blocks|key|1583604|text|或者你可以简单地使用filter来计算它，下面是我使用的函数：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1583605|ma+<-+function(x,+n+=+5){filter(x,+rep(1+/+n,+n),+sides+=+2)}|code-block|syntax|javascript|1583606|如果使用dplyr，请注意在上面的函数中指定stats::filter。|offset|length|style|CODE|1583607|entityMap^0|0|0|4|5|M|D|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|$]]

Or you can simply calculate it using filter, here's the function I use:

<pre><code>ma &lt;- function(x, n = 5){filter(x, rep(1 / n, n), sides = 2)}
</code></pre>

If you use <code>dplyr</code>, be careful to specify <code>stats::filter</code> in the function above.

blocks|key|1583667|text|caTools软件包具有非常快的滚动平均值/最小/最大/标准差和很少的其他功能。我只使用过runmean和runsd，它们是迄今为止提到的所有其他包中速度最快的。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1583668|entityMap^0|0|7|19|7|1H|5|0^^$0|@$1|2|3|4|5|6|7|H|8|@$9|I|A|J|B|C]|$9|K|A|L|B|C]|$9|M|A|N|B|C]]|D|@]|E|$]]|$1|F|3|-4|5|6|7|O|8|@]|D|@]|E|$]]]|G|$]]

The <code>caTools</code> package has very fast rolling mean/min/max/sd and few other functions. I've only worked with <code>runmean</code> and <code>runsd</code> and they are the fastest of any of the other packages mentioned to date.

blocks|key|1583725|text|对于用C%2B%2B编写的非常快速的移动平均值，您可以使用RcppRoll。只需调用roll_mean函数。文档可以在here上找到。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1583726|否则，这个(较慢的)+for循环应该可以完成此任务：|1583727|ma+<-+function(arr,+n=15){
++res+=+arr
++for(i+in+n:length(arr)){
++++res[i]+=+mean(arr[(i-n):i])
++}
++res
}|code-block|syntax|javascript|1583728|entityMap|0|LINK|mutability|MUTABLE|url|http://cran.r-project.org/web/packages/RcppRoll/RcppRoll.pdf^0|P|8|12|9|1J|4|0|0|0|0^^$0|@$1|2|3|4|5|6|7|U|8|@$9|V|A|W|B|C]|$9|X|A|Y|B|C]]|D|@$9|Z|A|10|1|11]]|E|$]]|$1|F|3|G|5|6|7|12|8|@]|D|@]|E|$]]|$1|H|3|I|5|J|7|13|8|@]|D|@]|E|$K|L]]|$1|M|3|-4|5|6|7|14|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|T]]]]

You could use <code>RcppRoll</code> for very quick moving averages written in C++. Just call the <code>roll_mean</code> function. Docs can be found <a href="http://cran.r-project.org/web/packages/RcppRoll/RcppRoll.pdf" rel="nofollow noreferrer">here</a>. 

Otherwise, this (slower) for loop should do the trick:

<pre><code>ma &lt;- function(arr, n=15){
 res = arr
 for(i in n:length(arr)){
 res[i] = mean(arr[(i-n):i])
 }
 res
}
</code></pre>

blocks|key|1583771|text|事实上，RcppRoll是非常好的。|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1583772|cantdutchthis发布的代码必须在窗口的第四行更正为固定：|1583773|ma+<-+function(arr,+n=15){
++res+=+arr
++for(i+in+n:length(arr)){
++++res[i]+=+mean(arr[(i-n%2B1):i])
++}
++res
}|code-block|syntax|javascript|1583774|另一种处理未命中的方法是使用here。|1583775|第三种方法，改进cantdutchthis代码以计算偏平均或不计算偏平均，如下所示：|1583776|++ma+<-+function(x,+n=2,parcial=TRUE){
++res+=+x+#set+the+first+values

++if+(parcial==TRUE){
++++for(i+in+1:length(x)){
++++++t<-max(i-n%2B1,1)
++++++res[i]+=+mean(x[t:i])
++++}
++++res

