blocks|key|2972440|text|如果你把对象传递给它，它就能工作(好吧，你没有，但是……)而不是作为字符向量：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2972441|df+%25.%25
++++group_by(asdfgfTgdsx,+asdfk30v0ja)+%25.%25
++++summarise(Value+=+mean(value))

>+df+%25.%25
%2B+++group_by(asdfgfTgdsx,+asdfk30v0ja)+%25.%25
%2B+++summarise(Value+=+mean(value))
Source:+local+data+frame+[9+x+3]
Groups:+asdfgfTgdsx

++asdfgfTgdsx+asdfk30v0ja++++++++Value
1+++++++++++A+++++++++++C++0.046538002
2+++++++++++C+++++++++++B+-0.286359899
3+++++++++++B+++++++++++A+-0.305159419
4+++++++++++C+++++++++++A+-0.004741504
5+++++++++++B+++++++++++B++0.520126476
6+++++++++++C+++++++++++C++0.086805492
7+++++++++++B+++++++++++C+-0.052613078
8+++++++++++A+++++++++++A++0.368410146
9+++++++++++A+++++++++++B++0.088462212|code-block|syntax|javascript|2972442|而df是你的data。|offset|length|style|CODE|2972443|?group_by说：|2972444|+...:+variables+to+group+by.+All+tbls+accept+variable+names,+some
++++++will+also+accept+functons+of+variables.+Duplicated+groups
++++++will+be+silently+dropped.|2972445|我将其解释为不是指名称的字符版本，而是指在foo$bar中如何引用它们；bar在这里不被引用。或者如何引用公式中的变量：foo+~+bar。|2972446|@Arun还提到你可以这样做：|2972447|df+%25.%25
++++group_by("asdfgfTgdsx",+"asdfk30v0ja")+%25.%25
++++summarise(Value+=+mean(value))|2972448|但您不能传入未计算的值不是数据对象中的变量名的内容。|2972449|我认为这是因为Hadley使用内部方法来查找您通过...参数传递的内容。|2972450|entityMap^0|0|0|1|2|6|4|0|0|9|0|0|L|7|10|3|1O|9|0|0|0|0|P|3|0^^$0|@$1|2|3|4|5|6|7|12|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|13|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|14|8|@$I|15|J|16|K|L]|$I|17|J|18|K|L]]|9|@]|A|$]]|$1|M|3|N|5|6|7|19|8|@$I|1A|J|1B|K|L]]|9|@]|A|$]]|$1|O|3|P|5|D|7|1C|8|@]|9|@]|A|$E|F]]|$1|Q|3|R|5|6|7|1D|8|@$I|1E|J|1F|K|L]|$I|1G|J|1H|K|L]|$I|1I|J|1J|K|L]]|9|@]|A|$]]|$1|S|3|T|5|6|7|1K|8|@]|9|@]|A|$]]|$1|U|3|V|5|D|7|1L|8|@]|9|@]|A|$E|F]]|$1|W|3|X|5|6|7|1M|8|@]|9|@]|A|$]]|$1|Y|3|Z|5|6|7|1N|8|@$I|1O|J|1P|K|L]]|9|@]|A|$]]|$1|10|3|-4|5|6|7|1Q|8|@]|9|@]|A|$]]]|11|$]]

It works if you pass it the objects (well, you aren't, but...) rather than as a character vector:

<pre><code>df %.%
 group_by(asdfgfTgdsx, asdfk30v0ja) %.%
 summarise(Value = mean(value))

&gt; df %.%
+ group_by(asdfgfTgdsx, asdfk30v0ja) %.%
+ summarise(Value = mean(value))
Source: local data frame [9 x 3]
Groups: asdfgfTgdsx

 asdfgfTgdsx asdfk30v0ja Value
1 A C 0.046538002
2 C B -0.286359899
3 B A -0.305159419
4 C A -0.004741504
5 B B 0.520126476
6 C C 0.086805492
7 B C -0.052613078
8 A A 0.368410146
9 A B 0.088462212
</code></pre>

where <code>df</code> was your <code>data</code>.

