Dplyr：如何在数据透视表中按分类组重新排列和拆分数据框 Table 在 R 中显示摘要统计信息
Dplyr: How to Rearrange and Split a Dataframe by A Categorical Group Within a Pivot Table Showing Summary Statistics in R
问题：
如果这是重复，我深表歉意，因为我不知道什么是我想要实现的目标的正确术语。我有一个名为 Country 的分类变量，我想直观地显示七个声学参数 （请参阅下面的数据结构和已经生成的摘要 table）。
根据数据，我使用 dplyr package （见下文）生成了描述性统计 (mean, standard deviation, standard error, min, max, q25, q75, and the coefficient of variation - CV) 的摘要 table。
我想生成 split version 我已经创建的 （见下文） 按分类值 [=15] 分组=]，因此，descriptive statistics 彼此堆叠成一个 table (arranged similarly to the example supplied).
正如在下面的示例中所观察到的，有一个名为 Year 的列，它漂亮而整齐地显示了出版物中每年的汇总统计数据。我的目标是制作一个类似麋鹿的例子，虽然，the 'Year' column would be called Country，而不是年，会有 two countries 的相似位置（如示例中的 Year - 见下文）并标记为“荷兰和法国”（如在虚拟数据中）。
正如在下面的示例中所观察到的，有一个名为 Year 的列，它漂亮而整齐地显示了出版物中每年的汇总统计数据。我的目标是制作一个类似麋鹿的例子，尽管 the 'Year' column (as shown in the example) would be called Country. I basically want to replicatethedescriptive summary statistics tablethat I havealready produced**(see below)** in the same arrangement (columns and rows) with an extra single column named 'Country' (located before the column变量) in which two countries (Holland and France) are 堆叠在一个 table` 中，因为结果更易于阅读。
摘要 table 的列名称为：
Country, variable, n (observations), Median, Mean, SD, SE, Min, Max, q25, q75, CV
我一直在研究数据 （下面的虚拟数据） 和汇总统计 R 代码 （下面） 和我无法理解如何做到这一点。
有谁知道如何在 dplyr 中生成这种类型的 table？
如果你能伸出援手，非常感谢？
目的：整理汇总统计table 类似于本例
参考
Morisaka, T., Shinohara, M., Nakahara, F. and Akamatsu, T., 2005. Geographic variations in the whistles among three Indo-Pacific bottlenose dolphin Tursiops aduncus populations in Japan. Fisheries Science, 71(3), pp.568-576
数据结构
'data.frame': 100 obs. of 12 variables:
$ ID : int 1 2 3 4 5 6 7 8 9 10 ...
$ Low.Freq : int 435 94103292 1 2688 8471 28818 654755585 468628164 342491 2288474 ...
$ High.Freq : int 6071 3210 6 7306092 6919054 666399 78 523880161 4700783 4173830 ...
$ Peak.Freq : int 87005561 9102 994839015 42745869 32840 62737133 2722 24 67404881 999242982 ...
$ Delta.Freq : int 5 78 88553 794 5 3859122 782 36 8756801 243169338 ...
$ Delta.Time : int 1361082 7926 499 5004 3494530 213 64551179 70 797 5 ...
$ Peak.Time : int 1465265 452894 545076172 8226275 5040875 700530 1 3639 20141 71712131 ...
$ Center_Freq: int 61907 8709547 300750537 45862 91417085 79892 47765 5477 18 4186 ...
$ Start.Freq : int 426355 22073538 680374 41771 54 6762844 599171 108 257451851 438814 ...
$ End.Freq : int 71000996 11613579 71377155 1942738 8760748 79 455 374 8 5 ...
$ Species : chr "Truncatus_Tursiops" "Truncatus_Tursiops" "Truncatus_Tursiops" "Truncatus_Tursiops" ...
$ Country : chr "Holland" "Holland" "Holland" "Holland" ...
来自 R-Code
的摘要统计信息 Table
# A tibble: 9 × 11
variable Median Mean n SD SE Min Max q25 q75 CV
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
2 Low.Freq 30645 47718421. 7157763188 160229651. 13082696. 0 936779338 392. 5065917. 336.
3 High.Freq 6020. 33588147. 5038222034 126884782. 10360099. 0 825466852 78.5 941394. 378.
4 Peak.Freq 45487 74707306. 11206095904 202504621. 16534433. 0 999242982 436. 32466176. 271.
5 Delta.Freq 20268. 31612255. 4741838252 113350682. 9255044. 0 754038591 93.2 2282342. 359.
6 Delta.Time 16852. 64582719. 9687407814 208416077. 17017101. 0 946706344 70.5 4181862. 323.
7 Peak.Time 35342 64781815. 9717272204 190695860. 15570252. 1 964147297 790. 6424504. 294.
8 Start.Freq 39416. 54517987. 8177697991 173895386. 14198499. 0 940000382 77.2 2694535 319.
9 End.Freq 71317 41475068. 6221260243 132873661. 10849089. 1 856943893 430. 7667247. 320.
R-代码：
library(dplyr)
library(tidyr)
#Function to calculate the coefficient of variation
cv <- function(x) 100*( sd(x)/mean(x))
Summary_Statistics <- Dummy[-1] %>%
dplyr::summarise(across(where(is.numeric), .fns =
list(n = length(n()),
Median = median,
Mean = mean,
n = sum,
SD = sd,
SE = ~sd(.)/sqrt(n()),
Min = min,
Max = max,
q25 = ~quantile(., 0.25),
q75 = ~quantile(., 0.75),
CV = cv
))) %>%
pivot_longer(everything(), names_sep = "_", names_to = c( "variable", ".value"))
虚拟数据
structure(list(ID = 1:100, Low.Freq = c(435L, 94103292L, 1L,
2688L, 8471L, 28818L, 654755585L, 468628164L, 342491L, 2288474L,
3915L, 411L, 267864894L, 3312618L, 5383L, 8989443L, 1894L, 534981L,
9544861L, 3437614L, 475386L, 7550764L, 48744L, 2317845L, 5126197L,
2445L, 8L, 557450L, 450259742L, 21006647L, 9L, 7234027L, 59L,
9L, 605L, 9199L, 3022L, 30218156L, 46423L, 38L, 88L, 396396244L,
28934316L, 7723L, 95688045L, 679354L, 716352L, 76289L, 332826763L,
6L, 90975L, 83103577L, 9529L, 229093L, 42810L, 5L, 18175302L,
1443751L, 5831L, 8303661L, 86L, 778L, 23947L, 8L, 9829740L, 2075838L,
7434328L, 82174987L, 2L, 94037071L, 9638653L, 5L, 3L, 65972L,
0L, 936779338L, 4885076L, 745L, 8L, 56456L, 125140L, 73043989L,
516476L, 7L, 4440739L, 612L, 3966L, 8L, 9255L, 84127L, 96218L,
5690L, 56L, 3561L, 78738L, 1803363L, 809369L, 7131L, 0L, 35502443L
), High.Freq = c(6071L, 3210L, 6L, 7306092L, 6919054L, 666399L,
78L, 523880161L, 4700783L, 4173830L, 30L, 811L, 341014L, 780L,
44749L, 91L, 201620707L, 74L, 1L, 65422L, 595L, 89093186L, 946520L,
6940919L, 655350L, 4L, 6L, 618L, 2006697L, 889L, 1398L, 28769L,
90519642L, 984L, 0L, 296209525L, 487088392L, 5L, 894L, 529L,
5L, 99106L, 2L, 926017L, 9078L, 1L, 21L, 88601017L, 575770L,
48L, 8431L, 194L, 62324996L, 5L, 81L, 40634727L, 806901520L,
6818173L, 3501L, 91780L, 36106039L, 5834347L, 58388837L, 34L,
3280L, 6507606L, 19L, 402L, 584L, 76L, 4078684L, 199L, 6881L,
92251L, 81715L, 40L, 327L, 57764L, 97668898L, 2676483L, 76L,
4694L, 817120L, 51L, 116712L, 666L, 3L, 42841L, 9724L, 21L, 4L,
359L, 2604L, 22L, 30490L, 5640L, 34L, 51923625L, 35544L, 59644L
), Peak.Freq = c(87005561L, 9102L, 994839015L, 42745869L, 32840L,
62737133L, 2722L, 24L, 67404881L, 999242982L, 3048L, 85315406L,
703037627L, 331264L, 8403609L, 3934064L, 50578953L, 370110665L,
3414L, 12657L, 40L, 432L, 7707L, 214L, 68588962L, 69467L, 75L,
500297L, 704L, 1L, 102659072L, 60896923L, 4481230L, 94124925L,
60164619L, 447L, 580L, 8L, 172L, 9478521L, 20L, 53L, 3072127L,
2160L, 27301893L, 8L, 4263L, 508L, 712409L, 50677L, 522433683L,
112844L, 193385L, 458269L, 93578705L, 22093131L, 6L, 9L, 1690461L,
0L, 4L, 652847L, 44767L, 21408L, 5384L, 304L, 721L, 651147L,
2426L, 586L, 498289375L, 945L, 6L, 816L, 46207L, 39135L, 6621028L,
66905L, 26905085L, 4098L, 0L, 14L, 88L, 530L, 97809006L, 90L,
6L, 260792844L, 9L, 833205723L, 99467321L, 5L, 8455640L, 54090L,
2L, 309L, 299161148L, 4952L, 454824L, 729805154L), Delta.Freq = c(5L,
78L, 88553L, 794L, 5L, 3859122L, 782L, 36L, 8756801L, 243169338L,
817789L, 8792384L, 7431L, 626921743L, 9206L, 95789L, 7916L, 8143453L,
6L, 4L, 6363L, 181125L, 259618L, 6751L, 33L, 37960L, 0L, 2L,
599582228L, 565585L, 19L, 48L, 269450424L, 70676581L, 7830566L,
4L, 86484313L, 21L, 90899794L, 2L, 72356L, 574280L, 869544L,
73418L, 6468164L, 2259L, 5938505L, 31329L, 1249L, 354L, 8817L,
3L, 2568L, 82809L, 29836269L, 5230L, 37L, 33752014L, 79307L,
1736L, 8522076L, 40L, 2289135L, 862L, 801448L, 8026L, 5L, 15L,
4393771L, 405914L, 71098L, 950288L, 8319L, 1396973L, 832L, 70L,
1746L, 61907L, 8709547L, 300750537L, 45862L, 91417085L, 79892L,
47765L, 5477L, 18L, 4186L, 2860L, 754038591L, 375L, 53809223L,
72L, 136L, 509L, 232325L, 13128104L, 1692L, 8581L, 23L, 7L),
Delta.Time = c(1361082L, 7926L, 499L, 5004L, 3494530L, 213L,
64551179L, 70L, 797L, 5L, 72588L, 86976L, 5163L, 635080L,
3L, 91L, 919806257L, 81443L, 3135427L, 4410972L, 5810L, 8L,
46603718L, 422L, 1083626L, 48L, 15699890L, 7L, 90167635L,
446459879L, 2332071L, 761660L, 49218442L, 381L, 46L, 493197L,
46L, 798597155L, 45342274L, 6265842L, 6L, 3445819L, 351L,
1761227L, 214L, 959L, 908996387L, 6L, 3855L, 9096604L, 152664L,
7970052L, 32366926L, 31L, 5201618L, 114L, 7806411L, 70L,
239L, 5065L, 2L, 1L, 14472831L, 122042249L, 8L, 495604L,
29L, 8965478L, 2875L, 959L, 39L, 9L, 690L, 933626665L, 85294L,
580093L, 95934L, 982058L, 65244056L, 137508L, 29L, 7621L,
7527L, 72L, 2L, 315L, 6L, 2413L, 8625150L, 51298109L, 851L,
890460L, 160736L, 6L, 850842734L, 2L, 7L, 76969113L, 190536L,
7855L), Peak.Time = c(1465265L, 452894L, 545076172L, 8226275L,
5040875L, 700530L, 1L, 3639L, 20141L, 71712131L, 686L, 923L,
770569738L, 69961L, 737458636L, 122403L, 199502046L, 6108L,
907L, 108078263L, 7817L, 4L, 6L, 69L, 721L, 786353L, 87486L,
1563L, 876L, 47599535L, 79295722L, 53L, 7378L, 591L, 6607935L,
954L, 6295L, 75514344L, 5742050L, 25647276L, 449L, 328566184L,
4L, 2L, 2703L, 21367543L, 63429043L, 708L, 782L, 909820L,
478L, 50L, 922L, 579882L, 7850L, 534L, 2157492L, 96L, 6L,
716L, 5L, 653290336L, 447854237L, 2L, 31972263L, 645L, 7L,
609909L, 4054695L, 455631L, 4919894L, 9L, 72713L, 9997L,
84090765L, 89742L, 5L, 5028L, 4126L, 23091L, 81L, 239635020L,
3576L, 898597785L, 6822L, 3798L, 201999L, 19624L, 20432923L,
18944093L, 930720236L, 1492302L, 300122L, 143633L, 5152743L,
417344L, 813L, 55792L, 78L, 14203776L), Center_Freq = c(61907L,
8709547L, 300750537L, 45862L, 91417085L, 79892L, 47765L,
5477L, 18L, 4186L, 2860L, 754038591L, 375L, 53809223L, 72L,
136L, 4700783L, 4173830L, 30L, 811L, 341014L, 780L, 44749L,
91L, 201620707L, 74L, 1L, 65422L, 595L, 89093186L, 946520L,
6940919L, 48744L, 2317845L, 5126197L, 2445L, 8L, 557450L,
450259742L, 21006647L, 9L, 7234027L, 59L, 9L, 651547554L,
45554L, 38493L, 91055218L, 38L, 1116474L, 2295482L, 3001L,
9L, 3270L, 141L, 53644L, 667983L, 565598L, 84L, 971L, 555498297L,
60431L, 6597L, 856943893L, 607815536L, 4406L, 79L, 4885076L,
745L, 8L, 56456L, 125140L, 73043989L, 516476L, 7L, 4440739L,
754038591L, 375L, 53809223L, 72L, 136L, 509L, 232325L, 13128104L,
1692L, 8581L, 23L, 5874213L, 4550L, 644668065L, 3712371L,
5928L, 8833L, 7L, 2186023L, 61627221L, 37297L, 716427989L,
21387L, 26639L), Start.Freq = c(426355L, 22073538L, 680374L,
41771L, 54L, 6762844L, 599171L, 108L, 257451851L, 438814L,
343045L, 4702L, 967787L, 1937L, 18L, 89301735L, 366L, 90L,
954L, 7337732L, 70891703L, 4139L, 10397931L, 940000382L,
7L, 38376L, 878528819L, 6287L, 738366L, 31L, 47L, 5L, 6L,
77848L, 2366508L, 45L, 3665842L, 7252260L, 6L, 61L, 3247L,
448348L, 1L, 705132L, 144L, 7423637L, 2L, 497L, 844927639L,
78978L, 914L, 131L, 7089563L, 927L, 9595581L, 2774463L, 1651L,
73509280L, 7L, 35L, 18L, 96L, 1L, 92545512L, 27354947L, 7556L,
65019L, 7480L, 71835L, 8249L, 64792L, 71537L, 349389666L,
280244484L, 82L, 6L, 40L, 353872L, 0L, 103L, 1255L, 4752L,
29L, 76L, 81185L, 14L, 9L, 470775630L, 818361265L, 57947209L,
44L, 24L, 41295L, 4L, 261449L, 9931404L, 773556640L, 930717L,
65007421L, 341175L), End.Freq = c(71000996L, 11613579L, 71377155L,
