I am attempting to build a barplot with error bars using the ggplot2 package showing 13 predictor variables on the x axis (the data frame behaviours can be found below). The predictors will ideally be grouped by the response variable (family) containing two levels (G8 and V4), represented by two coloured bars per predictor plus a key. I have tried to follow an example from the Cookbook for R (see below). I would like to summarise the data using the function summarySE from the Rmisc package to calculate the standard deviation, standard error of the mean, and a (default 95%) confidence interval, however, my code shows warning messages and returns NA's. I am unsure what the correct syntax is for the function summarySE(). How can I implement the R Cookbook example for my data?
My Code using the function `summarySE':
library(ggplot2) library(Rmisc) # (1) First Try - Equation 1 summary.behaviours <- summarySE(behaviours, measurevar="Family", groupvars=c("Swimming", "Not.Swimming", "Running", "Not.Running", "Fighting", "Not.Fighting", "Resting", "Not.Resting", "Hunting", "Not.Hunting", "Grooming", "Not.Grooming", "Other"), na.rm = TRUE) # (2) Second Try - Equation 2 summary.behaviours <- summarySE(behaviours, measurevar = c("Swimming", "Not.Swimming", "Running", "Not.Running", "Fighting", "Not.Fighting", "Resting", "Not.Resting", "Hunting", "Not.Hunting", "Grooming", "Not.Grooming", "Other"), groupvar="Family", na.rm = TRUE) Warning messages
Warning messages for Equation (1)
1: In mean.default(xx[, col], na.rm = na.rm) : argument is not numeric or logical: returning NA 2: In mean.default(xx[, col], na.rm = na.rm) : argument is not numeric or logical: returning NA and many more warnings of the same kind.
Error messages for equation (2):
Error in `[.data.frame`(xx, , col) : undefined columns selected Example code from the Cookbook for R
Reference: (ggplot2)/
summarySE provides the standard deviation, standard error of the mean, and a (default 95%) confidence interval
tgc <- summarySE(tg, measurevar="len", groupvars=c("supp","dose")) tgc #> supp dose N len sd se ci #> 1 OJ 0.5 10 13.23 4.459709 1.4102837 3.190283 #> 2 OJ 1.0 10 22.70 3.910953 1.2367520 2.797727 #> 3 OJ 2.0 10 26.06 2.655058 0.8396031 1.899314 #> 4 VC 0.5 10 7.98 2.746634 0.8685620 1.964824 #> 5 VC 1.0 10 16.77 2.515309 0.7954104 1.799343 #> 6 VC 2.0 10 26.14 4.797731 1.5171757 3.432090 # Use dose as a factor rather than numeric tgc2 <- tgc tgc2$dose <- factor(tgc2$dose) # Error bars represent standard error of the mean ggplot(tgc2, aes(x=dose, y=len, fill=supp)) + geom_bar(position=position_dodge(), stat="identity") + geom_errorbar(aes(ymin=len-se, ymax=len+se), width=.2, # Width of the error bars position=position_dodge(.9)) # Use 95% confidence intervals instead of SEM ggplot(tgc2, aes(x=dose, y=len, fill=supp)) + geom_bar(position=position_dodge(), stat="identity") + geom_errorbar(aes(ymin=len-ci, ymax=len+ci), width=.2, # Width of the error bars position=position_dodge(.9)) My data
behaviours <- structure(list(Family = structure(c(2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label = c("G8", "v4"), class = "factor"), Swimming = c(-0.4805568, 0.12600625, 0.06823834, 0.67480139, 0.64591744, 0.21265812, -0.01841352, 0.12600625, -0.2206012, 0.27042603, 0.03935439, -0.45167284, -0.04729748, -0.10506539, 0.0971223, -0.07618143, 0.29930998, 0.01047043, -0.24948516, -0.04729748, -0.01841352, -0.19171725, -0.4805568, 0.01047043, -0.42278889, -0.45167284, -0.30725307, 0.24154207, 1.45466817, -0.01841352, 0.38596185, 0.15489021, -0.04729748, 0.27042603, -0.07618143, -0.10506539, -0.01841352, 0.01047043, 0.06823834, -0.16283329, -0.01841352, -0.39390493, -0.04729748, 0.01047043, 0.01047043, 0.06823834, -0.04729748, -0.2206012, -0.16283329, -0.07618143, -0.2206012, -0.19171725, -0.16283329, -0.2206012, -0.13394934, -0.27836911, -0.04729748, 0.01047043, 0.12600625, 0.06823834, 0.06823834, 0.32819394, 0.32819394, -0.27836911, 0.18377416, 0.55926557, -0.19171725, -0.19171725, 0.01047043, -0.19171725, -0.01841352, -0.07618143, -0.13394934, -0.39390493, -0.04729748, -0.27836911, 0.70368535, 0.29930998, -0.13394934, 0.21265812), Not.Swimming = c(-0.0862927, -0.074481895, -0.056765686, -0.050860283, -0.050860283, -0.068576492, -0.068576492, 0.05543697, 0.114491, -0.021333268, -0.04495488, 0.008193747, -0.056765686, 0.008193747, 0.037720761, 0.01409915, 0.108585597, -0.074481895, 0.002288344, 0.049531567, 0.043626164, 0.049531567, 0.020004552, 0.008193747, 0.025909955, 0.031815358, 0.049531567, -0.039049477, -0.003617059, 0.002288344, 0.084963985, -0.080387298, 0.067247776, 0.031815358, 0.037720761, 0.025909955, 0.126301805, 0.031815358, 0.037720761, -0.050860283, -0.039049477, -0.003617059, 0.008193747, -0.039049477, -0.003617059, 0.008193747, 0.01409915, -0.015427865, 0.020004552, 0.031815358, 0.020004552, -0.033144074, -0.039049477, -0.009522462, -0.003617059, -0.04495488, -0.050860283, -0.04495488, -0.068576492, -0.033144074, -0.027238671, -0.068576492, 0.01409915, 0.002288344, 0.025909955, -0.009522462, -0.009522462, 0.025909955, 0.15582882, 0.002288344, -0.04495488, -0.015427865, 0.008193747, 0.037720761, 0.008193747, -0.015427865, -0.056765686, 0.079058582, -0.056765686, 0.025909955), Running = c(-0.157157188, 0.057316151, 0.064711783, 0.153459372, 0.072107416, 0.057316151, -0.053618335, 0.012942357, -0.03882707, 0.049920519, 0.012942357, -0.075805232, 0.035129254, -0.046222702, 0.109085578, -0.03882707, 0.057316151, 0.020337989, 0.035129254, 0.057316151, 0.005546724, -0.016640173, -0.142365923, 0.220020063, -0.149761556, -0.134970291, 0.042524886, 0.072107416, 0.064711783, 0.020337989, 0.049920519, 0.020337989, 0.138668107, 0.049920519, 0.020337989, -0.083200864, -0.024035805, -0.016640173, -0.03882707, -0.03882707, 0.005546724, -0.090596497, -0.00924454, -0.016640173, -0.075805232, -0.090596497, 0.012942357, -0.075805232, -0.061013967, -0.03882707, -0.112783394, -0.068409599, -0.090596497, -0.053618335, -0.075805232, -0.090596497, 0.064711783, 0.012942357, 0.042524886, -0.061013967, -0.061013967, 0.064711783, 0.175646269, -0.068409599, 0.027733621, 0.042524886, -0.03882707, -0.00924454, 0.027733621, -0.031431438, -0.046222702, -0.031431438, -0.068409599, -0.120179026, 0.035129254, -0.061013967, 0.39751524, 0.138668107, 0.020337989, 0.035129254), Not.Running = c(-0.438809944, -0.539013927, -0.539013927, -0.539013927, -0.472211271, -0.071395338, -0.071395338, 0.296019267, 0.563229889, -0.03799401, 0.195815284, -0.171599321, -0.305204632, 0.062209973, -0.104796666, 0.095611301, 0.028808645, -0.071395338, 0.329420595, 0.296019267, -0.171599321, -0.071395338, 0.596631217, 0.062209973, 0.028808645, -0.138197994, 0.095611301, -0.104796666, 0.296019267, 0.028808645, -0.03799401, -0.33860596, 0.129012629, 0.195815284, -0.03799401, 