blocks|key|2326785|text|我们可以melt第二个数据集从‘宽’到‘长’格式，merge与第一个数据集，以获得预期的输出。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2326786|library(reshape2)
res+<-+merge(A,+melt(B,+id.var='Year'),+
++++++++by.x=c('Country',+'Year'),+by.y=c('variable',+'Year'))
names(res)[4]+<-+'Rate'
res
#+++Country+Year+Number+Rate
#1++Canada+1997++++342+++456
#2++Canada+1998++++987+++999
#3+++++USA+1994++++455++++41|code-block|syntax|javascript|2326787|或者我们可以使用来自tidyr和right_join的tidyr和right_join来完成这个任务。|2326788|library(dplyr)
library(tidyr)
gather(B,+Country,Rate,+-Year)+%25>%25
+++++++++++++++++++++++right_join(.,+A)
#++Year+Country+Rate+Number
#1+1994+++++USA+++41++++455
#2+1997++Canada++456++++342
#3+1998++Canada++999++++987|2326789|或者，正如@DavidArenburg在评论中提到的那样，这也可以用data.table来完成。我们将“data.frame”转换为“data.table”(setDT(A))，将第二个数据集转换为melt，并加入on+'Year‘和'Country’。|2326790|library(data.table)#v1.9.6%2B
setDT(A)[melt(setDT(B),+1L,+variable+=+"Country",+value+=+"Rate"),+
++++++++++++++++on+=+c("Country",+"Year"),+
++++++++++++++++nomatch+=+0L]

#++++Country+Year+Number+Rate
#+1:+++++USA+1994++++455+++41
#+2:++Canada+1997++++342++456
#+3:++Canada+1998++++987++999|2326791|或者更短的版本(如果我们不太挑剔，没有变量名称)|2326792|setDT(A)[melt(B,+1L),+on+=+c(Country+=+"variable",+Year+=+"Year"),+nomatch+=+0L]|2326793|entityMap^0|4|4|P|5|0|0|A|5|G|A|R|5|X|A|0|0|Y|A|27|8|2R|4|2Z|2|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Y|8|@$9|Z|A|10|B|C]|$9|11|A|12|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|13|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|14|8|@$9|15|A|16|B|C]|$9|17|A|18|B|C]|$9|19|A|1A|B|C]|$9|1B|A|1C|B|C]]|D|@]|E|$]]|$1|M|3|N|5|H|7|1D|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|1E|8|@$9|1F|A|1G|B|C]|$9|1H|A|1I|B|C]|$9|1J|A|1K|B|C]|$9|1L|A|1M|B|C]]|D|@]|E|$]]|$1|Q|3|R|5|H|7|1N|8|@]|D|@]|E|$I|J]]|$1|S|3|T|5|6|7|1O|8|@]|D|@]|E|$]]|$1|U|3|V|5|H|7|1P|8|@]|D|@]|E|$I|J]]|$1|W|3|-4|5|6|7|1Q|8|@]|D|@]|E|$]]]|X|$]]

We can <code>melt</code> the second dataset from 'wide' to 'long' format, <code>merge</code> with the first dataset to get the expected output. 

<pre><code>library(reshape2)
res &lt;- merge(A, melt(B, id.var='Year'), 
 by.x=c('Country', 'Year'), by.y=c('variable', 'Year'))
names(res)[4] &lt;- 'Rate'
res
# Country Year Number Rate
#1 Canada 1997 342 456
#2 Canada 1998 987 999
#3 USA 1994 455 41
</code></pre>

<hr>

Or we can use <code>gather</code> from <code>tidyr</code> and <code>right_join</code> to get this done.

<pre><code>library(dplyr)
library(tidyr)
gather(B, Country,Rate, -Year) %&gt;%
 right_join(., A)
# Year Country Rate Number
#1 1994 USA 41 455
#2 1997 Canada 456 342
#3 1998 Canada 999 987
</code></pre>

<hr>

Or as @DavidArenburg mentioned in the comments, this can be also done with <code>data.table</code>. We convert the 'data.frame' to 'data.table' (<code>setDT(A)</code>), <code>melt</code> the second dataset and join <code>on</code> 'Year', and 'Country'.

