data1<-flights %>%
group_by(origin)%>%
summarise(n=n(),dly=mean(dep_delay,na.rm = T))
data1# A tibble: 3 × 3
origin n dly
<chr> <int> <dbl>
1 EWR 120835 15.1
2 JFK 111279 12.1
3 LGA 104662 10.3tidyverse初认识
1 第一题 编写代码
利用nycflights13包的flights数据集是2013年从纽约三大机场（JFK、LGA、EWR）起飞的所有航班的准点数据，共336776条记录。
计算纽约三大机场2013起飞航班数和平均延误时间（可使用group_by, summarise函数）
计算不同航空公司2013从纽约起飞航班数和平均延误时间
data2<-flights %>% group_by(carrier,dest)%>% summarise(n=n(),dly=mean(dep_delay,na.rm = T))`summarise()` has grouped output by 'carrier'. You can override using the `.groups` argument.data2# A tibble: 314 × 4 # Groups: carrier [16] carrier dest n dly <chr> <chr> <int> <dbl> 1 9E ATL 59 0.965 2 9E AUS 2 19 3 9E AVL 10 -2.6 4 9E BGR 1 34 5 9E BNA 474 19.1 6 9E BOS 914 14.8 7 9E BTV 2 -4.5 8 9E BUF 833 15.5 9 9E BWI 856 17.5 10 9E CAE 3 -3.67 # ℹ 304 more rows计算纽约三大机场排名前三个目的地和平均飞行距离(可使用group_by, summarise, arrange, slice_max函数)
library(dplyr)#用于arrange函数 data3<-flights %>% group_by(origin,dest)%>% summarise(n=n(),dis=mean(distance,na.rm = T)) %>% slice_max(n,n=3) %>% arrange(desc(n))`summarise()` has grouped output by 'origin'. You can override using the `.groups` argument.data3# A tibble: 9 × 4 # Groups: origin [3] origin dest n dis <chr> <chr> <int> <dbl> 1 JFK LAX 11262 2475 2 LGA ATL 10263 762 3 LGA ORD 8857 733 4 JFK SFO 8204 2586 5 LGA CLT 6168 544 6 EWR ORD 6100 719 7 JFK BOS 5898 187 8 EWR BOS 5327 200 9 EWR SFO 5127 2565
2 第二题 解释代码
代码含义：对数据框iris中的Species变量进行排序，arrange是排序函数，starts_with是表示以Sepal开始排序，desc表示是降序，%>%是管道操作符，用于将前一个操作的结果传递给下一个操作。它使得代码更易读和简洁
arrange(): 用于对数据框的行进行排序。Species: 首先按Species列（鸢尾花的种类）进行升序排序。Species有三个类别：setosa、versicolor和virginica。across(starts_with("Sepal")):starts_with("Sepal")选择所有以"Sepal"开头的列（即Sepal.Length和Sepal.Width）。across()用于对多列应用相同的操作。
desc(): 表示降序排序。这里对Sepal.Length和Sepal.Width列进行降序排序。tibble(iris) %>% arrange(Species,across(starts_with("Sepal"), desc))# A tibble: 150 × 5 Sepal.Length Sepal.Width Petal.Length Petal.Width Species <dbl> <dbl> <dbl> <dbl> <fct> 1 5.8 4 1.2 0.2 setosa 2 5.7 4.4 1.5 0.4 setosa 3 5.7 3.8 1.7 0.3 setosa 4 5.5 4.2 1.4 0.2 setosa 5 5.5 3.5 1.3 0.2 setosa 6 5.4 3.9 1.7 0.4 setosa 7 5.4 3.9 1.3 0.4 setosa 8 5.4 3.7 1.5 0.2 setosa 9 5.4 3.4 1.7 0.2 setosa 10 5.4 3.4 1.5 0.4 setosa # ℹ 140 more rows代码含义：
starwars: 这是dplyr包自带的一个数据集，包含了《星球大战》系列电影中的角色信息，如姓名、性别、身高等。group_by(gender): 这一步将数据按gender（性别）分组。也就是说，数据会按照不同的性别（如男性、女性等）分成不同的组。filter(mass > mean(mass, na.rm = TRUE)): 这一步对每个性别组进行过滤，保留那些mass（体重）大于该组平均体重的行。na.rm = TRUE表示在计算平均值时忽略缺失值（NA）starwars %>% group_by(gender) %>% filter(mass > mean(mass, na.rm = TRUE))# A tibble: 15 × 14 # Groups: gender [3] name height mass hair_color skin_color eye_color birth_year sex gender <chr> <int> <dbl> <chr> <chr> <chr> <dbl> <chr> <chr> 1 Darth … 202 136 none white yellow 41.9 male mascu… 2 Owen L… 178 120 brown, gr… light blue 52 male mascu… 3 Beru W… 165 75 brown light blue 47 fema… femin… 4 Chewba… 228 112 brown unknown blue 200 male mascu… 5 Jabba … 175 1358 <NA> green-tan… orange 600 herm… mascu… 6 Jek To… 180 110 brown fair blue NA <NA> <NA> 7 IG-88 200 140 none metal red 15 none mascu… 8 Bossk 190 113 none green red 53 male mascu… 9 Ayla S… 178 55 none blue hazel 48 fema… femin… 10 Gregar… 185 85 black dark brown NA <NA> <NA> 11 Lumina… 170 56.2 black yellow blue 58 fema… femin… 12 Zam We… 168 55 blonde fair, gre… yellow NA fema… femin… 13 Shaak … 178 57 none red, blue… black NA fema… femin… 14 Grievo… 216 159 none brown, wh… green, y… NA male mascu… 15 Tarfful 234 136 brown brown blue NA male mascu… # ℹ 5 more variables: homeworld <chr>, species <chr>, films <list>, # vehicles <list>, starships <list>代码含义：
select(name, homeworld, species)select()函数用于从数据集中选择指定的列。这里选择了
name（角色名称）、homeworld（母星）和species（物种）三列。
mutate(across(!name, as.factor))mutate()函数用于创建或修改数据集的列。across()是dplyr中的一个函数，用于对多列应用相同的操作。!name表示对除name列之外的所有列进行操作。as.factor()函数将指定的列转换为因子（factor）类型，通常用于分类数据。
starwars %>% select(name, homeworld, species) %>% mutate(across(!name, as.factor))# A tibble: 87 × 3 name homeworld species <chr> <fct> <fct> 1 Luke Skywalker Tatooine Human 2 C-3PO Tatooine Droid 3 R2-D2 Naboo Droid 4 Darth Vader Tatooine Human 5 Leia Organa Alderaan Human 6 Owen Lars Tatooine Human 7 Beru Whitesun Lars Tatooine Human 8 R5-D4 Tatooine Droid 9 Biggs Darklighter Tatooine Human 10 Obi-Wan Kenobi Stewjon Human # ℹ 77 more rows代码含义：
tibble(mtcars)将mtcars数据集转换为tibble格式。tibble是dplyr中一种更现代的、增强版的数据框，打印时更友好。vs是mtcars数据集中的一列，表示发动机类型（0表示V型发动机，1表示直列发动机）。这一步将数据按vs列分组，分成两组：vs = 0和vs = 1mutate(hp_cut = cut(hp, 3))
对hp列（马力）进行分箱操作，将其分为 3 个区间，并将结果存储在新列hp_cut中。cut()函数将连续变量转换为因子变量，默认按等宽区间划分。group_by(hp_cut)
再次按hp_cut列对数据进行分组。此时，数据会先按vs分组，再在每组内按hp_cut分组。tibble(mtcars) %>% group_by(vs) %>% mutate(hp_cut = cut(hp, 3)) %>% group_by(hp_cut)# A tibble: 32 × 12 # Groups: hp_cut [6] mpg cyl disp hp drat wt qsec vs am gear carb hp_cut <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <fct> 1 21 6 160 110 3.9 2.62 16.5 0 1 4 4 (90.8,172] 2 21 6 160 110 3.9 2.88 17.0 0 1 4 4 (90.8,172] 3 22.8 4 108 93 3.85 2.32 18.6 1 1 4 1 (75.7,99.3] 4 21.4 6 258 110 3.08 3.22 19.4 1 0 3 1 (99.3,123] 5 18.7 8 360 175 3.15 3.44 17.0 0 0 3 2 (172,254] 6 18.1 6 225 105 2.76 3.46 20.2 1 0 3 1 (99.3,123] 7 14.3 8 360 245 3.21 3.57 15.8 0 0 3 4 (172,254] 8 24.4 4 147. 62 3.69 3.19 20 1 0 4 2 (51.9,75.7] 9 22.8 4 141. 95 3.92 3.15 22.9 1 0 4 2 (75.7,99.3] 10 19.2 6 168. 123 3.92 3.44 18.3 1 0 4 4 (99.3,123] # ℹ 22 more rows
3 第三题 查找帮助理解函数
阅读 https://dplyr.tidyverse.org/reference/mutate-joins.html 内容，说明4个数据集链接函数函数的作用。分别举一个实际例子演示并解释其输出结果。
inner_join()：是dplyr包中的一个函数，用于将两个数据框（或表格）按照指定的列进行内连接。内连接的特点是只保留两个数据框中匹配的行，不匹配的行会被丢弃。#library(dplyr) df1 <- tibble(id = c(1, 2, 3), name = c("Alice", "Bob", "Charlie")) df2 <- tibble(id = c(2, 3, 4), score = c(90, 85, 88)) result <- inner_join(df1, df2, by = "id") print(result)# A tibble: 2 × 3 id name score <dbl> <chr> <dbl> 1 2 Bob 90 2 3 Charlie 85匹配的行：
df1和df2中id为 2 和 3 的行匹配成功。丢弃的行：
df1中id为 1 的行和df2中id为 4 的行被丢弃。合并的列：
name和score列被合并到结果中。
left_join()：用于将两个数据框（或表格）按照指定的列进行左连接。左连接的特点是保留左表（第一个表）的所有行，并将右表（第二个表）中匹配的行合并到左表中。如果右表中没有匹配的行，则用NA填充。df1 <- tibble(id = c(1, 2, 3), name = c("Alice", "Bob", "Charlie")) df2 <- tibble(id = c(2, 3, 4), score = c(90, 85, 88)) result <- left_join(df1, df2, by = "id") print(result)# A tibble: 3 × 3 id name score <dbl> <chr> <dbl> 1 1 Alice NA 2 2 Bob 90 3 3 Charlie 85保留左表的所有行：
df1中id为 1、2、3 的行都被保留。合并匹配的数据：
df2中id为 2 和 3 的行被合并到df1中。填充缺失值：
df1中id为 1 的行在df2中没有匹配的行，因此score列用NA填充。right_join()：用于将两个数据框（或表格）按照指定的列进行右连接。右连接的特点是保留右表（第二个表）的所有行，并将左表（第一个表）中匹配的行合并到右表中。如果左表中没有匹配的行，则用NA填充。df1 <- tibble(id = c(1, 2, 3), name = c("Alice", "Bob", "Charlie")) df2 <- tibble(id = c(2, 3, 4), score = c(90, 85, 88)) result <- right_join(df1, df2, by = "id") print(result)# A tibble: 3 × 3 id name score <dbl> <chr> <dbl> 1 2 Bob 90 2 3 Charlie 85 3 4 <NA> 88保留右表的所有行：
df2中id为 2、3、4 的行都被保留。合并匹配的数据：
df1中id为 2 和 3 的行被合并到df2中。填充缺失值：
df2中id为 4 的行在df1中没有匹配的行，因此name列用NA填充。
full_join()：用于将两个数据框（或表格）按照指定的列进行全连接。全连接的特点是保留两个表中的所有行，无论是否有匹配的行。如果某一行在其中一个表中没有匹配的行，则用NA填充。df1 <- tibble(id = c(1, 2, 3), name = c("Alice", "Bob", "Charlie")) df2 <- tibble(id = c(2, 3, 4), score = c(90, 85, 88)) result <- full_join(df1, df2, by = "id") print(result)# A tibble: 4 × 3 id name score <dbl> <chr> <dbl> 1 1 Alice NA 2 2 Bob 90 3 3 Charlie 85 4 4 <NA> 88保留两个表的所有行：
df1中id为 1、2、3 的行和df2中id为 2、3、4 的行都被保留。合并匹配的数据：
df1中id为 2 和 3 的行与df2中对应的行被合并。填充缺失值：
df1中id为 1 的行在df2中没有匹配的行，因此score列用NA填充；df2中id为 4 的行在df1中没有匹配的行，因此name列用NA填充。4 第四题 课堂练习1
##作业1 #install.packages("nycflights13") library(nycflights13) nycflights13::flights# A tibble: 336,776 × 19 year month day dep_time sched_dep_time dep_delay arr_time sched_arr_time <int> <int> <int> <int> <int> <dbl> <int> <int> 1 2013 1 1 517 515 2 830 819 2 2013 1 1 533 529 4 850 830 3 2013 1 1 542 540 2 923 850 4 2013 1 1 544 545 -1 1004 1022 5 2013 1 1 554 600 -6 812 837 6 2013 1 1 554 558 -4 740 728 7 2013 1 1 555 600 -5 913 854 8 2013 1 1 557 600 -3 709 723 9 2013 1 1 557 600 -3 838 846 10 2013 1 1 558 600 -2 753 745 # ℹ 336,766 more rows # ℹ 11 more variables: arr_delay <dbl>, carrier <chr>, flight <int>, # tailnum <chr>, origin <chr>, dest <chr>, air_time <dbl>, distance <dbl>, # hour <dbl>, minute <dbl>, time_hour <dttm>##(1) library(tidyr) library(tidyverse)#使用%>%函数的包 data1<-flights %>% group_by(origin)%>% summarise(n=n(),dly=mean(dep_delay,na.rm = T)) data1# A tibble: 3 × 3 origin n dly <chr> <int> <dbl> 1 EWR 120835 15.1 2 JFK 111279 12.1 3 LGA 104662 10.3data2<-flights %>% group_by(carrier,dest)%>% summarise(n=n(),dly=mean(dep_delay,na.rm = T))`summarise()` has grouped output by 