1942738L, 8760748L, 79L, 455L, 374L, 8L, 5L, 2266932L, 597833L,
155488L, 3020L, 4L, 554L, 4L, 16472L, 1945649L, 668181101L,
649780L, 22394365L, 93060602L, 172146L, 20472L, 23558847L,
190513L, 22759044L, 44L, 78450L, 205621181L, 218L, 69916344L,
23884L, 66L, 312148L, 7710564L, 4L, 422L, 744572L, 651547554L,
45554L, 38493L, 91055218L, 38L, 1116474L, 2295482L, 3001L,
9L, 3270L, 141L, 55595L, 38451L, 8660867L, 14L, 96L, 345L,
6L, 44L, 8235824L, 910517L, 1424326L, 87102566L, 53644L,
667983L, 565598L, 84L, 971L, 555498297L, 60431L, 6597L, 856943893L,
607815536L, 4406L, 79L, 7L, 28978746L, 7537295L, 6L, 633L,
345860066L, 802L, 1035131L, 602L, 2740L, 8065L, 61370968L,
429953765L, 981507L, 8105L, 343787257L, 44782L, 64184L, 12981359L,
123367978L, 818775L, 123745614L, 25345654L, 3L, 800889L),
Species = c("Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus"), Country = c("Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France")), class = "data.frame", row.names = c(NA,
-100L))
这能满足您的需求吗？ （如果你滚动到底部 :)）。它将上一期的答案与 group_by 相结合，从 pivot_longer 中排除了 Country 和 n，并且还重命名了 Center_Freq 以便在旋转时正确命名。
library(tidyverse)
Dummy <- structure(list(ID = 1:100, Low.Freq = c(435L, 94103292L, 1L,
2688L, 8471L, 28818L, 654755585L, 468628164L, 342491L, 2288474L,
3915L, 411L, 267864894L, 3312618L, 5383L, 8989443L, 1894L, 534981L,
9544861L, 3437614L, 475386L, 7550764L, 48744L, 2317845L, 5126197L,
2445L, 8L, 557450L, 450259742L, 21006647L, 9L, 7234027L, 59L,
9L, 605L, 9199L, 3022L, 30218156L, 46423L, 38L, 88L, 396396244L,
28934316L, 7723L, 95688045L, 679354L, 716352L, 76289L, 332826763L,
6L, 90975L, 83103577L, 9529L, 229093L, 42810L, 5L, 18175302L,
1443751L, 5831L, 8303661L, 86L, 778L, 23947L, 8L, 9829740L, 2075838L,
7434328L, 82174987L, 2L, 94037071L, 9638653L, 5L, 3L, 65972L,
0L, 936779338L, 4885076L, 745L, 8L, 56456L, 125140L, 73043989L,
516476L, 7L, 4440739L, 612L, 3966L, 8L, 9255L, 84127L, 96218L,
5690L, 56L, 3561L, 78738L, 1803363L, 809369L, 7131L, 0L, 35502443L
), High.Freq = c(6071L, 3210L, 6L, 7306092L, 6919054L, 666399L,
78L, 523880161L, 4700783L, 4173830L, 30L, 811L, 341014L, 780L,
44749L, 91L, 201620707L, 74L, 1L, 65422L, 595L, 89093186L, 946520L,
6940919L, 655350L, 4L, 6L, 618L, 2006697L, 889L, 1398L, 28769L,
90519642L, 984L, 0L, 296209525L, 487088392L, 5L, 894L, 529L,
5L, 99106L, 2L, 926017L, 9078L, 1L, 21L, 88601017L, 575770L,
48L, 8431L, 194L, 62324996L, 5L, 81L, 40634727L, 806901520L,
6818173L, 3501L, 91780L, 36106039L, 5834347L, 58388837L, 34L,
3280L, 6507606L, 19L, 402L, 584L, 76L, 4078684L, 199L, 6881L,
92251L, 81715L, 40L, 327L, 57764L, 97668898L, 2676483L, 76L,
4694L, 817120L, 51L, 116712L, 666L, 3L, 42841L, 9724L, 21L, 4L,
359L, 2604L, 22L, 30490L, 5640L, 34L, 51923625L, 35544L, 59644L
), Peak.Freq = c(87005561L, 9102L, 994839015L, 42745869L, 32840L,
62737133L, 2722L, 24L, 67404881L, 999242982L, 3048L, 85315406L,
703037627L, 331264L, 8403609L, 3934064L, 50578953L, 370110665L,
3414L, 12657L, 40L, 432L, 7707L, 214L, 68588962L, 69467L, 75L,
500297L, 704L, 1L, 102659072L, 60896923L, 4481230L, 94124925L,
60164619L, 447L, 580L, 8L, 172L, 9478521L, 20L, 53L, 3072127L,
2160L, 27301893L, 8L, 4263L, 508L, 712409L, 50677L, 522433683L,
112844L, 193385L, 458269L, 93578705L, 22093131L, 6L, 9L, 1690461L,
0L, 4L, 652847L, 44767L, 21408L, 5384L, 304L, 721L, 651147L,
2426L, 586L, 498289375L, 945L, 6L, 816L, 46207L, 39135L, 6621028L,
66905L, 26905085L, 4098L, 0L, 14L, 88L, 530L, 97809006L, 90L,
6L, 260792844L, 9L, 833205723L, 99467321L, 5L, 8455640L, 54090L,
2L, 309L, 299161148L, 4952L, 454824L, 729805154L), Delta.Freq = c(5L,
78L, 88553L, 794L, 5L, 3859122L, 782L, 36L, 8756801L, 243169338L,
817789L, 8792384L, 7431L, 626921743L, 9206L, 95789L, 7916L, 8143453L,
6L, 4L, 6363L, 181125L, 259618L, 6751L, 33L, 37960L, 0L, 2L,
599582228L, 565585L, 19L, 48L, 269450424L, 70676581L, 7830566L,
4L, 86484313L, 21L, 90899794L, 2L, 72356L, 574280L, 869544L,
73418L, 6468164L, 2259L, 5938505L, 31329L, 1249L, 354L, 8817L,
3L, 2568L, 82809L, 29836269L, 5230L, 37L, 33752014L, 79307L,
1736L, 8522076L, 40L, 2289135L, 862L, 801448L, 8026L, 5L, 15L,
4393771L, 405914L, 71098L, 950288L, 8319L, 1396973L, 832L, 70L,
1746L, 61907L, 8709547L, 300750537L, 45862L, 91417085L, 79892L,
47765L, 5477L, 18L, 4186L, 2860L, 754038591L, 375L, 53809223L,
72L, 136L, 509L, 232325L, 13128104L, 1692L, 8581L, 23L, 7L),
Delta.Time = c(1361082L, 7926L, 499L, 5004L, 3494530L, 213L,
64551179L, 70L, 797L, 5L, 72588L, 86976L, 5163L, 635080L,
3L, 91L, 919806257L, 81443L, 3135427L, 4410972L, 5810L, 8L,
46603718L, 422L, 1083626L, 48L, 15699890L, 7L, 90167635L,
446459879L, 2332071L, 761660L, 49218442L, 381L, 46L, 493197L,
46L, 798597155L, 45342274L, 6265842L, 6L, 3445819L, 351L,
1761227L, 214L, 959L, 908996387L, 6L, 3855L, 9096604L, 152664L,
7970052L, 32366926L, 31L, 5201618L, 114L, 7806411L, 70L,
239L, 5065L, 2L, 1L, 14472831L, 122042249L, 8L, 495604L,
29L, 8965478L, 2875L, 959L, 39L, 9L, 690L, 933626665L, 85294L,
580093L, 95934L, 982058L, 65244056L, 137508L, 29L, 7621L,
7527L, 72L, 2L, 315L, 6L, 2413L, 8625150L, 51298109L, 851L,
890460L, 160736L, 6L, 850842734L, 2L, 7L, 76969113L, 190536L,
7855L), Peak.Time = c(1465265L, 452894L, 545076172L, 8226275L,
5040875L, 700530L, 1L, 3639L, 20141L, 71712131L, 686L, 923L,
