0.1 引言

本文介绍在R语言中非导入的方式原始创建各类数据的方法。

0.2 标量(Scalars)

标量标量是只含一个元素的向量，可以用以下方式赋值：

f <- 3L
g <- "US"
h <- TRUE
class(f)
## [1] "integer"
mode(f)
## [1] "numeric"
typeof(f)
## [1] "integer"
class(g)
## [1] "character"
class(h)
## [1] "logical"

0.3 向量(Vector)

向量是用于存储数值型、字符型或逻辑型数据的一维数组。可以用c()创建。

(a <- c(1, 2, 5, 3, 6, -2, 4) )
## [1]  1  2  5  3  6 -2  4
(b <- c("one", "two", "three") )
## [1] "one"   "two"   "three"
(c <- c(TRUE, TRUE, TRUE, FALSE, TRUE, FALSE))
## [1]  TRUE  TRUE  TRUE FALSE  TRUE FALSE
class(a)
## [1] "numeric"
class(b)
## [1] "character"
class(c)
## [1] "logical"

0.4 矩阵(Matrix)

矩阵是一个二维数组，只是每个元素都拥有相同的模式（数值型、字符型或逻辑型）。

很多baseR的统计函数擅长处理此类数据。

可通过函数matrix()创建矩阵。 一般使用格式为：

myymatrix <- matrix(
  vector,
  nrow = number_of_rows,
  ncol = number_of_columns,
  byrow = logical_value,
  dimnames = list(char_vector_rownames,
                  char_vector_colnames)
)
cells    <- c(1,26,24,68) 
rnames   <- c("R1", "R2") 
cnames   <- c("C1", "C2") 
mymatrix <- matrix(cells, nrow=2, ncol=2, byrow=TRUE, 
                     dimnames=list(rnames, cnames))  
mymatrix
##    C1 C2
## R1  1 26
## R2 24 68

或者通过xtabs函数转换数据框建立。

ToothGrowth
len supp dose
4.2 VC 0.5
11.5 VC 0.5
7.3 VC 0.5
5.8 VC 0.5
6.4 VC 0.5
10.0 VC 0.5
11.2 VC 0.5
11.2 VC 0.5
5.2 VC 0.5
7.0 VC 0.5
16.5 VC 1.0
16.5 VC 1.0
15.2 VC 1.0
17.3 VC 1.0
22.5 VC 1.0
17.3 VC 1.0
13.6 VC 1.0
14.5 VC 1.0
18.8 VC 1.0
15.5 VC 1.0
23.6 VC 2.0
18.5 VC 2.0
33.9 VC 2.0
25.5 VC 2.0
26.4 VC 2.0
32.5 VC 2.0
26.7 VC 2.0
21.5 VC 2.0
23.3 VC 2.0
29.5 VC 2.0
15.2 OJ 0.5
21.5 OJ 0.5
17.6 OJ 0.5
9.7 OJ 0.5
14.5 OJ 0.5
10.0 OJ 0.5
8.2 OJ 0.5
9.4 OJ 0.5
16.5 OJ 0.5
9.7 OJ 0.5
19.7 OJ 1.0
23.3 OJ 1.0
23.6 OJ 1.0
26.4 OJ 1.0
20.0 OJ 1.0
25.2 OJ 1.0
25.8 OJ 1.0
21.2 OJ 1.0
14.5 OJ 1.0
27.3 OJ 1.0
25.5 OJ 2.0
26.4 OJ 2.0
22.4 OJ 2.0
24.5 OJ 2.0
24.8 OJ 2.0
30.9 OJ 2.0
26.4 OJ 2.0
27.3 OJ 2.0
29.4 OJ 2.0
23.0 OJ 2.0 
ToothGrowth %>% 
  select(dose, supp) %>% 
  xtabs(~., data = .)
##      supp
## dose  OJ VC
##   0.5 10 10
##   1   10 10
##   2   10 10

或者通过margin.table()转换数组建立

HairEyeColor
## , , Sex = Male
## 
##        Eye
## Hair    Brown Blue Hazel Green
##   Black    32   11    10     3
##   Brown    53   50    25    15
##   Red      10   10     7     7
##   Blond     3   30     5     8
## 
## , , Sex = Female
## 
##        Eye
## Hair    Brown Blue Hazel Green
##   Black    36    9     5     2
##   Brown    66   34    29    14
##   Red      16    7     7     7
##   Blond     4   64     5     8
margin.table(HairEyeColor, c(1, 2))
##        Eye
## Hair    Brown Blue Hazel Green
##   Black    68   20    15     5
##   Brown   119   84    54    29
##   Red      26   17    14    14
##   Blond     7   94    10    16

0.5 数组(Array)

数组与矩阵类似，但是维度可以大于2。数组可通过array函数创建，形式如下:

array(data = NA,
      dim = length(data),
      dimnames = NULL)
dim1 <- c("A1", "A2") 
dim2 <- c("B1", "B2", "B3") 
dim3 <- c("C1", "C2", "C3", "C4") 
z <- array(1:24, c(2, 3, 4), dimnames=list(d = dim1, e = dim2, f = dim3)) 
z
## , , f = C1
## 
##     e
## d    B1 B2 B3
##   A1  1  3  5
##   A2  2  4  6
## 
## , , f = C2
## 
##     e
## d    B1 B2 B3
##   A1  7  9 11
##   A2  8 10 12
## 
## , , f = C3
## 
##     e
## d    B1 B2 B3
##   A1 13 15 17
##   A2 14 16 18
## 
## , , f = C4
## 
##     e
## d    B1 B2 B3
##   A1 19 21 23
##   A2 20 22 24