'carrier'. You can override using the `.groups` argument.data2# A tibble: 314 × 4 # Groups: carrier [16] carrier dest n dly <chr> <chr> <int> <dbl> 1 9E ATL 59 0.965 2 9E AUS 2 19 3 9E AVL 10 -2.6 4 9E BGR 1 34 5 9E BNA 474 19.1 6 9E BOS 914 14.8 7 9E BTV 2 -4.5 8 9E BUF 833 15.5 9 9E BWI 856 17.5 10 9E CAE 3 -3.67 # ℹ 304 more rowslibrary(dplyr)#用于arrange函数 data3<-flights %>% group_by(origin,dest)%>% summarise(n=n(),dis=mean(distance,na.rm = T)) %>% slice_max(n,n=3) %>% arrange(desc(n))`summarise()` has grouped output by 'origin'. You can override using the `.groups` argument.data3# A tibble: 9 × 4 # Groups: origin [3] origin dest n dis <chr> <chr> <int> <dbl> 1 JFK LAX 11262 2475 2 LGA ATL 10263 762 3 LGA ORD 8857 733 4 JFK SFO 8204 2586 5 LGA CLT 6168 544 6 EWR ORD 6100 719 7 JFK BOS 5898 187 8 EWR BOS 5327 200 9 EWR SFO 5127 2565
##作业2课本31页习题
#1.5
#(1)
datasets::Titanic, , Age = Child, Survived = No
Sex
Class Male Female
1st 0 0
2nd 0 0
3rd 35 17
Crew 0 0
, , Age = Adult, Survived = No
Sex
Class Male Female
1st 118 4
2nd 154 13
3rd 387 89
Crew 670 3
, , Age = Child, Survived = Yes
Sex
Class Male Female
1st 5 1
2nd 11 13
3rd 13 14
Crew 0 0
, , Age = Adult, Survived = Yes
Sex
Class Male Female
1st 57 140
2nd 14 80
3rd 75 76
Crew 192 20data4<-data.frame(Titanic)
data4 Class Sex Age Survived Freq
1 1st Male Child No 0
2 2nd Male Child No 0
3 3rd Male Child No 35
4 Crew Male Child No 0
5 1st Female Child No 0
6 2nd Female Child No 0
7 3rd Female Child No 17
8 Crew Female Child No 0
9 1st Male Adult No 118
10 2nd Male Adult No 154
11 3rd Male Adult No 387
12 Crew Male Adult No 670
13 1st Female Adult No 4
14 2nd Female Adult No 13
15 3rd Female Adult No 89
16 Crew Female Adult No 3
17 1st Male Child Yes 5
18 2nd Male Child Yes 11
19 3rd Male Child Yes 13
20 Crew Male Child Yes 0
21 1st Female Child Yes 1
22 2nd Female Child Yes 13
23 3rd Female Child Yes 14
24 Crew Female Child Yes 0
25 1st Male Adult Yes 57
26 2nd Male Adult Yes 14
27 3rd Male Adult Yes 75
28 Crew Male Adult Yes 192
29 1st Female Adult Yes 140
30 2nd Female Adult Yes 80
31 3rd Female Adult Yes 76
32 Crew Female Adult Yes 20table1<-table(data4$Sex,data4$Survived)
addmargins(table1)#添加边际和
No Yes Sum
Male 8 8 16
Female 8 8 16
Sum 16 16 32table1
No Yes
Male 8 8
Female 8 8#(2)
#install.packages("vcd")
library(vcd)Warning: package 'vcd' was built under R version 4.3.3Loading required package: gridtable2<-structable(data4,direction = c("h","h","h","v"))#h代表行，v代表列
table2 Survived No Yes
Class Sex Age Freq
1st Male Child 0 1 0
1 0 0
3 0 0
4 0 0
5 0 1
11 0 0
13 0 0
14 0 0
17 0 0
20 0 0
35 0 0
57 0 0
75 0 0
76 0 0
80 0 0
89 0 0
118 0 0
140 0 0
154 0 0
192 0 0
387 0 0
670 0 0
Adult 0 0 0
1 0 0
3 0 0
4 0 0
5 0 0
11 0 0
13 0 0
14 0 0
17 0 0
20 0 0
35 0 0
57 0 1
75 0 0
76 0 0
80 0 0
89 0 0
118 1 0
140 0 0
154 0 0
192 0 0
387 0 0
670 0 0
Female Child 0 1 0
1 0 1
3 0 0
4 0 0
5 0 0
11 0 0
13 0 0
14 0 0
17 0 0
20 0 0
35 0 0
57 0 0
75 0 0
76 0 0
80 0 0
89 0 0
118 0 0
140 0 0
154 0 0
192 0 0
387 0 0
670 0 0
Adult 0 0 0
1 0 0
3 0 0
4 1 0
5 0 0
11 0 0
13 0 0
14 0 0
17 0 0
20 0 0
35 0 0
57 0 0
75 0 0
76 0 0
80 0 0
89 0 0
118 0 0
140 0 1
154 0 0
192 0 0
387 0 0
670 0 0
2nd Male Child 0 1 0
1 0 0
3 0 0
4 0 0
5 0 0
11 0 1
13 0 0
14 0 0
17 0 0
20 0 0
35 0 0
57 0 0
75 0 0
76 0 0
80 0 0
89 0 0
118 0 0
140 0 0
154 0 0
192 0 0
387 0 0
670 0 0
Adult 0 0 0
1 0 0
3 0 0
4 0 0
5 0 0
11 0 0
13 0 0
14 0 1
17 0 0
20 0 0
35 0 0
57 0 0
75 0 0
76 0 0
80 0 0
89 0 0
118 0 0
140 0 0
154 1 0
192 0 0
387 0 0
670 0 0
Female Child 0 1 0
1 0 0
3 0 0
4 0 0
5 0 0
11 0 0
13 0 1
14 0 0
17 0 0
20 0 0
35 0 0
57 0 0
75 0 0
76 0 0
80 0 0
89 0 0
118 0 0
140 0 0
154 0 0
192 0 0
387 0 0
670 0 0
Adult 0 0 0
1 0 0
3 0 0
4 0 0
5 0 0
11 0 0
13 1 0
14 0 0
17 0 0
20 0 0
35 0 0
57 0 0
75 0 0
76 0 0
80 0 1
89 0 0
118 0 0
140 0 0
154 0 0
192 0 0
387 0 0
670 0 0
3rd Male Child 0 0 0
1 0 0
3 0 0
4 0 0
5 0 0
11 0 0
13 0 1
14 0 0
17 0 0
20 0 0
35 1 0
57 0 0
75 0 0
76 0 0
80 0 0
89 0 0
118 0 0
140 0 0
154 0 0
192 0 0
387 0 0
670 0 0
Adult 0 0 0
1 0 0
3 0 0
4 0 0
5 0 0
11 0 0
13 0 0
14 0 0
17 0 0
20 0 0
35 0 0
57 0 0
75 0 1
76 0 0
80 0 0
89 0 0
118 0 0
140 0 0
154 0 0
192 0 0
387 1 0
670 0 0
Female Child 0 0 0
1 0 0
3 0 0
4 0 0
5 0 0
11 0 0
13 0 0
14 0 1
17 1 0
20 0 0
35 0 0
57 0 0
75 0 0
76 0 0
80 0 0
89 0 0
118 0 0
140 0 0
154 0 0
192 0 0
387 0 0
670 0 0
Adult 0 0 0
1 0 0
3 0 0
4 0 0
5 0 0
11 0 0
13 0 0
14 0 0
17 0 0
20 0 0
35 0 0
57 0 0
75 0 0
76 0 1
80 0 0
89 1 0
118 0 0
140 0 0
154 0 0
192 0 0
387 0 0
670 0 0
Crew Male Child 0 1 1
1 0 0
3 0 0
4 0 0
5 0 0
11 0 0
13 0 0
14 0 0
17 0 0
20 0 0
35 0 0
57 0 0
75 0 0
76 0 0
80 0 0
89 0 0
118 0 0
140 0 0
154 0 0
192 0 0
387 0 0
670 0 0
Adult 0 0 0
1 0 0
3 0 0
4 0 0
5 0 0
11 0 0
13 0 0
14 0 0
17 0 0
20 0 0
35 0 0
57 0 0
75 0 0
76 0 0
80 0 0
89 0 0
118 0 0
140 0 0
154 0 0
192 0 1
387 0 0
670 1 0
Female Child 0 1 1
1 0 0
3 0 0
4 0 0
5 0 0
11 0 0
13 0 0
14 0 0
17 0 0
20 0 0
35 0 0
57 0 0
75 0 0
76 0 0
80 0 0
89 0 0
118 0 0
140 0 0
154 0 0
192 0 0
387 0 0
670 0 0
Adult 0 0 0
1 0 0
3 1 0
4 0 0
5 0 0
11 0 0
13 0 0
14 0 0
17 0 0
20 0 1
35 0 0
57 0 0
75 0 0
76 0 0
80 0 0
89 0 0
118 0 0
140 0 0
154 0 0
192 0 0
387 0 0
670 0 0#(3)
df<-as.data.frame(table2)
df Class Sex Age Survived Freq Freq.1
1 1st Male Child No 0 1
2 2nd Male Child No 0 1
3 3rd Male Child No 0 0
4 Crew Male Child No 0 1
5 1st Female Child No 0 1
6 2nd Female Child No 0 1
7 3rd Female Child No 0 0
8 Crew Female Child No 0 1
9 1st Male Adult No 0 0
10 2nd Male Adult No 0 0
11 3rd Male Adult No 0 0
12 Crew Male Adult No 0 0
13 1st Female Adult No 0 0
14 2nd Female Adult No 0 0
15 3rd Female Adult No 0 0
16 Crew Female Adult No 0 0
17 1st Male Child Yes 0 0
18 2nd Male Child Yes 0 0
19 3rd Male Child Yes 0 0
20 Crew Male Child Yes 0 1
21 1st Female Child Yes 0 0
22 2nd Female Child Yes 0 0
23 3rd Female Child Yes 0 0
24 Crew Female Child Yes 0 1
25 1st Male Adult Yes 0 0
26 2nd Male Adult Yes 0 0
27 3rd Male Adult Yes 0 0
28 Crew Male Adult Yes 0 0
29 1st Female Adult Yes 0 0
30 2nd Female Adult Yes 0 0
31 3rd Female Adult Yes 0 0
32 Crew Female Adult Yes 0 0
33 1st Male Child No 1 0
34 2nd Male Child No 1 0
35 3rd Male Child No 1 0
36 Crew Male Child No 1 0
37 1st Female Child No 1 0
38 2nd Female Child No 1 0
39 3rd Female Child No 1 0
40 Crew Female Child No 1 0
41 1st Male Adult No 1 0
42 2nd Male Adult No 1 0
43 3rd Male Adult No 1 0
44 Crew Male Adult No 1 0
45 1st Female Adult No 1 0
46 2nd Female Adult No 1 0
47 3rd Female Adult No 1 0
48 Crew Female Adult No 1 0
49 1st Male Child Yes 1 0
50 2nd Male Child Yes 1 0
51 3rd Male Child Yes 1 0
52 Crew Male Child Yes 1 0
53 1st Female Child Yes 1 1
54 2nd Female Child Yes 1 0
55 3rd Female Child Yes 1 0
56 Crew Female Child Yes 1 0
57 1st Male Adult Yes 1 0
58 2nd Male Adult Yes 1 0
59 3rd Male Adult Yes 1 0
60 Crew Male Adult Yes 1 0
61 1st Female Adult Yes 1 0
62 2nd Female Adult Yes 1 0
63 3rd Female Adult Yes 1 0
64 Crew Female Adult Yes 1 0
65 1st Male Child No 3 0
66 2nd Male Child No 3 0
67 3rd Male Child No 3 0
68 Crew Male Child No 3 0
69 1st Female Child No 3 0
70 2nd Female Child No 3 0
71 3rd Female Child No 3 0
72 Crew Female Child No 3 0
73 1st Male Adult No 3 0
74 2nd Male Adult No 3 0
75 3rd Male Adult No 3 0
76 Crew Male Adult No 3 0
77 1st Female Adult No 3 0
78 2nd Female Adult No 3 0
79 3rd Female Adult No 3 0
80 Crew Female Adult No 3 1
81 1st Male Child Yes 3 0
82 2nd Male Child Yes 3 0
83 3rd Male Child Yes 3 0
84 Crew Male Child Yes 3 0
85 1st Female Child Yes 3 0
86 2nd Female Child Yes 3 0
87 3rd Female Child Yes 3 0
88 Crew Female Child Yes 3 0
89 1st Male Adult Yes 3 0
90 2nd Male Adult Yes 3 0
91 3rd Male Adult Yes 3 0
92 Crew Male Adult Yes 3 0
93 1st Female Adult Yes 3 0
94 2nd Female Adult Yes 3 0
95 3rd Female Adult Yes 3 0
96 Crew Female Adult Yes 3 0
97 1st Male Child No 4 0
98 2nd Male Child No 4 0
99 3rd Male Child No 4 0
100 Crew Male Child No 4 0
101 1st Female Child No 4 0
102 2nd Female Child No 4 0
103 3rd Female Child No 4 0
104 Crew Female Child No 4 0
105 1st Male Adult No 4 0
106 2nd Male Adult No 4 0
107 3rd Male Adult No 4 0
108 Crew Male Adult No 4 0
109 1st Female Adult No 4 1
110 2nd Female Adult No 4 0
111 3rd Female Adult No 4 0
112 Crew Female Adult No 4 0
113 1st Male Child Yes 4 0
114 2nd Male Child Yes 4 0
115 3rd Male Child Yes 4 0
116 Crew Male Child Yes 4 0
117 1st Female Child Yes 4 0
118 2nd Female Child Yes 4 0
119 3rd Female Child Yes 4 0
120 Crew Female Child Yes 4 0
121 1st Male Adult Yes 4 0
122 2nd Male Adult Yes 4 0
123 3rd Male Adult Yes 4 0
124 Crew Male Adult Yes 4 0
125 1st Female Adult Yes 4 0
