ggplot2 geom_line 错误答案

【问题标题】：Ggplot2 geom_line errorggplot2 geom_line 错误
【发布时间】：2013-11-29 18:50:26
【问题描述】：

我有一个数据集，其中包含多个国家/地区居住在城市/农村地区的人口比例的时间序列数据点。可悲的是，并非所有国家都有相同年份的数据。我一直在尝试制作一个简单的折线图来显示每年生活在不同地点的人的不同比例，但由于每个国家/地区的数据点数量不同，我遇到了麻烦。

我认为这是因为某些国家/地区只有一年的数据，并且使用来自ggplot2 的geom_line 会引发以下错误：

geom_path：每个组只包含一个观察。你需要调整群体审美？

我希望有一些方法可以覆盖这一点，或者只是绘制一个国家只有一年数据的单个点。有谁知道这是否可能，或者确实，如果这实际上是这个错误的意思？！！？

任何帮助都非常感谢！！！

谢谢

这是我的数据：

    structure(list(COUNTRY = structure(c(1L, 2L, 2L, 3L, 3L, 3L, 
4L, 4L, 4L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 
8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 11L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 1L, 
2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 
7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 11L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L, 
14L, 14L, 14L, 1L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 
6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L, 
9L, 9L, 10L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 
13L, 13L, 13L, 14L, 14L, 14L, 14L, 1L, 2L, 2L, 3L, 3L, 3L, 4L, 
4L, 4L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 
8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 11L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L, 1L, 2L, 
2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 
7L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 9L, 10L, 11L, 12L, 
12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L, 
14L, 14L, 1L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 5L, 6L, 
6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 
9L, 10L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 12L, 13L, 13L, 
13L, 13L, 14L, 14L, 14L, 14L, 1L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 
4L, 4L, 5L, 5L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 7L, 8L, 8L, 
8L, 9L, 9L, 9L, 9L, 9L, 10L, 11L, 12L, 12L, 12L, 12L, 12L, 12L, 
12L, 12L, 13L, 13L, 13L, 13L, 14L, 14L, 14L, 14L), class = "factor", .Label = c("Comoros", 
"Eritrea", "Ethiopia", "Kenya", "Lesotho", "Madagascar", "Malawi", 
"Namibia", "Rwanda", "South Africa", "Swaziland", "Tanzania", 
"Zambia", "Zimbabwe")), Year = structure(c(5L, 12L, 4L, 25L, 
16L, 9L, 22L, 13L, 7L, 2L, 23L, 15L, 22L, 14L, 6L, 1L, 24L, 15L, 
9L, 1L, 13L, 6L, 19L, 9L, 1L, 24L, 21L, 16L, 9L, 1L, 7L, 19L, 
24L, 13L, 8L, 5L, 1L, 18L, 10L, 4L, 20L, 11L, 5L, 1L, 24L, 17L, 
8L, 3L, 5L, 12L, 4L, 25L, 16L, 9L, 22L, 13L, 7L, 2L, 23L, 15L, 
22L, 14L, 6L, 1L, 24L, 15L, 9L, 1L, 13L, 6L, 19L, 9L, 1L, 24L, 
21L, 16L, 9L, 1L, 7L, 19L, 24L, 13L, 8L, 5L, 1L, 18L, 10L, 4L, 
20L, 11L, 5L, 1L, 24L, 17L, 8L, 3L, 5L, 12L, 4L, 25L, 16L, 9L, 
22L, 13L, 7L, 2L, 23L, 15L, 22L, 14L, 6L, 1L, 24L, 15L, 9L, 1L, 
13L, 6L, 19L, 9L, 1L, 24L, 21L, 16L, 9L, 1L, 7L, 19L, 24L, 13L, 
8L, 5L, 1L, 18L, 10L, 4L, 20L, 11L, 5L, 1L, 24L, 17L, 8L, 3L, 
5L, 12L, 4L, 25L, 16L, 9L, 22L, 13L, 7L, 2L, 23L, 15L, 22L, 14L, 
6L, 1L, 24L, 15L, 9L, 1L, 13L, 6L, 19L, 9L, 1L, 24L, 21L, 16L, 
9L, 1L, 7L, 19L, 24L, 13L, 8L, 5L, 1L, 18L, 10L, 4L, 20L, 11L, 
5L, 1L, 24L, 17L, 8L, 3L, 5L, 12L, 4L, 25L, 16L, 9L, 22L, 13L, 
7L, 2L, 23L, 15L, 22L, 14L, 6L, 1L, 24L, 15L, 9L, 1L, 13L, 6L, 
19L, 9L, 1L, 24L, 21L, 16L, 9L, 1L, 7L, 19L, 24L, 13L, 8L, 5L, 
1L, 18L, 10L, 4L, 20L, 11L, 5L, 1L, 24L, 17L, 8L, 3L, 5L, 12L, 
4L, 25L, 16L, 9L, 22L, 13L, 7L, 2L, 23L, 15L, 22L, 14L, 6L, 1L, 
24L, 15L, 9L, 1L, 13L, 6L, 19L, 9L, 1L, 24L, 21L, 16L, 9L, 1L, 
7L, 19L, 24L, 13L, 8L, 5L, 1L, 18L, 10L, 4L, 20L, 11L, 5L, 1L, 
24L, 17L, 8L, 3L, 5L, 12L, 4L, 25L, 16L, 9L, 22L, 13L, 7L, 2L, 
23L, 15L, 22L, 14L, 6L, 1L, 24L, 15L, 9L, 1L, 13L, 6L, 19L, 9L, 
1L, 24L, 21L, 16L, 9L, 1L, 7L, 19L, 24L, 13L, 8L, 5L, 1L, 18L, 
10L, 4L, 20L, 11L, 5L, 1L, 24L, 17L, 8L, 3L), class = "factor", .Label = c("1992", 
"1993", "1994", "1995", "1996", "1997", "1998", "1999", "2000", 
"2000/1", "2001/2", "2002", "2003", "2003/4", "2004", "2005", 
"2005/6", "2006", "2006/7", "2007", "2007/8", "2008/9", "2009", 
"2010", "2011")), location = 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, 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, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 
5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 5L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 
6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 6L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 
7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L, 7L), .Label = c("Urban", 
"Rural", "Total", "Capital.City", "Other.Cities.towns", "Urban.Non.slum", 
"Urban.Slum"), class = "factor"), percent = c(63.0434782608696, 
93.8, 87, 79.5642604795185, 65.4240807416892, 63.0791092522326, 
90.448386469558, 85.9419999774024, 92.7603614781794, 84.0437368780105, 
89.9792286718626, 91.0916571421351, 87.1132950026762, 73.8624315865239, 
60.8311005575454, 66.7, 96, 86.8, 90.6243926153181, 90.6911141749493, 
90.7602286016099, 93.0377175475414, 86.073106379954, 84.253722056373, 
77.8178199148702, 97.3, 91.8332260789258, 89.612164524266, 89.9070989918367, 
94.9, 85.1351949905457, 94.8358752154967, 92.9, 89.656599879838, 
90.2634019334124, 94.4, 91.6241263241579, 76.7337303943862, 68.4233513070184, 
74.15601627144, 88.4802888646634, 85.4643913454376, 89.7457528950664, 
81.3025210084024, 83.0579155525397, 71.5857386620092, 86.2324062094295, 
87.687478493975, 63.5379061371841, 78.5, 40.7, 51.7763728811622, 
32.2441768813334, 22.3138981723172, 83.3699691175754, 69.6742912391579, 
76.0526239692028, 83.7290062290807, 77.4758329101792, 83.8081963934296, 
67.5805226154664, 55.8951299980461, 41.9921451192584, 52.2, 92.5, 
77.6, 82.0322170392223, 85.2850090044269, 70.8031150919282, 47.108593681531, 
82.2215412952297, 78.3643348536815, 74.4253468485616, 94.8, 90.1711142192198, 
85.0338348718722, 86.3134329333052, 90.4, 79.2813256726705, 90.7077549957666, 
82.5, 77.7236217339155, 75.3278238729086, 77.7, 78.4592126267142, 
67.1145693585691, 55.3459024734839, 57.8463881286199, 83.5604620304044, 
83.9259722574938, 84.4589780509803, 73.3992444632325, 77.544833952707, 
63.0503715222555, 75.6808008503601, 85.6943513045284, 63.4, 84.2, 
51, 55.7151220012609, 34.9, 26.6, 85, 72.5, 79.2, 83.8, 80.3, 
84.9, 69.6, 59, 46, 54, 93, 78.7, 83.2, 85.9, 76.7, 57.5, 83.8, 
80.4, 75.6, 95, 90.4, 85.6, 86.9, 90.6, 82.2, 91.5, 84.5, 79.9, 
78.1, 80.9, 81.2, 68.1, 56.8, 59.6, 84.9, 84.4, 86.5, 77, 79.1337842548663, 
65.6, 79.1, 86.3, 68.421052631579, 96.1, 93.3, 93.461209969107, 
82.2712525836501, 88.2708936990495, 87.6298001816506, 87.6386027991385, 
93.1818181818183, 86.6666666666668, 88.1030398041979, 90.4761904761904, 
83.4297434324662, 86.3744073211853, 83.6107223166148, 78.3, NA, 
72.8, 80.952380952381, 87.5, 96.9073193030442, 99.1348508752745, 
85.5297651573129, 86.4793919321843, 79.4520547945208, 98.2, 92.4613307718678, 
85.4590408924955, 83.9378238341966, 92.1, 81.1594202898552, 96.0232554251852, 
NA, 88.0377726639494, 83.690767555447, 93.4, 90.0349966633017, 
71.2508707571865, 72, 79.4082828804656, 91.8032786885246, 84.5238095238095, 
87.8787878787881, 75.6097560975609, 81.0643061692494, 68.4708412135189, 
84.9056603773584, 89.5522388059702, 61.6438356164384, 91.7, 79.5, 
77.0004220956012, 61.061381883032, 58.756042602018, 91.2594694272412, 
85.20149612163, 92.4956062313464, 82.622382662868, 91.4036416540165, 
91.6169313256523, 89.2957214499669, 67.6757501795213, 48.1479760952102, 
NA, NA, 94.2, 94.3553068539161, 91.8799748693178, 89.3739230258784, 
92.1418739343887, 86.4757947454868, 81.0102236379536, 77.0100025126874, 
NA, 91.3720851411616, 92.2, 92.5003150086683, 97.8260869565219, 
87.1461797069698, 93.5168077834096, NA, 90.1780793791367, 92.9758067301415, 
94.9, 91.8829499602467, 81.749280834314, 65.1853441661798, 69.0503609949116, 
87.2562445664681, 85.8298270239758, 90.6673511683335, 83.2861189801694, 
84.9006282245266, 73.65452177457, 87.3075692692965, 85.5310215524833, 
83.3333333333333, NA, NA, 98.5990187756088, 84.4640706359058, 
NA, 93.9158337759274, 91.5744358611439, 100, NA, NA, NA, 88.7824144772468, 
85.1972665683085, 89.54493171236, NA, NA, 89.8, NA, 100, 97.6261376125643, 
96.3196943955923, 92.0952338262334, 87.9266080431752, 80.9429968520701, 
NA, NA, 92.8, 95.2886158200472, 100, 86.4199793410402, NA, NA, 
89.9001648604344, NA, NA, 91.5033109800214, 83.8918470610424, 
73.9339911532972, 88.6921281548131, 94.309068022859, 85.3299585067346, 
93.7362934447331, 86.5384615384618, 83.7424288707868, NA, 86.3836615391687, 
88.1866796344726, 58.1081081081081, NA, NA, 75.7976468146464, 
62.1289432084197, NA, 88.1488735873722, 84.2108238885019, 89.8335978405451, 
NA, NA, NA, 86.9222656846515, 70.3584041024493, 70.9023609260137, 
NA, NA, 85.9, NA, 89.8689917369566, 90.3864925686512, 92.628169473785, 
80.9468895007753, 78.7885741638367, 75.4005791241575, NA, NA, 
88.4, 87.7139456942162, 92.3809523809525, 83.7645232075473, NA, 
NA, 89.567507133125, NA, NA, 91.6433898994358, 73.6225283043976, 
65.9223049858496, 72.3148320483822, 86.2596215693035, 85.6224026570651, 
87.4940330171337, 78.7499999999997, 81.9949404453665, NA, 84.5563115043796, 
87.0190820047277)), .Names = c("COUNTRY", "Year", "location", 
"percent"), row.names = c(NA, -336L), class = "data.frame")