770569738L, 69961L, 737458636L, 122403L, 199502046L, 6108L,
907L, 108078263L, 7817L, 4L, 6L, 69L, 721L, 786353L, 87486L,
1563L, 876L, 47599535L, 79295722L, 53L, 7378L, 591L, 6607935L,
954L, 6295L, 75514344L, 5742050L, 25647276L, 449L, 328566184L,
4L, 2L, 2703L, 21367543L, 63429043L, 708L, 782L, 909820L,
478L, 50L, 922L, 579882L, 7850L, 534L, 2157492L, 96L, 6L,
716L, 5L, 653290336L, 447854237L, 2L, 31972263L, 645L, 7L,
609909L, 4054695L, 455631L, 4919894L, 9L, 72713L, 9997L,
84090765L, 89742L, 5L, 5028L, 4126L, 23091L, 81L, 239635020L,
3576L, 898597785L, 6822L, 3798L, 201999L, 19624L, 20432923L,
18944093L, 930720236L, 1492302L, 300122L, 143633L, 5152743L,
417344L, 813L, 55792L, 78L, 14203776L), Center_Freq = c(61907L,
8709547L, 300750537L, 45862L, 91417085L, 79892L, 47765L,
5477L, 18L, 4186L, 2860L, 754038591L, 375L, 53809223L, 72L,
136L, 4700783L, 4173830L, 30L, 811L, 341014L, 780L, 44749L,
91L, 201620707L, 74L, 1L, 65422L, 595L, 89093186L, 946520L,
6940919L, 48744L, 2317845L, 5126197L, 2445L, 8L, 557450L,
450259742L, 21006647L, 9L, 7234027L, 59L, 9L, 651547554L,
45554L, 38493L, 91055218L, 38L, 1116474L, 2295482L, 3001L,
9L, 3270L, 141L, 53644L, 667983L, 565598L, 84L, 971L, 555498297L,
60431L, 6597L, 856943893L, 607815536L, 4406L, 79L, 4885076L,
745L, 8L, 56456L, 125140L, 73043989L, 516476L, 7L, 4440739L,
754038591L, 375L, 53809223L, 72L, 136L, 509L, 232325L, 13128104L,
1692L, 8581L, 23L, 5874213L, 4550L, 644668065L, 3712371L,
5928L, 8833L, 7L, 2186023L, 61627221L, 37297L, 716427989L,
21387L, 26639L), Start.Freq = c(426355L, 22073538L, 680374L,
41771L, 54L, 6762844L, 599171L, 108L, 257451851L, 438814L,
343045L, 4702L, 967787L, 1937L, 18L, 89301735L, 366L, 90L,
954L, 7337732L, 70891703L, 4139L, 10397931L, 940000382L,
7L, 38376L, 878528819L, 6287L, 738366L, 31L, 47L, 5L, 6L,
77848L, 2366508L, 45L, 3665842L, 7252260L, 6L, 61L, 3247L,
448348L, 1L, 705132L, 144L, 7423637L, 2L, 497L, 844927639L,
78978L, 914L, 131L, 7089563L, 927L, 9595581L, 2774463L, 1651L,
73509280L, 7L, 35L, 18L, 96L, 1L, 92545512L, 27354947L, 7556L,
65019L, 7480L, 71835L, 8249L, 64792L, 71537L, 349389666L,
280244484L, 82L, 6L, 40L, 353872L, 0L, 103L, 1255L, 4752L,
29L, 76L, 81185L, 14L, 9L, 470775630L, 818361265L, 57947209L,
44L, 24L, 41295L, 4L, 261449L, 9931404L, 773556640L, 930717L,
65007421L, 341175L), End.Freq = c(71000996L, 11613579L, 71377155L,
1942738L, 8760748L, 79L, 455L, 374L, 8L, 5L, 2266932L, 597833L,
155488L, 3020L, 4L, 554L, 4L, 16472L, 1945649L, 668181101L,
649780L, 22394365L, 93060602L, 172146L, 20472L, 23558847L,
190513L, 22759044L, 44L, 78450L, 205621181L, 218L, 69916344L,
23884L, 66L, 312148L, 7710564L, 4L, 422L, 744572L, 651547554L,
45554L, 38493L, 91055218L, 38L, 1116474L, 2295482L, 3001L,
9L, 3270L, 141L, 55595L, 38451L, 8660867L, 14L, 96L, 345L,
6L, 44L, 8235824L, 910517L, 1424326L, 87102566L, 53644L,
667983L, 565598L, 84L, 971L, 555498297L, 60431L, 6597L, 856943893L,
607815536L, 4406L, 79L, 7L, 28978746L, 7537295L, 6L, 633L,
345860066L, 802L, 1035131L, 602L, 2740L, 8065L, 61370968L,
429953765L, 981507L, 8105L, 343787257L, 44782L, 64184L, 12981359L,
123367978L, 818775L, 123745614L, 25345654L, 3L, 800889L),
Species = c("Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus"), Country = c("Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France")), class = "data.frame", row.names = c(NA,
-100L))
#Function to calculate the coefficient of variation
cv <- function(x) 100*( sd(x)/mean(x))
Summary_Statistics <- Dummy[-1] %>%
rename(Center.Freq = Center_Freq) %>%
group_by(Country) %>%
summarise(across(where(is.numeric), .fns =
list(Median = median,
Mean = mean,
nsum = sum,
SD = sd,
SE = ~sd(.)/sqrt(n()),
Min = min,
Max = max,
q25 = ~quantile(., 0.25),
q75 = ~quantile(., 0.75),
CV = cv
)), n = n()) %>%
pivot_longer(-c(Country, n), names_sep = "_", names_to = c( "variable", ".value"))
Summary_Statistics
#> # A tibble: 18 × 13
#> Country n variable Median Mean nsum SD SE Min Max q25
#> <chr> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <dbl>
#> 1 France 52 Low.Freq 2.78e4 4.03e7 2.10e9 1.52e8 2.10e7 0 9.37e8 80.8
#> 2 France 52 High.Fr… 2.00e3 1.98e7 1.03e9 7.98e7 1.11e7 0 4.87e8 38.5
#> 3 France 52 Peak.Fr… 4.61e3 5.37e7 2.79e9 1.59e8 2.20e7 0 8.33e8 84.8
#> 4 France 52 Delta.F… 4.19e4 4.55e7 2.36e9 1.41e8 1.96e7 0 7.54e8 120
#> 5 France 52 Delta.T… 1.17e5 8.39e7 4.36e9 2.39e8 3.32e7 2 9.34e8 178.
#> 6 France 52 Peak.Ti… 6.43e4 5.52e7 2.87e9 1.83e8 2.54e7 2 9.31e8 1411.
#> 7 France 52 Center.… 5.71e4 7.25e7 3.77e9 1.94e8 2.69e7 1 7.54e8 574.
#> 8 France 52 Start.F… 5.52e3 7.17e7 3.73e9 2.08e8 2.88e7 0 8.79e8 30.5
#> 9 France 52 End.Freq 5.28e5 7.79e7 4.05e9 1.84e8 2.55e7 3 8.57e8 2256.
#> 10 Holland 48 Low.Freq 4.09e5 4.53e7 2.17e9 1.28e8 1.84e7 1 6.55e8 5016
#> 11 Holland 48 High.Fr… 5.51e4 4.09e7 1.96e9 1.39e8 2.01e7 1 8.07e8 168.
#> 12 Holland 48 Peak.Fr… 8.18e4 9.62e7 4.62e9 2.41e8 3.48e7 0 9.99e8 2000.
#> 13 Holland 48 Delta.F… 8.42e3 2.04e7 9.81e8 9.61e7 1.39e7 3 6.27e8 675
#> 14 Holland 48 Delta.T… 5.11e3 2.61e7 1.25e9 1.33e8 1.93e7 1 9.20e8 85.8
#> 15 Holland 48 Peak.Ti… 1.40e4 7.49e7 3.60e9 1.98e8 2.86e7 1 7.71e8 617.
#> 16 Holland 48 Center.… 4.53e4 6.96e7 3.34e9 1.99e8 2.87e7 8 8.57e8 652.
#> 17 Holland 48 Start.F… 6.49e4 5.14e7 2.47e9 1.83e8 2.63e7 1 9.40e8 810.