0.6 列表(List)

l <- list(names = 1:7, values = letters[1:10])
l
## $names
## [1] 1 2 3 4 5 6 7
## 
## $values
##  [1] "a" "b" "c" "d" "e" "f" "g" "h" "i" "j"

0.7 数据框(data frame)

由于不同的列可以包含不同模式（数值型、字符型等）的数据，数据框是在R中最常处理的数据结构。 数据框可通过函数data.frame()创建：

data.frame(
  ...,
  row.names = NULL,
  check.rows = FALSE,
  check.names = TRUE,
  fix.empty.names = TRUE,
  stringsAsFactors = default.stringsAsFactors()
)
L3 <- LETTERS[1:3]
fac <- sample(L3, 10, replace = TRUE)
(d <- data.frame(x = 1, y = 1:10, fac = fac))
x y fac
1 1 B
1 2 A
1 3 A
1 4 C
1 5 B
1 6 B
1 7 C
1 8 C
1 9 A
1 10 A

0.8 各类数据间的相互转化

0.8.1 转矢量

0.8.1.1 列表转矢量

l[[1]]
## [1] 1 2 3 4 5 6 7
l$names
## [1] 1 2 3 4 5 6 7
unlist(l)
##   names1   names2   names3   names4   names5   names6   names7  values1 
##      "1"      "2"      "3"      "4"      "5"      "6"      "7"      "a" 
##  values2  values3  values4  values5  values6  values7  values8  values9 
##      "b"      "c"      "d"      "e"      "f"      "g"      "h"      "i" 
## values10 
##      "j"

0.8.1.2 数据框转矢量

d[[1]]
##  [1] 1 1 1 1 1 1 1 1 1 1
d[['x']]
##  [1] 1 1 1 1 1 1 1 1 1 1
d %>% pull(x)
##  [1] 1 1 1 1 1 1 1 1 1 1

0.8.1.3 数组转矢量

as.vector(z)
##  [1]  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24

0.8.1.4 矩阵转矢量

mymatrix
##    C1 C2
## R1  1 26
## R2 24 68
as.vector(mymatrix)
## [1]  1 24 26 68

0.8.2 转数据框

0.8.2.1 列表转数据框

0.8.2.1.1 元素等长列表
l1 <- list(names = 1:7, values = letters[1:7])

df <- tibble(names = l1$names,
             values = l1$values)
df
names values
1 a
2 b
3 c
4 d
5 e
6 f
7 g 
# repurrrsive::sw_people
map_dfr(repurrrsive::sw_people, `[`, c("name", "mass", "height"))
name mass height
Luke Skywalker 77 172
C-3PO 75 167
R2-D2 32 96
Darth Vader 136 202
Leia Organa 49 150
Owen Lars 120 178
Beru Whitesun lars 75 165
R5-D4 32 97
Biggs Darklighter 84 183
Obi-Wan Kenobi 77 182
Anakin Skywalker 84 188
Wilhuff Tarkin unknown 180
Chewbacca 112 228
Han Solo 80 180
Greedo 74 173
Jabba Desilijic Tiure 1,358 175
Wedge Antilles 77 170
Jek Tono Porkins 110 180
Yoda 17 66
Palpatine 75 170
Boba Fett 78.2 183
IG-88 140 200
Bossk 113 190
Lando Calrissian 79 177
Lobot 79 175
Ackbar 83 180
Mon Mothma unknown 150
Arvel Crynyd unknown unknown
Wicket Systri Warrick 20 88
Nien Nunb 68 160
Qui-Gon Jinn 89 193
Nute Gunray 90 191
Finis Valorum unknown 170
Jar Jar Binks 66 196
Roos Tarpals 82 224
Rugor Nass unknown 206
Ric Olié unknown 183
Watto unknown 137
Sebulba 40 112
Quarsh Panaka unknown 183
Shmi Skywalker unknown 163
Darth Maul 80 175
Bib Fortuna unknown 180
Ayla Secura 55 178
Dud Bolt 45 94
Gasgano unknown 122
Ben Quadinaros 65 163
Mace Windu 84 188
Ki-Adi-Mundi 82 198
Kit Fisto 87 196
Eeth Koth unknown 171
Adi Gallia 50 184
Saesee Tiin unknown 188
Yarael Poof unknown 264
Plo Koon 80 188
Mas Amedda unknown 196
Gregar Typho 85 185
Cordé unknown 157
Cliegg Lars unknown 183
Poggle the Lesser 80 183
Luminara Unduli 56.2 170
Barriss Offee 50 166
Dormé unknown 165
Dooku 80 193
Bail Prestor Organa unknown 191
Jango Fett 79 183
Zam Wesell 55 168
Dexter Jettster 102 198
Lama Su 88 229
Taun We unknown 213
Jocasta Nu unknown 167
Ratts Tyerell 15 79
R4-P17 unknown 96
Wat Tambor 48 193
San Hill unknown 191
Shaak Ti 57 178
Grievous 159 216
Tarfful 136 234
Raymus Antilles 79 188
Sly Moore 48 178
Tion Medon 80 206
Finn unknown unknown
Rey unknown unknown
Poe Dameron unknown unknown
BB8 unknown unknown
Captain Phasma unknown unknown
Padmé Amidala 45 165
0.8.2.1.2 元素不等列表
#把列表中不等长的矢量转化成一个数据框
lst <- list(
  num = 1:5,
  alpha = letters[1:3]
)
lst
## $num
## [1] 1 2 3 4 5
## 
## $alpha
## [1] "a" "b" "c"
map_dfc(lst, `length<-`, max(lengths(lst)))
num alpha
1 a
2 b
3 c
4 NA
5 NA

