blocks|key|5781954|text|f+=+function(v,+ch)+sum(rle(as.character(v))$values+==+ch)
sapply(x,+f,+'a')
#+Red++Blue+Green+
#+2+++++1+++++3+|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|5781955|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>f = function(v, ch) sum(rle(as.character(v))$values == ch)
sapply(x, f, 'a')
# Red Blue Green 
# 2 1 3 
</code></pre>

blocks|key|4313462|text|下面是一个使用tidyverse的选项，我们将值转换为‘gather’格式，按'color‘分组，以及’rleid‘的游程长度id+(rleid)，将'a’的‘filter’元素取值，然后按'color‘分组，得到不同的'grp’元素的数量|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4313463|library(tidyverse)
library(data.table)
gather(x,+color,+value)+%25>%25+
+++group_by(color,+grp+=+rleid(value))+%25>%25+
+++filter(value+==+"a")+%25>%25+
+++group_by(color)+%25>%25
+++summarise(n+=+n_distinct(grp))
#+A+tibble:+3+x+2
#++color+++++n
#++<chr>+<int>
#1+Blue++++++1
#2+Green+++++3
#3+Red+++++++2|code-block|syntax|javascript|4313464|4313465|或summarise_all的一个选项|4313466|x+%25>%25+
+++summarise_all(list(~+n_distinct(setdiff((.+==+'a')+*+rleid(.),+0))))+%25>%25+
+++as.list+%25>%25+
+++enframe+%25>%25
+++unnest
#+A+tibble:+3+x+2
#++name++value
#++<chr>+<int>
#1+Red+++++++2
#2+Blue++++++1
#3+Green+++++3|4313467|entityMap^0|7|9|S|6|1G|5|1V|5|28|6|0|0|0|1|D|0|0^^$0|@$1|2|3|4|5|6|7|R|8|@$9|S|A|T|B|C]|$9|U|A|V|B|C]|$9|W|A|X|B|C]|$9|Y|A|Z|B|C]|$9|10|A|11|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|12|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|13|8|@]|D|@]|E|$]]|$1|L|3|M|5|6|7|14|8|@$9|15|A|16|B|C]]|D|@]|E|$]]|$1|N|3|O|5|H|7|17|8|@]|D|@]|E|$I|J]]|$1|P|3|-4|5|6|7|18|8|@]|D|@]|E|$]]]|Q|$]]

Here is an option with <code>tidyverse</code> where we <code>gather</code> into 'long' format, grouped by 'color', and the run-length-id (<code>rleid</code>) of 'value', <code>filter</code> the 'value' elements that are 'a', then grouped by 'color', get the number of distinct 'grp' elements

<pre><code>library(tidyverse)
library(data.table)
gather(x, color, value) %&gt;% 
 group_by(color, grp = rleid(value)) %&gt;% 
 filter(value == "a") %&gt;% 
 group_by(color) %&gt;%
 summarise(n = n_distinct(grp))
# A tibble: 3 x 2
# color n
# &lt;chr&gt; &lt;int&gt;
#1 Blue 1
#2 Green 3
#3 Red 2
</code></pre>

<hr>

Or an option with <code>summarise_all</code>

<pre><code>x %&gt;% 
 summarise_all(list(~ n_distinct(setdiff((. == 'a') * rleid(.), 0)))) %&gt;% 
 as.list %&gt;% 
 enframe %&gt;%
 unnest
# A tibble: 3 x 2
# name value
# &lt;chr&gt; &lt;int&gt;
#1 Red 2
#2 Blue 1
#3 Green 3
</code></pre>

blocks|key|4313489|text|这是另一个想法。我们可以合并和折叠所有字符串，拆分不是a的字符串，并计算包含a的元素的数量。result2是最终的结果。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|4313490|result+<-+sapply(x,+function(x)+{
++x2+<-+as.character(x)
++y+<-+paste0(x2,+collapse+=+"")
++z+<-+strsplit(y,+"[%5Ea]")[[1]]
++return(sum(grepl("a",+z)))
})

result2+<-+data.frame(Color+=+names(result),+a_sequence+=+unname(result))
result2
#+++Color+a_sequence
#+1+++Red++++++++++2
#+2++Blue++++++++++1
#+3+Green++++++++++3|code-block|syntax|javascript|4313491|entityMap^0|R|1|12|1|1A|7|0|0^^$0|@$1|2|3|4|5|6|7|M|8|@$9|N|A|O|B|C]|$9|P|A|Q|B|C]|$9|R|A|S|B|C]]|D|@]|E|$]]|$1|F|3|G|5|H|7|T|8|@]|D|@]|E|$I|J]]|$1|K|3|-4|5|6|7|U|8|@]|D|@]|E|$]]]|L|$]]

Here is another idea. We can combine and collapse all string, split the string that is not <code>a</code>, and count how many elements containing <code>a</code>. <code>result2</code> is the final outcome.

<pre><code>result &lt;- sapply(x, function(x) {
 x2 &lt;- as.character(x)
 y &lt;- paste0(x2, collapse = "")
 z &lt;- strsplit(y, "[^a]")[[1]]
 return(sum(grepl("a", z)))
})

result2 &lt;- data.frame(Color = names(result), a_sequence = unname(result))
result2
# Color a_sequence
# 1 Red 2
# 2 Blue 1
# 3 Green 3
</code></pre>

blocks|key|5782044|text|在base+R中，你可以这样做：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|5782045|+f+=+as.character(unique(unlist(x)))+
+t(sapply(x,function(y)table(factor(rle(as.character(y))$values,f))))
++++++a+b+c+d
Red+++2+1+1+1
Blue++1+1+0+0
Green+3+2+0+0|code-block|syntax|javascript|5782046|您的整个数据集似乎都包含在因子中，因此使用了代码as.character()|offset|length|style|CODE|5782047|entityMap^0|0|0|O|E|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]]|9|@]|A|$]]|$1|M|3|-4|5|6|7|T|8|@]|9|@]|A|$]]]|N|$]]

in base R you could do:

<pre><code> f = as.character(unique(unlist(x))) 
 t(sapply(x,function(y)table(factor(rle(as.character(y))$values,f))))
 a b c d
Red 2 1 1 1
Blue 1 1 0 0
Green 3 2 0 0
</code></pre>

It seems your whole dataset is in factors so the code <code>as.character()</code> is used

I would like to create a table or a new data frame that displays, for each column in the original data frame (of which there are many), how many times a sequence of a specific value occurs. For example, if I have the following data frame:

<pre><code>x &lt;- data.frame("Red" = c("a", "b", "a", "a", "c", "d"), "Blue" = c("b", "a", "a", "a", "a", "a"), "Green" = c("a", "a", "b", "a", "b", "a"))
</code></pre>

and I want to know, for each color (Red, Blue, and Green) how many times a run of the value "a" occurs (i.e., before it is interrupted by another value like b or c)

The correct answer would look something like this:

<pre><code> Color a_sequences
1 Red 2
2 Blue 1
3 Green 3
</code></pre>

I have found solutions on this site using the rle function on single vectors but I don't know how to scale this up for an entire data frame with multiple columns and obtain a new table or data frame with the sequence counts, as in the above table. Thank you!

Counting number of sequences of particular value for multiple columns in a data frame

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。

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我想创建一个表或一个新的数据框，为原始数据框(其中有许多列)中的每一列显示一个特定值的序列出现的次数。例如，如果我有以下数据帧：x <- data.frame("Red" = c("a", "b", "a", "a", "c", "d"), "Blue" = c("b", "a", "a", "a", "a", "a"...

问对数据帧中多列的特定值序列的个数进行计数
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问对数据帧中多列的特定值序列的个数进行计数EN