如何将多个变量(错误条)融为一体，以映射到geom_bar？

Question

首先，我的目标是为我的每个变量(震级、持续时间和距离域)生成图形，但是使用不同的错误条来进行训练和测试。：

几乎完成图

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我有一个像这样的数据框架：(下面是屏幕截图+ dput )。它显示了各种生物菌株在训练和测试过程中的响应(大小、距离、持续时间)及其标准误差(SEM)。例如，列车的持续时间响应在列"train_avg_dura“中，在测试中的响应是"test_avg_dura”。其中每一项的标准错误都出现在列train_duraSEM和test_duraSEM中。

df_group_sum.wide (数据帧)

dput data:
df_group_sum.wide <-
structure(list(strain = structure(1:8, .Label = c("N2", "acy-1(LOF)",
"acy-1(GOF)", "pde-4", "unc-43", "crh-1", "glr-1", "avr-14"), class = "factor"),
test_avg_dist = c(0.23102447163515, 0.198503787878788, 0.23892936802974,
0.247270588235294, 0.148316666666667, 0.195762711864407,
0.204740740740741, 0.238755154639175), test_avg_dura = c(1.04759733036707,
1.15537878787879, 0.914684014869888, 1.12286274509804, 0.828916666666667,
0.785491525423729, 0.788407407407407, 1.02309278350515),
test_avg_mag = c(0.112163461525871, 0.113447031611172, 0.15930172539742,
0.105397926645665, 0.0370000063024116, 0.0823626968797451,
0.0441620688813484, 0.135786546158742), test_distSEM = c(0.00460504533342531,
0.0050568065734325, 0.00945562739572128, 0.00524044558789062,
0.00882224860763199, 0.00983820301449839, 0.0162322856355826,
0.00738407922404085), test_duraSEM = c(0.0187491841242793,
0.0287113186085301, 0.0283764910080623, 0.0215386973519077,
0.0471018319675206, 0.0341593217329755, 0.0564553992545153,
0.0271939362203803), test_magSEM = c(0.00335619679815181,
0.00443251320170775, 0.00919066553588191, 0.00432150262248429,
0.00400887448034098, 0.00664866437888279, 0.00575860867691942,
0.00524462205156711), train_avg_dist = c(0.337652222222222,
0.294218518518519, 0.338651851851852, 0.311313725490196,
0.254675, 0.2737, 0.390688888888889, 0.314817948717949),
train_avg_dura = c(1.3543, 1.429, 1.19151851851852, 1.37256862745098,
1.236, 1.06376666666667, 1.41396296296296, 1.31512820512821
), train_avg_mag = c(0.1930557426236, 0.19297076970836, 0.212916856705011,
0.127417008935649, 0.0841239843171108, 0.117210954090848,
0.115413610503398, 0.179227387006556)), class = "data.frame", .Names = c("strain",
"test_avg_dist", "test_avg_dura", "test_avg_mag", "test_distSEM",
"test_duraSEM", "test_magSEM", "train_avg_dist", "train_avg_dura",
"train_avg_mag"), row.names = c(NA, -8L))

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问题是如何使用扫描电镜添加错误条，因为当我将变量映射到geom_bar时，需要将它们合并为一个变量，而不是两个变量。我认为这是一个融化的问题，但我无法弄清楚。

