ggplot-带有折线图的条形图-分组，stat_summary

Question

我已经看到这个问题回答了几次，但似乎无法使用我发现的各种帖子中的建议来解决。

我附有一个示例数据集（dput输出在底部）。我正在尝试此ggplot调用，以覆盖按年分组的“ scrapcount”列的折线图，但是混合了垂直线和一些水平连接。我应该如何正确地将其分组以每年获得一行？

p01 <- ggplot(data, aes(x = factor(MeltMonth))) +
          geom_bar(stat='identity', aes(y = weights)) +
          geom_line(aes(y = scrapcount * 1000000, group = factor(MeltYear), color = factor(MeltYear)))

样本数据：

data <- structure(list(MeltYear = c(2016, 2016, 2016, 2016, 2016, 2016, 
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018), MeltMonth = c(1, 
1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 
3, 3, 3, 3, 3, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 5, 
5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 7, 
7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 
9, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 
11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1, 1, 1, 1, 1, 
1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 
3, 4, 4, 4, 4, 4, 4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 
6, 6, 6, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 
8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10, 10, 10, 10, 
10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 
12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 1, 1, 1, 1, 1, 1, 1, 
1, 2, 2, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 3, 3, 3, 3, 4, 4, 4, 4, 
4, 4, 4, 5, 5, 5, 5, 5, 5, 5, 6, 6, 6, 6, 6, 6, 6, 7, 7, 7, 7, 
7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 10, 
10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11, 
11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12), ScrapPct = c("0", 
"0(T)", "28", "28(T)", "50", "50(T)", "70", NA, "0", "0(T)", 
"28", "28(T)", "50", "50(T)", "60", "70", "70(T)", NA, "0", "28", 
"28(T)", "50", "50(T)", "60(T)", "70", "70(T)", NA, "0", "28", 
"50", "50(T)", "60(T)", "70", "70(T)", NA, "(T)", "0", "0(T)", 
"25", "28", "28(T)", "50", "50(T)", "60", "70", "70(T)", "T", 
NA, "0", "0(T)", "25", "28", "28(T)", "50", "50(T)", "60", "70", 
"70(T)", NA, "0", "0(T)", "28", "28(T)", "50", "50(T)", "70", 
"70(T)", NA, "0", "0(T)", "28", "28(T)", "50", "50(T)", "70", 
"70(T)", NA, "0", "0(T)", "28", "28(T)", "50", "50(T)", "60(T)", 
"70", NA, "0", "0(T)", "28", "28(T)", "50", "50(T)", "60", "70", 
"70(T)", NA, "0", "0(T)", "25", "28", "28(T)", "50", "70", NA, 
"0", "0(T)", "28", "28(T)", "50", "50(T)", "70", "70(T)", NA, 
"0", "0(T)", "28", "28(T)", "50", "50(T)", "70", "70(T)", NA, 
"0", "0(T)", "28", "28(T)", "50", "50(T)", "70", NA, "0", "0(T)", 
"28", "28(T)", "50", "50(83 SCRAP)", "50(T)", "60", "70", NA, 
"0", "28", "28(T)", "28(T)(83 SCRAP)", "50", "50(T)", "60(T)", 
"70", "70(T)", NA, "0", "25", "28", "28(83 SCRAP)", "50", "70", 
