Better way for linear relationship plot between two variables I have got Demand, Temperature, and Price data. I would like to plot Demand against other two variables separately to see the linear relationship between them, but the I that I got is very ugly. I am wondering is there any other plot that I can use? contour plot? 
Thanks.
par(mfrow=c(1,2))
plot(data$Temp,data$Demand,ylab="Demand (MW)",
 xlab="Temperature (Celsius)",main = "Plot of Demand against Temperature")
abline(lm(data$Demand~data$Temp), col="red") 

plot(data$Price,data$Demand,ylab="Demand (MW)",
 xlab="Price (GBP)",main = "Plot of Demand against Price")
abline(lm(data$Demand~data$Price), col="red") 


 A: You obviously have a large amount of data. In such cases, a good alternative to the scatterplot is pre-binning the data, e.g., in hexagons. I recommend the hexbin package for R, which will give you a hexbinplot like this:

library(hexbin)

set.seed(1)
x <- rnorm(20000)
y <- rnorm(20000)
hbin <- hexbin(x,y, xbins = 40)
plot(hbin)

mkt asks why we should use hexagons, rather than, say, squares. Here is the argument made in the hexbin vignette:

Hexagons  have  symmetry  of  nearest  neighbors  which  is  lacking  in
  square bins.  Hexagons are the maximum number of sides a polygon can have for a regular tesselation of the plane, so in terms of packing a hexagon is 13% more efficient for covering the plane than squares.  This property translates into better sampling efficiency at least for elliptical shapes.  Lastly hexagons are visually less biased for displaying densities than other regular tesselations.  For instance with squares our eyes are drawn to the horizontal and vertical lines of the grid. 

The vignette offers the following example to underline the last point:

