在grid.arrange中绘制一个传说和间距很大的通用y轴和主要标题 [英] Plot a legend and well-spaced universal y-axis and main titles in grid.arrange
本文介绍了在grid.arrange中绘制一个传说和间距很大的通用y轴和主要标题的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
$ b
library（reshape）我有以下代码，它会生成两个包含到当前工作目录的图的pdf文件：
library（ggplot2）
require（ggplot2）
source（http://gridextra.googlecode.com/svn/trunk/R/arrange.r）
$ b （1L，2L，3L，4L，5L，6L，7L，
8L，9L，10L，11L，12L，13L，14L，15L，16L ，17L，18L，19L，20L，
21L，22L，23L，24L，25L，26L，27L，28L，29L，30L，31L，32L，33L，
34L，35L，36L，37L ，38L，39L，40L，41L，42L，43L，52L，44L，45L，
46L，47L，48L，49L，50L，51L，53L，54L，55L，56L，57L，58L，59L，标签= c（Baez，Blue，C147，C204，C21，
C278_PT，C294，C316 ，C485，C487_PigTa，C536，Carey，
Cool，Coyote，Deadpool，Epstein，Glass，Harrison，
丰收，Hazel，i-113，i-114，i-120_PigT，i-126，
i-127，Imagine，Jackstraw_杰里科，杰里 - 加尔克，裘德，
Kitty ，Majesty，Million，Monkey，Mozambique，Neil，
Nettie，Piggies，Psylocke，Queen，Ramble，Sinv-25 ，
Sinv12，Sol-11，Sol-18，Sol-20，Sol-49，Sol-6，Sol-J1，
Sol-M2，Sol-M3，SolB8，st_stephen，Starr，Sun_King，
Taxman，Tombstone_，Wallflower，Weight，Wigwam ，工人，
黄色），class =factor），All = c（0.3357,0.4166,0.0242，
0.9708,0.4518,0.0666,0,0.5925,0.2349,0.3242,0.3278 ，0.0246，
0.0352，0.0646，0.0563，0.6854，0.4664，0.8298，0.8831，NA，0.0078，
0.771，0.1376，0.0055，2E-04，如图1所示，0.2577，0.3326，0.0066，0.0024，
0.6136,0.4155,0.9931,0.2111,0.6373,0.0762,0.5153,0.1103，
0.0569,0,1,0.732,0,0.9225,0.1257,0.15858,0.9603,0.4629，
0 ，0.4155,0.0791,0.0777,0.3996,0.11212,0.6207,0.5766,0，
0.1347,0.3754,0.02737），X1_only = c（0.4758,0.3188,0.1465，$ b $ 0.3209,1.0278， 0.2065,0.6187,0.9377,0.8586,0.7292,0.0133，
1,1， 1,0.961,0.427,1,0.2203，NA，0.8919,0.2695,0.3724，
0.5798,0.5304,1,0.568,0.2291,0.4376,1,0.1572,0.20222，
0.9544,0.2462,0.9699,0.9299， 0.4439,0.2204,0.11135,0.3063,0.0311，
0.0384,0.2833,1,0.7024,0.7382,0.44923,0.4453,0.2341,0.7493，
0.0868,0.8801,0.8708,1.1,0.0491， 0.781，0.2037,0.1342，
0.5321,0.4787,0.7801），X78_only = c（0.3379,0.4102,0.2134，
0.6807,0.8242,1,0046,0.279,0.825,0.7563,0.6055,0.7472， b $ b 1,4,0.4958,0.0018,0.0175,1,1,0.5647，NA，0.2124,0.519,0.5204，
0.2272,0.03,1,0.0319，NA，0.4467,0.44473,0.1593,0.6066，
0.5907,0.0624,0.5699,0.66585,0.1414,0.546,0.6395,0.0102，b $ b 0.3112,0.791,0.07575,0.4155,0.9279,0.4834,0.3059,0.5967，
0.373,0.4114,0.9291,0.1119 ，0.7238,0.5993,0.7975,0.3283，
0.0511,0.4902,0.0438,2e-04,0.2357），X8_removed = c（0.0967，
0.5831,0.058,0.9268,0.3518,0.0629,0.60229， 0.2217,0.2602，
0.7123,0.0181,0.0348,0.1 482,0.1706,0.6748,0.33238,0.8134，
0.8032，NA，0.0246,0.5794,0.5204,0.0254,0.0056,1,0.6597，
0.3373,0.004,0.0087,0.9061,0.577,0.9565,0.4168， 0.7951，
0.1069,0.4071,0.1457,0.1453,0,0.8385,0.4658,0.0,0.7396，
0.0748,0.3677,0.9517,0.11188,0,0.5673,0.0396,0.0708,0.3645，
0.1147,0.5851,1,0.001,0.0614,0.131,0,0.4813），X8_only = c（0.1169，
0.8327,0.2169,0.0907,1,1,0.07,0.486,0.709,0.8882,0.4389， b $ b 1,0.7078,0.44496,0.1266,0.1945,0.4527,1,0.6518 NA 0.3594 b $ b 0.7715,0.134,0.2389,0.0203,1.01061 NA 0.1293 0.2558 b $ b 0.167 ，0.4815,0.7756,0.0403,0.22448,0.22265,0.0952,0.6658，
0.3405,0.0402,0.5906,0.2405,0.0086,0.5086,0.4709,1,0.0567，
0.4146,0.7554,0.104,0.1917,0.8625 ，1,1.1,0.8727,0.11439，
0.0452,1,0.5804,0.02764），X7_removed = c（0.2989,0.7268，
0.0087,0.8874,0.5853,0.0568,0.0762， 0.4226,0.3232,0.3972，
0.02,0.0159,0.0541,0.4919,0。 5951,0.5525,0.8114,0.5738，
NA，0.0062,0.7274,0.0155,0.0233,0.002,1,0.232,0.3476，
0.011,9e-04,0.5433,0.3725,0.7263,0.2462,0.4556， 0.0426，
0.7468，0.1235，0.0051，0，1，0.8962，0.0014，0，0.9892，0.1163，
0.1284，0.6873，0.3932，0，0.3722，0.0889，0.3782，0.4761，0.0484，
0.5321,0.5519,0,0.3453,0.0732,0,0.3483），X7_only = c（1，
0.5714,0.2825,0.8673,0.5557,0.6861,0.0044,0.11446,0.4957，
0.5248,0.8372 ，0.6665,0.6789,1,0.82,0.1759,0.3719,1，
0.704，NA，0.2585,0.4634,0.4283,0.6815,0.4161,1.01691，
NA，0.4563,1.0226,1 ，0.2349,0.5886,0.8154,0.8839,0.16131，
1,0.5112,0.1529,1,0.7245,4e-04,0.3095,0.6184,0.5542，
0.749,0.394,0.0298,0.1944,0.2881,0.7696 ，0.0637,0.652，
1,0.1494,1,0.3283,0.134,0.1992,0.0848,0.5826），X5_removed = c（1，
