ggplot heatmap不同类别的渐变颜色

时间：2017-02-24 16:38:28

标签： r ggplot2 categories heatmap

我正在尝试修改ggplot2 heatmaps: using different gradients for categories

中的实例

然而，我想绘制的值是离散的（我认为）。我已经将我的值（在存储过程中）标准化为0到1之间的百分比值。如果percent_value为0，那么我将显示白色。如果百分比值为1，那么我想显示完整的颜色。颜色从白色渐变到完整。每个类别都有自己的颜色。

这是我的代码...... 

library(RColorBrewer)
rm(list=ls())
yval <- c("51140/1234.5985/16:25:17" ,"51140/1234.5985/16:25:17" ,"51140/1234.5985/16:25:17" ,"51141/1234.5985/16:25:17" ,"51146/1234.5985/16:25:17" ,"51146/1234.5985/16:25:17" ,"51146/1234.5985/16:25:17" ,"51147/1234.5985/16:25:17" ,"51147/1234.5985/16:25:17" ,"51147/1234.5985/16:25:17" ,"51149/1234.5985/16:25:17" ,"51150/1234.5985/16:25:17" ,"51150/1234.5985/16:25:17" ,"51150/1234.5985/16:25:17" ,"51153/1234.5985/16:25:17" ,"51153/1234.5985/16:25:17" ,"51153/1234.5985/16:25:17")
cat <- c("cat1" ,"cat1" ,"cat1" ,"cat2" ,"cat1" ,"cat1" ,"cat1" ,"cat1" ,"cat1" ,"cat1" ,"cat2" ,"cat1" ,"cat1" ,"cat1" ,"cat1" ,"cat1" ,"cat1")
xval <- c("cat1.ant" ,"cat1.output3" ,"cat1.input5" ,"cat2.cat2_active_state" ,"cat1.input5" ,"cat1.output3" ,"cat1.ant" ,"cat1.ant" ,"cat1.output3" ,"cat1.input5" ,"cat2.cat2_active_state" ,"cat1.input5" ,"cat1.ant" ,"cat1.output3" ,"cat1.output3" ,"cat1.ant" ,"cat1.input5")
value <- c(0.75 ,1 ,1 ,0.1 ,1 ,1 ,0.75 ,0 ,1 ,1 ,1 ,1 ,0.75 ,1 ,1 ,0.75 ,1)
dat <- data.frame(xval, yval, cat, value)

n <- length(unique(dat$cat))
qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',]
col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals)))
sample_colours <- sample(col_vector, n)

# 2 categories. I've hard-coded the gradient ends in this example.
# I've tried translating the value up the number line to separate the categories into different colour bands.
gradientends <- c(0, 1, 2, 3)

interleave <- function(v1,v2)
{
  ord1 <- 2*(1:length(v1))-1
  ord2 <- 2*(1:length(v2))
  c(v1,v2)[order(c(ord1,ord2))]
}
colorends <- interleave(rep("white",n),sample_colours)

ggplot(dat, aes(x = xval, y = factor(yval))) + 
   geom_tile(aes(fill = value), colour = "grey80") + 
   geom_text(aes(label = value)) +
   scale_fill_gradientn(colours = colorends) + #, values = gradientends) +
   theme(axis.ticks = element_blank(), 
         axis.text.x = element_text(angle = 330, hjust = 0))

我尝试了各种方法，在我看来，scale_fill_gradient可能不是解决此问题的好方法。似乎缩放功能是＆＃34;调整＆＃34;因为取决于我正在绘制的值，我会得到正确或不正确的热图。

这种方法有没有解决方法，或者有更好的方法？

利安

1 个答案:

答案 0 :(得分：0)

我已经想出如何让我的例子工作。事实证明我得到了梯度错误，我应该在scale_fill_gradientn(colours = colorends, values = rescale(gradientends))进行重新缩放。说实话，我不太清楚这里发生了什么！假设gradientends正在以scale_fill_填充缩放的rescaleoffset值的相同方式重新调整value，因此所有内容都正确排列，没有溢出到相邻颜色块中。

