需要矢量化使用嵌套循环的解决方案（将数据帧从长格式转换为宽格式） [英] Need to vectorize solution that using nested loops (transform data frame from long to wide format)

问题描述

我有以下的数据框架，需要做从长到宽的格式转换：

pre $ code $符号边价
1 AB 1
2 AS 2
3 BB 3
4 CB 4
5 BS 5

说明：对于每个符号必须存在两条边，B和S边。需要查找这些线并将它们转换为宽格式：

code $符号，首先方（B或S），B方的价格，S方的价格]

如果一行存在，但另一行不存在，则将NA值设置为适当的价格值。例如，如果行与B方面存在，但与方S是缺少，然后把NA的价格S方。

输出结果必须如下：

 符号方price_B price_S 
 1 AB 1 2 
 2 BB 3 5 
 3 CB 4 NA 
  

对于符号A和B，我们有A和B两边的线，所以我们不用NA来转换它们。 B方首先为了然后我们只把B方放到方列。对于符号C，我们只有B面，而不是S面，所以我们把NA值放在price_S列。

如何进行矢量化？

解决方案

reshape 获取价格：

< pre $ lt; code> price< - reshape（x，direction ='wide'，idvar ='symbol'，timevar ='side'，v.names ='price'，sep =''）
价格
##符号priceB priceS
## 1 A 1 2
## 3 B 3 5
## 4 C 4 NA

集合获得第一个价格：



  first<  - 聚合（side〜symbol，data = x，FUN = head，n = 1）
 first 
 ##符号方
 ## 1 AB 
 ## 2 BB 
 ## 3 CB 
  


$ b $

  merge（first，价格）
 ##符号方priceB priceS 
 ## 1 AB 1 2 
 ## 2 BB 3 5 
 ## 3 C B 4 NA 
  




I have following data frame and need to do transformation from long into wide format:

symbol side price
1      A    B     1
2      A    S     2
3      B    B     3
4      C    B     4
5      B    S     5

Explanation: for every symbol must exists two lines with side B and side S. Need to find these lines and transform them into wide format:

[symbol, first-comed side (B or S), price of side B, price of side S]

If one line exists but another is missing, then put NA value to appropriate price value. For example if line with side B exists, but with side S is missing, then put NA to price of side S.

Output results must be following:

  symbol side price_B price_S
1      A    B      1      2
2      B    B      3      5
3      C    B      4     NA

For symbols A and B we have lines with sides A and B so we transform them without NA's. Side B was first in order then we put only B side to "side" column. For symbol C we have only side B but not side S so we put NA value to the "price_S" column.

How to vectorize it?

解决方案

reshape gets the prices:

prices <- reshape(x, direction='wide', idvar='symbol', timevar='side', v.names='price', sep='')
prices
##   symbol priceB priceS
## 1      A      1      2
## 3      B      3      5
## 4      C      4     NA

aggregate gets the first price:

first <- aggregate(side ~ symbol, data=x, FUN=head, n=1)
first
##   symbol side
## 1      A    B
## 2      B    B
## 3      C    B

merge puts them together:

merge(first, prices)
##   symbol side priceB priceS
## 1      A    B      1      2
## 2      B    B      3      5
## 3      C    B      4     NA

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