我有这个数据集：
> head(df)
# A tibble: 6 x 3
# Groups: type [1]
x y type
<dbl> <dbl> <dbl>
1 70.00 400.4200 1
2 72.00 400.4200 1
3 94.10 400.4310 1
4 105.60 400.4330 1
5 122.65 400.4360 1
6 132.20 400.4375 1
我按类型对数据进行了分组，并使用dplyrs总结计算了每个组的最大y值：
library(magrittr)
library(dplyr)
df %<>% group_by(type)
df_summary <- df %>% summarize(maximum = max(y))
根据预期给出了每组的最大值。
> df_summary
# A tibble: 14 x 2
type maximum
<dbl> <dbl>
1 1 400.4390
2 2 400.4395
3 3 400.4470
4 4 400.4450
5 5 400.4485
6 6 400.4360
7 7 400.4350
8 8 400.4250
9 9 400.4230
10 10 400.4235
11 11 400.4230
12 12 400.4260
13 13 400.4200
14 14 400.4145
我的第一个问题是：有没有办法使用汇总来获得属于最大y值的x值？我使用计算出的最大值来利用for循环从原始数据中得到我需要的东西：
for (i in unique(df_summary$type)){
if (i == 1){
aux <- df[(df$type == i) & (df$y == df_summary$maximum[i]),]
aux$value <- "maximum"
df_summary2 <- aux
} else {
aux <- df[(df$type == i) & (df$y == df_summary$maximum[i]),]
aux$value <- "maximum"
df_summary2 <- rbind(df_summary2, aux)
}
}
> df_summary2
# A tibble: 16 x 4
# Groups: type [14]
x y type value
<dbl> <dbl> <dbl> <chr>
1 139.70 400.4390 1 maximum
2 143.30 400.4395 2 maximum
3 426.70 400.4470 3 maximum
4 151.50 400.4450 4 maximum
5 146.20 400.4485 5 maximum
6 151.20 400.4485 5 maximum
7 99.90 400.4360 6 maximum
8 445.40 400.4350 7 maximum
9 323.40 400.4250 8 maximum
10 320.20 400.4230 9 maximum
11 97.90 400.4235 10 maximum
12 95.00 400.4230 11 maximum
13 108.55 400.4230 11 maximum
14 98.90 400.4260 12 maximum
15 108.70 400.4200 13 maximum
16 111.50 400.4145 14 maximum
有没有办法在不使用循环的情况下获得相同的结果？
数据：
structure(list(x = c(70, 72, 94.1, 105.6, 122.65, 132.2, 149.2,
162.2, 183.3, 188.8, 207.4, 222.9, 239.4, 247.4, 264.5, 275.5,
298.6, 302.6, 324.6, 336.6, 353.7, 360.7, 377.8, 392.8, 411.8,
417.8, 438.9, 451.9, 77.1, 86.65, 103.7, 115.2, 137.75, 139.3,
162.3, 173.4, 190.4, 200.4, 217.4, 230.5, 251.5, 256.6, 275.6,
290.6, 307.7, 315.7, 332.7, 343.8, 366.8, 369.8, 392.9, 403.9,
421, 429, 446, 70.3, 88.9, 94.4, 115.4, 128.5, 145.5, 154.6,
171.6, 183.6, 205.7, 207.7, 230.7, 241.8, 258.8, 267.8, 284.9,
297.9, 319, 325, 344, 359, 376.1, 383.1, 400.2, 411.2, 434.2,
438.2, 70.3, 81.4, 98.4, 105.9, 122.95, 138, 156.6, 162.6, 183.6,
198.6, 215.7, 224.7, 241.8, 252.8, 275.8, 277.8, 299.9, 311.45,
330, 340, 357, 370.1, 390.2, 396.2, 415.2, 430.2, 447.3, 454.3,
82, 93, 115.6, 119.1, 142.2, 153.2, 170.2, 177.75, 194.8, 209.85,
228.4, 234.4, 255.4, 268.5, 285.5, 294.6, 311.6, 322.6, 345.7,
347.7, 369.8, 381.8, 398.8, 407.8, 424.9, 437.9, 459, 75.8, 94.9,
109.9, 127, 134, 151, 162.1, 185.1, 189.1, 211.2, 223.2, 239.75,
247.3, 264.3, 279.4, 298.4, 303.9, 324.5, 337.5, 356.6, 367.1,
384.6, 396.6, 422.7, 424.7, 447.8, 458.8, 85.5, 95.05, 111.6,
125.6, 145.7, 151.7, 170.7, 185.8, 202.8, 209.8, 226.9, 238.4,
261, 265, 287, 299, 316.1, 323.1, 342.2, 357.2, 380.2, 385.3,
406.3, 419.4, 436.4, 445.4, 72.8, 84.3, 106.9, 108.9, 131.9,
143, 160, 169, 186.1, 199.1, 220.2, 226.2, 244.2, 259.75, 276.3,
284.3, 301.4, 312.4, 335.4, 338.95, 361.5, 372.6, 389.6, 397.6,
414.6, 429.7, 448.7, 453.8, 86.6, 99.7, 116.7, 126.25, 142.8,
154.8, 176.9, 178.9, 201.9, 213, 230, 239.5, 256.1, 270.1, 290.2,
296.2, 315.2, 332.2, 349.3, 356.3, 377.4, 388.4, 411.4, 414.5,
437.5, 448.6, 76.9, 84.9, 101.9, 117, 136, 141, 162.1, 175.1,
192.2, 201.7, 218.7, 230.2, 252.3, 254.3, 277.4, 288.4, 305.4,
314.95, 331.5, 345.5, 365.6, 371.6, 390.6, 405.7, 422.7, 429.8,
446.8, 457.8, 90, 93.5, 116.1, 127.1, 144.2, 152.2, 169.2, 184.2,
202.8, 208.3, 229.4, 242.4, 259.4, 268.5, 285.5, 297.5, 319.6,
321.6, 344.6, 355.7, 372.7, 381.8, 398.8, 412.3, 432.9, 438.9,
457.9, 82.3, 98.9, 106.4, 123.4, 134.5, 157.5, 161.05, 183.6,
194.6, 211.7, 219.7, 236.7, 251.8, 270.8, 275.8, 296.9, 309.9,
331, 340, 357, 368.55, 391.1, 393.1, 417.2, 429.2, 447.75, 457.3,
84.2, 97.2, 118.2, 123.75, 142.3, 157.85, 174.9, 182.4, 199.4,
210.5, 233.5, 237.5, 259.6, 271.6, 288.6, 295.7, 312.7, 327.8,
346.8, 352.8, 375.35, 390.4, 407.45, 421, 439.5, 451, 88.5, 90.5,
113.5, 124.6, 141.6, 150.6, 167.7, 180.7, 201.8, 206.8, 225.8,
240.9, 257.9, 265.9, 282.5, 294, 316.1, 320.1, 342.65, 354.2,
371.2, 378.2, 395.3, 410.3, 429.4, 434.4, 455.4), y = c(400.42,
400.42, 400.431, 400.433, 400.436, 400.4375, 400.4375, 400.4315,
400.42, 400.4175, 400.4075, 400.406, 400.409, 400.409, 400.411,
400.4075, 400.4065, 400.4065, 400.4105, 400.4135, 400.4215, 400.4215,
400.4265, 400.4295, 400.431, 400.434, 400.435, 400.4325, 400.4245,
400.428, 400.435, 400.4375, 400.439, 400.438, 400.432, 400.4285,
400.417, 400.411, 400.408, 400.4075, 400.4105, 400.41, 400.409,
400.4085, 400.412, 400.412, 400.4145, 400.4185, 400.4265, 400.427,
400.432, 400.4325, 400.4375, 400.4375, 400.436, 400.4155, 400.4205,
400.423, 400.4285, 400.4325, 400.441, 400.444, 400.436, 400.43,
400.4145, 400.4145, 400.4155, 400.4185, 400.4185, 400.4195, 400.4185,
400.419, 400.421, 400.421, 400.4255, 400.4325, 400.437, 400.4375,
400.4405, 400.443, 400.445, 400.445, 400.417, 400.422, 400.4265,
400.431, 400.4335, 400.4395, 400.443, 400.4415, 400.4295, 400.419,
400.416, 400.4145, 400.419, 400.421, 400.4195, 400.419, 400.42,
400.422, 400.4215, 400.421, 400.424, 400.426, 400.4285, 400.4295,
400.436, 400.439, 400.436, 400.433, 400.432, 400.4355, 400.436,
400.435, 400.445, 400.4465, 400.442, 400.442, 400.432, 400.425,
400.425, 400.426, 400.4285, 400.4265, 400.424, 400.424, 400.428,
400.4255, 400.422, 400.422, 400.422, 400.4235, 400.425, 400.428,
400.4345, 400.4345, 400.426, 400.4305, 400.434, 400.4345, 400.4325,
400.4335, 400.434, 400.433, 400.43, 400.4305, 400.4255, 400.424,
400.4265, 400.427, 400.4285, 400.424, 400.424, 400.425, 400.424,
400.421, 400.418, 400.417, 400.4215, 400.422, 400.4295, 400.43,
400.428, 400.424, 400.421, 400.4235, 400.424, 400.421, 400.425,
400.423, 400.422, 400.421, 400.421, 400.42, 400.423, 400.423,
400.426, 400.425, 400.422, 400.426, 400.428, 400.4275, 400.427,
400.426, 400.423, 400.4245, 400.425, 400.429, 400.433, 400.435,
400.4135, 400.414, 400.417, 400.4175, 400.416, 400.4175, 400.415,
400.414, 400.4155, 400.4185, 400.416, 400.4165, 400.421, 400.4225,
400.42, 400.419, 400.421, 400.423, 400.4225, 400.4205, 400.418,
400.4175, 400.4155, 400.4125, 400.413, 400.4185, 400.421, 400.419,
400.4165, 400.4175, 400.414, 400.4125, 400.416, 400.417, 400.413,
400.413, 400.414, 400.4145, 400.4155, 400.421, 400.42, 400.4195,
400.4175, 400.419, 400.421, 400.421, 400.4205, 400.4185, 400.4155,