++}else{
++++for(i+in+1:length(x)){
++++++t<-max(i-n%2B1,1)
++++++res[i]+=+mean(x[t:i])
++++}
++++res[-c(seq(1,n-1,1))]+#remove+the+n-1+first,i.e.,+res[c(-3,-4,...)]
++}
}|1583777|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/users/1600821/cantdutchthis|1|http://www.cookbook-r.com/Manipulating_data/Calculating_a_moving_average/|2^0|4|8|0|0|D|0|0|0|E|4|1|0|8|D|2|0|0^^$0|@$1|2|3|4|5|6|7|13|8|@$9|14|A|15|B|C]]|D|@]|E|$]]|$1|F|3|G|5|6|7|16|8|@]|D|@$9|17|A|18|1|19]]|E|$]]|$1|H|3|I|5|J|7|1A|8|@]|D|@]|E|$K|L]]|$1|M|3|N|5|6|7|1B|8|@]|D|@$9|1C|A|1D|1|1E]]|E|$]]|$1|O|3|P|5|6|7|1F|8|@]|D|@$9|1G|A|1H|1|1I]]|E|$]]|$1|Q|3|R|5|J|7|1J|8|@]|D|@]|E|$K|L]]|$1|S|3|-4|5|6|7|1K|8|@]|D|@]|E|$]]]|T|$U|$5|V|W|X|E|$Y|Z]]|10|$5|V|W|X|E|$Y|11]]|12|$5|V|W|X|E|$Y|Z]]]]

In fact <code>RcppRoll</code> is very good.

The code posted by <a href="https://stackoverflow.com/users/1600821/cantdutchthis">cantdutchthis</a> must be corrected in the fourth line to the window be fixed:

<pre><code>ma &lt;- function(arr, n=15){
 res = arr
 for(i in n:length(arr)){
 res[i] = mean(arr[(i-n+1):i])
 }
 res
}
</code></pre>

Another way, which handles missings, is given <a href="http://www.cookbook-r.com/Manipulating_data/Calculating_a_moving_average/" rel="nofollow noreferrer">here</a>. 

A third way, improving <a href="https://stackoverflow.com/users/1600821/cantdutchthis">cantdutchthis</a> code to calculate partial averages or not, follows:

<pre><code> ma &lt;- function(x, n=2,parcial=TRUE){
 res = x #set the first values

 if (parcial==TRUE){
 for(i in 1:length(x)){
 t&lt;-max(i-n+1,1)
 res[i] = mean(x[t:i])
 }
 res

 }else{
 for(i in 1:length(x)){
 t&lt;-max(i-n+1,1)
 res[i] = mean(x[t:i])
 }
 res[-c(seq(1,n-1,1))] #remove the n-1 first,i.e., res[c(-3,-4,...)]
 }
}
</code></pre>

blocks|key|1368698|text|以补充cantdutchthis和Rodrigo+Remedio的回答；|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1368699|moving_fun+<-+function(x,+w,+FUN,+...)+{
++#+x:+a+double+vector
++#+w:+the+length+of+the+window,+i.e.,+the+section+of+the+vector+selected+to+apply+FUN
++#+FUN:+a+function+that+takes+a+vector+and+return+a+summarize+value,+e.g.,+mean,+sum,+etc.
++#+Given+a+double+type+vector+apply+a+FUN+over+a+moving+window+from+left+to+the+right,+
++#++++when+a+window+boundary+is+not+a+legal+section,+i.e.+lower_bound+and+i+(upper+bound)+
++#++++are+not+contained+in+the+length+of+the+vector,+return+a+NA_real_
++if+(w+<+1)+{
++++stop("The+length+of+the+window+'w'+must+be+greater+than+0")
++}
++output+<-+x
++for+(i+in+1:length(x))+{
+++++#+plus+1+because+the+index+is+inclusive+with+the+upper_bound+'i'
++++lower_bound+<-+i+-+w+%2B+1
++++if+(lower_bound+<+1)+{
++++++output[i]+<-+NA_real_
++++}+else+{
++++++output[i]+<-+FUN(x[lower_bound:i,+...])
++++}
++}
++output
}

#+example
v+<-+seq(1:10)

#+compute+a+MA(2)
moving_fun(v,+2,+mean)

#+compute+moving+sum+of+two+periods
moving_fun(v,+2,+sum)|code-block|syntax|javascript|1368700|entityMap|0|LINK|mutability|MUTABLE|url|https://stackoverflow.com/users/1600821/cantdutchthis|1|https://stackoverflow.com/users/5490241/rodrigo-remedio^0|3|D|0|H|F|1|0|0^^$0|@$1|2|3|4|5|6|7|S|8|@]|9|@$A|T|B|U|1|V]|$A|W|B|X|1|Y]]|C|$]]|$1|D|3|E|5|F|7|Z|8|@]|9|@]|C|$G|H]]|$1|I|3|-4|5|6|7|10|8|@]|9|@]|C|$]]]|J|$K|$5|L|M|N|C|$O|P]]|Q|$5|L|M|N|C|$O|R]]]]

