blocks|key|3949766|text|这里有一个非常简洁的方法：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3949767|+head(my.data)
++id+treatment
1++1+++++++++A
2++1+++++++++B
3++1+++++++++C
4++1+++++++++D
5++1+++++++++E
6++2+++++++++A

group_by(my.data,+id)+%25>%25+filter(n_distinct(treatment)+==+5)
#+A+tibble:+5+x+2
#+Groups:+++id+[1]
+++++id+treatment+++++
++<int>+<chr>+++++
1+++++1+A++++++++++++
2+++++1+B++++++++++++
3+++++1+C++++++++++++
4+++++1+D++++++++++++
5+++++1+E+++|code-block|syntax|javascript|3949768|解释|3949769|在内部，n_distinct(treatment)会统计每个id类别中唯一值的实例，因为您是按该列分组的。则filter保留出现5次的id。|offset|length|style|CODE|3949770|entityMap^0|0|0|0|4|L|U|2|1V|2|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|T|8|@$K|U|L|V|M|N]|$K|W|L|X|M|N]|$K|Y|L|Z|M|N]]|9|@]|A|$]]|$1|O|3|-4|5|6|7|10|8|@]|9|@]|A|$]]]|P|$]]

Here's a super concise way to do it:

<pre><code> head(my.data)
 id treatment
1 1 A
2 1 B
3 1 C
4 1 D
5 1 E
6 2 A

group_by(my.data, id) %&gt;% filter(n_distinct(treatment) == 5)
# A tibble: 5 x 2
# Groups: id [1]
 id treatment 
 &lt;int&gt; &lt;chr&gt; 
1 1 A 
2 1 B 
3 1 C 
4 1 D 
5 1 E 
</code></pre>

<h1>Explanation</h1>

Internally, <code>n_distinct(treatment)</code> tallies up instances of the unique values in each <code>id</code> category, since you grouped by that column. Then filter retains the <code>id</code>s that occurred 5 times.

blocks|key|3953814|text|要返回包含所有处理的ID组的子集，请执行类似以下操作：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3953815|install.packages("dplyr")
library(dplyr)

treatments+<-+c("A",+"B",+"C",+"D",+"E")

df+%25>%25+group_by(id)+%25>%25+filter(all(treatments+%25in%25+treatment))

####+OUTPUT+####

#+A+tibble:+5+x+2
#+Groups:+++id+[1]
+++++id+treatment
++<int>+<fct>++++
1+++++1+A++++++++
2+++++1+B++++++++
3+++++1+C++++++++
4+++++1+D++++++++
5+++++1+E++++++++|code-block|syntax|javascript|3953816|这里的主要优点是它正确地处理了重复的条件。也就是说，在一个ID组中有相同条件的可能性很小，例如：|3953817|#+A+tibble:+11+x+2
++++++id+treatment
+++<dbl>+<chr>++++
+1+++++1+A++++++++
+2+++++1+A++++++++
+3+++++1+B++++++++
+4+++++1+C++++++++
+5+++++1+D++++++++
+6+++++1+E++++++++
+7+++++2+A++++++++
+8+++++2+A++++++++
+9+++++2+B++++++++
10+++++2+B++++++++
11+++++2+C++++++++|3953818|上面的代码将返回包含每个条件的任何组的所有观察结果：|3953819|#+A+tibble:+6+x+2
#+Groups:+++id+[1]
+++++id+treatment
++<dbl>+<chr>++++
1+++++1+A++++++++
2+++++1+A++++++++
3+++++1+B++++++++
4+++++1+C++++++++
5+++++1+D++++++++
6+++++1+E++++++++|3953820|entityMap^0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|Q|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|R|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|S|8|@]|9|@]|A|$]]|$1|I|3|J|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|K|3|L|5|6|7|U|8|@]|9|@]|A|$]]|$1|M|3|N|5|D|7|V|8|@]|9|@]|A|$E|F]]|$1|O|3|-4|5|6|7|W|8|@]|9|@]|A|$]]]|P|$]]

To return a subset of ID groups that contain all treatments do something like the following:

<pre class="lang-r prettyprint-override"><code>install.packages("dplyr")
library(dplyr)

treatments &lt;- c("A", "B", "C", "D", "E")

df %&gt;% group_by(id) %&gt;% filter(all(treatments %in% treatment))