0.8.2.2 数组转数据数据框

z %>% as.table() %>% as.data.frame()
d e f Freq
A1 B1 C1 1
A2 B1 C1 2
A1 B2 C1 3
A2 B2 C1 4
A1 B3 C1 5
A2 B3 C1 6
A1 B1 C2 7
A2 B1 C2 8
A1 B2 C2 9
A2 B2 C2 10
A1 B3 C2 11
A2 B3 C2 12
A1 B1 C3 13
A2 B1 C3 14
A1 B2 C3 15
A2 B2 C3 16
A1 B3 C3 17
A2 B3 C3 18
A1 B1 C4 19
A2 B1 C4 20
A1 B2 C4 21
A2 B2 C4 22
A1 B3 C4 23
A2 B3 C4 24 
z %>% margin.table(c(1, 2))
##     e
## d    B1 B2 B3
##   A1 40 48 56
##   A2 44 52 60
z %>% margin.table(2)
## e
##  B1  B2  B3 
##  84 100 116

0.8.2.3 矩阵转数据框

mymatrix
##    C1 C2
## R1  1 26
## R2 24 68
mymatrix %>% as_tibble(rownames = "name")
name C1 C2
R1 1 26
R2 24 68

0.8.2.4 矢量转数据框

a  %>%  enframe()
name value
1 1
2 2
3 5
4 3
5 6
6 -2
7 4

0.8.3 转数组

HE <- HairEyeColor %>% as.data.frame()
HE
Hair Eye Sex Freq
Black Brown Male 32
Brown Brown Male 53
Red Brown Male 10
Blond Brown Male 3
Black Blue Male 11
Brown Blue Male 50
Red Blue Male 10
Blond Blue Male 30
Black Hazel Male 10
Brown Hazel Male 25
Red Hazel Male 7
Blond Hazel Male 5
Black Green Male 3
Brown Green Male 15
Red Green Male 7
Blond Green Male 8
Black Brown Female 36
Brown Brown Female 66
Red Brown Female 16
Blond Brown Female 4
Black Blue Female 9
Brown Blue Female 34
Red Blue Female 7
Blond Blue Female 64
Black Hazel Female 5
Brown Hazel Female 29
Red Hazel Female 7
Blond Hazel Female 5
Black Green Female 2
Brown Green Female 14
Red Green Female 7
Blond Green Female 8 
HE_array <- xtabs(Freq ~ Hair + Eye + Sex, data = HE)
HE_array
## , , Sex = Male
## 
##        Eye
## Hair    Brown Blue Hazel Green
##   Black    32   11    10     3
##   Brown    53   50    25    15
##   Red      10   10     7     7
##   Blond     3   30     5     8
## 
## , , Sex = Female
## 
##        Eye
## Hair    Brown Blue Hazel Green
##   Black    36    9     5     2
##   Brown    66   34    29    14
##   Red      16    7     7     7
##   Blond     4   64     5     8
HairEyeColor
## , , Sex = Male
## 
##        Eye
## Hair    Brown Blue Hazel Green
##   Black    32   11    10     3
##   Brown    53   50    25    15
##   Red      10   10     7     7
##   Blond     3   30     5     8
## 
## , , Sex = Female
## 
##        Eye
## Hair    Brown Blue Hazel Green
##   Black    36    9     5     2
##   Brown    66   34    29    14
##   Red      16    7     7     7
##   Blond     4   64     5     8

0.9 数组、数据框相互转化实例

# 原始数据为数组
HEC_array <- HairEyeColor
HEC_array
## , , Sex = Male
## 
##        Eye
## Hair    Brown Blue Hazel Green
##   Black    32   11    10     3
##   Brown    53   50    25    15
##   Red      10   10     7     7
##   Blond     3   30     5     8
## 
## , , Sex = Female
## 
##        Eye
## Hair    Brown Blue Hazel Green
##   Black    36    9     5     2
##   Brown    66   34    29    14
##   Red      16    7     7     7
##   Blond     4   64     5     8
# 数组转为数据框
HEC_counts <- HEC_array %>% 
  as.data.frame()
HEC_counts
Hair Eye Sex Freq
Black Brown Male 32
Brown Brown Male 53
Red Brown Male 10
Blond Brown Male 3
Black Blue Male 11
Brown Blue Male 50
Red Blue Male 10
Blond Blue Male 30
Black Hazel Male 10
Brown Hazel Male 25
Red Hazel Male 7
Blond Hazel Male 5
Black Green Male 3
Brown Green Male 15
Red Green Male 7
Blond Green Male 8
Black Brown Female 36
Brown Brown Female 66
Red Brown Female 16
Blond Brown Female 4
Black Blue Female 9
Brown Blue Female 34
Red Blue Female 7
Blond Blue Female 64
Black Hazel Female 5
Brown Hazel Female 29
Red Hazel Female 7
Blond Hazel Female 5
Black Green Female 2
Brown Green Female 14
Red Green Female 7
Blond Green Female 8 
# 汇总频数型数据框转数组

