如何将一个数据帧的值添加到另一个数据帧，并由接收数据帧进行双重条件？

Question

我正在尝试以一种特定的方式合并两个数据框。它们与这两个相似：

df1 <- data.frame(Countries = c("Portugal", "Andorra", "Spain", "Portugal", "Portugal", "Portugal", "Spain"),
                 Year_of_order = c("2015", "2016", "2014", "2016", "2014", "2015", "2015"),
                 Type_of_order = c("vegetables", "meat", "fruits", "fruits", "meat", "vegetable", "meat")
)

df2 <- data.frame(Names = c("Andorra", "Andorra", "Andorra", "Andorra", "Portugal", "Portugal", "Portugal", "Spain", "Spain", "Spain"),
                 Year = c("2014", "2015", "2016", "2017", "2014", "2015", "2016", "2014", "2015", "2016"),
                 Budget_spent = c("452", "444", "429", "456", "854", "876", "833", "102", "104", "96")
)

第二个数据帧是

pivot_longer

的乘积，这就是为什么它看起来有点奇怪。

我想要的是向 df1 添加一个新列，称为

Budget_spent

：其值应该是 df2 中对应的

Year

和

Names

中指示的值。基本上，当 df1

Year_of_order

和

Countries

都匹配 df2 的

Year

和

Names

时，我想要获得 df2 的

Budget_spent

的值。据我了解，我需要进行双条件匹配，但也许我错了。

如果我的解释不清楚，我希望输出如下：

>df1
   Countries  Year_of_order  Type_of_order  Budget_spent
1  Portugal   2015           vegetables     876
2  Andorra    2016           meat           429
3  Spain      2014           fruits         102
4  Portugal   2016           fruits         833
5  Portugal   2014           meat           854
6  Portugal   2015           vegetable      876
7  Spain      2015           meat           104

我尝试了很多东西（

match

，

merge

，

if

，

when

，

for row

），但收效甚微，主要是因为我什至无法从概念上掌握我应该应用什么流程：

#Here's the most complete attempt, the rest look quite torn apart

df1$Budget_spent <- for (row in df1$Countries) {
  match(df1$Countries, df2$Names)+
  match(df1$Year_of_order, df2$Year)+
  df2$Budget_spent}

#Error message: argument non-numeric for binary operator

如果您提供有关如何解决该问题的任何帮助或指示，我将非常感激。

Answer 1

left_join(df1, df2, join_by(Countries == Names, Year_of_order == Year))
  Countries Year_of_order Type_of_order Budget_spent
1  Portugal          2015    vegetables          876
2   Andorra          2016          meat          429
3     Spain          2014        fruits          102
4  Portugal          2016        fruits          833
5  Portugal          2014          meat          854
6  Portugal          2015     vegetable          876
7     Spain          2015          meat          104