Question

我有一个数据框，其中包含不同日期的不同货币金额，并且想要创建一个新列，显示在该日期它等于的金额。

例如我的数据可能如下

data <- data.frame(
  date = seq.Date(from = as.Date('2017-01-01'), to = as.Date('2017-01-10'), length = 10),
  currency = c('EUR', 'GBP', 'USD', 'HKD', 'GBP', 'EUR', 'CAN', 'AUD', 'EUR', 'GBP'),
  amount = 1:10

）

但是我想要一个通用函数来执行此操作，无论日期范围和数据框架中的货币如何都可以使用。

我很确定我可以使用getFX包中的quantmod函数执行此操作，但无法完成最佳方法！

我确信此操作已经完成，所以我们将不胜感激。

由于

Answer 1

使用功能?merge，?lapply并按照以下步骤操作：

<强>步骤：

1）创建货币对EUR / GBP，USD / GBP

2）获取外汇数据

3）重塑/修改外汇数据

4）与原始数据合并并计算fx翻译量

library("quantmod")
library("reshape2") #for melt function


#replaced CAN by CAD for Canadian Dollar

currDF  <- data.frame(
date = seq.Date(from = as.Date('2017-01-01'), to = as.Date('2017-01-10'), length = 10),
currency = c('EUR', 'GBP', 'USD', 'HKD', 'GBP', 'EUR', 'CAD', 'AUD', 'EUR', 'GBP'),
amount = 1:10,stringsAsFactors=FALSE)
currDF

外汇数据：

#Set start and end dates
startDt = as.Date("2017-01-01")
endDt = as.Date("2017-01-10")


#create currency pair combinations i.e. EUR/GBP, USDGBP 
currCombinations = paste(setdiff(unique(currDF$currency),"GBP"),"GBP",sep="/")

currCombinations
#[1] "EUR/GBP" "USD/GBP" "HKD/GBP" "CAD/GBP" "AUD/GBP"

#get FX data for each currency pair and merge them into a single xts dataset
#see ?lapply, ?do.call and ?merge.xts
#note auto.assign needs to be FALSE

fxData = do.call(merge.xts,lapply(currCombinations,function(x) 
   getFX(x,from=startDt,to=endDt,auto.assign=FALSE))) 

fxData
#            EUR.GBP  USD.GBP  HKD.GBP  CAD.GBP  AUD.GBP
#2017-01-01 0.852550 0.810242 0.104474 0.602773 0.583503
#2017-01-02 0.852550 0.810242 0.104474 0.602773 0.583503
#2017-01-03 0.850220 0.814326 0.104987 0.606361 0.587630
#2017-01-04 0.850310 0.815139 0.105102 0.609074 0.591082
#2017-01-05 0.853695 0.810205 0.104480 0.610352 0.591940
#2017-01-06 0.855625 0.808914 0.104311 0.610882 0.592583
#2017-01-07 0.857335 0.813948 0.104951 0.614860 0.593995
#2017-01-08 0.857330 0.813942 0.104951 0.614847 0.593990
#2017-01-09 0.864095 0.819833 0.105707 0.619112 0.600639
#2017-01-10 0.870830 0.822656 0.106077 0.622248 0.605533

#remove .GBP from all columns above
colnames(fxData) = gsub("[.]GBP","",colnames(fxData))

#set conversion factor for GBP = 1
fxData$GBP = 1

数据重塑：

#create data.frame from xts data
fxData_DF = data.frame(date=index(fxData),coredata(fxData),stringsAsFactors=FALSE)

#To make fx dataset(wide format) amenable for merging with currDF
#we convert it to long format by using melt from reshape2 package see,?melt

fxMolten = melt(fxData_DF,id="date",variable.name="currency",value.name="conversionFactor")


head(fxMolten,2)
#        date currency conversionFactor
#1 2017-01-01      EUR          0.85255
#2 2017-01-02      EUR          0.85255
tail(fxMolten,2)
#         date currency conversionFactor
#59 2017-01-09      GBP                1
#60 2017-01-10      GBP                1

合并数据：

#For each currency and date, we need a conversion factor hence
#we merge both datasets by columns date and currency 

fxMerged = merge(currDF,fxMolten,by=c("date","currency"))

#calculate FX translated amount
fxMerged$convAmount = fxMerged$amount * fxMerged$conversionFactor

fxMerged
#         date currency amount conversionFactor convAmount
#1  2017-01-01      EUR      1         0.852550   0.852550
#2  2017-01-02      GBP      2         1.000000   2.000000
#3  2017-01-03      USD      3         0.814326   2.442978
#4  2017-01-04      HKD      4         0.105102   0.420408
#5  2017-01-05      GBP      5         1.000000   5.000000
#6  2017-01-06      EUR      6         0.855625   5.133750
#7  2017-01-07      CAD      7         0.614860   4.304020
#8  2017-01-08      AUD      8         0.593990   4.751920
#9  2017-01-09      EUR      9         0.864095   7.776855
#10 2017-01-10      GBP     10         1.000000  10.000000

不同日期的多种货币的货币兑换

1 个答案: