RCura

# This is the server logic for a Shiny web application.
# You can find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com
#

library(shiny)

shinyServer(function(input, output) {

RCura

#distance 846 tours

distTow<-dist(TowSpdf@coords,method="euclidean")

matdistTow<-as.matrix(distTow)
matdistTow[matdistTow==0]<-NA
min(matdistTow,na.rm=TRUE)/1000
max(matdistTow,na.rm=TRUE)/1000
mean(matdistTow,na.rm=TRUE)/1000

RCura

model closest_to
global {
	int nb_agents_min <- 50;
	int nb_agents_max <- 5000;
	bool agent1_use_geom <- false;
	bool agent2_use_geom <- false;
	geometry agent1_geom <- circle(3);
	geometry agent2_geom <- circle(5);
	
	

RCura

/**
 *  closest_to
 *  Author: Robin Cura
 *  Description: 
 */

model closest_to

global {

RCura

model si
// A simple infection spreading model

global {
    int number_people <- 300;  // The init number of people
    int number_I <- 1 ;	// The init number of infected
    float infection_rate <- 0.1 ; // The infection rate
	float infection_distance <- 5.0 ; // infection distance (in meters)
	int immune_step <- 30 ; // number of steps before becoming immune after infection
	int end_immunity_step <-50; // number of steps before not being immune anymore

RCura

diff --git a/msi.gama.application/gama1.7.Eclipse3_8_2.product b/msi.gama.application/gama1.7.Eclipse3_8_2.product
index b97e304..7649748 100644
--- a/msi.gama.application/gama1.7.Eclipse3_8_2.product
+++ b/msi.gama.application/gama1.7.Eclipse3_8_2.product
@@ -268,6 +268,10 @@ openFile
       <feature id="org.eclipse.ecf.core.ssl.feature"/>
       <feature id="org.eclipse.ecf.filetransfer.ssl.feature"/>
       <feature id="org.eclipse.e4.rcp"/>
+      <feature id="ummisco.gama.feature.models"/>
+      <feature id="ummisco.gama.feature.dependencies"/>

RCura

voteNames <- paste("Vote", 1:9, sep="")
outputHRR <- rollcall(data = depVotes, yea = 1, nay = 6, missing = c(8,9), notInLegis = 0, legis.names = legisNames, vote.names = voteNames)
outputOC <- oc(rcObject = outputHRR, dims = 2, minvotes = 1, polarity = c(4,323))

RCura

        rank correctYea wrongYea wrongNay correctNay volume   coord1D       coord2D
Legis1   9.5          1        0        0          3  0.154 0.1393427  2.439471e-04
Legis2   9.5          1        0        0          3  0.213 0.1393427  2.439471e-04
Legis3   7.0          2        0        0          2  0.131 0.1392035 -9.211715e-25
Legis4   7.0          2        0        0          2  0.029 0.1392035 -9.211715e-25
Legis5   7.0          2        0        0          2  0.070 0.1392035 -9.211715e-25
Legis6   3.0          0        0        0          0  0.000 0.0000000  0.000000e+00
Legis7   3.0          0        0        0          0  0.000 0.0000000  0.000000e+00
Legis8   3.0          0        0        0          0  0.000 0.0000000  0.000000e+00
Legis9   3.0          0        0        0          0  0.000 0.0000000  0.000000e+00

RCura

# Create dummy data
a <- rnorm(n = 100, mean = 50, sd = 5)
b <- rnorm(n = 100, mean = 50, sd = 5)
c <- rnorm(n = 100, mean = 50, sd = 5)
mydf <- data.frame(A = a, B = b, C = c, row.names = c(1:100))
library(ade4)
# Running a Correspondence Analysis
myCA <- dudi.coa(df = mydf,scannf = FALSE,  nf = 2)
# Getting distances for Hclust
myDist <- dist.dudi(myCA, amongrow = TRUE)

RCura

require(shiny)
require(sp)
require(rgdal)
runApp(
  list(
    ui = bootstrapPage(
      fileInput('inputdata', 'Input file',accept=c('.csv')),
      plotOutput("xyPlot"),
      downloadButton('downloadShp', 'DownloadSHP')
    ),