lapply 到 运行 运行 两个匿名函数同时

lapply to run to run two anonymous functions simultaneously

我有这样的数据。我正在使用 survey 包生成名为 vars 的向量中每个变量的 MEANSEFREQ

library(survey)

df <- data.frame(sex = c('F', 'M', NA, 'M', 'M', 'M', 'F', 'F'),
                 married = c(1,1,1,1,0,0,1,1),
                 pens = c(0, 1, 1, NA, 1, 1, 0, 0),
                 weight = c(1.12, 0.55, 1.1, 0.6, 0.23, 0.23, 0.66, 0.67))


vars <- c("sex","married","pens")

这是我的调查设计:

design <- svydesign(ids=~1, data=df, weights=~weight)

我正在使用lapply寻找手段:

lapply(vars, function(x) 
    svymean(as.formula(paste0('~interaction(', x, ')')), design, na.rm = T))

我正在使用 lapply 查找频率:

lapply(vars, function(x) 
    svytable(as.formula(paste0('~interaction(', x, ')')), design))

有没有办法运行lapply把这两个函数当做同一个tine?我希望我的输出看起来像这样:

                   mean     SE     freq
interaction(sex)F 0.60345 0.2067    2.45
interaction(sex)M 0.39655 0.2067    1.61

我试过了:

lapply(vars, function(x) 
  svymean(as.formula(paste0('~interaction(', x, ')')),
  svytable(as.formula(paste0('~interaction(', x, ')')), design)))

最好的方法就是将函数 return 的结果都放在一个列表中。但老实说,如果在 lapply 之外创建函数变得越来越复杂,我发现它会更好。所以这就是我可能会做的:

myfun <- function(x){
  means <- svymean(as.formula(paste0('~interaction(', x, ')')), design, na.rm = T)
  table <- svytable(as.formula(paste0('~interaction(', x, ')')), design)
  results <- list(svymean = means, svytable = table)
  return(results)
}

lapply(vars, myfun)

您显然可以将其作为匿名函数来执行,例如...

lapply(vars, function(x){
      means <- svymean(as.formula(paste0('~interaction(', x, ')')), design, na.rm = T)
      table <- svytable(as.formula(paste0('~interaction(', x, ')')), design)
      results <- list(svymean = means, svytable = table)
      return(results)
    })

您甚至不一定需要存储中间结果

lapply(vars, function(x){
      list(svymean = svymean(as.formula(paste0('~interaction(', x, ')')), design, na.rm = T), svytable = svytable(as.formula(paste0('~interaction(', x, ')')), design))})

但希望你会同意这并不漂亮。

这会将频率和均值放在同一行:

lapply(vars, function(x){
       out <-data.frame(eval(bquote(svymean(~interaction(.(as.name(x))), design, na.rm = T))))
       table <- eval(bquote(svytable(~.(as.name(x)),design)))
       out$freq <- table
       out 
       })