从一个数据框中提取数据并放入另一个数据框

Question

我想知道在有两个不同的过滤条件时，如何将一个数据框的数据提取到另一个数据框中。

我的数据框 A 是：

  TransactionNo       Date AccountNo TransactionType Amount Currency
1       1000001 2019-07-01     10001           Spend -12.44      SGD
2       1000002 2019-07-01     10001           Spend -31.92      CNY
3       1000003 2019-07-03     10001           Spend  -8.08      USD
4       1000004 2019-07-04     10001           Spend  -6.02      SGD
5       1000005 2019-07-05     10001           Spend  -5.04      USD
6       1000006 2019-07-06     10001           Spend  -8.43      SGD

我的数据框 B 是

  Date                  USD   CNY   SGD
  <dttm>              <dbl> <dbl> <dbl>
1 2019-07-01 00:00:00  1.35 0.198     1
2 2019-07-02 00:00:00  1.40 0.198     1
3 2019-07-03 00:00:00  1.36 0.197     1
4 2019-07-04 00:00:00  1.37 0.197     1
5 2019-07-05 00:00:00  1.38 0.197     1
6 2019-07-06 00:00:00  1.39 0.197     1

我想在数据框A中创建一个名为Exchange的新列。这个新列的条目可以来自于数据框B。取决于在两个数据框中货币和日期是否匹配。例如，数据框A中的第一行将是1，第二行是0.198，第三行是1.36。

最终代码大致如下：

A <- mutate(A, Exchange = XXXXXXXXXXX)

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- Chen JG Thomas

Answer 1

在基本的R语言中，创建一个包含行和列索引的索引，使用cbind函数来绑定这些索引，并提取相应的值。

i1 <- match(as.Date(A$Date), as.Date(B$Date))
j1 <- match(A$Currency, names(B)[-1])
A$Exchange <- B[-1][cbind(i1, j1)]
A$Exchange
#[1] 1.000 0.198 1.360 1.000 1.380 1.000

---

或者使用tidyverse，我们可以将数据从“宽”格式转换为“长”格式（'B'），然后进行联接。

library(dplyr)
library(tidyr)
B %>%
  mutate(Date = as.Date(Date)) %>% 
  pivot_longer(cols = -Date, names_to = 'Currency', values_to = 'Exchange') %>% 
  right_join(A %>%
               mutate(Date = as.Date(Date)))
# A tibble: 6 x 7
#  Date       Currency Exchange TransactionNo AccountNo TransactionType Amount
#  <date>     <chr>       <dbl>         <int>     <int> <chr>            <dbl>
#1 2019-07-01 SGD         1           1000001     10001 Spend           -12.4 
#2 2019-07-01 CNY         0.198       1000002     10001 Spend           -31.9 
#3 2019-07-03 USD         1.36        1000003     10001 Spend            -8.08
#4 2019-07-04 SGD         1           1000004     10001 Spend            -6.02
#5 2019-07-05 USD         1.38        1000005     10001 Spend            -5.04
#6 2019-07-06 SGD         1           1000006     10001 Spend            -8.43

数据

A <- structure(list(TransactionNo = 1000001:1000006, Date = c("2019-07-01", 
"2019-07-01", "2019-07-03", "2019-07-04", "2019-07-05", "2019-07-06"
), AccountNo = c(10001L, 10001L, 10001L, 10001L, 10001L, 10001L
), TransactionType = c("Spend", "Spend", "Spend", "Spend", "Spend", 
"Spend"), Amount = c(-12.44, -31.92, -8.08, -6.02, -5.04, -8.43
), Currency = c("SGD", "CNY", "USD", "SGD", "USD", "SGD")),
class = "data.frame", row.names = c("1", 
"2", "3", "4", "5", "6"))

B <- structure(list(Date = c("2019-07-01 00:00:00", "2019-07-02 00:00:00", 
"2019-07-03 00:00:00", "2019-07-04 00:00:00", "2019-07-05 00:00:00", 
"2019-07-06 00:00:00"), USD = c(1.35, 1.4, 1.36, 1.37, 1.38, 
1.39), CNY = c(0.198, 0.198, 0.197, 0.197, 0.197, 0.197), SGD = c(1L, 
1L, 1L, 1L, 1L, 1L)), class = "data.frame", row.names = c("1", 
"2", "3", "4", "5", "6"))

- akrun

Answer 2

虽然不如Akrun的方法高效，但这是另一种基于Base R的解决方案：

# Reshape B from wide to long: 

    long_B <-
      data.frame(
        reshape(
          B,
          direction = "long",
          varying = names(B)[names(B) != "Date"],
          v.names = "Exchange",
          idvar = "Date",
          timevar = "Currency",
          times = names(B)[names(B) != "Date"]
        ),
        row.names = NULL
      )

# Left join on intersecting vectors: 

a_left_join_b <-
  merge(A,
        long_B,
        by = intersect(colnames(A), colnames(long_B)),
        all.x = TRUE)

一个整洁的宇宙替代方案：

ab <- 

  B %>% 

  gather(key = "Currency", value = "Exchange", -Date) %>% 

  right_join(A, by = intersect(colnames(.), colnames(A)), all.y = TRUE)

数据：

A <-
  structure(
    list(
      TransactionNo = 1000001:1000006,
      Date = as.POSIXct(
        c(
          "2019-07-01",
          "2019-07-01",
          "2019-07-03",
          "2019-07-04",
          "2019-07-05",
          "2019-07-06"
        ),
        tz = Sys.timezone()
      ),
      AccountNo = c(10001L, 10001L, 10001L, 10001L, 10001L, 10001L),
      TransactionType = c("Spend", "Spend", "Spend", "Spend", "Spend",
                          "Spend"),
      Amount = c(-12.44, -31.92, -8.08, -6.02, -5.04, -8.43),
      Currency = c("SGD", "CNY", "USD", "SGD", "USD", "SGD")
    ),
    class = "data.frame",
    row.names = c("1",
                  "2", "3", "4", "5", "6")
  )

B <-
  structure(
    list(
      Date = as.POSIXct(
        c(
          "2019-07-01 00:00:00",
          "2019-07-02 00:00:00",
          "2019-07-03 00:00:00",
          "2019-07-04 00:00:00",
          "2019-07-05 00:00:00",
          "2019-07-06 00:00:00"
        ),
        tz = Sys.timezone()
      ),
      USD = c(1.35, 1.4, 1.36, 1.37, 1.38,
              1.39),
      CNY = c(0.198, 0.198, 0.197, 0.197, 0.197, 0.197),
      SGD = c(1L,
              1L, 1L, 1L, 1L, 1L)
    ),
    class = "data.frame",
    row.names = c("1",
                  "2", "3", "4", "5", "6")
  )

- hello_friend