NA, "0", "28", "50", "50(83 GRADE SCRAP)", "70", NA, "0", "0(T)", 
"25", "28", "28(T)", "50", "50(T)", "60(T)", "70", "70(T)", NA, 
"0", "0(T)", "25", "28", "28(83 GRADE)", "28(83 GRADES SCRAP)", 
"28(T)", "50", "50(T)", "70", "70(T)", NA, "0", "28", "28(T)", 
"50", "50(T)", "70", "70(T)", NA, "0", "0(T)", "25", "28", "28(83 GRADE)", 
"28(83 GRADE)*", "50", "50(T)", "70", "70(T)", NA, "0", "0(T)", 
"25", "28", "28(83 GRADE)", "28(T)", "50", "50(T)", "70", "70(T)", 
NA, "0", "25", "28", "28(83 GRADE)", "28(T)", "50", "50(T)", 
"60", "70", NA, "0", "28", "28 (83 GRADE)", "28(83 GRADE)", "50", 
"60", "70", NA, "0", "25", "28", "50", "70", "TURNINGS PUCKS", 
NA, "0", "0(T)", "25", "28", "28(83 GRADE)", "50", "60", "70", 
NA, "0", "28", "28(83 GRADE)", "50", "60", "70", NA, "0", "0(T)", 
"25", "28", "50", "70", NA, "0", "0(T)", "28", "50", "60", "70", 
NA, "0", "0(T)", "28", "50", "60", "70", NA, "0", "0(T)", "28", 
"28(83 GRADE)", "50", "60", "70", NA, "0", "0(T)", "28", "28(83 GRADE)", 
"50", "60", "70", NA, "0", "0(T)", "2", "25", "28", "28(83 GRADE)", 
"28(T)", "50", "70", NA, "0", "0(T)", "25", "28", "28(83 GRADE)", 
"28(T)", "50", "50(T)", "70", NA, "0", "25", "28", "28(83 GRADE)", 
"28(T)", "50", "60", "70", NA), counts = c(11L, 1L, 21L, 1L, 
38L, 6L, 4L, 4L, 6L, 1L, 1L, 5L, 23L, 41L, 2L, 5L, 2L, 1L, 3L, 
1L, 8L, 8L, 49L, 1L, 4L, 1L, 2L, 9L, 3L, 20L, 17L, 3L, 18L, 1L, 
9L, 1L, 10L, 1L, 1L, 3L, 2L, 23L, 22L, 3L, 12L, 11L, 1L, 2L, 
10L, 7L, 1L, 7L, 2L, 16L, 12L, 2L, 14L, 19L, 6L, 8L, 4L, 8L, 
2L, 16L, 6L, 9L, 4L, 5L, 24L, 5L, 6L, 7L, 17L, 5L, 20L, 3L, 3L, 
20L, 1L, 3L, 5L, 16L, 8L, 3L, 23L, 1L, 8L, 8L, 9L, 11L, 19L, 
11L, 2L, 14L, 4L, 4L, 30L, 1L, 1L, 10L, 1L, 33L, 16L, 7L, 32L, 
2L, 12L, 1L, 23L, 2L, 23L, 1L, 1L, 16L, 5L, 8L, 10L, 20L, 8L, 
22L, 6L, 1L, 15L, 20L, 3L, 5L, 16L, 2L, 31L, 1L, 18L, 3L, 8L, 
2L, 32L, 2L, 1L, 4L, 24L, 5L, 7L, 9L, 2L, 4L, 29L, 10L, 2L, 19L, 
4L, 1L, 19L, 1L, 11L, 5L, 35L, 13L, 3L, 27L, 5L, 27L, 2L, 21L, 
5L, 21L, 1L, 2L, 13L, 1L, 18L, 4L, 1L, 21L, 3L, 3L, 35L, 1L, 
1L, 12L, 2L, 1L, 7L, 12L, 12L, 19L, 3L, 3L, 18L, 9L, 1L, 11L, 
6L, 18L, 11L, 5L, 21L, 2L, 4L, 3L, 6L, 1L, 9L, 16L, 46L, 12L, 
8L, 18L, 2L, 1L, 8L, 2L, 8L, 18L, 19L, 18L, 7L, 6L, 17L, 1L, 
26L, 1L, 6L, 21L, 2L, 4L, 21L, 3L, 23L, 35L, 1L, 2L, 27L, 1L, 
28L, 5L, 19L, 2L, 26L, 27L, 32L, 1L, 7L, 27L, 1L, 1L, 29L, 1L, 
11L, 1L, 39L, 4L, 6L, 29L, 4L, 27L, 3L, 30L, 10L, 17L, 2L, 2L, 
34L, 38L, 17L, 5L, 19L, 5L, 22L, 28L, 1L, 19L, 1L, 16L, 1L, 22L, 
31L, 2L, 41L, 3L, 23L, 2L, 26L, 3L, 24L, 2L, 33L, 7L, 15L, 4L, 
27L, 2L, 20L, 1L, 18L, 3L, 8L, 1L, 1L, 1L, 34L, 3L, 1L, 34L, 
31L, 3L, 16L, 1L, 3L, 30L, 3L, 6L, 40L, 3L, 40L, 1L, 8L, 1L, 
30L, 1L, 1L, 30L, 4L, 33L, 6L), weights = c(207434, 20558, 405457, 
20983, 773304, 124639, 82286, 61910, 113145, 20605, 20614, 103468, 
