SummarySE（Rmisc软件包）生成一个带误差线的条形图（ggplot2） [英] SummarySE (Rmisc package) to produce a barplot with error bars (ggplot2)

问题描述

我正在尝试使用 ggplot2 包在x轴上显示13个预测变量（数据框 behavior 可以在下面找到）。理想情况下，预测变量将按照包含两个级别（G8和V4）的响应变量（ family ）进行分组，每个预测变量加上一个键，用两个彩色条表示。我试图遵循Cookbook for R的一个例子（见下文）。我想用 Rmisc 包中的函数 summarySE 来总结数据以计算标准偏差，标准误差平均值和一个（默认95％）置信区间，但是，我的代码显示警告消息并返回NA。我不确定函数 summarySE（）的正确语法。如何为我的数据实现R Cookbook示例？

使用函数`summarySE'的My Code：

  library（ ggplot2）
 library（Rmisc）
 
＃（1）First Try  -  Equation 1 
 
 summary.behaviours<  -  summarySE（behaviour，
 measurevar =Family，
 groupvars = c（游泳，
Not.Swimming，
Running，
Not.Running，
战斗，
Not.Fighting，
休息，
Not.Resting，
狩猎，
Not.Hunting，
 梳理，
Not.Grooming，
其他），
 na.rm = TRUE）
＃（2）第二次尝试 - 等式2 
 
 summary.behaviours<  -  summarySE（行为，
 measurevar = c（游泳，
Not.Swimming，
正在运行，
Not 。运行，
战斗，
Not.Fighting，
休息，
Not.Resting，
狩猎，
 Not.Hunting，
美容，
不合格，
其他），
 groupvar =家庭，
 na.rm = TRUE）
  

警告信息

警告公式（1）的消息

  1：在mean.default（xx [，col]，na.rm = na.rm ）：
参数不是数字或逻辑：返回NA 
 2：在mean.default（xx [，col]，na.rm = na.rm）中：
参数不是数字或逻辑：返回NA 
  

以及更多相同类型的警告。

公式（2）的错误信息：

 `[.data.frame`（xx ，，col）：未定义的列被选中
  

Ex充足的代码来自Cookbook for R

参考：

有一点小小的评论：我个人比较喜欢箱形图来比较各种变量的分布情况：

  ggplot（long_behaviours，aes（x = variable，y = value，fill = Family））+ 
 geom_boxplot（）+ 
 theme（axis.text.x = element_text（angle = 45 ，hjust = 1））
  

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))

解决方案

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))

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))

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