在 ggplot 的一个方面用箭头注释
Annotate with an arrow in one facet for a ggplot
我想用箭头和文本“训练”来注释黑色虚线。但是 annotate 函数会注释所有方面。我只想要“remembered/cen”方面的箭头。
这是我的数据集：
data10 <- structure(list(Group = c("Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered"), Condition = c("CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS"), test = c("Pre-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test"), trial = c(1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 13, 14, 15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16), Variables = c("Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time"), Eye_Mx = c(1.150583333, 1.273916667, 1.213083333,
1.065166667, 1.2373, 1.19925, 0.93675, 0.950833333, 0.616916667,
0.440416667, 0.598083333, 0.618583333, 0.693545455, 0.667583333,
0.873666667, 0.51825, 1.220454545, 1.034583333, 0.874583333,
1.015166667, 0.532222222, 0.714454545, 0.905583333, 0.898333333,
0.641666667, 0.787666667, 0.609833333, 0.623583333, 0.69925,
0.7188, 0.61725, 0.661166667, 1.349, 1.585416667, 1.0145, 1.201090909,
0.810545455, 0.591090909, 1.1416, 0.697166667, 0.431166667, 0.804583333,
0.289666667, 0.63875, 0.46825, 0.633, 0.418833333, 0.691166667,
1.219125, 0.7033, 0.524666667, 0.724818182, 0.648583333, 0.639181818,
0.596583333, 0.509416667, 0.576272727, 0.483222222, 0.388222222,
0.647, 0.42575, 0.269818182, 0.488333333, 0.5903, 1.869083333,
2.066181818, 2.124166667, 2.31525, 2.0943, 1.93625, 1.786916667,
1.922583333, 1.470833333, 1.421454545, 1.519083333, 1.508833333,
1.575909091, 1.5135, 1.8025, 1.541, 1.800454545, 1.888666667,
1.85575, 2.201666667, 1.55725, 1.7781, 1.748, 1.767583333, 1.489333333,
1.4259, 1.436916667, 1.5855, 1.535666667, 1.4013, 1.3855, 1.356666667,
1.852888889, 2.463636364, 2.031, 2.195727273, 1.804454545, 1.709090909,
2.1938, 1.97625, 1.256833333, 1.704363636, 1.418083333, 1.371166667,
1.459166667, 1.46725, 1.183666667, 1.407, 2.348625, 1.8981, 1.973583333,
1.746727273, 1.6805, 1.963, 1.68075, 1.872583333, 1.345636364,
1.339222222, 1.311222222, 1.316833333, 1.215833333, 1.053636364,
1.415916667, 1.2292), sd = c(0.948671172, 0.678775831, 0.820965004,
0.771358286, 1.11350558, 0.598444974, 0.794668727, 0.824723627,
0.481933503, 0.314103185, 0.469586754, 0.576648697, 0.629203681,
0.528873667, 0.975212642, 0.406696922, 0.986302019, 0.821480975,
0.776634401, 0.804389643, 0.52690957, 0.881839936, 0.881676756,
0.842954149, 0.49820502, 0.551171205, 0.611370269, 0.630794947,
0.605911653, 0.612136659, 0.504005614, 0.478993231, 0.896792758,
1.545713396, 1.479810742, 1.481512366, 1.016337185, 0.827241616,
1.987092303, 0.874371549, 0.557526165, 1.312183015, 0.163762763,
1.081580084, 0.682258832, 0.99675364, 0.582176455, 1.069035235,
1.352635886, 1.003522136, 0.705413397, 0.93395362, 0.764277848,
0.989686599, 0.875251492, 0.582424316, 0.618786084, 0.971365119,
0.4453251, 1.057255968, 0.710771044, 0.157439397, 0.584064339,
0.966582301, 0.807429305, 0.578682092, 0.911954428, 1.146678771,
0.977409848, 0.7173858, 0.692368328, 0.84760684, 0.426626052,
0.392027133, 0.463031406, 0.346331904, 0.435984278, 0.625301164,
0.733525794, 0.468399014, 0.911551574, 0.845252338, 0.560227896,
1.191183013, 0.503701088, 0.686482249, 0.812501692, 0.649220856,
0.448065201, 0.520082782, 0.465629478, 0.601450142, 0.498518229,
0.432112652, 0.422273393, 0.374147354, 0.631002663, 1.659917846,
1.024954525, 1.202822771, 0.652806306, 0.768222032, 1.742846509,
0.782477781, 0.398411581, 0.98639944, 0.580826286, 0.781519247,
0.683742619, 0.717473487, 0.26632937, 0.748351886, 1.884740371,