469209, 824891, 40609, 103194, 41078, 19747, 40659, 12966, 164561, 
166481, 988762, 20301, 82447, 20150, 36892, 126047, 46905, 386183, 
324783, 62331, 339607, 12822, 159687, 20606, 139596, 20767, 10781, 
47873, 40499, 454152, 421639, 61942, 233491, 204732, 20609, 16852, 
188002, 125642, 10906, 137620, 41018, 287540, 215056, 41362, 
260027, 370500, 101522, 109587, 81951, 165314, 41309, 291380, 
121235, 180902, 66739, 82713, 347050, 103145, 123289, 143572, 
331285, 102734, 406688, 61605, 41479, 294302, 20544, 61436, 101996, 
315582, 149754, 61943, 458560, 796, 116777, 165160, 184396, 226196, 
363011, 226587, 41220, 263464, 78782, 47515, 521489, 20615, 10940, 
206040, 20568, 621913, 295439, 121067, 584891, 34120, 247005, 
20446, 447898, 31850, 439005, 20280, 20795, 247398, 102617, 153709, 
194364, 382904, 155901, 394641, 121533, 17776, 228900, 396567, 
61301, 97307, 313930, 37251, 589038, 10876, 291268, 62104, 164330, 
38625, 659758, 41353, 20362, 81574, 484278, 95130, 103180, 184596, 
40916, 82539, 578253, 196441, 41283, 386344, 80641, 4524, 302367, 
10904, 212691, 102562, 682525, 264078, 47083, 474865, 101992, 
527168, 40921, 423338, 79317, 329508, 20551, 21425, 248116, 18992, 
363368, 81955, 20544, 419755, 58566, 35268, 529855, 20411, 10056, 
195126, 26996, 14011, 135918, 228135, 224486, 383255, 60683, 
52010, 273119, 176914, 21049, 216170, 117754, 330027, 219768, 
78750, 325536, 33499, 41276, 56717, 121974, 13701, 162125, 281719, 
872330, 236383, 126602, 250243, 33408, 10873, 146578, 40011, 
163488, 317153, 311218, 325656, 138412, 104445, 238556, 10732, 
491907, 20490, 117758, 372741, 27347, 82084, 389548, 37320, NA, 
654496, 20512, 40933, 464243, 20472, 547677, 84113, 257198, 21176, 
477812, 522095, 627723, 13605, 81221, NA, 20473, 9786, 575789, 
20550, 211013, 20434, 766259, 62348, 101575, 568512, 82770, 535744, 
61289, 597523, 156643, 271068, 40732, 20816, 620291, 733227, 
310834, 75924, 282832, 98107, 433121, 514064, 20641, 355869, 
308, 238412, 18500, 397917, 567989, 41108, 779787, 40822, 369426, 
40708, 456805, 47740, 467083, 41388, NA, 114518, 218779, 75025, 
495130, 40359, 390211, 20806, 344959, 43407, 107150, 14774, 18447, 
9812, 652705, 61640, 20162, 642691, 601030, 42583, 230841, 14764, 
31447, 575805, 61468, 117472, 766114, 60831, 750711, 19982, 120162, 
10239, 553813, 19018, 19955, NA, 79198, 597375, 107136), scrapcount = c(1L, 
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 3L, 0L, 1L, 1L, 
0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 0L, 
1L, 0L, 0L, 0L, 0L, 1L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
3L, 0L, 0L, 1L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
0L, 1L, 0L, 2L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 2L, 0L, 1L, 
0L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 3L, 0L, 0L, 