更新：

我用来绘制图形的已熔化的数据帧如下：

structure(list(strain = structure(c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 
8L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L), .Label = c("N2", "acy-1(LOF)", 
"acy-1(GOF)", "pde-4", "unc-43", "crh-1", "glr-1", "avr-14"), class = "factor"), 
    variable = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
    5L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 
    7L, 7L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 
    9L, 9L, 9L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 10L, 11L, 
    11L, 11L, 11L, 11L, 11L, 11L, 11L, 12L, 12L, 12L, 12L, 12L, 
    12L, 12L, 12L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 13L, 14L, 
    14L, 14L, 14L, 14L, 14L, 14L, 14L, 15L, 15L, 15L, 15L, 15L, 
    15L, 15L, 15L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 16L, 17L, 
    17L, 17L, 17L, 17L, 17L, 17L, 17L, 18L, 18L, 18L, 18L, 18L, 
    18L, 18L, 18L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 19L, 20L, 
    20L, 20L, 20L, 20L, 20L, 20L, 20L), .Label = c("test_avg_dist", 
    "test_avg_dura", "test_avg_mag", "test_avg_prob", "test_avg_spd", 
    "test_distSEM", "test_duraSEM", "test_magSEM", "test_probSEM", 
    "test_spdSEM", "train_avg_dist", "train_avg_dura", "train_avg_mag", 
    "train_avg_prob", "train_avg_spd", "train_distSEM", "train_duraSEM", 
    "train_magSEM", "train_probSEM", "train_spdSEM"), class = "factor"), 
    value = c(0.23102447163515, 0.198503787878788, 0.23892936802974, 
    0.247270588235294, 0.148316666666667, 0.195762711864407, 
    0.204740740740741, 0.238755154639175, 1.04759733036707, 1.15537878787879, 
    0.914684014869888, 1.12286274509804, 0.828916666666667, 0.785491525423729, 
    0.788407407407407, 1.02309278350515, 0.112163461525871, 0.113447031611172, 
    0.15930172539742, 0.105397926645665, 0.0370000063024116, 
    0.0823626968797451, 0.0441620688813484, 0.135786546158742, 
    0.457040018571118, 0.563727434411572, 0.624264612406578, 
    0.392625726149316, 0.219488346025285, 0.355836464305103, 
    0.158243463050796, 0.549997886634136, 0.218104671667048, 
    0.175578055416405, 0.256197987699313, 0.218534931269605, 
    0.181253278716812, 0.235434749265196, 0.236043513165036, 
    0.229165553562148, 0.00460504533342531, 0.0050568065734325, 
    0.00945562739572128, 0.00524044558789062, 0.00882224860763199, 
    0.00983820301449839, 0.0162322856355826, 0.00738407922404085, 
    0.0187491841242793, 0.0287113186085301, 0.0283764910080623, 
    0.0215386973519077, 0.0471018319675206, 0.0341593217329755, 
    0.0564553992545153, 0.0271939362203803, 0.00335619679815181, 
    0.00443251320170775, 0.00919066553588191, 0.00432150262248429, 
    0.00400887448034098, 0.00664866437888279, 0.00575860867691942, 
    0.00524462205156711, 0.00460504533342531, 0.0050568065734325, 
    0.00945562739572128, 0.00524044558789062, 0.00882224860763199, 
    0.00983820301449839, 0.0162322856355826, 0.00738407922404085, 
    0.00148090077905166, 0.00224725406956702, 0.00293788372166611, 
    0.00142518092482957, 0.00475313026432338, 0.00259537819051875, 
    0.00439432015310276, 0.00179190641262238, 0.337652222222222, 
    0.294218518518519, 0.338651851851852, 0.311313725490196, 
    0.254675, 0.2737, 0.390688888888889, 0.314817948717949, 1.3543, 
    1.429, 1.19151851851852, 1.37256862745098, 1.236, 1.06376666666667, 
    1.41396296296296, 1.31512820512821, 0.1930557426236, 0.19297076970836, 
    0.212916856705011, 0.127417008935649, 0.0841239843171108, 
    0.117210954090848, 0.115413610503398, 0.179227387006556, 
    0.525206741295172, 0.606796097537911, 0.592920766963248, 
    0.383218177729097, 0.294853306191478, 0.37983654970313, 0.244065736387288, 
    0.529995494304863, 0.245519078777542, 0.204069564920836, 
    0.279438682643543, 0.223741850875084, 0.203505986396722, 
    0.244494243449087, 0.263225928969608, 0.235094347033923, 
    0.00509151719343593, 0.00741331297357774, 0.0110354960774679, 
    0.0058641318136066, 0.0114389388703232, 0.0108143010933781, 
    0.0182904578688527, 0.00913426247712326, 0.0167858570502119, 
    0.0279705569908445, 0.030133138276768, 0.0219057666071679, 
    0.0479637760140276, 0.0332974908188985, 0.0605392786801207, 
    0.0323033076008837, 0.00498395111761598, 0.0081988397756359, 
    0.0107052683837969, 0.00442352355941589, 0.00723029142814287, 
    0.00764631328347674, 0.00980735575566329, 0.00789476278044047, 
    0.00509151719343593, 0.00741331297357774, 0.0110354960774679, 
    0.0058641318136066, 0.0114389388703232, 0.0108143010933781, 
    0.0182904578688527, 0.00913426247712326, 0.00139403793044242, 
    0.00220415921330836, 0.00299625483623813, 0.00144528089431754, 
    0.00441088530148196, 0.00248394605240026, 0.00319027562414684, 
    0.00174638373495128)), row.names = c(NA, -160L), .Names = c("strain", 
"variable", "value"), class = "data.frame")

我用来绘制此图的代码(删除SEM行之后)如下：

    (abs_bar_mag <- 
    df_group_sum.long %>% 
    filter(grepl("mag", variable)) %>% 
    ggplot(aes(x = strain,
               y = value,
               fill = variable))+
    scale_fill_manual(values=c("lightseagreen", "indianred1"))+
    geom_bar(stat="identity", position = "dodge") + 
    #geom_errorbar(aes(ymin=value-1, ymax=value+1), width=.1, position = position_dodge(width=0.9)) +
    theme(panel.background = element_blank()) +
    theme(text = element_text(size = 20),
          axis.line = element_line(colour = "black")) +
    ggtitle("") +
    theme(plot.title = element_text(size = 30, hjust = 0.5, face = "bold"),
          axis.text = element_text(size = 70),
          strip.text = element_text(size = 40),
          axis.text.x = element_text(angle = 65, hjust = 1,  size = 40),
          axis.title.y = (element_text(size = 65)))
  +
    labs(colour = "",
         y = "Magnitude",
         x = "") +
    scale_colour_manual(values = rev())
  )

