如何 melt/gather 多个变量（误差线）合二为一以映射到 geom_bar？
How do I melt/gather multiple variables (error bars) into one for mapping to geom_bar?
我将从我的目标开始，即为我的每个变量生成图表（幅度 [mag]、持续时间 [dura] 和距离 [dist]，但 火车和测试。 :
即将完成的图表
我有一个如下所示的数据框：（屏幕截图 + 下面的输出）。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）及其标准误差 (SEM)。例如，训练时的持续时间响应在 "train_avg_dura" 列中，测试时的持续时间响应在 "test_avg_dura" 列中。每个标准误差在 train_duraSEM 和 test_duraSEM
列中
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))
我遇到的问题是如何使用 SEM 添加误差线，因为当我将变量映射到 geom_bar 时，我需要将它们合并到一个变量中而不是两个变量中。我认为这是一个融化问题，但我无法弄清楚。
更新：
我用来绘制图表的熔化数据框如下：
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())
)
感谢您提出的任何建议或解决方案！
谢谢，
亚兰
这里的问题是 avg 列和 SEM（标准误差）列需要放在一起。这需要同时 重塑两个值列 。 See section 3.a of Efficient reshaping using data.tables 了解更多详情。
因此，我们从宽格式 (df_group_sum.wide) 的数据开始。为了与 OP 提供的代码一致，仅绘制了幅度。
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 = "")
OP 还提供了 data.frame 的长格式 df_group_sum.long，它确实包含比 df_group_sum.wide 更多的数据。现在也应该绘制这些。
通过查看变量名
unique(df_group_sum.long$variable)
[1] test_avg_dist test_avg_dura test_avg_mag test_avg_prob test_avg_spd
[6] test_distSEM test_duraSEM test_magSEM test_probSEM test_spdSEM
[11] train_avg_dist train_avg_dura train_avg_mag train_avg_prob train_avg_spd
[16] train_distSEM train_duraSEM train_magSEM train_probSEM train_spdSEM
20 Levels: test_avg_dist test_avg_dura test_avg_mag test_avg_prob ... train_spdSEM
data.frame 似乎包含五个不同变量（dist、dura、[=29=）的汇总数据（avg 和 SEM） ], prob, spd) 两个数据集 (train 和 test)。同样，avg 和 SEM 需要在一行中保持在一起，以便绘制带有误差线的条形图。
很遗憾，命名方案不一致。如果包含标准误差的变量的命名类似于 train_avg_mag，例如 train_SEM_mag 而不是 train_magSEM.
会更好
因此，第一步是拆分 variable 个名称以分别获得不同的组：
library(data.table)
DT <- data.table(df_group_sum.long)
DT[, c("dataset", "measure", "variable") :=
DT[, tstrsplit(variable, "_|SEM$")][is.na(V3), `:=`(V3 = V2, V2 = "SEM")]]
DT
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])
"test" "train"
unique(DT[, measure])
"avg" "SEM"
现在，使用连接更新将缩写变量名替换为其全名：
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多面图。 dcast() 用于将数据从长格式重塑为宽格式，其中每行有两个度量 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))
我将从我的目标开始，即为我的每个变量生成图表（幅度 [mag]、持续时间 [dura] 和距离 [dist]，但 火车和测试。 :
即将完成的图表
我有一个如下所示的数据框：（屏幕截图 + 下面的输出）。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）及其标准误差 (SEM)。例如，训练时的持续时间响应在 "train_avg_dura" 列中，测试时的持续时间响应在 "test_avg_dura" 列中。每个标准误差在 train_duraSEM 和 test_duraSEM
列中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))
我遇到的问题是如何使用 SEM 添加误差线，因为当我将变量映射到 geom_bar 时，我需要将它们合并到一个变量中而不是两个变量中。我认为这是一个融化问题，但我无法弄清楚。
更新：
我用来绘制图表的熔化数据框如下：
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())
)
感谢您提出的任何建议或解决方案！
谢谢， 亚兰
这里的问题是 avg 列和 SEM（标准误差）列需要放在一起。这需要同时 重塑两个值列 。 See section 3.a of Efficient reshaping using data.tables 了解更多详情。
因此，我们从宽格式 (df_group_sum.wide) 的数据开始。为了与 OP 提供的代码一致，仅绘制了幅度。
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 = "")
OP 还提供了 data.frame 的长格式 df_group_sum.long，它确实包含比 df_group_sum.wide 更多的数据。现在也应该绘制这些。
通过查看变量名
unique(df_group_sum.long$variable)
[1] test_avg_dist test_avg_dura test_avg_mag test_avg_prob test_avg_spd [6] test_distSEM test_duraSEM test_magSEM test_probSEM test_spdSEM [11] train_avg_dist train_avg_dura train_avg_mag train_avg_prob train_avg_spd [16] train_distSEM train_duraSEM train_magSEM train_probSEM train_spdSEM 20 Levels: test_avg_dist test_avg_dura test_avg_mag test_avg_prob ... train_spdSEM
data.frame 似乎包含五个不同变量（dist、dura、[=29=）的汇总数据（avg 和 SEM） ], prob, spd) 两个数据集 (train 和 test)。同样，avg 和 SEM 需要在一行中保持在一起，以便绘制带有误差线的条形图。
很遗憾，命名方案不一致。如果包含标准误差的变量的命名类似于 train_avg_mag，例如 train_SEM_mag 而不是 train_magSEM.
因此，第一步是拆分 variable 个名称以分别获得不同的组：
library(data.table)
DT <- data.table(df_group_sum.long)
DT[, c("dataset", "measure", "variable") :=
DT[, tstrsplit(variable, "_|SEM$")][is.na(V3), `:=`(V3 = V2, V2 = "SEM")]]
DT
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])
"test" "train"
unique(DT[, measure])
"avg" "SEM"
现在，使用连接更新将缩写变量名替换为其全名：
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多面图。 dcast() 用于将数据从长格式重塑为宽格式，其中每行有两个度量 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))