ggplot::geom_boxplot() 如何改变R中一个框组的宽度
ggplot::geom_boxplot() How to change the width of one box group in R
我想将类别 "random" 中框的宽度调整为与绘图中其他框的宽度相同。它现在是一个单独的组，而其他组包含两个子组......关于如何做到这一点的任何想法？
使用 geom_boxplot(width=0.2) 只是改变所有框的宽度。到目前为止，我使用了以下代码：
ggplot(TablePerCatchmentAndYear,aes(x=NoiseType, y= POA, fill = TempRes)) +
geom_boxplot(lwd=0.05) + ylim(c(-1.25, 1)) + theme(legend.position='bottom') +
ggtitle('title')+ scale_fill_discrete(name = '')
我为此使用的数据如下table:
TablePerCatchmentAndYear = structure(list(CatchmentModelType = c("2126_Murg_2009_dry_bench_hourly",
"2126_Murg_2009_dry_bench_hourly", "2126_Murg_2009_dry_bench_hourly",
"2126_Murg_2009_dry_bench_hourly", "2126_Murg_2009_dry_bench_hourly",
"2126_Murg_2009_dry_bench_hourly", "2126_Murg_2009_dry_bench_hourly",
"2126_Murg_2009_dry_bench_hourly", "2126_Murg_2009_dry_bench_hourly",
"2126_Murg_2009_dry_bench_hourly", "2126_Murg_2009_dry_LogNormSDdivBy4_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy4_hourly", "2126_Murg_2009_dry_LogNormSDdivBy4_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy4_hourly", "2126_Murg_2009_dry_LogNormSDdivBy4_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy4_hourly", "2126_Murg_2009_dry_LogNormSDdivBy4_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy4_hourly", "2126_Murg_2009_dry_LogNormSDdivBy4_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy4_hourly", "2126_Murg_2009_dry_LogNormSDdivBy2_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy2_hourly", "2126_Murg_2009_dry_LogNormSDdivBy2_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy2_hourly", "2126_Murg_2009_dry_LogNormSDdivBy2_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy2_hourly", "2126_Murg_2009_dry_LogNormSDdivBy2_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy2_hourly", "2126_Murg_2009_dry_LogNormSDdivBy2_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy2_hourly", "2126_Murg_2009_dry_LogNormSDdivBy1_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy1_hourly", "2126_Murg_2009_dry_LogNormSDdivBy1_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy1_hourly", "2126_Murg_2009_dry_LogNormSDdivBy1_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy1_hourly", "2126_Murg_2009_dry_LogNormSDdivBy1_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy1_hourly", "2126_Murg_2009_dry_LogNormSDdivBy1_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy1_hourly", "2126_Murg_2009_dry_random_hourly",
"2126_Murg_2009_dry_random_hourly", "2126_Murg_2009_dry_random_hourly",
"2126_Murg_2009_dry_random_hourly", "2126_Murg_2009_dry_random_hourly",
"2126_Murg_2009_dry_random_hourly", "2126_Murg_2009_dry_random_hourly",
"2126_Murg_2009_dry_random_hourly", "2126_Murg_2009_dry_random_hourly",
"2126_Murg_2009_dry_random_hourly", "2126_Murg_2009_dry_bench_weekly",
"2126_Murg_2009_dry_bench_weekly", "2126_Murg_2009_dry_bench_weekly",
"2126_Murg_2009_dry_bench_weekly", "2126_Murg_2009_dry_bench_weekly",
"2126_Murg_2009_dry_bench_weekly", "2126_Murg_2009_dry_bench_weekly",
"2126_Murg_2009_dry_bench_weekly", "2126_Murg_2009_dry_bench_weekly",
