r - 如何根据值扩展数据框？

Question

这个问题在这里已经有了答案:





Expand ranges defined by "from" and "to" columns

(9 个回答)


4年前关闭。




我有以下输入数据框:

df <- data.frame(x=c('a','b','c'),y=c(4,5,6),from=c(1,2,3),to=c(2,4,6))  
df
  x y  from to
1 a 4  1    2
2 b 5  2    4
3 c 6  3    6

现在我想将每一行乘以 from 和 to 之间的值，即 ('a',4) 跨越两行，即 1,2 .预期结果如下所示:

exp <- data.frame(x=c('a','a','b','b','b','c','c','c','c'),
                  y=c(4,4,5,5,5,6,6,6,6),
                  z=c(1,2,2,3,4,3,4,5,6))
exp
  x y z
1 a 4 1
2 a 4 2
3 b 5 2
4 b 5 3
5 b 5 4
6 c 6 3
7 c 6 4
8 c 6 5
9 c 6 6

在没有循环的情况下完成此任务的最惯用方法是什么？

Answer 1

一种“非tidyverse”方式:

data.frame(
  x = c('a', 'b', 'c'),
  y = c(4, 5, 6),
  from = c(1, 2, 3),
  to = c(2, 4, 6),
  stringsAsFactors = FALSE
) -> xdf

do.call(rbind.data.frame, lapply(1:nrow(xdf), function(i) {
  data.frame(x = xdf$x[i], y=xdf$y[i], z=xdf$from[i]:xdf$to[i], stringsAsFactors=FALSE)
}))

一种“tidyverse”方式:

library(tidyverse)

data_frame(
  x = c('a', 'b', 'c'),
  y = c(4, 5, 6),
  from = c(1, 2, 3),
  to = c(2, 4, 6)
) -> xdf

rowwise(xdf) %>% 
  do(data_frame(x = .$x, y=.$y, z=.$from:.$to))

另一种尚未在下面进行基准测试的“tidyverse”方式:

xdf %>% 
  rowwise() %>% 
  do( merge( as_tibble(.), tibble(z=.$from:.$to), by=NULL) ) %>%
  select( -from, -to )     # Omit this line if you want to keep all original columns.

既然你问了 abt 性能:

library(microbenchmark)

data.table::data.table(
  x = c('a','b','c'),
  y = c(4,5,6),
  from = c(1,2,3),
  to = c(2,4,6)
) -> xdt1

data.frame(
  x = c('a', 'b', 'c'),
  y = c(4, 5, 6),
  from = c(1, 2, 3),
  to = c(2, 4, 6),
  stringsAsFactors = FALSE
) -> xdf1 

data.table ops 经常就地修改，所以在执行 op 之前保持一个公平的竞争环境并制作每个数据帧/表的副本。

在大多数现代系统上，时间损失约为 100 纳秒。

microbenchmark(

  data.table = {
    xdt2 <- xdt1
    xdt2[, diff:= (to - from) + 1]
    xdt2 <- xdt2[rep(1:.N, diff)]
    xdt2[,z := seq(from,to), by=.(x,y,from,to)]
    xdt2[,c("x", "y", "z")]
  }, 

  base = {
    xdf2 <- xdf1
    do.call(rbind.data.frame, lapply(1:nrow(xdf2), function(i) {
      data.frame(x = xdf2$x[i], y=xdf2$y[i], z=xdf2$from[i]:xdf2$to[i], stringsAsFactors=FALSE)
    }))
  }, 

  tidyverse = {
    xdf2 <- xdf1
    dplyr::rowwise(xdf2) %>% 
      dplyr::do(dplyr::data_frame(x = .$x, y=.$y, z=.$from:.$to))
  }, 

  plyr = {
    xdf2 <- xdf1
    plyr::mdply(xdf2, function(x,y,from,to) data.frame(x,y,z=seq(from,to)))[c("x","y","z")]
  },

  times = 1000

)
## Unit: microseconds
##        expr       min         lq       mean    median         uq        max neval
##  data.table   920.361  1072.9265  1257.2321  1178.832  1280.2660  10628.552  1000
##        base   677.069   761.3145   884.4136   825.472   915.8985   5366.515  1000
##   tidyverse 15926.127 17231.5015 19201.4798 17994.919 20014.4140 166901.570  1000
##        plyr  1938.838  2196.4205  2448.5314  2322.949  2501.5075   5735.255  1000