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如何根据数据列的值向X轴添加其他值？

Letin 发表于 Dev
18
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我正在使用循环绘制多个基因的散点图。每个基因产生多个png文件。每个基因/ png文件包含两个散点图：左侧为Group1，右侧为Group2。每个组都包含健康和不健康的样本。到目前为止，我已经成功地成功导出了代码。

但是，我现在要做的是为每个健康和不健康的组在每个“时间点”的x轴上添加样本数。这基于“样本”列。对于每个时间点，它应显示为“（（健康状态下的样品数量，不健康状态下的样品数量）”）。谁能帮助我实现这一目标？

我当前的2个基因的示例数据框'data'如下：

Biomarkers  TimePoint   Group   Scale   Readings    Condition   samples
Gene1   52.5    Group1  25  0.027   Healthy 33
Gene1   52.5    Group2  25  0.024   Healthy 35
Gene1   57.5    Group1  25  0.029   Healthy 39
Gene1   57.5    Group2  25  0.023   Healthy 46
Gene1   62.5    Group1  25  0.030   Healthy 38
Gene1   62.5    Group2  25  0.024   Healthy 42
Gene1   67.5    Group1  25  0.033   Healthy 23
Gene1   67.5    Group2  25  0.026   Healthy 16
Gene2   52.5    Group1  25  0.051   Healthy 33
Gene2   52.5    Group2  25  0.046   Healthy 35
Gene2   57.5    Group1  25  0.052   Healthy 39
Gene2   57.5    Group2  25  0.048   Healthy 46
Gene2   62.5    Group1  25  0.049   Healthy 38
Gene2   62.5    Group2  25  0.051   Healthy 42
Gene2   67.5    Group1  25  0.051   Healthy 23
Gene2   67.5    Group2  25  0.052   Healthy 16
Gene1   52.5    Group1  25.01   0.026   Unhealthy   41
Gene1   52.5    Group2  25.01   0.023   Unhealthy   57
Gene1   57.5    Group1  25.01   0.027   Unhealthy   79
Gene1   57.5    Group2  25.01   0.024   Unhealthy   70
Gene1   62.5    Group1  25.01   0.030   Unhealthy   93
Gene1   62.5    Group2  25.01   0.025   Unhealthy   84
Gene1   67.5    Group1  25.01   0.033   Unhealthy   98
Gene1   67.5    Group2  25.01   0.022   Unhealthy   64
Gene2   52.5    Group1  25.01   0.043   Unhealthy   36
Gene2   52.5    Group2  25.01   0.044   Unhealthy   57
Gene2   57.5    Group1  25.01   0.043   Unhealthy   79
Gene2   57.5    Group2  25.01   0.043   Unhealthy   70
Gene2   62.5    Group1  25.01   0.043   Unhealthy   93
Gene2   62.5    Group2  25.01   0.044   Unhealthy   84
Gene2   67.5    Group1  25.01   0.044   Unhealthy   98
Gene2   67.5    Group2  25.01   0.044   Unhealthy   64
Gene1   52.5    Group1  50  0.035   Healthy 33
Gene1   52.5    Group2  50  0.029   Healthy 35
Gene1   57.5    Group1  50  0.039   Healthy 39
Gene1   57.5    Group2  50  0.031   Healthy 46
Gene1   62.5    Group1  50  0.038   Healthy 38
Gene1   62.5    Group2  50  0.030   Healthy 42
Gene1   67.5    Group1  50  0.040   Healthy 23
Gene1   67.5    Group2  50  0.035   Healthy 16
Gene2   52.5    Group1  50  0.058   Healthy 33
Gene2   52.5    Group2  50  0.053   Healthy 35
Gene2   57.5    Group1  50  0.059   Healthy 39
Gene2   57.5    Group2  50  0.056   Healthy 46
Gene2   62.5    Group1  50  0.057   Healthy 38
Gene2   62.5    Group2  50  0.058   Healthy 42
Gene2   67.5    Group1  50  0.061   Healthy 23
Gene2   67.5    Group2  50  0.058   Healthy 16
Gene1   52.5    Group1  50.01   0.038   Unhealthy   41
Gene1   52.5    Group2  50.01   0.030   Unhealthy   57
Gene1   57.5    Group1  50.01   0.038   Unhealthy   79
Gene1   57.5    Group2  50.01   0.031   Unhealthy   70
Gene1   62.5    Group1  50.01   0.040   Unhealthy   93
Gene1   62.5    Group2  50.01   0.032   Unhealthy   84
Gene1   67.5    Group1  50.01   0.043   Unhealthy   98
Gene1   67.5    Group2  50.01   0.033   Unhealthy   64
Gene2   52.5    Group1  50.01   0.052   Unhealthy   36
Gene2   52.5    Group2  50.01   0.051   Unhealthy   57
Gene2   57.5    Group1  50.01   0.052   Unhealthy   79
Gene2   57.5    Group2  50.01   0.051   Unhealthy   70
Gene2   62.5    Group1  50.01   0.052   Unhealthy   93
Gene2   62.5    Group2  50.01   0.052   Unhealthy   84
Gene2   67.5    Group1  50.01   0.053   Unhealthy   98
Gene2   67.5    Group2  50.01   0.051   Unhealthy   64
Gene1   52.5    Group1  75  0.045   Healthy 33
Gene1   52.5    Group2  75  0.038   Healthy 35
Gene1   57.5    Group1  75  0.048   Healthy 39
Gene1   57.5    Group2  75  0.041   Healthy 46
Gene1   62.5    Group1  75  0.047   Healthy 38
Gene1   62.5    Group2  75  0.040   Healthy 42
Gene1   67.5    Group1  75  0.050   Healthy 23
Gene1   67.5    Group2  75  0.043   Healthy 16
Gene2   52.5    Group1  75  0.066   Healthy 33
Gene2   52.5    Group2  75  0.064   Healthy 35
Gene2   57.5    Group1  75  0.065   Healthy 39
Gene2   57.5    Group2  75  0.064   Healthy 46
Gene2   62.5    Group1  75  0.068   Healthy 38
Gene2   62.5    Group2  75  0.071   Healthy 42
Gene2   67.5    Group1  75  0.070   Healthy 23
Gene2   67.5    Group2  75  0.071   Healthy 16
Gene1   52.5    Group1  75.01   0.057   Unhealthy   41
Gene1   52.5    Group2  75.01   0.041   Unhealthy   57
Gene1   57.5    Group1  75.01   0.056   Unhealthy   79
Gene1   57.5    Group2  75.01   0.040   Unhealthy   70
Gene1   62.5    Group1  75.01   0.057   Unhealthy   93
Gene1   62.5    Group2  75.01   0.043   Unhealthy   84
Gene1   67.5    Group1  75.01   0.059   Unhealthy   98
Gene1   67.5    Group2  75.01   0.046   Unhealthy   64
Gene2   52.5    Group1  75.01   0.063   Unhealthy   36
Gene2   52.5    Group2  75.01   0.060   Unhealthy   57
Gene2   57.5    Group1  75.01   0.061   Unhealthy   79
Gene2   57.5    Group2  75.01   0.062   Unhealthy   70
Gene2   62.5    Group1  75.01   0.062   Unhealthy   93
Gene2   62.5    Group2  75.01   0.062   Unhealthy   84
Gene2   67.5    Group1  75.01   0.061   Unhealthy   98
Gene2   67.5    Group2  75.01   0.060   Unhealthy   64
Gene1   52.5    Group1  100 0.056   Healthy 33
Gene1   52.5    Group2  100 0.046   Healthy 35
Gene1   57.5    Group1  100 0.063   Healthy 39
Gene1   57.5    Group2  100 0.048   Healthy 46
Gene1   62.5    Group1  100 0.060   Healthy 38
Gene1   62.5    Group2  100 0.052   Healthy 42
Gene1   67.5    Group1  100 0.064   Healthy 23
Gene1   67.5    Group2  100 0.055   Healthy 16
Gene2   52.5    Group1  100 0.082   Healthy 33
Gene2   52.5    Group2  100 0.074   Healthy 35
Gene2   57.5    Group1  100 0.070   Healthy 39
Gene2   57.5    Group2  100 0.075   Healthy 46
Gene2   62.5    Group1  100 0.074   Healthy 38
Gene2   62.5    Group2  100 0.078   Healthy 42
Gene2   67.5    Group1  100 0.080   Healthy 23
Gene2   67.5    Group2  100 0.075   Healthy 16
Gene1   52.5    Group1  100.01  0.090   Unhealthy   41
Gene1   52.5    Group2  100.01  0.060   Unhealthy   57
Gene1   57.5    Group1  100.01  0.093   Unhealthy   79
Gene1   57.5    Group2  100.01  0.053   Unhealthy   70
Gene1   62.5    Group1  100.01  0.089   Unhealthy   93
Gene1   62.5    Group2  100.01  0.057   Unhealthy   84
Gene1   67.5    Group1  100.01  0.089   Unhealthy   98
Gene1   67.5    Group2  100.01  0.065   Unhealthy   64
Gene2   52.5    Group1  100.01  0.074   Unhealthy   36
Gene2   52.5    Group2  100.01  0.074   Unhealthy   57
Gene2   57.5    Group1  100.01  0.077   Unhealthy   79
Gene2   57.5    Group2  100.01  0.078   Unhealthy   70
Gene2   62.5    Group1  100.01  0.073   Unhealthy   93
Gene2   62.5    Group2  100.01  0.073   Unhealthy   84
Gene2   67.5    Group1  100.01  0.072   Unhealthy   98
Gene2   67.5    Group2  100.01  0.074   Unhealthy   64

我的数据的Dput是：

dput(data)
structure(list(Biomarkers = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("Gene1", 
"Gene2"), class = "factor"), TimePoint = c(52.5, 52.5, 57.5, 
57.5, 62.5, 62.5, 67.5, 67.5, 52.5, 52.5, 57.5, 57.5, 62.5, 62.5, 
67.5, 67.5, 52.5, 52.5, 57.5, 57.5, 62.5, 62.5, 67.5, 67.5, 52.5, 
52.5, 57.5, 57.5, 62.5, 62.5, 67.5, 67.5, 52.5, 52.5, 57.5, 57.5, 
62.5, 62.5, 67.5, 67.5, 52.5, 52.5, 57.5, 57.5, 62.5, 62.5, 67.5, 
67.5, 52.5, 52.5, 57.5, 57.5, 62.5, 62.5, 67.5, 67.5, 52.5, 52.5, 
57.5, 57.5, 62.5, 62.5, 67.5, 67.5, 52.5, 52.5, 57.5, 57.5, 62.5, 
62.5, 67.5, 67.5, 52.5, 52.5, 57.5, 57.5, 62.5, 62.5, 67.5, 67.5, 
52.5, 52.5, 57.5, 57.5, 62.5, 62.5, 67.5, 67.5, 52.5, 52.5, 57.5, 
57.5, 62.5, 62.5, 67.5, 67.5, 52.5, 52.5, 57.5, 57.5, 62.5, 62.5, 
67.5, 67.5, 52.5, 52.5, 57.5, 57.5, 62.5, 62.5, 67.5, 67.5, 52.5, 
52.5, 57.5, 57.5, 62.5, 62.5, 67.5, 67.5, 52.5, 52.5, 57.5, 57.5, 
62.5, 62.5, 67.5, 67.5), Group = structure(c(1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L), .Label = c("Group1", 
"Group2"), class = "factor"), Scale = c(25, 25, 25, 25, 25, 25, 
25, 25, 25, 25, 25, 25, 25, 25, 25, 25, 25.01, 25.01, 25.01, 
25.01, 25.01, 25.01, 25.01, 25.01, 25.01, 25.01, 25.01, 25.01, 
25.01, 25.01, 25.01, 25.01, 50, 50, 50, 50, 50, 50, 50, 50, 50, 
50, 50, 50, 50, 50, 50, 50, 50.01, 50.01, 50.01, 50.01, 50.01, 
50.01, 50.01, 50.01, 50.01, 50.01, 50.01, 50.01, 50.01, 50.01, 
50.01, 50.01, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 75, 
75, 75, 75, 75, 75.01, 75.01, 75.01, 75.01, 75.01, 75.01, 75.01, 
75.01, 75.01, 75.01, 75.01, 75.01, 75.01, 75.01, 75.01, 75.01, 
100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 100, 
100, 100, 100, 100.01, 100.01, 100.01, 100.01, 100.01, 100.01, 
100.01, 100.01, 100.01, 100.01, 100.01, 100.01, 100.01, 100.01, 
100.01, 100.01), Readings = c(0.027, 0.024, 0.029, 0.023, 0.03, 
