如何使用R中的函数sec.axis（）自定义百分比（0-100%，按5%的间隔）的辅助y轴比例_R_Ggplot2_Graphics_Boxplot_Yaxis

如何使用R中的函数sec.axis（）自定义百分比（0-100%，按5%的间隔）的辅助y轴比例

r graphics

如何使用R中的函数sec.axis（）自定义百分比（0-100%，按5%的间隔）的辅助y轴比例,r,ggplot2,graphics,boxplot,yaxis,R,Ggplot2,Graphics,Boxplot,Yaxis,概述我想制作一个二次y轴，显示0-100%和5%间隔之间的百分比（即0、5、10、15等）到目前为止，我成功地生成了第二个y轴，但我不知道如何以5%的间隔更改比例问题我成功地更改了第二个y轴的比例，但我的代码也将第一个y轴更改为相同的比例，从而影响了框的视觉显示，并将它们垂直挤压在一起期望的结果第一个y轴比例中的比例表示纬度坐标，因此无法更改此比例有人知道我如何使用下面的R代码和数据框，在不影响第一个y轴比例的情况下，自定义R中第二个y轴的比例（百分比0-100%，乘以5%）如果

概述

我想制作一个二次y轴，显示0-100%和5%间隔之间的百分比（即0、5、10、15等）

到目前为止，我成功地生成了第二个y轴，但我不知道如何以5%的间隔更改比例

问题

我成功地更改了第二个y轴的比例，但我的代码也将第一个y轴更改为相同的比例，从而影响了框的视觉显示，并将它们垂直挤压在一起

期望的结果

第一个y轴比例中的比例表示纬度坐标，因此无法更改此比例

有人知道我如何使用下面的R代码和数据框，在不影响第一个y轴比例的情况下，自定义R中第二个y轴的比例（百分比0-100%，乘以5%）

如果有人能帮忙，我将不胜感激

R代码

        ##New Plot Window 
          dev.new()
        ##Leave space for z axis
          par(mar = c(5, 4, 4, 4) + 0.3)

        ##Potentially set the second data set for Canopy Cover index onto a different scale 0-100%
        ##I am not sure if this object is useful to produce the scale for the secondary y-axis
             Canopy_Scale_ylabel <- seq(0, 100, by = 5)

        ##First Plot for the key parameters and latitude 

        QuercusParameterLat1<-ggplot(MeltedParameterLatitude1, aes(x = Key_Parameters, y = Latitude, fill=Key_Parameter_Category_Values)) + 
                                     geom_boxplot() +
                                     theme(axis.text.x = element_text(angle = 15, hjust = 1), text = element_text(size=10)) + 
                                     scale_x_discrete(labels=c("Stand Density Index", "Urbansiation Index", "Phenological Index"))+
                                     theme(panel.background = element_blank(), 
                                     panel.grid.major = element_blank(), 
                                     panel.grid.minor = element_blank(),
                                     panel.border = element_blank()) + 
                                     theme(axis.line.x = element_line(color="black", size = 0.8),
                                     axis.line.y = element_line(color="black", size = 0.8)) + 
                                     labs(x = "Key Parameters", y = "Latitude", size = 0.5) +
                                     theme(legend.position="right")
        ##Rename the legend title
        p11 <- QuercusParameterLat1 + guides(fill=guide_legend(title="Key Parameter Categories"))

# now adding the secondary axis, following the example in the help file ?scale_y_continuous
        # and, very important, reverting the above transformation
        p_yaxis_scale1 <- p11 + scale_y_continuous(sec.axis = sec_axis(~.+1, name = "Canopy Index %"), limit=c(0, 100))

