R 增加ggplot中x轴因子之间的间距

我在ggplot2中使用geom_pointrange函数，以绘制5名受试者在不同条件下的一些测量分布。为了避免主题重叠，我按照如下方式构建了该图：

Final = structure(list(Subject = structure(c(1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L), .Label = c("1", "2", "3", "4", "5"), class = "factor"), 
    X00.conditionName = structure(c(1L, 1L, 2L, 2L, 3L, 3L, 4L, 
    4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L, 
    4L, 4L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 1L, 1L, 2L, 2L, 3L, 
    3L, 4L, 4L), .Label = c("EyeClose-Haptic", "mixed-Haptic_Visual", 
    "only-Haptic", "only-Visual"), class = "factor"), X03.totalTargetNumber = c(2L, 
    3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 
    2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 
    3L, 2L, 3L, 2L, 3L, 2L, 3L, 2L, 3L), Accuracy = c(0.075870763, 
    0.0907863686, 0.0222156611, 0.0492028585333333, 0.0301178471, 
    0.0736098328666667, 0.0329723832, 0.0455095300666667, 0.065151615, 
    0.0979033533333333, 0.0247176775, 0.0335825226666667, 0.027385248, 
    0.0462643053333333, 0.037272505, 0.0652166726666667, 0.043005086, 
    0.061848328, 0.031106749, 0.0275656054, 0.026701889, 0.0373967466666667, 
    0.028998468, 0.03219287, 0.0597213356, 0.0851717708333333, 
    0.030286913, 0.0779058462666667, 0.043368508, 0.051437624, 
    0.029002474, 0.0479204566666667, 0.094555739, 0.0856268291666667, 
    0.031908514, 0.0310441326666667, 0.036311762, 0.0496942306666667, 
    0.054625148, 0.0482682121666667), upperCI = c(0.116082073022708, 
    0.139632763787946, 0.0315087794760623, 0.0727058964327625, 
    0.0468512606854127, 0.116787586356955, 0.0444933233012107, 
    0.062820743812494, 0.0858551911272202, 0.136013260005381, 
    0.0327074347874691, 0.0460471773903695, 0.035302995136302, 
    0.0740077338495226, 0.0641795522210299, 0.131047110446756, 
    0.0572545979325947, 0.0809511078363974, 0.0414215170576924, 
    0.0341480438532189, 0.0382253716300962, 0.0519626825555577, 
    0.0377955915789704, 0.0430125127419472, 0.0903928001427357, 
    0.114245467448517, 0.0461054194398361, 0.129350863514659, 
    0.0635159480110737, 0.0717647837071829, 0.0371919026867606, 
    0.0615899295823839, 0.170222051412597, 0.128502458351433, 
    0.046712862081242, 0.0388340720489338, 0.0574188259607336, 
    0.0786845830951613, 0.0844193698576058, 0.0784830058409822
    ), lowerCI = c(0.0356594529772922, 0.0419399734120541, 0.0129225427239377, 
    0.0256998206339042, 0.0133844335145873, 0.0304320793763786, 
    0.0214514430987893, 0.0281983163208393, 0.0444480388727798, 
    0.059793446661286, 0.0167279202125309, 0.0211178679429639, 
    0.019467500863698, 0.0185208768171441, 0.0103654577789701, 
    -0.000613765113422152, 0.0287555740674053, 0.0427455481636026, 
    0.0207919809423076, 0.0209831669467811, 0.0151784063699038, 
    0.0228308107777757, 0.0202013444210296, 0.0213732272580528, 
    0.0290498710572643, 0.0560980742181497, 0.0144684065601638, 
    0.0264608290186746, 0.0232210679889263, 0.0311104642928171, 
    0.0208130453132394, 0.0342509837509495, 0.018889426587403, 
    0.0427511999819006, 0.017104165918758, 0.0232541932843995, 
    0.0152046980392664, 0.0207038782381721, 0.0248309261423941, 
    0.0180534184923511), CondLevel = c("EyeClose-Haptic2", "EyeClose-Haptic3", 
    "mixed-Haptic_Visual2", "mixed-Haptic_Visual3", "only-Haptic2", 
    "only-Haptic3", "only-Visual2", "only-Visual3", "EyeClose-Haptic2", 
    "EyeClose-Haptic3", "mixed-Haptic_Visual2", "mixed-Haptic_Visual3", 
    "only-Haptic2", "only-Haptic3", "only-Visual2", "only-Visual3", 
    "EyeClose-Haptic2", "EyeClose-Haptic3", "mixed-Haptic_Visual2", 
    "mixed-Haptic_Visual3", "only-Haptic2", "only-Haptic3", "only-Visual2", 
    "only-Visual3", "EyeClose-Haptic2", "EyeClose-Haptic3", "mixed-Haptic_Visual2", 
    "mixed-Haptic_Visual3", "only-Haptic2", "only-Haptic3", "only-Visual2", 
    "only-Visual3", "EyeClose-Haptic2", "EyeClose-Haptic3", "mixed-Haptic_Visual2", 
    "mixed-Haptic_Visual3", "only-Haptic2", "only-Haptic3", "only-Visual2", 
    "only-Visual3")), .Names = c("Subject", "X00.conditionName", 
"X03.totalTargetNumber", "Accuracy", "upperCI", "lowerCI", "CondLevel"
), row.names = c(NA, -40L), class = "data.frame")


