Dplyr: How to Rearrange and Split a Dataframe by A Categorical Group Within a Pivot Table Showing Summary Statistics in R
Question
Issue:
I apologise if this is a repeat, as I've no idea whats' the correct terminology for what I'm trying to achieve. I have a categorical variable called Country and I want to visually display seven acoustic parameters (see the structure of the data below and the summary table already produced) .
From the data, I have produced a summary table of descriptive statistics (mean, standard deviation, standard error, min, max, q25, q75, and the coefficient of variation - CV) using the dplyr package (see below).
I would like to produce a split version of the descriptive summary statistic table I've already created (see below) grouped by the categorical value Country , whereby, the descriptive statistics are stacked on top of each other in one single table (arranged similarly to the example supplied) .
As observed in the example below , there is a column called Year , which nicely and neatly shows the summary statistics per year in the publication. My aim is to produce an example of similar elk, although, the 'Year' column would be called Country , and instead of years, there'd be two countries of similar position (as Year in the example - see below ) and labelled as `Holland and France' (as in the Dummy Data).
As observed in the example below , there is a column called Year , which nicely and neatly shows the summary statistics per year in the publication. My aim is to produce an example of similar elk, although, the 'Year' column (as shown in the example) would be called Country . I basically want to . I basically want to replicate the descriptive summary statistics table that I have already produced **(see below)** in the same arrangement (columns and rows) with an extra single column named 'Country' (located before the column Variable ) in which two countries (Holland and France) are stacked together in a single table` because the results would be easier to read.
The column names for the summary table would be:
Country, variable, n (observations), Median, Mean, SD, SE, Min, Max, q25, q75, CV
I have been playing around with the data (dummy data below) and the summary statistics R-code (below) and I cannot fathom how to do this.
Does anyone know how to produce this type of table in dplyr?
Many thanks in advance if you can lend a hand?
Aim: To arrange the summary statistics table similar to this example.
Reference
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
Structure of the data
'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" ...
Summary Statistics Table from R-Code
# 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-code:
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"))
Dummy Data
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))
1 answers
solution1
2 ACCPTED 2022-05-23 11:45:15
Does this give you what you want? (If you scroll to the bottom:)). It combines the answer from the previous issue with group_by , excluding Country and n from the pivot_longer and also renaming Center_Freq so it names correctly when pivoting.
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>
Created on 2022-05-23 by the reprex package (v2.0.1)
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