structure(list(Key_Parameters = structure(c(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, 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, 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, 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, 
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, 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, 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, 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, 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, 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, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Stand_density_index", 
"Urbanisation_index", "Phenological_Index"), class = "factor"), 
    Latitude = c(51.4175, 52.12087, 52.0269, 52.0269, 52.0269, 
    52.0269, 52.947709, 52.947709, 51.491811, 51.491811, 52.59925, 
    52.59925, 52.59925, 52.59925, 51.60157, 51.60157, 52.6888, 
    52.6888, 52.6888, 52.6888, 50.697802, 50.697802, 50.697802, 
    50.697802, 53.62417, 50.446841, 50.446841, 53.959679, 53.959679, 
    53.959679, 53.959679, 51.78375, 51.78375, 51.78375, 51.78375, 
    51.456965, 51.456965, 51.456965, 51.456965, 51.3651, 51.3651, 
    51.3651, 51.3651, 52.01182, 52.01182, 52.01182, 52.01182, 
    50.114277, 50.114277, 51.43474, 51.43474, 51.10676, 51.10676, 
    51.10676, 51.10676, 50.435984, 50.435984, 50.435984, 50.435984, 
    51.78666, 51.78666, 52.441088, 52.441088, 52.552344, 49.259471, 
    49.259471, 49.259471, 49.259471, 51.746642, 51.746642, 51.746642, 
    51.746642, 52.2501, 52.2501, 52.2501, 52.2501, 52.423336, 
    52.423336, 52.423336, 52.423336, 53.615575, 53.615575, 53.615575, 
    53.615575, 51.08474, 51.08474, 51.08474, 53.19329, 53.19329, 
    53.19329, 53.19329, 55.96785, 55.96785, 56.52664, 56.52664, 
    56.52664, 56.52664, 51.8113, 51.8113, 51.8113, 51.8113, 52.580157, 
    52.580157, 52.580157, 52.580157, 50.52008, 50.52008, 50.52008, 
    50.52008, 51.48417, 51.48417, 51.48417, 51.48417, 54.58243, 
    54.58243, 54.58243, 54.58243, 52.58839, 52.58839, 52.58839, 
    52.58839, 52.717283, 52.717283, 52.717283, 52.717283, 50.740764, 
    50.740764, 50.740764, 50.740764, 52.57937, 52.57937, 52.57937, 
    52.57937, 50.736531, 50.736531, 50.79926, 50.79926, 50.79926, 
    53.675996, 53.675996, 51.36445, 51.36445, 51.36445, 51.36445, 
    52.122402, 52.122402, 52.122402, 52.16104, 52.16104, 55.91913, 
    51.6528, 51.6528, 51.6528, 51.6528, 51.88485, 51.88485, 51.88485, 
    51.88485, 52.34015, 52.34015, 52.34015, 52.026042, 52.026042, 
    52.026042, 52.026042, 51.319032, 51.319032, 51.319032, 51.319032, 
    51.51357, 51.51357, 51.51357, 51.51357, 53.43202, 53.43202, 
    53.43202, 53.43202, 51.50823, 51.50823, 51.50823, 51.50823, 
    51.4175, 52.12087, 52.0269, 52.0269, 52.0269, 52.0269, 52.947709, 
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    52.6888, 50.697802, 50.697802, 50.697802, 50.697802, 53.62417, 
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    51.78375, 51.78375, 51.78375, 51.78375, 51.456965, 51.456965, 
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    52.441088, 52.441088, 52.552344, 49.259471, 49.259471, 49.259471, 
    49.259471, 51.746642, 51.746642, 51.746642, 51.746642, 52.2501, 
    52.2501, 52.2501, 52.2501, 52.423336, 52.423336, 52.423336, 
    52.423336, 53.615575, 53.615575, 53.615575, 53.615575, 51.08474, 
    51.08474, 51.08474, 53.19329, 53.19329, 53.19329, 53.19329, 
    55.96785, 55.96785, 56.52664, 56.52664, 56.52664, 56.52664, 
    51.8113, 51.8113, 51.8113, 51.8113, 52.580157, 52.580157, 
    52.580157, 52.580157, 50.52008, 50.52008, 50.52008, 50.52008, 
    51.48417, 51.48417, 51.48417, 51.48417, 54.58243, 54.58243, 
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    52.717283, 52.717283, 52.717283, 52.717283, 50.740764, 50.740764, 
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    50.736531, 50.736531, 50.79926, 50.79926, 50.79926, 53.675996, 
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    52.122402, 52.122402, 52.16104, 52.16104, 55.91913, 51.6528, 
    51.6528, 51.6528, 51.6528, 51.88485, 51.88485, 51.88485, 
    51.88485, 52.34015, 52.34015, 52.34015, 52.026042, 52.026042, 
    52.026042, 52.026042, 51.319032, 51.319032, 51.319032, 51.319032, 
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    45L, 45L, 35L, 85L, 95L, 85L, 95L, 85L, 85L, 85L, 75L, 45L, 
    25L, 75L, 65L, 75L, 75L, 95L, 95L, 95L, 95L, 95L, 65L, 85L, 
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    85L, 65L, 45L, 75L, 75L, 65L, 65L, 75L, 65L, 95L, 95L, 95L, 
    85L, 65L, 75L, 75L, 75L, 65L, 75L, 35L, 75L, 75L, 75L, 75L, 
    25L, 45L, 45L, 35L, 85L, 95L, 85L, 95L), Key_Parameter_Category_Values = structure(c(3L, 
    1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 4L, 4L, 4L, 4L, 
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-543L))

您需要设置

中断

非

限制

以在

缩放y连续

高dww的可能重复中上升5秒。感谢您再次回复我的帖子，非常感谢。我对制作复杂的情节相当陌生，所以这是一个陡峭的学习曲线，但很好。我的最后期限是2天，我已经在这些地块上工作了4-5天。我按照你的建议重新编辑了上面的代码（添加了中断和限制）。不幸的是，我没有成功。我还创建了一个具有正确比例的对象，名为Canopy_Scale_ylabel。如果这是可能的，我将特别感谢任何建议。非常感谢。我产生了一些可爱的错误信息，我把它们纳入了上面的问题中。我走对了吗？如果你能提供建议，非常感谢。这真是一个复制品。例如，查看并使用那里的建议。除了在发帖前检查重复项（如果你在谷歌上搜索“ggplot secondary axis breaks”，上面的链接是第一个点击的链接），你应该在这里为你的问题构造最少的例子。在发布之前，应该消除与实际问题无关的大量数据和代码，否则会浪费人们的时间来处理所有额外的内容。您需要设置

中断

非

限制

以5秒的时间在

缩放和连续

高dww的可能重复。感谢您再次回复我的帖子，非常感谢。我对制作复杂的情节相当陌生，所以这是一个陡峭的学习曲线，但很好。我的最后期限是2天，我已经在这些地块上工作了4-5天。我按照你的建议重新编辑了上面的代码（添加了中断和限制）。不幸的是，我没有成功。我还创建了一个具有正确比例的对象，名为Canopy_Scale_ylabel。如果这是可能的，我将特别感谢任何建议。非常感谢。我产生了一些可爱的错误信息，我把它们纳入了上面的问题中。我走对了吗？如果你能提供建议，非常感谢。这真是一个复制品。例如，查看并使用那里的建议。除了在发帖前检查重复项（如果你在谷歌上搜索“ggplot secondary axis breaks”，上面的链接是第一个点击的链接），你应该在这里为你的问题构造最少的例子。许多与实际问题无关的数据和代码应该在发布之前删除，否则会浪费人们的时间费力地处理所有额外的内容。

##There are 20 tick marks for 0-100 by 5
##n=20

p_yaxis_scale <- p11 + scale_y_continuous(sec.axis = sec_axis(~.+1, name = "Canopy Index %"), breaks=scales::pretty_breaks(MeltedParameterLatitude1$Category_Index, n=20))

