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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: http://www.cookbook-r.com/Graphs/Plotting_means_and_error_bars_(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))
Alice Hobbs
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    Please clean up this question. Here are the most obvious issues (but there might be more): 1) You post data called `mydat`, but your function call works with `behaviours`. Are they the same? 2) Your `structure()` misses a comma after `.Label = c("G8", "v4")` 3) The data contains fields `Not.Fighting` and `Not.Hunting`, but inside `summarySE()` you use `Not.fighting` and `Not.hunting`. 4) Your title and the example code in the last section mention `ggplot2`, but your problem happens before you use `ggplot2`. – Stibu Mar 27 '16 at 20:26
  • 5) Even after I corrected this issues (with guessing; I might be wrong...), I get many more error messages than those you mention. – Stibu Mar 27 '16 at 20:26
  • Appart from all that: the help for `summarySE` says that `groupvars` is "a vector containing names of columns that contain grouping variables". It seems that you are using this function completely wrong. You can group by several variables, but only summarize one. You try to group by one variable (which is fine) and summarize several (which won't work). – Stibu Mar 27 '16 at 20:31
  • I have corrected the typo's. The grouping variable is family - "G8" and "V4". This is a classification exercise and I would like to graphically show differences in variability between these families. I have switched the grouping variable to groupvar and predictors to measurevar, and another warning message was returned. At this point, I really do not understand these warning messages. – Alice Hobbs Mar 28 '16 at 08:07
  • I tried to edit your question in something more useful. But there are still many problems that I cannot solve, for instance, the column `Resting` contains only 76 values instead of 80. Also, I get different error messages than you do. I know the answer to your question (that is, if I understand it correctly), but I will not answer as long as it is such a mess. – Stibu Mar 28 '16 at 08:42
  • Hi Stibu, Thank you for your help. It is greatly appreciated. I re-copied the dataframe into the text and I believe that each column now contains 80 observations. – Alice Hobbs Mar 28 '16 at 09:57

1 Answers1

6

Both the code variants that you posted won't work because they are using the function summarySE() wrongly:

  • Version 1: You use Family as the measurement variable, which means that you ask the function to give you mean, standard deviation, etc. of Family.
  • Version2: You correctly group by Family, but now you supply many measurement variables. This does not work because summarySE() 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 for sd, 13 columns for ci, 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))

enter image description here

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))

enter image description here

Stibu
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  • Hiya Stibu. Words cannot express my gratitude for your help. I successfully produced the barplots with error bars; however, the box plots, which are definitely nicer, were not as successful. My biggest apologies, but I placed: (1) the code which I used to produce the long-format dataframe; (2) the code to summarise the data using the function summarySE(); (3) the code and a graphical representation of the bar plots; and (4) the code and a jpeg showing my failed attempt to produce box plots. Do you know how to fix the box plot problem? Thank you so much, I have learned a lot from you. – Alice Hobbs Mar 28 '16 at 17:25
  • You are welcome; I'm glad it helped and that you learned something. Regarding the box plot: you are using the wrong data. There is not need to apply `summarySE()` first in that situation, because `geom_boxplot()` does all the necessary calculations itself. So you should use `long_behaviours` for the boxplot (not `long_behaviours1`). – Stibu Mar 28 '16 at 17:49
  • Great! Success with the boxplots! This has been a great interactive experience. Take care and Thank you again from a very happy person – Alice Hobbs Mar 28 '16 at 18:02