这是工作代码。我已按照SO指南中的建议将数据放入dput（）ofrmat中。我在rescaloffset中包含了geom_text和rm(list=ls()) library(RColorBrewer) dat <- structure(list(xval = structure(c(5L, 3L, 2L, 4L, 2L, 3L, 1L, 1L, 3L, 2L, 4L, 2L, 1L, 3L, 3L, 1L, 2L), .Label = c("cat1.ant", "cat1.input5", "cat1.output3", "cat2.cat2_active_state", "cat3.ant" ), class = "factor"), yval = structure(c(1L, 1L, 1L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 5L, 6L, 6L, 6L, 7L, 7L, 7L), .Label = c("51140/1234.5985/16:25:17", "51141/1234.5985/16:25:17", "51146/1234.5985/16:25:17", "51147/1234.5985/16:25:17", "51149/1234.5985/16:25:17", "51150/1234.5985/16:25:17", "51153/1234.5985/16:25:17" ), class = "factor"), cat = structure(c(3L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("cat1", "cat2", "cat3"), class = "factor"), value = c(0.75, 1, 1, 0.1, 1, 1, 0.75, 0, 1, 1, 1, 1, 0.75, 1, 1, 0.75, 1), rescaleoffset = c(200.75, 1, 1, 100.1, 1, 1, 0.75, 0, 1, 1, 101, 1, 0.75, 1, 1, 0.75, 1 )), .Names = c("xval", "yval", "cat", "value", "rescaleoffset" ), row.names = c(NA, -17L), class = "data.frame") n <- length(unique(dat$cat)) qual_col_pals = brewer.pal.info[brewer.pal.info$category == 'qual',] col_vector = unlist(mapply(brewer.pal, qual_col_pals$maxcolors, rownames(qual_col_pals))) sample_colours <- sample(col_vector, n) dat$rescaleoffset <- dat$value + 100*(as.numeric(dat$cat)-1) scalerange <- range(dat$value) gradientends <- scalerange + rep(c(0,100,200), each=2) colorends <- c("white", "red", "white", "green", "white", "blue") ggplot(dat, aes(xval, yval)) + geom_tile(aes(fill = rescaleoffset), colour = "white") + geom_text(aes(label = paste(format(round(value, 5), nsmall = 5), format(round(rescaleoffset, 5), nsmall = 5), sep='\n'))) + scale_fill_gradientn(colours = colorends, values = rescale(gradientends)) + scale_x_discrete("", expand = c(0, 0)) + scale_y_discrete("", expand = c(0, 0)) + theme_grey(base_size = 9) + theme(axis.ticks = element_blank(), axis.text.x = element_text(angle = 330, hjust = 0))+ theme(legend.background = element_rect(fill="gray90", size=30, linetype="dotted")) 值（这有助于调试）。我还添加了另一个类别来使其复杂化。

dat <- structure(list(heatmap_row_display = structure(c(2L, 6L, 5L, 
8L, 4L, 3L, 7L, 9L, 1L, 3L, 7L, 9L, 4L, 1L, 4L, 1L, 3L, 7L, 9L
), .Label = c("051140/1084.8158/16:25:17", "051141/1084.8466/16:25:17", 
"051146/1084.8803/16:25:17", "051147/1084.8876/16:25:17", "051148/1084.8965/16:25:17", 
"051149/1084.9465/16:25:17", "051150/1084.9525/16:25:17", "051152/1084.9965/16:25:17", 
"051153/1085.0193/16:25:17"), class = "factor"), msg_no = c(51141L, 
51149L, 51148L, 51152L, 51147L, 51146L, 51150L, 51153L, 51140L, 
51146L, 51150L, 51153L, 51147L, 51140L, 51147L, 51140L, 51146L, 
51150L, 51153L), relative_time_ms = c(1084.8466, 1084.9465, 1084.8965, 
1084.9965, 1084.8876, 1084.8803, 1084.9525, 1085.0193, 1084.8158, 
1084.8803, 1084.9525, 1085.0193, 1084.8876, 1084.8158, 1084.8876, 
1084.8158, 1084.8803, 1084.9525, 1085.0193), pcan_rx_datetime_adjusted = structure(c(1487089517, 
1487089517, 1487089517, 1487089517, 1487089517, 1487089517, 1487089517, 
1487089517, 1487089517, 1487089517, 1487089517, 1487089517, 1487089517, 
1487089517, 1487089517, 1487089517, 1487089517, 1487089517, 1487089517
), class = c("POSIXct", "POSIXt"), tzone = ""), block_name = structure(c(1L, 
1L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L), .Label = c("BCU1", "BCU2", "IDC1_status"), class = "factor"), 
    pcan_attribute = structure(c(1L, 1L, 2L, 2L, 3L, 3L, 3L, 
    3L, 3L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L), .Label = c("BCU1.BCU1_active_state", 
    "BCU2.BCU2_active_state", "IDC1_status.IDC1_ant", "IDC1_status.IDC1_input5", 
    "IDC1_status.IDC1_output3"), class = "factor"), data_value_as_string = c(1L, 
    1L, 1L, 1L, 0L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 1L, 1L), data_value = c(1L, 1L, 1L, 1L, 0L, 3L, 3L, 3L, 
    3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), value = c(1, 
    1, 1, 1, 0, 0.75, 0.75, 0.75, 0.75, 1, 1, 1, 1, 1, 1, 1, 
    1, 1, 1), colour = structure(c(2L, 2L, 1L, 1L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("cyan", 
    "darkviolet", "deeppink"), class = "factor"), rescaleoffset = c(1, 
    1, 101, 101, 200, 200.75, 200.75, 200.75, 200.75, 201, 201, 
    201, 201, 201, 201, 201, 201, 201, 201)), .Names = c("heatmap_row_display", 
"msg_no", "relative_time_ms", "pcan_rx_datetime_adjusted", "block_name", 
"pcan_attribute", "data_value_as_string", "data_value", "value", 
"colour", "rescaleoffset"), row.names = c(NA, 19L), class = "data.frame")

n <- length(unique(dat$block_name))
# Do it this way to avoid reordering the colours in the data frame
sample_colours <- levels(factor(dat$colour, levels=unique(dat$colour)))

# Rescale all the values into categories of 100
dat$rescaleoffset <- dat$value + 100*(as.numeric(dat$block_name)-1)

scalerange <- range(dat$value)
# Mark the end of each gradient for each category block.
gradientends <- scalerange + rep(seq(0, (n - 1) * 100, by = 100), each=2)

# Interleave two vectors, used to interleave "white" with each of the category colours. 
# "white" is used to colour the values on lowest end of each category's gradient range.
interleave <- function(v1,v2)
{
  ord1 <- 2*(1:length(v1))-1
  ord2 <- 2*(1:length(v2))
  c(v1,v2)[order(c(ord1,ord2))]
}
colorends <- interleave(rep("white",n),sample_colours)

p <- ggplot(dat, aes(pcan_attribute, heatmap_row_display)) + 
  geom_tile(aes(fill = rescaleoffset), colour = "white") + 
  geom_text(aes(label = paste(format(round(value, 1), nsmall = 1), sep='\n')), size=rel(2.0)) +
  scale_fill_gradientn(colours = colorends, values = rescale(gradientends)) + 
  scale_x_discrete("", expand = c(0, 0)) + 
  scale_y_discrete("", expand = c(0, 0)) + 
  theme_grey(base_size = 9) + 
  theme(axis.ticks = element_blank(), 
        axis.text.x = element_text(angle = 330, hjust = 0))+
  theme(legend.background = element_rect(fill="gray90", size=30, linetype="dotted")) 

print(p)

尽管这些值是数字且看起来是连续的，但它们实际上代表了离散的分类值。总的来说，我很满意这一点，正是我正在寻找的，尽管在格式化和参数化方面还需要一些工作。

编辑：现在我真的很困惑。这是一组类似的数据，但它没有按照我的预期进行绘图。我希望BCU1 catgory是紫色（不是白色），因为它的值为1.0。有一些我不理解缩放的东西。有人可以帮忙吗？

System.arrayCopy()
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