   ##Error message

     Error in pretty.default(x, n, ...) : invalid 'min.n' argument

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3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
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3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L), .Label = c("Stand_density_index", 
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    85L, 65L, 55L, 75L, 85L, 85L, 95L, 85L, 75L, 75L, 85L, 65L, 
    45L, 75L, 75L, 65L, 65L, 75L, 65L, 95L, 95L, 95L, 85L, 65L, 
    75L, 75L, 75L, 65L, 75L, 35L, 75L, 75L, 75L, 75L, 25L, 45L, 
    45L, 35L, 85L, 95L, 85L, 95L, 85L, 85L, 85L, 75L, 45L, 25L, 
    75L, 65L, 75L, 75L, 95L, 95L, 95L, 95L, 95L, 65L, 85L, 65L, 
    95L, 85L, 85L, 85L, 75L, 75L, 65L, 85L, 85L, 75L, 75L, 85L, 
    65L, 95L, 85L, 95L, 95L, 75L, 75L, 85L, 85L, 85L, 85L, 85L, 
    75L, 85L, 85L, 85L, 85L, 75L, 75L, 85L, 85L, 65L, 75L, 85L, 
    75L, 95L, 95L, 95L, 95L, 75L, 65L, 95L, 95L, 55L, 75L, 65L, 
    75L, 65L, 95L, 75L, 95L, 65L, 75L, 75L, 85L, 85L, 65L, 95L, 
    65L, 65L, 65L, 65L, 65L, 65L, 85L, 85L, 75L, 95L, 85L, 85L, 
    75L, 45L, 55L, 35L, 35L, 25L, 25L, 95L, 85L, 75L, 85L, 85L, 
    75L, 75L, 65L, 75L, 85L, 65L, 45L, 95L, 95L, 95L, 95L, 65L, 
    75L, 45L, 35L, 75L, 95L, 95L, 85L, 75L, 65L, 85L, 95L, 75L, 
    85L, 85L, 95L, 65L, 65L, 45L, 65L, 85L, 35L, 95L, 85L, 85L, 
    85L, 85L, 65L, 55L, 75L, 85L, 85L, 95L, 85L, 75L, 75L, 85L, 
    65L, 45L, 75L, 75L, 65L, 65L, 75L, 65L, 95L, 95L, 95L, 85L, 
    65L, 75L, 75L, 75L, 65L, 75L, 35L, 75L, 75L, 75L, 75L, 25L, 
    45L, 45L, 35L, 85L, 95L, 85L, 95L, 85L, 85L, 85L, 75L, 45L, 
    25L, 75L, 65L, 75L, 75L, 95L, 95L, 95L, 95L, 95L, 65L, 85L, 
    65L, 95L, 85L, 85L, 85L, 75L, 75L, 65L, 85L, 85L, 75L, 75L, 
    85L, 65L, 95L, 85L, 95L, 95L, 75L, 75L, 85L, 85L, 85L, 85L, 
    85L, 75L, 85L, 85L, 85L, 85L, 75L, 75L, 85L, 85L, 65L, 75L, 
    85L, 75L, 95L, 95L, 95L, 95L, 75L, 65L, 95L, 95L, 55L, 75L, 
    65L, 75L, 65L, 95L, 75L, 95L, 65L, 75L, 75L, 85L, 85L, 65L, 
    95L, 65L, 65L, 65L, 65L, 65L, 65L, 85L, 85L, 75L, 95L, 85L, 
    85L, 75L, 45L, 55L, 35L, 35L, 25L, 25L, 95L, 85L, 75L, 85L, 
    85L, 75L, 75L, 65L, 75L, 85L, 65L, 45L, 95L, 95L, 95L, 95L, 
    65L, 75L, 45L, 35L, 75L, 95L, 95L, 85L, 75L, 65L, 85L, 95L, 
    75L, 85L, 85L, 95L, 65L, 65L, 45L, 65L, 85L, 35L, 95L, 85L, 
    85L, 85L, 85L, 65L, 55L, 75L, 85L, 85L, 95L, 85L, 75L, 75L, 
    85L, 65L, 45L, 75L, 75L, 65L, 65L, 75L, 65L, 95L, 95L, 95L, 
    85L, 65L, 75L, 75L, 75L, 65L, 75L, 35L, 75L, 75L, 75L, 75L, 
    25L, 45L, 45L, 35L, 85L, 95L, 85L, 95L), Key_Parameter_Category_Values = structure(c(3L, 
    1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 1L, 1L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 3L, 2L, 2L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 3L, 
    4L, 4L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 
    2L, 3L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 4L, 
    4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 2L, 2L, 2L, 2L, 
    3L, 3L, 3L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 2L, 
    2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 2L, 1L, 1L, 2L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 3L, 
    3L, 3L, 3L, 4L, 4L, 4L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 2L, 
    2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 4L, 
    4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 
    2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 
    2L, 2L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 4L, 
    4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 
    4L, 1L, 1L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
    1L, 2L, 2L, 2L, 2L, 4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 4L, 4L, 
    2L, 2L, 2L, 3L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 4L, 4L, 1L, 
    3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 
    4L, 4L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 
    1L, 2L, 4L, 2L, 2L, 4L, 4L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 
    3L, 3L, 1L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 3L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 4L, 4L, 1L, 2L, 2L, 2L, 2L, 1L, 1L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 2L, 2L, 2L, 
    2L, 2L, 3L, 3L, 3L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
    3L, 3L, 3L, 3L, 4L, 1L, 1L, 1L, 1L, 3L, 2L, 3L, 3L, 3L, 3L, 
    4L, 3L, 2L, 3L, 2L, 2L, 2L, 1L, 3L, 1L, 4L, 2L, 4L, 3L, 3L, 
    3L, 2L, 2L, 2L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 3L, 4L, 3L, 3L, 3L, 2L, 3L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
    3L, 2L), .Label = c("1", "2", "3", "4"), class = "factor")), class = "data.frame", row.names = c(NA, 
-543L))