#### OUTPUT ####

# A tibble: 5 x 2
# Groups: id [1]
 id treatment
 &lt;int&gt; &lt;fct&gt; 
1 1 A 
2 1 B 
3 1 C 
4 1 D 
5 1 E 
</code></pre>

The main advantage here is that it deals with repeated conditions correctly. That is to say, in the off chance that you have the identical condition within an ID group, e.g.:

<pre class="lang-r prettyprint-override"><code># A tibble: 11 x 2
 id treatment
 &lt;dbl&gt; &lt;chr&gt; 
 1 1 A 
 2 1 A 
 3 1 B 
 4 1 C 
 5 1 D 
 6 1 E 
 7 2 A 
 8 2 A 
 9 2 B 
10 2 B 
11 2 C 
</code></pre>

The above code will return all observations of any groups containing every condition:

<pre class="lang-r prettyprint-override"><code># A tibble: 6 x 2
# Groups: id [1]
 id treatment
 &lt;dbl&gt; &lt;chr&gt; 
1 1 A 
2 1 A 
3 1 B 
4 1 C 
5 1 D 
6 1 E 
</code></pre>

blocks|key|3949966|text|还有一种dplyr可能性：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3949967|df+%25>%25
+group_by(id)+%25>%25
+filter(grepl("A,B,C,D,E",+paste(treatment,+collapse+=+","),+fixed+=+TRUE))

+++++id+treatment
++<int>+<chr>++++
1+++++1+A++++++++
2+++++1+B++++++++
3+++++1+C++++++++
4+++++1+D++++++++
5+++++1+E+|code-block|syntax|javascript|3949968|在这里，它还考虑了排序，这意味着它过滤掉了治疗不进行A,B,C,D,E的情况。|3949969|如果顺序不重要，则可以先排列"treatment“列：|3949970|df+%25>%25
+group_by(id)+%25>%25
+arrange(treatment,+.by_group+=+TRUE)+%25>%25
+filter(grepl("A,B,C,D,E",+paste(treatment,+collapse+=+","),+fixed+=+TRUE))|3949971|考虑@gersht展示的一个场景，可以将其修改为：|3949972|df+%25>%25
+group_by(id)+%25>%25
+filter(grepl("A,B,C,D,E",+paste(unique(treatment),+collapse+=+","),+fixed+=+TRUE))|3949973|或者：|3949974|df+%25>%25
+group_by(id)+%25>%25
+arrange(treatment,+.by_group+=+TRUE)+%25>%25
+filter(grepl("A,B,C,D,E",+paste(unique(treatment),+collapse+=+","),+fixed+=+TRUE))|3949975|entityMap^0|4|5|0|0|Q|9|0|0|0|0|0|0|0^^$0|@$1|2|3|4|5|6|7|10|8|@$9|11|A|12|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|13|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|14|8|@$9|15|A|16|B|C]]|D|@]|E|$]]|$1|M|3|N|5|6|7|17|8|@]|D|@]|E|$]]|$1|O|3|P|5|H|7|18|8|@]|D|@]|E|$I|J]]|$1|Q|3|R|5|6|7|19|8|@]|D|@]|E|$]]|$1|S|3|T|5|H|7|1A|8|@]|D|@]|E|$I|J]]|$1|U|3|V|5|6|7|1B|8|@]|D|@]|E|$]]|$1|W|3|X|5|H|7|1C|8|@]|D|@]|E|$I|J]]|$1|Y|3|-4|5|6|7|1D|8|@]|D|@]|E|$]]]|Z|$]]

Also a <code>dplyr</code> possibility:

<pre><code>df %&gt;%
 group_by(id) %&gt;%
 filter(grepl("A,B,C,D,E", paste(treatment, collapse = ","), fixed = TRUE))

 id treatment
 &lt;int&gt; &lt;chr&gt; 
1 1 A 
2 1 B 
3 1 C 
4 1 D 
5 1 E 
</code></pre>

Here it takes into account also the ordering, meaning it filters out cases where treatment is not going <code>A,B,C,D,E</code>.

