R 将所有行粘贴到一起的最快方法

我想将所有行逐列粘贴到同一单元格中

例如，我有一个如下表：

Col1 AAABBC

Col2 AAABBB

Col3 ABBBAA 

库（TIBLE）
tibble：：tribble(
~Col1，~Col2，~Col3，
“AA”、“AA”、“AB”，
“AB”，“AB”，“BB”，
“BC”、“BB”、“AA”
)

我想要的输出是一个3X1表，如下所示：

Col1 AAABBC

Col2 AAABBB

Col3 ABBBAA 

然而，实际情况更复杂，因为我的原始表有600000行和2000列。我想知道实现这一目标最快的方法是什么。我尝试了循环，但它花了很长时间才完成逐行粘贴

感谢您的帮助，谢谢

库（data.table）
dt结果
#>1:AAABBC
#>2:AAABBB
#>3:ABBBAA
#或
dtv1
#>1:AAABBC
#>2:AAABBB
#>3:ABBBAA

于2021-03-18年由（v0.3.0）创建

第二种方法应该比第一种方法快。

lappy（df，paste，collapse=”“）

---

这将返回一个列表。如果需要向量，请使用

sapply

而不是

lappy

。如果您需要数据帧，请将整个调用包装在

data.frame

中。如果您有足够的内存来存储多个数据实例，则使用

doParallel

包的这种方法可以工作。这里我使用的是

tidyverse

family

library(tidyverse)
library(doParallel)

n <- 1000
# Generate a 1000 rows df with ~3000 columns
big_table <- do.call("rbind", replicate(n, data, simplify = FALSE))
lapply(1:10, function(x) {big_table <<- bind_cols(big_table, big_table); return(x)})

# Get the list of column names
col_list <- names(big_table)
# Define number of cores you want to process
number_of_parallel_cores <- 4
col_group <- split(col_list, sort(rep_len(1:number_of_parallel_cores, length(col_list))))

# Running the code with timer
system.time({
  registerDoParallel(number_of_parallel_cores)
  combine_data <- bind_rows(foreach(i_col_group = col_group) %dopar% {
    big_table %>%
      select(one_of(i_col_group)) %>%
      summarize(across(.fns = paste, collapse = "")) %>%
      pivot_longer(cols = everything(), names_to = "col_names", values_to = "values")
  })
})

输出

   col_names values                                                                                                                                                                                                               
   <chr>     <chr>                                                                                                                                                                                                                
 1 Col1...1  AAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAA…
 2 Col2...2  AAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAA…
 3 Col3...3  ABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAAB…
 4 Col1...4  AAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAA…
 5 Col2...5  AAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAA…
 6 Col3...6  ABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAAB…
 7 Col1...7  AAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAA…
 8 Col2...8  AAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAA…
 9 Col3...9  ABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAABBBAAAB…
10 Col1...10 AAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAAABBCAA…
# … with 3,062 more rows

---

我们可以使用

dapply

from

collapse

，它针对行操作进行了优化

library(collapse)
dapply(df1, paste, collapse="", MARGIN = 1)
#[1] "AAAAAB" "ABABBB" "BCBBAA"

根据

？dapply

dapply有效地将函数应用于类似矩阵的对象的列或行，并在默认情况下返回具有相同类型和属性的对象。或者，也可以在普通矩阵或data.frame中返回结果。还提供了简单的并行性

---

天哪，你太棒了。这真的很有帮助！！！非常感谢！！！

system.time(
  big_table %>%
    select(one_of(col_list)) %>%
    summarize(across(.fns = paste, collapse = "")) %>%
    pivot_longer(cols = everything(), names_to = "col_names", values_to = "values")
)

   user  system elapsed 
  0.021   0.000   0.022 

library(collapse)
dapply(df1, paste, collapse="", MARGIN = 1)
#[1] "AAAAAB" "ABABBB" "BCBBAA"