[英]How to avoid the connection lines in geom_line or geom_path when there is no data?
我正在使用 geom_path 繪制平均值的時間序列，並使用 geom_ribbon 添加具有最小最大值的功能區。 時間序列中存在一些數據差距，但 plot 保持連接線。 在隨附的 plot 中，最后一個面板顯示了數據中的差距。 該數據沒有 x 或 y 條目。 有什么辦法可以控制這個？
這是頂部面板的 plot 代碼：
ggplot(stat_total, aes(color=gas)) +
geom_path(aes(x=date_mean, y=conc_mean, color=gas), size=1.2, na.rm = T) +
geom_ribbon(aes(x=date_mean, ymin=conc_min, ymax=conc_max, fill=gas), color="grey70", alpha=0.4, na.rm = T)+
scale_x_datetime(date_breaks = "3 weeks" , date_labels = "%d-%b") +
xlab(NULL) +
ylab('[ppb]') +
theme_bw() +
facet_wrap(gas~.,scales = 'free_x',ncol = 1,nrow=2)
以及數據樣本：
structure(list(day = c(6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 10L, 10L,
11L, 11L, 12L, 12L, 13L, 13L, 15L, 15L, 16L, 16L, 17L, 17L, 18L,
18L, 20L, 20L, 21L, 21L, 25L, 25L, 26L, 26L, 27L, 27L, 28L, 28L,
1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 12L, 12L, 13L, 13L, 14L,
14L, 15L, 15L, 16L, 16L, 17L, 17L, 18L, 18L, 19L, 19L, 20L, 20L,
22L, 22L, 23L, 23L, 24L, 24L, 25L, 25L, 26L, 26L, 27L, 27L, 28L,
28L, 29L, 29L, 30L, 30L, 31L, 31L, 2L, 2L, 3L, 3L, 4L, 4L, 5L,
5L, 6L, 6L, 7L, 7L, 8L, 8L, 9L, 9L, 11L, 11L, 12L, 12L, 13L,
13L, 26L, 26L, 27L, 27L, 28L, 28L, 29L, 29L, 30L, 30L, 1L, 1L,
2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 14L, 14L, 15L, 15L, 16L, 16L,
17L, 17L, 18L, 18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L, 22L, 23L,
23L, 24L, 24L, 25L, 25L, 26L, 26L, 27L, 27L, 28L, 28L, 29L, 29L,
30L, 30L, 31L, 31L, 1L, 1L, 2L, 2L, 3L, 3L, 4L, 4L, 5L, 5L, 6L,
6L, 7L, 7L, 8L, 8L, 9L, 9L, 15L, 15L, 16L, 16L, 17L, 17L, 18L,
18L, 19L, 19L, 20L, 20L, 21L, 21L, 22L, 22L, 24L, 24L, 25L, 25L,
26L, 26L), month = c(2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,
2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3, 3,
3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4,
4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5, 5,
5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6,
6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6),
gas = c("AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC", "BVOC", "AVOC",
"BVOC", "AVOC", "BVOC"), date_mean = structure(c(1549475100,
1549475100, 1549542360, 1549542360, 1549620787.5, 1549620787.5,
1549710663.15789, 1549710663.15789, 1549801042.10526, 1549801042.10526,
1549885680, 1549885680, 1549971100, 1549971100, 1550052300,
1550052300, 1550263680, 1550263680, 1550312871.42857, 1550312871.42857,
1550406436.36364, 1550406436.36364, 1550475600, 1550475600,
1550686320, 1550686320, 1550756700, 1550756700, 1551105981.81818,
1551105981.81818, 1551177428.57143, 1551177428.57143, 1551260700,
1551260700, 1551351176.47059, 1551351176.47059, 1551442263.15789,
1551442263.15789, 1551537771.42857, 1551537771.42857, 1551617052.63158,
1551617052.63158, 1551703500, 1551703500, 1551782925, 1551782925,
1552427550, 1552427550, 1552499742.85714, 1552499742.85714,
1552551075, 1552551075, 1552645800, 1552645800, 1552737120,
1552737120, 1552830942.85714, 1552830942.85714, 1552885875,
1552885875, 1553019075, 1553019075, 1553065457.14286, 1553065457.14286,
1553274000, 1553274000, 1553350725, 1553350725, 1553430857.14286,
1553430857.14286, 1553519076.92308, 1553519076.92308, 1553572800,
1553572800, 1553714100, 1553714100, 1553774717.64706, 1553774717.64706,
1553857842.85714, 1553857842.85714, 1553942057.14286, 1553942057.14286,
1553995800, 1553995800, 1554210800, 1554210800, 1554313000,
1554313000, 1554383442.85714, 1554383442.85714, 1554463080,
1554463080, 1554551672.72727, 1554551672.72727, 1554640740,
1554640740, 1554723600, 1554723600, 1554809760, 1554809760,
1555006320, 1555006320, 1555067250, 1555067250, 1555150950,
1555150950, 1556319600, 1556319600, 1556373600, 1556373600,
1556453400, 1556453400, 1556533800, 1556533800, 1556646300,
1556646300, 1556707628.57143, 1556707628.57143, 1556797800,
1556797800, 1556888123.07692, 1556888123.07692, 1556974800,
1556974800, 1557050072.72727, 1557050072.72727, 1557869400,
1557869400, 1557914563.63636, 1557914563.63636, 1558005726.31579,
1558005726.31579, 1558092937.5, 1558092937.5, 1558178600,
1558178600, 1558265611.76471, 1558265611.76471, 1558351376.47059,
1558351376.47059, 1558436400, 1558436400, 1558525050, 1558525050,
1558612164.70588, 1558612164.70588, 1558699300, 1558699300,
1558783320, 1558783320, 1558874400, 1558874400, 1558935600,
1558935600, 1559079900, 1559079900, 1559128950, 1559128950,
1559216747.36842, 1559216747.36842, 1559301900, 1559301900,
1559387300, 1559387300, 1559474258.82353, 1559474258.82353,
1559561717.64706, 1559561717.64706, 1559649494.11765, 1559649494.11765,
1559733300, 1559733300, 1559816485.71429, 1559816485.71429,
1559908270.58824, 1559908270.58824, 1559994750, 1559994750,
1560075187.5, 1560075187.5, 1560612150, 1560612150, 1560686600,
1560686600, 1560744720, 1560744720, 1560897000, 1560897000,
1560945494.11765, 1560945494.11765, 1561031258.82353, 1561031258.82353,
1561124353.84615, 1561124353.84615, 1561174650, 1561174650,
1561397760, 1561397760, 1561469250, 1561469250, 1561509600,
1561509600), class = c("POSIXct", "POSIXt"), tzone = "UTC"),
conc_mean = c(2.21485, 4.51665, 1.07492666666667, 3.61554666666667,
1.2719875, 3.3012125, 0.765063157894737, 3.71997368421053,
