R 按组计算描述性统计
我有一个从两所不同大学收集的数据集。每一个都包括学生的信息，如国家、年级、年龄等。我想提取每个国家的年级和年龄的最小值、平均值、最大值、标准差。他们在每个大学中按国家分组，并创建表格 我使用的代码如下。我正在为每个大学重复最小值、最大值和标准偏差的代码。重复这个过程是可以的，但是当我创建一个我想要的表时，我需要返回excel来合并我从这个代码中获得的统计信息。那么在R中有没有直接的方法来实现这一点呢R 按组计算描述性统计,r,dplyr,statistics,grouping,R,Dplyr,Statistics,Grouping,我有一个从两所不同大学收集的数据集。每一个都包括学生的信息，如国家、年级、年龄等。我想提取每个国家的年级和年龄的最小值、平均值、最大值、标准差。他们在每个大学中按国家分组，并创建表格 我使用的代码如下。我正在为每个大学重复最小值、最大值和标准偏差的代码。重复这个过程是可以的，但是当我创建一个我想要的表时，我需要返回excel来合并我从这个代码中获得的统计信息。那么在R中有没有直接的方法来实现这一点呢 stats_gr <- data %>% select(Country, Gra
stats_gr <- data %>%
select(Country, Grades, Age) %>%
group_by(country) %>%
summarise(Grades = mean(Grades), Age=mean(Age))
library(stargazer)
stats_gr <- data %>%
select(Country, Grades, Age) %>%
group_by(country) %>% stargazer(type="text")
我用knitr的kable函数解决了这个问题 我生成的假数据集只是为了填充表。这是一个单一的数据集，其中包括来自每个国家两所大学的数据。 这种方法的优点是，如果您想使用rmarkdown来编织到word，它将很好地工作。如果你这样做，桌子就会像这样
您可以使用相关的kable参数控制位数、表格标题或列对齐方式。不确定这是否是您想要的，但您可以写：SummarSegrades\u mean=meanGrades、Grades\u in=minGrades、Grades\u max=maxGrades、Grades\u sd=sdGrades、Age\u mean=meanAge。或者，作为一种替代方法，您可以使用do而不是summary并调用psych:：descripe或任何其他预制的descriptives函数，您可以在select之后输出数据帧吗？即使只是dputheaddata也会有帮助。我想要的是在R中提取这个表。我不想使用excel在单个表中编译统计数据。@emilykoth。我猜数据是我的原始数据。在这个函数中，我应该用什么变量替换head。它向我显示了这条线统计N平均值St.Dev.Min Pctl25 Pctl75 Max。它工作得非常好。非常感谢你！对不起，还有一件事。如果我想将Grade&Age设置为它们的min、mean、max、sd的顶部，我应该向脚本中添加什么代码？kableExtra包将这样做以导出到HTML，但我不确定导出到word是否有效。
library(dplyr)
df <- tibble::tribble(
~University, ~Countries, ~Grades, ~Age,
"University-1", "USA", 46, 29,
"University-1", "UK", 84, 30,
"University-1", "Sweden", 5, 28,
"University-1", "Spain", 40, 26,
"University-1", "Portugal", 49, 29,
"University-1", "Italy", 16, 24,
"University-1", "USA", 34, 19,
"University-1", "UK", 66, 28,
"University-1", "Sweden", 9, 25,
"University-1", "Spain", 80, 20,
"University-1", "Portugal", 55, 20,
"University-1", "Italy", 4, 21,
"University-1", "USA", 93, 18,
"University-1", "UK", 62, 28,
"University-1", "Sweden", 80, 30,
"University-2", "Spain", 1, 22,
"University-2", "Portugal", 56, 25,
"University-2", "Italy", 9, 29,
"University-2", "USA", 40, 21,
"University-2", "UK", 54, 20,
"University-2", "Sweden", 60, 24,
"University-2", "Spain", 77, 21,
"University-2", "Portugal", 22, 18,
"University-2", "Italy", 53, 29,
"University-2", "USA", 11, 21,
"University-2", "UK", 65, 27,
"University-2", "Sweden", 24, 27,
"University-2", "Spain", 18, 23,
"University-2", "Portugal", 73, 19,
"University-2", "Italy", 79, 22,
"University-1", "USA", 2, 26,
"University-1", "UK", 83, 23,
"University-1", "Sweden", 5, 19,
"University-1", "Spain", 75, 19,
"University-1", "Portugal", 12, 21,
"University-1", "Italy", 68, 29,
"University-1", "USA", 100, 21,
"University-1", "UK", 49, 21,
"University-1", "Sweden", 81, 20,
"University-1", "Spain", 99, 23,
"University-1", "Portugal", 82, 24,
"University-1", "Italy", 23, 26,
"University-1", "USA", 86, 30,
"University-1", "UK", 50, 20,
"University-1", "Sweden", 4, 19,
"University-2", "Spain", 12, 25,
"University-2", "Portugal", 12, 21,
"University-2", "Italy", 45, 21,
"University-2", "USA", 16, 26,
"University-2", "UK", 56, 23,
"University-2", "Sweden", 63, 24,
"University-2", "Spain", 37, 28,
"University-2", "Portugal", 86, 21,
"University-2", "Italy", 95, 18,
"University-2", "USA", 56, 20,
"University-2", "UK", 27, 20,
"University-2", "Sweden", 3, 27,
"University-2", "Spain", 18, 27,
"University-2", "Portugal", 68, 27,
"University-2", "Italy", 48, 21
)
df %>%
group_by(University,Countries) %>%
summarise(Grades_min = min(Grades),
Grades_mean = mean(Grades),
Grades_max = max(Grades),
Grades_sd = sd(Grades),
Age_min = min(Age),
Age_mean= mean(Age),
Age_max = max(Age),
Age_sd = sd(Age)) %>%
knitr::kable(col.names = c("University",
"Country",
"Min Grade",
"Mean Grade",
"Max Grade",
"Grade SD",
"Min Age",
"Mean Age",
"Max Age",
"Age SD"))
|University |Country | Min Grade| Mean Grade| Max Grade| Grade SD| Min Age| Mean Age| Max Age| Age SD|
|:------------|:--------|---------:|----------:|---------:|--------:|-------:|--------:|-------:|--------:|
|University-1 |Italy | 4| 27.75000| 68| 27.95681| 21| 25.00000| 29| 3.366502|
|University-1 |Portugal | 12| 49.50000| 82| 28.82707| 20| 23.50000| 29| 4.041452|
|University-1 |Spain | 40| 73.50000| 99| 24.61030| 19| 22.00000| 26| 3.162278|
|University-1 |Sweden | 4| 30.66667| 81| 38.64022| 19| 23.50000| 30| 4.847680|
|University-1 |UK | 49| 65.66667| 84| 15.31883| 20| 25.00000| 30| 4.195235|
|University-1 |USA | 2| 60.16667| 100| 38.98931| 18| 23.83333| 30| 5.192944|
|University-2 |Italy | 9| 54.83333| 95| 29.81554| 18| 23.33333| 29| 4.589844|
|University-2 |Portugal | 12| 52.83333| 86| 29.54601| 18| 21.83333| 27| 3.488075|
|University-2 |Spain | 1| 27.16667| 77| 27.06597| 21| 24.33333| 28| 2.804758|
|University-2 |Sweden | 3| 37.50000| 63| 29.03446| 24| 25.50000| 27| 1.732051|
|University-2 |UK | 27| 50.50000| 65| 16.38088| 20| 22.50000| 27| 3.316625|
|University-2 |USA | 11| 30.75000| 56| 21.06142| 20| 22.00000| 26| 2.708013|