require(ggplot2)

pdf("Pilot2.pdf", w = 12, h = 8)
limits <- aes(ymax = upperCI, ymin=lowerCI)
BaseLayer = ggplot(data = Final, aes (x = X00.conditionName, y = Accuracy, color = Subject, group = Subject ))
BaseLayer + geom_pointrange(limits, position=position_dodge(width=1), size = 1.5) + 
theme(axis.text=element_text(size=14), axis.title=element_text(size=14), axis.text.x = element_text(angle = 25, hjust = 1)) + 
facet_grid (.~X03.totalTargetNumber) + ggtitle ("Pilot 2") + xlab ("Condition")
dev.off()

Final=结构（列表）（主题=结构，
1L，1L，2L，2L，2L，2L，2L，2L，2L，2L，3L，3L，3L，3L，3L，3L，
3L，3L，4L，4L，4L，4L，4L，4L，4L，5L，5L，5L，5L，5L，
5L，5L），.Label=c（“1”、“2”、“3”、“4”、“5”），class=“factor”），
X00.conditionName=结构（c（1L、1L、2L、2L、3L、3L、4L、，
4L，1L，1L，2L，2L，3L，3L，4L，4L，1L，1L，2L，2L，3L，3L，
4L，4L，1L，1L，2L，2L，3L，3L，4L，4L，1L，1L，2L，2L，3L，
3L、4L、4L），.Label=c（“闭眼触觉”、“混合触觉”，
“仅触觉”、“仅视觉”），class=“factor”），X03.totalTargetNumber=c（2L，
3L、2L、3L、2L、3L、2L、3L、2L、3L、2L、3L、2L、2L、3L、2L、3L、2L、3L、，
2L、3L、2L、3L、2L、3L、2L、3L、2L、3L、2L、3L、2L、3L、2L、3L、2L、，
3L，2L，3L，2L，3L，2L，3L，2L，3L），精度=c（0.075870763，
0.0907863686, 0.0222156611, 0.0492028585333333, 0.0301178471, 
0.0736098328666667, 0.0329723832, 0.0455095300666667, 0.065151615, 
0.0979033533333333, 0.0247176775, 0.0335825226666667, 0.027385248, 
0.0462643053333333, 0.037272505, 0.0652166726666667, 0.043005086, 
0.061848328, 0.031106749, 0.0275656054, 0.026701889, 0.0373967466666667, 
0.028998468, 0.03219287, 0.0597213356, 0.0851717708333333, 
0.030286913, 0.0779058462666667, 0.043368508, 0.051437624, 
0.029002474, 0.0479204566666667, 0.094555739, 0.0856268291666667, 
0.031908514, 0.0310441326666667, 0.036311762, 0.0496942306666667, 
0.054625148，0.0482682166667），上限CI=c（0.116082073022708，
0.139632763787946, 0.0315087794760623, 0.0727058964327625, 
0.0468512606854127, 0.116787586356955, 0.0444933233012107, 
0.062820743812494, 0.0858551911272202, 0.136013260005381, 
0.0327074347874691, 0.0460471773903695, 0.035302995136302, 
0.0740077338495226, 0.0641795522210299, 0.131047110446756, 
0.0572545979325947, 0.0809511078363974, 0.0414215170576924, 
0.0341480438532189, 0.0382253716300962, 0.0519626825555577, 
0.0377955915789704, 0.0430125127419472, 0.0903928001427357, 
0.114245467448517, 0.0461054194398361, 0.129350863514659, 