0.375805263157895, 1.10004210526316, 0.675033333333333, 1.17912,
1.23057222222222, 3.79774444444444, 0.204633333333333, 0.578241666666667,
0.54028, 0.23396, 0.702907142857143, 0.971378571428571, 0.813372727272727,
1.31120909090909, 0.87175, 1.3416, 1.15376, 3.93216, 0.3061,
1.58768333333333, 0.325572727272727, 0.530245454545455, 0.735842857142857,
1.18681428571429, 0.489575, 0.8701375, 0.431847058823529,
0.618288235294118, 0.572268421052632, 1.00910526315789, 0.475021428571429,
1.11840714285714, 0.437810526315789, 0.73228947368421, 0.677941666666667,
1.26760833333333, 0.4298875, 0.667275, 0, 0.141375, 0.396471428571429,
0.566985714285714, 0.562175, 0.5603625, 0.415214285714286,
1.04814285714286, 0.139766666666667, 0.1184, 0.158435714285714,
0.493435714285714, 0.738375, 2.1870375, 0.5032125, 1.860325,
0, 0, 0.80184, 1.6749, 0.629425, 1.32535, 0.621492857142857,
2.09426428571429, 0.521784615384615, 0.8041, 0.0966, 0.02106,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0.11013,
0.45911, 0.945981818181818, 2.15627272727273, 0.44487, 0.68837,
0.8569, 1.47154444444444, 0.40066, 0.88519, 0, 0, 0.278175,
0.1233125, 0.199175, 0.108025, 0.1002, 0.1679, 0.157933333333333,
0.303033333333333, 0.231433333333333, 0.330433333333333,
0.5878, 0.694266666666667, 1.13938333333333, 0.78425, 3.01142142857143,
0.8532, 2.96855333333333, 0.905413333333333, 2.63885384615385,
0.831161538461539, 0.0564933333333333, 0.110113333333333,
0.0251636363636364, 0.0381454545454545, 0.032775, 0.070375,
0.171754545454545, 0.179809090909091, 0.868431578947368,
0.290926315789474, 1.460875, 0.3505375, 0.515116666666667,
0.2017, 0.170970588235294, 0.0566647058823529, 2.00161764705882,
0.891194117647059, 2.27995882352941, 1.07888823529412, 0.4599,
0.172966666666667, 0.292129411764706, 0.3191, 1.30511111111111,
0.858427777777778, 0.90774, 0.82456, 0.538777777777778, 0.221883333333333,
0.509583333333333, 0.280516666666667, 0.24795, 0.14805, 0.08165,
0.09388125, 0.0355947368421053, 0.0266210526315789, 0.0540666666666667,
0.0445833333333333, 0.0329111111111111, 0.0137111111111111,
0.431323529411765, 0.138288235294118, 0.946082352941176,
0.597052941176471, 0.0175, 0.00785294117647059, 0.03314375,
0.019, 0.04485, 0.0101714285714286, 1.12921176470588, 0.166876470588235,
2.01030625, 1.2114875, 1.25706875, 0.54935, 0.0532833333333333,
0.05245, 0.0311222222222222, 0.00601666666666667, 0, 0, 0,
0, 0, 0, 0, 0, 0.168461538461538, 0.0720230769230769, 0,
0, 0.46362, 0.17162, 0.347108333333333, 0.1352, 0.255366666666667,
0.0637), conc_min = c(0.9481, 1.016, 0, 0, 0, 0, 0.1382,
0.4736, 0.1568, 0.2592, 0.1855, 0.1443, 0.3351, 0.4526, 0.0364,
0.0148, 0.3338, 0.0614, 0.1845, 0.193, 0.298, 0.2129, 0,
0, 0.182, 0.3781, 0.1973, 0.5151, 0, 0, 0.289, 0.0466, 0.076,
0.0312, 0.1458, 0.0806, 0.1124, 0.0219, 0.1038, 0.0628, 0,
0, 0, 0, 0, 0, 0, 0.0911, 0, 0, 0.3236, 0.0391, 0.0757, 0.0159,
0.0289, 0, 0.0117, 0.0052, 0.1448, 0.1749, 0, 0, 0, 0, 0,
0, 0.2611, 0.1329, 0.1001, 0.6807, 0.0311, 0.0042, 0.0149,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0.2353, 0.5611, 0.0524, 0.0392, 0.2764, 0.3357, 0, 0, 0,
0, 0, 0, 0, 0, 0.1002, 0.1679, 0.0878, 0.2908, 0, 0, 0.4953,
0.4679, 0.7842, 0.2518, 1.1497, 0.4129, 1.4005, 0.3776, 1.0426,
0.3828, 0.0077, 0.0047, 0, 0, 0.026, 0.0039, 0.0241, 0.0029,
0.0522, 0.0555, 0.1238, 0.0305, 0.025, 0.0009, 0.0211, 0.0007,
0.035, 0.0093, 0.2304, 0.1012, 0.0358, 0.0139, 0.0711, 0.0259,
0.2234, 0.1971, 0.012, 0, 0.0079, 0, 0, 0, 0.0348, 0.0258,
0.006, 0, 0.0055, 0, 0.0081, 0, 0.0109, 0, 0.0144, 0, 0.0276,
0.0015, 0.0047, 0, 0.007, 0, 0.0114, 0, 0.0062, 0, 0.2045,
0.3129, 0, 0, 0.0173, 0.0009, 0.0123, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0.1093, 0.0679, 0.0855, 0.0256, 0.0927,
0.0266), conc_max = c(5.2082, 9.4515, 2.6412, 9.5067, 3.374,
10.2935, 1.9887, 7.3334, 1.1261, 2.2172, 2.521, 2.9801, 3.3107,
7.9089, 0.701, 1.181, 0.9013, 0.7176, 1.3709, 2.6799, 2.4004,
2.6443, 1.7978, 3.5656, 2.2826, 9.0001, 0.4704, 3.1122, 1.1959,
1.0669, 1.8055, 2.8748, 1.4114, 2.7354, 0.9683, 1.6872, 1.7906,
3.068, 1.1533, 3.1572, 1.61, 1.8917, 3.1135, 3.3496, 0.8959,
1.6323, 0, 0.1973, 1.1029, 1.7997, 1.0299, 1.3705, 1.7949,
5.4322, 0.4341, 0.3075, 0.6009, 1.5614, 1.6237, 6.4092, 1.6444,
4.1521, 0, 0, 1.6438, 4.4297, 1.512, 2.4371, 2.0231, 6.2908,
1.5731, 2.59, 0.3182, 0.0694, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0.5511, 2.499, 1.596, 3.4777, 1.018,
1.5773, 1.8561, 2.5637, 1.0436, 3.3362, 0, 0, 1.3413, 0.3713,
0.6086, 0.4185, 0.1002, 0.1679, 0.2585, 0.3129, 0.6006, 0.7198,
0.668, 1.1023, 1.8961, 1.2774, 6.3908, 1.2608, 5.7836, 1.9329,
4.8889, 2.1084, 0.4252, 0.5633, 0.0532, 0.1212, 0.0488, 0.262,
0.3876, 1.006, 3.4004, 1.1248, 4.1029, 1.2065, 2.13, 0.6134,
0.7077, 0.2737, 6.3182, 2.0403, 6.3883, 2.3115, 1.4964, 0.5299,
1.2378, 0.9909, 4.1648, 2.5412, 4.6703, 2.0224, 2.8942, 0.9106,
1.1358, 0.7632, 0.4456, 0.2783, 0.4417, 0.5307, 0.0934, 0.1239,
0.2766, 0.2853, 0.1005, 0.1172, 4.3601, 1.3379, 4.5632, 2.9013,
0.0615, 0.0915, 0.0648, 0.1201, 0.1214, 0.0886, 7.008, 1.001,
4.6935, 5.0903, 7.8913, 1.6407, 0.1217, 0.2257, 0.1106, 0.0603,
0, 0, 0, 0, 0, 0, 0, 0, 1.0643, 0.5233, 0, 0, 0.6608, 0.2956,
0.8226, 0.3397, 0.3421, 0.1225)), class = c("tbl_df", "tbl",
"data.frame"), row.names = c(NA, -202L))
這是另一個比我最初想象的稍微復雜的問題，但我認為我有一個似乎可行的解決方案。 乍一看，您似乎可以只設置data=stat_total[which(stat_total$conc_mean,=0),] ，這意味着只會繪制那些大於 0 的值......但這不起作用。 原因很簡單， ggplot仍將通過 geom_path 一直連接線並通過geom_path繪制功能geom_ribbon ，因為數據存在於這些 0 值的右側和左側。
這里的關鍵是要了解我們想要更改和分配group=審美。 這控制幾何圖形的連接性，如線條。 它很容易通過以下方式演示：
d <- data.frame(x=1:10, y=1:10, grp=c(rep(1,4),2,rep(3,5)))
ggplot(d, aes(x,y)) + theme_bw() +
geom_line(aes(group=grp)) + geom_point()
因此，您的示例的理論解決方案將涉及讓group=美學應用於不等於零的stat_total$conc_mean的“部分”，同時在stat_total$conc_mean等於零時也不進行繪圖。 至關重要的是，“部分”需要具有不同的group=審美價值。 如果他們不這樣做，那么我們將像您現在一樣將整個事物連接起來，因為再次 - 在這些零的右側和左側仍然存在數據，因此ggplot只會在它們之間畫一條線。
首先，我按stat_total$gas和stat_total$date_mean排列了您的數據框。
df <- arrange(stat_total, gas, date_mean)
然后，我想
(1)創建一個列，該列基本上指示stat_total$conc_mean為 0 或包含的值 > 0。我承認沒有這一步可能更優雅地完成目標，但這部分也使得更容易遵循解決方案.
df$a <- ifelse(df$conc_mean==0, NA, 1)
(2)使用 function 創建一個新的分組列。 function 遍歷一個向量並將一個計數（ g_num ）存儲到一個返回向量中，當有一個數字時 position ，但存儲NA並在找到NA時增加g_num 。 結果是一個返回向量，其中包含我們想要的數字序列。
my_func <- function(x) {
g_num <- 1
return_vect <- vector(mode='double',length=length(x))
for(i in 1:length(x)) {
if (is.na(x[i])){
return_vect[i] <- NA
g_num <- g_num+1
}
else {
return_vect[i] <- g_num
}
}
return(return_vect)
}
# create the new column
df$g <- my_func(df$a)
它是如何工作的一個例子如下所示：
> test <- c(1,1,1,NA,NA,1,1,NA,1,1)
> test
[1] 1 1 1 NA NA 1 1 NA 1 1
> my_func(test)
[1] 1 1 1 NA NA 3 3 NA 4 4
(3) Plot 它。 它與您的原始代碼相同，但我們使用新列作為group=美學，並且只有 plot 值 > 0 用於stat_total$conc_mean （因此您避免在某些部分的圖表底部出現一條線。
ggplot(df[which(df$conc_mean!=0),], aes(color=gas, group=g)) +
geom_path(aes(x=date_mean, y=conc_mean, color=gas), size=1.2, na.rm = T) +
geom_ribbon(aes(x=date_mean, ymin=conc_min, ymax=conc_max, fill=gas), color="grey70", alpha=0.4, na.rm = T)+
scale_x_datetime(date_breaks = "3 weeks" , date_labels = "%d-%b") +
xlab(NULL) +
ylab('[ppb]') +
theme_bw() +
facet_wrap(gas~.,scales = 'free_x',ncol = 1,nrow=2)
美國各州 shapefile 的 R geom_point vs geom_line vs geom_path
[英]R geom_point vs geom_line vs geom_path for US states shapefile
使用 geom_line 或 geom_path 連接具有不同分類變量的點
[英]use geom_line or geom_path to connect points with different categorical variable
當點被變量躲避而線被另一個變量躲避時，如何對齊 geom_line 和 geom_point？
[英]How to align geom_line and geom_point when points are dodged by a variable and lines by another?
geom_line() 錯誤 geom_path：每組僅包含一個觀察值。 需要調整群體審美嗎？
[英]geom_line() error geom_path: Each group consists of only one observation. Do you need to adjust the group aesthetic?
使用stat ='count'時用ggplot（geom_line）分隔行
[英]Separating Lines with ggplot (geom_line) when using stat='count'
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