<code>?group_by</code> says:

<pre><code> ...: variables to group by. All tbls accept variable names, some
 will also accept functons of variables. Duplicated groups
 will be silently dropped.
</code></pre>

which I interpret to mean not the character versions of the names, but how you would refer to them in <code>foo$bar</code>; <code>bar</code> is not quoted here. Or how you'd refer to variables in a formula: <code>foo ~ bar</code>.

@Arun also mentions that you can do:

<pre><code>df %.%
 group_by("asdfgfTgdsx", "asdfk30v0ja") %.%
 summarise(Value = mean(value))
</code></pre>

But you can't pass in something that unevaluated is not a name of a variable in the data object.

I presume this is due to the internal methods Hadley is using to look up the things you pass in via the <code>...</code> argument.

blocks|key|164070|text|目前在dplyr中对此的支持非常弱，最终我认为其语法将是这样的：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|164071|df+%25.%25+group_by(.groups+=+c("asdfgfTgdsx",+"asdfk30v0ja"))|code-block|syntax|javascript|164072|但这可能不会有一段时间(因为我需要考虑所有的后果)。|164073|同时，您可以使用regroup()，它接受一个符号列表：|offset|length|style|CODE|164074|library(dplyr)

df+<-++data.frame(
++asihckhdoydk+=+sample(LETTERS[1:3],+100,+replace=TRUE),
++a30mvxigxkgh+=+sample(LETTERS[1:3],+100,+replace=TRUE),
++value+=+rnorm(100)
)

df+%25.%25
++regroup(list(quote(asihckhdoydk),+quote(a30mvxigxkgh)))+%25.%25
++summarise(n+=+n())|164075|如果您有一个列名的字符矢量，可以使用lapply()和as.symbol()将它们转换为正确的结构|164076|vars+<-+setdiff(names(df),+"value")
vars2+<-+lapply(vars,+as.symbol)

df+%25.%25+regroup(vars2)+%25.%25+summarise(n+=+n())|164077|entityMap^0|0|0|0|8|9|0|0|I|8|R|B|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|X|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Y|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|Z|8|@$K|10|L|11|M|N]]|9|@]|A|$]]|$1|O|3|P|5|D|7|12|8|@]|9|@]|A|$E|F]]|$1|Q|3|R|5|6|7|13|8|@$K|14|L|15|M|N]|$K|16|L|17|M|N]]|9|@]|A|$]]|$1|S|3|T|5|D|7|18|8|@]|9|@]|A|$E|F]]|$1|U|3|-4|5|6|7|19|8|@]|9|@]|A|$]]]|V|$]]

The support for this in dplyr is currently pretty weak, eventually I think the syntax will be something like:

<pre><code>df %.% group_by(.groups = c("asdfgfTgdsx", "asdfk30v0ja"))
</code></pre>

But that probably won't be there for a while (because I need to think through all the consequences).

In the meantime, you can use <code>regroup()</code>, which takes a list of symbols:

<pre><code>library(dplyr)

df &lt;- data.frame(
 asihckhdoydk = sample(LETTERS[1:3], 100, replace=TRUE),
 a30mvxigxkgh = sample(LETTERS[1:3], 100, replace=TRUE),
 value = rnorm(100)
)

df %.%
 regroup(list(quote(asihckhdoydk), quote(a30mvxigxkgh))) %.%
 summarise(n = n())
</code></pre>

If you have have a character vector of column names, you can convert them to the right structure with <code>lapply()</code> and <code>as.symbol()</code>:

<pre><code>vars &lt;- setdiff(names(df), "value")
vars2 &lt;- lapply(vars, as.symbol)

df %.% regroup(vars2) %.% summarise(n = n())
</code></pre>

blocks|key|164083|text|在dplyr完全支持字符串参数之前，也许这个要点是有用的：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|164084|https://gist.github.com/skranz/9681509|offset|length|164085|它包含大量使用字符串参数的包装函数，如s_group_by、s_mutate、s_filter等。您可以将它们与普通的dplyr函数混合使用。例如|164086|cols+=+c("cyl","gear")
mtcars+%25.%25
++s_group_by(cols)+%25.%25++
++s_summarise("avdisp=mean(disp),+max(disp)")+%25.%25
++arrange(avdisp)|code-block|syntax|javascript|164087|entityMap|0|LINK|mutability|MUTABLE|url^0|0|0|12|0|0|0|0^^$0|@$1|2|3|4|5|6|7|T|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|U|8|@]|9|@$D|V|E|W|1|X]]|A|$]]|$1|F|3|G|5|6|7|Y|8|@]|9|@]|A|$]]|$1|H|3|I|5|J|7|Z|8|@]|9|@]|A|$K|L]]|$1|M|3|-4|5|6|7|10|8|@]|9|@]|A|$]]]|N|$O|$5|P|Q|R|A|$S|C]]]]