In order to complement the answer of <a href="https://stackoverflow.com/users/1600821/cantdutchthis">cantdutchthis</a> and <a href="https://stackoverflow.com/users/5490241/rodrigo-remedio">Rodrigo Remedio</a>;

<pre><code>moving_fun &lt;- function(x, w, FUN, ...) {
 # x: a double vector
 # w: the length of the window, i.e., the section of the vector selected to apply FUN
 # FUN: a function that takes a vector and return a summarize value, e.g., mean, sum, etc.
 # Given a double type vector apply a FUN over a moving window from left to the right, 
 # when a window boundary is not a legal section, i.e. lower_bound and i (upper bound) 
 # are not contained in the length of the vector, return a NA_real_
 if (w &lt; 1) {
 stop("The length of the window 'w' must be greater than 0")
 }
 output &lt;- x
 for (i in 1:length(x)) {
 # plus 1 because the index is inclusive with the upper_bound 'i'
 lower_bound &lt;- i - w + 1
 if (lower_bound &lt; 1) {
 output[i] &lt;- NA_real_
 } else {
 output[i] &lt;- FUN(x[lower_bound:i, ...])
 }
 }
 output
}

# example
v &lt;- seq(1:10)

# compute a MA(2)
moving_fun(v, 2, mean)

# compute moving sum of two periods
moving_fun(v, 2, sum)
</code></pre>

blocks|key|1583849|text|虽然有点慢，但您还可以使用zoo::rollapply对矩阵执行计算。|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1583850|reqd_ma+<-+rollapply(x,+FUN+=+mean,+width+=+n)|code-block|syntax|javascript|1583851|其中x是数据集，FUN+=+mean是函数；您也可以将其更改为min、max、sd等，width是滚动窗口。|1583852|entityMap^0|0|0|0^^$0|@$1|2|3|4|5|6|7|K|8|@]|9|@]|A|$]]|$1|B|3|C|5|D|7|L|8|@]|9|@]|A|$E|F]]|$1|G|3|H|5|6|7|M|8|@]|9|@]|A|$]]|$1|I|3|-4|5|6|7|N|8|@]|9|@]|A|$]]]|J|$]]

Though a bit slow but you can also use zoo::rollapply to perform calculations on matrices.

<pre><code>reqd_ma &lt;- rollapply(x, FUN = mean, width = n)
</code></pre>

where x is the data set, FUN = mean is the function; you can also change it to min, max, sd etc and width is the rolling window.

blocks|key|1583914|text|可以使用runner包来移动函数。在本例中为mean_run函数。cummean的问题是它不能处理NA值，但mean_run却能。runner包也支持不规则的时间序列，窗口可以依赖于日期：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1583915|library(runner)
set.seed(11)
x1+<-+rnorm(15)
x2+<-+sample(c(rep(NA,5),+rnorm(15)),+15,+replace+=+TRUE)
date+<-+Sys.Date()+%2B+cumsum(sample(1:3,+15,+replace+=+TRUE))

mean_run(x1)
#>++[1]+-0.5910311+-0.2822184+-0.6936633+-0.8609108+-0.4530308+-0.5332176
#>++[7]+-0.2679571+-0.1563477+-0.1440561+-0.2300625+-0.2844599+-0.2897842
#>+[13]+-0.3858234+-0.3765192+-0.4280809

mean_run(x2,+na_rm+=+TRUE)
#>++[1]+-0.18760011+-0.09022066+-0.06543317++0.03906450+-0.12188853+-0.13873536
#>++[7]+-0.13873536+-0.14571604+-0.12596067+-0.11116961+-0.09881996+-0.08871569
#>+[13]+-0.05194292+-0.04699909+-0.05704202

mean_run(x2,+na_rm+=+FALSE+)
#>++[1]+-0.18760011+-0.09022066+-0.06543317++0.03906450+-0.12188853+-0.13873536
#>++[7]++++++++++NA++++++++++NA++++++++++NA++++++++++NA++++++++++NA++++++++++NA
#>+[13]++++++++++NA++++++++++NA++++++++++NA

mean_run(x2,+na_rm+=+TRUE,+k+=+4)
#>++[1]+-0.18760011+-0.09022066+-0.06543317++0.03906450+-0.10546063+-0.16299272
#>++[7]+-0.21203756+-0.39209010+-0.13274756+-0.05603811+-0.03894684++0.01103493
#>+[13]++0.09609256++0.09738460++0.04740283

mean_run(x2,+na_rm+=+TRUE,+k+=+4,+idx+=+date)
#>+[1]+-0.187600111+-0.090220655+-0.004349696++0.168349653+-0.206571573+-0.494335093
#>+[7]+-0.222969541+-0.187600111+-0.087636571++0.009742884++0.009742884++0.012326968
#>+[13]++0.182442234++0.125737145++0.059094786|code-block|syntax|javascript|1583916|用户还可以指定其他选项，如lag，并且只滚动at特定的索引。有关package和function文档的更多信息。|1583917|entityMap|0|LINK|mutability|MUTABLE|url|https://gogonzo.github.io/runner/|1|https://gogonzo.github.io/runner/index.html|2|https://gogonzo.github.io/runner/reference/mean_run.html^0|4|6|M|8|X|7|1D|2|1I|8|1T|6|4|6|0|0|0|D|3|M|2|W|7|1|14|8|2|0^^$0|@$1|2|3|4|5|6|7|Y|8|@$9|Z|A|10|B|C]|$9|11|A|12|B|C]|$9|13|A|14|B|C]|$9|15|A|16|B|C]|$9|17|A|18|B|C]|$9|19|A|1A|B|C]]|D|@$9|1B|A|1C|1|1D]]|E|$]]|$1|F|3|G|5|H|7|1E|8|@]|D|@]|E|$I|J]]|$1|K|3|L|5|6|7|1F|8|@$9|1G|A|1H|B|C]|$9|1I|A|1J|B|C]]|D|@$9|1K|A|1L|1|1M]|$9|1N|A|1O|1|1P]]|E|$]]|$1|M|3|-4|5|6|7|1Q|8|@]|D|@]|E|$]]]|N|$O|$5|P|Q|R|E|$S|T]]|U|$5|P|Q|R|E|$S|V]]|W|$5|P|Q|R|E|$S|X]]]]

One can use <a href="https://gogonzo.github.io/runner/" rel="nofollow noreferrer"><code>runner</code></a> package for moving functions. In this case <code>mean_run</code> function. Problem with <code>cummean</code> is that it doesn't handle <code>NA</code> values, but <code>mean_run</code> does. <code>runner</code> package also supports irregular time series and windows can depend on date:

<pre class="lang-r prettyprint-override"><code>library(runner)
set.seed(11)
x1 &lt;- rnorm(15)
x2 &lt;- sample(c(rep(NA,5), rnorm(15)), 15, replace = TRUE)
date &lt;- Sys.Date() + cumsum(sample(1:3, 15, replace = TRUE))

mean_run(x1)
#&gt; [1] -0.5910311 -0.2822184 -0.6936633 -0.8609108 -0.4530308 -0.5332176
#&gt; [7] -0.2679571 -0.1563477 -0.1440561 -0.2300625 -0.2844599 -0.2897842
#&gt; [13] -0.3858234 -0.3765192 -0.4280809

mean_run(x2, na_rm = TRUE)
#&gt; [1] -0.18760011 -0.09022066 -0.06543317 0.03906450 -0.12188853 -0.13873536
#&gt; [7] -0.13873536 -0.14571604 -0.12596067 -0.11116961 -0.09881996 -0.08871569
#&gt; [13] -0.05194292 -0.04699909 -0.05704202

mean_run(x2, na_rm = FALSE )
#&gt; [1] -0.18760011 -0.09022066 -0.06543317 0.03906450 -0.12188853 -0.13873536
#&gt; [7] NA NA NA NA NA NA
#&gt; [13] NA NA NA

mean_run(x2, na_rm = TRUE, k = 4)
#&gt; [1] -0.18760011 -0.09022066 -0.06543317 0.03906450 -0.10546063 -0.16299272
#&gt; [7] -0.21203756 -0.39209010 -0.13274756 -0.05603811 -0.03894684 0.01103493
#&gt; [13] 0.09609256 0.09738460 0.04740283

mean_run(x2, na_rm = TRUE, k = 4, idx = date)
#&gt; [1] -0.187600111 -0.090220655 -0.004349696 0.168349653 -0.206571573 -0.494335093
#&gt; [7] -0.222969541 -0.187600111 -0.087636571 0.009742884 0.009742884 0.012326968
#&gt; [13] 0.182442234 0.125737145 0.059094786
</code></pre>