#> 18 Holland 48 End.Freq 5.46e4 3.37e7 1.62e9 1.24e8 1.80e7 4 6.68e8 294
#> # … with 2 more variables: q75 <dbl>, CV <dbl>
由 reprex package (v2.0.1)
于 2022-05-23 创建
问题：
如果这是重复，我深表歉意，因为我不知道什么是我想要实现的目标的正确术语。我有一个名为 Country 的分类变量，我想直观地显示七个声学参数 （请参阅下面的数据结构和已经生成的摘要 table）。
根据数据，我使用 dplyr package （见下文）生成了描述性统计 (mean, standard deviation, standard error, min, max, q25, q75, and the coefficient of variation - CV) 的摘要 table。
我想生成 split version 我已经创建的 （见下文） 按分类值 [=15] 分组=]，因此，descriptive statistics 彼此堆叠成一个 table (arranged similarly to the example supplied).
正如在下面的示例中所观察到的，有一个名为 Year 的列，它漂亮而整齐地显示了出版物中每年的汇总统计数据。我的目标是制作一个类似麋鹿的例子，虽然，the 'Year' column would be called Country，而不是年，会有 two countries 的相似位置（如示例中的 Year - 见下文）并标记为“荷兰和法国”（如在虚拟数据中）。
正如在下面的示例中所观察到的，有一个名为 Year 的列，它漂亮而整齐地显示了出版物中每年的汇总统计数据。我的目标是制作一个类似麋鹿的例子，尽管 the 'Year' column (as shown in the example) would be called Country. I basically want to replicatethedescriptive summary statistics tablethat I havealready produced**(see below)** in the same arrangement (columns and rows) with an extra single column named 'Country' (located before the column变量) in which two countries (Holland and France) are 堆叠在一个 table` 中，因为结果更易于阅读。
摘要 table 的列名称为：
Country, variable, n (observations), Median, Mean, SD, SE, Min, Max, q25, q75, CV
我一直在研究数据 （下面的虚拟数据） 和汇总统计 R 代码 （下面） 和我无法理解如何做到这一点。
有谁知道如何在 dplyr 中生成这种类型的 table？
如果你能伸出援手，非常感谢？
目的：整理汇总统计table 类似于本例
参考
Morisaka, T., Shinohara, M., Nakahara, F. and Akamatsu, T., 2005. Geographic variations in the whistles among three Indo-Pacific bottlenose dolphin Tursiops aduncus populations in Japan. Fisheries Science, 71(3), pp.568-576
数据结构
'data.frame': 100 obs. of 12 variables:
$ ID : int 1 2 3 4 5 6 7 8 9 10 ...
$ Low.Freq : int 435 94103292 1 2688 8471 28818 654755585 468628164 342491 2288474 ...
$ High.Freq : int 6071 3210 6 7306092 6919054 666399 78 523880161 4700783 4173830 ...
$ Peak.Freq : int 87005561 9102 994839015 42745869 32840 62737133 2722 24 67404881 999242982 ...
$ Delta.Freq : int 5 78 88553 794 5 3859122 782 36 8756801 243169338 ...
$ Delta.Time : int 1361082 7926 499 5004 3494530 213 64551179 70 797 5 ...
$ Peak.Time : int 1465265 452894 545076172 8226275 5040875 700530 1 3639 20141 71712131 ...
$ Center_Freq: int 61907 8709547 300750537 45862 91417085 79892 47765 5477 18 4186 ...
$ Start.Freq : int 426355 22073538 680374 41771 54 6762844 599171 108 257451851 438814 ...
$ End.Freq : int 71000996 11613579 71377155 1942738 8760748 79 455 374 8 5 ...
$ Species : chr "Truncatus_Tursiops" "Truncatus_Tursiops" "Truncatus_Tursiops" "Truncatus_Tursiops" ...
$ Country : chr "Holland" "Holland" "Holland" "Holland" ...
来自 R-Code
的摘要统计信息 Table # A tibble: 9 × 11
variable Median Mean n SD SE Min Max q25 q75 CV
<chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
2 Low.Freq 30645 47718421. 7157763188 160229651. 13082696. 0 936779338 392. 5065917. 336.
3 High.Freq 6020. 33588147. 5038222034 126884782. 10360099. 0 825466852 78.5 941394. 378.
4 Peak.Freq 45487 74707306. 11206095904 202504621. 16534433. 0 999242982 436. 32466176. 271.
5 Delta.Freq 20268. 31612255. 4741838252 113350682. 9255044. 0 754038591 93.2 2282342. 359.
6 Delta.Time 16852. 64582719. 9687407814 208416077. 17017101. 0 946706344 70.5 4181862. 323.
7 Peak.Time 35342 64781815. 9717272204 190695860. 15570252. 1 964147297 790. 6424504. 294.
8 Start.Freq 39416. 54517987. 8177697991 173895386. 14198499. 0 940000382 77.2 2694535 319.
9 End.Freq 71317 41475068. 6221260243 132873661. 10849089. 1 856943893 430. 7667247. 320.
R-代码：
library(dplyr)
library(tidyr)
#Function to calculate the coefficient of variation
cv <- function(x) 100*( sd(x)/mean(x))
Summary_Statistics <- Dummy[-1] %>%
dplyr::summarise(across(where(is.numeric), .fns =
list(n = length(n()),
Median = median,
Mean = mean,
n = sum,
SD = sd,
SE = ~sd(.)/sqrt(n()),
Min = min,
Max = max,
q25 = ~quantile(., 0.25),
q75 = ~quantile(., 0.75),
CV = cv
))) %>%
pivot_longer(everything(), names_sep = "_", names_to = c( "variable", ".value"))
虚拟数据
structure(list(ID = 1:100, Low.Freq = c(435L, 94103292L, 1L,
2688L, 8471L, 28818L, 654755585L, 468628164L, 342491L, 2288474L,
3915L, 411L, 267864894L, 3312618L, 5383L, 8989443L, 1894L, 534981L,
9544861L, 3437614L, 475386L, 7550764L, 48744L, 2317845L, 5126197L,
2445L, 8L, 557450L, 450259742L, 21006647L, 9L, 7234027L, 59L,
9L, 605L, 9199L, 3022L, 30218156L, 46423L, 38L, 88L, 396396244L,
28934316L, 7723L, 95688045L, 679354L, 716352L, 76289L, 332826763L,
6L, 90975L, 83103577L, 9529L, 229093L, 42810L, 5L, 18175302L,
1443751L, 5831L, 8303661L, 86L, 778L, 23947L, 8L, 9829740L, 2075838L,
7434328L, 82174987L, 2L, 94037071L, 9638653L, 5L, 3L, 65972L,
0L, 936779338L, 4885076L, 745L, 8L, 56456L, 125140L, 73043989L,
516476L, 7L, 4440739L, 612L, 3966L, 8L, 9255L, 84127L, 96218L,
5690L, 56L, 3561L, 78738L, 1803363L, 809369L, 7131L, 0L, 35502443L
), High.Freq = c(6071L, 3210L, 6L, 7306092L, 6919054L, 666399L,
78L, 523880161L, 4700783L, 4173830L, 30L, 811L, 341014L, 780L,
44749L, 91L, 201620707L, 74L, 1L, 65422L, 595L, 89093186L, 946520L,
6940919L, 655350L, 4L, 6L, 618L, 2006697L, 889L, 1398L, 28769L,
90519642L, 984L, 0L, 296209525L, 487088392L, 5L, 894L, 529L,
5L, 99106L, 2L, 926017L, 9078L, 1L, 21L, 88601017L, 575770L,
48L, 8431L, 194L, 62324996L, 5L, 81L, 40634727L, 806901520L,
6818173L, 3501L, 91780L, 36106039L, 5834347L, 58388837L, 34L,
3280L, 6507606L, 19L, 402L, 584L, 76L, 4078684L, 199L, 6881L,