0.1453,0.0176,0.8428,0.22277,0.2563,0,0.5326， 0.1549,0.4405，
0.395,0.0195,0.08,0.1069,0.0316,0.6298,0.5157,1， 0.5967，
NA，0.0265,0.5703,0.2667,0.3485,0.0021,1,0.1821,0.3006，
0.007,0.0112,0.1964,0.44427,0.769,0.11214,0.6064,0.0914，
0.4188， 0.021，0.0814，0，0.8372，0.8052，0，0，0.8662，0.7917，
0.0924，0.9316，0.7399，0，0.2031，0.0701，0.0652，0.6636，0.0513，
0.2049，0.7161，0， 0.0407，0.1729，0，0.3079），X5_only = C（0.0642，
0.631，0.5193，0.979，0.5348，0.1304，0.02％，0.0217，0.0871，0.2022，
0.7602，1，0.3532，0.5292，1 ，0.3677,0.0896,0.3702,0.6084，
NA，0.1518,0.3467,0.1117,0.0252,0.7894,1,0.9842,0.7315，
0.8511,0.0717,0.085,0.7955,0.3517,1.08263,0.6102 ，0.268，
0.1071,0.33837,0.0175,0.5887,1，NA，0.1198,0.8537,0.0101，
0.3807,0.4939,0.1469,0.1368,0.5458,0.2514,1.03692,1.0b $ b 0.4877,0.5787,0.6025,0.5888,1,0.3472,1），X 4_removed = c（0.4492，
0.3821,0.0121,0.9957,0.5158,0.0498,0.0788,0.8003,0.11716，
0.661， 0.0194,0.0511,0.1862,0.0188,0.6454,0.5077,1,0.8794 ，
NA，0.3458，0.6059，0.1315，0.0099，0.003，1，0.0585，0.4635，
0.0357，0.0289，0.6835，0.2247，0.8437，0.3585，0.6074，0.1926，
0.3432，0.3615 ，0.0322,0,0.8418,0.7076,0,0.9281,0.7697，
0.1011,0.3068,0.971,0.4686,0,0.3731,0.1024,0.0683,0.8112，
0.3742,0.7381,0.22738,0.0089,0.2366 ，0.6924,0,0.1984），X4_only = c（0.6485，
0.0709,0.11639,0.6908,1,1,0.44469,0.639,0.0378,0.5116，
0.0026,0.6549,0.6928,0.2884,1， 0.4386,0.6246,0.6188,1，
NA，0.0966,0.3946,0.7223,0.1357,0.8912,1,0.4741,0.7526，
0.2005,0.013,1,0.45,0.1086,0.11184,0.8975,0.3181， 0.9958，
0.0644,0.0975,0.0721,1,1,1,7e-04,0.2754,0.4852,0.065，
0.747,0.4823,0.11971,0.6178,0.3781,1,0.362,0.1168,0.382，
0.4267,8e-04,0.1880,0.2115,0.2937,1），X3_removed = c（0.3009，
0.3414,0.02,0.9935,0.4216,0.1273,0.60406,0.22728,0.5307，
0.477,0.0612,0.0627,0.0808,0.1636,0.6506,0.6507,0.8122，
0.9 531，NA，0.0144,0.9274,0.146,0.0171,1e-04,1,0.2732,0
0.4153,0.0141,0.0105,0.6892,0.3701,0.9965,0.0418,0.5436，
0.2755,0.4803,0.0959， 0.1199,0,0.833,0.5373,0,0,0.9701，
0.1054,0.15858,0.9964,0.6849,0.023.210,0.1072,0.3401,0.3629，
0.2504,0.6056,0.5372,2e-04， 0.1168,1.0,0.224），X3_only = c（1，
0.9325,0.772,0.5505,1,0.2068,0.0829,0.17,0.8951,0.0225，
0.8263,0.2111,0.5087,0.768,0.2471 ，0.6294,0.2815,1,0.0496，
NA，0.3364,0.6286,0.2102,0.6816,0.372,1,0.7311,0.5138，
0.0683,0.11996,0.6998,1,0.6988,0.44426,0.66669,0.0412 ，0.6081，
1，0.237，6e-04,0.6349,0.7124,1,0.2314,0.0398,1,0.3487，
0.8153,0.11271,0.11145,0.8641,0.4056,0.11488,1,0.2357,0.26 ，
1,0.2678,0.5537,0.0317,0.0467,1），X2_removed = c（0.6335，
0.349,0.2095,0.9777,0.8928,0.0571,0,0.4285,0.2036,0.3168，
0.3668,0.0854,0.413,0.0608,0.0526,0.7608,0.3094,0.8186，
0.9273，NA，0.00 14,0.6512,0.44424,0.0275,0.2121,1.0308，
0.2381,0.0173,0.0075,0.7423,0.6126,0.979,0.11716,0.862， 0.6888,0,0.6213,0.0935，
0.1351,0.6946,0.4708,0.11458,0.899,0.4391,0.0277,0.5004，
0.3974,0.8854,0.2696,0.01846,0.5871,0.02966），其中， ，X2_only = c（0.191，
0.4397,0.0403,0.3606,0.0089,1,0033,0.659,0.1818,0.0949，
0.5521,0.137,0.0014,1，NA，0.8585,1,1,0.9437 ，NA，0.4086，
0.1699,0.0648,0.9087,0.0011,1,0.1291,0.5329,0.22315,0.22844，
0.6429,0.0488,0.1814,0.8658,0.0869,0.8394,0.5938,0.1782，
0,0.0098,1,1,1,0.1742,0.3911,0.8523,0.7331,0.1271,0.5119，
0，0.0105,0.0035,1,0.5665,0.072,0.22928,0.4224,0.5491，
0.4274 ，0.1054,0,0.5817），X1_removed = c（0.1653,0.7658,0.0718，
0.7705,0.4193,0.1894,0,0.5167,0.1053,0.2823,0.0496,0.11439，
0.0258,0.0676,0.03117， 0.5465,0.4909,0.66464,0.9383，NA，0.0124，
0.9288,0。 069,0.0116,6e-04,1,0.3301,0.508,0.0175,8e-04，
0.6016,0.7442,0.9609,0.4151,0.6049,0.1266,0.4281,0.22719，
0.0039,0,0.315， 1,0,0.8931,0.1124,0.3804,0.9233,0.3355，
0,0.3542,0.0363,0.0679,0.2652,0.122,0.4025,0.8155,2e-04，
0.2642,0.3629,0.0363， 0.2897）），.Names = C（ 基因座， 所有， X1_only，
的X78_only， X8_removed， X8_only， X7_removed， X7_only，
的X5_removed，X5_only，X4_removed，X4_only，X3_removed，
X3_only，X2_removed，X2_only，X1_removed），class =data.frame，行。 names = c（NA，
-62L））
＃现在创建这个大数据集的子集
split1_data< -droplevels（子集（数据，数据$ Loci％
split2_data< -droplevels（数据，数据$ Loci％在％data $ Loci [9:16]））
split3_data< -droplevels（subset（data ，数据$ Loci％在％data $ Loci [17:24]））
split4_data< -droplevels（子集（数据，数据$ Loci％在％data $ Loci [25:32]））
split5_data< -droplevels（子集（数据，数据$位点％数据$ Loci [33:40]））
split6_data< -droplevels（子集（数据，数据$ Loci％在％data $ Loci [41:48]））
split7_data< -droplevels （子集（数据，数据$ Loci％在％data $ Loci [49:56]））
split8_data< -droplevels（subset（data，data $ Loci％in％data $ Loci [57:62]））
＃并融化其中的每一个
split1_datam< -melt（split1_data，id =Loci）
split2_datam< -melt（split2_data，id =Loci）
split3_datam< -melt（split3_data，id =Loci）
split4_datam< -melt（split4_data，id =Loci）
split5_datam< -melt（split5_data，id =Loci）
split6_datam< -melt（split6_data，id =Loci）
split7_datam< -melt（split7_data，id =Loci）
split8_datam< -melt（split8_data，id =Loci）
＃并绘制每个融化子集的图形
p1 < - ggplot（split1_datam，aes（x = Loci，y = value，color = variable，width = 。）+ geom_bar（position =dodge）+ geom_hline（yintercept = 0.05）+ opts（legend.position =none，axis.text.x = theme_text（angle = 90，size = 8））+ scale_y_discrete （打破s = seq（0,1））+ ylab（NULL）
p2 < - ggplot（split2_datam，aes（x = Loci，y = value，color = variable，width = .15））+ geom_bar（position =dodge）+ geom_hline（yintercept = 0.05）+ opts（legend.position =none，axis.text.x = theme_text（angle = 90，size = 8））+ scale_y_discrete（breaks = seq 0））+ scale_fill_grey（）+ ylab（NULL）
p3 <-p < - ggplot（split3_datam，aes（x = Loci，y = value，color = variable，width =。 15））
p3 <-p3 + geom_bar（position =dodge）+ geom_hline（yintercept = 0.05）+ opts（legend.position =none，axis.text.x = theme_text（angle = 90， size = 8））+ scale_y_discrete（breaks = seq（0,1））+ ylab（NULL）
p4 <-p < - ggplot（split4_datam，aes（x = Loci，y = value ，color = variable，width = .15））
p4 <-p4 + geom_bar（position =dodge）+ geom_hline（yintercept = 0.05）+ scale_y_discrete（breaks = seq（0,1））+ opts legend.position =none，axis.text.x = theme_text（angle = 90，size = 8））+ ylab（NULL）
p5 <-p < - ggplot （split5_datam，aes（x = Loci，y = value，co lor = variable，width = .15））
p5 <-p5 + geom_bar（position =dodge）+ geom_hline（yintercept = 0.05）+ opts（legend.position =none，axis.text.x = theme_text（angle = 90，size = 8））+ scale_y_discrete（breaks = seq（0,1））+ ylab（NULL）
p6 <-p < - ggplot split6_datam，aes（x = Loci，y = value，color = variable，width = .15））
p6 <-p6 + geom_bar（position =dodge）+ geom_hline（yintercept = 0.05）+ scale_y_discrete = seq（0,1））+ opts（legend.position =none，axis.text.x = theme_text（angle = 90，size = 8））+ ylab（NULL）
p7 < ; - p < - ggplot（split7_datam，aes（x = Loci，y = value，color = variable，width = .15））
p7 <-p7 + geom_bar（position =dodge）+ geom_hline yintercept = 0.05）+ scale_y_discrete（breaks = seq（0,1））+ opts（legend.position =none，axis.text.x = theme_text（angle = 90，size = 8））+ ylab（NULL）
p8 <-p < - ggplot（split8_datam，aes（x = Loci，y = value，color = variable，width = .15））
p8 <-p8 + geom_bar（position =dodge）+ geom_hline（yinterc ept = 0.05）+ scale_y_discrete（breaks = seq（0,1））+ opts（legend.position =none，axis.text.x = theme_text（angle = 90，size = 8））+ ylab（NULL）
＃对于为整个多元素创造一个图例不利尝试？
leg< - ggplotGrob（p1 + opts（keep =legend_box））
##一个需要提供具有定义宽度的图例
legend = gTree（children = gList （leg），cl =legendGrob）
widthDetails.legendGrob< - 函数（x）单位（2，cm）
＃将前四个图绘制在一起
pdf（sensivitity_page_2_plots.pdf）
grid.arrange（p1，p2，p3，p4，nrow = 2，legend = legend，main =Sensitivity，left =Pvalue）
dev .off（）
＃与后四个
同名pdf（sensivitity_page_2_plots.pdf）
leg< - ggplotGrob（p5 + opts（keep = legend_box））
##我们需要提供一个定义宽度的图例
legend = gTree（children = gList（leg），cl =legendGrob）
widthDetails。 legendGrob < - function（x）unit（2，cm）
grid.arrange（p5，p6，p7，p8，nrow = 2，legend = legend，main =Sensitivity， left =Pvalue）
dev.off（）
图表看起来像这样：
我有以下问题：
- 我怎样才能将y轴标签放在不与边缘接近的地方？
- 我如何为主标题做同样的事情
- 为什么图例不出现？ （这部分代码已从grid.arrange文档中解除）。而且，如果这样做，我该如何正确地分配空间？如果没有其他人使用grid.arrange？
- 如果没有，是否有另一种方法来应用layout（） - 就像在ggplot中定位一样？
- How can I space the y-axis label so that it's not so close to the edge?
- How can I do the same for the main title
- Why is the legend not appearing? (that portion of the code was lifted from the grid.arrange documentation). And, when it does, how do I space that properly? Anybody else out there use grid.arrange?
- If not, is there another way to apply layout()-like positioning in ggplot?
非常感谢大家！
解决方案
据我所知， p1没有图例 - 因此没有图例被提取，因此在调用 grid.arrange 时没有绘制图例。
这是一个更简单的例子。它应该让你开始。
编辑： ggplot2版本0.9.3.1的代码更新
＃加载所需的软件包
library（ggplot2）
library（gtable）
library（grid）
library（gridExtra）
＃生成一些数据
df < - data.frame（x = factor（rep（1：5，2）），Groups = factor（rep（c（Group 1， 组2），5）））
＃获得四个图
p1 < - ggplot（data = df，aes（x = x，y = sample（1:10， 10），fill = Groups））+
geom_bar（position =dodge，stat =identity）+ theme（axis.title.y = element_blank（））
p2 < - ggplot（ data = df，aes（x = x，y = sample（1:10，10），fill = Groups））+
geom_bar（position =dodge，stat =identity）+ theme（axis。 title.y = element_blank（））
p3 < - ggplot（data = df，aes（x = x，y = sample（1:10，10），fill = Groups））+
geom_bar （position =dodge，stat =identity）+ theme（axis.title.y = element_blank（））
p4 < - ggplot（data = df，aes（x = x，y = sample 1:10 ，10），fill = Groups））+
geom_bar（position =dodge，stat =identity）+ theme（axis.title.y = element_blank（））
＃从p1中提取图例
legend = gtable_filter（ggplotGrob（p1），guide-box）
＃grid.draw（legend）＃确保图例已被提取
＃排列要绘制的元素。
＃内部的arrangeGrob（）函数安排四个图，主标题
＃和全局y轴标题。
＃外部grid.arrange（）函数排列并绘制arrangeGrob对象和图例。
grid.arrange（legendGet（p1 + theme（legend.position =none），
p2 + theme（legend.position =none），
p3 + theme（legend。 position =none），
p4 + theme（legend.position =none），
nrow = 2，
top = textGrob（Main Title，vjust = 1，gp = gpar（fontface =bold，cex = 1.5）），
left = textGrob（Global Y-axis Label，rot = 90，vjust = 1）），
legend，
width = unit.c（unit（1，npc） - legend $ width，legend $ width），
nrow = 1）
请注意宽度如何使用图例的宽度。结果是：
主标题和全局y轴标题使用 vjust 定位。如果你想让全局的Y轴标题占用更多的空间，那么将它创建为textGrob，并使用 widths 来设置它的宽度。在这里，内部安排了四个地块和主要标题。外部grid.arrange排列并绘制全局y轴标题，arrangeGrob对象和图例。全局Y轴标题的宽度设置为三行。
label = textGrob（Global Y-axis Label ，rot = 90，vjust = 0.5）
grid.arrange（label，
arrangeGrob（p1 + theme（legend.position =none），
p2 + theme（legend.position = 无），
p3 + theme（legend.position =none），
p4 + theme（legend.position =none），
nrow = 2，
top = textGrob（Main Title，vjust = 1，gp = gpar（fontface =bold，cex = 1.5））），
legend，
widths = unit.c（unit（3 ，lines），unit（1，npc） - unit（3，lines） - legend $ width，legend $ width），
nrow = 1）
使用您的数据和您的代码为了对数据进行子集和重塑，我绘制了前四个绘图，从第一个绘图中提取了该图例，然后安排了t他绘制，传说和标签。代码运行没有问题。
这些图有一些问题（还有，您的代码无法生成您帖子中显示的图）。我做了一些小改动。
＃加载库
库（ggplot2）
库（gridExtra）
库（ reshape2）
###
＃您的帖子中用于获取数据，子集和重塑数据的代码。
###
＃并为每个融化的子集创建一个图
p1 < - ggplot（split1_datam，aes（x = Loci，y = value，fill =变量））+
geom_bar（position =dodge，stat =identity）+ geom_hline（yintercept = 0.05）+
theme（axis.text.x = element_text（angle = 90，size = 8））+
ylab（NULL）
p2 < - ggplot（split2_datam，aes（x = Loci，y = value，fill = variable））+
geom_bar position =dodge，stat =identity）+ geom_hline（yintercept = 0.05）+
theme（axis.text.x = element_text（angle = 90，size = 8））+
ylab NULL）
p3 < - ggplot（split3_datam，aes（x = Loci，y = value，fill = variable））+
geom_bar（position =dodge，stat =identity ）+ geom_hline（yintercept = 0.05）+
theme（axis.text.x = element_text（angle = 90，size = 8））+
ylab（NULL）
p4 < - ggplot（split4_datam，aes（x = Loci，y = value，fill = variable））+
geom_bar（position =dodge，stat =identity）+ ge om_hline（yintercept = 0.05）+
theme（axis.text.x = element_text（angle = 90，size = 8））+
ylab（NULL）
＃Extracxt传说从p1
legend = gtable_filter（ggplotGrob（p1），guide-box）
＃grid.draw（legend）＃确保图例已被提取
＃按照之前的排列和绘制绘图
label = textGrob（p value，rot = 90，vjust = 0.5）
grid.arrange（label，
arrangeGrob（p1 + theme（legend。 （legend.position =none），
p2 + theme（legend.position =none），
p3 + theme（legend.position =none），
p4 + theme .position =none），
nrow = 2，
top = textGrob（Sensitivity，vjust = 1，gp = gpar（fontface =bold，cex = 1.5）），
legend，
widths = unit.c（unit（2，lines），unit（1，npc） - unit（2，lines） - legend $ width，legend $ width） ，nrow = 1）
p>
I have the following code which will generate two pdf files containing plots to the current working directory:
library(reshape)
library(ggplot2)
require(ggplot2)
source("http://gridextra.googlecode.com/svn/trunk/R/arrange.r")
data<-structure(list(Loci = 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, 25L, 26L, 27L, 28L, 29L, 30L, 31L, 32L, 33L,
34L, 35L, 36L, 37L, 38L, 39L, 40L, 41L, 42L, 43L, 52L, 44L, 45L,
46L, 47L, 48L, 49L, 50L, 51L, 53L, 54L, 55L, 56L, 57L, 58L, 59L,
60L, 61L, 62L), .Label = c("Baez", "Blue", "C147", "C204", "C21",
"C278_PT", "C294", "C316", "C485", "C487_PigTa", "C536", "Carey",
"Cool", "Coyote", "Deadpool", "Epstein", "Glass", "Harrison",
"Harvest", "Hazel", "i-113", "i-114", "i-120_PigT", "i-126",
"i-127", "Imagine", "Jackstraw_", "Jericho", "Jerry-Garc", "Jude",
"Kitty", "Majesty", "Million", "Monkey", "Mozambique", "Neil",
"Nettie", "Piggies", "Psylocke", "Queen", "Ramble", "Sinv-25",
"Sinv12", "Sol-11", "Sol-18", "Sol-20", "Sol-49", "Sol-6", "Sol-J1",
"Sol-M2", "Sol-M3", "SolB8", "st_stephen", "Starr", "Sun_King",
"Taxman", "Tombstone_", "Wallflower", "Weight", "Wigwam", "Workingman",
"Yellow"), class = "factor"), All = c(0.3357, 0.4166, 0.0242,
0.9708, 0.4518, 0.0666, 0, 0.5925, 0.2349, 0.3242, 0.3278, 0.0246,
0.0352, 0.0646, 0.0563, 0.6854, 0.4664, 0.8298, 0.8831, NA, 0.0078,
0.771, 0.1376, 0.0055, 2e-04, 1, 0.2577, 0.3326, 0.0066, 0.0024,
0.6136, 0.4155, 0.9931, 0.2111, 0.6373, 0.0762, 0.5153, 0.1103,
0.0569, 0, 1, 0.732, 0, 0, 0.9225, 0.1257, 0.1658, 0.9603, 0.4629,
0, 0.4155, 0.0791, 0.0777, 0.3996, 0.1212, 0.6207, 0.5766, 0,
0.1347, 0.3754, 0, 0.2737), X1_only = c(0.4758, 0.3188, 0.1465,
0.3209, 1, 0.0278, 0.2065, 0.6187, 0.9377, 0.8586, 0.7292, 0.0133,
1, 1, 1, 0.961, 0.427, 1, 0.2203, NA, 0.8919, 0.2695, 0.3724,
0.5798, 0.5304, 1, 0.568, 0.2291, 0.4376, 1, 0.1572, 0.2022,
0.9544, 0.2462, 0.9699, 0.4439, 0.2204, 0.1135, 0.3063, 0.0311,
0.0384, 0.2833, 1, 0.7024, 0.7382, 0.4923, 0.4453, 0.2341, 0.7493,
0.0868, 0.8801, 0.8708, 1, 1, 1, 0.0491, 0.291, 0.2037, 0.1342,
0.5321, 0.4787, 0.7801), X78_only = c(0.3379, 0.4102, 0.2134,
0.6807, 0.8242, 1, 0.0046, 0.279, 0.825, 0.7563, 0.6055, 0.7472,
1, 0.4958, 0.0018, 0.0175, 1, 1, 0.5647, NA, 0.2124, 0.519, 0.5204,
0.2272, 0.03, 1, 0.0319, NA, 0.4467, 0.4473, 0.1593, 0.6066,
0.5907, 0.0624, 0.5699, 0.6585, 0.1414, 0.546, 0.6395, 0.0102,
0.3112, 0.791, 0, 0.7753, 0.4155, 0.9279, 0.4834, 0.3059, 0.5967,
0.373, 0.4114, 0.9291, 0.1159, 0.7238, 0.5993, 0.7975, 0.3283,
0.0511, 0.4902, 0.0438, 2e-04, 0.2357), X8_removed = c(0.0967,
0.5831, 0.058, 0.9268, 0.3518, 0.0629, 0, 0.6229, 0.2217, 0.2602,
0.7123, 0.0181, 0.0348, 0.1482, 0.1706, 0.6748, 0.3238, 0.8134,
0.8032, NA, 0.0246, 0.5794, 0.5204, 0.0254, 0.0056, 1, 0.6597,
0.3373, 0.004, 0.0087, 0.9061, 0.577, 0.9565, 0.4168, 0.7951,
0.1069, 0.4071, 0.1457, 0.1453, 0, 0.8385, 0.4658, 0, 0, 0.7396,
0.0748, 0.3677, 0.9571, 0.1188, 0, 0.5673, 0.0396, 0.0708, 0.3645,
0.1147, 0.5851, 1, 0.001, 0.0614, 0.131, 0, 0.4813), X8_only = c(0.1169,
0.8327, 0.2169, 0.0907, 1, 1, 0.07, 0.486, 0.709, 0.8882, 0.4389,
1, 0.7078, 0.4496, 0.1266, 0.1945, 0.4527, 1, 0.6518, NA, 0.3594,
0.7715, 0.134, 0.2389, 0.0203, 1, 0.1061, NA, 0.1293, 0.2558,
0.167, 0.4815, 0.7756, 0.0403, 0.2448, 0.2265, 0.0952, 0.6658,
0.3405, 0.0402, 0.5906, 0.2405, 0.0086, 0.5086, 0.4709, 1, 0.0567,
0.4146, 0.7554, 0.104, 0.1917, 0.8625, 1, 1, 1, 0.8727, 0.1439,
0.0452, 1, 0.5804, 0, 0.2764), X7_removed = c(0.2989, 0.7268,
0.0087, 0.8874, 0.5853, 0.0568, 0, 0.7622, 0.4226, 0.3232, 0.3972,
0.02, 0.0159, 0.0541, 0.4919, 0.5951, 0.5525, 0.8114, 0.5738,
NA, 0.0062, 0.7274, 0.0155, 0.0233, 0.002, 1, 0.232, 0.3476,
0.011, 9e-04, 0.5433, 0.3725, 0.7263, 0.2462, 0.4556, 0.0426,
0.7468, 0.1235, 0.0051, 0, 1, 0.8962, 0.0014, 0, 0.9892, 0.1163,
0.1284, 0.6873, 0.3932, 0, 0.3722, 0.0889, 0.3782, 0.4761, 0.0484,
0.5321, 0.5519, 0, 0.3453, 0.0732, 0, 0.3483), X7_only = c(1,
0.5714, 0.2825, 0.8673, 0.5557, 0.6861, 0.0044, 0.1146, 0.4957,
0.5248, 0.8372, 0.6665, 0.6789, 1, 0.0082, 0.1759, 0.3719, 1,
0.704, NA, 0.2585, 0.4634, 0.4283, 0.6815, 0.4161, 1, 0.1691,
NA, 0.4563, 1, 0.226, 1, 0.2349, 0.5886, 0.8154, 0.8839, 0.1631,
1, 0.5112, 0.1529, 1, 0.7245, 4e-04, 0.3095, 0.6184, 0.5542,
0.749, 0.394, 0.0298, 0.1994, 0.2881, 0.7696, 0.0637, 0.652,
1, 0.1494, 1, 0.3283, 0.134, 0.1992, 0.0848, 0.5826), X5_removed = c(1,
0.1453, 0.0176, 0.8428, 0.2277, 0.2563, 0, 0.5326, 0.1549, 0.4405,
0.395, 0.0195, 0.08, 0.1069, 0.0316, 0.6298, 0.5157, 1, 0.5967,
NA, 0.0265, 0.5703, 0.2667, 0.3485, 0.0021, 1, 0.1821, 0.3006,
0.007, 0.0112, 0.1964, 0.4427, 0.769, 0.1214, 0.6064, 0.0914,
0.4188, 0.021, 0.0814, 0, 0.8372, 0.8052, 0, 0, 0.8662, 0.7917,
0.0924, 0.9316, 0.7399, 0, 0.2031, 0.0701, 0.0652, 0.6636, 0.0513,
0.2049, 0.7161, 0, 0.0407, 0.1729, 0, 0.3079), X5_only = c(0.0642,
0.631, 0.5193, 0.979, 0.5348, 0.1304, 0.02, 0.0217, 0.0871, 0.2022,
0.7602, 1, 0.3532, 0.5292, 1, 0.3677, 0.0896, 0.3702, 0.6084,
NA, 0.1518, 0.3467, 0.1171, 0.0252, 0.7894, 1, 0.9842, 0.7315,
0.8511, 0.0717, 0.0585, 0.7955, 0.3517, 1, 0.8263, 0.6102, 0.268,
0.1071, 0.3837, 0.0175, 0.5887, 1, NA, 0.1198, 0.8537, 0.0101,
0.3807, 0.4939, 0.1469, 0.1368, 0.5458, 0.2514, 1, 0.3692, 1,
0.4877, 0.5787, 0.6025, 0.5888, 1, 0.3472, 1), X4_removed = c(0.4492,
0.3821, 0.0121, 0.9957, 0.5158, 0.0498, 0, 0.718, 0.8003, 0.1716,
0.661, 0.0194, 0.0511, 0.1862, 0.0188, 0.6454, 0.5077, 1, 0.8794,
NA, 0.3458, 0.6059, 0.1315, 0.0099, 0.003, 1, 0.0585, 0.4635,
0.0357, 0.0289, 0.6835, 0.2247, 0.8437, 0.3585, 0.6074, 0.1926,
0.3432, 0.3615, 0.0322, 0, 0.8418, 0.7076, 0, 0.9281, 0.7697,
0.1011, 0.3068, 0.971, 0.4686, 0, 0.3731, 0.1024, 0.0683, 0.8112,
0.3742, 0.7381, 0.2738, 0.0089, 0.2366, 0.6924, 0, 0.1984), X4_only = c(0.6485,
0.0709, 0.1639, 0.6908, 1, 1, 0.4469, 0.639, 0.0378, 0.5116,
0.0026, 0.6549, 0.6928, 0.2884, 1, 0.4386, 0.6246, 0.6188, 1,
NA, 0.0966, 0.3946, 0.7223, 0.1357, 0.8912, 1, 0.4741, 0.7526,
0.2005, 0.013, 1, 0.455, 0.1086, 0.1184, 0.8975, 0.3181, 0.9958,
0.0644, 0.0975, 0.0721, 1, 1, 1, 7e-04, 0.2754, 0.4852, 0.065,
0.747, 0.4823, 0.1971, 0.6178, 0.3781, 1, 0.362, 0.1168, 0.382,
0.4267, 8e-04, 0.188, 0.2115, 0.2937, 1), X3_removed = c(0.3009,
0.3414, 0.02, 0.9935, 0.4216, 0.1273, 0, 0.6406, 0.2728, 0.5307,
0.477, 0.0612, 0.0627, 0.0808, 0.1636, 0.6506, 0.6507, 0.8122,
0.9531, NA, 0.0144, 0.9274, 0.1646, 0.0171, 1e-04, 1, 0.2732,
0.4153, 0.0141, 0.0105, 0.6892, 0.3701, 0.9956, 0.0418, 0.5436,
0.2755, 0.4803, 0.0959, 0.1199, 0, 0.833, 0.5373, 0, 0, 0.9701,
0.1054, 0.1558, 0.9964, 0.6849, 0, 0.2023, 0.1072, 0.3401, 0.3629,
0.2504, 0.6056, 0.5372, 2e-04, 0.1168, 1, 0, 0.242), X3_only = c(1,
0.9325, 0.772, 0.5505, 1, 0.2068, 0.0829, 0.17, 0.8951, 0.0225,
0.8263, 0.2111, 0.5087, 0.768, 0.2471, 0.6294, 0.2815, 1, 0.0496,
NA, 0.3364, 0.6286, 0.2102, 0.6816, 0.372, 1, 0.7311, 0.5138,
0.0683, 0.1996, 0.6998, 1, 0.6988, 0.4426, 0.6669, 0.0412, 0.6081,
1, 0.237, 6e-04, 0.6349, 0.7124, 1, 0.2314, 0.0398, 1, 0.3487,
0.8153, 0.1271, 0.1145, 0.8641, 0.4056, 0.1488, 1, 0.2357, 0.26,
1, 0.2678, 0.5537, 0.0317, 0.0467, 1), X2_removed = c(0.6335,
0.349, 0.2095, 0.9777, 0.8928, 0.0571, 0, 0.4285, 0.2036, 0.3168,
0.3668, 0.0854, 0.413, 0.0608, 0.0526, 0.7608, 0.3094, 0.8186,
0.9273, NA, 0.0014, 0.6512, 0.4424, 0.0275, 0.2121, 1, 0.3008,
0.2381, 0.0173, 0.0075, 0.7423, 0.6126, 0.979, 0.1716, 0.862,
0.0245, 0.5096, 0.2795, 0.4794, 0, 1, 0.6888, 0, 0, 0.6213, 0.0935,
0.1351, 0.6946, 0.4708, 0.1458, 0.899, 0.4391, 0.0727, 0.5004,
0.3974, 0.8854, 0.2696, 0, 0.1846, 0.5871, 0, 0.2966), X2_only = c(0.191,
0.4397, 0.0403, 0.3606, 0.0089, 1, 0.0033, 0.659, 0.1818, 0.0949,
0.5521, 0.1637, 0.0014, 1, NA, 0.8585, 1, 1, 0.9437, NA, 0.4086,
0.1699, 0.0648, 0.9087, 0.0011, 1, 0.1291, 0.5329, 0.2315, 0.2844,
0.6429, 0.0488, 0.1814, 0.8658, 0.0869, 0.8394, 0.5938, 0.1722,
0, 0.0098, 1, 1, 1, 0.1742, 0.3911, 0.8523, 0.7331, 0.1271, 0.5119,
0, 0.0105, 0.0035, 1, 0.5665, 0.072, 0.2928, 0.4224, 0.5491,
0.4274, 0.1054, 0, 0.5817), X1_removed = c(0.1653, 0.7658, 0.0718,
0.7705, 0.4193, 0.1894, 0, 0.5167, 0.1053, 0.2823, 0.0496, 0.1439,
0.0258, 0.0676, 0.031, 0.5465, 0.4909, 0.6464, 0.9383, NA, 0.0124,
0.9288, 0.069, 0.0116, 6e-04, 1, 0.3301, 0.508, 0.0175, 8e-04,
0.6016, 0.7442, 0.9609, 0.4151, 0.6049, 0.1266, 0.4281, 0.2719,
0.0039, 0, 0.315, 1, 0, 0, 0.8931, 0.1124, 0.3804, 0.9233, 0.3355,
0, 0.3542, 0.0363, 0.0679, 0.2652, 0.122, 0.4025, 0.8155, 2e-04,
0.2642, 0.3629, 0, 0.2897)), .Names = c("Loci", "All", "X1_only",
"X78_only", "X8_removed", "X8_only", "X7_removed", "X7_only",
"X5_removed", "X5_only", "X4_removed", "X4_only", "X3_removed",
