可以定义“中间”范围在scale_fill_gradient2（）？ [英] Is it possible to define the "mid" range in scale_fill_gradient2()?
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问题描述
我使用 ggplot（）创建一个热图，并且想要使用 scale_fill_gradient2（）。我发现，然而，中间的颜色太宽，往往显示一些我的数据负面（例如使用黑色）。是否可以定义被认为是中的范围，以使其更窄？
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?
资料集：
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))
代码使用 reshape2 ， ggplot2 和 scale ：
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
您可以尝试 scale_fill_gradientn 。通过在 values = rescale（...）中使用合适的数字，可以提高接近零的颜色标度的分辨率。
You can try scale_fill_gradientn. Resolution of the colour scale may be increased for values close to zero by using suitable numbers in values = rescale(...).
scale_fill_gradientn(colours = c("cyan", "black", "red"),
values = rescale(c(-0.5, -0.05, 0, 0.05, 0.5)))
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