HEC_counts %>% 
  xtabs(Freq ~ ., data = .)
## , , Sex = Male
## 
##        Eye
## Hair    Brown Blue Hazel Green
##   Black    32   11    10     3
##   Brown    53   50    25    15
##   Red      10   10     7     7
##   Blond     3   30     5     8
## 
## , , Sex = Female
## 
##        Eye
## Hair    Brown Blue Hazel Green
##   Black    36    9     5     2
##   Brown    66   34    29    14
##   Red      16    7     7     7
##   Blond     4   64     5     8
## 创建频数转观测函数
counts_to_cases <- function(x, count_col = "Freq"){
  idx <- rep(seq_len(nrow(x)), x[[count_col]])
  x[[count_col]] <- NULL
  x[idx, ]
}

# 数据框由汇总频数型转为重复观测型
HEC_cases <- HEC_counts %>% 
  counts_to_cases() 
HEC_cases
Hair Eye Sex
1 Black Brown Male
1.1 Black Brown Male
1.2 Black Brown Male
1.3 Black Brown Male
1.4 Black Brown Male
1.5 Black Brown Male
1.6 Black Brown Male
1.7 Black Brown Male
1.8 Black Brown Male
1.9 Black Brown Male
1.10 Black Brown Male
1.11 Black Brown Male
1.12 Black Brown Male
1.13 Black Brown Male
1.14 Black Brown Male
1.15 Black Brown Male
1.16 Black Brown Male
1.17 Black Brown Male
1.18 Black Brown Male
1.19 Black Brown Male
1.20 Black Brown Male
1.21 Black Brown Male
1.22 Black Brown Male
1.23 Black Brown Male
1.24 Black Brown Male
1.25 Black Brown Male
1.26 Black Brown Male
1.27 Black Brown Male
1.28 Black Brown Male
1.29 Black Brown Male
1.30 Black Brown Male
1.31 Black Brown Male
2 Brown Brown Male
2.1 Brown Brown Male
2.2 Brown Brown Male
2.3 Brown Brown Male
2.4 Brown Brown Male
2.5 Brown Brown Male
2.6 Brown Brown Male
2.7 Brown Brown Male
2.8 Brown Brown Male
2.9 Brown Brown Male
2.10 Brown Brown Male
2.11 Brown Brown Male
2.12 Brown Brown Male
2.13 Brown Brown Male
2.14 Brown Brown Male
2.15 Brown Brown Male
2.16 Brown Brown Male
2.17 Brown Brown Male
2.18 Brown Brown Male
2.19 Brown Brown Male
2.20 Brown Brown Male
2.21 Brown Brown Male
2.22 Brown Brown Male
2.23 Brown Brown Male
2.24 Brown Brown Male
2.25 Brown Brown Male
2.26 Brown Brown Male
2.27 Brown Brown Male
2.28 Brown Brown Male
2.29 Brown Brown Male
2.30 Brown Brown Male
2.31 Brown Brown Male
2.32 Brown Brown Male
2.33 Brown Brown Male
2.34 Brown Brown Male
2.35 Brown Brown Male
2.36 Brown Brown Male
2.37 Brown Brown Male
2.38 Brown Brown Male
2.39 Brown Brown Male
2.40 Brown Brown Male
2.41 Brown Brown Male
2.42 Brown Brown Male
2.43 Brown Brown Male
2.44 Brown Brown Male
2.45 Brown Brown Male
2.46 Brown Brown Male
2.47 Brown Brown Male
2.48 Brown Brown Male
2.49 Brown Brown Male
2.50 Brown Brown Male
2.51 Brown Brown Male
2.52 Brown Brown Male
3 Red Brown Male
3.1 Red Brown Male
3.2 Red Brown Male
3.3 Red Brown Male
3.4 Red Brown Male
3.5 Red Brown Male
3.6 Red Brown Male
3.7 Red Brown Male
3.8 Red Brown Male
3.9 Red Brown Male
4 Blond Brown Male
4.1 Blond Brown Male
4.2 Blond Brown Male
5 Black Blue Male
5.1 Black Blue Male
5.2 Black Blue Male
5.3 Black Blue Male
5.4 Black Blue Male
5.5 Black Blue Male
5.6 Black Blue Male
5.7 Black Blue Male
5.8 Black Blue Male
5.9 Black Blue Male
5.10 Black Blue Male
6 Brown Blue Male
6.1 Brown Blue Male
6.2 Brown Blue Male
6.3 Brown Blue Male
6.4 Brown Blue Male
6.5 Brown Blue Male
6.6 Brown Blue Male
6.7 Brown Blue Male
6.8 Brown Blue Male
6.9 Brown Blue Male
6.10 Brown Blue Male
6.11 Brown Blue Male
6.12 Brown Blue Male
6.13 Brown Blue Male
6.14 Brown Blue Male
6.15 Brown Blue Male
6.16 Brown Blue Male
6.17 Brown Blue Male
6.18 Brown Blue Male
6.19 Brown Blue Male
6.20 Brown Blue Male
6.21 Brown Blue Male
6.22 Brown Blue Male
6.23 Brown Blue Male
6.24 Brown Blue Male
6.25 Brown Blue Male
6.26 Brown Blue Male
6.27 Brown Blue Male
6.28 Brown Blue Male
6.29 Brown Blue Male
6.30 Brown Blue Male
6.31 Brown Blue Male
6.32 Brown Blue Male
6.33 Brown Blue Male
6.34 Brown Blue Male