Until dplyr has full support for string arguments, perhaps this gist is useful:

<a href="https://gist.github.com/skranz/9681509">https://gist.github.com/skranz/9681509</a>

It contains bunch of wrapper functions like s_group_by, s_mutate, s_filter, etc that use string arguments. You can mix them with the normal dplyr functions. For example

<pre><code>cols = c("cyl","gear")
mtcars %.%
 s_group_by(cols) %.% 
 s_summarise("avdisp=mean(disp), max(disp)") %.%
 arrange(avdisp)
</code></pre>

blocks|key|2972535|text|data+=+data.frame(
++my.a+=+sample(LETTERS[1:3],+100,+replace=TRUE),
++my.b+=+sample(LETTERS[1:3],+100,+replace=TRUE),
++value+=+rnorm(100)
)

group_by(data,newcol=paste(my.a,my.b,sep="_"))+%25>%25+summarise(Value=mean(value))|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|2972536|unstyled|entityMap^0|0^^$0|@$1|2|3|4|5|6|7|G|8|@]|9|@]|A|$B|C]]|$1|D|3|-4|5|E|7|H|8|@]|9|@]|A|$]]]|F|$]]

<pre><code>data = data.frame(
 my.a = sample(LETTERS[1:3], 100, replace=TRUE),
 my.b = sample(LETTERS[1:3], 100, replace=TRUE),
 value = rnorm(100)
)

group_by(data,newcol=paste(my.a,my.b,sep="_")) %&gt;% summarise(Value=mean(value))
</code></pre>

blocks|key|166882|text|现在，通过名称以下划线结尾的dplyr函数的变体，可以支持dplyr中列的字符串规范。例如，对应于group_by函数，有一个可以接受字符串参数的group_by_函数。This+vignette详细描述了这些函数的语法。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|166883|下面的代码片段干净利落地解决了@sharoz最初提出的问题(注意需要写出.dots参数)：|166884|#+Given+data+and+columns+from+the+OP

data+%25>%25
++++group_by_(.dots+=+columns)+%25>%25
++++summarise(Value+=+mean(value))|code-block|syntax|javascript|166885|(请注意，%25.%25现在使用%25>%25运算符，并且不推荐使用dplyr+)。|166886|entityMap|0|LINK|mutability|MUTABLE|url|http://cran.r-project.org/web/packages/dplyr/vignettes/nse.html^0|E|5|T|5|1D|8|21|9|2D|D|0|0|10|5|0|0|5|3|C|3|0^^$0|@$1|2|3|4|5|6|7|W|8|@$9|X|A|Y|B|C]|$9|Z|A|10|B|C]|$9|11|A|12|B|C]|$9|13|A|14|B|C]]|D|@$9|15|A|16|1|17]]|E|$]]|$1|F|3|G|5|6|7|18|8|@$9|19|A|1A|B|C]]|D|@]|E|$]]|$1|H|3|I|5|J|7|1B|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|1C|8|@$9|1D|A|1E|B|C]|$9|1F|A|1G|B|C]]|D|@]|E|$]]|$1|O|3|-4|5|6|7|1H|8|@]|D|@]|E|$]]]|P|$Q|$5|R|S|T|E|$U|V]]]]

String specification of columns in <code>dplyr</code> are now supported through variants of the <code>dplyr</code> functions with names finishing in an underscore. For example, corresponding to the <code>group_by</code> function there is a <code>group_by_</code> function that may take string arguments. <a href="http://cran.r-project.org/web/packages/dplyr/vignettes/nse.html">This vignette</a> describes the syntax of these functions in detail.

