Tag Archives: cholera

Non classé

Another Interactive Map for the Cholera Dataset

5 Comments

Following my previous post, François (aka @FrancoisKeck) posted a comment mentionning another package I could use to get an interactive map, the rleafmap package. And the heatmap was here easy to include.

The first part is still the same, to get the data,

> require(rleafmap)
> library(sp)
> library(rgdal)
> library(maptools)
> library(KernSmooth)
> setwd("/home/arthur/Documents/")
> deaths <- readShapePoints("Cholera_Deaths")
> df_deaths <- data.frame(deaths@coords)
> coordinates(df_deaths)=~coords.x1+coords.x2
> proj4string(df_deaths)=CRS("+init=epsg:27700") 
> df_deaths = spTransform(df_deaths,CRS("+proj=longlat +datum=WGS84"))
> df=data.frame(df_deaths@coords)

To get a first visualisation, use

> stamen_bm <- basemap("stamen.toner")
> j_snow <- spLayer(df_deaths, stroke = FALSE)
> writeMap(stamen_bm, j_snow, width = 1000, height = 750, setView = c( mean(df[,1]),mean(df[,2])), setZoom = 14)

and again, using the + and the – in the top left area, we can zoom in, or out. Or we can do it manually,

> writeMap(stamen_bm, j_snow, width = 1000, height = 750, setView = c( mean(df[,1]),mean(df[,2])), setZoom = 16)

To get the heatmap, use

> library(spatstat)
> library(maptools)

> win <- owin(xrange = bbox(df_deaths)[1,] + c(-0.01,0.01), yrange = bbox(df_deaths)[2,] + c(-0.01,0.01))
> df_deaths_ppp <- ppp(coordinates(df_deaths)[,1],  coordinates(df_deaths)[,2], window = win)
> 
> df_deaths_ppp_d <- density.ppp(df_deaths_ppp, 
  sigma = min(bw.ucv(df[,1]),bw.ucv(df[,2])))
 
> df_deaths_d <- as.SpatialGridDataFrame.im(df_deaths_ppp_d)
> df_deaths_d$v[df_deaths_d$v < 10^3] <- NA

> stamen_bm <- basemap("stamen.toner")
> mapquest_bm <- basemap("mapquest.map")
 
> j_snow <- spLayer(df_deaths, stroke = FALSE)
> df_deaths_den <- spLayer(df_deaths_d, layer = "v", cells.alpha = seq(0.1, 0.8, length.out = 12))
> my_ui <- ui(layers = "topright")

> writeMap(stamen_bm, mapquest_bm, j_snow, df_deaths_den, width = 1000, height = 750, interface = my_ui, setView = c( mean(df[,1]),mean(df[,2])), setZoom = 16)

The amazing thing here are the options in the top right corner. For instance, we can remove some layers, e.g. to remove the points

or to change the background

To get an html file, instead of a standard visualisation in RStudio, use

> writeMap(stamen_bm, mapquest_bm, j_snow, df_deaths_den, width = 450, height = 350, interface = my_ui, setView = c( mean(df[,1]),mean(df[,2])), setZoom = 16, directView ="browser")

which will generate the html table (as well as some additional files actually) above. Awesome, isn’t it?

Maps

Interactive Maps for John Snow’s Cholera Data

5 Comments

This week, in Istanbul, for the second training on data science, we’ve been discussing classification and regression models, but also visualisation. Including maps. And we did have a brief introduction to the  leaflet package,

devtools::install_github("rstudio/leaflet")
require(leaflet)

To see what can be done with that package, we will use one more time the John Snow’s cholera dataset, discussed in previous posts (one to get a visualisation on a google map background, and the second one on an openstreetmap background),

library(sp)
library(rgdal)
library(maptools)
setwd("/cholera/")
deaths <- readShapePoints("Cholera_Deaths")
df_deaths <- data.frame(deaths@coords)
coordinates(df_deaths)=~coords.x1+coords.x2
proj4string(df_deaths)=CRS("+init=epsg:27700") 
df_deaths = spTransform(df_deaths,CRS("+proj=longlat +datum=WGS84"))
df=data.frame(df_deaths@coords)
lng=df$coords.x1
lat=df$coords.x2

Once installed the leaflet package, we can use the package at the RStudio console (which is what we will do here), or within R Markdown documents, and within Shiny applications. But because of restriction we got on this blog (rules of hypotheses.org) So there will be only copies of my screen. But if you run the code, in RStudio you will get interactvive maps in the viewer window.