0.0635159480110737, 0.0717647837071829, 0.0371919026867606, 
0.0615899295823839, 0.170222051412597, 0.128502458351433, 
0.046712862081242, 0.0388340720489338, 0.0574188259607336, 
0.0786845830951613, 0.0844193698576058, 0.0784830058409822
)，lowerCI=c（0.0356594529772922，0.0419399734120541，0.012922542723937，
0.0256998206339042, 0.0133844335145873, 0.0304320793763786, 
0.0214514430987893, 0.0281983163208393, 0.0444480388727798, 
0.059793446661286, 0.0167279202125309, 0.0211178679429639, 
0.019467500863698, 0.0185208768171441, 0.0103654577789701, 
-0.000613765113422152, 0.0287555740674053, 0.0427455481636026, 
0.0207919809423076, 0.0209831669467811, 0.0151784063699038, 
0.0228308107777757, 0.0202013444210296, 0.0213732272580528, 
0.0290498710572643, 0.0560980742181497, 0.0144684065601638, 
0.0264608290186746, 0.0232210679889263, 0.0311104642928171, 
0.0208130453132394, 0.0342509837509495, 0.018889426587403, 
0.0427511999819006, 0.017104165918758, 0.0232541932843995, 
0.0152046980392664, 0.0207038782381721, 0.0248309261423941, 
0.0180534184923511），CondLevel=c（“EyeClose-Haptic2”、“EyeClose-Haptic3”，
“混合触觉视觉2”、“混合触觉视觉3”、“仅限触觉2”，
“only-Haptic3”、“only-Visual2”、“only-Visual3”、“EyeClose-Haptic2”，
“EyeClose-Haptic3”、“混合触觉视觉2”、“混合触觉视觉3”，
“only-Haptic2”、“only-Haptic3”、“only-Visual2”、“only-Visual3”，
“EyeClose-Haptic2”、“EyeClose-Haptic3”、“混合触觉视觉2”，
“混合触觉视觉3”、“仅触觉2”、“仅触觉3”、“仅视觉2”，
“only-Visual3”、“EyeClose-Haptic2”、“EyeClose-Haptic3”、“mixed-Haptic_Visual2”，
“混合触觉视觉3”、“仅触觉2”、“仅触觉3”、“仅视觉2”，
“only-Visual3”、“EyeClose-Haptic2”、“EyeClose-Haptic3”、“mixed-Haptic_Visual2”，
“混合触觉视觉3”、“仅触觉2”、“仅触觉3”、“仅视觉2”，
“only-Visual3”），.name=c（“主题”，“X00.conditionName”，
“X03.总目标编号”、“精度”、“上限CI”、“下限CI”、“条件级别”
)，row.names=c（NA，-40L），class=“data.frame”）
需要（ggplot2）
pdf（“Pilot2.pdf”，w=12，h=8）
限制最好的解决方案是使用镶嵌面创建8个独立的高矮曲线图，其中包含所有这些特征，这些特征之间由一条白色细沟分隔，顶部有一个实心标签。您可能保留或丢失X轴标签。它创建了一个由8个图形组成的图形，比在大图上更好地进行通信

像这样：

除了垂直面之外，最好的解决方案是使用镶嵌面创建8个独立的高矮地块，其中包含所有这些特征，这些特征之间用一条白色细沟隔开，顶部有一个实心标签。您可能保留或丢失X轴标签。它创建了一个由8个图形组成的图形，比在大图上更好地进行通信

像这样：

除非你的是verticle
不确定这个解决方案，或者我遗漏了什么。如果我保留aes（x=X00.conditionName，y=accurity，color=Subject，group=Subject），然后像你建议的那样，我在条件和级别的组合上刻面，我仍然在每个刻面中绘制了4个条件，这显然是不好的。是的，但是每个x类别都有自己的绘图，那么每个主题之间的差异（相对于类别而言，这将是显而易见的，并且更容易知道你是在看第一个纯触觉还是最后一个混合触觉视觉。根据各自的条件制作两组四个面……两个数字，2和3不确定这个解决方案，或者我遗漏了一些