我有一个有两列的数据帧。第一列包含类别，如“第一”，“第二”，“第三”，第二列有数字，表示我从“类别”中看到特定组的次数。
例如:
Category Frequency
First 10
First 15
First 5
Second 2
Third 14
Third 20
Second 3
我想按类别对数据进行排序，并将所有频率相加:
Category Frequency
First 30
Second 5
Third 34
在R中怎么做呢?
当前回答
你可以用函数群。sum来自包Rfast。
Category <- Rfast::as_integer(Category,result.sort=FALSE) # convert character to numeric. R's as.numeric produce NAs.
result <- Rfast::group.sum(Frequency,Category)
names(result) <- Rfast::Sort(unique(Category)
# 30 5 34
Rfast有许多组函数和组。和就是其中之一。
其他回答
library(tidyverse)
x <- data.frame(Category= c('First', 'First', 'First', 'Second', 'Third', 'Third', 'Second'),
Frequency = c(10, 15, 5, 2, 14, 20, 3))
count(x, Category, wt = Frequency)
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。
几年后，只是为了添加另一个简单的基本R解决方案，由于某种原因这里没有给出——xtabs
xtabs(Frequency ~ Category, df)
# Category
# First Second Third
# 30 5 34
或者你想要回数据帧
as.data.frame(xtabs(Frequency ~ Category, df))
# Category Freq
# 1 First 30
# 2 Second 5
# 3 Third 34
使用cast代替reccast(注意'Frequency'现在是'value')
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)
得到:
Category (all)
First 30
Second 5
Third 34
另一种解决方案是在矩阵或数据帧中按组返回和，并且简短快速:
rowsum(x$Frequency, x$Category)