126 2nd Female Adult Yes 4 0
127 3rd Female Adult Yes 4 0
128 Crew Female Adult Yes 4 0
129 1st Male Child No 5 0
130 2nd Male Child No 5 0
131 3rd Male Child No 5 0
132 Crew Male Child No 5 0
133 1st Female Child No 5 0
134 2nd Female Child No 5 0
135 3rd Female Child No 5 0
136 Crew Female Child No 5 0
137 1st Male Adult No 5 0
138 2nd Male Adult No 5 0
139 3rd Male Adult No 5 0
140 Crew Male Adult No 5 0
141 1st Female Adult No 5 0
142 2nd Female Adult No 5 0
143 3rd Female Adult No 5 0
144 Crew Female Adult No 5 0
145 1st Male Child Yes 5 1
146 2nd Male Child Yes 5 0
147 3rd Male Child Yes 5 0
148 Crew Male Child Yes 5 0
149 1st Female Child Yes 5 0
150 2nd Female Child Yes 5 0
151 3rd Female Child Yes 5 0
152 Crew Female Child Yes 5 0
153 1st Male Adult Yes 5 0
154 2nd Male Adult Yes 5 0
155 3rd Male Adult Yes 5 0
156 Crew Male Adult Yes 5 0
157 1st Female Adult Yes 5 0
158 2nd Female Adult Yes 5 0
159 3rd Female Adult Yes 5 0
160 Crew Female Adult Yes 5 0
161 1st Male Child No 11 0
162 2nd Male Child No 11 0
163 3rd Male Child No 11 0
164 Crew Male Child No 11 0
165 1st Female Child No 11 0
166 2nd Female Child No 11 0
167 3rd Female Child No 11 0
168 Crew Female Child No 11 0
169 1st Male Adult No 11 0
170 2nd Male Adult No 11 0
171 3rd Male Adult No 11 0
172 Crew Male Adult No 11 0
173 1st Female Adult No 11 0
174 2nd Female Adult No 11 0
175 3rd Female Adult No 11 0
176 Crew Female Adult No 11 0
177 1st Male Child Yes 11 0
178 2nd Male Child Yes 11 1
179 3rd Male Child Yes 11 0
180 Crew Male Child Yes 11 0
181 1st Female Child Yes 11 0
182 2nd Female Child Yes 11 0
183 3rd Female Child Yes 11 0
184 Crew Female Child Yes 11 0
185 1st Male Adult Yes 11 0
186 2nd Male Adult Yes 11 0
187 3rd Male Adult Yes 11 0
188 Crew Male Adult Yes 11 0
189 1st Female Adult Yes 11 0
190 2nd Female Adult Yes 11 0
191 3rd Female Adult Yes 11 0
192 Crew Female Adult Yes 11 0
193 1st Male Child No 13 0
194 2nd Male Child No 13 0
195 3rd Male Child No 13 0
196 Crew Male Child No 13 0
197 1st Female Child No 13 0
198 2nd Female Child No 13 0
199 3rd Female Child No 13 0
200 Crew Female Child No 13 0
201 1st Male Adult No 13 0
202 2nd Male Adult No 13 0
203 3rd Male Adult No 13 0
204 Crew Male Adult No 13 0
205 1st Female Adult No 13 0
206 2nd Female Adult No 13 1
207 3rd Female Adult No 13 0
208 Crew Female Adult No 13 0
209 1st Male Child Yes 13 0
210 2nd Male Child Yes 13 0
211 3rd Male Child Yes 13 1
212 Crew Male Child Yes 13 0
213 1st Female Child Yes 13 0
214 2nd Female Child Yes 13 1
215 3rd Female Child Yes 13 0
216 Crew Female Child Yes 13 0
217 1st Male Adult Yes 13 0
218 2nd Male Adult Yes 13 0
219 3rd Male Adult Yes 13 0
220 Crew Male Adult Yes 13 0
221 1st Female Adult Yes 13 0
222 2nd Female Adult Yes 13 0
223 3rd Female Adult Yes 13 0
224 Crew Female Adult Yes 13 0
225 1st Male Child No 14 0
226 2nd Male Child No 14 0
227 3rd Male Child No 14 0
228 Crew Male Child No 14 0
229 1st Female Child No 14 0
230 2nd Female Child No 14 0
231 3rd Female Child No 14 0
232 Crew Female Child No 14 0
233 1st Male Adult No 14 0
234 2nd Male Adult No 14 0
235 3rd Male Adult No 14 0
236 Crew Male Adult No 14 0
237 1st Female Adult No 14 0
238 2nd Female Adult No 14 0
239 3rd Female Adult No 14 0
240 Crew Female Adult No 14 0
241 1st Male Child Yes 14 0
242 2nd Male Child Yes 14 0
243 3rd Male Child Yes 14 0
244 Crew Male Child Yes 14 0
245 1st Female Child Yes 14 0
246 2nd Female Child Yes 14 0
247 3rd Female Child Yes 14 1
248 Crew Female Child Yes 14 0
249 1st Male Adult Yes 14 0
250 2nd Male Adult Yes 14 1
251 3rd Male Adult Yes 14 0
252 Crew Male Adult Yes 14 0
253 1st Female Adult Yes 14 0
254 2nd Female Adult Yes 14 0
255 3rd Female Adult Yes 14 0
256 Crew Female Adult Yes 14 0
257 1st Male Child No 17 0
258 2nd Male Child No 17 0
259 3rd Male Child No 17 0
260 Crew Male Child No 17 0
261 1st Female Child No 17 0
262 2nd Female Child No 17 0
263 3rd Female Child No 17 1
264 Crew Female Child No 17 0
265 1st Male Adult No 17 0
266 2nd Male Adult No 17 0
267 3rd Male Adult No 17 0
268 Crew Male Adult No 17 0
269 1st Female Adult No 17 0
270 2nd Female Adult No 17 0
271 3rd Female Adult No 17 0
272 Crew Female Adult No 17 0
273 1st Male Child Yes 17 0
274 2nd Male Child Yes 17 0
275 3rd Male Child Yes 17 0
276 Crew Male Child Yes 17 0
