r - data.frames 列表中特定 data.frame 列的高效函数

Question

我有一个列表 data.frame s。例如

set.seed(1)
my_list <- list()
ids = c("a","b","c","d","e")
for(i in 1:5){
  my_list[[i]] <- data.frame(id = ids, p = rnorm(length(ids)), m = rnorm(length(ids)), hp = runif(length(ids)), hm = runif(length(ids)), d = rnorm(length(ids)), a = rnorm(length(ids)))
}

我想要的是有效地为每个 id(在“id”列中)计算列表中所有数据框的“p”、“m”、“d”和“a”列的方差。理想情况下，这将返回 data.frame像这样(基于上面绘制的值):

> result.df
  id     var_p     var_m      var_d     var_a
1  a 0.2371569 1.7810729 0.08264279 0.5074250
2  b 0.1091675 0.2107997 1.15051229 1.1578691
3  c 0.5385789 0.7650123 0.44215343 0.3137903
4  d 1.0174542 0.7818498 0.06414317 0.6079849
5  e 0.7343667 1.2870542 1.41615858 0.7362462

Answer 1

使用 my_list

library(plyr)
df = do.call(rbind, my_list)
out = ddply(df, .(id), colwise(var, c('p','m','d','a')))

#> out
#  id         p         m          d         a
#1  a 0.2371569 1.7810729 0.08264279 0.5074250
#2  b 0.1091675 0.2107997 1.15051229 1.1578691
#3  c 0.5385789 0.7650123 0.44215343 0.3137903
#4  d 1.0174542 0.7818498 0.06414317 0.6079849
#5  e 0.7343667 1.2870542 1.41615858 0.7362462

或基本 R 替代方案，使用 lapply 的组合和 apply

df = do.call(rbind, my_list)
df1 = do.call(rbind, 
      lapply(split(df, df$id), 
      function(x) apply(subset(x, select = c(p,m,d,a)), 2, var)))

out = transform(df1, id = row.names(df1))

#> out
#          p         m          d         a id
#a 0.2371569 1.7810729 0.08264279 0.5074250  a
#b 0.1091675 0.2107997 1.15051229 1.1578691  b
#c 0.5385789 0.7650123 0.44215343 0.3137903  c
#d 1.0174542 0.7818498 0.06414317 0.6079849  d
#e 0.7343667 1.2870542 1.41615858 0.7362462  e

或使用 doBy

library(doBy)
df = do.call(rbind, my_list)
out = summaryBy( p + m + d + a ~ id , data = df, keep.names=TRUE, FUN = var)

#> out
#  id         p         m          d         a
#1  a 0.2371569 1.7810729 0.08264279 0.5074250
#2  b 0.1091675 0.2107997 1.15051229 1.1578691
#3  c 0.5385789 0.7650123 0.44215343 0.3137903
#4  d 1.0174542 0.7818498 0.06414317 0.6079849
#5  e 0.7343667 1.2870542 1.41615858 0.7362462

或使用 sqldf

library(sqldf)
df = do.call(rbind, my_list)
out = sqldf("select id, variance(p), variance(m), 
             variance(d), variance(a) from df group by id")

#> out
#  id variance(p) variance(m) variance(d) variance(a)
#1  a   0.2371569   1.7810729  0.08264279   0.5074250
#2  b   0.1091675   0.2107997  1.15051229   1.1578691
#3  c   0.5385789   0.7650123  0.44215343   0.3137903
#4  d   1.0174542   0.7818498  0.06414317   0.6079849
#5  e   0.7343667   1.2870542  1.41615858   0.7362462