如何计算不同组中的嵌套类别及其在R中的平均值

我们如何使用dplyr或其他有用的库从下面的示例计算以下内容：

每个州的学校总数

每所学校的学生总数

按性别分列的每所学校学生总数

按性别和类型分列的每所学校的学生总数

按性别划分的项目1和项目3的平均值

每个州按性别划分的第1项和第3项的平均值

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非常感谢。

TL/DR：在询问之前，你应该做一些研究。

你知道，在Stackoverflow上我们试着互相帮助。在请求帮助之前，我们通常会进行彻底的研究。在您的情况下，您可以阅读

dplyr

或

tidyverse

文档。我知道这有时很无聊，但总比从随机用户那里得到答案要好

研究

分组依据

和

通过从RStudio控制台请求功能总结

（例如，

？分组依据

）

#1各州的学校总数
按州划分的学校百分比
按（州）分组%>%
摘要（编号=n（））

在示例数据集中，您有唯一的学校名称。这就是为什么结果可能会令人困惑和毫无意义

# 2 total number of students

students <- df %>%
  group_by(schools) %>%
  summarise(students= n())

# by gender

students_gender <- df %>%
  group_by(Gender) %>%
  summarise(stud_gend = n())

# by gender and type

stud_gend_type <- df %>%
  group_by(Gender, type) %>%
  summarise(studs = n())

#2学生总数
学生%
组别(学校)%>%
总结（学生=n（）
#按性别
学生性别%
按性别划分的组别%>%
总结（stud\u gend=n（）
#按性别和类型分列
螺柱性别类型%
按（性别、类型）分组%>%
总结（螺柱=n（）

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如你所见，原理非常简单。所以，我把最后两项任务留给您自己完成。

在下面的代码中，我已经完成了部分任务的基本内容。您从dplyr库中获取的主要功能是groupby（）和summary（）：

library(dplyr)

ID = 1:50
states = rep(c("TS", "NE", "AR", "MO", "WA"),times = c(10, 10, 10, 10, 10))
schools = randomNames::randomNames(50) ## 5 first last names separated by a space
Gender = rep(c("male", "female"),times = c(18,32))
type = rep(c("private", "public"),times = c(20,30))
item1 = rnorm(50, mean=25, sd=5)
item2 = rnorm(50, mean=30, sd=5)
item3 = rnorm(50, mean=15, sd=5)
df = data.frame(ID, states, schools, Gender, type, item1, item2, item3)

head(df)

# total number of schools by each state,

df %>% 
  group_by(states) %>% 
  summarise(number = n())

# total number of students by each school,
# total number of students by each school by Gender,
# total number of students by each school by Gender and type,

df %>% 
  group_by(schools,Gender,type) %>% 
  summarise(number = n())

# mean of item1 and item3 by Gender,
# mean of item1 and item3 by Gender for each state,

df %>% 
  group_by(Gender,states) %>% 
  summarise(item1 = mean(item1),
            item2 = mean(item2))

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为了不在这些问题上得到否定的观点，我会做一些尝试。您标记了“dplyr”，表示您知道在哪里查找。因此，这可能会让人觉得是试图让别人回答你的练习。非常感谢！

# 2 total number of students

students <- df %>%
  group_by(schools) %>%
  summarise(students= n())

# by gender

students_gender <- df %>%
  group_by(Gender) %>%
  summarise(stud_gend = n())

# by gender and type

stud_gend_type <- df %>%
  group_by(Gender, type) %>%
  summarise(studs = n())

library(dplyr)

ID = 1:50
states = rep(c("TS", "NE", "AR", "MO", "WA"),times = c(10, 10, 10, 10, 10))
schools = randomNames::randomNames(50) ## 5 first last names separated by a space
Gender = rep(c("male", "female"),times = c(18,32))
type = rep(c("private", "public"),times = c(20,30))
item1 = rnorm(50, mean=25, sd=5)
item2 = rnorm(50, mean=30, sd=5)
item3 = rnorm(50, mean=15, sd=5)
df = data.frame(ID, states, schools, Gender, type, item1, item2, item3)

head(df)

# total number of schools by each state,

df %>% 
  group_by(states) %>% 
  summarise(number = n())

# total number of students by each school,
# total number of students by each school by Gender,
# total number of students by each school by Gender and type,

df %>% 
  group_by(schools,Gender,type) %>% 
  summarise(number = n())

# mean of item1 and item3 by Gender,
# mean of item1 and item3 by Gender for each state,

df %>% 
  group_by(Gender,states) %>% 
  summarise(item1 = mean(item1),
            item2 = mean(item2))