在同一ggplot中绘制离散和连续缩放 [英] Plotting discrete and continuous scales in same ggplot

问题描述

我想使用ggplot2绘制一些不同的数据项目，使用两种不同的颜色比例（一个连续的和一个离散的来自两个不同的df）。我可以单独绘制我想要的样式，但我不能让它们一起工作。看起来你不可能有两个不同的颜色比例在同一个图中操作？我已经看到类似的问题使元素在顶部阴谋透明，所以他们不会不透明的元素下面：

  grid.newpage（）
 pushViewport（viewport（layout = grid.layout（1，1，widths = unit（1，npc））））
 print（p1 + theme（legend.position =none），vp = viewport（layout.pos.row = 1，layout.pos.col = 1））
 print（p2 + theme（legend.position =none），vp = viewport（layout.pos.row = 1，layout.pos.col = 1 ））
  

查看我的回答 here ，以了解如何将图例添加到网格布局中的另一个位置。

I would like to plot some different data items using ggplot2, using two different colour scales (one continuous and one discrete from two different df's). I can plot either exactly how I would like individually, but I cannot make them work together. It seems like you cannot have two different colour scales operating in the same plot? I have seen similar questions here and here, and this had led me to believe that what I would like to achieve is simply not possible in ggplot2, but just in case I am wrong I would like to illustrate my problem to see if there is a work-around.

I have some GIS stream data which has some categorical attributes attached to it, which I can plot (p1 in code below) to get:

I also have a set of locations which have a continuous response, which I can also plot (p2 in code below) to get: However I can't combine the two (p3 in code below). I get this error

Error in scales[[prev_aes]] : attempt to select less than one element

Commenting out the line scale_colour_hue("Strahler order") + changes the error to

Error: Discrete value supplied to continuous scale

Basically it seems that ggplot2 uses the same scale type (continuous or discrete) for the geom_path call and the geom_point calls. So when I pass the discrete variable, factor(Strahler), to the scale_colour_gradientn scale, the plot fails.

Is there a way around this? It would be amazing if there was a data argument to a scales function to tell it where it should be mapping or setting attributes from. Is this even possible?

Many thanks and reproducible code below:

library(ggplot2)

### Download df's   ###
oldwd <- getwd(); tmp <- tempdir(); setwd(tmp)
url <- "http://dl.dropbox.com/u/44829974/Data.zip"
f <- paste(tmp,"\\tmp.zip",sep="")
download.file(url,f)
unzip(f)


### Read in data    ###
riv_df <- read.table("riv_df.csv", sep=",",h=T)
afr_df <- read.table("afr_df.csv", sep=",",h=T)
vil_df <- read.table("vil_df.csv", sep=",",h=T)


### Min and max for plot area   ###
xmin <- -18; xmax <- 3; ymin <- 4; ymax <- 15


### Plot river data ###
p1 <-   ggplot(riv_df, aes(long, lat)) + 
    geom_map( mapping = aes( long , lat , map_id = id ) , fill = "white" , data = afr_df , map = afr_df ) +
    geom_path( colour = "grey95" , mapping = aes( long , lat , group = group , size = 1 ) , data = afr_df ) +
    geom_path( aes( group = id , alpha = I(Strahler/6) , colour = factor(Strahler) , size = Strahler/6 ) ) +
    scale_alpha( guide = "none" ) +
    scale_colour_hue("Strahler order") +
    scale_x_continuous( limits = c( xmin , xmax ) , expand = c( 0 , 0 ) ) +
    scale_y_continuous( limits = c( ymin , ymax ) , expand = c( 0 , 0 ) ) +
    coord_map()
print(p1) # This may take a little while depending on computer speed...



### Plot response data  ###
p2 <- ggplot( NULL ) +
    geom_point( aes( X , Y , colour = Z) , size = 2 , shape = 19 , data = vil_df ) +
    scale_colour_gradientn( colours = rev(heat.colors(25)) , guide="colourbar" ) +
    coord_equal()
print(p2)



### Plot both together  ###
p3 <-   ggplot(riv_df, aes(long, lat)) + 
    geom_map( mapping = aes( long , lat , map_id = id ) , fill = "white" , data = afr_df , map = afr_df ) +
    geom_path( colour = "grey95" , mapping = aes( long , lat , group = group , size = 1 ) , data = afr_df ) +
    geom_path( aes( group = id , alpha = I(Strahler/6) , colour = factor(Strahler) , size = Strahler/6 ) ) +
    scale_colour_hue("Strahler order") +
    scale_alpha( guide = "none" ) +
    scale_x_continuous( limits = c( xmin , xmax ) , expand = c( 0 , 0 ) ) +
    scale_y_continuous( limits = c( ymin , ymax ) , expand = c( 0 , 0 ) ) +
    geom_point( aes( X , Y , colour = Z) , size = 2 , shape = 19 , data = vil_df ) +
    scale_colour_gradientn( colours = rev(heat.colors(25)) , guide="colourbar" ) +
    coord_map()
print(p3)
#Error in scales[[prev_aes]] : attempt to select less than one element

### Clear-up downloaded files   ###
unlink(tmp,recursive=T)
setwd(oldwd)

Cheers,

Simon

解决方案

You can do this. You need to tell grid graphics to overlay one plot on top of the other. You have to get margins and spacing etc, exactly right, and you have to think about the transparency of the top layers. In short... it's not worth it. As well as possibly making the plot confusing.

However, I thought some people might like a pointer on how to acheive this. N.B. I used code from this gist to make the elements in the top plot transparent so they don't opaque the elements below:

grid.newpage()
pushViewport( viewport( layout = grid.layout( 1 , 1 , widths = unit( 1 , "npc" ) ) ) ) 
print( p1 + theme(legend.position="none") , vp = viewport( layout.pos.row = 1 , layout.pos.col = 1 ) )
print( p2 + theme(legend.position="none") , vp = viewport( layout.pos.row = 1 , layout.pos.col = 1 ) )

See my answer here for how to add legends into another position on the grid layout.

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