0.875399141, 0.661320505, 0.703044393, 0.49535084, 0.954243365,
0.645801986, 1.293963499, 0.649359573, 0.623769945, 0.256283426,
0.8611224, 0.495113363, 0.158687285, 0.522609442, 0.635988959
), se = c(0.273857778, 0.195945704, 0.236992183, 0.222671957,
0.352121382, 0.172756183, 0.229401102, 0.238077204, 0.139122219,
0.090673779, 0.135558019, 0.16646414, 0.189712048, 0.152672677,
0.281519641, 0.117403289, 0.297381248, 0.237141131, 0.22419504,
0.232207288, 0.175636523, 0.265884745, 0.254518156, 0.243339902,
0.143819401, 0.159109422, 0.176487395, 0.182094816, 0.174911628,
0.193574608, 0.145493889, 0.138273435, 0.298930919, 0.446209023,
0.467957245, 0.446692786, 0.306437191, 0.249422732, 0.62837376,
0.252409325, 0.160943941, 0.378794609, 0.047274238, 0.312225276,
0.19695116, 0.287737991, 0.168059866, 0.30860389, 0.478229004,
0.317341563, 0.203635307, 0.281597612, 0.220628011, 0.298401737,
0.252663342, 0.168131418, 0.186571024, 0.323788373, 0.1484417,
0.305203509, 0.205181927, 0.047469764, 0.168604852, 0.305660162,
0.233084763, 0.174479216, 0.263258567, 0.331017649, 0.309084133,
0.207091442, 0.19986952, 0.244683019, 0.123156333, 0.118200628,
0.133665654, 0.099977409, 0.131454206, 0.180508898, 0.211750657,
0.135215148, 0.274843141, 0.244003332, 0.161723863, 0.343864917,
0.178085227, 0.217084748, 0.244978478, 0.187413918, 0.129345282,
0.164464616, 0.134415652, 0.173623701, 0.143909817, 0.136646019,
0.121899828, 0.108007038, 0.210334221, 0.500484062, 0.32411908,
0.362664711, 0.196828507, 0.231627658, 0.551136458, 0.225881879,
0.115011517, 0.297410621, 0.167670106, 0.225605174, 0.197379493,
0.207116755, 0.076882667, 0.216030581, 0.666356349, 0.276825515,
0.190906786, 0.21197586, 0.14299547, 0.2877152, 0.186426975,
0.373535087, 0.195789278, 0.207923315, 0.085427809, 0.248584625,
0.142926917, 0.047846017, 0.150864351, 0.201117368), ci = c(0.602756906,
0.431273588, 0.521616278, 0.490097673, 0.796553907, 0.380233796,
0.504908421, 0.524004393, 0.306205939, 0.199571642, 0.298361189,
0.366385102, 0.422704785, 0.336030297, 0.619620551, 0.258402896,
0.662606712, 0.52194411, 0.493449956, 0.511084796, 0.405018549,
0.59242813, 0.560190685, 0.535587514, 0.316544368, 0.350197476,
0.388446137, 0.400787988, 0.384977898, 0.437896186, 0.320229889,
0.304337779, 0.689335936, 0.982099437, 1.058592834, 0.99529355,
0.682784611, 0.555748479, 1.421480202, 0.555549178, 0.354235225,
0.833721312, 0.104049895, 0.6872032, 0.433486581, 0.633307048,
0.369897272, 0.679232583, 1.1308319, 0.71787649, 0.448198289,
0.627438579, 0.485598977, 0.664880504, 0.556108267, 0.370054755,
0.415706148, 0.746657327, 0.342307174, 0.671748394, 0.451602376,
0.105769226, 0.371096776, 0.691451324, 0.513016105, 0.388763919,
0.5794282, 0.728564932, 0.699196885, 0.455805192, 0.439909848,
0.538543693, 0.271065261, 0.263367411, 0.29419612, 0.220048794,
0.292898224, 0.397297405, 0.466060055, 0.297606535, 0.61238868,
0.537047714, 0.355951823, 0.756841578, 0.421104648, 0.491079817,
0.545846064, 0.412495252, 0.284687047, 0.37204481, 0.295846856,
0.382143188, 0.316743371, 0.30911477, 0.268299713, 0.237721887,
0.485031584, 1.115147982, 0.733208298, 0.808067333, 0.438561244,
0.516098584, 1.246757287, 0.497162663, 0.253138642, 0.66267216,
0.369039416, 0.49655364, 0.434429334, 0.455860905, 0.169217609,
0.475480104, 1.575682382, 0.626222821, 0.420183003, 0.47231165,
0.314730908, 0.641069416, 0.410323006, 0.822145184, 0.436245697,
0.479472024, 0.19699688, 0.54713107, 0.314580023, 0.106607569,
0.332050198, 0.454959094)), class = c("spec_tbl_df", "tbl_df",
"tbl", "data.frame"), row.names = c(NA, -128L), spec = structure(list(
cols = list(Group = structure(list(), class = c("collector_character",
"collector")), Condition = structure(list(), class = c("collector_character",
"collector")), test = structure(list(), class = c("collector_character",
"collector")), trial = structure(list(), class = c("collector_double",
"collector")), Variables = structure(list(), class = c("collector_character",
"collector")), Eye_Mx = structure(list(), class = c("collector_double",
"collector")), sd = structure(list(), class = c("collector_double",
"collector")), se = structure(list(), class = c("collector_double",
"collector")), ci = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1), class = "col_spec"))
这是我的 ggplot 代码：
p <- ggplot(data10, aes(x = trial, y = Eye_Mx)) +
geom_line(aes(color = Variables, linetype = Variables), lwd=1.2) +
scale_color_manual(values = c("darkred", "steelblue")) + facet_grid(Condition ~ Group)+ theme_bw() + xlab("Trial Pre- / Post-test") + ylab("Hand and Eye Movement time (s)") +
scale_x_continuous(limits = c(1,16), breaks = seq(1,16,1)) + theme(axis.text.x = element_text(size = 10,face="bold", angle = 90),#, angle = 10, hjust = .5, vjust = .5),
axis.text.y = element_text(size = 10, face = "bold"),
axis.title.y = element_text(vjust= 1.8, size = 16),
axis.title.x = element_text(vjust= -0.5, size = 16),
axis.title = element_text(face = "bold")) + theme(legend.position="top")+
geom_vline(xintercept=8.5, linetype="dashed", color = "black", size=1.5)
p + guides(fill=guide_legend(title="Variables:")) + theme(legend.text=element_text(size=14),legend.title=element_text(size=14) ) +
theme(strip.text = element_text(face="bold", size=12)) + annotate("segment", x = 11, xend = 9, y = 4, yend = 3, colour = "black", size=1, alpha=0.9, arrow=arrow())
您可以创建一个额外的数据框（例如 arrowdf），其中包含您想要注释的 Condition 和 Group。我还将您的所有 theme() 论点分组，以便它们更加清晰。
arrowdf <- tibble(Condition = "CEN", Group = "Remembered")
ggplot(data10, aes(x = trial, y = Eye_Mx)) +
geom_line(aes(color = Variables, linetype = Variables), lwd=1.2) +
scale_color_manual(values = c("darkred", "steelblue")) +
facet_grid(Condition ~ Group)+
theme_bw() +
xlab("Trial Pre- / Post-test") +
ylab("Hand and Eye Movement time (s)") +
scale_x_continuous(limits = c(1,16), breaks = seq(1,16,1)) +
theme(axis.text.x = element_text(size = 10,face="bold", angle = 90),
axis.text.y = element_text(size = 10, face = "bold"),
axis.title.y = element_text(vjust= 1.8, size = 16),
axis.title.x = element_text(vjust= -0.5, size = 16),
axis.title = element_text(face = "bold"),
legend.text=element_text(size=14),
legend.title=element_text(size=14),
strip.text = element_text(face="bold", size=12),
legend.position="top")+
geom_vline(xintercept=8.5, linetype="dashed", color = "black", size=1.5) +
guides(fill=guide_legend(title="Variables:")) +
geom_segment(data = arrowdf,
aes(x = 11, xend = 9, y = 4, yend = 3),
colour = "black", size = 1, alpha=0.9, arrow = arrow())
我想用箭头和文本“训练”来注释黑色虚线。但是 annotate 函数会注释所有方面。我只想要“remembered/cen”方面的箭头。
这是我的数据集：
data10 <- structure(list(Group = c("Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Visible", "Visible", "Visible", "Visible",
"Visible", "Visible", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered", "Remembered",
"Remembered", "Remembered", "Remembered", "Remembered"), Condition = c("CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN", "CEN",
"CEN", "CEN", "CEN", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS", "IPS",
"IPS"), test = c("Pre-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test", "Pre-test", "Pre-test", "Pre-test",
"Pre-test", "Pre-test", "Pre-test", "Pre-test", "Pre-test", "Post-test",
"Post-test", "Post-test", "Post-test", "Post-test", "Post-test",
"Post-test", "Post-test"), trial = c(1, 2, 3, 4, 5, 6, 7, 8,
9, 10, 11, 12, 13, 14, 15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,
11, 12, 13, 14, 15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12,
13, 14, 15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14,
15, 16, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16,
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1, 2,
3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 1, 2, 3, 4,
5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16), Variables = c("Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Eye movement time", "Eye movement time", "Eye movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time", "Hand movement time", "Hand movement time",
"Hand movement time"), Eye_Mx = c(1.150583333, 1.273916667, 1.213083333,
1.065166667, 1.2373, 1.19925, 0.93675, 0.950833333, 0.616916667,
0.440416667, 0.598083333, 0.618583333, 0.693545455, 0.667583333,
0.873666667, 0.51825, 1.220454545, 1.034583333, 0.874583333,
1.015166667, 0.532222222, 0.714454545, 0.905583333, 0.898333333,
0.641666667, 0.787666667, 0.609833333, 0.623583333, 0.69925,
0.7188, 0.61725, 0.661166667, 1.349, 1.585416667, 1.0145, 1.201090909,
0.810545455, 0.591090909, 1.1416, 0.697166667, 0.431166667, 0.804583333,
0.289666667, 0.63875, 0.46825, 0.633, 0.418833333, 0.691166667,
1.219125, 0.7033, 0.524666667, 0.724818182, 0.648583333, 0.639181818,
0.596583333, 0.509416667, 0.576272727, 0.483222222, 0.388222222,
0.647, 0.42575, 0.269818182, 0.488333333, 0.5903, 1.869083333,
2.066181818, 2.124166667, 2.31525, 2.0943, 1.93625, 1.786916667,
1.922583333, 1.470833333, 1.421454545, 1.519083333, 1.508833333,
1.575909091, 1.5135, 1.8025, 1.541, 1.800454545, 1.888666667,
1.85575, 2.201666667, 1.55725, 1.7781, 1.748, 1.767583333, 1.489333333,
1.4259, 1.436916667, 1.5855, 1.535666667, 1.4013, 1.3855, 1.356666667,
1.852888889, 2.463636364, 2.031, 2.195727273, 1.804454545, 1.709090909,
2.1938, 1.97625, 1.256833333, 1.704363636, 1.418083333, 1.371166667,
1.459166667, 1.46725, 1.183666667, 1.407, 2.348625, 1.8981, 1.973583333,
1.746727273, 1.6805, 1.963, 1.68075, 1.872583333, 1.345636364,
1.339222222, 1.311222222, 1.316833333, 1.215833333, 1.053636364,
1.415916667, 1.2292), sd = c(0.948671172, 0.678775831, 0.820965004,
0.771358286, 1.11350558, 0.598444974, 0.794668727, 0.824723627,
0.481933503, 0.314103185, 0.469586754, 0.576648697, 0.629203681,
0.528873667, 0.975212642, 0.406696922, 0.986302019, 0.821480975,
0.776634401, 0.804389643, 0.52690957, 0.881839936, 0.881676756,
0.842954149, 0.49820502, 0.551171205, 0.611370269, 0.630794947,
0.605911653, 0.612136659, 0.504005614, 0.478993231, 0.896792758,
1.545713396, 1.479810742, 1.481512366, 1.016337185, 0.827241616,
1.987092303, 0.874371549, 0.557526165, 1.312183015, 0.163762763,
1.081580084, 0.682258832, 0.99675364, 0.582176455, 1.069035235,
1.352635886, 1.003522136, 0.705413397, 0.93395362, 0.764277848,
0.989686599, 0.875251492, 0.582424316, 0.618786084, 0.971365119,
0.4453251, 1.057255968, 0.710771044, 0.157439397, 0.584064339,
0.966582301, 0.807429305, 0.578682092, 0.911954428, 1.146678771,
0.977409848, 0.7173858, 0.692368328, 0.84760684, 0.426626052,
0.392027133, 0.463031406, 0.346331904, 0.435984278, 0.625301164,
0.733525794, 0.468399014, 0.911551574, 0.845252338, 0.560227896,
1.191183013, 0.503701088, 0.686482249, 0.812501692, 0.649220856,
0.448065201, 0.520082782, 0.465629478, 0.601450142, 0.498518229,
0.432112652, 0.422273393, 0.374147354, 0.631002663, 1.659917846,
1.024954525, 1.202822771, 0.652806306, 0.768222032, 1.742846509,
0.782477781, 0.398411581, 0.98639944, 0.580826286, 0.781519247,
0.683742619, 0.717473487, 0.26632937, 0.748351886, 1.884740371,
0.875399141, 0.661320505, 0.703044393, 0.49535084, 0.954243365,
0.645801986, 1.293963499, 0.649359573, 0.623769945, 0.256283426,
0.8611224, 0.495113363, 0.158687285, 0.522609442, 0.635988959
), se = c(0.273857778, 0.195945704, 0.236992183, 0.222671957,
0.352121382, 0.172756183, 0.229401102, 0.238077204, 0.139122219,
0.090673779, 0.135558019, 0.16646414, 0.189712048, 0.152672677,
0.281519641, 0.117403289, 0.297381248, 0.237141131, 0.22419504,
0.232207288, 0.175636523, 0.265884745, 0.254518156, 0.243339902,
0.143819401, 0.159109422, 0.176487395, 0.182094816, 0.174911628,
0.193574608, 0.145493889, 0.138273435, 0.298930919, 0.446209023,
0.467957245, 0.446692786, 0.306437191, 0.249422732, 0.62837376,
0.252409325, 0.160943941, 0.378794609, 0.047274238, 0.312225276,
0.19695116, 0.287737991, 0.168059866, 0.30860389, 0.478229004,
0.317341563, 0.203635307, 0.281597612, 0.220628011, 0.298401737,
0.252663342, 0.168131418, 0.186571024, 0.323788373, 0.1484417,
0.305203509, 0.205181927, 0.047469764, 0.168604852, 0.305660162,
0.233084763, 0.174479216, 0.263258567, 0.331017649, 0.309084133,
0.207091442, 0.19986952, 0.244683019, 0.123156333, 0.118200628,
0.133665654, 0.099977409, 0.131454206, 0.180508898, 0.211750657,
0.135215148, 0.274843141, 0.244003332, 0.161723863, 0.343864917,
0.178085227, 0.217084748, 0.244978478, 0.187413918, 0.129345282,
0.164464616, 0.134415652, 0.173623701, 0.143909817, 0.136646019,
0.121899828, 0.108007038, 0.210334221, 0.500484062, 0.32411908,
0.362664711, 0.196828507, 0.231627658, 0.551136458, 0.225881879,
0.115011517, 0.297410621, 0.167670106, 0.225605174, 0.197379493,
0.207116755, 0.076882667, 0.216030581, 0.666356349, 0.276825515,
0.190906786, 0.21197586, 0.14299547, 0.2877152, 0.186426975,
0.373535087, 0.195789278, 0.207923315, 0.085427809, 0.248584625,
0.142926917, 0.047846017, 0.150864351, 0.201117368), ci = c(0.602756906,
0.431273588, 0.521616278, 0.490097673, 0.796553907, 0.380233796,
0.504908421, 0.524004393, 0.306205939, 0.199571642, 0.298361189,
0.366385102, 0.422704785, 0.336030297, 0.619620551, 0.258402896,
0.662606712, 0.52194411, 0.493449956, 0.511084796, 0.405018549,
0.59242813, 0.560190685, 0.535587514, 0.316544368, 0.350197476,
0.388446137, 0.400787988, 0.384977898, 0.437896186, 0.320229889,
0.304337779, 0.689335936, 0.982099437, 1.058592834, 0.99529355,
0.682784611, 0.555748479, 1.421480202, 0.555549178, 0.354235225,
0.833721312, 0.104049895, 0.6872032, 0.433486581, 0.633307048,
0.369897272, 0.679232583, 1.1308319, 0.71787649, 0.448198289,
0.627438579, 0.485598977, 0.664880504, 0.556108267, 0.370054755,
0.415706148, 0.746657327, 0.342307174, 0.671748394, 0.451602376,
0.105769226, 0.371096776, 0.691451324, 0.513016105, 0.388763919,
0.5794282, 0.728564932, 0.699196885, 0.455805192, 0.439909848,
0.538543693, 0.271065261, 0.263367411, 0.29419612, 0.220048794,
0.292898224, 0.397297405, 0.466060055, 0.297606535, 0.61238868,
0.537047714, 0.355951823, 0.756841578, 0.421104648, 0.491079817,
0.545846064, 0.412495252, 0.284687047, 0.37204481, 0.295846856,
0.382143188, 0.316743371, 0.30911477, 0.268299713, 0.237721887,