277 1st Female Child Yes 17 0
278 2nd Female Child Yes 17 0
279 3rd Female Child Yes 17 0
280 Crew Female Child Yes 17 0
281 1st Male Adult Yes 17 0
282 2nd Male Adult Yes 17 0
283 3rd Male Adult Yes 17 0
284 Crew Male Adult Yes 17 0
285 1st Female Adult Yes 17 0
286 2nd Female Adult Yes 17 0
287 3rd Female Adult Yes 17 0
288 Crew Female Adult Yes 17 0
289 1st Male Child No 20 0
290 2nd Male Child No 20 0
291 3rd Male Child No 20 0
292 Crew Male Child No 20 0
293 1st Female Child No 20 0
294 2nd Female Child No 20 0
295 3rd Female Child No 20 0
296 Crew Female Child No 20 0
297 1st Male Adult No 20 0
298 2nd Male Adult No 20 0
299 3rd Male Adult No 20 0
300 Crew Male Adult No 20 0
301 1st Female Adult No 20 0
302 2nd Female Adult No 20 0
303 3rd Female Adult No 20 0
304 Crew Female Adult No 20 0
305 1st Male Child Yes 20 0
306 2nd Male Child Yes 20 0
307 3rd Male Child Yes 20 0
308 Crew Male Child Yes 20 0
309 1st Female Child Yes 20 0
310 2nd Female Child Yes 20 0
311 3rd Female Child Yes 20 0
312 Crew Female Child Yes 20 0
313 1st Male Adult Yes 20 0
314 2nd Male Adult Yes 20 0
315 3rd Male Adult Yes 20 0
316 Crew Male Adult Yes 20 0
317 1st Female Adult Yes 20 0
318 2nd Female Adult Yes 20 0
319 3rd Female Adult Yes 20 0
320 Crew Female Adult Yes 20 1
321 1st Male Child No 35 0
322 2nd Male Child No 35 0
323 3rd Male Child No 35 1
324 Crew Male Child No 35 0
325 1st Female Child No 35 0
326 2nd Female Child No 35 0
327 3rd Female Child No 35 0
328 Crew Female Child No 35 0
329 1st Male Adult No 35 0
330 2nd Male Adult No 35 0
331 3rd Male Adult No 35 0
332 Crew Male Adult No 35 0
333 1st Female Adult No 35 0
334 2nd Female Adult No 35 0
335 3rd Female Adult No 35 0
336 Crew Female Adult No 35 0
337 1st Male Child Yes 35 0
338 2nd Male Child Yes 35 0
339 3rd Male Child Yes 35 0
340 Crew Male Child Yes 35 0
341 1st Female Child Yes 35 0
342 2nd Female Child Yes 35 0
343 3rd Female Child Yes 35 0
344 Crew Female Child Yes 35 0
345 1st Male Adult Yes 35 0
346 2nd Male Adult Yes 35 0
347 3rd Male Adult Yes 35 0
348 Crew Male Adult Yes 35 0
349 1st Female Adult Yes 35 0
350 2nd Female Adult Yes 35 0
351 3rd Female Adult Yes 35 0
352 Crew Female Adult Yes 35 0
353 1st Male Child No 57 0
354 2nd Male Child No 57 0
355 3rd Male Child No 57 0
356 Crew Male Child No 57 0
357 1st Female Child No 57 0
358 2nd Female Child No 57 0
359 3rd Female Child No 57 0
360 Crew Female Child No 57 0
361 1st Male Adult No 57 0
362 2nd Male Adult No 57 0
363 3rd Male Adult No 57 0
364 Crew Male Adult No 57 0
365 1st Female Adult No 57 0
366 2nd Female Adult No 57 0
367 3rd Female Adult No 57 0
368 Crew Female Adult No 57 0
369 1st Male Child Yes 57 0
370 2nd Male Child Yes 57 0
371 3rd Male Child Yes 57 0
372 Crew Male Child Yes 57 0
373 1st Female Child Yes 57 0
374 2nd Female Child Yes 57 0
375 3rd Female Child Yes 57 0
376 Crew Female Child Yes 57 0
377 1st Male Adult Yes 57 1
378 2nd Male Adult Yes 57 0
379 3rd Male Adult Yes 57 0
380 Crew Male Adult Yes 57 0
381 1st Female Adult Yes 57 0
382 2nd Female Adult Yes 57 0
383 3rd Female Adult Yes 57 0
384 Crew Female Adult Yes 57 0
385 1st Male Child No 75 0
386 2nd Male Child No 75 0
387 3rd Male Child No 75 0
388 Crew Male Child No 75 0
389 1st Female Child No 75 0
390 2nd Female Child No 75 0
391 3rd Female Child No 75 0
392 Crew Female Child No 75 0
393 1st Male Adult No 75 0
394 2nd Male Adult No 75 0
395 3rd Male Adult No 75 0
396 Crew Male Adult No 75 0
397 1st Female Adult No 75 0
398 2nd Female Adult No 75 0
399 3rd Female Adult No 75 0
400 Crew Female Adult No 75 0
401 1st Male Child Yes 75 0
402 2nd Male Child Yes 75 0
403 3rd Male Child Yes 75 0
404 Crew Male Child Yes 75 0
405 1st Female Child Yes 75 0
406 2nd Female Child Yes 75 0
407 3rd Female Child Yes 75 0
408 Crew Female Child Yes 75 0
409 1st Male Adult Yes 75 0
410 2nd Male Adult Yes 75 0
411 3rd Male Adult Yes 75 1
412 Crew Male Adult Yes 75 0
413 1st Female Adult Yes 75 0
414 2nd Female Adult Yes 75 0
415 3rd Female Adult Yes 75 0
416 Crew Female Adult Yes 75 0
417 1st Male Child No 76 0
418 2nd Male Child No 76 0
419 3rd Male Child No 76 0
420 Crew Male Child No 76 0
421 1st Female Child No 76 0
422 2nd Female Child No 76 0
423 3rd Female Child No 76 0
424 Crew Female Child No 76 0
425 1st Male Adult No 76 0