"2126_Murg_2009_dry_bench_weekly", "2126_Murg_2009_dry_LogNormSDdivBy4_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy4_weekly", "2126_Murg_2009_dry_LogNormSDdivBy4_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy4_weekly", "2126_Murg_2009_dry_LogNormSDdivBy4_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy4_weekly", "2126_Murg_2009_dry_LogNormSDdivBy4_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy4_weekly", "2126_Murg_2009_dry_LogNormSDdivBy4_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy4_weekly", "2126_Murg_2009_dry_LogNormSDdivBy2_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy2_weekly", "2126_Murg_2009_dry_LogNormSDdivBy2_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy2_weekly", "2126_Murg_2009_dry_LogNormSDdivBy2_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy2_weekly", "2126_Murg_2009_dry_LogNormSDdivBy2_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy2_weekly", "2126_Murg_2009_dry_LogNormSDdivBy2_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy2_weekly", "2126_Murg_2009_dry_LogNormSDdivBy1_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy1_weekly", "2126_Murg_2009_dry_LogNormSDdivBy1_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy1_weekly", "2126_Murg_2009_dry_LogNormSDdivBy1_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy1_weekly", "2126_Murg_2009_dry_LogNormSDdivBy1_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy1_weekly", "2126_Murg_2009_dry_LogNormSDdivBy1_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy1_weekly"), NoiseType = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("bench", "LogNormSDdivBy1",
"LogNormSDdivBy2", "LogNormSDdivBy4", "random"), class = "factor"),
TempRes = 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, 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), .Label = c("hourly", "weekly"), class = "factor"),
yearChar = c("dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry"), Parameterset = c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L), Reff = c(0.6626,
0.6959, 0.7128, 0.6351, 0.7056, 0.6755, 0.655, 0.7155, 0.6839,
0.6564, 0.543, 0.5652, 0.5405, 0.5698, 0.5395, 0.5548, 0.5652,
0.5699, 0.5892, 0.578, 0.5366, 0.5052, 0.5389, 0.5194, 0.5555,
0.5529, 0.5315, 0.5092, 0.5137, 0.5417, 0.6635, 0.6427, 0.6561,
0.6702, 0.7035, 0.6789, 0.6631, 0.6544, 0.6432, 0.6384, 0.2273,
0.1757, -0.0048, 0.1647, 0.2586, 0.2926, 0.0739, 0.2607,
0.0799, 0.3595, 0.6679, 0.6712, 0.6557, 0.6906, 0.6777, 0.6748,
0.6531, 0.6779, 0.6708, 0.6446, 0.6227, 0.6404, 0.6474, 0.6221,
0.6089, 0.6159, 0.6194, 0.6382, 0.6323, 0.6198, 0.4703, 0.5456,
0.5883, 0.5114, 0.5188, 0.6257, 0.6036, 0.4501, 0.5154, 0.6,
0.2172, 0.245, 0.3625, 0.2793, 0.4073, 0.3257, 0.3435, 0.4297,
0.4375, 0.3451), LogReff = c(0.6498, 0.6665, 0.684, 0.6078,
0.6845, 0.6375, 0.6325, 0.6871, 0.6661, 0.6396, 0.5571, 0.5735,
0.5398, 0.5763, 0.5389, 0.5612, 0.5657, 0.578, 0.5999, 0.5881,
0.5806, 0.5445, 0.5782, 0.5724, 0.5832, 0.6113, 0.5763, 0.5439,
0.5626, 0.5757, 0.6855, 0.6787, 0.7003, 0.6393, 0.6684, 0.6924,
0.6897, 0.6956, 0.6408, 0.6801, 0.2823, -0.6217, -0.5084,
0.1936, 0.2246, 0.5335, 0.0143, 0.3124, -1.2437, -1.2655,
0.7041, 0.6973, 0.6834, 0.7032, 0.7116, 0.7042, 0.6811, 0.7148,
0.693, 0.6994, 0.6543, 0.6724, 0.6962, 0.657, 0.6783, 0.6621,
0.655, 0.6763, 0.6668, 0.6557, 0.6393, 0.6671, 0.726, 0.6832,
0.6848, 0.725, 0.7171, 0.6249, 0.6998, 0.7267, 0.3785, 0.4655,
0.5272, 0.5249, 0.5853, 0.4842, 0.4172, 0.6045, 0.5857, 0.5238
), VolumeError = c(0.9267, 0.931, 0.9401, 0.9225, 0.9507,
0.923, 0.9243, 0.9536, 0.9312, 0.9285, 0.8689, 0.8718, 0.8716,
0.8716, 0.8683, 0.8658, 0.8691, 0.8703, 0.8764, 0.8745, 0.8786,
0.8773, 0.8875, 0.8924, 0.8837, 0.8862, 0.8865, 0.8779, 0.8792,
0.8901, 0.8119, 0.8109, 0.8412, 0.8254, 0.8271, 0.8509, 0.8161,
0.8259, 0.8386, 0.8263, 0.8507, 0.5669, 0.4859, 0.6478, 0.6046,