If the order is not important, then you can arrange the "treatment" column first:

<pre><code>df %&gt;%
 group_by(id) %&gt;%
 arrange(treatment, .by_group = TRUE) %&gt;%
 filter(grepl("A,B,C,D,E", paste(treatment, collapse = ","), fixed = TRUE))
</code></pre>

Considering a scenario shown by @gersht, it can be modified to:

<pre><code>df %&gt;%
 group_by(id) %&gt;%
 filter(grepl("A,B,C,D,E", paste(unique(treatment), collapse = ","), fixed = TRUE))
</code></pre>

Or:

<pre><code>df %&gt;%
 group_by(id) %&gt;%
 arrange(treatment, .by_group = TRUE) %&gt;%
 filter(grepl("A,B,C,D,E", paste(unique(treatment), collapse = ","), fixed = TRUE))
</code></pre>

blocks|key|3954141|text|我认为给出一个基本的R解决方案会很有趣，|type|unstyled|depth|inlineStyleRanges|entityRanges|data|3954142|DF+=+data.frame(id+=+c(rep(1,5),rep(2,3)),+treatment+=+c('A','C','B','D','E','A','B','C'))|code-block|syntax|javascript|3954143|请注意，我在treatment中略微改变了顺序，以考虑到它的可能性。|offset|length|style|CODE|3954144|do.call(rbind,
++lapply(split(DF,DF$id),
++++function(X){
++++++if(identical(sort(unique(X$treatment)),c('A','B','C','D','E')))+X
++++++else+NULL}))|3954145|我考虑到这样一种可能性，即一个id可以有多个相同的处理，因为尚不清楚data.frame的其余部分是什么样子，并且id组的处理可能不会排序。|3954146|entityMap^0|0|0|6|9|0|0|F|2|Y|A|1L|2|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|T|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|U|8|@$I|V|J|W|K|L]]|9|@]|A|$]]|$1|M|3|N|5|D|7|X|8|@]|9|@]|A|$E|F]]|$1|O|3|P|5|6|7|Y|8|@$I|Z|J|10|K|L]|$I|11|J|12|K|L]|$I|13|J|14|K|L]]|9|@]|A|$]]|$1|Q|3|-4|5|6|7|15|8|@]|9|@]|A|$]]]|R|$]]

I thought it would be interesting to give a base R solution,

<pre><code>DF = data.frame(id = c(rep(1,5),rep(2,3)), treatment = c('A','C','B','D','E','A','B','C'))
</code></pre>

Note that I permuted the order slightly in <code>treatment</code> to account for its possibility.

<pre><code>do.call(rbind,
 lapply(split(DF,DF$id),
 function(X){
 if(identical(sort(unique(X$treatment)),c('A','B','C','D','E'))) X
 else NULL}))
</code></pre>

I allow for the possibility that an <code>id</code> can have multiples of the same treatment since it is unclear what the rest of the <code>data.frame</code> looks like and that the treatments for an <code>id</code> group might not be sorted.

blocks|key|3954159|text|下面是base+R中使用subset和ave的方法(借用@steveb的数据)：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|3954160|treatments+<-+LETTERS[1:5]
subset(treatment.df,+as.logical(ave(treatment,+id,+FUN+=+function(x)+all(treatments+%25in%25+x))))
#+++id+treatment
#+1++1+++++++++A
#+2++1+++++++++B
#+3++1+++++++++C
#+4++1+++++++++D
#+5++1+++++++++E|code-block|syntax|javascript|3954161|entityMap^0|C|6|J|3|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|R|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|S|8|@]|D|@]|E|$]]]|L|$]]

Here's a way in base R using <code>subset</code> and <code>ave</code> (borrowing @steveb's data):

<pre><code>treatments &lt;- LETTERS[1:5]
subset(treatment.df, as.logical(ave(treatment, id, FUN = function(x) all(treatments %in% x))))
# id treatment
# 1 1 A
# 2 1 B
# 3 1 C
# 4 1 D
# 5 1 E
</code></pre>

I have a data frame with many columns and rows with many ids, the following data only shows 2 ids.

<pre><code>id treatment 
1 A 
1 B 
1 C 
1 D 
1 E 
2 A 
2 B 
2 C 
</code></pre>

I want to get the subgroup of id with the five treatments (A,B,C,D,E).
So the output table would look like this:

<pre><code>id treatment
1 A 
1 B 
1 C 
1 D 
1 E 
</code></pre>

Thank you very much.

How to select multiple rows based on the other column?

我有一个数据框，有很多列和行，有很多ids，下面的数据只显示2个ids。id  treatment  1   A          1   B          1   C          1   D          1   E          2   A          2   B         2   C...

问如何根据另一列选择多行？
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问如何根据另一列选择多行？EN

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问如何根据另一列选择多行？
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