and图热图与密度图误差

Question

这个新帖子指的是前一篇文章(闪亮应用程序中的热图)。

在这里可以找到示例数据集：示例中使用的示例数据集

结果的密度图和显示每个位置的数据集中最大值的图表似乎不匹配。第三个ggplot有一些问题，我不知道如何解决。

• 我将第三个ggplot在scale_fill_gradientn中的比例设置为0到100。但是，生成的地块的热图颜色与刻度应该显示的颜色不一样。例如，94.251应该是一个较暗的区域，但它不会出现在图表上。
• 第三个ggplot中最大值的一些文本与坐标位置的矩形不匹配。我想解决这个问题。
• 我还希望第一个ggplot中的密度图显示一个混合图，类似于这个示例密度图中显示的混合图。我不太确定该怎么做

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library(grid)
library(ggplot2)


sensor.data <- read.csv("Sample_Dataset.csv") 

# Create position -> coord conversion 
pos.names <- names(sensor.data)[ grep("*Pos",names(sensor.data)) ] # Get column names with "Pos" in them 
mock.coords <<- list() 
lapply(pos.names, function(name){ 
  }) 
mock.coords <- list ("Position1"=data.frame("x"=0.1,"y"=0.2), 
                     "Position2"=data.frame("x"=0.2,"y"=0.4), 
                     "Position3"=data.frame("x"=0.3,"y"=0.6), 
                     "Position4"=data.frame("x"=0.4,"y"=0.65), 
                     "Position5"=data.frame("x"=0.5,"y"=0.75), 
                     "Position6"=data.frame("x"=0.6,"y"=0.6), 
                     "Position7"=data.frame("x"=0.7,"y"=0.6), 
                     "Position8"=data.frame("x"=0.8,"y"=0.43), 
                     "Position9"=data.frame("x"=0.9,"y"=0.27), 
                     "Position10"=data.frame("x"=0.75,"y"=0.12))

# Change format of your data matrix 
df.l <- list() 
cnt <- 1 

for (i in 1:nrow(sensor.data)){ 
  for (j in 1:length(pos.names)){ 
    name <- pos.names[j] 
    curr.coords <- mock.coords[[name]] 
    df.l[[cnt]] <- data.frame("x.pos"=curr.coords$x, 
                              "y.pos"=curr.coords$y, 
                              "heat" =sensor.data[i,j]) 
    cnt <- cnt + 1 
  } 
} 

df <- do.call(rbind, df.l) 


# Load image 
    library(jpeg)
download.file("http://www.expresspcb.com/wp-content/uploads/2015/06/PhotoProductionPCB_TL_800.jpg","pcb.jpg")
img <- readJPEG("/home/oskar/pcb.jpg")

g <- rasterGrob(img, interpolate=TRUE,width=1,height=1) 

# Show overlay of image and heatmap 
ggplot(data=df,aes(x=x.pos,y=y.pos,fill=heat)) + 
  annotation_custom(g, xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=Inf) + 
  stat_density2d( alpha=0.2,aes(fill = ..level..), geom="polygon" ) + 
  scale_fill_gradientn(colours = rev( rainbow(3) )) + 
  scale_x_continuous(expand=c(0,0)) + 
  scale_y_continuous(expand=c(0,0)) + 
  ggtitle("Density") 


# # Show where max temperature is 
# dat.max = df[which.max(df$heat),] 
# 
# ggplot(data=coords,aes(x=x,y=y)) + 
#   annotation_custom(g, xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=Inf) + 
#   geom_point(data=dat.max,aes(x=x.pos,y=y.pos), shape=21,size=5,color="black",fill="red") + 
#   geom_text(data=dat.max,aes(x=x.pos,y=y.pos,label=round(heat,3)),vjust=-1,color="red",size=10) + 
#   ggtitle("Max Temp Position") 

# bin data manually 

# Manually set number of rows and columns in the matrix containing sums of heat for each square in grid 
nrows <- 30 
ncols <- 30 

# Define image coordinate ranges 
x.range <- c(0,1) # x-coord range 
y.range <- c(0,1) # x-coord range 

# Create matrix and set all entries to 0 
heat.density.dat <- matrix(nrow=nrows,ncol=ncols) 
heat.density.dat[is.na(heat.density.dat)] <- 0 

# Subdivide the coordinate ranges to n+1 values so that i-1,i gives a segments start and stop coordinates 
x.seg <- seq(from=min(x.range),to=max(x.range),length.out=ncols+1) 
y.seg <- seq(from=min(y.range),to=max(y.range),length.out=nrows+1) 

