当你需要在不同的列上应用不同的聚合函数(并且你必须/想要坚持以R为基底)时，我发现它非常有用(并且有效):

e.g.

假设输入如下:

DF <-                
data.frame(Categ1=factor(c('A','A','B','B','A','B','A')),
           Categ2=factor(c('X','Y','X','X','X','Y','Y')),
           Samples=c(1,2,4,3,5,6,7),
           Freq=c(10,30,45,55,80,65,50))

> DF
  Categ1 Categ2 Samples Freq
1      A      X       1   10
2      A      Y       2   30
3      B      X       4   45
4      B      X       3   55
5      A      X       5   80
6      B      Y       6   65
7      A      Y       7   50

我们要按1类和2类进行分组，并计算Freq的样本和均值。 下面是使用ave的一个可能的解决方案:

# create a copy of DF (only the grouping columns)
DF2 <- DF[,c('Categ1','Categ2')]

# add sum of Samples by Categ1,Categ2 to DF2 
# (ave repeats the sum of the group for each row in the same group)
DF2$GroupTotSamples <- ave(DF$Samples,DF2,FUN=sum)

# add mean of Freq by Categ1,Categ2 to DF2 
# (ave repeats the mean of the group for each row in the same group)
DF2$GroupAvgFreq <- ave(DF$Freq,DF2,FUN=mean)

# remove the duplicates (keep only one row for each group)
DF2 <- DF2[!duplicated(DF2),]

结果:

> DF2
  Categ1 Categ2 GroupTotSamples GroupAvgFreq
1      A      X               6           45
2      A      Y               9           40
3      B      X               7           50
6      B      Y               6           65

你也可以使用by()函数:

x2 <- by(x$Frequency, x$Category, sum)
do.call(rbind,as.list(x2))

其他那些包(plyr，重塑)的好处是返回data.frame，但是by()值得熟悉一下，因为它是一个基函数。

使用聚合:

aggregate(x$Frequency, by=list(Category=x$Category), FUN=sum)
  Category  x
1    First 30
2   Second  5
3    Third 34

在上面的例子中，可以在列表中指定多个维度。相同数据类型的多个聚合指标可以通过cbind合并:

aggregate(cbind(x$Frequency, x$Metric2, x$Metric3) ...

(嵌入@thelatemail评论)，聚合也有一个公式界面

aggregate(Frequency ~ Category, x, sum)

或者，如果希望聚合多个列，可以使用。符号(也适用于一列)

aggregate(. ~ Category, x, sum)

或tapply:

tapply(x$Frequency, x$Category, FUN=sum)
 First Second  Third 
    30      5     34 

使用这些数据:

x <- data.frame(Category=factor(c("First", "First", "First", "Second",
                                      "Third", "Third", "Second")), 
                    Frequency=c(10,15,5,2,14,20,3))

你也可以使用dplyr包来实现这个目的:

library(dplyr)
x %>% 
  group_by(Category) %>% 
  summarise(Frequency = sum(Frequency))

#Source: local data frame [3 x 2]
#
#  Category Frequency
#1    First        30
#2   Second         5
#3    Third        34

或者，对于多个摘要列(也适用于一个列):

x %>% 
  group_by(Category) %>% 
  summarise(across(everything(), sum))

下面是一些关于如何使用dplyr函数(使用内置数据集mtcars)按组总结数据的例子:

# several summary columns with arbitrary names
mtcars %>% 
  group_by(cyl, gear) %>%                            # multiple group columns
  summarise(max_hp = max(hp), mean_mpg = mean(mpg))  # multiple summary columns

# summarise all columns except grouping columns using "sum" 
mtcars %>% 
  group_by(cyl) %>% 
  summarise(across(everything(), sum))

# summarise all columns except grouping columns using "sum" and "mean"
mtcars %>% 
  group_by(cyl) %>% 
  summarise(across(everything(), list(mean = mean, sum = sum)))

# multiple grouping columns
mtcars %>% 
  group_by(cyl, gear) %>% 
  summarise(across(everything(), list(mean = mean, sum = sum)))

# summarise specific variables, not all
mtcars %>% 
  group_by(cyl, gear) %>% 
  summarise(across(c(qsec, mpg, wt), list(mean = mean, sum = sum)))

# summarise specific variables (numeric columns except grouping columns)
mtcars %>% 
  group_by(gear) %>% 
  summarise(across(where(is.numeric), list(mean = mean, sum = sum)))

有关更多信息，包括%>%操作符，请参阅dplyr介绍。

虽然我最近对大多数这些类型的操作都转换为dplyr，但sqldf包对于某些事情仍然非常好(恕我直言，可读性更强)。

下面是一个示例，说明如何使用sqldf回答这个问题

x <- data.frame(Category=factor(c("First", "First", "First", "Second",
                                  "Third", "Third", "Second")), 
                Frequency=c(10,15,5,2,14,20,3))

sqldf("select 
          Category
          ,sum(Frequency) as Frequency 
       from x 
       group by 
          Category")

##   Category Frequency
## 1    First        30
## 2   Second         5
## 3    Third        34

对于dplyr 1.1.0及以上版本，你可以在总结中使用.by。这个快捷方式避免使用group_by，并返回一个未分组的数据帧:

library(dplyr)
x %>%  
  summarise(Frequency = sum(Frequency), .by = Category)