0.85, 0.9425, 0.9153, 0.5295, 0.6555, 0.9777, 0.994, 0.9915,
0.9899, 0.9738, 0.9833, 0.9694, 0.9981, 0.9964, 0.9818, 0.997,
0.9822, 0.9954, 0.9996, 0.9768, 0.9644, 0.9974, 0.9962, 0.998,
0.9995, 0.9962, 0.9684, 0.99, 0.9625, 0.9595, 0.9853, 0.9783,
0.9227, 0.9661, 0.9783, 0.7664, 0.8786, 0.7615, 0.799, 0.7369,
0.7722, 0.8399, 0.7354, 0.771, 0.7745), MAREMeasure = c(0.532,
0.543, 0.557, 0.497, 0.5581, 0.5176, 0.5166, 0.5621, 0.5447,
0.5234, 0.445, 0.4554, 0.4322, 0.4579, 0.4298, 0.4448, 0.4487,
0.4582, 0.4762, 0.4675, 0.4718, 0.4432, 0.4725, 0.4721, 0.4725,
0.4989, 0.4711, 0.4428, 0.4577, 0.4704, 0.7183, 0.7166, 0.7144,
0.6848, 0.6943, 0.7034, 0.7202, 0.7194, 0.6832, 0.7105, 0.4913,
0.5758, 0.5658, 0.5817, 0.6574, 0.6191, 0.1196, 0.3526, 0.5357,
0.5475, 0.5931, 0.5882, 0.5782, 0.5886, 0.5984, 0.5945, 0.5728,
0.6089, 0.5834, 0.5962, 0.5434, 0.5581, 0.5848, 0.5467, 0.5703,
0.5478, 0.546, 0.5633, 0.555, 0.5458, 0.6243, 0.6468, 0.6736,
0.6175, 0.6219, 0.6604, 0.6769, 0.5766, 0.6301, 0.6793, 0.5227,
0.6047, 0.647, 0.6657, 0.661, 0.6324, 0.5751, 0.6707, 0.6532,
0.6621), POA = c(0.692775, 0.7091, 0.723475, 0.6656, 0.724725,
0.6884, 0.6821, 0.729575, 0.706475, 0.686975, 0.6035, 0.616475,
0.596025, 0.6189, 0.594125, 0.60665, 0.612175, 0.6191, 0.635425,
0.627025, 0.6169, 0.59255, 0.619275, 0.614075, 0.623725,
0.637325, 0.61635, 0.59345, 0.6033, 0.619475, 0.7198, 0.712225,
0.728, 0.704925, 0.723325, 0.7314, 0.722275, 0.723825, 0.70145,
0.713825, 0.4629, 0.174175, 0.134625, 0.39695, 0.4363, 0.5738,
0.287575, 0.46025, -0.02465, 0.07425, 0.7357, 0.737675, 0.7272,
0.743075, 0.740375, 0.7392, 0.7191, 0.749925, 0.7359, 0.7305,
0.70435, 0.713275, 0.73095, 0.70635, 0.708575, 0.69755, 0.70445,
0.7185, 0.713025, 0.7052, 0.682525, 0.706975, 0.744475, 0.69365,
0.69625, 0.7491, 0.743975, 0.643575, 0.70285, 0.746075, 0.4712,
0.54845, 0.57455, 0.567225, 0.597625, 0.553625, 0.543925,
0.610075, 0.61185, 0.576375)), .Names = c("CatchmentModelType",
"NoiseType", "TempRes", "yearChar", "Parameterset", "Reff", "LogReff",
"VolumeError", "MAREMeasure", "POA"), row.names = c(1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 1501L, 1502L, 1503L, 1504L, 1505L,
1506L, 1507L, 1508L, 1509L, 1510L, 1001L, 1002L, 1003L, 1004L,
1005L, 1006L, 1007L, 1008L, 1009L, 1010L, 501L, 502L, 503L, 504L,
505L, 506L, 507L, 508L, 509L, 510L, 36001L, 36002L, 36003L, 36004L,
36005L, 36006L, 36007L, 36008L, 36009L, 36010L, 401L, 402L, 403L,
404L, 405L, 406L, 407L, 408L, 409L, 410L, 1901L, 1902L, 1903L,
1904L, 1905L, 1906L, 1907L, 1908L, 1909L, 1910L, 1401L, 1402L,
1403L, 1404L, 1405L, 1406L, 1407L, 1408L, 1409L, 1410L, 901L,
902L, 903L, 904L, 905L, 906L, 907L, 908L, 909L, 910L), class = "data.frame")
可以修改 second solution here 以适合您的情况：
第 1 步。使用 tidyr 包中的 complete 将假数据添加到数据集：
TablePerCatchmentAndYear2 <- TablePerCatchmentAndYear %>%
dplyr::select(NoiseType, TempRes, POA) %>%
tidyr::complete(NoiseType, TempRes, fill = list(POA = 100))
# 100 is arbitrarily chosen here as a very large value beyond the range of
# POA values in the boxplot
步骤 2. 绘图，但将 y 轴限制设置在 coord_cartesian:
范围内
ggplot(dat2,aes(x=NoiseType, y= POA, fill = TempRes)) +
geom_boxplot(lwd=0.05) + coord_cartesian(ylim = c(-1.25, 1)) + theme(legend.position='bottom') +
ggtitle('title')+ scale_fill_discrete(name = '')
原因是使用 ylim() 命令设置限制会导致每周随机噪声类型的空箱线图 space 消失。 ylim 的帮助文件指出：
Note that, by default, any values outside the limits will be replaced
with NA.
