如何重塑数据框并将循环列转置为数据框行？ [英] How to reshape dataframe and transpose recurring columns to dataframe rows?

问题描述

我有一个包含重复列的数据框（间隔为5）。

I have a dataframe that has recurring columns (the interval is 5).

此刻我的数据帧

这就是它的样子：我有5种类型的列，它们随时间重复一次。重复出现的列的名称都有一个后缀，也可以删除/重命名，以便它们都可以匹配。

So this is how it looks: I have 5 type of columns and they repeat time over time. The recurring columns have a suffix in their name, this can be removed/renamed as well, so that they would all match.

我想做的是转置这些重复出现的列排成一行，所以最后我只有5列（日期，PX_LAST，PX_HIGH，PX_VOLUME，名称）。然后，我将能够按日期，名称等对数据框进行分组，并执行许多其他操作。

What I would like to do is to transpose these recurring columns to rows, so that I would have only 5 columns in the end (Dates, PX_LAST, PX_HIGH, PX_VOLUME, Name). Then I would be able to group the dataframe by Dates, Name etc and do many other things.

我尝试了使用管道运算符％>％进行一些操作，但此刻目前还没有真正起作用。既然我没有什么想法了，我想，也许您可​​以帮帮我。

I tried some manipulations with pipe operator %>%, but it didn't really work at the moment. Since I don't have any ideas left, I thought, that maybe you could help me out.

预先感谢！

推荐答案

一个选择是将数据拆分到列表基于列名，然后一起 rbind

One option would be to split the data into a list of data.frame based on the column names and then rbind them together

nm1 <-  sub("\\.\\d+", "", names(dft))
i1 <- ave(seq_along(dft), nm1, FUN = seq_along)
out <- do.call(rbind, lapply(split.default(dft, i1), 
      function(x) setNames(x, sub("\\.\\d+", "", names(x)))))
row.names(out) <- NULL
out
#  Date Age
#1    1  21
#2    2  15
#3    1  32
#4    2  12

---

或另一种选择是遍历唯一名称，对数据进行子集，取消列表，并转换为 data.frame

---

Or another option is to loop through the unique names, subset the data, unlist, and convert to data.frame

un1 <- unique(nm1)
setNames(data.frame(lapply(un1, 
       function(x) unlist(dft[grep(x, names(dft))]))), un1)

data

data

dft <- data.frame("Date" = 1:2, "Age" = c(21,15), "Date" = 1:2, "Age" = c(32,12))

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