92251L, 81715L, 40L, 327L, 57764L, 97668898L, 2676483L, 76L,
4694L, 817120L, 51L, 116712L, 666L, 3L, 42841L, 9724L, 21L, 4L,
359L, 2604L, 22L, 30490L, 5640L, 34L, 51923625L, 35544L, 59644L
), Peak.Freq = c(87005561L, 9102L, 994839015L, 42745869L, 32840L,
62737133L, 2722L, 24L, 67404881L, 999242982L, 3048L, 85315406L,
703037627L, 331264L, 8403609L, 3934064L, 50578953L, 370110665L,
3414L, 12657L, 40L, 432L, 7707L, 214L, 68588962L, 69467L, 75L,
500297L, 704L, 1L, 102659072L, 60896923L, 4481230L, 94124925L,
60164619L, 447L, 580L, 8L, 172L, 9478521L, 20L, 53L, 3072127L,
2160L, 27301893L, 8L, 4263L, 508L, 712409L, 50677L, 522433683L,
112844L, 193385L, 458269L, 93578705L, 22093131L, 6L, 9L, 1690461L,
0L, 4L, 652847L, 44767L, 21408L, 5384L, 304L, 721L, 651147L,
2426L, 586L, 498289375L, 945L, 6L, 816L, 46207L, 39135L, 6621028L,
66905L, 26905085L, 4098L, 0L, 14L, 88L, 530L, 97809006L, 90L,
6L, 260792844L, 9L, 833205723L, 99467321L, 5L, 8455640L, 54090L,
2L, 309L, 299161148L, 4952L, 454824L, 729805154L), Delta.Freq = c(5L,
78L, 88553L, 794L, 5L, 3859122L, 782L, 36L, 8756801L, 243169338L,
817789L, 8792384L, 7431L, 626921743L, 9206L, 95789L, 7916L, 8143453L,
6L, 4L, 6363L, 181125L, 259618L, 6751L, 33L, 37960L, 0L, 2L,
599582228L, 565585L, 19L, 48L, 269450424L, 70676581L, 7830566L,
4L, 86484313L, 21L, 90899794L, 2L, 72356L, 574280L, 869544L,
73418L, 6468164L, 2259L, 5938505L, 31329L, 1249L, 354L, 8817L,
3L, 2568L, 82809L, 29836269L, 5230L, 37L, 33752014L, 79307L,
1736L, 8522076L, 40L, 2289135L, 862L, 801448L, 8026L, 5L, 15L,
4393771L, 405914L, 71098L, 950288L, 8319L, 1396973L, 832L, 70L,
1746L, 61907L, 8709547L, 300750537L, 45862L, 91417085L, 79892L,
47765L, 5477L, 18L, 4186L, 2860L, 754038591L, 375L, 53809223L,
72L, 136L, 509L, 232325L, 13128104L, 1692L, 8581L, 23L, 7L),
Delta.Time = c(1361082L, 7926L, 499L, 5004L, 3494530L, 213L,
64551179L, 70L, 797L, 5L, 72588L, 86976L, 5163L, 635080L,
3L, 91L, 919806257L, 81443L, 3135427L, 4410972L, 5810L, 8L,
46603718L, 422L, 1083626L, 48L, 15699890L, 7L, 90167635L,
446459879L, 2332071L, 761660L, 49218442L, 381L, 46L, 493197L,
46L, 798597155L, 45342274L, 6265842L, 6L, 3445819L, 351L,
1761227L, 214L, 959L, 908996387L, 6L, 3855L, 9096604L, 152664L,
7970052L, 32366926L, 31L, 5201618L, 114L, 7806411L, 70L,
239L, 5065L, 2L, 1L, 14472831L, 122042249L, 8L, 495604L,
29L, 8965478L, 2875L, 959L, 39L, 9L, 690L, 933626665L, 85294L,
580093L, 95934L, 982058L, 65244056L, 137508L, 29L, 7621L,
7527L, 72L, 2L, 315L, 6L, 2413L, 8625150L, 51298109L, 851L,
890460L, 160736L, 6L, 850842734L, 2L, 7L, 76969113L, 190536L,
7855L), Peak.Time = c(1465265L, 452894L, 545076172L, 8226275L,
5040875L, 700530L, 1L, 3639L, 20141L, 71712131L, 686L, 923L,
770569738L, 69961L, 737458636L, 122403L, 199502046L, 6108L,
907L, 108078263L, 7817L, 4L, 6L, 69L, 721L, 786353L, 87486L,
1563L, 876L, 47599535L, 79295722L, 53L, 7378L, 591L, 6607935L,
954L, 6295L, 75514344L, 5742050L, 25647276L, 449L, 328566184L,
4L, 2L, 2703L, 21367543L, 63429043L, 708L, 782L, 909820L,
478L, 50L, 922L, 579882L, 7850L, 534L, 2157492L, 96L, 6L,
716L, 5L, 653290336L, 447854237L, 2L, 31972263L, 645L, 7L,
609909L, 4054695L, 455631L, 4919894L, 9L, 72713L, 9997L,
84090765L, 89742L, 5L, 5028L, 4126L, 23091L, 81L, 239635020L,
3576L, 898597785L, 6822L, 3798L, 201999L, 19624L, 20432923L,
18944093L, 930720236L, 1492302L, 300122L, 143633L, 5152743L,
417344L, 813L, 55792L, 78L, 14203776L), Center_Freq = c(61907L,
8709547L, 300750537L, 45862L, 91417085L, 79892L, 47765L,
5477L, 18L, 4186L, 2860L, 754038591L, 375L, 53809223L, 72L,
136L, 4700783L, 4173830L, 30L, 811L, 341014L, 780L, 44749L,
91L, 201620707L, 74L, 1L, 65422L, 595L, 89093186L, 946520L,
6940919L, 48744L, 2317845L, 5126197L, 2445L, 8L, 557450L,
450259742L, 21006647L, 9L, 7234027L, 59L, 9L, 651547554L,
45554L, 38493L, 91055218L, 38L, 1116474L, 2295482L, 3001L,
9L, 3270L, 141L, 53644L, 667983L, 565598L, 84L, 971L, 555498297L,
60431L, 6597L, 856943893L, 607815536L, 4406L, 79L, 4885076L,
745L, 8L, 56456L, 125140L, 73043989L, 516476L, 7L, 4440739L,
754038591L, 375L, 53809223L, 72L, 136L, 509L, 232325L, 13128104L,
1692L, 8581L, 23L, 5874213L, 4550L, 644668065L, 3712371L,
5928L, 8833L, 7L, 2186023L, 61627221L, 37297L, 716427989L,
21387L, 26639L), Start.Freq = c(426355L, 22073538L, 680374L,
41771L, 54L, 6762844L, 599171L, 108L, 257451851L, 438814L,
343045L, 4702L, 967787L, 1937L, 18L, 89301735L, 366L, 90L,
954L, 7337732L, 70891703L, 4139L, 10397931L, 940000382L,
7L, 38376L, 878528819L, 6287L, 738366L, 31L, 47L, 5L, 6L,
77848L, 2366508L, 45L, 3665842L, 7252260L, 6L, 61L, 3247L,
448348L, 1L, 705132L, 144L, 7423637L, 2L, 497L, 844927639L,
78978L, 914L, 131L, 7089563L, 927L, 9595581L, 2774463L, 1651L,
73509280L, 7L, 35L, 18L, 96L, 1L, 92545512L, 27354947L, 7556L,
65019L, 7480L, 71835L, 8249L, 64792L, 71537L, 349389666L,
280244484L, 82L, 6L, 40L, 353872L, 0L, 103L, 1255L, 4752L,
29L, 76L, 81185L, 14L, 9L, 470775630L, 818361265L, 57947209L,
44L, 24L, 41295L, 4L, 261449L, 9931404L, 773556640L, 930717L,
65007421L, 341175L), End.Freq = c(71000996L, 11613579L, 71377155L,
1942738L, 8760748L, 79L, 455L, 374L, 8L, 5L, 2266932L, 597833L,
155488L, 3020L, 4L, 554L, 4L, 16472L, 1945649L, 668181101L,
649780L, 22394365L, 93060602L, 172146L, 20472L, 23558847L,
190513L, 22759044L, 44L, 78450L, 205621181L, 218L, 69916344L,
23884L, 66L, 312148L, 7710564L, 4L, 422L, 744572L, 651547554L,
45554L, 38493L, 91055218L, 38L, 1116474L, 2295482L, 3001L,