coord_cartesian 的帮助文件指出：
Setting limits on the coordinate system will zoom the plot (like
you're looking at it with a magnifying glass), and will not change the
underlying data like setting limits on a scale will.
备选方案
这将使所有框保持相同的宽度，而不管沿 x 轴的每个类别是否有不同数量的因子水平。它通过展平 "x variable"~"fill factor variable" 关系的层次性质来实现这一点，因此 "x variable"~"fill factor variable" 的每个组合在箱线图。
第 1 步。定义每个箱线图沿 x 轴的位置，将 x 轴作为数字而不是分类：
TablePerCatchmentAndYear3 <- TablePerCatchmentAndYear %>%
mutate(NoiseType.Numeric = as.numeric(factor(NoiseType))) %>%
mutate(NoiseType.Numeric = NoiseType.Numeric + case_when(NoiseType != "random" & TempRes == "hourly" ~ -0.2,
NoiseType != "random" & TempRes == "weekly" ~ +0.2,
TRUE ~ 0))
# check the result
TablePerCatchmentAndYear3 %>%
select(NoiseType, TempRes, NoiseType.Numeric) %>%
unique() %>% arrange(NoiseType.Numeric)
NoiseType TempRes NoiseType.Numeric
1 bench hourly 0.8
2 bench weekly 1.2
3 LogNormSDdivBy1 hourly 1.8
4 LogNormSDdivBy1 weekly 2.2
5 LogNormSDdivBy2 hourly 2.8
6 LogNormSDdivBy2 weekly 3.2
7 LogNormSDdivBy4 hourly 3.8
8 LogNormSDdivBy4 weekly 4.2
9 random hourly 5.0
第 2 步。绘图，用分类标签标记数字 x 轴：
ggplot(TablePerCatchmentAndYear3,
aes(x = NoiseType.Numeric, y = POA, fill = TempRes, group = NoiseType.Numeric)) +
geom_boxplot() +
scale_x_continuous(name = "NoiseType", breaks = c(1, 2, 3, 4, 5), minor_breaks = NULL,
labels = sort(unique(dat$NoiseType)), expand = c(0, 0)) +
coord_cartesian(ylim = c(-1.25, 1), xlim = c(0.5, 5.5)) +
theme(legend.position='bottom') +
ggtitle('title')+ scale_fill_discrete(name = '')
注意：就个人而言，我不推荐此解决方案。很难自动化/概括，因为它需要根据存在的填充变量级别的数量进行不同的手动调整。但是，如果您真的需要它作为一次性用例，就在这里。
从 ggplot2 3.0.0 开始，position_dodge() 中有一个 preserve 参数允许保留单个元素的宽度。还有第二个闪避功能，position_dodge2()，它改变了元素在重叠绘图区域的分布方式。
如果您希望所有框的宽度相同且单个框以其 x 标记为中心，您可以在 position_dodge2() 中使用 preserve = "single"。
ggplot(TablePerCatchmentAndYear, aes(x = NoiseType, y = POA, fill = TempRes)) +
geom_boxplot(lwd = 0.05, position = position_dodge2(preserve = "single") ) +
ylim(-1.25, 1) +
theme(legend.position='bottom') +
scale_fill_discrete(name = '')
如果你想让所有方框的宽度相同 和 将单个元素组的闪避与其他组镜像，你可以添加 preserve = "single" 到 position_dodge().
