如何动态访问数据帧列表中的特定属性 [英] How To Dynamically Access Specific Attribute In A List of Data Frames

问题描述

我有一个数据，它是数据帧的列表.我正在尝试访问列表中每个数据框内的特定属性.可以使用以下代码提取特定属性DP.UniqueId.

I have a data which is a list of data frames. I am trying to access a specific attribute inside each of the data frames in the list. It's possible to extract the specific attribute DP.UniqueId using below code.

> attr(new_data$A$AA, "SpotfireColumnMetaData")$DP.UniqueId
[1] "A-024"

此方法的问题在于它不是动态的.另外，我拥有的大数据列表中有成千上万个数据帧，对于每个数据帧，我都想提取此特定的DP.UniqueId属性.

Problem with this approach is that it's not dynamic. Also, the big data I have has thousands of data frames in the list and for each of the data frames I want to extract this specific DP.UniqueId attribute.

如果必须应用lapply()或for loop()来动态获取此属性，则必须将其引用为:

If I have to apply lapply() or for loop() to get this attribute dynamically, then I will have to reference it as:

> attr(new_data[1][1], "SpotfireColumnMetaData")$DP.UniqueId
NULL

但是看起来像在R中一样，attr()不能像上面的代码那样被引用.有没有一种方法可以动态提取特定属性并将其存储在数据框中?

But it looks like in R, attr() can't be referenced like above code does. Is there a way to extract specific attributes dynamically and store it in a data frame?

数据

new_data <- list(A = structure(list(AA = structure(5.49485, SpotfireColumnMetaData = list(
  DP.TestNumber = "111", DP.Type = "", DP.TestName = "ABC", 
  DP.Info = "PTR", DP.TestUnit = "Mohm", DP.Statistic = "raw", 
  DP.Program = "", DP.ScaleFactor = 0L, DP.FilteredOutCells = 0L, 
  Limits.Prod.Lower = 2, Limits.Prod.Target = NaN, Limits.Prod.Upper = 7, 
  Limits.Spec.Lower = -Inf, Limits.Spec.Target = NaN, Limits.Spec.Upper = Inf, 
  Limits.Outlier.Lower = -Inf, Limits.Outlier.Target = NaN, 
  Limits.Outlier.Upper = Inf, Limits.Whatif.Lower = -Inf, Limits.Whatif.Target = NaN, 
  Limits.Whatif.Upper = Inf, DP.ParamType = "PARAMETRIC", DP.BlockId = "", 
  DP.Scratch = "", DP.ColumnId = "", Dp.BaseName = "", DP.FTR.testtxt = "", 
  DP.PTR.testtxt = "A  -1 <> B", DP.DTR.textdat = "", 
  DP.MPR.pinnum = "0", DP.UniqueId = "A-024"))), class = "data.frame", row.names = c(NA,-1L)),
  B = structure(list(BB = structure(0.08707662, SpotfireColumnMetaData = list(
  DP.TestNumber = "112", DP.Type = "", DP.TestName = "ABC", 
DP.Info = "PTR", DP.TestUnit = "Mohm", DP.Statistic = "raw", 
DP.Program = "", DP.ScaleFactor = 0L, DP.FilteredOutCells = 0L, 
Limits.Prod.Lower = 2, Limits.Prod.Target = NaN, Limits.Prod.Upper = 7, 
Limits.Spec.Lower = -Inf, Limits.Spec.Target = NaN, Limits.Spec.Upper = Inf, 
Limits.Outlier.Lower = -Inf, Limits.Outlier.Target = NaN, 
Limits.Outlier.Upper = Inf, Limits.Whatif.Lower = -Inf, Limits.Whatif.Target = NaN, 
Limits.Whatif.Upper = Inf, DP.ParamType = "PARAMETRIC", DP.BlockId = "", 
DP.Scratch = "", DP.ColumnId = "", Dp.BaseName = "", DP.FTR.testtxt = "", 
DP.PTR.testtxt = "A  -1 <> B", DP.DTR.textdat = "", 
DP.MPR.pinnum = "0", DP.UniqueId = "B-025"))), class = "data.frame", row.names = c(NA,-1L)))

推荐答案

似乎可以使用purrr包轻松完成此操作.例如

It seems like you can accomplish this easily with the purrr package. For example

library(purrr)
new_data %>% map(pluck, 1, attr_getter("SpotfireColumnMetaData"), "DP.UniqueId")
# $A
# [1] "A-024"
# $B
# [1] "B-025"

map()将遍历初始列表，然后pluck()可以处理深度提取.

The map() will iterate over the initial list, and then pluck() can take care of the deep extraction.

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