426 2nd Male Adult No 76 0
427 3rd Male Adult No 76 0
428 Crew Male Adult No 76 0
429 1st Female Adult No 76 0
430 2nd Female Adult No 76 0
431 3rd Female Adult No 76 0
432 Crew Female Adult No 76 0
433 1st Male Child Yes 76 0
434 2nd Male Child Yes 76 0
435 3rd Male Child Yes 76 0
436 Crew Male Child Yes 76 0
437 1st Female Child Yes 76 0
438 2nd Female Child Yes 76 0
439 3rd Female Child Yes 76 0
440 Crew Female Child Yes 76 0
441 1st Male Adult Yes 76 0
442 2nd Male Adult Yes 76 0
443 3rd Male Adult Yes 76 0
444 Crew Male Adult Yes 76 0
445 1st Female Adult Yes 76 0
446 2nd Female Adult Yes 76 0
447 3rd Female Adult Yes 76 1
448 Crew Female Adult Yes 76 0
449 1st Male Child No 80 0
450 2nd Male Child No 80 0
451 3rd Male Child No 80 0
452 Crew Male Child No 80 0
453 1st Female Child No 80 0
454 2nd Female Child No 80 0
455 3rd Female Child No 80 0
456 Crew Female Child No 80 0
457 1st Male Adult No 80 0
458 2nd Male Adult No 80 0
459 3rd Male Adult No 80 0
460 Crew Male Adult No 80 0
461 1st Female Adult No 80 0
462 2nd Female Adult No 80 0
463 3rd Female Adult No 80 0
464 Crew Female Adult No 80 0
465 1st Male Child Yes 80 0
466 2nd Male Child Yes 80 0
467 3rd Male Child Yes 80 0
468 Crew Male Child Yes 80 0
469 1st Female Child Yes 80 0
470 2nd Female Child Yes 80 0
471 3rd Female Child Yes 80 0
472 Crew Female Child Yes 80 0
473 1st Male Adult Yes 80 0
474 2nd Male Adult Yes 80 0
475 3rd Male Adult Yes 80 0
476 Crew Male Adult Yes 80 0
477 1st Female Adult Yes 80 0
478 2nd Female Adult Yes 80 1
479 3rd Female Adult Yes 80 0
480 Crew Female Adult Yes 80 0
481 1st Male Child No 89 0
482 2nd Male Child No 89 0
483 3rd Male Child No 89 0
484 Crew Male Child No 89 0
485 1st Female Child No 89 0
486 2nd Female Child No 89 0
487 3rd Female Child No 89 0
488 Crew Female Child No 89 0
489 1st Male Adult No 89 0
490 2nd Male Adult No 89 0
491 3rd Male Adult No 89 0
492 Crew Male Adult No 89 0
493 1st Female Adult No 89 0
494 2nd Female Adult No 89 0
495 3rd Female Adult No 89 1
496 Crew Female Adult No 89 0
497 1st Male Child Yes 89 0
498 2nd Male Child Yes 89 0
499 3rd Male Child Yes 89 0
500 Crew Male Child Yes 89 0
501 1st Female Child Yes 89 0
502 2nd Female Child Yes 89 0
503 3rd Female Child Yes 89 0
504 Crew Female Child Yes 89 0
505 1st Male Adult Yes 89 0
506 2nd Male Adult Yes 89 0
507 3rd Male Adult Yes 89 0
508 Crew Male Adult Yes 89 0
509 1st Female Adult Yes 89 0
510 2nd Female Adult Yes 89 0
511 3rd Female Adult Yes 89 0
512 Crew Female Adult Yes 89 0
513 1st Male Child No 118 0
514 2nd Male Child No 118 0
515 3rd Male Child No 118 0
516 Crew Male Child No 118 0
517 1st Female Child No 118 0
518 2nd Female Child No 118 0
519 3rd Female Child No 118 0
520 Crew Female Child No 118 0
521 1st Male Adult No 118 1
522 2nd Male Adult No 118 0
523 3rd Male Adult No 118 0
524 Crew Male Adult No 118 0
525 1st Female Adult No 118 0
526 2nd Female Adult No 118 0
527 3rd Female Adult No 118 0
528 Crew Female Adult No 118 0
529 1st Male Child Yes 118 0
530 2nd Male Child Yes 118 0
531 3rd Male Child Yes 118 0
532 Crew Male Child Yes 118 0
533 1st Female Child Yes 118 0
534 2nd Female Child Yes 118 0
535 3rd Female Child Yes 118 0
536 Crew Female Child Yes 118 0
537 1st Male Adult Yes 118 0
538 2nd Male Adult Yes 118 0
539 3rd Male Adult Yes 118 0
540 Crew Male Adult Yes 118 0
541 1st Female Adult Yes 118 0
542 2nd Female Adult Yes 118 0
543 3rd Female Adult Yes 118 0
544 Crew Female Adult Yes 118 0
545 1st Male Child No 140 0
546 2nd Male Child No 140 0
547 3rd Male Child No 140 0
548 Crew Male Child No 140 0
549 1st Female Child No 140 0
550 2nd Female Child No 140 0
551 3rd Female Child No 140 0
552 Crew Female Child No 140 0
553 1st Male Adult No 140 0
554 2nd Male Adult No 140 0
555 3rd Male Adult No 140 0
556 Crew Male Adult No 140 0
557 1st Female Adult No 140 0
558 2nd Female Adult No 140 0
559 3rd Female Adult No 140 0
560 Crew Female Adult No 140 0
561 1st Male Child Yes 140 0
562 2nd Male Child Yes 140 0
563 3rd Male Child Yes 140 0
564 Crew Male Child Yes 140 0
565 1st Female Child Yes 140 0
566 2nd Female Child Yes 140 0
567 3rd Female Child Yes 140 0
568 Crew Female Child Yes 140 0
569 1st Male Adult Yes 140 0
570 2nd Male Adult Yes 140 0
571 3rd Male Adult Yes 140 0
572 Crew Male Adult Yes 140 0
