raster - Plotting map resulted from kriging in R

I want to plot a map weighted by prices. I got a data frame of latitudes, longitudes and prices:

> str(data)
'data.frame':   1253 obs. of  3 variables:
 $ lon  : num  11.2 11.1 11 11 11.3 ...
 $ lat  : num  49.6 49.4 49.3 49.4 49.3 ...
 $ price: num  1.4 1.37 1.4 1.36 1.4 ...

> head(data)
       lon      lat    price
1 11.21702 49.58926 1.395000
2 11.07774 49.37093 1.369000

3 11.04533 49.33787 1.397000
4 10.97310 49.43500 1.357333
5 11.34080 49.31650 1.399000
6 11.20708 49.36844 1.399000

My second step is to transform the data frame into a spatial data type:

coordinates(data) = ~lon+lat
proj4string(data) = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84")

And to plot it:

spplot(data, scales=list(draw=T), sp.layout=list("sp.points", data, pch=""), main="My Data")

(Should look like a small map of Germany)

At this point I got a plot with my points colored from black (lowest price) to yellow (highest price). But there is also a lot of white space in my plot. Now I want to fill the white space with assumptions, so it looks like a heatmap at the end.

I read some articles about "Kriging", but I'm not sure how to code this. I also read, that I need a raster. Therefore I tried the following lines according to this article How to make RASTER from irregular point data without interpolation:

s100 <- as.matrix(as.data.frame(data), ncol=3, byrow=TRUE)
e <- extent(s100[,1:2])
r <- raster(e, ncol=10, nrow=2)
x <- rasterize(s100[,1:2], r, fun='mean')