<pre><code>library(data.table)#v1.9.6+
setDT(A)[melt(setDT(B), 1L, variable = "Country", value = "Rate"), 
 on = c("Country", "Year"), 
 nomatch = 0L]

# Country Year Number Rate
# 1: USA 1994 455 41
# 2: Canada 1997 342 456
# 3: Canada 1998 987 999
</code></pre>

Or a shorter version (if we are not too picky no variable names)

<pre><code>setDT(A)[melt(B, 1L), on = c(Country = "variable", Year = "Year"), nomatch = 0L]
</code></pre>

blocks|key|1860391|text|下面是另一种使用data.table的方法，它不需要将第二个数据表转换为长表单：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1860392|require(data.table)+#+1.9.6%2B
A[B,+Rate+:=+get(Country),+by=.EACHI,+on="Year"]
#++++Country+Year+Number+Rate
#+1:+++++USA+1994++++455+++41
#+2:++Canada+1997++++342++456
#+3:++Canada+1998++++987++999|code-block|syntax|javascript|1860393|其中A和B是data.tables，而Country是字符类型的。|1860394|entityMap^0|8|A|0|0|2|1|4|1|J|7|0^^$0|@$1|2|3|4|5|6|7|O|8|@$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|S|8|@$9|T|A|U|B|C]|$9|V|A|W|B|C]|$9|X|A|Y|B|C]]|D|@]|E|$]]|$1|M|3|-4|5|6|7|Z|8|@]|D|@]|E|$]]]|N|$]]

Here's another way using <code>data.table</code> that doesn't require converting the 2nd data table to long form:

<pre><code>require(data.table) # 1.9.6+
A[B, Rate := get(Country), by=.EACHI, on="Year"]
# Country Year Number Rate
# 1: USA 1994 455 41
# 2: Canada 1997 342 456
# 3: Canada 1998 987 999
</code></pre>

where <code>A</code> and <code>B</code> are data.tables, and <code>Country</code> is of character type.

I need to look up a value in a data frame based on multiple criteria in another data frame. Example

<pre><code>A=
Country Year Number
USA 1994 455
Canada 1997 342
Canada 1998 987
</code></pre>

must have added a column by the name of "rate" coming from

<pre><code>B=
Year USA Canada
1993 21 654
1994 41 321
1995 56 789
1996 85 123
1997 65 456
1998 1 999
</code></pre>

So that the final data frame is

<pre><code>C=
Country Year Number Rate
USA 1994 455 41
Canada 1997 342 456
Canada 1998 987 999
</code></pre>

In other words: Look up year and country from A in B and result is C. I would like to do this without a loop. I would like a general approach, such that I would be able to look up based on more than two criteria.

Looking up values without loop in R

翻译质量差，导致语言生硬或混乱。

没有提供实际的解决方法或示例。

解答不清晰，无法理解或解决问题。

页面排版不美观，阅读体验差。

文章

问答

视频

学习中心

腾讯云实验室

直播

竞赛

腾讯云代码分析专区

腾讯iOA零信任安全管理系统专区

腾讯云架构师技术同盟交流圈

腾讯云数据库专区

腾讯云顾问专区

腾讯云原生专区

腾讯混元专区

腾讯云TCE专区

腾讯云Lighthouse专区

腾讯云HAI专区

腾讯云Edgeone专区

腾讯云存储专区

腾讯云智能专区

腾讯轻联专区 

腾讯云开发专区

TAPD专区

腾讯轻量云游戏服专区

腾讯云最具价值专家

腾讯云架构师技术同盟

腾讯云创作之星

腾讯云开发者先锋

腾讯云代码助手

云原生构建

TAPD 敏捷项目管理

Cloud Studio

SDK中心

API中心

命令行工具

涵盖代码开发、场景应用、自动测试全流程，助你从零构建专属AI助手

一站式MCP教程库，解锁AI应用新玩法

我需要根据另一个数据帧中的多个标准在数据帧中查找一个值。示例A=Country Year NumberUSA     1994 455Canada  1997 342Canada  1998 987必须添加了一个名为"rate“的列。B=Year   USA   Canada1993   21    6541994  ...

问在R中查找不带循环的值
EN

Stack Overflow用户

社区

活动

圈层

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问在R中查找不带循环的值EN