带有colorRampPalette的viridis在R中输出不同的矢量长度
我在为数字向量指定特定颜色时遇到了一些问题。我的兴趣是使用中的带有colorRampPalette的viridis在R中输出不同的矢量长度,r,gplots,viridis,R,Gplots,Viridis,我在为数字向量指定特定颜色时遇到了一些问题。我的兴趣是使用中的VAR1.color作为heatmap.2中的ColSidecolors。我的数据帧有1655行，没有缺失值。我使用VAR1的最大值生成colorRampPalette向量，但是，当我使用函数创建长度相等的向量，以便为每个观察指定颜色时，生成的颜色向量的长度与原始颜色向量的长度不同 31>var2Color mm$VAR2.color您已经遇到了我为什么构建的一个用例—为值分配颜色 set.seed(81) ## random va
VAR1.colorheatmap.2ColSidecolors31>var2Color mm$VAR2.color您已经遇到了我为什么构建的一个用例—为值分配颜色
set.seed(81)
## random values that best replicate my data
mm <- data.frame(VAR1 = abs(rnorm(n = 1655, 2, 100)),
VAR2 = abs(rnorm(n = 1655, 2, 88)))
library(colourvalues)
mm$VAR1.color <- colour_values( mm$VAR1 )
set.seed（81）
##最能复制我的数据的随机值
嗯，会有帮助吗？你好！是的，那非常有用。Awnser:mm$VAR1.color是的！这是非常有用的。在couriosity之外，有没有办法将颜色聚焦到特定的范围？i、 e.数据是0-200，但80%的数据在0到40之间，例如mm$VAR1.color我还没有做这个功能，但是你可以在数据中添加另一个变量mm$VAR3 40，40，mm$VAR1）
和VAR3上的颜色。我的解决方案是使用tmp
对象<代码>tmp 40]
1 > library(viridis)
2 > set.seed(81)
3 >
4 > mm <- data.frame(VAR1 = abs(rnorm(n = 1655, 2, 100)),
VAR2 = abs(rnorm(n = 1655, 2, 88)))
5 > head(mm)
6 VAR1 VAR2
7 1 101.50747 2.436640
8 2 50.82978 5.620249
9 3 88.32233 11.304224
10 4 58.74447 92.727843
11 5 32.13192 141.090015
12 6 12.21342 5.785531
13 >
14 > dim(mm)
15 [1] 1655 2
16 > any(is.na(mm))
17 [1] FALSE
18 >
19 > var1Color <- colorRampPalette(viridis(ceiling(max(mm$VAR1)), option = "A"))
20 > mm$VAR1.color <- var1Color(ceiling(max(mm$VAR1)))[mm$VAR1]
21 Error in `$<-.data.frame`(`*tmp*`, VAR1.color, value = c("#651A80", "#251254", :
22 replacement has 1641 rows, data has 1655
23 >
24 > mm$VAR1.color <- NA
25 > mm$VAR1.color <- var1Color(ceiling(max(mm$VAR1)))[mm$VAR1]
26 Error in `$<-.data.frame`(`*tmp*`, VAR1.color, value = c("#651A80", "#251254", :
27 replacement has 1641 rows, data has 1655
28 >
29 > any(is.na(mm$VAR2))
30 [1] FALSE
31 > var2Color <- colorRampPalette(viridis(ceiling(max(mm$VAR2)), option = "A"))
32 > mm$VAR2.color <- var2Color(ceiling(max(mm$VAR2)))[mm$VAR2]
33 Error in `$<-.data.frame`(`*tmp*`, VAR2.color, value = c("#010005", "#020109", :
34 replacement has 1643 rows, data has 1655
35 >
36 > sessionInfo()
37 R Under development (unstable) (2018-10-16 r75445)
38 Platform: x86_64-apple-darwin16.7.0 (64-bit)
39 Running under: macOS Sierra 10.12.6
40
41 Matrix products: default
42 BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
43 LAPACK: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libLAPACK.dylib
44
45 locale:
46 [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
47
48 attached base packages:
49 [1] parallel stats graphics grDevices utils datasets methods
50 [8] base
51
52 other attached packages:
53 [1] wesanderson_0.3.6 RColorBrewer_1.1-2 viridis_0.5.1
54 [4] viridisLite_0.3.0 multcomp_1.4-8 TH.data_1.0-10
55 [7] MASS_7.3-51 survival_2.42-6 mvtnorm_1.0-8
56 [10] factoextra_1.0.5 ggfortify_0.4.5 ggplot2_3.1.0
57 [13] pheatmap_1.0.12 copynumber_1.23.0 BiocGenerics_0.29.1
58 [16] vegan_2.5-4 lattice_0.20-35 permute_0.9-4
59 [19] gplots_3.0.1
60
61 loaded via a namespace (and not attached):
62 [1] ggrepel_0.8.0 Rcpp_1.0.0 tidyr_0.8.2
63 [4] zoo_1.8-4 gtools_3.8.1 assertthat_0.2.0
64 [7] R6_2.4.0 GenomeInfoDb_1.19.2 plyr_1.8.4
65 [10] stats4_3.6.0 pillar_1.3.1 zlibbioc_1.29.0
66 [13] rlang_0.3.1 lazyeval_0.2.1 gdata_2.18.0
67 [16] S4Vectors_0.21.10 Matrix_1.2-14 splines_3.6.0
68 [19] stringr_1.4.0 RCurl_1.95-4.11 munsell_0.5.0
69 [22] compiler_3.6.0 pkgconfig_2.0.2 mgcv_1.8-24
70 [25] tidyselect_0.2.5 tibble_2.0.1 gridExtra_2.3
71 [28] GenomeInfoDbData_1.2.0 IRanges_2.17.4 codetools_0.2-15
72 [31] crayon_1.3.4 dplyr_0.8.0.1 withr_2.1.2
73 [34] bitops_1.0-6 grid_3.6.0 nlme_3.1-137
74 [37] gtable_0.2.0 magrittr_1.5 scales_1.0.0
75 [40] KernSmooth_2.23-15 stringi_1.3.1 XVector_0.23.0
76 [43] sandwich_2.5-0 tools_3.6.0 glue_1.3.0
77 [46] purrr_0.3.0 colorspace_1.4-0 cluster_2.0.7-1
78 [49] GenomicRanges_1.35.1 caTools_1.17.1.1
79 !>
set.seed(81)
## random values that best replicate my data
mm <- data.frame(VAR1 = abs(rnorm(n = 1655, 2, 100)),
VAR2 = abs(rnorm(n = 1655, 2, 88)))
library(colourvalues)
mm$VAR1.color <- colour_values( mm$VAR1 )
mm$h <- 10
barplot( height = mm$h, col = mm$VAR1.color, border = NA, space = 0, yaxt = 'n')
mm <- mm[ with(mm, order(VAR1)), ]
barplot( height = mm$h, col = mm$VAR1.color, border = NA, space = 0, yaxt = 'n')