0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 0L, 2L, 0L, 0L, 0L, 1L, 0L, 1L, 
0L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 
1L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, 2L, 0L, 0L, 0L, 1L, 0L, 
1L, 0L, 2L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 1L, 0L, 1L, 7L, 1L, 0L, 
1L, 0L, 0L, 2L, 2L, 0L, 5L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 1L, 1L, 
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 5L, 0L, 0L, 1L, 0L, 0L, 0L, 
0L, 1L, 1L, 0L, 1L, 2L, 1L, 1L, 0L, 1L, 0L, 0L, 2L, 0L, 2L, 0L, 
0L, 1L, 0L, 0L, 0L, 1L, 0L, 8L, 0L, 1L, 0L, 0L, 1L, 0L, 0L, 0L, 
1L, 0L, 0L, 0L, 0L, 0L, 0L, 4L, 0L, 0L, 1L, 0L, 0L, 0L, 2L, 0L, 
0L, 0L, 0L, 0L, 1L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 2L, 0L, 3L, 1L, 
2L, 2L, 0L, 2L, 1L, 3L, 0L, 3L, 0L, 0L, 0L, 1L, 0L, 5L, 0L, 1L, 
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 2L, 2L, 0L, 0L, 
0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 
2L, 0L)), class = c("grouped_df", "tbl_df", "tbl", "data.frame"
), row.names = c(NA, -323L), vars = c("MeltYear", "MeltMonth"
), drop = TRUE, indices = list(0:7, 8:17, 18:26, 27:34, 35:47, 
    48:58, 59:67, 68:76, 77:85, 86:95, 96:103, 104:112, 113:121, 
    122:129, 130:139, 140:149, 150:156, 157:162, 163:173, 174:185, 
    186:193, 194:204, 205:215, 216:225, 226:233, 234:240, 241:249, 
    250:256, 257:263, 264:270, 271:277, 278:285, 286:293, 294:303, 
    304:313, 314:322), group_sizes = c(8L, 10L, 9L, 8L, 13L, 
11L, 9L, 9L, 9L, 10L, 8L, 9L, 9L, 8L, 10L, 10L, 7L, 6L, 11L, 
12L, 8L, 11L, 11L, 10L, 8L, 7L, 9L, 7L, 7L, 7L, 7L, 8L, 8L, 10L, 
10L, 9L), biggest_group_size = 13L, labels = structure(list(MeltYear = c(2016, 
2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 2016, 
2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 2017, 
2017, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 2018, 
2018, 2018), MeltMonth = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 
12, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 1, 2, 3, 4, 5, 6, 
7, 8, 9, 10, 11, 12)), class = "data.frame", row.names = c(NA, 
-36L), vars = c("MeltYear", "MeltMonth"), drop = TRUE))

当前输出：

为了显示我要执行的操作，我使用正确的分组制作了另一张图的示例：

Answer 1

利用上面@aosmith的输入，正确地识别出我对每个“年/月”组的“ scrapcount”都有多个观测值，因此我想出了以下几点来通过stat_summary来校正我的图形：

p01 <- ggplot(data, aes(x = factor(MeltMonth), group = factor(MeltYear))) +
        geom_bar(stat='identity', fill = 'antiquewhite3', aes(y = weights)) +
        stat_summary(fun.y = 'sum', geom = 'line', aes(y = scrapcount*350000, color = factor(MeltYear))) +
        scale_y_continuous(sec.axis = sec_axis(~./350000, name = 'Count - C1 & C2 Codes')) +
        labs(title = "Weight by Application, Monthly, 2016-2018", x = "Month", y = "Weight", color = "Year")