6.35 Brown Blue Male
6.36 Brown Blue Male
6.37 Brown Blue Male
6.38 Brown Blue Male
6.39 Brown Blue Male
6.40 Brown Blue Male
6.41 Brown Blue Male
6.42 Brown Blue Male
6.43 Brown Blue Male
6.44 Brown Blue Male
6.45 Brown Blue Male
6.46 Brown Blue Male
6.47 Brown Blue Male
6.48 Brown Blue Male
6.49 Brown Blue Male
7 Red Blue Male
7.1 Red Blue Male
7.2 Red Blue Male
7.3 Red Blue Male
7.4 Red Blue Male
7.5 Red Blue Male
7.6 Red Blue Male
7.7 Red Blue Male
7.8 Red Blue Male
7.9 Red Blue Male
8 Blond Blue Male
8.1 Blond Blue Male
8.2 Blond Blue Male
8.3 Blond Blue Male
8.4 Blond Blue Male
8.5 Blond Blue Male
8.6 Blond Blue Male
8.7 Blond Blue Male
8.8 Blond Blue Male
8.9 Blond Blue Male
8.10 Blond Blue Male
8.11 Blond Blue Male
8.12 Blond Blue Male
8.13 Blond Blue Male
8.14 Blond Blue Male
8.15 Blond Blue Male
8.16 Blond Blue Male
8.17 Blond Blue Male
8.18 Blond Blue Male
8.19 Blond Blue Male
8.20 Blond Blue Male
8.21 Blond Blue Male
8.22 Blond Blue Male
8.23 Blond Blue Male
8.24 Blond Blue Male
8.25 Blond Blue Male
8.26 Blond Blue Male
8.27 Blond Blue Male
8.28 Blond Blue Male
8.29 Blond Blue Male
9 Black Hazel Male
9.1 Black Hazel Male
9.2 Black Hazel Male
9.3 Black Hazel Male
9.4 Black Hazel Male
9.5 Black Hazel Male
9.6 Black Hazel Male
9.7 Black Hazel Male
9.8 Black Hazel Male
9.9 Black Hazel Male
10 Brown Hazel Male
10.1 Brown Hazel Male
10.2 Brown Hazel Male
10.3 Brown Hazel Male
10.4 Brown Hazel Male
10.5 Brown Hazel Male
10.6 Brown Hazel Male
10.7 Brown Hazel Male
10.8 Brown Hazel Male
10.9 Brown Hazel Male
10.10 Brown Hazel Male
10.11 Brown Hazel Male
10.12 Brown Hazel Male
10.13 Brown Hazel Male
10.14 Brown Hazel Male
10.15 Brown Hazel Male
10.16 Brown Hazel Male
10.17 Brown Hazel Male
10.18 Brown Hazel Male
10.19 Brown Hazel Male
10.20 Brown Hazel Male
10.21 Brown Hazel Male
10.22 Brown Hazel Male
10.23 Brown Hazel Male
10.24 Brown Hazel Male
11 Red Hazel Male
11.1 Red Hazel Male
11.2 Red Hazel Male
11.3 Red Hazel Male
11.4 Red Hazel Male
11.5 Red Hazel Male
11.6 Red Hazel Male
12 Blond Hazel Male
12.1 Blond Hazel Male
12.2 Blond Hazel Male
12.3 Blond Hazel Male
12.4 Blond Hazel Male
13 Black Green Male
13.1 Black Green Male
13.2 Black Green Male
14 Brown Green Male
14.1 Brown Green Male
14.2 Brown Green Male
14.3 Brown Green Male
14.4 Brown Green Male
14.5 Brown Green Male
14.6 Brown Green Male
14.7 Brown Green Male
14.8 Brown Green Male
14.9 Brown Green Male
14.10 Brown Green Male
14.11 Brown Green Male
14.12 Brown Green Male
14.13 Brown Green Male
14.14 Brown Green Male
15 Red Green Male
15.1 Red Green Male
15.2 Red Green Male
15.3 Red Green Male
15.4 Red Green Male
15.5 Red Green Male
15.6 Red Green Male
16 Blond Green Male
16.1 Blond Green Male
16.2 Blond Green Male
16.3 Blond Green Male
16.4 Blond Green Male
16.5 Blond Green Male
16.6 Blond Green Male
16.7 Blond Green Male
17 Black Brown Female
17.1 Black Brown Female
17.2 Black Brown Female
17.3 Black Brown Female
17.4 Black Brown Female
17.5 Black Brown Female
17.6 Black Brown Female
17.7 Black Brown Female
17.8 Black Brown Female
17.9 Black Brown Female
17.10 Black Brown Female
17.11 Black Brown Female
17.12 Black Brown Female
17.13 Black Brown Female
17.14 Black Brown Female
17.15 Black Brown Female
17.16 Black Brown Female
17.17 Black Brown Female
17.18 Black Brown Female
17.19 Black Brown Female
17.20 Black Brown Female
17.21 Black Brown Female
17.22 Black Brown Female
17.23 Black Brown Female
17.24 Black Brown Female
17.25 Black Brown Female
17.26 Black Brown Female
17.27 Black Brown Female
17.28 Black Brown Female
17.29 Black Brown Female
17.30 Black Brown Female
17.31 Black Brown Female
17.32 Black Brown Female
17.33 Black Brown Female
17.34 Black Brown Female
17.35 Black Brown Female
