Difference between revisions of "Wikisym 2012 Demo"
From Testiwiki
Juha Villman (talk | contribs) (→Large quantity of points on a static Google Maps) |
m |
||
| (16 intermediate revisions by one other user not shown) | |||
| Line 1: | Line 1: | ||
| − | + | [[Category:Code under inspection]] | |
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | + | ==Health effects of Drinking Water Model== | |
| − | |||
| − | + | This example is model which is built for calculating health effects of drinking water and how water treatment processess affect to the outcome. | |
| − | + | * [[Ground water pathogen concentrations]] | |
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
| − | |||
<rcode | <rcode | ||
| Line 168: | Line 14: | ||
'Ground water - Clean';Ground water - Clean; | 'Ground water - Clean';Ground water - Clean; | ||
'Ground water - Surface water stress';Ground water - Surface water stress; | 'Ground water - Surface water stress';Ground water - Surface water stress; | ||
| − | 'Surface water - | + | 'Surface water - Low stress';Surface water - Low stress; |
| − | 'Surface water - | + | 'Surface water - Medium stress';Surface water - Medium stress; |
| − | 'Surface water - | + | 'Surface water - High stress';Surface water - High stress| |
| − | category: | + | category:Ground water: Pathogenic concentrations| |
| − | name:Kampylo|description: | + | name:Kampylo|description:Cambylobacter-concentration estimation (microbe/l)|default:'Use water source specific classification'| |
| − | name:Ecoli|description:E.coli O157:H7 - | + | name:Ecoli|description:E.coli O157:H7 -concentration estimation (microbe/l)|default:'Use water source specific classification'| |
| − | name:Rota|description:Rotavirus- | + | name:Rota|description:Rotavirus-concentration estimation (microbe/l)|default:'Use water source specific classification'| |
| − | name:Noro|description:Norovirus- | + | name:Noro|description:Norovirus-concentration estimation (microbe/l)|default:'Use water source specific classification'| |
| − | name:Crypto|description:Cryptosporidium- | + | name:Crypto|description:Cryptosporidium-concentration estimation (microbe/l)|default:'Use water source specific classification'| |
| − | name:Giardia|description:Giardia- | + | name:Giardia|description:Giardia-concentration estimation (microbe/l)|default:'Use water source specific classification'| |
| − | name:Kaupunni|description: | + | name:Kaupunni|description:City default values|default:'Custom'|type:selection| |
options: | options: | ||
| − | 'Custom'; | + | 'Custom';Use values defined above; |
| + | 'Op_en5799';Gotham City; | ||
'Op_fi2603';Kuopio| | 'Op_fi2603';Kuopio| | ||
| − | name:Puhdistus|description: | + | name:Puhdistus|description:Available purification methods|type:checkbox| |
options: | options: | ||
| − | 1; | + | 1;Traditional purification; |
| − | 2; | + | 2;Highly effective purification; |
| − | 3; | + | 3;Enhanced purification; |
| − | 4; | + | 4;Slow sand filtration; |
| − | 5; | + | 5;Limestone filtration; |
| − | 6; | + | 6;Activated carbon filtration; |
| − | 7;UV | + | 7;UV filtration; |
| − | 8; | + | 8;Ozonisation| |
| − | default:1;4;5;6|category: | + | default:1;4;5;6|category:Water purification: Purification processess and chlorinesation| |
| − | name:KlooriAnnos|default:1.5|description: | + | name:KlooriAnnos|default:1.5|description:Chlorine dose (mg/l)| |
| − | name:VedeKulu|default:1153|description: | + | name:VedeKulu|default:1153|description:Water consumption (ml)|category:Water network and consumers| |
| − | name:Vaestonkoko|default:100000|description: | + | name:Vaestonkoko|default:100000|description:Population |
" | " | ||
> | > | ||
| Line 214: | Line 61: | ||
#Patogeenien pitoisuudet | #Patogeenien pitoisuudet | ||
| − | Fetch2(data.frame(Name = "RaaPatPitLuo", Key = "AEmnj6ZNfhIHAt2X"), evaluate = TRUE) | + | #Fetch2(data.frame(Name = "RaaPatPitLuo", Key = "AEmnj6ZNfhIHAt2X"), evaluate = TRUE) |
| + | # fetching data from english Opasnet | ||
| + | #Fetch2(data.frame(Name = "RaaPatPitLuo", Key = "kjRoRPqqAzhaG8qR"), evaluate = TRUE) | ||
| + | temp <- tidy(op_baseGetData("opasnet_base", "Op_en5800"), objname = "RaaPatPitLuo") | ||
| + | print(xtable(temp), type = "html") | ||
| + | |||
| + | RaaPatPitLuo <- new("ovariable", | ||
| + | name = "RaaPatPitLuo", | ||
| + | data = temp | ||
| + | ) | ||
| − | + | # RaaPatPitLuo@output <- RaaPatPitLuo@output[RaaPatPitLuo@output$Raakavesilähde == i.raw.class, ] | |
| + | RaaPatPitLuo@output <- RaaPatPitLuo@output[RaaPatPitLuo@output$Water_source == i.raw.class, ] | ||
RaaPatPitLuo@output <- merge( | RaaPatPitLuo@output <- merge( | ||
RaaPatPitLuo@output, | RaaPatPitLuo@output, | ||
| Line 590: | Line 447: | ||
print(xtable(dose.response[,c("Pathogen", "Exp.pat", "P.inf", "VedDesTehSource")]), type="html") # Patogeeneille altistuminen ja infektion todennäköisyys | print(xtable(dose.response[,c("Pathogen", "Exp.pat", "P.inf", "VedDesTehSource")]), type="html") # Patogeeneille altistuminen ja infektion todennäköisyys | ||
| − | cat("<span style='font-size: 1.2em;font-weight:bold;'> | + | cat("<span style='font-size: 1.2em;font-weight:bold;'>Estimated health effects</span>\n") |
| + | |||
| + | cat(sum((1 - (1 - temp$P.ill.g.inf * temp$P.inf)^365) * Vaestonkoko, na.rm = TRUE), " stomach flus per year \n") | ||
| + | |||
| + | cat(sum(Health.effects$DALYs, na.rm = TRUE), " DALY's from stomach flus \n") | ||
| + | </rcode> | ||
| + | |||
| + | == Polygons on dynamic Google Maps == | ||
| + | This example plots municipalities of Finland on Google Maps using data from National Land Survey of Finland. | ||
| + | <rcode name="polygons_on_google_maps"> | ||
| + | |||
| + | library(OpasnetUtilsExt) | ||
| + | library(sorvi) | ||
| + | library(rgdal) | ||
| + | |||
| + | # Get the shape data of Finnish municipalities using soRvi library | ||
| + | data(MML) | ||
| + | shp <- MML[["1_milj_Shape_etrs_shape"]][["kunta1_p"]] | ||
| + | |||
| + | # Set the projection | ||
| + | epsg4326String <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs") | ||
| + | proj4string(shp)<-("+init=epsg:3047") | ||
| + | shp2<-spTransform(shp,epsg4326String) | ||
| + | |||
| + | # Create the KML data using the shape | ||
| + | out<-sapply(slot(shp2,"polygons"),function(x){kmlPolygon(x,name="name",col='#df0000aa',lwd=1,border='black',description="desc") }) | ||
| + | data<-paste( | ||
| + | paste(kmlPolygon(kmlname="This will be layer name", kmldescription="<i>More info about layer here</i>")$header, collapse="\n"), | ||