400.413, 400.4105, 400.411, 400.4175, 400.4185, 400.4195, 400.42,
400.4215, 400.42, 400.419, 400.4175, 400.415, 400.4175, 400.4165,
400.4165, 400.415, 400.414, 400.416, 400.4165, 400.4145, 400.416,
400.416, 400.416, 400.414, 400.4115, 400.408, 400.4085, 400.4045,
400.4015, 400.406, 400.4065, 400.4065, 400.405, 400.4225, 400.4225,
400.42, 400.419, 400.422, 400.419, 400.4185, 400.417, 400.4185,
400.4155, 400.4185, 400.4175, 400.42, 400.4185, 400.416, 400.417,
400.42, 400.42, 400.417, 400.4125, 400.4105, 400.4105, 400.405,
400.4065, 400.409, 400.411, 400.409, 400.4215, 400.426, 400.4235,
400.4195, 400.4215, 400.4185, 400.42, 400.42, 400.4195, 400.416,
400.4165, 400.4175, 400.4195, 400.4195, 400.4185, 400.4175, 400.4185,
400.4185, 400.415, 400.414, 400.409, 400.4065, 400.406, 400.4065,
400.4095, 400.41, 400.4075, 400.415, 400.4165, 400.416, 400.413,
400.417, 400.414, 400.412, 400.411, 400.414, 400.4125, 400.413,
400.414, 400.4155, 400.4155, 400.414, 400.413, 400.415, 400.416,
400.4125, 400.411, 400.4065, 400.4065, 400.402, 400.4045, 400.4085,
400.407, 400.4125, 400.4125, 400.4115, 400.4085, 400.411, 400.409,
400.4025, 400.4035, 400.4055, 400.4055, 400.408, 400.404, 400.4085,
400.4085, 400.402, 400.4055, 400.4085, 400.41, 400.4045, 400.4035,
400.405, 400.4045, 400.3995, 400.4025, 400.403, 400.405, 400.4045
), type = c(1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,
1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 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, 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, 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,
7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7, 7,
7, 7, 7, 7, 7, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8,
8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 8, 9, 9, 9, 9, 9, 9, 9, 9, 9,
9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 9, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10, 10,
10, 10, 10, 10, 10, 10, 10, 10, 10, 11, 11, 11, 11, 11, 11, 11,
11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11, 11,
11, 11, 11, 11, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12,
12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 12, 13,
13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13, 13,
13, 13, 13, 13, 13, 13, 13, 13, 13, 14, 14, 14, 14, 14, 14, 14,
14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14, 14,
14, 14, 14, 14)), .Names = c("x", "y", "type"), row.names = c(NA,
-379L), class = c("tbl_df", "tbl", "data.frame"))
答案 0 :(得分：1)
我们可以使用FontData到which.max slice值为＆＃39; y＆＃39;对于每种类型＆＃39;
max如果有联系，请使用df %>%
group_by(type) %>%
slice(which.max(y)) %>%
mutate(value = "maximum")
来比较＆＃39; y＆＃39;使用==的＆＃39; y＆＃39;，max行或filter
slice答案 1 :(得分：1)
或arrange和slice的组合：
df %>%
group_by(type) %>%
arrange(y) %>%
slice(n())
结果：
# A tibble: 14 x 3
# Groups: type [14]
x y type
<dbl> <dbl> <dbl>
1 149.20 400.4375 1
2 137.75 400.4390 2
3 438.20 400.4450 3
4 156.60 400.4430 4
5 153.20 400.4465 5
6 109.90 400.4345 6
7 445.40 400.4350 7
8 312.40 400.4230 8
9 332.20 400.4210 9
10 101.90 400.4215 10
11 93.50 400.4225 11
12 98.90 400.4260 12
13 142.30 400.4170 13
14 90.50 400.4125 14