18 Brown Brown Female
18.1 Brown Brown Female
18.2 Brown Brown Female
18.3 Brown Brown Female
18.4 Brown Brown Female
18.5 Brown Brown Female
18.6 Brown Brown Female
18.7 Brown Brown Female
18.8 Brown Brown Female
18.9 Brown Brown Female
18.10 Brown Brown Female
18.11 Brown Brown Female
18.12 Brown Brown Female
18.13 Brown Brown Female
18.14 Brown Brown Female
18.15 Brown Brown Female
18.16 Brown Brown Female
18.17 Brown Brown Female
18.18 Brown Brown Female
18.19 Brown Brown Female
18.20 Brown Brown Female
18.21 Brown Brown Female
18.22 Brown Brown Female
18.23 Brown Brown Female
18.24 Brown Brown Female
18.25 Brown Brown Female
18.26 Brown Brown Female
18.27 Brown Brown Female
18.28 Brown Brown Female
18.29 Brown Brown Female
18.30 Brown Brown Female
18.31 Brown Brown Female
18.32 Brown Brown Female
18.33 Brown Brown Female
18.34 Brown Brown Female
18.35 Brown Brown Female
18.36 Brown Brown Female
18.37 Brown Brown Female
18.38 Brown Brown Female
18.39 Brown Brown Female
18.40 Brown Brown Female
18.41 Brown Brown Female
18.42 Brown Brown Female
18.43 Brown Brown Female
18.44 Brown Brown Female
18.45 Brown Brown Female
18.46 Brown Brown Female
18.47 Brown Brown Female
18.48 Brown Brown Female
18.49 Brown Brown Female
18.50 Brown Brown Female
18.51 Brown Brown Female
18.52 Brown Brown Female
18.53 Brown Brown Female
18.54 Brown Brown Female
18.55 Brown Brown Female
18.56 Brown Brown Female
18.57 Brown Brown Female
18.58 Brown Brown Female
18.59 Brown Brown Female
18.60 Brown Brown Female
18.61 Brown Brown Female
18.62 Brown Brown Female
18.63 Brown Brown Female
18.64 Brown Brown Female
18.65 Brown Brown Female
19 Red Brown Female
19.1 Red Brown Female
19.2 Red Brown Female
19.3 Red Brown Female
19.4 Red Brown Female
19.5 Red Brown Female
19.6 Red Brown Female
19.7 Red Brown Female
19.8 Red Brown Female
19.9 Red Brown Female
19.10 Red Brown Female
19.11 Red Brown Female
19.12 Red Brown Female
19.13 Red Brown Female
19.14 Red Brown Female
19.15 Red Brown Female
20 Blond Brown Female
20.1 Blond Brown Female
20.2 Blond Brown Female
20.3 Blond Brown Female
21 Black Blue Female
21.1 Black Blue Female
21.2 Black Blue Female
21.3 Black Blue Female
21.4 Black Blue Female
21.5 Black Blue Female
21.6 Black Blue Female
21.7 Black Blue Female
21.8 Black Blue Female
22 Brown Blue Female
22.1 Brown Blue Female
22.2 Brown Blue Female
22.3 Brown Blue Female
22.4 Brown Blue Female
22.5 Brown Blue Female
22.6 Brown Blue Female
22.7 Brown Blue Female
22.8 Brown Blue Female
22.9 Brown Blue Female
22.10 Brown Blue Female
22.11 Brown Blue Female
22.12 Brown Blue Female
22.13 Brown Blue Female
22.14 Brown Blue Female
22.15 Brown Blue Female
22.16 Brown Blue Female
22.17 Brown Blue Female
22.18 Brown Blue Female
22.19 Brown Blue Female
22.20 Brown Blue Female
22.21 Brown Blue Female
22.22 Brown Blue Female
22.23 Brown Blue Female
22.24 Brown Blue Female
22.25 Brown Blue Female
22.26 Brown Blue Female
22.27 Brown Blue Female
22.28 Brown Blue Female
22.29 Brown Blue Female
22.30 Brown Blue Female
22.31 Brown Blue Female
22.32 Brown Blue Female
22.33 Brown Blue Female
23 Red Blue Female
23.1 Red Blue Female
23.2 Red Blue Female
23.3 Red Blue Female
23.4 Red Blue Female
23.5 Red Blue Female
23.6 Red Blue Female
24 Blond Blue Female
24.1 Blond Blue Female
24.2 Blond Blue Female
24.3 Blond Blue Female
24.4 Blond Blue Female
24.5 Blond Blue Female
24.6 Blond Blue Female
24.7 Blond Blue Female
24.8 Blond Blue Female
24.9 Blond Blue Female
24.10 Blond Blue Female
24.11 Blond Blue Female
24.12 Blond Blue Female
24.13 Blond Blue Female
24.14 Blond Blue Female
24.15 Blond Blue Female
24.16 Blond Blue Female
24.17 Blond Blue Female
24.18 Blond Blue Female
24.19 Blond Blue Female
24.20 Blond Blue Female
