是否可以定义“中"?scale_fill_gradient2() 的范围? [英] Is it possible to define the "mid" range in scale_fill_gradient2()?

问题描述

我正在使用 ggplot() 创建热图，并希望使用 scale_fill_gradient2() 的 3 种配色方案.但是，我发现中间颜色太宽，并且倾向于负面地显示我的一些数据(例如使用黑色").是否可以定义被视为中"的范围，使其更窄?如果没有，有没有更好的方法可以这样做?

I am creating a heat map using ggplot(), and would like to utilize the 3 color scheme of scale_fill_gradient2(). I've found, however that the middle color is too broad and tends to display some of my data negatively (using "black" for example). Is it possible to define the range that is considered "mid," to make it more narrow? If not, is there a better way that I may do so?

数据集:

structure(list(var1 = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L), .Label = c("A", "B", "C", "D", "E", "F", "G"), class = "factor"), 
var2 = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 
11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 
23L, 24L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 
13L, 14L, 15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 
1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 
15L, 16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 1L, 2L, 
3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 
16L, 17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 1L, 2L, 3L, 
4L, 5L, 6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 1L, 2L, 3L, 4L, 5L, 
6L, 7L, 8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 
19L, 20L, 21L, 22L, 23L, 24L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 9L, 10L, 11L, 12L, 13L, 14L, 15L, 16L, 17L, 18L, 19L, 
20L, 21L, 22L, 23L, 24L), .Label = c("a", "b", "c", "d", 
"e", "f", "g", "h", "i", "j", "k", "l", "m", "n", "o", "p", 
"q", "r", "s", "t", "u", "v", "w", "x"), class = "factor"), 
corr = c(0.039063517, -0.012531832, 0.096287532, -0.156156609, 
-0.097044878, 0.144426494, 0.102142979, 0.061426893, 0.051079225, 
0.271860908, 0.156812951, 0.259456277, -0.121838722, -0.157440078, 
-0.037827967, -0.01929319, -0.108895665, -0.066815122, 0.254285337, 
0.12688199, -0.064394035, 0.00112601, 0.173774179, 0.01179886, 
0.105171013, 0.088559148, 0.033584364, -0.368075609, -0.272671354, 
-0.456557935, -0.441008229, -0.118498286, -0.309056047, 0.051624421, 
0.087594347, -0.0264506, 0.081249807, -0.194887615, -0.135397719, 
-0.078688964, -0.059544125, -0.065410158, 0.211446055, 0.027338504, 
-0.06185598, -0.007720807, 0.092997248, -0.177812491, 0.133226267, 
0.075247459, 0.04586679, -0.37972917, -0.254410003, -0.447919321, 
-0.426264017, -0.150347417, -0.270786314, 0.143483685, 0.230384468, 
-0.012297462, 0.096957204, -0.134348613, -0.056239035, -0.038059581, 
-0.040273741, -0.131126698, 0.222754865, 0.067883188, -0.154724805, 
-0.076366467, 0.152747678, -0.160657826, 0.104652439, 0.029599007, 
-0.02194356, -0.349623751, -0.228046427, -0.363148102, -0.326649794, 
-0.160199177, -0.253621489, 0.242711785, 0.242808683, 0.005937022, 
0.120849407, -0.131358491, 0.004558011, -0.056873299, 0.048745675, 
-0.147360978, 0.204314713, 0.033110005, -0.154644375, -0.095999454, 
0.202067265, -0.182694112, 0.071256125, 0.062783632, 0.078250678, 
-0.348208521, -0.237899511, -0.235431913, -0.249280276, -0.129802124, 
-0.084153729, 0.201859004, 0.292353092, -0.000270168, 0.130668616, 
-0.101519227, -0.024695776, -0.024373331, -0.05976031, -0.07861482, 
0.150316096, 0.100494694, -0.107507982, -0.06543542, 0.220813403, 
-0.159374132, 0.139677124, 0.005974473, 0.011484774, -0.423918147, 
-0.286712875, -0.286979217, -0.2730134, -0.095014441, -0.134216671, 
0.215551068, 0.432571228, 0.029945066, 0.178996421, -0.114005412, 
-0.098205883, 0.009661061, -0.07163473, 0.010722592, 0.175801779, 
0.170582102, -0.199407024, -0.15933157, 0.246575881, -0.084929336, 
0.160642274, 0.025941888, -0.058676465, -0.297996589, -0.180504241, 
-0.236672516, -0.228203539, -0.181349045, -0.091893083, 0.277145017, 
0.38119709, 0.019554381, 0.18725277, -0.019416086, 0.000417258, 
0.134560252, 0.059330986, -0.091994882, 0.11807573, 0.175219716, 
-0.169272253, -0.094240107, 0.180532375, 0.04322002)), .Names = c("var1", "var2", "corr"), class = "data.frame", row.names = c(NA, -168L))

使用reshape2、ggplot2和scales的代码:

sample.m <- melt(sample)
sample.m$var2 <- as.character(sample.m$var2)
sample.m$var2 <- factor(sample.m$var2, levels=unique(sample.m$var2),ordered=TRUE)
sample.m$var1 <- as.character(sample.m$var1)
sample.m$var1 <- factor(sample.m$var1, levels=unique(sample.m$var1),ordered=TRUE)
p <- ggplot(sample.m, aes(var2,var1)) + geom_tile(aes(fill=value), colour="transparent") +    scale_fill_gradient2(low="cyan", mid="black", high="red", midpoint=0, limits=c(-0.6,0.6))
base_size <- 9
p + theme_bw() + labs(x = "", y = "") + scale_x_discrete(expand = c(0,0)) + theme(legend.position = "bottom", axis.ticks = element_blank(), axis.text.x = element_text(size = base_size * 0.8, angle = 310, hjust = 0, colour = "black"), axis.text.y = element_text(size = base_size * 0.8,colour = "black"))

推荐答案

你可以试试 scale_fill_gradientn 和 values 参数.来自 ?scale_fill_gradientn:

You can try scale_fill_gradientn and the values argument. From ?scale_fill_gradientn:

如果颜色不应该沿着渐变均匀定位，则该向量给出颜色向量中每种颜色的位置(0 和 1 之间).有关将任意范围映射到 0 和 1 之间的便利函数，请参阅 rescale.

if colours should not be evenly positioned along the gradient this vector gives the position (between 0 and 1) for each colour in the colours vector. See rescale for a convience function to map an arbitrary range to between 0 and 1.

因此，可以通过在 values = rescale(...) 中使用合适的数字来提高接近于零的值的色阶分辨率.

Thus, resolution of the colour scale for values close to zero may be increased by using suitable numbers in values = rescale(...).

scale_fill_gradientn(colours = c("cyan", "black", "red"),
                       values = scales::rescale(c(-0.5, -0.05, 0, 0.05, 0.5)))

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