The following snippet cleanly solves the problem that @sharoz originally posed (note the need to write out the <code>.dots</code> argument):

<pre><code># Given data and columns from the OP

data %&gt;%
 group_by_(.dots = columns) %&gt;%
 summarise(Value = mean(value))
</code></pre>

(Note that dplyr now uses the <code>%&gt;%</code> operator, and <code>%.%</code> is deprecated).

blocks|key|2759957|text|为了完整地编写代码，这里使用新语法更新Hadley的答案：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2759958|library(dplyr)

df+<-++data.frame(
++++asihckhdoydk+=+sample(LETTERS[1:3],+100,+replace=TRUE),
++++a30mvxigxkgh+=+sample(LETTERS[1:3],+100,+replace=TRUE),
++++value+=+rnorm(100)
)

#+Columns+you+want+to+group+by
grp_cols+<-+names(df)[-3]

#+Convert+character+vector+to+list+of+symbols
dots+<-+lapply(grp_cols,+as.symbol)

#+Perform+frequency+counts
df+%25>%25
++++group_by_(.dots=dots)+%25>%25
++++summarise(n+=+n())|code-block|syntax|javascript|2759959|输出：|2759960|Source:+local+data+frame+[9+x+3]
Groups:+asihckhdoydk

++asihckhdoydk+a30mvxigxkgh++n
1++++++++++++A++++++++++++A+10
2++++++++++++A++++++++++++B+10
3++++++++++++A++++++++++++C+13
4++++++++++++B++++++++++++A+14
5++++++++++++B++++++++++++B+10
6++++++++++++B++++++++++++C+12
7++++++++++++C++++++++++++A++9
8++++++++++++C++++++++++++B+12
9++++++++++++C++++++++++++C+10|2759961|entityMap^0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|N|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

Just so as to write the code in full, here's an update on Hadley's answer with the new syntax:

<pre><code>library(dplyr)

df &lt;- data.frame(
 asihckhdoydk = sample(LETTERS[1:3], 100, replace=TRUE),
 a30mvxigxkgh = sample(LETTERS[1:3], 100, replace=TRUE),
 value = rnorm(100)
)

# Columns you want to group by
grp_cols &lt;- names(df)[-3]

# Convert character vector to list of symbols
dots &lt;- lapply(grp_cols, as.symbol)

# Perform frequency counts
df %&gt;%
 group_by_(.dots=dots) %&gt;%
 summarise(n = n())
</code></pre>

output:

<pre><code>Source: local data frame [9 x 3]
Groups: asihckhdoydk

 asihckhdoydk a30mvxigxkgh n
1 A A 10
2 A B 10
3 A C 13
4 B A 14
5 B B 10
6 B C 12
7 C A 9
8 C B 12
9 C C 10
</code></pre>

blocks|key|2972644|text|这里的答案中缺少的一个(很小的)情况是，当要分组的变量在流水线中途动态生成时：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|2972645|library(wakefield)
df_foo+=+r_series(rnorm,+10,+1000)
df_foo+%25>%25+
++#+1.+create+quantized+versions+of+base+variables
++mutate_each(
++++funs(Quantized+=+.+>+0)
++)+%25>%25+
++#+2.+group_by+the+indicator+variables
++group_by_(
++++.dots+=+grep("Quantized",+names(.),+value+=+TRUE)
++++)+%25>%25+
++#+3.+summarize+the+base+variables
++summarize_each(
++++funs(sum(.,+na.rm+=+TRUE)),+contains("X_")
++)|code-block|syntax|javascript|2972646|这基本上展示了如何结合使用grep和group_by_(.dots+=+...)来实现这一点。|offset|length|style|CODE|2972647|entityMap^0|0|0|D|4|I|M|0^^$0|@$1|2|3|4|5|6|7|O|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|P|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|Q|8|@$I|R|J|S|K|L]|$I|T|J|U|K|L]]|9|@]|A|$]]|$1|M|3|-4|5|6|7|V|8|@]|9|@]|A|$]]]|N|$]]

One (tiny) case that is missing from the answers here, that I wanted to make explicit, is when the variables to group by are generated dynamically midstream in a pipeline: 

