){O=D[D.length-2].cN?"</span>":"";y+=O;L--;D.length--}var r=D[D.length-1];D.length--;D[D.length-1].buffer="";if(r.starts){I(r.starts,"")}return R.rE}if(w(M,R)){throw"Illegal"}}var E=e[B];var D=[E.dM];var A=0;var x=0;var y="";try{var s,u=0;E.dM.buffer="";do{s=p(C,u);var t=G(s[0],s[1],s[2]);u+=s[0].length;if(!t){u+=s[1].length}}while(!s[2]);if(D.length>1){throw"Illegal"}return{r:A,keyword_count:x,value:y}}catch(H){if(H=="Illegal"){return{r:0,keyword_count:0,value:m(C)}}else{throw H}}}function g(t){var p={keyword_count:0,r:0,value:m(t)};var r=p;for(var q in e){if(!e.hasOwnProperty(q)){continue}var s=d(q,t);s.language=q;if(s.keyword_count+s.r>r.keyword_count+r.r){r=s}if(s.keyword_count+s.r>p.keyword_count+p.r){r=p;p=s}}if(r.language){p.second_best=r}return p}function i(r,q,p){if(q){r=r.replace(/^((<[^>]+>|\t)+)/gm,function(t,w,v,u){return w.replace(/\t/g,q)})}if(p){r=r.replace(/\n/g,"<br>")}return r}function n(t,w,r){var x=h(t,r);var v=a(t);var y,s;if(v){y=d(v,x)}else{return}var q=c(t);if(q.length){s=document.createElement("pre");s.innerHTML=y.value;y.value=k(q,c(s),x)}y.value=i(y.value,w,r);var u=t.className;if(!u.match("(\\s|^)(language-)?"+v+"(\\s|$)")){u=u?(u+" "+v):v}if(/MSIE [678]/.test(navigator.userAgent)&&t.tagName=="CODE"&&t.parentNode.tagName=="PRE"){s=t.parentNode;var p=document.createElement("div");p.innerHTML="<pre><code>"+y.value+"</code></pre>";t=p.firstChild.firstChild;p.firstChild.cN=s.cN;s.parentNode.replaceChild(p.firstChild,s)}else{t.innerHTML=y.value}t.className=u;t.result={language:v,kw:y.keyword_count,re:y.r};if(y.second_best){t.second_best={language:y.second_best.language,kw:y.second_best.keyword_count,re:y.second_best.r}}}function o(){if(o.called){return}o.called=true;var r=document.getElementsByTagName("pre");for(var p=0;p<r.length;p++){var q=b(r[p]);if(q){n(q,hljs.tabReplace)}}}function l(){if(window.addEventListener){window.addEventListener("DOMContentLoaded",o,false);window.addEventListener("load",o,false)}else{if(window.attachEvent){window.attachEvent("onload",o)}else{window.onload=o}}}var e={};this.LANGUAGES=e;this.highlight=d;this.highlightAuto=g;this.fixMarkup=i;this.highlightBlock=n;this.initHighlighting=o;this.initHighlightingOnLoad=l;this.IR="[a-zA-Z][a-zA-Z0-9_]*";this.UIR="[a-zA-Z_][a-zA-Z0-9_]*";this.NR="\\b\\d+(\\.\\d+)?";this.CNR="\\b(0[xX][a-fA-F0-9]+|(\\d+(\\.\\d*)?|\\.\\d+)([eE][-+]?\\d+)?)";this.BNR="\\b(0b[01]+)";this.RSR="!|!=|!==|%|%=|&|&&|&=|\\*|\\*=|\\+|\\+=|,|\\.|-|-=|/|/=|:|;|<|<<|<<=|<=|=|==|===|>|>=|>>|>>=|>>>|>>>=|\\?|\\[|\\{|\\(|\\^|\\^=|\\||\\|=|\\|\\||~";this.ER="(?![\\s\\S])";this.BE={b:"\\\\.",r:0};this.ASM={cN:"string",b:"'",e:"'",i:"\\n",c:[this.BE],r:0};this.QSM={cN:"string",b:'"',e:'"',i:"\\n",c:[this.BE],r:0};this.CLCM={cN:"comment",b:"//",e:"$"};this.CBLCLM={cN:"comment",b:"/\\*",e:"\\*/"};this.HCM={cN:"comment",b:"#",e:"$"};this.NM={cN:"number",b:this.NR,r:0};this.CNM={cN:"number",b:this.CNR,r:0};this.BNM={cN:"number",b:this.BNR,r:0};this.inherit=function(r,s){var p={};for(var q in r){p[q]=r[q]}if(s){for(var q in s){p[q]=s[q]}}return p}}();hljs.LANGUAGES.cpp=function(){var a={keyword:{"false":1,"int":1,"float":1,"while":1,"private":1,"char":1,"catch":1,"export":1,virtual:1,operator:2,sizeof:2,dynamic_cast:2,typedef:2,const_cast:2,"const":1,struct:1,"for":1,static_cast:2,union:1,namespace:1,unsigned:1,"long":1,"throw":1,"volatile":2,"static":1,"protected":1,bool:1,template:1,mutable:1,"if":1,"public":1,friend:2,"do":1,"return":1,"goto":1,auto:1,"void":2,"enum":1,"else":1,"break":1,"new":1,extern:1,using:1,"true":1,"class":1,asm:1,"case":1,typeid:1,"short":1,reinterpret_cast:2,"default":1,"double":1,register:1,explicit:1,signed:1,typename:1,"try":1,"this":1,"switch":1,"continue":1,wchar_t:1,inline:1,"delete":1,alignof:1,char16_t:1,char32_t:1,constexpr:1,decltype:1,noexcept:1,nullptr:1,static_assert:1,thread_local:1,restrict:1,_Bool:1,complex:1},built_in:{std:1,string:1,cin:1,cout:1,cerr:1,clog:1,stringstream:1,istringstream:1,ostringstream:1,auto_ptr:1,deque:1,list:1,queue:1,stack:1,vector:1,map:1,set:1,bitset:1,multiset:1,multimap:1,unordered_set:1,unordered_map:1,unordered_multiset:1,unordered_multimap:1,array:1,shared_ptr:1}};return{dM:{k:a,i:"</",c:[hljs.CLCM,hljs.CBLCLM,hljs.QSM,{cN:"string",b:"'\\\\?.",e:"'",i:"."},{cN:"number",b:"\\b(\\d+(\\.\\d*)?|\\.\\d+)(u|U|l|L|ul|UL|f|F)"},hljs.CNM,{cN:"preprocessor",b:"#",e:"$"},{cN:"stl_container",b:"\\b(deque|list|queue|stack|vector|map|set|bitset|multiset|multimap|unordered_map|unordered_set|unordered_multiset|unordered_multimap|array)\\s*<",e:">",k:a,r:10,c:["self"]}]}}}();hljs.LANGUAGES.r={dM:{c:[hljs.HCM,{cN:"number",b:"\\b0[xX][0-9a-fA-F]+[Li]?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+(?:[eE][+\\-]?\\d*)?L\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\b\\d+\\.(?!\\d)(?:i\\b)?",e:hljs.IMMEDIATE_RE,r:1},{cN:"number",b:"\\b\\d+(?:\\.\\d*)?(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"number",b:"\\.\\d+(?:[eE][+\\-]?\\d*)?i?\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"keyword",b:"(?:tryCatch|library|setGeneric|setGroupGeneric)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\.",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\.\\.\\d+(?![\\w.])",e:hljs.IMMEDIATE_RE,r:10},{cN:"keyword",b:"\\b(?:function)",e:hljs.IMMEDIATE_RE,r:2},{cN:"keyword",b:"(?:if|in|break|next|repeat|else|for|return|switch|while|try|stop|warning|require|attach|detach|source|setMethod|setClass)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"literal",b:"(?:NA|NA_integer_|NA_real_|NA_character_|NA_complex_)\\b",e:hljs.IMMEDIATE_RE,r:10},{cN:"literal",b:"(?:NULL|TRUE|FALSE|T|F|Inf|NaN)\\b",e:hljs.IMMEDIATE_RE,r:1},{cN:"identifier",b:"[a-zA-Z.][a-zA-Z0-9._]*\\b",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"<\\-(?!\\s*\\d)",e:hljs.IMMEDIATE_RE,r:2},{cN:"operator",b:"\\->|<\\-",e:hljs.IMMEDIATE_RE,r:1},{cN:"operator",b:"%%|~",e:hljs.IMMEDIATE_RE},{cN:"operator",b:">=|<=|==|!=|\\|\\||&&|=|\\+|\\-|\\*|/|\\^|>|<|!|&|\\||\\$|:",e:hljs.IMMEDIATE_RE,r:0},{cN:"operator",b:"%",e:"%",i:"\\n",r:1},{cN:"identifier",b:"`",e:"`",r:0},{cN:"string",b:'"',e:'"',c:[hljs.BE],r:0},{cN:"string",b:"'",e:"'",c:[hljs.BE],r:0},{cN:"paren",b:"[[({\\])}]",e:hljs.IMMEDIATE_RE,r:0}]}};
hljs.initHighlightingOnLoad();
</script>
</head>
<body>
<h1>Shine Mosquito, shine !! (DRAFT)</h1>
<p>Iowa State University Medical Entomology Laboratory and Iowa Department of Public Health and the University Hygienic Lab (Iowa City, IA), monitors mosquito populations and mosquito-borne disease in the State of Iowa. </p>
<p>We will use shiny package to develop a web application to help entomologist to visualize, summarize and analyze the data produced by the surveillance program.</p>
<p>At this point we have written a shiny app based on part of the entire data set, we are using yearly mosquito counts for 8 sites and 20 years of data. However complete data has more sites and years on weakly basis. The shiny app have 4 tabset panels each of them with a different plot, we describe each tabset with an example. </p>
<hr/>
<h2>- 1) Tabset 1: Individual species description</h2>
<p>In the first tabset we present a figure for individual species. In this figure we can select only 1 species at time, and additionally select multiple sites. The information presented in this plot is yearly species proportion facet per site. In the each plot there are a reference line that represents the mean proportion for each species across sites. </p>
<p>Choices on shiny: Species (only one) and Sites. </p>
<pre><code class="r">d <- subset(msq.spyr, (specie == "Aedes.vexans") & (site == "Gvalley"))
ggplot(data = d, aes(x = year, y = prop.spst), color = site) + geom_point(size = 4) +
geom_line() + geom_line(aes(x = year, y = prop.spyr), color = I("red")) +
facet_wrap(facets = ~site, scales = "free")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk tab1"/> </p>
<p>As an example, we show here the proportion of Aedes.Vexan on Green Valley over time, the red line represent the average proportion of this species on each year. So on average across all years and sites, this species represent around 75% of the mosquito counts. However on Green Valley the Aedes.Vexan proportion is most of the time below the mean, What happen in Green Valley ? The Vexans don't like it anymore !!!! </p>
<hr/>
<h2>- 2) Second tabset: Spatial comparison </h2>
<p>Our second tabset has a map, here we can select multiple species and multiple sites, the idea is to find species that share habitats and other species which are likely to be in different places. </p>
<pre><code class="r">d <- subset(msq.spyr, specie %in% c("Aedes.triseriatus", "Culex.pipiens.group"))
d1 <- ddply(d, .(specie, subregion), summarise, prop.diff = mean((prop.spst -
prop.spyr)/prop.spyr), prop.st = mean(prop.spst), prop.yr = mean(prop.spyr),
lgst = prop.st/(1 - prop.st), lgyr = prop.yr/(1 - prop.yr), or = log(lgst/lgyr),
rr = prop.st/prop.yr)
msq.ia <- merge(d1, ia2, by = "subregion")
p <- ggplot() + geom_polygon(data = ia.s, aes(x = long, y = lat, group = region,
order = order)) + theme(axis.text = element_blank(), axis.title = element_blank(),
axis.line = element_blank(), axis.ticks = element_blank(), panel.border = element_blank(),
panel.grid = element_blank())
p + geom_point(data = msq.ia, aes(x = long, y = lat, group = group)) + facet_wrap(~specie)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk tab2"/> </p>
<hr/>
<h2>- 3) Third tabset: Community description</h2>
<p>One important feature for the entomologist is to study the community characteristics, they are several indices reflecting different aspects of the mosquito community composition. In this plot we can choose multiple indices and multiple sites to plot the yearly value of each index on each site. These are community level plots.</p>
