从R中的多种货币转换为欧元
Convert into EURO from multiple currencies in R
我有以下数据框：
structure(list(currency = c("NZD", "NZD", "NZD", "NZD", "NZD",
"EUR", "SEK", "EUR"), price = c(580.9, 539.75, 567.8,
802, 486, 365, 5088, 111)), class = "data.frame")
我想添加一个新列，其中“价格”的值为欧元，采用 2019 年的平均汇率。
我已经安装了 priceR 包并测试了以下功能，但它不允许从多种货币转换。
historical_exchange_rates("NZD", to = "USD",
start_date = "2019-01-01", end_date = "2019-12-31")
添加一个包含 2019 年平均欧元价格的新列的优雅方法是什么？
输出应如下所示：
currency price price_euro
NZD 380.86 500
SEK 531.75 800
######## 编辑#####
我设法用货币创建了这个 df，然后我将 left_join 到我的主 df。
我想知道有没有更优雅的解决方案
#include currencies
currency <- unique(mydf$currency)
#Loop over each of them
currency_df <- do.call(cbind, lapply(currency, function(x) {
historical_exchange_rates(currency, to = "EUR",
start_date = "2019-01-01", end_date = "2019-12-31")}))
#remove duplicated columns (date)
currency_df <- currency_df[, !duplicated(colnames(currency_df), fromLast = TRUE)]
#clean the currency df
currency_df <- currency_df %>%
#gets the average of all numeric columns
summarise_if(is.numeric, mean, na.rm =TRUE) %>%
#reshape from wide to long
pivot_longer(cols = starts_with("one_")) %>%
#extract currency name to link to main table
mutate(currency = gsub(".*one_(.*)_equivalent.*","\1",name))
货币数据框（在do.call之后）：
structure(list(date = structure(17906, class = "Date"), one_NZD_equivalent_to_x_EUR = 0.587717,
date = structure(17906, class = "Date"), one_KES_equivalent_to_x_EUR = 0.008648,
date = structure(17906, class = "Date"), one_USD_equivalent_to_x_EUR = 0.865426,
date = structure(17906, class = "Date"), one_AED_equivalent_to_x_EUR = 0.235849,
date = structure(17906, class = "Date"), one_EUR_equivalent_to_x_EUR = 1,
date = structure(17906, class = "Date"), one_TRY_equivalent_to_x_EUR = 0.158195,
date = structure(17906, class = "Date"), one_CZK_equivalent_to_x_EUR = 0.039034,
date = structure(17906, class = "Date"), one_PLN_equivalent_to_x_EUR = 0.23245,
date = structure(17906, class = "Date"), one_ZAR_equivalent_to_x_EUR = 0.062471,
date = structure(17906, class = "Date"), one_GBP_equivalent_to_x_EUR = 1.10791,
class = "data.frame")
具有平均值和整形的currency_df
structure(list(name = c("one_NZD_equivalent_to_x_EUR", "one_KES_equivalent_to_x_EUR",
"one_USD_equivalent_to_x_EUR", "one_AED_equivalent_to_x_EUR",
"one_EUR_equivalent_to_x_EUR", "one_CHF_equivalent_to_x_EUR",
"one_SEK_equivalent_to_x_EUR", "one_NOK_equivalent_to_x_EUR",
"one_DKK_equivalent_to_x_EUR", "one_TRY_equivalent_to_x_EUR",
"one_CZK_equivalent_to_x_EUR", "one_PLN_equivalent_to_x_EUR",
"one_ZAR_equivalent_to_x_EUR", "one_GBP_equivalent_to_x_EUR",
"one_HKD_equivalent_to_x_EUR", "one_SGD_equivalent_to_x_EUR",
"one_INR_equivalent_to_x_EUR", "one_AUD_equivalent_to_x_EUR",
"one_AOA_equivalent_to_x_EUR"), value = c(0.588651219178082,
0.0930875424657534, 0.89324564109589, 0.307407216438356, 1, 0.89912858630137,
0.0945236684931507, 0.101572109589041, 0.133948753424658, 0.157569854794521,
0.0389822712328767, 0.232789550684932, 0.0618727479452055, 1.14057644657534,
0.11402897260274, 0.654955421917808, 0.0127049808219178, 0.620929498630137,
1), currency = c("NZD", "KES", "USD", "AED", "EUR", "CHF", "SEK",
"NOK", "DKK", "TRY", "CZK", "PLN", "ZAR", "GBP", "HKD", "SGD",
"INR", "AUD", "AOA")), row.names = c(NA, -19L), class = c("tbl_df",
"tbl", "data.frame"))
如果我理解你的问题，你只是想向包含欧元价格的现有数据集添加一列。我认为您对加入数据集有正确的想法，但是由于您提供的函数格式化答案的方式，所以只需要处理一些丑陋的事情。我还想象您想要一种将其推广到更大的类似结构数据集的方法，因此将其功能化是更可取的。