0.396223251, 0.362821923, -0.138197994, 0.26261794, -0.405408616, -0.205000649, 0.129012629, 0.195815284, -0.205000649, -0.004592683, -0.205000649, -0.071395338, -0.171599321, -0.104796666, -0.138197994, -0.104796666, -0.071395338, -0.104796666, -0.03799401, -0.004592683, -0.238401977, 0.028808645, -0.305204632, -0.305204632, -0.271803305, -0.03799401, -0.372007288, 0.095611301, 0.195815284, 0.162413956, 0.229216612, 0.229216612, 0.396223251, 0.630032545, 0.463025906, 0.496427234, 0.062209973, -0.071395338, 0.229216612, -0.071395338, -0.071395338, -0.205000649, 0.229216612, -0.305204632, 0.396223251), Fighting = c(-0.67708172, -0.58224128, -0.11436177, -0.34830152, -0.84568695, -0.32933343, 0.35984044, -0.3251183, 1.51478626, 0.11114773, 0.27975296, -0.89626852, 0.12379312, 0.66965255, 1.56536783, 0.56427428, -0.71291033, -0.75927677, -0.75295407, -1.00164679, -1.03958296, 0.82139726, -1.07541157, -1.0311527, -0.98900139, -1.06908888, -1.20186549, 0.58324237, -0.9700333, 0.22917139, 0.41042201, -1.11545531, -0.19023412, 0.25446217, -0.05324237, 0.09007207, 1.21129685, 0.62539368, 1.32932051, 0.40199175, 0.44625062, 0.60221046, 0.33665722, -0.63493041, -0.282967, -0.32722587, -0.11646933, -0.10171637, 0.13643851, -0.57802615, 0.05002833, -0.1607282, -0.29139726, 0.13222338, -0.41152848, 0.68229794, -0.24292325, -0.11646933, -0.21341734, -0.24292325, -0.24292325, 0.09007207, -0.34197883, -0.30825778, -0.08696342, -0.8119659, 0.49683219, -0.13754498, -0.4831857, 0.39988418, 0.90148474, 0.28396809, 1.05322945, 1.24923303, 0.47154141, 1.27873894, 0.05002833, 1.54218461, 0.74763247, 0.11747042), Not.Fighting = c(-0.097624192, -0.160103675, -0.092996082, -0.234153433, -0.136963126, -0.15778962, -0.15778962, -0.023574435, 0.00188017, -0.224897213, -0.109194467, -0.069855533, -0.123078796, -0.111508522, -0.143905291, -0.099938247, -0.118450687, 1.519900201, 0.177748344, 0.108326696, 0.652129604, 0.638245274, -0.072169588, 0.087500202, -0.18093017, -0.146219346, -0.049029039, -0.125392851, -0.134649071, -0.060599313, -0.086053918, -0.197128554, -0.083739863, -0.092996082, 0.844196163, 0.055103433, 1.971140911, -0.111508522, -0.224897213, -0.187872334, -0.160103675, -0.194814499, -0.053657149, -0.206384774, 0.108326696, -0.164731785, 0.187004564, 0.025020719, 0.057417488, 0.434608441, 0.057417488, 0.073615872, -0.035144709, -0.051343094, -0.134649071, -0.185558279, 0.013450444, -0.134649071, -0.215640993, -0.185558279, -0.005061995, -0.238781543, -0.099938247, -0.16704584, -0.208698829, 0.048161268, 0.048161268, -0.037458764, 0.16154996, 0.031962884, -0.102252302, -0.123078796, -0.139277181, -0.208698829, -0.118450687, -0.072169588, -0.044400929, -0.030516599, -0.132335016, -0.037458764), Resting = c(0.01081204879, -0.03398160805, 0.057108797, -0.04063432116, -0.13084281035, -0.02997847693, 0.12732080268, -0.1028170581, 0.08155320398, -0.17932134171, -0.14338902206, -0.02058415581, -0.11528274705, -0.11764091337, 0.04389156236, 0.01399844913, -0.05755560242, 0.04711630687, 0.0158428036, 0.093485909, 0.09677967302, 0.02053612974, -0.03608286844, 0.07805238146, -9.686695e-05, -0.02285413055, -0.00424187149, 0.01446241356, 0.03187450017, 0.11323315542, -0.01171898422, -0.06499053655, -0.07758659568, -0.07399758157, -0.11503350996, 0.02167111711, 0.01904454162, 0.05768779393, 0.05555202379, -0.01031175326, -0.00458313459, 0.17430774591, 0.00481502094, -0.00928412956, 0.09047589183, 0.08917985896, -0.05671203072, -0.05333390954, 0.08541446168, 0.10140397965, -0.02509342995, -0.0369877908, 0.04609635201, 0.06524159499, 0.0845977309, -0.03239032508, -0.03208740616, 0.06264952925, 0.05241547086, -0.03437271856, -0.03437271856, -0.06747523863, -0.01270059491, 0.10014629095, -0.02872845706, -0.00950652573, 0.04867308008, 0.02486518629, -0.05951115497, -0.02353665674, -0.01967923345, -0.10148651548, -0.00480936518, -0.00098261723, -0.13970798195, -0.00286148145, -0.05492902692, 0.10732815358, 0.11660744219, -0.02016620439 ), Not.Resting = c(-0.77046287, 0.773856776, -2.593072768, -2.837675606, -1.680828329, -0.947623773, -0.947623773, -2.607366431, -0.637055341, -1.818396455, 2.170944974, -0.658126752, -0.808243774, 2.377766908, 2.111220276, -0.322326312, 2.218858946, 3.920878638, -0.304945754, 1.038591535, 1.752268128, 0.907465624, 1.137774798, -3.663486997, 2.350924346, 0.067293462, -1.898454393, -2.497647463, -4.471716512, -1.465081244, -0.232806371, -3.043893581, -2.323908986, 1.437404886, 1.079056696, 1.110865131, 1.404724068, -1.706664294, 0.736746935, -0.005516985, 1.727170333, 1.685228831, 1.836016918, 0.46617392, 1.697173771, 1.057314221, 0.933704227, 0.482480775, 0.680713089, 0.090780703, 0.680713089, -0.982921741, -2.281900378, 0.97208909, 0.027767791, -0.1628815, -0.530221948, -0.385741863, -0.972251823, 0.002267358, -1.134447998, 0.626424009, -0.722750217, -0.382722075, -0.356550578, -1.851614124, -1.851614124, 1.731465143, 0.254319006, 2.043778341, -0.28991392, 1.386940871, 0.054207713, 0.594212936, 1.551821303, 3.100704184, 0.327263666, -1.055195336, -1.134447998, 1.730726972), Hunting = c(-0.67708172, -0.58224128, -0.11436177, -0.34830152, -0.84568695, -0.32933343, 0.35984044, -0.3251183, 1.51478626, 0.11114773, 0.27975296, -0.89626852, 0.12379312, 0.66965255, 1.56536783, 0.56427428, -0.71291033, -0.75927677, -0.75295407, -1.00164679, -1.03958296, 0.82139726, -1.07541157, -1.0311527, -0.98900139, -1.06908888, -1.20186549, 0.58324237, -0.9700333, 0.22917139, 0.41042201, -1.11545531, -0.19023412, 0.25446217, -0.05324237, 0.09007207, 1.21129685, 0.62539368, 1.32932051, 0.40199175, 0.44625062, 0.60221046, 0.33665722, -0.63493041, -0.282967, -0.32722587, -0.11646933, -0.10171637, 0.13643851, -0.57802615, 0.05002833, -0.1607282, -0.29139726, 0.13222338, -0.41152848, 0.68229794, -0.24292325, -0.11646933, -0.21341734, -0.24292325, -0.24292325, 0.09007207, -0.34197883, -0.30825778, -0.08696342, -0.8119659, 0.49683219, -0.13754498, -0.4831857, 0.39988418, 0.90148474, 0.28396809, 1.05322945, 1.24923303, 0.47154141, 1.27873894, 0.05002833, 1.54218461, 0.74763247, 0.11747042), Not.Hunting = c(-0.097624192, -0.160103675, -0.092996082, -0.234153433, -0.136963126, -0.15778962, -0.15778962, -0.023574435, 0.00188017, -0.224897213, -0.109194467, -0.069855533, -0.123078796, -0.111508522, -0.143905291, -0.099938247, -0.118450687, 1.519900201, 0.177748344, 0.108326696, 0.652129604, 0.638245274, -0.072169588, 0.087500202, -0.18093017, -0.146219346, -0.049029039, -0.125392851, -0.134649071, -0.060599313, -0.086053918, -0.197128554, -0.083739863, -0.092996082, 0.844196163, 0.055103433, 1.971140911, -0.111508522, -0.224897213, -0.187872334, -0.160103675, -0.194814499, -0.053657149, -0.206384774, 0.108326696, -0.164731785, 0.187004564, 0.025020719, 0.057417488, 0.434608441, 0.057417488, 