573 1st Female Adult Yes 140 1
574 2nd Female Adult Yes 140 0
575 3rd Female Adult Yes 140 0
576 Crew Female Adult Yes 140 0
577 1st Male Child No 154 0
578 2nd Male Child No 154 0
579 3rd Male Child No 154 0
580 Crew Male Child No 154 0
581 1st Female Child No 154 0
582 2nd Female Child No 154 0
583 3rd Female Child No 154 0
584 Crew Female Child No 154 0
585 1st Male Adult No 154 0
586 2nd Male Adult No 154 1
587 3rd Male Adult No 154 0
588 Crew Male Adult No 154 0
589 1st Female Adult No 154 0
590 2nd Female Adult No 154 0
591 3rd Female Adult No 154 0
592 Crew Female Adult No 154 0
593 1st Male Child Yes 154 0
594 2nd Male Child Yes 154 0
595 3rd Male Child Yes 154 0
596 Crew Male Child Yes 154 0
597 1st Female Child Yes 154 0
598 2nd Female Child Yes 154 0
599 3rd Female Child Yes 154 0
600 Crew Female Child Yes 154 0
601 1st Male Adult Yes 154 0
602 2nd Male Adult Yes 154 0
603 3rd Male Adult Yes 154 0
604 Crew Male Adult Yes 154 0
605 1st Female Adult Yes 154 0
606 2nd Female Adult Yes 154 0
607 3rd Female Adult Yes 154 0
608 Crew Female Adult Yes 154 0
609 1st Male Child No 192 0
610 2nd Male Child No 192 0
611 3rd Male Child No 192 0
612 Crew Male Child No 192 0
613 1st Female Child No 192 0
614 2nd Female Child No 192 0
615 3rd Female Child No 192 0
616 Crew Female Child No 192 0
617 1st Male Adult No 192 0
618 2nd Male Adult No 192 0
619 3rd Male Adult No 192 0
620 Crew Male Adult No 192 0
621 1st Female Adult No 192 0
622 2nd Female Adult No 192 0
623 3rd Female Adult No 192 0
624 Crew Female Adult No 192 0
625 1st Male Child Yes 192 0
626 2nd Male Child Yes 192 0
627 3rd Male Child Yes 192 0
628 Crew Male Child Yes 192 0
629 1st Female Child Yes 192 0
630 2nd Female Child Yes 192 0
631 3rd Female Child Yes 192 0
632 Crew Female Child Yes 192 0
633 1st Male Adult Yes 192 0
634 2nd Male Adult Yes 192 0
635 3rd Male Adult Yes 192 0
636 Crew Male Adult Yes 192 1
637 1st Female Adult Yes 192 0
638 2nd Female Adult Yes 192 0
639 3rd Female Adult Yes 192 0
640 Crew Female Adult Yes 192 0
641 1st Male Child No 387 0
642 2nd Male Child No 387 0
643 3rd Male Child No 387 0
644 Crew Male Child No 387 0
645 1st Female Child No 387 0
646 2nd Female Child No 387 0
647 3rd Female Child No 387 0
648 Crew Female Child No 387 0
649 1st Male Adult No 387 0
650 2nd Male Adult No 387 0
651 3rd Male Adult No 387 1
652 Crew Male Adult No 387 0
653 1st Female Adult No 387 0
654 2nd Female Adult No 387 0
655 3rd Female Adult No 387 0
656 Crew Female Adult No 387 0
657 1st Male Child Yes 387 0
658 2nd Male Child Yes 387 0
659 3rd Male Child Yes 387 0
660 Crew Male Child Yes 387 0
661 1st Female Child Yes 387 0
662 2nd Female Child Yes 387 0
663 3rd Female Child Yes 387 0
664 Crew Female Child Yes 387 0
665 1st Male Adult Yes 387 0
666 2nd Male Adult Yes 387 0
667 3rd Male Adult Yes 387 0
668 Crew Male Adult Yes 387 0
669 1st Female Adult Yes 387 0
670 2nd Female Adult Yes 387 0
671 3rd Female Adult Yes 387 0
672 Crew Female Adult Yes 387 0
673 1st Male Child No 670 0
674 2nd Male Child No 670 0
675 3rd Male Child No 670 0
676 Crew Male Child No 670 0
677 1st Female Child No 670 0
678 2nd Female Child No 670 0
679 3rd Female Child No 670 0
680 Crew Female Child No 670 0
681 1st Male Adult No 670 0
682 2nd Male Adult No 670 0
683 3rd Male Adult No 670 0
684 Crew Male Adult No 670 1
685 1st Female Adult No 670 0
686 2nd Female Adult No 670 0
687 3rd Female Adult No 670 0
688 Crew Female Adult No 670 0
689 1st Male Child Yes 670 0
690 2nd Male Child Yes 670 0
691 3rd Male Child Yes 670 0
692 Crew Male Child Yes 670 0
693 1st Female Child Yes 670 0
694 2nd Female Child Yes 670 0
695 3rd Female Child Yes 670 0
696 Crew Female Child Yes 670 0
697 1st Male Adult Yes 670 0
698 2nd Male Adult Yes 670 0
699 3rd Male Adult Yes 670 0
700 Crew Male Adult Yes 670 0
701 1st Female Adult Yes 670 0
702 2nd Female Adult Yes 670 0
703 3rd Female Adult Yes 670 0
704 Crew Female Adult Yes 670 0#1.6
sj<-rnorm(1000,200,20) # 产生1000个均值为200、标准差为10的正态分布随机数
biao<-table(cut(sj,breaks = 10,right = FALSE,dig.lab = 4))
biaoge<-data.frame(biao)
biaoge Var1 Freq
1 [121.8,136.7) 3
2 [136.7,151.5) 6
3 [151.5,166.2) 34
4 [166.2,181) 134
5 [181,195.7) 269
6 [195.7,210.4) 262
7 [210.4,225.2) 191
8 [225.2,239.9) 70
9 [239.9,254.7) 23
10 [254.7,269.6) 8