"X3_only", "X2_removed", "X2_only", "X1_removed"), class = "data.frame", row.names = c(NA,
-62L))
#now make subsets of this big dataset
split1_data<-droplevels(subset(data,data$Loci %in% data$Loci[1:8]))
split2_data<-droplevels(subset(data,data$Loci %in% data$Loci[9:16]))
split3_data<-droplevels(subset(data,data$Loci %in% data$Loci[17:24]))
split4_data<-droplevels(subset(data,data$Loci %in% data$Loci[25:32]))
split5_data<-droplevels(subset(data,data$Loci %in% data$Loci[33:40]))
split6_data<-droplevels(subset(data,data$Loci %in% data$Loci[41:48]))
split7_data<-droplevels(subset(data,data$Loci %in% data$Loci[49:56]))
split8_data<-droplevels(subset(data,data$Loci %in% data$Loci[57:62]))
#and melt each of them
split1_datam<-melt(split1_data,id="Loci")
split2_datam<-melt(split2_data,id="Loci")
split3_datam<-melt(split3_data,id="Loci")
split4_datam<-melt(split4_data,id="Loci")
split5_datam<-melt(split5_data,id="Loci")
split6_datam<-melt(split6_data,id="Loci")
split7_datam<-melt(split7_data,id="Loci")
split8_datam<-melt(split8_data,id="Loci")
#and make a plot for each of the melted subsets
p1<- ggplot(split1_datam, aes(x =Loci, y = value, color = variable, width=.15))+ geom_bar(position="dodge")+ geom_hline(yintercept=0.05)+ opts(legend.position="none",axis.text.x = theme_text(angle=90, size=8)) + scale_y_discrete(breaks=seq(0,1)) + ylab(NULL)
p2<- ggplot(split2_datam, aes(x =Loci, y = value, color = variable, width=.15)) + geom_bar(position="dodge") + geom_hline(yintercept=0.05)+ opts(legend.position = "none", axis.text.x = theme_text(angle=90, size=8)) + scale_y_discrete(breaks=seq(0,1))+ scale_fill_grey() + ylab(NULL)
p3<-p <- ggplot(split3_datam, aes(x =Loci, y = value, color = variable, width=.15))
p3<-p3 + geom_bar(position="dodge") + geom_hline(yintercept=0.05)+ opts(legend.position = "none", axis.text.x = theme_text(angle=90, size=8)) + scale_y_discrete(breaks=seq(0,1)) + ylab(NULL)
p4<-p <- ggplot(split4_datam, aes(x =Loci, y = value, color = variable, width=.15))
p4<-p4 + geom_bar(position="dodge") + geom_hline(yintercept=0.05)+ scale_y_discrete(breaks=seq(0,1))+opts(legend.position="none", axis.text.x = theme_text(angle=90, size=8)) + ylab(NULL)
p5<-p <- ggplot(split5_datam, aes(x =Loci, y = value, color = variable, width=.15))
p5<-p5 + geom_bar(position="dodge") + geom_hline(yintercept=0.05)+ opts(legend.position = "none", axis.text.x = theme_text(angle=90, size=8))+ scale_y_discrete(breaks=seq(0,1)) + ylab(NULL)
p6<-p <- ggplot(split6_datam, aes(x =Loci, y = value, color = variable, width=.15))
p6<-p6 + geom_bar(position="dodge") + geom_hline(yintercept=0.05) + scale_y_discrete(breaks=seq(0,1))+ opts(legend.position = "none", axis.text.x = theme_text(angle=90, size=8)) + ylab(NULL)
p7<-p <- ggplot(split7_datam, aes(x =Loci, y = value, color = variable, width=.15))
p7<-p7 + geom_bar(position="dodge") + geom_hline(yintercept=0.05) + scale_y_discrete(breaks=seq(0,1))+ opts(legend.position = "none", axis.text.x = theme_text(angle=90, size=8)) + ylab(NULL)
p8<-p <- ggplot(split8_datam, aes(x =Loci, y = value, color = variable, width=.15))
p8<-p8 + geom_bar(position="dodge") + geom_hline(yintercept=0.05) + scale_y_discrete(breaks=seq(0,1))+opts(legend.position="none",axis.text.x = theme_text(angle=90, size=8)) + ylab(NULL)
#make a bad attempt at creating a legend for the entire multiplot?
leg <- ggplotGrob(p1 + opts(keep="legend_box"))
## one needs to provide the legend with a well-defined width
legend=gTree(children=gList(leg), cl="legendGrob")
widthDetails.legendGrob <- function(x) unit(2, "cm")
#plot the first four plots together
pdf("sensivitity_page_2_plots.pdf")
grid.arrange(p1, p2, p3, p4, nrow=2, legend=legend, main ="Sensitivity", left = "Pvalue")
dev.off()
#same with the next four
pdf("sensivitity_page_2_plots.pdf")
leg <- ggplotGrob(p5 + opts(keep="legend_box"))
## one needs to provide the legend with a well-defined width
legend=gTree(children=gList(leg), cl="legendGrob")
widthDetails.legendGrob <- function(x) unit(2, "cm")
grid.arrange(p5, p6, p7, p8, nrow=2, legend=legend, main ="Sensitivity", left = "Pvalue")
dev.off()
The plots will look like this:
I have the following questions:
Thanks so much, everyone!