0.485031584, 1.115147982, 0.733208298, 0.808067333, 0.438561244,
0.516098584, 1.246757287, 0.497162663, 0.253138642, 0.66267216,
0.369039416, 0.49655364, 0.434429334, 0.455860905, 0.169217609,
0.475480104, 1.575682382, 0.626222821, 0.420183003, 0.47231165,
0.314730908, 0.641069416, 0.410323006, 0.822145184, 0.436245697,
0.479472024, 0.19699688, 0.54713107, 0.314580023, 0.106607569,
0.332050198, 0.454959094)), class = c("spec_tbl_df", "tbl_df",
"tbl", "data.frame"), row.names = c(NA, -128L), spec = structure(list(
cols = list(Group = structure(list(), class = c("collector_character",
"collector")), Condition = structure(list(), class = c("collector_character",
"collector")), test = structure(list(), class = c("collector_character",
"collector")), trial = structure(list(), class = c("collector_double",
"collector")), Variables = structure(list(), class = c("collector_character",
"collector")), Eye_Mx = structure(list(), class = c("collector_double",
"collector")), sd = structure(list(), class = c("collector_double",
"collector")), se = structure(list(), class = c("collector_double",
"collector")), ci = structure(list(), class = c("collector_double",
"collector"))), default = structure(list(), class = c("collector_guess",
"collector")), skip = 1), class = "col_spec"))
这是我的 ggplot 代码：
p <- ggplot(data10, aes(x = trial, y = Eye_Mx)) +
geom_line(aes(color = Variables, linetype = Variables), lwd=1.2) +
scale_color_manual(values = c("darkred", "steelblue")) + facet_grid(Condition ~ Group)+ theme_bw() + xlab("Trial Pre- / Post-test") + ylab("Hand and Eye Movement time (s)") +
scale_x_continuous(limits = c(1,16), breaks = seq(1,16,1)) + theme(axis.text.x = element_text(size = 10,face="bold", angle = 90),#, angle = 10, hjust = .5, vjust = .5),
axis.text.y = element_text(size = 10, face = "bold"),
axis.title.y = element_text(vjust= 1.8, size = 16),
axis.title.x = element_text(vjust= -0.5, size = 16),
axis.title = element_text(face = "bold")) + theme(legend.position="top")+
geom_vline(xintercept=8.5, linetype="dashed", color = "black", size=1.5)
p + guides(fill=guide_legend(title="Variables:")) + theme(legend.text=element_text(size=14),legend.title=element_text(size=14) ) +
theme(strip.text = element_text(face="bold", size=12)) + annotate("segment", x = 11, xend = 9, y = 4, yend = 3, colour = "black", size=1, alpha=0.9, arrow=arrow())
您可以创建一个额外的数据框（例如 arrowdf），其中包含您想要注释的 Condition 和 Group。我还将您的所有 theme() 论点分组，以便它们更加清晰。
arrowdf <- tibble(Condition = "CEN", Group = "Remembered")
ggplot(data10, aes(x = trial, y = Eye_Mx)) +
geom_line(aes(color = Variables, linetype = Variables), lwd=1.2) +
scale_color_manual(values = c("darkred", "steelblue")) +
facet_grid(Condition ~ Group)+
theme_bw() +
xlab("Trial Pre- / Post-test") +
ylab("Hand and Eye Movement time (s)") +
scale_x_continuous(limits = c(1,16), breaks = seq(1,16,1)) +
theme(axis.text.x = element_text(size = 10,face="bold", angle = 90),
axis.text.y = element_text(size = 10, face = "bold"),
axis.title.y = element_text(vjust= 1.8, size = 16),
axis.title.x = element_text(vjust= -0.5, size = 16),
axis.title = element_text(face = "bold"),
legend.text=element_text(size=14),
legend.title=element_text(size=14),
strip.text = element_text(face="bold", size=12),
legend.position="top")+
geom_vline(xintercept=8.5, linetype="dashed", color = "black", size=1.5) +
guides(fill=guide_legend(title="Variables:")) +
geom_segment(data = arrowdf,
aes(x = 11, xend = 9, y = 4, yend = 3),
colour = "black", size = 1, alpha=0.9, arrow = arrow())