0.024, 0.033, 0.026, 0.051, 0.046, 0.052, 0.048, 0.049, 0.051, 
0.051, 0.052, 0.026, 0.023, 0.027, 0.024, 0.03, 0.025, 0.033, 
0.022, 0.043, 0.044, 0.043, 0.043, 0.043, 0.044, 0.044, 0.044, 
0.035, 0.029, 0.039, 0.031, 0.038, 0.03, 0.04, 0.035, 0.058, 
0.053, 0.059, 0.056, 0.057, 0.058, 0.061, 0.058, 0.038, 0.03, 
0.038, 0.031, 0.04, 0.032, 0.043, 0.033, 0.052, 0.051, 0.052, 
0.051, 0.052, 0.052, 0.053, 0.051, 0.045, 0.038, 0.048, 0.041, 
0.047, 0.04, 0.05, 0.043, 0.066, 0.064, 0.065, 0.064, 0.068, 
0.071, 0.07, 0.071, 0.057, 0.041, 0.056, 0.04, 0.057, 0.043, 
0.059, 0.046, 0.063, 0.06, 0.061, 0.062, 0.062, 0.062, 0.061, 
0.06, 0.056, 0.046, 0.063, 0.048, 0.06, 0.052, 0.064, 0.055, 
0.082, 0.074, 0.07, 0.075, 0.074, 0.078, 0.08, 0.075, 0.09, 0.06, 
0.093, 0.053, 0.089, 0.057, 0.089, 0.065, 0.074, 0.074, 0.077, 
0.078, 0.073, 0.073, 0.072, 0.074), Condition = structure(c(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, 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, 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, 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), .Label = c("Healthy", 
"Unhealthy"), class = "factor"), samples = c(33L, 35L, 39L, 46L, 
38L, 42L, 23L, 16L, 33L, 35L, 39L, 46L, 38L, 42L, 23L, 16L, 41L, 
57L, 79L, 70L, 93L, 84L, 98L, 64L, 36L, 57L, 79L, 70L, 93L, 84L, 
98L, 64L, 33L, 35L, 39L, 46L, 38L, 42L, 23L, 16L, 33L, 35L, 39L, 
46L, 38L, 42L, 23L, 16L, 41L, 57L, 79L, 70L, 93L, 84L, 98L, 64L, 
36L, 57L, 79L, 70L, 93L, 84L, 98L, 64L, 33L, 35L, 39L, 46L, 38L, 
42L, 23L, 16L, 33L, 35L, 39L, 46L, 38L, 42L, 23L, 16L, 41L, 57L, 
79L, 70L, 93L, 84L, 98L, 64L, 36L, 57L, 79L, 70L, 93L, 84L, 98L, 
64L, 33L, 35L, 39L, 46L, 38L, 42L, 23L, 16L, 33L, 35L, 39L, 46L, 
38L, 42L, 23L, 16L, 41L, 57L, 79L, 70L, 93L, 84L, 98L, 64L, 36L, 
57L, 79L, 70L, 93L, 84L, 98L, 64L)), class = "data.frame", row.names = c(NA, 
-128L))

