entityMap|blocks|key|ee80k|text|你可以这样做：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|536qe|library(dplyr)

df1+%25>%25+select(-id_df2)+%25>%25
++bind_rows(df2)+%25>%25
++mutate(id+=+gsub("_.*",+"",+id))+%25>%25
++replace(.,+is.na(.),+0)+%25>%25
++group_by(id,+type)+%25>%25
++summarise_at(vars(contains("count")),+funs(sum))|code-block|syntax|javascript|4tdf1|其中，输出为：|6jp4o|#+A+tibble:+4+x+4
#+Groups:+++id+[?]
++id+++++type+count1+count2
++<chr>+<dbl>++<dbl>++<dbl>
1+four++++++5++++++1++++++1
2+one+++++++8++++++1++++++0
3+three+++++6++++++1++++++1
4+two+++++++7++++++3++++++0|4afoa|但也包括：|19iv8|df1+%25>%25+select(-id_df2)+%25>%25
++bind_rows(df2)+%25>%25
++mutate(id+=+ifelse(grepl("_",+id),+id,+paste0(id,+"_a")))+%25>%25
++replace(.,+is.na(.),+0)+%25>%25
++group_by(id,+type)+%25>%25
++summarise_at(vars(contains("count")),+funs(sum))|a2pd9|如果您对保留_a部件感兴趣。|offset|length|style|CODE|93ubq|另一种方法是使用join，转换为long，然后向后传播，如：|424gm|library(tidyverse)

df1+%25>%25+
++left_join(df2,+by+=+c("id_df2"+=+"id"))+%25>%25
++gather(var,+val,+-id)+%25>%25
++mutate(var+=+gsub("\\..*",+"",+var))+%25>%25
++distinct(id,+var,+val)+%25>%25
++filter(!var+==+"id_df2")+%25>%25
++group_by(id,+var)+%25>%25
++summarise(val+=+sum(as.numeric(val),+na.rm+=+T))+%25>%25
++spread(var,+val)+|18qi9|给予：|70ogo|#+A+tibble:+4+x+4
#+Groups:+++id+[4]
++id++++++count1+count2++type
++<fct>++++<dbl>++<dbl>+<dbl>
1+four_a+++++++1++++++1+++++5
2+one_a++++++++1++++++0+++++8
3+three_a++++++1++++++1+++++6
4+two_a++++++++3++++++0+++++7|3vtm|如果_a结束有特殊目的，这可能是有意义的，例如，还有带有_b、_c等的组(在这种情况下，上面的方法将失败)。|977ib^0|0|0|0|0|0|0|6|2|0|0|0|0|0|2|2|S|2|V|2|0^^$0|$]|1|@$2|3|4|5|6|7|8|16|9|@]|A|@]|B|$]]|$2|C|4|D|6|E|8|17|9|@]|A|@]|B|$F|G]]|$2|H|4|I|6|7|8|18|9|@]|A|@]|B|$]]|$2|J|4|K|6|E|8|19|9|@]|A|@]|B|$F|G]]|$2|L|4|M|6|7|8|1A|9|@]|A|@]|B|$]]|$2|N|4|O|6|E|8|1B|9|@]|A|@]|B|$F|G]]|$2|P|4|Q|6|7|8|1C|9|@$R|1D|S|1E|T|U]]|A|@]|B|$]]|$2|V|4|W|6|7|8|1F|9|@]|A|@]|B|$]]|$2|X|4|Y|6|E|8|1G|9|@]|A|@]|B|$F|G]]|$2|Z|4|10|6|7|8|1H|9|@]|A|@]|B|$]]|$2|11|4|12|6|E|8|1I|9|@]|A|@]|B|$F|G]]|$2|13|4|14|6|7|8|1J|9|@$R|1K|S|1L|T|U]|$R|1M|S|1N|T|U]|$R|1O|S|1P|T|U]]|A|@]|B|$]]|$2|15|4|-4|6|7|8|1Q|9|@]|A|@]|B|$]]]]

You could do:

<pre><code>library(dplyr)

df1 %&gt;% select(-id_df2) %&gt;%
 bind_rows(df2) %&gt;%
 mutate(id = gsub("_.*", "", id)) %&gt;%
 replace(., is.na(.), 0) %&gt;%
 group_by(id, type) %&gt;%
 summarise_at(vars(contains("count")), funs(sum))
</code></pre>

Where the output is:

<pre><code># A tibble: 4 x 4
# Groups: id [?]
 id type count1 count2
 &lt;chr&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;
1 four 5 1 1
2 one 8 1 0
3 three 6 1 1
4 two 7 3 0
</code></pre>

But also:

<pre><code>df1 %&gt;% select(-id_df2) %&gt;%
 bind_rows(df2) %&gt;%
 mutate(id = ifelse(grepl("_", id), id, paste0(id, "_a"))) %&gt;%
 replace(., is.na(.), 0) %&gt;%
 group_by(id, type) %&gt;%
 summarise_at(vars(contains("count")), funs(sum))
</code></pre>

If you're interested in keeping the <code>_a</code> part.

Another approach would be with a join, transforming to long, and then spreading back, like:

<pre><code>library(tidyverse)

df1 %&gt;% 
 left_join(df2, by = c("id_df2" = "id")) %&gt;%
 gather(var, val, -id) %&gt;%
 mutate(var = gsub("\\..*", "", var)) %&gt;%
 distinct(id, var, val) %&gt;%
 filter(!var == "id_df2") %&gt;%
 group_by(id, var) %&gt;%
 summarise(val = sum(as.numeric(val), na.rm = T)) %&gt;%
 spread(var, val) 
</code></pre>

Giving:

<pre><code># A tibble: 4 x 4
# Groups: id [4]
 id count1 count2 type
 &lt;fct&gt; &lt;dbl&gt; &lt;dbl&gt; &lt;dbl&gt;
1 four_a 1 1 5
2 one_a 1 0 8
3 three_a 1 1 6
4 two_a 3 0 7
</code></pre>

This may be of interest if <code>_a</code> ending has a special purpose, e.g. there are also groups with <code>_b</code>, <code>_c</code> etc. (the above method would fail in this case).

entityMap|blocks|key|8ets7|text|稍微不那么优雅，但仍然有效：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|evini|result_2+<-+df2+%25>%25+
++mutate(id+=+paste0(id,+"_a"))+%25>%25
++bind_rows(df1)+%25>%25+
++select(-id_df2)+%25>%25+
++replace(.,+is.na(.),+0)+%25>%25
++group_by(id)+%25>%25+
++summarise(count1+=+sum(count1),+count2+=+sum(count2),+type+=+max(type))+%25>%25+
++mutate(id_df2+=+as.factor(id))+%25>%25+
++select(c(id_df2,+type,+count1,+count2),+-id)|code-block|syntax|javascript|brhj9^0|0|0^^$0|$]|1|@$2|3|4|5|6|7|8|I|9|@]|A|@]|B|$]]|$2|C|4|D|6|E|8|J|9|@]|A|@]|B|$F|G]]|$2|H|4|-4|6|7|8|K|9|@]|A|@]|B|$]]]]

slightly less elegant, but works nonetheless:

<pre><code>result_2 &lt;- df2 %&gt;% 
 mutate(id = paste0(id, "_a")) %&gt;%
 bind_rows(df1) %&gt;% 
 select(-id_df2) %&gt;% 
 replace(., is.na(.), 0) %&gt;%
 group_by(id) %&gt;% 
 summarise(count1 = sum(count1), count2 = sum(count2), type = max(type)) %&gt;% 
 mutate(id_df2 = as.factor(id)) %&gt;% 
 select(c(id_df2, type, count1, count2), -id)
</code></pre>

I have two dataframes. For some rows of df1, there's a matching row in df2. Now some columns of df1 should be manipulated so that they contain the sum of their own value and the equivalent value out of df2.

In the following example, columns 'count1' and 'count2' should be summed up, but not column 'type'.

<pre><code>df1 &lt;- data.frame(id = c("one_a", "two_a", "three_a", "four_a"), type = c(8,7,6,5), count1 = c(1,2,1,NA), count2 = c(NA,0,1,0), id_df2 = c("one", "two", "three", "four"))
df2 &lt;- data.frame(id = c("one", "two", "four"), type = c(8,7,5), count1 = c(0,1,1), count2 = c(0,0,1))
result &lt;- data.frame(id = c("one_a", "two_a", "three_a", "four_a"), type = c(8,7,6,5), count1 = c(1,3,1,1), count2 = c(0,0,1,1))

&gt; df1
 id type count1 count2 id_df2
1 one_a 8 1 NA one
2 two_a 7 2 0 two
3 three_a 6 1 1 three
4 four_a 5 NA 0 four

&gt; df2
 id type count1 count2
1 one 8 0 0
2 two 7 1 0
3 four 5 1 1

&gt; result
 id type count1 count2
1 one_a 8 1 0
2 two_a 7 3 0
3 three_a 6 1 1
4 four_a 5 1 1
</code></pre>

There have been similar questions and I tried to find a solution by splitting the dataframes apart and merging them afterwards. I just wondered if there's a more elegant way to do this. My original dataset has about 300 columns, so I'm looking for a solution which is scalable.

Thanks in advance
chuckmorris

Sum up two dataframes partly

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

我有两个数据帧。对于df1的某些行，在df2中有匹配的行。现在，应该对df1的一些列进行操作，使它们包含自己的值和df2中的等价值之和。 在下面的示例中，应该对列'count1‘和'count2’求和，而不是对列'type‘求和。 df1 <- data.frame(id = c("one_a", "two_a", ...

问部分总结两个数据帧
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

问部分总结两个数据帧EN