9L, 3270L, 141L, 55595L, 38451L, 8660867L, 14L, 96L, 345L,
6L, 44L, 8235824L, 910517L, 1424326L, 87102566L, 53644L,
667983L, 565598L, 84L, 971L, 555498297L, 60431L, 6597L, 856943893L,
607815536L, 4406L, 79L, 7L, 28978746L, 7537295L, 6L, 633L,
345860066L, 802L, 1035131L, 602L, 2740L, 8065L, 61370968L,
429953765L, 981507L, 8105L, 343787257L, 44782L, 64184L, 12981359L,
123367978L, 818775L, 123745614L, 25345654L, 3L, 800889L),
Species = c("Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus"), Country = c("Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France")), class = "data.frame", row.names = c(NA,
-100L))
这能满足您的需求吗？ （如果你滚动到底部 :)）。它将上一期的答案与 group_by 相结合，从 pivot_longer 中排除了 Country 和 n，并且还重命名了 Center_Freq 以便在旋转时正确命名。
library(tidyverse)
Dummy <- structure(list(ID = 1:100, Low.Freq = c(435L, 94103292L, 1L,
2688L, 8471L, 28818L, 654755585L, 468628164L, 342491L, 2288474L,
3915L, 411L, 267864894L, 3312618L, 5383L, 8989443L, 1894L, 534981L,
9544861L, 3437614L, 475386L, 7550764L, 48744L, 2317845L, 5126197L,
2445L, 8L, 557450L, 450259742L, 21006647L, 9L, 7234027L, 59L,
9L, 605L, 9199L, 3022L, 30218156L, 46423L, 38L, 88L, 396396244L,
28934316L, 7723L, 95688045L, 679354L, 716352L, 76289L, 332826763L,
6L, 90975L, 83103577L, 9529L, 229093L, 42810L, 5L, 18175302L,
1443751L, 5831L, 8303661L, 86L, 778L, 23947L, 8L, 9829740L, 2075838L,
7434328L, 82174987L, 2L, 94037071L, 9638653L, 5L, 3L, 65972L,
0L, 936779338L, 4885076L, 745L, 8L, 56456L, 125140L, 73043989L,
516476L, 7L, 4440739L, 612L, 3966L, 8L, 9255L, 84127L, 96218L,
5690L, 56L, 3561L, 78738L, 1803363L, 809369L, 7131L, 0L, 35502443L
), High.Freq = c(6071L, 3210L, 6L, 7306092L, 6919054L, 666399L,
78L, 523880161L, 4700783L, 4173830L, 30L, 811L, 341014L, 780L,
44749L, 91L, 201620707L, 74L, 1L, 65422L, 595L, 89093186L, 946520L,
6940919L, 655350L, 4L, 6L, 618L, 2006697L, 889L, 1398L, 28769L,
90519642L, 984L, 0L, 296209525L, 487088392L, 5L, 894L, 529L,
5L, 99106L, 2L, 926017L, 9078L, 1L, 21L, 88601017L, 575770L,
48L, 8431L, 194L, 62324996L, 5L, 81L, 40634727L, 806901520L,
6818173L, 3501L, 91780L, 36106039L, 5834347L, 58388837L, 34L,
3280L, 6507606L, 19L, 402L, 584L, 76L, 4078684L, 199L, 6881L,
92251L, 81715L, 40L, 327L, 57764L, 97668898L, 2676483L, 76L,
4694L, 817120L, 51L, 116712L, 666L, 3L, 42841L, 9724L, 21L, 4L,
359L, 2604L, 22L, 30490L, 5640L, 34L, 51923625L, 35544L, 59644L
), Peak.Freq = c(87005561L, 9102L, 994839015L, 42745869L, 32840L,
62737133L, 2722L, 24L, 67404881L, 999242982L, 3048L, 85315406L,
703037627L, 331264L, 8403609L, 3934064L, 50578953L, 370110665L,
3414L, 12657L, 40L, 432L, 7707L, 214L, 68588962L, 69467L, 75L,
500297L, 704L, 1L, 102659072L, 60896923L, 4481230L, 94124925L,
60164619L, 447L, 580L, 8L, 172L, 9478521L, 20L, 53L, 3072127L,
2160L, 27301893L, 8L, 4263L, 508L, 712409L, 50677L, 522433683L,
112844L, 193385L, 458269L, 93578705L, 22093131L, 6L, 9L, 1690461L,
0L, 4L, 652847L, 44767L, 21408L, 5384L, 304L, 721L, 651147L,
2426L, 586L, 498289375L, 945L, 6L, 816L, 46207L, 39135L, 6621028L,
66905L, 26905085L, 4098L, 0L, 14L, 88L, 530L, 97809006L, 90L,
6L, 260792844L, 9L, 833205723L, 99467321L, 5L, 8455640L, 54090L,
2L, 309L, 299161148L, 4952L, 454824L, 729805154L), Delta.Freq = c(5L,
78L, 88553L, 794L, 5L, 3859122L, 782L, 36L, 8756801L, 243169338L,
817789L, 8792384L, 7431L, 626921743L, 9206L, 95789L, 7916L, 8143453L,
6L, 4L, 6363L, 181125L, 259618L, 6751L, 33L, 37960L, 0L, 2L,
599582228L, 565585L, 19L, 48L, 269450424L, 70676581L, 7830566L,
4L, 86484313L, 21L, 90899794L, 2L, 72356L, 574280L, 869544L,
73418L, 6468164L, 2259L, 5938505L, 31329L, 1249L, 354L, 8817L,
3L, 2568L, 82809L, 29836269L, 5230L, 37L, 33752014L, 79307L,
1736L, 8522076L, 40L, 2289135L, 862L, 801448L, 8026L, 5L, 15L,
4393771L, 405914L, 71098L, 950288L, 8319L, 1396973L, 832L, 70L,
1746L, 61907L, 8709547L, 300750537L, 45862L, 91417085L, 79892L,
47765L, 5477L, 18L, 4186L, 2860L, 754038591L, 375L, 53809223L,
72L, 136L, 509L, 232325L, 13128104L, 1692L, 8581L, 23L, 7L),
Delta.Time = c(1361082L, 7926L, 499L, 5004L, 3494530L, 213L,
64551179L, 70L, 797L, 5L, 72588L, 86976L, 5163L, 635080L,
3L, 91L, 919806257L, 81443L, 3135427L, 4410972L, 5810L, 8L,
46603718L, 422L, 1083626L, 48L, 15699890L, 7L, 90167635L,
446459879L, 2332071L, 761660L, 49218442L, 381L, 46L, 493197L,
46L, 798597155L, 45342274L, 6265842L, 6L, 3445819L, 351L,
1761227L, 214L, 959L, 908996387L, 6L, 3855L, 9096604L, 152664L,
7970052L, 32366926L, 31L, 5201618L, 114L, 7806411L, 70L,
239L, 5065L, 2L, 1L, 14472831L, 122042249L, 8L, 495604L,
29L, 8965478L, 2875L, 959L, 39L, 9L, 690L, 933626665L, 85294L,
580093L, 95934L, 982058L, 65244056L, 137508L, 29L, 7621L,
7527L, 72L, 2L, 315L, 6L, 2413L, 8625150L, 51298109L, 851L,
890460L, 160736L, 6L, 850842734L, 2L, 7L, 76969113L, 190536L,
7855L), Peak.Time = c(1465265L, 452894L, 545076172L, 8226275L,
5040875L, 700530L, 1L, 3639L, 20141L, 71712131L, 686L, 923L,
770569738L, 69961L, 737458636L, 122403L, 199502046L, 6108L,
907L, 108078263L, 7817L, 4L, 6L, 69L, 721L, 786353L, 87486L,
1563L, 876L, 47599535L, 79295722L, 53L, 7378L, 591L, 6607935L,
954L, 6295L, 75514344L, 5742050L, 25647276L, 449L, 328566184L,
4L, 2L, 2703L, 21367543L, 63429043L, 708L, 782L, 909820L,
478L, 50L, 922L, 579882L, 7850L, 534L, 2157492L, 96L, 6L,
716L, 5L, 653290336L, 447854237L, 2L, 31972263L, 645L, 7L,
609909L, 4054695L, 455631L, 4919894L, 9L, 72713L, 9997L,
84090765L, 89742L, 5L, 5028L, 4126L, 23091L, 81L, 239635020L,
3576L, 898597785L, 6822L, 3798L, 201999L, 19624L, 20432923L,
18944093L, 930720236L, 1492302L, 300122L, 143633L, 5152743L,
417344L, 813L, 55792L, 78L, 14203776L), Center_Freq = c(61907L,
8709547L, 300750537L, 45862L, 91417085L, 79892L, 47765L,
5477L, 18L, 4186L, 2860L, 754038591L, 375L, 53809223L, 72L,
136L, 4700783L, 4173830L, 30L, 811L, 341014L, 780L, 44749L,
91L, 201620707L, 74L, 1L, 65422L, 595L, 89093186L, 946520L,
6940919L, 48744L, 2317845L, 5126197L, 2445L, 8L, 557450L,
450259742L, 21006647L, 9L, 7234027L, 59L, 9L, 651547554L,
45554L, 38493L, 91055218L, 38L, 1116474L, 2295482L, 3001L,
9L, 3270L, 141L, 53644L, 667983L, 565598L, 84L, 971L, 555498297L,
60431L, 6597L, 856943893L, 607815536L, 4406L, 79L, 4885076L,
745L, 8L, 56456L, 125140L, 73043989L, 516476L, 7L, 4440739L,
754038591L, 375L, 53809223L, 72L, 136L, 509L, 232325L, 13128104L,
1692L, 8581L, 23L, 5874213L, 4550L, 644668065L, 3712371L,
5928L, 8833L, 7L, 2186023L, 61627221L, 37297L, 716427989L,
21387L, 26639L), Start.Freq = c(426355L, 22073538L, 680374L,
41771L, 54L, 6762844L, 599171L, 108L, 257451851L, 438814L,
343045L, 4702L, 967787L, 1937L, 18L, 89301735L, 366L, 90L,
954L, 7337732L, 70891703L, 4139L, 10397931L, 940000382L,
7L, 38376L, 878528819L, 6287L, 738366L, 31L, 47L, 5L, 6L,
77848L, 2366508L, 45L, 3665842L, 7252260L, 6L, 61L, 3247L,
448348L, 1L, 705132L, 144L, 7423637L, 2L, 497L, 844927639L,
78978L, 914L, 131L, 7089563L, 927L, 9595581L, 2774463L, 1651L,
73509280L, 7L, 35L, 18L, 96L, 1L, 92545512L, 27354947L, 7556L,
65019L, 7480L, 71835L, 8249L, 64792L, 71537L, 349389666L,
280244484L, 82L, 6L, 40L, 353872L, 0L, 103L, 1255L, 4752L,
29L, 76L, 81185L, 14L, 9L, 470775630L, 818361265L, 57947209L,
44L, 24L, 41295L, 4L, 261449L, 9931404L, 773556640L, 930717L,
65007421L, 341175L), End.Freq = c(71000996L, 11613579L, 71377155L,
1942738L, 8760748L, 79L, 455L, 374L, 8L, 5L, 2266932L, 597833L,
155488L, 3020L, 4L, 554L, 4L, 16472L, 1945649L, 668181101L,
649780L, 22394365L, 93060602L, 172146L, 20472L, 23558847L,
190513L, 22759044L, 44L, 78450L, 205621181L, 218L, 69916344L,
23884L, 66L, 312148L, 7710564L, 4L, 422L, 744572L, 651547554L,
45554L, 38493L, 91055218L, 38L, 1116474L, 2295482L, 3001L,
9L, 3270L, 141L, 55595L, 38451L, 8660867L, 14L, 96L, 345L,
6L, 44L, 8235824L, 910517L, 1424326L, 87102566L, 53644L,
667983L, 565598L, 84L, 971L, 555498297L, 60431L, 6597L, 856943893L,
607815536L, 4406L, 79L, 7L, 28978746L, 7537295L, 6L, 633L,
345860066L, 802L, 1035131L, 602L, 2740L, 8065L, 61370968L,
429953765L, 981507L, 8105L, 343787257L, 44782L, 64184L, 12981359L,
123367978L, 818775L, 123745614L, 25345654L, 3L, 800889L),
Species = c("Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Grampus_griseus", "Grampus_griseus", "Grampus_griseus",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Truncatus_Tursiops",
"Truncatus_Tursiops", "Truncatus_Tursiops", "Delphinus_Delphinus",
"Delphinus_Delphinus", "Delphinus_Delphinus", "Delphinus_Delphinus",
"Delphinus_Delphinus"), Country = c("Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "Holland", "Holland", "Holland",
"Holland", "Holland", "Holland", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France", "France", "France", "France", "France",
"France", "France")), class = "data.frame", row.names = c(NA,
-100L))
#Function to calculate the coefficient of variation
cv <- function(x) 100*( sd(x)/mean(x))
Summary_Statistics <- Dummy[-1] %>%
rename(Center.Freq = Center_Freq) %>%
group_by(Country) %>%
summarise(across(where(is.numeric), .fns =
list(Median = median,
Mean = mean,
nsum = sum,
SD = sd,
SE = ~sd(.)/sqrt(n()),
Min = min,
Max = max,
q25 = ~quantile(., 0.25),
q75 = ~quantile(., 0.75),
CV = cv
)), n = n()) %>%
pivot_longer(-c(Country, n), names_sep = "_", names_to = c( "variable", ".value"))
Summary_Statistics
#> # A tibble: 18 × 13
#> Country n variable Median Mean nsum SD SE Min Max q25
#> <chr> <int> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <int> <int> <dbl>
#> 1 France 52 Low.Freq 2.78e4 4.03e7 2.10e9 1.52e8 2.10e7 0 9.37e8 80.8
#> 2 France 52 High.Fr… 2.00e3 1.98e7 1.03e9 7.98e7 1.11e7 0 4.87e8 38.5
#> 3 France 52 Peak.Fr… 4.61e3 5.37e7 2.79e9 1.59e8 2.20e7 0 8.33e8 84.8
#> 4 France 52 Delta.F… 4.19e4 4.55e7 2.36e9 1.41e8 1.96e7 0 7.54e8 120
#> 5 France 52 Delta.T… 1.17e5 8.39e7 4.36e9 2.39e8 3.32e7 2 9.34e8 178.
#> 6 France 52 Peak.Ti… 6.43e4 5.52e7 2.87e9 1.83e8 2.54e7 2 9.31e8 1411.
#> 7 France 52 Center.… 5.71e4 7.25e7 3.77e9 1.94e8 2.69e7 1 7.54e8 574.
#> 8 France 52 Start.F… 5.52e3 7.17e7 3.73e9 2.08e8 2.88e7 0 8.79e8 30.5
#> 9 France 52 End.Freq 5.28e5 7.79e7 4.05e9 1.84e8 2.55e7 3 8.57e8 2256.
#> 10 Holland 48 Low.Freq 4.09e5 4.53e7 2.17e9 1.28e8 1.84e7 1 6.55e8 5016
#> 11 Holland 48 High.Fr… 5.51e4 4.09e7 1.96e9 1.39e8 2.01e7 1 8.07e8 168.
#> 12 Holland 48 Peak.Fr… 8.18e4 9.62e7 4.62e9 2.41e8 3.48e7 0 9.99e8 2000.
#> 13 Holland 48 Delta.F… 8.42e3 2.04e7 9.81e8 9.61e7 1.39e7 3 6.27e8 675
#> 14 Holland 48 Delta.T… 5.11e3 2.61e7 1.25e9 1.33e8 1.93e7 1 9.20e8 85.8
#> 15 Holland 48 Peak.Ti… 1.40e4 7.49e7 3.60e9 1.98e8 2.86e7 1 7.71e8 617.
#> 16 Holland 48 Center.… 4.53e4 6.96e7 3.34e9 1.99e8 2.87e7 8 8.57e8 652.
#> 17 Holland 48 Start.F… 6.49e4 5.14e7 2.47e9 1.83e8 2.63e7 1 9.40e8 810.
#> 18 Holland 48 End.Freq 5.46e4 3.37e7 1.62e9 1.24e8 1.80e7 4 6.68e8 294
#> # … with 2 more variables: q75 <dbl>, CV <dbl>
由 reprex package (v2.0.1)
于 2022-05-23 创建