Terreno una leyenda y el eje y universal, bien espaciados y títulos principales en grid.arrange

Question

tengo el siguiente código que generará dos archivos PDF que contiene las parcelas en el directorio de trabajo actual:

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() 

Las parcelas se tener este aspecto:

tengo las siguientes preguntas:

1. ¿Cómo puedo espacio de la etiqueta del eje y por lo que no es tan cerca del borde?
2. ¿Cómo puedo hacer lo mismo con el título principal
3. ¿Por qué no aparece la leyenda? (esa parte del código se eliminó de la cuadrícula. Documentación de arreglos). Y, cuando lo hace, ¿cómo espacio eso correctamente? ¿Hay alguien más que use grid.arrange?
4. Si no es así, ¿hay alguna otra forma de aplicar layout() como posicionamiento en ggplot?

¡Muchísimas gracias a todos!

Answer 1

Por lo que puedo decir, su p1 no tiene una leyenda, por lo tanto, no hay leyenda que extraer, y por lo tanto no hay leyenda que se dibujará en la llamada a grid.arrange.

Aquí hay un ejemplo más simple. Debería comenzar.

EDIT: actualizar el código para la versión ggplot2 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) 

Nota cómo el widths utiliza el ancho de la leyenda. El resultado es:

El título principal y el título global eje y se colocaron utilizando vjust. Si desea, por ejemplo, el título global del eje y para ocupar más espacio, créelo como un TextGrob y use widths para establecer su ancho. Aquí, el organizador interior organiza las cuatro parcelas y el título principal. El grid.arrange externo organiza y dibuja el título global del eje y, el objeto arrangeGrob y la leyenda. El ancho del título del eje y global está establecido en tres líneas.

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) 

EDITAR

El uso de sus datos, y su código de creación de subconjuntos y la remodelación de los datos, he dibujado los primeros cuatro parcelas, se extrae la leyenda de la primera parcela, entonces dispuestas las parcelas, leyenda y etiqueta El código corrió sin problemas.

Hubo algunos problemas con los trazados (y además, su código no pudo haber producido los trazados que se muestran en su publicación). Hice algunos cambios menores.

# 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)