我现在拥有的代码是这样的：

# Load libraries
library(ggplot2)
library(magrittr)
library(dplyr)
library(gridExtra)
library(grid)

proc_plot <- function(sub) {

  data_Group1 <- sub[sub$Group == "Group1", ]
  data_Group2 <- sub[sub$Group == "Group2", ]

  min_rdg <- min(data_Group1$Readings, data_Group2$Readings)
  max_rdg <- max(data_Group1$Readings, data_Group2$Readings)

  # Group1
  graph_Group1 <- ggplot(data_Group1, aes(x = TimePoint, y = Readings, group = Scale)) +
    labs(title="Group1", x="Time point", y="Readings") +
    scale_x_continuous(breaks = c(52.5, 57.5, 62.5, 67.5),
                       labels = c("1", "2", "3", "4")) +
    geom_line(aes(color = Scale, linetype=Condition), na.rm = TRUE, size = 0.8) +
    geom_point(aes(color = Scale),size = 2.5, na.rm = TRUE) +
    scale_color_continuous(name = "Scale", breaks = c(25, 50, 75, 100)) +
    scale_y_continuous(limits = c(min_rdg, max_rdg)) +
    theme(legend.key.height = unit(2.3, "cm"))

  # Group2
  graph_Group2 <- ggplot(data_Group2, aes(x = TimePoint, y = Readings, group = Scale)) +
    labs(title="Group2", x="Time point", y="Readings") +
    scale_x_continuous(breaks = c(52.5, 57.5, 62.5, 67.5),
                       labels = c("1", "2", "3", "4")) +
    geom_line(aes(color = Scale, linetype=Condition), na.rm = TRUE, size = 0.8) +
    geom_point(aes(color = Scale), size = 2.5, na.rm = TRUE) +
    scale_color_continuous(name = "Scale", breaks = c(25, 50, 75, 100)) +
    scale_y_continuous(limits = c(min_rdg, max_rdg)) +
    theme(legend.key.height = unit(2.3, "cm"))

  png (paste0("ScatterPlot_", sub$Biomarkers[[1]], ".png"), height=600, width=1111)
    output <- grid.arrange(graph_Group1, graph_Group2, nrow = 1, 
                           top=textGrob(sub$Biomarkers[[1]], gp=gpar(fontsize=20)))
  dev.off()

  return(output)
}


# BUILD PLOT LIST AND PNG FILES
plot_list <- by(data, data$Biomarkers, proc_plot)

dev.off()
grid.draw(plot_list$Gene1)

dev.off()
grid.draw(plot_list$Gene2)

我还在下面附加了Gene1的示例png文件。我已经用红色手动添加了数字以突出显示并显示出每个基因/ png文件确实需要的数字（但黑色）。在此处输入图片说明

任何帮助表示赞赏。感谢您。

格雷戈尔·托马斯（Gregor Thomas）

您可以\n在标签中使用换行符。例如，

scale_x_continuous(breaks = c(52.5, 57.5, 62.5, 67.5),
                   labels = c("1\n(33, 41)", "2\n(39, 79)", "3\n(38, 93)", "4\n(23, 98)"))

您可以通过编程方式执行以下操作：

lab_df = data_Group1 %>% group_by(TimePoint) %>% 
  summarize(label = sprintf("(%s, %s)", first(samples[Condition == "Healthy"]), first(samples[Condition == "Unhealthy"])))
lab_df                                                  
# # A tibble: 4 x 2
#   TimePoint label   
#       <dbl> <chr>   
# 1      52.5 (33, 41)
# 2      57.5 (39, 79)
# 3      62.5 (38, 93)
# 4      67.5 (23, 98)

ggplot(...) + ... +
  scale_x_continuous(
    breaks = lab_df$TimePoint,
    labels = paste(1:nrow(lab_df), lab_df$label, sep = "\n")
  )

全方位服务解决方案。简化为使用for循环而不是单独处理组，而是通过程序处理标签。

proc_plot <- function(sub) {
  lab_df = sub %>% group_by(TimePoint, Group) %>% 
    summarize(label = sprintf(
      "(%s, %s)", 
      first(samples[Condition == "Healthy"]),
      first(samples[Condition == "Unhealthy"])
    )) %>%
    arrange(Group, TimePoint) # make sure things are in order

  min_rdg <- min(sub$Readings)
  max_rdg <- max(sub$Readings)

  graphs = list()

  for (i in unique(sub$Group)) {
    this_lab = lab_df[lab_df$Group == i, ]
    graphs[[i]] =  ggplot(sub[sub$Group == i, ], aes(x = TimePoint, y = Readings, group = Scale)) +
      labs(title = i, x = "Time point", y = "Readings") +
      scale_x_continuous(breaks = this_lab$TimePoint,
                         labels = paste(1:nrow(this_lab), this_lab$label, sep = "\n")) +
      geom_line(aes(color = Scale, linetype=Condition), na.rm = TRUE, size = 0.8) +
      geom_point(aes(color = Scale),size = 2.5, na.rm = TRUE) +
      scale_color_continuous(name = "Scale", breaks = c(25, 50, 75, 100)) +
      scale_y_continuous(limits = c(min_rdg, max_rdg)) +
      theme(legend.key.height = unit(2.3, "cm"))
  }

  png (paste0("ScatterPlot_", sub$Biomarkers[[1]], ".png"), height=600, width=1111)
    output <- grid.arrange(grobs = graphs, nrow = 1, 
                           top = textGrob(sub$Biomarkers[[1]], gp = gpar(fontsize = 20)))
  dev.off()
  return(output)
}

proc_plot(sub[sub$Biomarkers == "Gene1", ])

在此处输入图片说明

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