如何按組分組變量？

Question

假設我有兩列數據。第一個包含「First」，「Second」，「Third」等類別。第二個包含的數字表示我看到「First」的次數。

例如：

Category  Frequency 
First  10 
First  15 
First  5 
Second  2 
Third  14 
Third  20 
Second  3 

我想按類別對數據進行排序，總結頻率：

Category  Frequency 
First  30 
Second  5 
Third  34 

我將如何做到這一點的R'

Answer 1

使用aggregate：

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

---

（嵌入@thelatemail評論），aggregate有一個公式界面太

aggregate(Frequency ~ Category, x, sum) 

或者，如果你想要聚合多列，您可以使用.表示法（適用於一列）

aggregate(. ~ Category, x, sum) 

---

或tapply：

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

---

使用該數據：

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

Answer 2

如果x與您的數據的數據幀，那麼下面會做你想要什麼：

require(reshape) 
recast(x, Category ~ ., fun.aggregate=sum) 

Answer 3

library(plyr) 
ddply(tbl, .(Category), summarise, sum = sum(Frequency)) 

Answer 4

只是爲了增加了第三種選擇：

require(doBy) 
summaryBy(Frequency~Category, data=yourdataframe, FUN=sum) 

編輯：這是一個非常古老的答案。現在我會推薦使用group_by並從dplyr中進行彙總，就像在@docendo中一樣。

Answer 5

這有點related to this question。

您也可以只使用由（）功能：

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

那些其他包（plyr，重塑）有返回data.frame的好處，但它是值得熟悉的（ ），因爲它是一個基本功能。

Answer 6

由rcs提供的答案作品很簡單。不過，如果你正在處理更大的數據集，需要一個性能提升有一個更快的替代方案：

library(data.table) 
data = data.table(Category=c("First","First","First","Second","Third", "Third", "Second"), 
        Frequency=c(10,15,5,2,14,20,3)) 
data[, sum(Frequency), by = Category] 
# Category V1 
# 1: First 30 
# 2: Second 5 
# 3: Third 34 
system.time(data[, sum(Frequency), by = Category]) 
# user system elapsed 
# 0.008  0.001  0.009 

我們來比較一下使用的數據是一樣的。框架和凌駕於：

data = data.frame(Category=c("First","First","First","Second","Third", "Third", "Second"), 
        Frequency=c(10,15,5,2,14,20,3)) 
system.time(aggregate(data$Frequency, by=list(Category=data$Category), FUN=sum)) 
# user system elapsed 
# 0.008  0.000  0.015 

如果你想保持柱這是語法：

data[,list(Frequency=sum(Frequency)),by=Category] 
# Category Frequency 
# 1: First  30 
# 2: Second   5 
# 3: Third  34 

的差異將成爲大數據集更明顯，如下面的代碼演示：

data = data.table(Category=rep(c("First", "Second", "Third"), 100000), 
        Frequency=rnorm(100000)) 
system.time(data[,sum(Frequency),by=Category]) 
# user system elapsed 
# 0.055  0.004  0.059 
data = data.frame(Category=rep(c("First", "Second", "Third"), 100000), 
        Frequency=rnorm(100000)) 
system.time(aggregate(data$Frequency, by=list(Category=data$Category), FUN=sum)) 
# user system elapsed 
# 0.287  0.010  0.296 

---

對於多個聚合，你可以結合lapply和.SD如下

data[, lapply(.SD, sum), by = Category] 
# Category Frequency 
# 1: First  30 
# 2: Second   5 
# 3: Third  34 

Answer 7

最近，您還可以使用dplyr包用於這一目的：

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，多個摘要列（有一列工程太）：

x %>% 
    group_by(Category) %>% 
    summarise_each(funs(sum)) 

更新爲dplyr> = 0.5：summarise_each已被summarise_all，summarise_at和summarise_if替換爲dplyr中的函數族。

或者，如果你有多列小組通過，您可以指定所有的人都在用逗號分隔的group_by：

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

欲瞭解更多信息，包括%>%運營商，看到introduction to dplyr。

Answer 8

若干年後，只需添加一個不存在這裏一些reason- xtabs

xtabs(Frequency ~ Category, df) 
# Category 
# First Second Third 
# 30  5  34 

另一種簡單的基礎R解決方案或者，如果想回data.frame

as.data.frame(xtabs(Frequency ~ Category, df)) 
# Category Freq 
# 1 First 30 
# 2 Second 5 
# 3 Third 34 

Answer 9

雖然我最近成爲轉換爲dplyr大多數這些類型的操作，sqldf包仍然是非常好的（和恕我直言更可讀）的一些事情。

下面是一個如何這個問題可以sqldf

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 

Answer 10

使用cast代替recast與回答一個例子（注意現在'value''Frequency'是）

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

install.packages("reshape") 

result<-cast(df, Category ~ . ,fun.aggregate=sum) 

獲得：

Category (all) 
First  30 
Second 5 
Third  34