R 如何将多个变量（误差条）融合/收集为一个，以映射到几何图形条？_R_Ggplot2_Melt_Geom Bar

R 如何将多个变量（误差条）融合/收集为一个，以映射到几何图形条？

R 如何将多个变量（误差条）融合/收集为一个，以映射到几何图形条？,r,ggplot2,melt,geom-bar,R,Ggplot2,Melt,Geom Bar,我将从我的目标开始，即为我的每个变量（幅值[mag]、持续时间[dura]和距离[dist]）生成图形，但要为训练和测试生成不同的误差条。：几乎完成图我有一个这样的数据框：（下面的截图+dput）。它显示了各种生物菌株在训练和测试期间的响应（幅度、距离、持续时间）以及它们的标准误差（SEM）。例如，训练时的持续时间响应在列“训练平均持续时间”中，而测试时的持续时间响应是“测试平均持续时间”.每一项的标准误差在列_duraSEM和测试_duraSEM中 df_组_总和（数据帧）我用于绘制此

我将从我的目标开始，即为我的每个变量（幅值[mag]、持续时间[dura]和距离[dist]）生成图形，但要为训练和测试生成不同的误差条。：

几乎完成图

我有一个这样的数据框：（下面的截图+dput）。它显示了各种生物菌株在训练和测试期间的响应（幅度、距离、持续时间）以及它们的标准误差（SEM）。例如，训练时的持续时间响应在列“训练平均持续时间”中，而测试时的持续时间响应是“测试平均持续时间”.每一项的标准误差在列_duraSEM和测试_duraSEM中

df_组_总和（数据帧）

我用于绘制此图的代码（删除SEM行后）如下所示：

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")

    (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())
  )

（abs\u bar\u mag%
过滤器（grepl（“mag”，变量））%>%
ggplot（aes（x=应变，
y=值，
填充=可变）+
刻度填充手册（数值=c（“lightseagreen”、“indianred1”））+
几何图形栏（stat=“identity”，position=“dodge”）+
#几何误差条（aes（ymin=value-1，ymax=value+1），宽度=0.1，位置=position\u减淡（宽度=0.9））+
主题（panel.background=element\u blank（））+
主题（文本=元素\文本（大小=20），
轴线=元素线（颜色=“黑色”））+
标题（“”）+
主题（plot.title=element\u text（大小=30，hjust=0.5，face=“bold”），
axis.text=元素\文本（大小=70），
strip.text=元素\文本（尺寸=40），
axis.text.x=元素\文本（角度=65，尺寸=1，尺寸=40），
axis.title.y=（元素_文本（大小=65）））
+
实验室（颜色=”，
y=“震级”，
x=“”）+
比例\颜色\手册（值=rev（））
)

我非常感谢您提供的任何建议或解决方案

谢谢，

Aram

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。有关详细信息，请参阅

因此，我们从宽格式的数据（

df_group_sum.wide

）开始。为了与OP提供的代码一致，只绘制了震级

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

OP还提供了一个长格式的data.frame

df_group_sum.long

，它包含的数据确实比

df_group_sum.wide

多。现在也应该绘制这些数据

通过查看变量名

unique(df_group_sum.long$variable)

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，以便绘制带有错误条的条形图

不幸的是，命名方案不一致。如果包含标准错误的变量命名类似于

train\u avg\u mag

，例如

train\u SEM\u mag

，而不是

train\u magSEM

，则会更好

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))

因此，第一步是拆分

变量

名称，以分别获得不同的组：

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

“区”“dura”“mag”“prob”“spd”

“测试”“列车”

“平均”“扫描电镜”

现在，使用连接更新将缩写变量名替换为全名：

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的形状。有关详细信息，请参阅