一次重塑多个值 [英] Reshape multiple values at once

问题描述

我有一个很长的数据集，我想扩大范围，我很好奇是否有一种方法可以使用R中的reshape2或tidyr包一步完成全部操作。

I have a long data set I would like to make wide and I'm curious if there is a way to do this all in one step using the reshape2 or tidyr packages in R.

数据框 df 看起来像这样：

id  type    transactions    amount
20  income       20          100
20  expense      25          95
30  income       50          300
30  expense      45          250

我想了解以下内容：

id  income_transactions expense_transactions    income_amount   expense_amount
20       20                           25                 100             95
30       50                           45                 300             250

我知道我可以通过以下方式使用reshape2：

I know I can get part of the way there with reshape2 via for example:

dcast(df, id ~  type, value.var="transactions")

但是有没有办法一次将整个df整形，一次处理交易和金额变量？理想情况下，使用新的更合适的列名？

But is there a way to reshape the entire df in one shot addressing both the "transactions" and "amount" variables at once? And ideally with new more appropriate column names?

推荐答案

在 reshape2中，可以使用 recast （尽管以我的经验来看，这不是一个众所周知的函数）。

In "reshape2", you can use recast (though in my experience, this isn't a widely known function).

library(reshape2)
recast(mydf, id ~ variable + type, id.var = c("id", "type"))
#   id transactions_expense transactions_income amount_expense amount_income
# 1 20                   25                  20             95           100
# 2 30                   45                  50            250           300

您还可以使用基R的 reshape ：

reshape(mydf, direction = "wide", idvar = "id", timevar = "type")
#   id transactions.income amount.income transactions.expense amount.expense
# 1 20                  20           100                   25             95
# 3 30                  50           300                   45            250

或者，您可以融化和 dcast ，如下所示（此处带有 data.table）：

Or, you can melt and dcast, like this (here with "data.table"):

library(data.table)
library(reshape2)
dcast.data.table(melt(as.data.table(mydf), id.vars = c("id", "type")), 
                 id ~ variable + type, value.var = "value")
#    id transactions_expense transactions_income amount_expense amount_income
# 1: 20                   25                  20             95           100
# 2: 30                   45                  50            250           300

在 dcast.data.table 的更高版本中，来自 data.table（1.9.8）您将可以直接执行此操作。如果我正确理解的话，@ Arun尝试实现的内容将是重新整形，而无需首先融化数据，这就是当前在中发生的情况重铸，本质上是熔化 + dcast 操作序列的包装。

In later versions of dcast.data.table from "data.table" (1.9.8) you will be able to do this directly. If I understand correctly, what @Arun is trying to implement would be doing the reshaping without first having to melt the data, which is what happens presently with recast, which is essentially a wrapper for a melt + dcast sequence of operations.

为了更全面，这里是 tidyr 的方法：

And, for thoroughness, here's the tidyr approach:

library(dplyr)
library(tidyr)
mydf %>% 
  gather(var, val, transactions:amount) %>% 
  unite(var2, type, var) %>% 
  spread(var2, val)
#   id expense_amount expense_transactions income_amount income_transactions
# 1 20             95                   25           100                  20
# 2 30            250                   45           300                  50

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