| + | paste(unlist(out["style",]), collapse="\n"), | ||
| + | paste(unlist(out["content",]), collapse="\n"), | ||
| + | paste(kmlPolygon()$footer, collapse="\n"), | ||
| + | sep='' | ||
| + | ) | ||
| + | |||
| + | # Show the KML data on Google Maps | ||
| + | google.show_kml_data_on_maps(data) | ||
| + | |||
| + | </rcode> | ||
| + | |||
| + | == Points on dynamic Google Maps == | ||
| + | This examples plots buildings of Kuopio on Google Maps. User can give the minimum age of buildings to plot as an input parameter. | ||
| + | |||
| + | |||
| + | <rcode name='plots_on_dynamic_maps' variables="name:age|description:Building minimum age|default:120"> | ||
| + | |||
| + | library(rgdal) | ||
| + | library(RColorBrewer) | ||
| + | library(classInt) | ||
| + | library(OpasnetUtilsExt) | ||
| + | library(RODBC) | ||
| + | |||
| + | if (age > 190) | ||
| + | { | ||
| + | age <- 190 | ||
| + | } | ||
| + | |||
| + | shp <- spatial_db_query(paste('SELECT * FROM kuopio_house WHERE ika >= ',age,';',sep='')) | ||
| + | |||
| + | coordinates(shp)=c("y_koord","x_koord") | ||
| + | |||
| + | plotvar<-shp@data$ika | ||
| + | nclr<-8 | ||
| + | plotclr<-brewer.pal(nclr,"BuPu") | ||
| + | class<-classIntervals(plotvar,nclr,style="quantile") | ||
| + | colcode<-findColours(class,plotclr) | ||
| + | |||
| + | epsg4326String <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs") | ||
| + | proj4string(shp)<-("+init=epsg:3067") | ||
| + | shp2<-spTransform(shp,epsg4326String) | ||
| + | |||
| + | |||
| + | kmlname<-"Kuopio house data" | ||
| + | kmldescription<-"Random stuff about here" | ||
| + | icon<-"http://maps.google.com/mapfiles/kml/pal2/icon18.png" | ||
| + | name<-paste("Value: ",shp2$ika) | ||
| + | description <- paste("<b>Age:</b>",shp2$ika,"<br><b>Building ID:</b>",shp2$rakennustunnus) | ||
| + | |||
| + | data <- google.point_kml(shp2,kmlname,kmldescription,name,description,icon,colcode) | ||
| + | google.show_kml_data_on_maps(data) | ||
| + | |||
| + | </rcode> | ||
| − | + | == Large quantity of points on a static Google Maps == | |
| + | This example plots large number of point data on static Google Maps. The map produced in this example shows the age (in years) distribution of buildings within Kuopio. User can select the number of age classes (4,6 or 8) and the type of classification. | ||
| − | cat( | + | <rcode name='static_gmaps_test' |
| + | graphics='1' | ||
| + | Variables="name:myclasses|description:Number of classes|type:selection|options:4;4;6;6;8;8|default:8| | ||
| + | name:classtype|description:Type of classification|type:selection|options:'equal';Equal Int;'quantile';Quantile;'sd';Standard deviation;'jenks';Jenks|default:'quantile' | ||
| + | " | ||
| + | > | ||
| + | #code goes here | ||
| + | library(RgoogleMaps) | ||
| + | library(rgdal) | ||
| + | library(maptools) | ||
| + | library(RColorBrewer) | ||
| + | library(classInt) | ||
| + | library(OpasnetUtilsExt) | ||
| + | |||
| + | shp<-readOGR('PG:host=localhost user=postgres dbname=spatial_db','kuopio_house') | ||
| + | plotvar<-shp@data$ika | ||
| + | nclr<-myclasses | ||
| + | plotclr<-brewer.pal(nclr,"Reds") | ||
| + | class<-classIntervals(plotvar,nclr,style=classtype) | ||
| + | colcode<-findColours(class,plotclr) | ||
| + | |||
| + | epsg4326String <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs") | ||
| + | proj4string(shp)<-("+init=epsg:3067") | ||
| + | shp2<-spTransform(shp,epsg4326String) | ||
| + | #get marker information for all points | ||
| + | mymarkers<-cbind.data.frame(lat=c(shp2@coords[,2]),lon=c(shp2@coords[,1]),color=colcode); | ||
| + | |||
| + | #get the bounding box: | ||
| + | bb <- qbbox(lat = mymarkers[,"lat"], lon = mymarkers[,"lon"]) | ||
| + | |||
| + | #MyMap function without the "file destination" parameter | ||
| + | MyRmap<-function (lonR, latR, center, size = c(640, 640), | ||
| + | MINIMUMSIZE = FALSE, RETURNIMAGE = TRUE, GRAYSCALE = FALSE, | ||
| + | NEWMAP = TRUE, zoom, verbose = 1, ...) | ||
| + | { | ||
| + | if (missing(zoom)) | ||
| + | zoom <- min(MaxZoom(latR, lonR, size)) | ||
| + | if (missing(center)) { | ||
| + | lat.center <- mean(latR) | ||
| + | lon.center <- mean(lonR) | ||
| + | } | ||
| + | else { | ||
| + | lat.center <- center[1] | ||
| + | lon.center <- center[2] | ||
| + | } | ||
| + | if (MINIMUMSIZE) { | ||
| + | ll <- LatLon2XY(latR[1], lonR[1], zoom) | ||
| + | ur <- LatLon2XY(latR[2], lonR[2], zoom) | ||
| + | cr <- LatLon2XY(lat.center, lon.center, zoom) | ||
| + | ll.Rcoords <- Tile2R(ll, cr) | ||
| + | ur.Rcoords <- Tile2R(ur, cr) | ||
| + | if (verbose > 1) { | ||
| + | cat("ll:") | ||
| + | print(ll) | ||
| + | print(ll.Rcoords) | ||
| + | cat("ur:") | ||
| + | print(ur) | ||
| + | print(ur.Rcoords) | ||
| + | cat("cr:") | ||
| + | print(cr) | ||
| + | } | ||
| + | size[1] <- 2 * max(c(ceiling(abs(ll.Rcoords$X)), ceiling(abs(ur.Rcoords$X)))) + | ||
| + | 1 | ||
| + | size[2] <- 2 * max(c(ceiling(abs(ll.Rcoords$Y)), ceiling(abs(ur.Rcoords$Y)))) + | ||
| + | 1 | ||
| + | if (verbose) | ||
| + | cat("new size: ", size, "\n") | ||
| + | } | ||
| + | return(google.get_map(center = c(lat.center, lon.center), zoom = zoom, | ||
| + | size = size, RETURNIMAGE = RETURNIMAGE, | ||
| + | GRAYSCALE = GRAYSCALE, verbose = verbose, ...)) | ||
| + | } | ||
| + | |||
| + | MyMap<-MyRmap(bb$lonR,bb$latR,maptype="mobile",scale="2") | ||
| + | |||
| + | PlotOnStaticMap(MyMap,size=c(640,640)) | ||
| + | |||
| + | PlotOnStaticMap(MyMap,size=c(640,640),lat=mymarkers[,"lat"],lon=mymarkers[,"lon"],pch=19,cex=0.3,col=colcode,add=T) | ||
| + | |||
| + | legend("topleft", legend=names(attr(colcode, "table")),title="Building Age (Yr)", fill=attr(colcode, "palette"), cex=1.0, bty="y",bg="white") | ||
</rcode> | </rcode> | ||
Latest revision as of 09:28, 27 August 2013
Contents
Health effects of Drinking Water Model
This example is model which is built for calculating health effects of drinking water and how water treatment processess affect to the outcome.
Polygons on dynamic Google Maps
This example plots municipalities of Finland on Google Maps using data from National Land Survey of Finland.
Points on dynamic Google Maps
This examples plots buildings of Kuopio on Google Maps. User can give the minimum age of buildings to plot as an input parameter.
Large quantity of points on a static Google Maps
This example plots large number of point data on static Google Maps. The map produced in this example shows the age (in years) distribution of buildings within Kuopio. User can select the number of age classes (4,6 or 8) and the type of classification.