ggplot(TablePerCatchmentAndYear, aes(x = NoiseType, y = POA, fill = TempRes)) +
geom_boxplot(lwd = 0.05, position = position_dodge(preserve = "single") ) +
ylim(-1.25, 1) +
theme(legend.position='bottom') +
scale_fill_discrete(name = '')
我想将类别 "random" 中框的宽度调整为与绘图中其他框的宽度相同。它现在是一个单独的组，而其他组包含两个子组......关于如何做到这一点的任何想法？
geom_boxplot(width=0.2) 只是改变所有框的宽度。到目前为止，我使用了以下代码：
ggplot(TablePerCatchmentAndYear,aes(x=NoiseType, y= POA, fill = TempRes)) +
geom_boxplot(lwd=0.05) + ylim(c(-1.25, 1)) + theme(legend.position='bottom') +
ggtitle('title')+ scale_fill_discrete(name = '')
我为此使用的数据如下table:
TablePerCatchmentAndYear = structure(list(CatchmentModelType = c("2126_Murg_2009_dry_bench_hourly",
"2126_Murg_2009_dry_bench_hourly", "2126_Murg_2009_dry_bench_hourly",
"2126_Murg_2009_dry_bench_hourly", "2126_Murg_2009_dry_bench_hourly",
"2126_Murg_2009_dry_bench_hourly", "2126_Murg_2009_dry_bench_hourly",
"2126_Murg_2009_dry_bench_hourly", "2126_Murg_2009_dry_bench_hourly",
"2126_Murg_2009_dry_bench_hourly", "2126_Murg_2009_dry_LogNormSDdivBy4_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy4_hourly", "2126_Murg_2009_dry_LogNormSDdivBy4_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy4_hourly", "2126_Murg_2009_dry_LogNormSDdivBy4_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy4_hourly", "2126_Murg_2009_dry_LogNormSDdivBy4_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy4_hourly", "2126_Murg_2009_dry_LogNormSDdivBy4_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy4_hourly", "2126_Murg_2009_dry_LogNormSDdivBy2_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy2_hourly", "2126_Murg_2009_dry_LogNormSDdivBy2_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy2_hourly", "2126_Murg_2009_dry_LogNormSDdivBy2_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy2_hourly", "2126_Murg_2009_dry_LogNormSDdivBy2_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy2_hourly", "2126_Murg_2009_dry_LogNormSDdivBy2_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy2_hourly", "2126_Murg_2009_dry_LogNormSDdivBy1_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy1_hourly", "2126_Murg_2009_dry_LogNormSDdivBy1_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy1_hourly", "2126_Murg_2009_dry_LogNormSDdivBy1_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy1_hourly", "2126_Murg_2009_dry_LogNormSDdivBy1_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy1_hourly", "2126_Murg_2009_dry_LogNormSDdivBy1_hourly",
"2126_Murg_2009_dry_LogNormSDdivBy1_hourly", "2126_Murg_2009_dry_random_hourly",
"2126_Murg_2009_dry_random_hourly", "2126_Murg_2009_dry_random_hourly",
"2126_Murg_2009_dry_random_hourly", "2126_Murg_2009_dry_random_hourly",
"2126_Murg_2009_dry_random_hourly", "2126_Murg_2009_dry_random_hourly",
"2126_Murg_2009_dry_random_hourly", "2126_Murg_2009_dry_random_hourly",
"2126_Murg_2009_dry_random_hourly", "2126_Murg_2009_dry_bench_weekly",
"2126_Murg_2009_dry_bench_weekly", "2126_Murg_2009_dry_bench_weekly",
"2126_Murg_2009_dry_bench_weekly", "2126_Murg_2009_dry_bench_weekly",
"2126_Murg_2009_dry_bench_weekly", "2126_Murg_2009_dry_bench_weekly",
"2126_Murg_2009_dry_bench_weekly", "2126_Murg_2009_dry_bench_weekly",