First step. To load a map, centered initially in London, use

m = leaflet()%>% addTiles() 
m %>% fitBounds(-.141,  51.511, -.133, 51.516)

In the viewer window of RStudio, it is just like on OpenStreetMap, e.g. we can zoom-in, or zoom-out (with the standard + and – in the top left corner)

And we can add additional material, such as the location of the deaths from cholera (since we now have the same coordinate representation system here)

rd=.5
op=.8
clr="blue"
m = leaflet() %>% addTiles()
m %>% addCircles(lng,lat, radius = rd,opacity=op,col=clr)

We can also add some heatmap.

X=cbind(lng,lat)
kde2d <- bkde2D(X, bandwidth=c(bw.ucv(X[,1]),bw.ucv(X[,2])))

But there is no heatmap function (so far) so we have to do it manually,

x=kde2d$x1
y=kde2d$x2
z=kde2d$fhat
CL=contourLines(x , y , z)

We have now a list that contains lists of polygons corresponding to isodensity curves. To visualise of of then, use

m = leaflet() %>% addTiles() 
m %>% addPolygons(CL[[5]]$x,CL[[5]]$y,fillColor = "red", stroke = FALSE)

Of course, we can get at the same time the points and the polygon

m = leaflet() %>% addTiles() 
m %>% addCircles(lng,lat, radius = rd,opacity=op,col=clr) %>%
  addPolygons(CL[[5]]$x,CL[[5]]$y,fillColor = "red", stroke = FALSE)

Continue reading Interactive Maps for John Snow’s Cholera Data →

Maps

John Snow, and Google Maps

7 Comments

In my previous post, I discussed how to use OpenStreetMaps (and standard plotting functions of R) to visualize John Snow’s dataset. But it is also possible to use Google Maps (and ggplot2 types of graphs).

library(ggmap)
get_london <- get_map(c(-.137,51.513), zoom=17)
london <- ggmap(get_london)

Again, the tricky part comes from the fact that the coordinate representation system, here, is not the same as the one used on Robin Wilson’s blog.

> library(maptools)
> setwd("/cholera/")
> deaths <- readShapePoints("Cholera_Deaths")
> head(deaths@coords)
coords.x1 coords.x2
0  529308.7  181031.4
1  529312.2  181025.2
2  529314.4  181020.3
3  529317.4  181014.3
4  529320.7  181007.9
5  529336.7  181006.0
> X <- deaths@coords

or, use d X_deaths.RData. So now, we have to change it

df_deaths <- data.frame(X)
library(sp)
library(rgdal)
coordinates(df_deaths)=~coords.x1+coords.x2
proj4string(df_deaths)=CRS("+init=epsg:27700") 
df_deaths = spTransform(df_deaths,CRS("+proj=longlat +datum=WGS84"))

Here, we have the same coordinate system as the one used in Google Maps. Now, we can add a layer, with the points,

london + geom_point(aes(x=coords.x1, y=coords.x2),data=data.frame(df_deaths@coords),col="red")

Again, it is possible to add the density, as an additional layer,

london + geom_point(aes(x=coords.x1, y=coords.x2), 
data=data.frame(df_deaths@coords),col="red")+
geom_density2d(data = data.frame(df_deaths@coords), 
aes(x = coords.x1, y=coords.x2), size = 0.3) + 
stat_density2d(data = data.frame(df_deaths@coords), 
aes(x = coords.x1, y=coords.x2,fill = ..level.., alpha = ..level..),size = 0.01, bins = 16, geom = "polygon") + scale_fill_gradient(low = "green", high = "red",guide = FALSE) + 
scale_alpha(range = c(0, 0.3), guide = FALSE)

 

Maps

John Snow, and OpenStreetMap

11 Comments

While I was working for a training on data visualization, I wanted to get a nice visual for John Snow’s cholera dataset. This dataset can actually be found in a great package of famous historical datasets.

library(HistData)
data(Snow.deaths)
data(Snow.streets)

One can easily visualize the deaths, on a simplified map, with the streets (here simple grey segments, see Vincent Arel-Bundock’s post)

plot(Snow.deaths[,c("x","y")], col="red", pch=19, cex=.7,xlab="", ylab="", xlim=c(3,20), ylim=c(3,20))
slist <- split(Snow.streets[,c("x","y")],as.factor(Snow.streets[,"street"]))
invisible(lapply(slist, lines, col="grey"))

Continue reading John Snow, and OpenStreetMap →