0.073615872, -0.035144709, -0.051343094, -0.134649071, -0.185558279, 0.013450444, -0.134649071, -0.215640993, -0.185558279, -0.005061995, -0.238781543, -0.099938247, -0.16704584, -0.208698829, 0.048161268, 0.048161268, -0.037458764, 0.16154996, 0.031962884, -0.102252302, -0.123078796, -0.139277181, -0.208698829, -0.118450687, -0.072169588, -0.044400929, -0.030516599, -0.132335016, -0.037458764), Grooming = c(0.01081204879, -0.03398160805, 0.057108797, -0.04063432116, -0.13084281035, -0.02997847693, 0.12732080268, -0.1028170581, 0.08155320398, -0.17932134171, -0.14338902206, -0.02058415581, -0.11528274705, -0.11764091337, 0.04389156236, 0.01399844913, -0.05755560242, 0.04711630687, 0.0158428036, 0.093485909, 0.09677967302, 0.02053612974, -0.03608286844, 0.07805238146, -9.686695e-05, -0.02285413055, -0.00424187149, 0.01446241356, 0.03187450017, 0.11323315542, -0.01171898422, -0.06499053655, -0.07758659568, -0.07399758157, -0.11503350996, 0.02167111711, 0.01904454162, 0.05768779393, 0.05555202379, -0.01031175326, -0.00458313459, 0.17430774591, 0.00481502094, -0.00928412956, 0.09047589183, 0.08917985896, -0.05671203072, -0.05333390954, 0.08541446168, 0.10140397965, -0.02509342995, -0.0369877908, 0.04609635201, 0.06524159499, 0.0845977309, -0.03239032508, -0.03208740616, 0.06264952925, 0.05241547086, -0.03437271856, -0.03437271856, -0.06747523863, -0.01270059491, 0.10014629095, -0.02872845706, -0.00950652573, 0.04867308008, 0.02486518629, -0.05951115497, -0.02353665674, -0.01967923345, -0.10148651548, -0.00480936518, -0.00098261723, -0.13970798195, -0.00286148145, -0.05492902692, 0.10732815358, 0.11660744219, -0.02016620439), Not.Grooming = c(-0.77046287, 0.773856776, -2.593072768, -2.837675606, -1.680828329, -0.947623773, -0.947623773, -2.607366431, -0.637055341, -1.818396455, 2.170944974, -0.658126752, -0.808243774, 2.377766908, 2.111220276, -0.322326312, 2.218858946, 3.920878638, -0.304945754, 1.038591535, 1.752268128, 0.907465624, 1.137774798, -3.663486997, 2.350924346, 0.067293462, -1.898454393, -2.497647463, -4.471716512, -1.465081244, -0.232806371, -3.043893581, -2.323908986, 1.437404886, 1.079056696, 1.110865131, 1.404724068, -1.706664294, 0.736746935, -0.005516985, 1.727170333, 1.685228831, 1.836016918, 0.46617392, 1.697173771, 1.057314221, 0.933704227, 0.482480775, 0.680713089, 0.090780703, 0.680713089, -0.982921741, -2.281900378, 0.97208909, 0.027767791, -0.1628815, -0.530221948, -0.385741863, -0.972251823, 0.002267358, -1.134447998, 0.626424009, -0.722750217, -0.382722075, -0.356550578, -1.851614124, -1.851614124, 1.731465143, 0.254319006, 2.043778341, -0.28991392, 1.386940871, 0.054207713, 0.594212936, 1.551821303, 3.100704184, 0.327263666, -1.055195336, -1.134447998, 1.730726972), Other = c(0.019502286, -0.290451956, 0.359948884, 0.557840914, 0.117453376, 0.126645924, 0.126645924, 0.196486873, 0.152780228, 0.354469789, -0.261430968, 0.176448238, -0.007374708, -0.557848621, -0.213674557, -0.005819262, -0.470070992, -0.786078864, 0.006063789, -0.27184265, -0.349418792, -0.338096262, -0.165119403, 0.346566439, -0.344191931, 0.074321265, 0.179825379, 0.278407054, 0.593125727, 0.199177375, -0.058900625, 0.633875622, 0.428150308, -0.206023441, -0.436958199, -0.291839246, -0.907641911, 0.448567295, -0.127186127, 0.024715134, -0.41634503, -0.330697382, -0.469720666, -0.047494017, -0.301732446, -0.138901021, 0.098101379, -0.002063769, -0.02832419, 0.071630763, -0.02832419, 