"2126_Murg_2009_dry_bench_weekly", "2126_Murg_2009_dry_LogNormSDdivBy4_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy4_weekly", "2126_Murg_2009_dry_LogNormSDdivBy4_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy4_weekly", "2126_Murg_2009_dry_LogNormSDdivBy4_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy4_weekly", "2126_Murg_2009_dry_LogNormSDdivBy4_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy4_weekly", "2126_Murg_2009_dry_LogNormSDdivBy4_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy4_weekly", "2126_Murg_2009_dry_LogNormSDdivBy2_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy2_weekly", "2126_Murg_2009_dry_LogNormSDdivBy2_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy2_weekly", "2126_Murg_2009_dry_LogNormSDdivBy2_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy2_weekly", "2126_Murg_2009_dry_LogNormSDdivBy2_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy2_weekly", "2126_Murg_2009_dry_LogNormSDdivBy2_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy2_weekly", "2126_Murg_2009_dry_LogNormSDdivBy1_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy1_weekly", "2126_Murg_2009_dry_LogNormSDdivBy1_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy1_weekly", "2126_Murg_2009_dry_LogNormSDdivBy1_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy1_weekly", "2126_Murg_2009_dry_LogNormSDdivBy1_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy1_weekly", "2126_Murg_2009_dry_LogNormSDdivBy1_weekly",
"2126_Murg_2009_dry_LogNormSDdivBy1_weekly"), NoiseType = structure(c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L,
5L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 4L, 4L, 4L, 4L, 4L,
4L, 4L, 4L, 4L, 4L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), .Label = c("bench", "LogNormSDdivBy1",
"LogNormSDdivBy2", "LogNormSDdivBy4", "random"), class = "factor"),
TempRes = 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, 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), .Label = c("hourly", "weekly"), class = "factor"),
yearChar = c("dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry", "dry",
"dry", "dry"), Parameterset = c(1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L,
2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L,
6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L,
10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L,
8L, 9L, 10L, 1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, 9L, 10L), Reff = c(0.6626,
0.6959, 0.7128, 0.6351, 0.7056, 0.6755, 0.655, 0.7155, 0.6839,
0.6564, 0.543, 0.5652, 0.5405, 0.5698, 0.5395, 0.5548, 0.5652,
0.5699, 0.5892, 0.578, 0.5366, 0.5052, 0.5389, 0.5194, 0.5555,
0.5529, 0.5315, 0.5092, 0.5137, 0.5417, 0.6635, 0.6427, 0.6561,
0.6702, 0.7035, 0.6789, 0.6631, 0.6544, 0.6432, 0.6384, 0.2273,
0.1757, -0.0048, 0.1647, 0.2586, 0.2926, 0.0739, 0.2607,
0.0799, 0.3595, 0.6679, 0.6712, 0.6557, 0.6906, 0.6777, 0.6748,
0.6531, 0.6779, 0.6708, 0.6446, 0.6227, 0.6404, 0.6474, 0.6221,
0.6089, 0.6159, 0.6194, 0.6382, 0.6323, 0.6198, 0.4703, 0.5456,
0.5883, 0.5114, 0.5188, 0.6257, 0.6036, 0.4501, 0.5154, 0.6,
0.2172, 0.245, 0.3625, 0.2793, 0.4073, 0.3257, 0.3435, 0.4297,
0.4375, 0.3451), LogReff = c(0.6498, 0.6665, 0.684, 0.6078,
0.6845, 0.6375, 0.6325, 0.6871, 0.6661, 0.6396, 0.5571, 0.5735,
0.5398, 0.5763, 0.5389, 0.5612, 0.5657, 0.578, 0.5999, 0.5881,
0.5806, 0.5445, 0.5782, 0.5724, 0.5832, 0.6113, 0.5763, 0.5439,
0.5626, 0.5757, 0.6855, 0.6787, 0.7003, 0.6393, 0.6684, 0.6924,
0.6897, 0.6956, 0.6408, 0.6801, 0.2823, -0.6217, -0.5084,
0.1936, 0.2246, 0.5335, 0.0143, 0.3124, -1.2437, -1.2655,
0.7041, 0.6973, 0.6834, 0.7032, 0.7116, 0.7042, 0.6811, 0.7148,
0.693, 0.6994, 0.6543, 0.6724, 0.6962, 0.657, 0.6783, 0.6621,
0.655, 0.6763, 0.6668, 0.6557, 0.6393, 0.6671, 0.726, 0.6832,
0.6848, 0.725, 0.7171, 0.6249, 0.6998, 0.7267, 0.3785, 0.4655,
0.5272, 0.5249, 0.5853, 0.4842, 0.4172, 0.6045, 0.5857, 0.5238
), VolumeError = c(0.9267, 0.931, 0.9401, 0.9225, 0.9507,
0.923, 0.9243, 0.9536, 0.9312, 0.9285, 0.8689, 0.8718, 0.8716,