我确信有更有效的方法，但对于像我这样的爱整洁的人来说，这个方法很有效。
library(priceR);library(tidyverse)
#Data frame
df <- data.frame(
currency = c("NZD", "NZD", "NZD", "NZD", "NZD", "EUR", "SEK", "EUR"),
price = c(580.9, 539.75, 567.8, 802, 486, 365, 5088, 111)
)
#Function to pull conversions
avg_ex <- function(x){
historical_exchange_rates(x, to = "EUR",start_date = "2019-01-01", end_date = "2019-12-31") %>%
`colnames<-`(c('date','conv')) %>% summarise(mean(conv)) %>% as.numeric
}
#Apply across all needed
conversions = sapply(unique(df$currency),avg_ex) %>% data.frame() %>% rownames_to_column() %>%
`colnames<-`(c('currency','conv'))
#Join and convert
df %>% left_join(conversions,by='currency') %>%
mutate(price_euro = price*conv)
生成以下输出
currency price conv price_euro
1 NZD 580.90 0.58865122 341.9475
2 NZD 539.75 0.58865122 317.7245
3 NZD 567.80 0.58865122 334.2362
4 NZD 802.00 0.58865122 472.0983
5 NZD 486.00 0.58865122 286.0845
6 EUR 365.00 1.00000000 365.0000
7 SEK 5088.00 0.09452367 480.9364
8 EUR 111.00 1.00000000 111.0000
我认为您已经有了解决方案，但如果它有用，这里有一种生成具有多种货币的数据框的便捷方法：
library(priceR)
library(tidyverse)
nz_to_us <- historical_exchange_rates("NZD", to = "USD",
start_date = "2019-01-01", end_date = "2019-12-31")
nz_to_eu <- historical_exchange_rates("NZD", to = "EUR",
start_date = "2019-01-01", end_date = "2019-12-31")
nz_to_us %>%
left_join(nz_to_eu, by = "date")
date one_NZD_equivalent_to_x_USD one_NZD_equivalent_to_x_EUR
1 2019-01-01 0.672695 0.585309
2 2019-01-02 0.671352 0.585720
3 2019-01-03 0.663423 0.586166
4 2019-01-04 0.668918 0.587234
5 2019-01-05 0.668918 0.587234
6 2019-01-06 0.668918 0.587234
7 2019-01-07 0.674390 0.591051
8 2019-01-08 0.675413 0.588339
9 2019-01-09 0.674081 0.588408
10 2019-01-10 0.679107 0.587717
我有以下数据框：
structure(list(currency = c("NZD", "NZD", "NZD", "NZD", "NZD",
"EUR", "SEK", "EUR"), price = c(580.9, 539.75, 567.8,
802, 486, 365, 5088, 111)), class = "data.frame")
我想添加一个新列，其中“价格”的值为欧元，采用 2019 年的平均汇率。
我已经安装了 priceR 包并测试了以下功能，但它不允许从多种货币转换。
historical_exchange_rates("NZD", to = "USD",
start_date = "2019-01-01", end_date = "2019-12-31")
添加一个包含 2019 年平均欧元价格的新列的优雅方法是什么？
输出应如下所示：
currency price price_euro
NZD 380.86 500
SEK 531.75 800
######## 编辑#####
我设法用货币创建了这个 df，然后我将 left_join 到我的主 df。 我想知道有没有更优雅的解决方案
#include currencies
currency <- unique(mydf$currency)
#Loop over each of them
currency_df <- do.call(cbind, lapply(currency, function(x) {
historical_exchange_rates(currency, to = "EUR",
start_date = "2019-01-01", end_date = "2019-12-31")}))
#remove duplicated columns (date)
currency_df <- currency_df[, !duplicated(colnames(currency_df), fromLast = TRUE)]
#clean the currency df
currency_df <- currency_df %>%
#gets the average of all numeric columns
summarise_if(is.numeric, mean, na.rm =TRUE) %>%
#reshape from wide to long
pivot_longer(cols = starts_with("one_")) %>%
#extract currency name to link to main table
mutate(currency = gsub(".*one_(.*)_equivalent.*","\1",name))
货币数据框（在do.call之后）：
structure(list(date = structure(17906, class = "Date"), one_NZD_equivalent_to_x_EUR = 0.587717,
date = structure(17906, class = "Date"), one_KES_equivalent_to_x_EUR = 0.008648,
date = structure(17906, class = "Date"), one_USD_equivalent_to_x_EUR = 0.865426,
date = structure(17906, class = "Date"), one_AED_equivalent_to_x_EUR = 0.235849,
date = structure(17906, class = "Date"), one_EUR_equivalent_to_x_EUR = 1,
date = structure(17906, class = "Date"), one_TRY_equivalent_to_x_EUR = 0.158195,
date = structure(17906, class = "Date"), one_CZK_equivalent_to_x_EUR = 0.039034,
date = structure(17906, class = "Date"), one_PLN_equivalent_to_x_EUR = 0.23245,
date = structure(17906, class = "Date"), one_ZAR_equivalent_to_x_EUR = 0.062471,
date = structure(17906, class = "Date"), one_GBP_equivalent_to_x_EUR = 1.10791,
class = "data.frame")
具有平均值和整形的currency_df
structure(list(name = c("one_NZD_equivalent_to_x_EUR", "one_KES_equivalent_to_x_EUR",
"one_USD_equivalent_to_x_EUR", "one_AED_equivalent_to_x_EUR",
"one_EUR_equivalent_to_x_EUR", "one_CHF_equivalent_to_x_EUR",
"one_SEK_equivalent_to_x_EUR", "one_NOK_equivalent_to_x_EUR",
"one_DKK_equivalent_to_x_EUR", "one_TRY_equivalent_to_x_EUR",
"one_CZK_equivalent_to_x_EUR", "one_PLN_equivalent_to_x_EUR",
"one_ZAR_equivalent_to_x_EUR", "one_GBP_equivalent_to_x_EUR",
"one_HKD_equivalent_to_x_EUR", "one_SGD_equivalent_to_x_EUR",
"one_INR_equivalent_to_x_EUR", "one_AUD_equivalent_to_x_EUR",
"one_AOA_equivalent_to_x_EUR"), value = c(0.588651219178082,
0.0930875424657534, 0.89324564109589, 0.307407216438356, 1, 0.89912858630137,
0.0945236684931507, 0.101572109589041, 0.133948753424658, 0.157569854794521,
0.0389822712328767, 0.232789550684932, 0.0618727479452055, 1.14057644657534,
0.11402897260274, 0.654955421917808, 0.0127049808219178, 0.620929498630137,
1), currency = c("NZD", "KES", "USD", "AED", "EUR", "CHF", "SEK",
"NOK", "DKK", "TRY", "CZK", "PLN", "ZAR", "GBP", "HKD", "SGD",
"INR", "AUD", "AOA")), row.names = c(NA, -19L), class = c("tbl_df",
"tbl", "data.frame"))
如果我理解你的问题，你只是想向包含欧元价格的现有数据集添加一列。我认为您对加入数据集有正确的想法，但是由于您提供的函数格式化答案的方式，所以只需要处理一些丑陋的事情。我还想象您想要一种将其推广到更大的类似结构数据集的方法，因此将其功能化是更可取的。
我确信有更有效的方法，但对于像我这样的爱整洁的人来说，这个方法很有效。
library(priceR);library(tidyverse)
#Data frame
df <- data.frame(
currency = c("NZD", "NZD", "NZD", "NZD", "NZD", "EUR", "SEK", "EUR"),
price = c(580.9, 539.75, 567.8, 802, 486, 365, 5088, 111)
)
#Function to pull conversions
avg_ex <- function(x){
historical_exchange_rates(x, to = "EUR",start_date = "2019-01-01", end_date = "2019-12-31") %>%
`colnames<-`(c('date','conv')) %>% summarise(mean(conv)) %>% as.numeric
}
#Apply across all needed
conversions = sapply(unique(df$currency),avg_ex) %>% data.frame() %>% rownames_to_column() %>%
`colnames<-`(c('currency','conv'))
#Join and convert
df %>% left_join(conversions,by='currency') %>%
mutate(price_euro = price*conv)
生成以下输出
currency price conv price_euro
1 NZD 580.90 0.58865122 341.9475
2 NZD 539.75 0.58865122 317.7245
3 NZD 567.80 0.58865122 334.2362
4 NZD 802.00 0.58865122 472.0983
5 NZD 486.00 0.58865122 286.0845
6 EUR 365.00 1.00000000 365.0000
7 SEK 5088.00 0.09452367 480.9364
8 EUR 111.00 1.00000000 111.0000
我认为您已经有了解决方案，但如果它有用，这里有一种生成具有多种货币的数据框的便捷方法：
library(priceR)
library(tidyverse)
nz_to_us <- historical_exchange_rates("NZD", to = "USD",
start_date = "2019-01-01", end_date = "2019-12-31")
nz_to_eu <- historical_exchange_rates("NZD", to = "EUR",
start_date = "2019-01-01", end_date = "2019-12-31")
nz_to_us %>%
left_join(nz_to_eu, by = "date")
date one_NZD_equivalent_to_x_USD one_NZD_equivalent_to_x_EUR
1 2019-01-01 0.672695 0.585309
2 2019-01-02 0.671352 0.585720
3 2019-01-03 0.663423 0.586166
4 2019-01-04 0.668918 0.587234
5 2019-01-05 0.668918 0.587234
6 2019-01-06 0.668918 0.587234
7 2019-01-07 0.674390 0.591051
8 2019-01-08 0.675413 0.588339
9 2019-01-09 0.674081 0.588408
10 2019-01-10 0.679107 0.587717