24.21 Blond Blue Female
24.22 Blond Blue Female
24.23 Blond Blue Female
24.24 Blond Blue Female
24.25 Blond Blue Female
24.26 Blond Blue Female
24.27 Blond Blue Female
24.28 Blond Blue Female
24.29 Blond Blue Female
24.30 Blond Blue Female
24.31 Blond Blue Female
24.32 Blond Blue Female
24.33 Blond Blue Female
24.34 Blond Blue Female
24.35 Blond Blue Female
24.36 Blond Blue Female
24.37 Blond Blue Female
24.38 Blond Blue Female
24.39 Blond Blue Female
24.40 Blond Blue Female
24.41 Blond Blue Female
24.42 Blond Blue Female
24.43 Blond Blue Female
24.44 Blond Blue Female
24.45 Blond Blue Female
24.46 Blond Blue Female
24.47 Blond Blue Female
24.48 Blond Blue Female
24.49 Blond Blue Female
24.50 Blond Blue Female
24.51 Blond Blue Female
24.52 Blond Blue Female
24.53 Blond Blue Female
24.54 Blond Blue Female
24.55 Blond Blue Female
24.56 Blond Blue Female
24.57 Blond Blue Female
24.58 Blond Blue Female
24.59 Blond Blue Female
24.60 Blond Blue Female
24.61 Blond Blue Female
24.62 Blond Blue Female
24.63 Blond Blue Female
25 Black Hazel Female
25.1 Black Hazel Female
25.2 Black Hazel Female
25.3 Black Hazel Female
25.4 Black Hazel Female
26 Brown Hazel Female
26.1 Brown Hazel Female
26.2 Brown Hazel Female
26.3 Brown Hazel Female
26.4 Brown Hazel Female
26.5 Brown Hazel Female
26.6 Brown Hazel Female
26.7 Brown Hazel Female
26.8 Brown Hazel Female
26.9 Brown Hazel Female
26.10 Brown Hazel Female
26.11 Brown Hazel Female
26.12 Brown Hazel Female
26.13 Brown Hazel Female
26.14 Brown Hazel Female
26.15 Brown Hazel Female
26.16 Brown Hazel Female
26.17 Brown Hazel Female
26.18 Brown Hazel Female
26.19 Brown Hazel Female
26.20 Brown Hazel Female
26.21 Brown Hazel Female
26.22 Brown Hazel Female
26.23 Brown Hazel Female
26.24 Brown Hazel Female
26.25 Brown Hazel Female
26.26 Brown Hazel Female
26.27 Brown Hazel Female
26.28 Brown Hazel Female
27 Red Hazel Female
27.1 Red Hazel Female
27.2 Red Hazel Female
27.3 Red Hazel Female
27.4 Red Hazel Female
27.5 Red Hazel Female
27.6 Red Hazel Female
28 Blond Hazel Female
28.1 Blond Hazel Female
28.2 Blond Hazel Female
28.3 Blond Hazel Female
28.4 Blond Hazel Female
29 Black Green Female
29.1 Black Green Female
30 Brown Green Female
30.1 Brown Green Female
30.2 Brown Green Female
30.3 Brown Green Female
30.4 Brown Green Female
30.5 Brown Green Female
30.6 Brown Green Female
30.7 Brown Green Female
30.8 Brown Green Female
30.9 Brown Green Female
30.10 Brown Green Female
30.11 Brown Green Female
30.12 Brown Green Female
30.13 Brown Green Female
31 Red Green Female
31.1 Red Green Female
31.2 Red Green Female
31.3 Red Green Female
31.4 Red Green Female
31.5 Red Green Female
31.6 Red Green Female
32 Blond Green Female
32.1 Blond Green Female
32.2 Blond Green Female
32.3 Blond Green Female
32.4 Blond Green Female
32.5 Blond Green Female
32.6 Blond Green Female
32.7 Blond Green Female 
# 或者用slice
HEC_counts %>% 
  slice(rep(1:n(), Freq))
Hair Eye Sex Freq
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Black Brown Male 32
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Brown Brown Male 53
Red Brown Male 10
Red Brown Male 10
Red Brown Male 10
Red Brown Male 10
Red Brown Male 10
Red Brown Male 10
Red Brown Male 10
Red Brown Male 10
Red Brown Male 10
Red Brown Male 10
Blond Brown Male 3
Blond Brown Male 3
Blond Brown Male 3
Black Blue Male 11
Black Blue Male 11
Black Blue Male 11
Black Blue Male 11
Black Blue Male 11
Black Blue Male 11
Black Blue Male 11
Black Blue Male 11
Black Blue Male 11
Black Blue Male 11
Black Blue Male 11
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Brown Blue Male 50
Red Blue Male 10
Red Blue Male 10
Red Blue Male 10
Red Blue Male 10
Red Blue Male 10
Red Blue Male 10
Red Blue Male 10
Red Blue Male 10
Red Blue Male 10
Red Blue Male 10
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Blond Blue Male 30
Black Hazel Male 10
Black Hazel Male 10
Black Hazel Male 10
Black Hazel Male 10
Black Hazel Male 10
Black Hazel Male 10
Black Hazel Male 10
Black Hazel Male 10
Black Hazel Male 10
Black Hazel Male 10
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Brown Hazel Male 25
Red Hazel Male 7
Red Hazel Male 7
Red Hazel Male 7
Red Hazel Male 7
Red Hazel Male 7
Red Hazel Male 7
Red Hazel Male 7
Blond Hazel Male 5
Blond Hazel Male 5
Blond Hazel Male 5
Blond Hazel Male 5
Blond Hazel Male 5
Black Green Male 3
Black Green Male 3
Black Green Male 3
Brown Green Male 15
Brown Green Male 15
Brown Green Male 15
Brown Green Male 15
Brown Green Male 15
Brown Green Male 15
Brown Green Male 15
Brown Green Male 15
Brown Green Male 15
Brown Green Male 15
Brown Green Male 15
Brown Green Male 15
Brown Green Male 15
Brown Green Male 15
Brown Green Male 15
Red Green Male 7
Red Green Male 7
Red Green Male 7
Red Green Male 7
Red Green Male 7
Red Green Male 7
Red Green Male 7
Blond Green Male 8
Blond Green Male 8
Blond Green Male 8
Blond Green Male 8
Blond Green Male 8
Blond Green Male 8
Blond Green Male 8
Blond Green Male 8
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Black Brown Female 36
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Brown Brown Female 66
Red Brown Female 16
Red Brown Female 16
Red Brown Female 16
Red Brown Female 16
Red Brown Female 16
Red Brown Female 16
Red Brown Female 16
Red Brown Female 16
Red Brown Female 16
Red Brown Female 16
Red Brown Female 16
Red Brown Female 16
Red Brown Female 16
Red Brown Female 16
Red Brown Female 16
Red Brown Female 16
Blond Brown Female 4
Blond Brown Female 4
Blond Brown Female 4
Blond Brown Female 4
Black Blue Female 9
Black Blue Female 9
Black Blue Female 9
Black Blue Female 9
Black Blue Female 9
Black Blue Female 9
Black Blue Female 9
Black Blue Female 9
Black Blue Female 9
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Brown Blue Female 34
Red Blue Female 7
Red Blue Female 7
Red Blue Female 7
Red Blue Female 7
Red Blue Female 7
Red Blue Female 7
Red Blue Female 7
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Blond Blue Female 64
Black Hazel Female 5
Black Hazel Female 5
Black Hazel Female 5
Black Hazel Female 5
Black Hazel Female 5
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Brown Hazel Female 29
Red Hazel Female 7
Red Hazel Female 7
Red Hazel Female 7
Red Hazel Female 7
Red Hazel Female 7
Red Hazel Female 7
Red Hazel Female 7
Blond Hazel Female 5
Blond Hazel Female 5
Blond Hazel Female 5
Blond Hazel Female 5
Blond Hazel Female 5
Black Green Female 2
Black Green Female 2
Brown Green Female 14
Brown Green Female 14
Brown Green Female 14
Brown Green Female 14
Brown Green Female 14
Brown Green Female 14
Brown Green Female 14
Brown Green Female 14
Brown Green Female 14
Brown Green Female 14
Brown Green Female 14
Brown Green Female 14
Brown Green Female 14
Brown Green Female 14
Red Green Female 7
Red Green Female 7
Red Green Female 7
Red Green Female 7
Red Green Female 7
Red Green Female 7
Red Green Female 7
Blond Green Female 8
Blond Green Female 8
Blond Green Female 8
Blond Green Female 8
Blond Green Female 8
Blond Green Female 8
Blond Green Female 8
Blond Green Female 8 
# 重复观测型数据汇成数组
HEC_cases %>% 
  table()
## , , Sex = Male
## 
##        Eye
## Hair    Brown Blue Hazel Green
##   Black    32   11    10     3
##   Brown    53   50    25    15
##   Red      10   10     7     7
##   Blond     3   30     5     8
## 
## , , Sex = Female
## 
##        Eye
## Hair    Brown Blue Hazel Green
##   Black    36    9     5     2
##   Brown    66   34    29    14
##   Red      16    7     7     7
##   Blond     4   64     5     8