Question

鉴于此data.frame：

set.seed(4)
df <- data.frame(x = rep(1:5, each = 2), y = sample(50:100, 10, T))
#    x  y
# 1  1 78
# 2  1 53
# 3  2 93
# 4  2 96
# 5  3 61
# 6  3 82
# 7  4 53
# 8  4 76
# 9  5 91
# 10 5 99

我想编写一些简单的函数（即特征工程）来为x创建功能，然后将每个结果data.frames连接在一起。例如：

library(dplyr)
count_x <- function(df) df %>% group_by(x) %>% summarise(count_x = n())
sum_y   <- function(df) df %>% group_by(x) %>% summarise(sum_y = sum(y))
mean_y  <- function(df) df %>% group_by(x) %>% summarise(mean_y = mean(y))  
# and many more...

这可以通过plyr::join_all完成，但我想知道dplyr或data.table是否有更好（或更高效）的方法？

df_with_features <- plyr::join_all(list(count_x(df), sum_y(df), mean_y(df)),
                                   by = 'x', type = 'full')

# > df_with_features
#   x count_x sum_y mean_y
# 1 1       2   131   65.5
# 2 2       2   189   94.5
# 3 3       2   143   71.5
# 4 4       2   129   64.5
# 5 5       2   190   95.0

Answer 1

将@ SimonOHanlon的data.table方法与@ Jaap的Reduce和merge技术结合起来可以产生最高效的结果：

library(data.table)
setDT(df)
count_x_dt <- function(dt) dt[, list(count_x = .N), keyby = x]
sum_y_dt   <- function(dt) dt[, list(sum_y = sum(y)), keyby = x]
mean_y_dt  <- function(dt) dt[, list(mean_y = mean(y)), keyby = x]

Reduce(function(...) merge(..., all = TRUE, by = c("x")), 
       list(count_x_dt(df), sum_y_dt(df), mean_y_dt(df)))

更新以包含tidyverse / purrr（purrr::redcue）方法：

library(tidyverse)
list(count_x(df), sum_y(df), mean_y(df)) %>% 
  reduce(left_join)

Answer 2

在data.table的说法中，这相当于具有已排序的键控data.table并使用该键来连接各种data.tables。

e.g。

require(data.table)
setDT(df)  #df is now a data.table
df_count <- df[ , list(count_x=.N),by=x]
df_sum <- df[ , list(sum_y = sum(y)),by=x]
#  merge.data.table executes a fast join on the shared key
merge(df_count,df_sum)
#   x count_x sum_y
#1: 1       2   129
#2: 2       2   128
#3: 3       2   154
#4: 4       2   182
#5: 5       2   151

在你的例子中，你可能会这样写：

count_x <- function(dt) dt[ , list(N = .N) , keyby=x ]
sum_y <- function(dt) dt[ , list(Sum=sum(y)),keyby=x]

#  Then merge...
merge(sum_y(df),count_x(df))
#   x Sum N
#1: 1 129 2
#2: 2 128 2
#3: 3 154 2
#4: 4 182 2
#5: 5 151 2

是否有一个dplyr或data.table等效于plyr :: join_all？通过数据框列表加入？

2 个答案: