加快 R 中嵌套 for 循环的速度，并在 excel 中存储超出其限制的非常大的数据框

发布于 2025-01-13 02:58:17 字数 841 浏览 0 评论 0原文

我在 R 中使用以下代码行花了 36 个小时或更长时间来努力创建一个数据框。任何使此代码更快的建议都将受到赞赏。然而，到最后，我需要将此数据框存储在 Excel 文件中，但我注意到此特定代码需要超过 3 亿行，这超出了典型的 Excel 工作表长度。我期待获得帮助，将其存储在 Excel（或记事本）文件中以供将来使用。

library(writexl)
team_b <- 10:120
individual_b <- 1:84
team_s <- 1:250
individual_s <- 1:150

d <- data.frame()
for (i in team_b) {
  for (j in individual_b) {
    for (k in team_s) {
      for (l in individual_s) {
        sc <- l/k 
        bu <- j/i 
        sr <- l/j 
        pi <- sc/bu
        if(bu>0.7||sc>0.7||sr>6){
          c = "unrealistic"
        }
        else{
          c = "realistic"
        }
        d <- rbind(d, data.frame(i,j,k,l,sc,bu,sr,pi,c)) 
      }
    }
  }
}
colnames(d) <- c("T_b", "I_b", "T_s", "I_s", "BU", "SC", "SR", "PI", "Comment")
#View(d)

write_xlsx(d, "d.xlsx")

原文

I'm struggling to create a dataframe using the following codelines in R for complete 36 hours and more. Any suggestions to make this code faster would be admired. However, by the end, I need to store this data frame in an excel file, but I noticed that this particular code required over 300 million rows, which exceeds the typical excel sheet length. I look forward to get help in storing this in an excel (or notepad) file for future use as well.

library(writexl)
team_b <- 10:120
individual_b <- 1:84
team_s <- 1:250
individual_s <- 1:150

d <- data.frame()
for (i in team_b) {
  for (j in individual_b) {
    for (k in team_s) {
      for (l in individual_s) {
        sc <- l/k 
        bu <- j/i 
        sr <- l/j 
        pi <- sc/bu
        if(bu>0.7||sc>0.7||sr>6){
          c = "unrealistic"
        }
        else{
          c = "realistic"
        }
        d <- rbind(d, data.frame(i,j,k,l,sc,bu,sr,pi,c)) 
      }
    }
  }
}
colnames(d) <- c("T_b", "I_b", "T_s", "I_s", "BU", "SC", "SR", "PI", "Comment")
#View(d)

write_xlsx(d, "d.xlsx")

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乖乖公主 2025-01-20 02:58:18

library(data.table)
d <- CJ(team_b, individual_b, team_s, individual_s) # generate all combinations
setnames(d, c('i', 'j', 'k', 'l'))
d[, sc := l/k] 
d[, bu := j/i] 
d[, sr := l/j] 
d[, pi := sc/bu]
d[, c := ifelse(bu > 0.7 | sc > 0.7 | sr > 6, "unrealistic", "realistic")]

library(data.table)
d <- CJ(team_b, individual_b, team_s, individual_s) # generate all combinations
setnames(d, c('i', 'j', 'k', 'l'))
d[, sc := l/k] 
d[, bu := j/i] 
d[, sr := l/j] 
d[, pi := sc/bu]
d[, c := ifelse(bu > 0.7 | sc > 0.7 | sr > 6, "unrealistic", "realistic")]

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