<pre><code class="r">d <- subset(msq.ind.aux, (site %in% c("Gvalley", "Union")) & (Index %in% c("Simpson",
"SpeciesRichness")))
ggplot(data = d, aes(x = year, y = Index.val)) + geom_point(size = 4) + geom_line() +
geom_line(aes(x = year, y = Index.val), color = I("red")) + facet_grid(facets = Index ~
site, scales = "free")
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk tab3"/> </p>
<p>We have choose Green Valley and Union stations with Species Richness and Simpson Index. Both measures are related to the diversity in the community. </p>
<hr/>
<h2>- 4) Fourth tabset: Identify extreme communities</h2>
<p>Besides describing each community we may be interesting in comparing them, in particular we want to know if all communities are alike or there are some site-year observations that are different from the rest. </p>
<p>Working with species proportion within one site and year (instead of total counts) we compute an “average community” which simply taking average of each species proportion.
Then for each observations we compute the distance respect to that average community. </p>
<p>We plot a two panel figure, on the right is the estimated density of the distances to the mid point community. The vertical red line represent the 90 quantile. </p>
<p>On the left panel there is a scatter plot of two community variables and we colored the points corresponding to communities further from the center community, this is with distance bigger than its 90% quantile. </p>
<pre><code class="r">q <- quantile(msq$distout, probs = 0.9)
d <- data.frame(lax = msq[, "PrecipationMinus2"], lay = msq[, "DominanceBP"],
rare = as.factor(msq$distout > q))
p1 <- qplot(x = dat.den$x, y = dat.den$y, geom = "line", size = I(1.5)) + geom_vline(xintercept = q,
color = I("red")) + ylab("") + xlab("Distance to Average Community")
p2 <- qplot(data = d, lax, lay, color = rare) + scale_color_manual(values = c("black",
"red")) + xlab(as.character("PrecipationMinus2")) + ylab(as.character("DominanceBP")) +
theme(legend.position = "bottom")
grid.arrange(p1, p2, ncol = 2)
</code></pre>
<p><img src="data:image/png;base64,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" alt="plot of chunk tab4"/> </p>
<p>On the shiny it is possible to choose the quantile that determine which communities are consider extreme and also the variables plot in the right panel. In this example the selected quantile is 90% and the variables are Precipitation (horizontal) and Dominance index (vertical). Basically we can see all extreme communities shows big values in Dominance but are not extreme on precipitation. </p>
<h2>- Next Steps</h2>
<p>We have just got the complete data set, with more than 30 sites over 15 Iowa counties (instead of 8 sites on 4 counties), and data back to 70's (not all years for every site). So probably next week will be the data processing steps (cleaning and summarizing) with no much advance on shiny. </p>
<p>On the application aspects, we may include more tools to measure distances among communities, as an MDS analysis of community distance matrix using {\tt vegan} package.</p>
</body>
</html>