解决方案 As far as I can tell, your p1 does not have a legend - hence there's no legend to be extracted, and thus no legend to be drawn in the call to grid.arrange.
Here's a simpler example. It should get you started.
EDIT: Code update for ggplot2 version 0.9.3.1
# Load the required packages
library(ggplot2)
library(gtable)
library(grid)
library(gridExtra)
# Generate some data
df <- data.frame(x = factor(rep(1:5, 2)), Groups = factor(rep(c("Group 1", "Group 2"), 5)))
# Get four plots
p1 <- ggplot(data = df, aes(x=x, y = sample(1:10, 10), fill = Groups)) +
geom_bar(position = "dodge", stat = "identity") + theme(axis.title.y = element_blank())
p2 <- ggplot(data = df, aes(x=x, y = sample(1:10, 10), fill = Groups)) +
geom_bar(position = "dodge", stat = "identity") + theme(axis.title.y = element_blank())
p3 <- ggplot(data = df, aes(x=x, y = sample(1:10, 10), fill = Groups)) +
geom_bar(position = "dodge", stat = "identity") + theme(axis.title.y = element_blank())
p4 <- ggplot(data = df, aes(x=x, y = sample(1:10, 10), fill = Groups)) +
geom_bar(position = "dodge", stat = "identity") + theme(axis.title.y = element_blank())
# Extracxt the legend from p1
legend = gtable_filter(ggplotGrob(p1), "guide-box")
# grid.draw(legend) # Make sure the legend has been extracted
# Arrange the elements to be plotted.
# The inner arrangeGrob() function arranges the four plots, the main title,
# and the global y-axis title.
# The outer grid.arrange() function arranges and draws the arrangeGrob object and the legend.
grid.arrange(arrangeGrob(p1 + theme(legend.position="none"),
p2 + theme(legend.position="none"),
p3 + theme(legend.position="none"),
p4 + theme(legend.position="none"),
nrow = 2,
top = textGrob("Main Title", vjust = 1, gp = gpar(fontface = "bold", cex = 1.5)),
left = textGrob("Global Y-axis Label", rot = 90, vjust = 1)),
legend,
widths=unit.c(unit(1, "npc") - legend$width, legend$width),
nrow=1)
Note how the widths uses the width of the legend. The result is:
The main title and the global y-axis title were positioned using vjust. If you want, say, the global y-axis title to take more space, then create it as a textGrob, and use widths to set its width. Here, the inner arrangeGrob arranges the four plots and the main title. The outer grid.arrange arranges and draws the global y-axis title, the arrangeGrob object, and the legend. The width of the global y-axis title is set to three lines.
label = textGrob("Global Y-axis Label", rot = 90, vjust = 0.5)
grid.arrange(label,
arrangeGrob(p1 + theme(legend.position="none"),
p2 + theme(legend.position="none"),
p3 + theme(legend.position="none"),
p4 + theme(legend.position="none"),
nrow = 2,
top = textGrob("Main Title", vjust = 1, gp = gpar(fontface = "bold", cex = 1.5))),
legend,
widths=unit.c(unit(3, "lines"), unit(1, "npc") - unit(3, "lines") - legend$width, legend$width),
nrow=1)
EDIT
Using your data, and your code for subsetting and reshaping the data, I've drawn the first four plots, extracted the legend from the first plot, then arranged the plots, legend, and label. The code ran with no problems.
There were some problems with the plots (and also, your code could not have produced the plots shown in your post). I made some minor changes.
# Load the libraries
library(ggplot2)
library(gridExtra)
library(reshape2)
###
# Your code from your post for getting the data, subsetting, and reshaping the data.
###
#and make a plot for each of the melted subsets
p1 <- ggplot(split1_datam, aes(x = Loci, y = value, fill = variable)) +
geom_bar(position = "dodge", stat = "identity")+ geom_hline(yintercept = 0.05) +
theme(axis.text.x = element_text(angle = 90, size = 8)) +
ylab(NULL)
p2 <- ggplot(split2_datam, aes(x = Loci, y = value, fill = variable)) +
geom_bar(position = "dodge", stat = "identity")+ geom_hline(yintercept = 0.05) +
theme(axis.text.x = element_text(angle = 90, size = 8)) +
ylab(NULL)
p3 <- ggplot(split3_datam, aes(x = Loci, y = value, fill = variable)) +
geom_bar(position = "dodge", stat = "identity")+ geom_hline(yintercept = 0.05) +
theme(axis.text.x = element_text(angle = 90, size = 8)) +
ylab(NULL)
p4 <- ggplot(split4_datam, aes(x = Loci, y = value, fill = variable)) +
geom_bar(position = "dodge", stat = "identity")+ geom_hline(yintercept = 0.05) +
theme(axis.text.x = element_text(angle = 90, size = 8)) +
ylab(NULL)
# Extracxt the legend from p1
legend = gtable_filter(ggplotGrob(p1), "guide-box")
# grid.draw(legend) # Make sure the legend has been extracted
# Arrange and draw the plot as before
label = textGrob("p value", rot = 90, vjust = 0.5)
grid.arrange(label,
arrangeGrob(p1 + theme(legend.position="none"),
p2 + theme(legend.position="none"),
p3 + theme(legend.position="none"),
p4 + theme(legend.position="none"),
nrow = 2,
top = textGrob("Sensitivity", vjust = 1, gp = gpar(fontface = "bold", cex = 1.5))),
legend,
widths=unit.c(unit(2, "lines"), unit(1, "npc") - unit(2, "lines") - legend$width, legend$width), nrow=1)
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