<pre><code>library(wakefield)
df_foo = r_series(rnorm, 10, 1000)
df_foo %&gt;% 
 # 1. create quantized versions of base variables
 mutate_each(
 funs(Quantized = . &gt; 0)
 ) %&gt;% 
 # 2. group_by the indicator variables
 group_by_(
 .dots = grep("Quantized", names(.), value = TRUE)
 ) %&gt;% 
 # 3. summarize the base variables
 summarize_each(
 funs(sum(., na.rm = TRUE)), contains("X_")
 )
</code></pre>

This basically shows how to use <code>grep</code> in conjunction with <code>group_by_(.dots = ...)</code> to achieve this.

blocks|key|2760021|text|自从这个问题发布后，dplyr添加了group_by的作用域版本(documentation+here)。这使您可以使用与select相同的函数，如下所示：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2760022|data+=+data.frame(
++++asihckhdoydkhxiydfgfTgdsx+=+sample(LETTERS[1:3],+100,+replace=TRUE),
++++a30mvxigxkghc5cdsvxvyv0ja+=+sample(LETTERS[1:3],+100,+replace=TRUE),
++++value+=+rnorm(100)
)

#+get+the+columns+we+want+to+average+within
columns+=+names(data)[-3]

library(dplyr)
df1+<-+data+%25>%25
++group_by_at(vars(one_of(columns)))+%25>%25
++summarize(Value+=+mean(value))

#compare+plyr+for+reference
df2+<-+plyr::ddply(data,+columns,+plyr::summarize,+value=mean(value))
table(df1+==+df2,+useNA+=+'ifany')
##+TRUE+
##++27+|code-block|syntax|javascript|2760023|您的示例问题的输出与预期一致(参见与上面的plyr和下面的输出的比较)：|2760024|#+A+tibble:+9+x+3
#+Groups:+++asihckhdoydkhxiydfgfTgdsx+[?]
++asihckhdoydkhxiydfgfTgdsx+a30mvxigxkghc5cdsvxvyv0ja+++++++Value
+++++++++++++++++++++<fctr>++++++++++++++++++++<fctr>+++++++<dbl>
1+++++++++++++++++++++++++A+++++++++++++++++++++++++A++0.04095002
2+++++++++++++++++++++++++A+++++++++++++++++++++++++B++0.24943935
3+++++++++++++++++++++++++A+++++++++++++++++++++++++C+-0.25783892
4+++++++++++++++++++++++++B+++++++++++++++++++++++++A++0.15161805
5+++++++++++++++++++++++++B+++++++++++++++++++++++++B++0.27189974
6+++++++++++++++++++++++++B+++++++++++++++++++++++++C++0.20858897
7+++++++++++++++++++++++++C+++++++++++++++++++++++++A++0.19502221
8+++++++++++++++++++++++++C+++++++++++++++++++++++++B++0.56837548
9+++++++++++++++++++++++++C+++++++++++++++++++++++++C+-0.22682998|2760025|请注意，由于dplyr::summarize一次只剥离了一层分组，因此在生成的tibble中仍然有一些分组正在进行(这有时会让人感到惊讶，后来会出人意料)。如果您想绝对安全地避免意外的分组行为，您始终可以在汇总之后将%25>%25+ungroup添加到您的管道中。|2760026|entityMap|0|LINK|mutability|MUTABLE|url|https://www.rdocumentation.org/packages/dplyr/versions/0.7.1/topics/group_by_all^0|I|8|1P|6|X|I|0|0|0|0|0|6|G|30|B|0^^$0|@$1|2|3|4|5|6|7|Y|8|@$9|Z|A|10|B|C]|$9|11|A|12|B|C]]|D|@$9|13|A|14|1|15]]|E|$]]|$1|F|3|G|5|H|7|16|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|17|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|18|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|19|8|@$9|1A|A|1B|B|C]|$9|1C|A|1D|B|C]]|D|@]|E|$]]|$1|Q|3|-4|5|6|7|1E|8|@]|D|@]|E|$]]]|R|$S|$5|T|U|V|E|$W|X]]]]

Since this question was posted, dplyr added scoped versions of <code>group_by</code> (<a href="https://www.rdocumentation.org/packages/dplyr/versions/0.7.1/topics/group_by_all" rel="noreferrer">documentation here</a>). This lets you use the same functions you would use with <code>select</code>, like so:

<pre><code>data = data.frame(
 asihckhdoydkhxiydfgfTgdsx = sample(LETTERS[1:3], 100, replace=TRUE),
 a30mvxigxkghc5cdsvxvyv0ja = sample(LETTERS[1:3], 100, replace=TRUE),
 value = rnorm(100)
)

# get the columns we want to average within
columns = names(data)[-3]

library(dplyr)
df1 &lt;- data %&gt;%
 group_by_at(vars(one_of(columns))) %&gt;%
 summarize(Value = mean(value))

#compare plyr for reference
df2 &lt;- plyr::ddply(data, columns, plyr::summarize, value=mean(value))
table(df1 == df2, useNA = 'ifany')
## TRUE 
## 27 
</code></pre>

The output from your example question is as expected (see comparison to plyr above and output below):

<pre><code># A tibble: 9 x 3
# Groups: asihckhdoydkhxiydfgfTgdsx [?]
 asihckhdoydkhxiydfgfTgdsx a30mvxigxkghc5cdsvxvyv0ja Value
 &lt;fctr&gt; &lt;fctr&gt; &lt;dbl&gt;
1 A A 0.04095002
2 A B 0.24943935
3 A C -0.25783892
4 B A 0.15161805
5 B B 0.27189974
6 B C 0.20858897
7 C A 0.19502221
8 C B 0.56837548
9 C C -0.22682998
</code></pre>

Note that since <code>dplyr::summarize</code> only strips off one layer of grouping at a time, you've still got some grouping going on in the resultant tibble (which can sometime catch people by suprise later down the line). If you want to be absolutely safe from unexpected grouping behavior, you can always add <code>%&gt;% ungroup</code> to your pipeline after you summarize.

blocks|key|2972713|text|有关将.dots参数用作dplyr::group_by函数的字符矢量输入的一般示例：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|2972714|iris+%25>%25+
++++group_by(.dots+="Species")+%25>%25+
++++summarise(meanpetallength+=+mean(Petal.Length))|code-block|syntax|javascript|2972715|或者不使用分组变量的硬编码名称(根据OP的要求)：|2972716|iris+%25>%25+
++++group_by(.dots+=+names(iris)[5])+%25>%25+
++++summarise_at("Petal.Length",+mean)|2972717|以OP为例：|2972718|data+%25>%25+
++++group_by(.dots+=names(data)[-3])+%25>%25+
++++summarise_at("value",+mean)|2972719|参见dplyr+vignette+on+programming，解释代词、准引语、quosures和tidyeval。|2972720|entityMap|0|LINK|mutability|MUTABLE|url|https://cran.r-project.org/web/packages/dplyr/vignettes/programming.html^0|3|5|C|F|0|0|0|0|0|0|2|T|0|0^^$0|@$1|2|3|4|5|6|7|12|8|@$9|13|A|14|B|C]|$9|15|A|16|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|17|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|18|8|@]|D|@]|E|$]]|$1|M|3|N|5|H|7|19|8|@]|D|@]|E|$I|J]]|$1|O|3|P|5|6|7|1A|8|@]|D|@]|E|$]]|$1|Q|3|R|5|H|7|1B|8|@]|D|@]|E|$I|J]]|$1|S|3|T|5|6|7|1C|8|@]|D|@$9|1D|A|1E|1|1F]]|E|$]]|$1|U|3|-4|5|6|7|1G|8|@]|D|@]|E|$]]]|V|$W|$5|X|Y|Z|E|$10|11]]]]

General example on using the <code>.dots</code> argument as character vector input to the <code>dplyr::group_by</code> function :

<pre><code>iris %&gt;% 
 group_by(.dots ="Species") %&gt;% 
 summarise(meanpetallength = mean(Petal.Length))
</code></pre>

Or without a hard coded name for the grouping variable (as asked by the OP):

<pre><code>iris %&gt;% 
 group_by(.dots = names(iris)[5]) %&gt;% 
 summarise_at("Petal.Length", mean)
</code></pre>

With the example of the OP:

<pre><code>data %&gt;% 
 group_by(.dots =names(data)[-3]) %&gt;% 
 summarise_at("value", mean)
</code></pre>

See also the <a href="https://cran.r-project.org/web/packages/dplyr/vignettes/programming.html" rel="nofollow noreferrer">dplyr vignette on programming</a> which explains pronouns, quasiquotation, quosures, and tidyeval.

blocks|key|163049|text|使用dplyr+1.0.0中的across()进行更新|type|unstyled|depth|inlineStyleRanges|entityRanges|data|163050|上面的所有答案仍然有效，并且.dots参数的解决方案很有侵入性。|163051|但是如果你想找一个更好记的解决方案，新的across()就派上用场了。它是由Hadley+Wickham发布的2020-04-03，可以在mutate()和summarise()中使用，并取代范围变体，如_at或_all。最重要的是，它非常优雅地用引用/不引用(如!!!+rlang::syms()+)取代了繁琐的非标准评估(NSE)。|offset|length|style|CODE|163052|因此，使用across的解决方案看起来非常具有可读性：|163053|data+%25>%25
++group_by(across(all_of(columns)))+%25>%25
++summarize(Value+=+mean(value))|code-block|syntax|javascript|163054|entityMap^0|0|0|K|8|1X|8|26|B|2U|3|2Y|4|3O|H|0|5|6|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|6|7|T|8|@]|9|@]|A|$]]|$1|D|3|E|5|6|7|U|8|@$F|V|G|W|H|I]|$F|X|G|Y|H|I]|$F|Z|G|10|H|I]|$F|11|G|12|H|I]|$F|13|G|14|H|I]|$F|15|G|16|H|I]]|9|@]|A|$]]|$1|J|3|K|5|6|7|17|8|@$F|18|G|19|H|I]]|9|@]|A|$]]|$1|L|3|M|5|N|7|1A|8|@]|9|@]|A|$O|P]]|$1|Q|3|-4|5|6|7|1B|8|@]|9|@]|A|$]]]|R|$]]

<h2>Update with across() from dplyr 1.0.0</h2>
All the answers above are still working, and the solutions with the .dots argument are intruiging.
BUT if you look for a solution that is easier to remember, the new <code>across()</code> comes in handy. It was published 2020-04-03 by Hadley Wickham and can be used in <code>mutate()</code> and <code>summarise()</code> and replace the scoped variants like <code>_at</code> or <code>_all</code>. Above all, it replaces very elegantly the cumbersome non-standard evaluation (NSE) with quoting/unquoting such as <code>!!! rlang::syms()</code>.
So the solution with <code>across</code> looks very readable:
<pre><code>data %&gt;%
 group_by(across(all_of(columns))) %&gt;%
 summarize(Value = mean(value))
</code></pre>

I'm trying to transfer my understanding of plyr into dplyr, but I can't figure out how to group by multiple columns.

<pre><code># make data with weird column names that can't be hard coded
data = data.frame(
 asihckhdoydkhxiydfgfTgdsx = sample(LETTERS[1:3], 100, replace=TRUE),
 a30mvxigxkghc5cdsvxvyv0ja = sample(LETTERS[1:3], 100, replace=TRUE),
 value = rnorm(100)
)

# get the columns we want to average within
columns = names(data)[-3]

# plyr - works
ddply(data, columns, summarize, value=mean(value))

# dplyr - raises error
data %.%
 group_by(columns) %.%
 summarise(Value = mean(value))
#&gt; Error in eval(expr, envir, enclos) : index out of bounds
</code></pre>

What am I missing to translate the plyr example into a dplyr-esque syntax?

Edit 2017: Dplyr has been updated, so a simpler solution is available. See the currently selected answer.

Group by multiple columns in dplyr, using string vector input

我试图将我对plyr的理解转化为dplyr，但我不知道如何按多列进行分组。# make data with weird column names that can't be hard codeddata = data.frame(  asihckhdoydkhxiydfgfTgdsx = sample(LETTERS[...

问在dplyr中按多个列分组，使用字符串向量输入
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