如何使用乘法变量将数据从宽到长整形？

Question

我有一个非常大的数据集，需要从宽到长整形。

这是我的数据集的演示，其中包含所有情况：

genename    case1   case2   case3   strand
TP53            1       0       1      pos
TNN             0       0       1      pos
CD13            0       0       0      pos
AP35            1       1       1      neg

只有在1存在的情况下，情况才会保留并重新成形为纵向，如下所示：

genename    case    strand
TP53       case1       pos
TP53       case3       pos
TNN        case3       pos
AP35       case1       neg
AP35       case2       neg
AP35       case3       neg

如何在R中处理这种重塑？

Answer 1

tidyverse

df <- read.table(text="genename    case1   case2   case3   strand
TP53            1       0       1      pos
TNN             0       0       1      pos
CD13            0       0       0      pos
AP35            1       1       1      neg", header =T)

library(tidyverse)

df %>% 
  gather( case, case_value, c(case1, case2, case3) ) %>%
  filter( case_value == 1 )

#   genename strand  case case_value
# 1     TP53    pos case1          1
# 2     AP35    neg case1          1
# 3     AP35    neg case2          1
# 4     TP53    pos case3          1
# 5      TNN    pos case3          1
# 6     AP35    neg case3          1

data.table

library(data.table)
data.table::melt( setDT(df), id.vars = c("genename", "strand"), measure.vars = c("case1", "case2", "case3") )[value == 1, ][]

#    genename strand variable value
# 1:     TP53    pos    case1     1
# 2:     AP35    neg    case1     1
# 3:     AP35    neg    case2     1
# 4:     TP53    pos    case3     1
# 5:      TNN    pos    case3     1
# 6:     AP35    neg    case3     1

基准

microbenchmark::microbenchmark(
tidyverse = { df %>% 
  gather( case, case_value, c(case1, case2, case3) ) %>%
  filter( case_value == 1 )},
data.table = { melt( setDT(df), id.vars = c("genename", "strand"), measure.vars = c("case1", "case2", "case3") )[value == 1, ][] },
times = 1000)

# Unit: milliseconds
#       expr      min       lq     mean   median       uq      max neval
# tidyverse 2.335393 2.569323 3.157647 2.737729 3.089605 29.29513  1000
# data.table 1.374062 1.551656 1.845519 1.676229 1.838309 28.23499  1000