# List to hold found values 
a <- list() 
cnt <- 1 
for( ri in 2:(nrows+1)){ 
  x.vals <- x.seg [c(ri-1,ri)] 

  for ( ci in 2:(ncols+1)){ 
    # Get current segments, for example x.vals = [0.2, 0.3] 
    y.vals <- y.seg [c(ci-1,ci)] 

    # Find which of the entries in the data.frame that has x or y coordinates in the current grid 
    x.inds <- which( ( (df$x.pos >= min(x.vals)) & (df$x.pos <= max(x.vals)))==T ) 
    y.inds <- which( ((df$y.pos >= min(y.vals)) & (df$y.pos <= max(y.vals)))==T ) 

    # Find which entries has both x and y in current grid 
    inds <- intersect( x.inds , y.inds ) 

    # If there's any such coordinates 
    if (length(inds) > 0){ 
      # Append to list 
      a[[cnt]] <- data.frame("x.start"=min(x.vals), "x.stop"=max(x.vals), 
                             "y.start"=min(y.vals), "y.stop"=max(y.vals), 
                             "acc.heat"=sum(df$heat[inds],na.rm = T) ) 
      print(length(df$heat[inds])) 
      # Increment counter variable 
      cnt <- cnt + 1 
    } 
  } 
} 

# Construct data.frame from list 
heat.dens.df <- do.call(rbind,a) 

# Plot again 
ggplot(data=heat.dens.df,aes(x=x.start,y=y.start)) + 
  annotation_custom(g, xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=Inf) + 
  geom_rect(data=heat.dens.df, aes(xmin=x.start, xmax=x.stop, ymin=y.start, ymax=y.stop, fill=acc.heat), alpha=0.5) + 
  scale_fill_gradientn(colours = rev( rainbow(3) )) + 
  scale_x_continuous(expand=c(0,0)) + 
  scale_y_continuous(expand=c(0,0))

mock.coords <- list ("Position1"=data.frame("x"=0.1,"y"=0.2), 
                     "Position2"=data.frame("x"=0.2,"y"=0.4), 
                     "Position3"=data.frame("x"=0.3,"y"=0.6), 
                     "Position4"=data.frame("x"=0.4,"y"=0.65), 
                     "Position5"=data.frame("x"=0.5,"y"=0.75), 
                     "Position6"=data.frame("x"=0.6,"y"=0.6), 
                     "Position7"=data.frame("x"=0.7,"y"=0.6), 
                     "Position8"=data.frame("x"=0.8,"y"=0.43), 
                     "Position9"=data.frame("x"=0.9,"y"=0.27), 
                     "Position10"=data.frame("x"=0.75,"y"=0.12)) 

# Show where max temperature is 
heat.dat <- sensor.data[pos.names] 

# Get max for each position 
max.df <- apply(heat.dat,2,max) 
dat.max.l <- lapply(1:length(max.df), function(i){ 
  h.val <- max.df[i] 
  c.name <- names(h.val) 
  c.coords <- mock.coords[[c.name]] 
  data.frame("x.pos"=c.coords$x, "y.pos"=c.coords$y,"heat"=h.val) 
}) 

coords <- data.frame("x"=c(0,1),"y"=c(0,1)) 
dat.max <- do.call(rbind,dat.max.l) 

ggplot(data=coords,aes(x=x,y=y)) + 
  annotation_custom(g, xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=Inf) + 
  geom_point(data=dat.max,aes(x=x.pos,y=y.pos), shape=13,size=5,color="black",fill="red") + 
  geom_text(data=dat.max,aes(x=x.pos,y=y.pos,label=round(heat,3)),vjust=-1,color="red",size=10) + 
  geom_rect(data=heat.dens.df, aes(xmin=x.start, xmax=x.stop, ymin=y.start, ymax=y.stop, fill=acc.heat,x=NULL,y=NULL), alpha=0.5) + 
  scale_fill_gradientn(limits = c(0,100), colours = rev( rainbow(3) )) + 
  scale_x_continuous(expand=c(0,0)) + 
  scale_y_continuous(expand=c(0,0))

Answer 1

几件事。

• 若要对文本进行居中，请删除geom_text中的geom_text值。
• 为了为这些数据创建一个热图，我们需要某种类型的插值或平滑，因为您只有10个点的数据(或者只有几个数据点)

这可能是一种解决办法：

library(grid)
library(ggplot2)


sensor.data <- read.csv("/home/oskar/Downloads/Sample_Dataset.csv - Sample_Dataset.csv.csv") 

# Create position -> coord conversion 
pos.names <- names(sensor.data)[ grep("*Pos",names(sensor.data)) ] # Get column names with "Pos" in them 
mock.coords <- list ("Position1"=data.frame("x"=0.1,"y"=0.2), 
                     "Position2"=data.frame("x"=0.2,"y"=0.4), 
                     "Position3"=data.frame("x"=0.3,"y"=0.6), 
                     "Position4"=data.frame("x"=0.4,"y"=0.65), 
                     "Position5"=data.frame("x"=0.5,"y"=0.75), 
                     "Position6"=data.frame("x"=0.6,"y"=0.6), 
                     "Position7"=data.frame("x"=0.7,"y"=0.6), 
                     "Position8"=data.frame("x"=0.8,"y"=0.43), 
                     "Position8.1"=data.frame("x"=0.85,"y"=0.49), 
                     "Position9"=data.frame("x"=0.9,"y"=0.27), 
                     "Position10"=data.frame("x"=0.75,"y"=0.12))

# Change format of your data matrix 
df.l <- list() 
cnt <- 1 

for (i in 1:nrow(sensor.data)){ 
  for (j in 1:length(pos.names)){ 
    name <- pos.names[j] 
    curr.coords <- mock.coords[[name]] 
    df.l[[cnt]] <- data.frame("x.pos"=curr.coords$x, 
                              "y.pos"=curr.coords$y, 
                              "heat" =sensor.data[i,j]) 
    cnt <- cnt + 1 
  } 
} 

df <- do.call(rbind, df.l) 

# Load image 
library(jpeg)
download.file("http://www.expresspcb.com/wp-content/uploads/2015/06/PhotoProductionPCB_TL_800.jpg","pcb.jpg")
img <- readJPEG("/home/oskar/pcb.jpg")
g <- rasterGrob(img, interpolate=TRUE,width=1,height=1) 

# Manually set number of rows and columns in the matrix containing max of heat for each square in grid
nrows <- 50
ncols <- 50

# Define image coordinate ranges
x.range <- c(0,1) # x-coord range
y.range <- c(0,1) # x-coord range

x.bounds <- seq(from=min(x.range),to=max(x.range),length.out = ncols + 1)
y.bounds <- seq(from=min(y.range),to=max(y.range),length.out = nrows + 1)

# Create matrix and set all entries to 0
heat.max.dat <<- matrix(nrow=nrows,ncol=ncols)

lapply(1:length(mock.coords), function(i){
  c <- mock.coords[[i]]
  # calculate where in matrix this fits
  x <- c$x; y <- c$y
  x.ind <- findInterval(x, x.bounds)
  y.ind <- findInterval(y, y.bounds)
  heat.max.dat[x.ind,y.ind] <<- max(sensor.data[names(mock.coords)[i]])
})
heat.max.dat[is.na(heat.max.dat)]<-0

require(fields)
# Look at the image plots to see how the smoothing works
#image(heat.max.dat)
h.mat.interp <- image.smooth(heat.max.dat)
#image(h.mat.interp$z)

mat <- h.mat.interp$z

require(reshape2)
m.dat <- melt(mat)
# Change to propper coors, image is assumed to have coors between 0-1
m.dat$Var1 <-  seq(from=min(x.range),to=max(x.range),length.out=ncols)[m.dat$Var1]
m.dat$Var2 <-  seq(from=min(y.range),to=max(y.range),length.out=ncols)[m.dat$Var2]

# Show where max temperature is 
heat.dat <- sensor.data[pos.names] 

# Get max for each position 
max.df <- apply(heat.dat,2,max) 
dat.max.l <- lapply(1:length(max.df), function(i){ 
  h.val <- max.df[i] 
  c.name <- names(h.val) 
  c.coords <- mock.coords[[c.name]] 
  data.frame("x.pos"=c.coords$x, "y.pos"=c.coords$y,"heat"=h.val) 
}) 

dat.max <- do.call(rbind,dat.max.l) 

coords <- data.frame("x"=c(0,1),"y"=c(0,1)) 
ggplot(data=coords,aes(x=x,y=y)) + 
  annotation_custom(g, xmin=-Inf, xmax=Inf, ymin=-Inf, ymax=Inf) +
  geom_raster(data=m.dat,aes(x=Var1,y=Var2,fill=value), interpolate = TRUE, alpha=0.5) + 
  scale_fill_gradientn(colours = rev( rainbow(3) ),guide=FALSE) +
  geom_text(data=dat.max,aes(x=x.pos,y=y.pos,label=round(heat,3)),vjust=0,color="white",size=5) +
  scale_x_continuous(expand=c(0,0)) + 
  scale_y_continuous(expand=c(0,0))

最后我明白了

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