0.8716, 0.8683, 0.8658, 0.8691, 0.8703, 0.8764, 0.8745, 0.8786,
0.8773, 0.8875, 0.8924, 0.8837, 0.8862, 0.8865, 0.8779, 0.8792,
0.8901, 0.8119, 0.8109, 0.8412, 0.8254, 0.8271, 0.8509, 0.8161,
0.8259, 0.8386, 0.8263, 0.8507, 0.5669, 0.4859, 0.6478, 0.6046,
0.85, 0.9425, 0.9153, 0.5295, 0.6555, 0.9777, 0.994, 0.9915,
0.9899, 0.9738, 0.9833, 0.9694, 0.9981, 0.9964, 0.9818, 0.997,
0.9822, 0.9954, 0.9996, 0.9768, 0.9644, 0.9974, 0.9962, 0.998,
0.9995, 0.9962, 0.9684, 0.99, 0.9625, 0.9595, 0.9853, 0.9783,
0.9227, 0.9661, 0.9783, 0.7664, 0.8786, 0.7615, 0.799, 0.7369,
0.7722, 0.8399, 0.7354, 0.771, 0.7745), MAREMeasure = c(0.532,
0.543, 0.557, 0.497, 0.5581, 0.5176, 0.5166, 0.5621, 0.5447,
0.5234, 0.445, 0.4554, 0.4322, 0.4579, 0.4298, 0.4448, 0.4487,
0.4582, 0.4762, 0.4675, 0.4718, 0.4432, 0.4725, 0.4721, 0.4725,
0.4989, 0.4711, 0.4428, 0.4577, 0.4704, 0.7183, 0.7166, 0.7144,
0.6848, 0.6943, 0.7034, 0.7202, 0.7194, 0.6832, 0.7105, 0.4913,
0.5758, 0.5658, 0.5817, 0.6574, 0.6191, 0.1196, 0.3526, 0.5357,
0.5475, 0.5931, 0.5882, 0.5782, 0.5886, 0.5984, 0.5945, 0.5728,
0.6089, 0.5834, 0.5962, 0.5434, 0.5581, 0.5848, 0.5467, 0.5703,
0.5478, 0.546, 0.5633, 0.555, 0.5458, 0.6243, 0.6468, 0.6736,
0.6175, 0.6219, 0.6604, 0.6769, 0.5766, 0.6301, 0.6793, 0.5227,
0.6047, 0.647, 0.6657, 0.661, 0.6324, 0.5751, 0.6707, 0.6532,
0.6621), POA = c(0.692775, 0.7091, 0.723475, 0.6656, 0.724725,
0.6884, 0.6821, 0.729575, 0.706475, 0.686975, 0.6035, 0.616475,
0.596025, 0.6189, 0.594125, 0.60665, 0.612175, 0.6191, 0.635425,
0.627025, 0.6169, 0.59255, 0.619275, 0.614075, 0.623725,
0.637325, 0.61635, 0.59345, 0.6033, 0.619475, 0.7198, 0.712225,
0.728, 0.704925, 0.723325, 0.7314, 0.722275, 0.723825, 0.70145,
0.713825, 0.4629, 0.174175, 0.134625, 0.39695, 0.4363, 0.5738,
0.287575, 0.46025, -0.02465, 0.07425, 0.7357, 0.737675, 0.7272,
0.743075, 0.740375, 0.7392, 0.7191, 0.749925, 0.7359, 0.7305,
0.70435, 0.713275, 0.73095, 0.70635, 0.708575, 0.69755, 0.70445,
0.7185, 0.713025, 0.7052, 0.682525, 0.706975, 0.744475, 0.69365,
0.69625, 0.7491, 0.743975, 0.643575, 0.70285, 0.746075, 0.4712,
0.54845, 0.57455, 0.567225, 0.597625, 0.553625, 0.543925,
0.610075, 0.61185, 0.576375)), .Names = c("CatchmentModelType",
"NoiseType", "TempRes", "yearChar", "Parameterset", "Reff", "LogReff",
"VolumeError", "MAREMeasure", "POA"), row.names = c(1L, 2L, 3L,
4L, 5L, 6L, 7L, 8L, 9L, 10L, 1501L, 1502L, 1503L, 1504L, 1505L,
1506L, 1507L, 1508L, 1509L, 1510L, 1001L, 1002L, 1003L, 1004L,
1005L, 1006L, 1007L, 1008L, 1009L, 1010L, 501L, 502L, 503L, 504L,
505L, 506L, 507L, 508L, 509L, 510L, 36001L, 36002L, 36003L, 36004L,
36005L, 36006L, 36007L, 36008L, 36009L, 36010L, 401L, 402L, 403L,
404L, 405L, 406L, 407L, 408L, 409L, 410L, 1901L, 1902L, 1903L,
1904L, 1905L, 1906L, 1907L, 1908L, 1909L, 1910L, 1401L, 1402L,
1403L, 1404L, 1405L, 1406L, 1407L, 1408L, 1409L, 1410L, 901L,
902L, 903L, 904L, 905L, 906L, 907L, 908L, 909L, 910L), class = "data.frame")
可以修改 second solution here 以适合您的情况：
第 1 步。使用 tidyr 包中的 complete 将假数据添加到数据集：
TablePerCatchmentAndYear2 <- TablePerCatchmentAndYear %>%
dplyr::select(NoiseType, TempRes, POA) %>%
tidyr::complete(NoiseType, TempRes, fill = list(POA = 100))
# 100 is arbitrarily chosen here as a very large value beyond the range of
# POA values in the boxplot
步骤 2. 绘图，但将 y 轴限制设置在 coord_cartesian:
ggplot(dat2,aes(x=NoiseType, y= POA, fill = TempRes)) +
geom_boxplot(lwd=0.05) + coord_cartesian(ylim = c(-1.25, 1)) + theme(legend.position='bottom') +
ggtitle('title')+ scale_fill_discrete(name = '')
原因是使用 ylim() 命令设置限制会导致每周随机噪声类型的空箱线图 space 消失。 ylim 的帮助文件指出：
Note that, by default, any values outside the limits will be replaced with NA.
coord_cartesian 的帮助文件指出：
Setting limits on the coordinate system will zoom the plot (like you're looking at it with a magnifying glass), and will not change the underlying data like setting limits on a scale will.
备选方案
这将使所有框保持相同的宽度，而不管沿 x 轴的每个类别是否有不同数量的因子水平。它通过展平 "x variable"~"fill factor variable" 关系的层次性质来实现这一点，因此 "x variable"~"fill factor variable" 的每个组合在箱线图。
第 1 步。定义每个箱线图沿 x 轴的位置，将 x 轴作为数字而不是分类：
TablePerCatchmentAndYear3 <- TablePerCatchmentAndYear %>%
mutate(NoiseType.Numeric = as.numeric(factor(NoiseType))) %>%
mutate(NoiseType.Numeric = NoiseType.Numeric + case_when(NoiseType != "random" & TempRes == "hourly" ~ -0.2,
NoiseType != "random" & TempRes == "weekly" ~ +0.2,
TRUE ~ 0))
# check the result
TablePerCatchmentAndYear3 %>%
select(NoiseType, TempRes, NoiseType.Numeric) %>%
unique() %>% arrange(NoiseType.Numeric)
NoiseType TempRes NoiseType.Numeric
1 bench hourly 0.8
2 bench weekly 1.2
3 LogNormSDdivBy1 hourly 1.8
4 LogNormSDdivBy1 weekly 2.2
5 LogNormSDdivBy2 hourly 2.8
6 LogNormSDdivBy2 weekly 3.2
7 LogNormSDdivBy4 hourly 3.8
8 LogNormSDdivBy4 weekly 4.2
9 random hourly 5.0
第 2 步。绘图，用分类标签标记数字 x 轴：
ggplot(TablePerCatchmentAndYear3,
aes(x = NoiseType.Numeric, y = POA, fill = TempRes, group = NoiseType.Numeric)) +
geom_boxplot() +
scale_x_continuous(name = "NoiseType", breaks = c(1, 2, 3, 4, 5), minor_breaks = NULL,
labels = sort(unique(dat$NoiseType)), expand = c(0, 0)) +
coord_cartesian(ylim = c(-1.25, 1), xlim = c(0.5, 5.5)) +
theme(legend.position='bottom') +
ggtitle('title')+ scale_fill_discrete(name = '')
注意：就个人而言，我不推荐此解决方案。很难自动化/概括，因为它需要根据存在的填充变量级别的数量进行不同的手动调整。但是，如果您真的需要它作为一次性用例，就在这里。
从 ggplot2 3.0.0 开始，position_dodge() 中有一个 preserve 参数允许保留单个元素的宽度。还有第二个闪避功能，position_dodge2()，它改变了元素在重叠绘图区域的分布方式。
如果您希望所有框的宽度相同且单个框以其 x 标记为中心，您可以在 position_dodge2() 中使用 preserve = "single"。
ggplot(TablePerCatchmentAndYear, aes(x = NoiseType, y = POA, fill = TempRes)) +
geom_boxplot(lwd = 0.05, position = position_dodge2(preserve = "single") ) +
ylim(-1.25, 1) +
theme(legend.position='bottom') +
scale_fill_discrete(name = '')
如果你想让所有方框的宽度相同 和 将单个元素组的闪避与其他组镜像，你可以添加 preserve = "single" 到 position_dodge().
ggplot(TablePerCatchmentAndYear, aes(x = NoiseType, y = POA, fill = TempRes)) +
geom_boxplot(lwd = 0.05, position = position_dodge(preserve = "single") ) +
ylim(-1.25, 1) +
theme(legend.position='bottom') +
scale_fill_discrete(name = '')