我很感激你的任何建议或解决方案！

谢谢你，阿拉姆

Answer 1

这里的问题是avg列和SEM (标准错误)列需要保持在一起。这需要同时重塑两个值列。See section 3.a of 为您提供更多细节。

因此，我们从宽格式的数据(df_group_sum.wide)开始。为了与OP提供的代码保持一致，只绘制了幅度。

library(data.table)
library(ggplot2)

molten <- melt(
  data.table(df_group_sum.wide), id.vars = "strain", 
  measure.vars = patterns("avg_mag$", "magSEM$"),
  value.name = c("avg", "SEM"))[
    , variable := forcats::lvls_revalue(variable, c("test_mag", "train_mag"))][]
molten

strain variable avg SEM 1: N2 test\_mag 0.11216346 0.003356197 2: acy-1(LOF) test\_mag 0.11344703 0.004432513 3: acy-1(GOF) test\_mag 0.15930173 0.009190666 4: pde-4 test\_mag 0.10539793 0.004321503 5: unc-43 test\_mag 0.03700001 0.004008874 6: crh-1 test\_mag 0.08236270 0.006648664 7: glr-1 test\_mag 0.04416207 0.005758609 8: avr-14 test\_mag 0.13578655 0.005244622 9: N2 train\_mag 0.19305574 NA 10: acy-1(LOF) train\_mag 0.19297077 NA 11: acy-1(GOF) train\_mag 0.21291686 NA 12: pde-4 train\_mag 0.12741701 NA 13: unc-43 train\_mag 0.08412398 NA 14: crh-1 train\_mag 0.11721095 NA 15: glr-1 train\_mag 0.11541361 NA 16: avr-14 train\_mag 0.17922739 NA

ggplot(molten, 
  aes(strain, avg, ymin = avg - SEM, ymax = avg + SEM, fill = variable)) +
geom_col(position = "dodge") + 
geom_errorbar(width=.1, position = position_dodge(width=0.9)) +
scale_fill_manual(values=c("lightseagreen", "indianred1")) +
theme_bw() +  
labs(fill = "", y = "Magnitude", x = "")

​

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OP还提供了一个长格式的data.frame，它确实包含了比df_group_sum.wide更多的数据。现在也应该把这些画出来。

通过查看变量名

unique(df_group_sum.long$variable)

test_avg_dist test_avg_dura test_avg_mag test_avg_prob test_avg_spd 6 test_distSEM test_duraSEM test_magSEM test_probSEM test_spdSEM 11 train_avg_dist train_avg_dura train_avg_mag train_avg_prob train_avg_spd 16 train_distSEM train_duraSEM train_avg_spd 20级:en21 en23#.train_spdSEM

data.frame似乎包含两个数据集(train和test)的五个不同变量(dist、dura、mag、prob、spd)的聚合数据(avg和SEM)。同样，avg和SEM需要保持在一行上，用错误条来绘制条形图。

不幸的是，命名方案是不一致的。如果包含标准错误的变量被命名为类似于train_avg_mag (例如，train_SEM_mag而不是train_magSEM )，情况会更好。

因此，第一步是将variable名称分开，分别得到不同的组：

library(data.table)
DT <- data.table(df_group_sum.long)
DT[, c("dataset", "measure", "variable") := 
     DT[, tstrsplit(variable, "_|SEM$")][is.na(V3), `:=`(V3 = V2, V2 = "SEM")]]
DT

strain variable value dataset measure 1: N2 dist 0.231024472 test avg 2: acy-1(LOF) dist 0.198503788 test avg 3: acy-1(GOF) dist 0.238929368 test avg 4: pde-4 dist 0.247270588 test avg 5: unc-43 dist 0.148316667 test avg --- 156: pde-4 spd 0.001445281 train SEM 157: unc-43 spd 0.004410885 train SEM 158: crh-1 spd 0.002483946 train SEM 159: glr-1 spd 0.003190276 train SEM 160: avr-14 spd 0.001746384 train SEM

unique(DT[, variable])

"dist“"dura”"mag“"prob”"spd“

unique(DT[, dataset])

“试验”“火车”

unique(DT[, measure])

"avg“"SEM”

现在，使用联接上的更新将缩写变量名替换为它们的全名。

abbr2full <- data.table(
  variable = c("dist", "dura", "mag"), 
  full = c("Distance", "Duration", "Magnitude")
)
DT[abbr2full, on = "variable", variable := full][]

最后，创建了所有五个变量的平面图。dcast()用于将数据从long重新格式化为宽格式，其中每一行都有两个度量标准avg和SEM。

library(ggplot2)
ggplot(dcast(DT, ... ~ measure), 
       aes(strain, avg, ymin = avg - SEM, ymax = avg + SEM, fill = dataset)) +
  geom_col(position = "dodge") + 
  geom_errorbar(width=.1, position = position_dodge(width=0.9)) +
  scale_fill_manual(values=c("lightseagreen", "indianred1")) +
  theme_bw() +  
  labs(fill = "", y = "Average", x = "") + 
  facet_wrap(~ variable, scales = "free_y") +
  theme(axis.text.x = element_text(angle = 65, hjust = 1))

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