Produce smooth error bar around multiple line graph using standard error available in the data frame. I already have the stadard error in the data frame so I could use data +/- se.
Produce smooth error bar around multiple line graph using standard error available in the data frame. I already have the stadard error in the data frame so I could use data +/- se.
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"))
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))