我想用 ggplot2 生成一个由 COUNTRY 分面的简单图。我可以使用geom_point 做到这一点：

ggplot(meas_melt, aes(Year, percent, colour=location))+ geom_point() + facet_wrap(~COUNTRY)

但是，如果我尝试使用 geom_line (ggplot(meas_melt, aes(Year, percent, colour=location))+ geom_line() + facet_wrap(~COUNTRY)) 生成线图

我收到以下错误：

geom_path：每个组只包含一个观察。你需要调整群体审美？

我曾认为这可能是因为一些国家/地区只有一年的数据，因此我将日期子集化以删除这三个国家/地区，如下所示：

ggplot(meas_melt, aes(Year, percent, colour=location))+ geom_line(data=meas_melt[!meas_melt$COUNTRY %in% c('Comoros','South Africa','Swaziland'),]) + facet_wrap(~COUNTRY)

但是，我得到了同样的错误！

【问题讨论】：

标签： r ggplot2

【解决方案1】：

@Sven 的回答是正确的，但只解决了部分问题。请注意，没有针对科摩罗、南非或斯威士兰的情节。这是因为在您的数据中，有时年份是 2006 年或 2007 年，有时是“2006/7”。

data[meas_melt$COUNTRY=="Swaziland",]
      COUNTRY   Year           location  percent
32  Swaziland 2006/7              Urban 94.83588
80  Swaziland 2006/7              Rural 90.70775
128 Swaziland 2006/7              Total 91.50000
176 Swaziland 2006/7       Capital.City 96.02326
224 Swaziland 2006/7 Other.Cities.towns 93.51681
272 Swaziland 2006/7     Urban.Non.slum       NA
320 Swaziland 2006/7         Urban.Slum       NA

那些国家真的只有一个“年”（因此，没有线）。更重要的是，这些奇数年份名称会扭曲您的 x 轴。您可以看到使用scales="free" 参数到facet_wrap(...)：

ggplot(meas_melt, aes(x=Year,y=percent, color=location)) + 
  geom_line(aes(group=location)) +facet_wrap(~COUNTRY, scales="free") + 
  theme(axis.text.x=element_text(angle=90, vjust=0.5, size=8), 
        legend.position="bottom")

产生这个：

【讨论】：

【解决方案2】：

您必须在geom_line 中指定aes(group = location)：

library(ggplot2)
ggplot(meas_melt, aes(Year, percent, colour=location)) +
  geom_line(aes(group = location)) + 
  facet_wrap(~COUNTRY)

【讨论】：