r - 计算两个分组变量的每个组合的列总和

Question

这个问题在这里已经有了答案:





How to sum a variable by group

(15 个回答)


6年前关闭。




我有一个看起来像这样的数据集:

 Type Age   count1  count2  Year   Pop1   Pop2  TypeDescrip
  A   35    1        1      1990   30000  50000  alpha                                 
  A   35    3        1      1990   30000  50000  alpha 
  A   45    2        3      1990   20000  70000  alpha 
  B   45    2        1      1990   20000  70000  beta
  B   45    4        5      1990   20000  70000  beta 

我想添加在 Type 和 Age 列中匹配的行数。所以理想情况下，我最终会得到一个如下所示的数据集:

 Type  Age  count1  count2  Year   Pop1   Pop2  TypeDescrip 
  A   35    4        2      1990   30000  50000  alpha 
  A   45    2        3      1990   20000  70000  alpha 
  B   45    6        6      1990   20000  70000  beta 

我试过使用嵌套 duplicated()声明如下:

typedup = duplicated(df$Type)
bothdup = duplicated(df[(typedup == TRUE),]$Age)

但这会返回重复的年龄或类型的索引，不一定当一行具有两者的重复项时。

我也试过 tapply:

tapply(c(df$count1, df$count2), c(df$Age, df$Type), sum)

但是这个输出很难处理。完成后我想要一个 data.frame。

我不想使用 for 循环，因为我的数据集非常大。

Answer 1

尝试

library(dplyr)
df1 %>%
     group_by(Type, Age) %>% 
     summarise_each(funs(sum))
#    Type Age count1 count2
#1    A  35      4      2
#2    A  45      2      3
#3    B  45      6      6

在 dplyr 的较新版本中

df1 %>%
     group_by(Type, Age) %>%
     summarise_all(sum)

或使用 base R

 aggregate(.~Type+Age, df1, FUN=sum)
 #    Type Age count1 count2
 #1    A  35      4      2
 #2    A  45      2      3
 #3    B  45      6      6

或者

library(data.table)
setDT(df1)[, lapply(.SD, sum), .(Type, Age)] 
#   Type Age count1 count2
#1:    A  35      4      2
#2:    A  45      2      3
#3:    B  45      6      6

更新

基于新的数据集，

 df2 %>%
     group_by(Type, Age,Pop1, Pop2, TypeDescrip) %>% 
     summarise_each(funs(sum), matches('^count'))
 #    Type Age  Pop1  Pop2 TypeDescrip count1 count2
 #1    A  35 30000 50000       alpha      4      2
 #2    A  45 20000 70000        beta      2      3
 #3    B  45 20000 70000        beta      6      6

数据

 df1 <- structure(list(Type = c("A", "A", "A", "B", "B"), Age = c(35L, 
 35L, 45L, 45L, 45L), count1 = c(1L, 3L, 2L, 2L, 4L), count2 = c(1L, 
 1L, 3L, 1L, 5L)), .Names = c("Type", "Age", "count1", "count2"
 ), class = "data.frame", row.names = c(NA, -5L))

 df2 <- structure(list(Type = c("A", "A", "A", "B", "B"), Age = c(35L, 
 35L, 45L, 45L, 45L), count1 = c(1L, 3L, 2L, 2L, 4L), count2 = c(1L, 
 1L, 3L, 1L, 5L), Year = c(1990L, 1990L, 1990L, 1990L, 1990L), 
   Pop1 = c(30000L, 30000L, 20000L, 20000L, 20000L), Pop2 = c(50000L, 
   50000L, 70000L, 70000L, 70000L), TypeDescrip = c("alpha", 
   "alpha", "beta", "beta", "beta")), .Names = c("Type", "Age", 
  "count1", "count2", "Year", "Pop1", "Pop2", "TypeDescrip"),
   class =   "data.frame", row.names = c(NA, -5L))