0.295110588, 0.347112947, -0.083577573, -0.036886152, 0.189045953, 0.467596992, 0.303378276, 0.218879697, 0.092005711, 0.27011134, -0.012909856, 0.262292068, 0.107125772, 0.123422927, 0.299426602, 0.299426602, -0.326871824, -0.022088391, -0.428508341, -0.014675497, -0.114462294, 0.087227267, -0.031519161, -0.159318008, -0.397875854, 0.101520559, 0.244481505, 0.529968994, -0.32661959)), .Names = c("Family", "Swimming", "Not.Swimming", "Running", "Not.Running", "Fighting", "Not.Fighting", "Resting", "Not.Resting", "Hunting", "Not.Hunting", "Grooming", "Not.Grooming", "Other"), class = "data.frame", row.names = c(NA, -80L)) 1 Answer
Both the code variants that you posted won't work because they are using the function summarySE() wrongly:
- Version 1: You use
Familyas the measurement variable, which means that you ask the function to give you mean, standard deviation, etc. ofFamily. - Version2: You correctly group by
Family, but now you supply many measurement variables. This does not work becausesummarySE()expects a single measurement variable. Try to imagine how the output table should look with several measurement variables and you will notice that this won't be possible. You would have 13 columns forsd, 13 columns forci, etc.
The problem with your data is that Swimming", "Not.Swimming", "Running", etc. are actually values not variables. (Explaining this in detail is too much for this answer; see here if you need more information.) So, you need to convert your data into so-called long format:
library(tidyr) long_behaviours <- gather(behaviours, variable, value, -Family) long_behaviours[c(1, 120, 313, 730), ] ## Family variable value ## 1 v4 Swimming -0.48055680 ## 120 G8 Not.Swimming -0.05086028 ## 313 G8 Not.Running -0.07139534 ## 730 v4 Not.Hunting -0.22489721 As you can see from the few lines that I "randomly" picked from the resulting data frame, there is now a column that gives you the predictor and a single column with the numeric value. Now, you can use value as the measurement variable in summarySE and group by the other two:
library(Rmisc) sum_behaviours <- summarySE(long_behaviours, measurevar = "value", groupvar = c("Family", "variable"), na.rm = TRUE) head(sum_behaviours) ## Family variable N value sd se ci ## 1 G8 Fighting 50 0.157977831 0.58253445 0.082382813 0.16555446 ## 2 G8 Grooming 50 0.003784713 0.06611479 0.009350043 0.01878961 ## 3 G8 Hunting 50 0.157977831 0.58253445 0.082382813 0.16555446 ## 4 G8 Not.Fighting 50 -0.007098363 0.33806726 0.047809930 0.09607765 ## 5 G8 Not.Grooming 50 0.202045803 1.30151612 0.184062175 0.36988679 ## 6 G8 Not.Hunting 50 -0.007098363 0.33806726 0.047809930 0.09607765 You have now a table with mean, standard deviation, etc. for each Family and variable. This is the data you need to produce the plot according to the example from the R-Cookbook:
library(ggplot2) ggplot(sum_behaviours, aes(x = variable, y = value, fill = Family)) + geom_bar(position=position_dodge(), stat="identity") + geom_errorbar(aes(ymin = value - ci, ymax = value + ci), width=.2, position=position_dodge(.9)) + theme(axis.text.x = element_text(angle = 45, hjust = 1)) 
A little side remark: I personally prefer a box plot to compare the distributions of various variables with each other:
ggplot(long_behaviours, aes(x = variable, y = value, fill = Family)) + geom_boxplot() + theme(axis.text.x = element_text(angle = 45, hjust = 1))