One can also specify other options like <code>lag</code>, and roll only <code>at</code> specific indexes. More in <a href="https://gogonzo.github.io/runner/index.html" rel="nofollow noreferrer">package</a> and <a href="https://gogonzo.github.io/runner/reference/mean_run.html" rel="nofollow noreferrer">function</a> documentation.

blocks|key|1583969|text|滑块包可以用来做这个。它有一个专门设计的界面，给人的感觉类似于purrr。它接受任何任意函数，并且可以返回任何类型的输出。数据帧甚至是逐行迭代的。pkgdown站点是here。|type|unstyled|depth|inlineStyleRanges|entityRanges|offset|length|data|1583970|library(slider)

x+<-+1:3

#+Mean+of+the+current+value+%2B+1+value+before+it
#+returned+as+a+double+vector
slide_dbl(x,+~mean(.x,+na.rm+=+TRUE),+.before+=+1)
#>+[1]+1.0+1.5+2.5


df+<-+data.frame(x+=+x,+y+=+x)

#+Slide+row+wise+over+data+frames
slide(df,+~.x,+.before+=+1)
#>+[[1]]
#>+++x+y
#>+1+1+1
#>+
#>+[[2]]
#>+++x+y
#>+1+1+1
#>+2+2+2
#>+
#>+[[3]]
#>+++x+y
#>+1+2+2
#>+2+3+3|code-block|syntax|javascript|1583971|slider和data.table的frollapply()的开销应该非常低(比zoo快得多)。对于这个简单的示例，frollapply()看起来要快一点，但请注意，它只接受数字输入，并且输出必须是标量数值。滑块函数是完全通用的，您可以返回任何数据类型。|style|CODE|1583972|library(slider)
library(zoo)
library(data.table)

x+<-+1:50000+%2B+0L

bench::mark(
++slider+=+slide_int(x,+function(x)+1L,+.before+=+5,+.complete+=+TRUE),
++zoo+=+rollapplyr(x,+FUN+=+function(x)+1L,+width+=+6,+fill+=+NA),
++datatable+=+frollapply(x,+n+=+6,+FUN+=+function(x)+1L),
++iterations+=+200
)
#>+#+A+tibble:+3+x+6
#>+++expression++++++min+++median+`itr/sec`+mem_alloc+`gc/sec`
#>+++<bch:expr>+<bch:tm>+<bch:tm>+++++<dbl>+<bch:byt>++++<dbl>
#>+1+slider++++++19.82ms+++26.4ms+++++38.4++++829.8KB+++++19.0
#>+2+zoo++++++++177.92ms++211.1ms++++++4.71++++17.9MB+++++24.8
#>+3+datatable++++7.78ms+++10.9ms+++++87.9++++807.1KB+++++38.7|1583973|entityMap|0|LINK|mutability|MUTABLE|url|https://davisvaughan.github.io/slider/^0|2B|4|0|0|0|I|C|1M|C|0|0^^$0|@$1|2|3|4|5|6|7|W|8|@]|9|@$A|X|B|Y|1|Z]]|C|$]]|$1|D|3|E|5|F|7|10|8|@]|9|@]|C|$G|H]]|$1|I|3|J|5|6|7|11|8|@$A|12|B|13|K|L]|$A|14|B|15|K|L]]|9|@]|C|$]]|$1|M|3|N|5|F|7|16|8|@]|9|@]|C|$G|H]]|$1|O|3|-4|5|6|7|17|8|@]|9|@]|C|$]]]|P|$Q|$5|R|S|T|C|$U|V]]]]

The slider package can be used for this. It has an interface that has been specifically designed to feel similar to purrr. It accepts any arbitrary function, and can return any type of output. Data frames are even iterated over row wise. The pkgdown site is <a href="https://davisvaughan.github.io/slider/" rel="nofollow noreferrer">here</a>.



<pre class="lang-r prettyprint-override"><code>library(slider)

x &lt;- 1:3

# Mean of the current value + 1 value before it
# returned as a double vector
slide_dbl(x, ~mean(.x, na.rm = TRUE), .before = 1)
#&gt; [1] 1.0 1.5 2.5


df &lt;- data.frame(x = x, y = x)

# Slide row wise over data frames
slide(df, ~.x, .before = 1)
#&gt; [[1]]
#&gt; x y
#&gt; 1 1 1
#&gt; 
#&gt; [[2]]
#&gt; x y
#&gt; 1 1 1
#&gt; 2 2 2
#&gt; 
#&gt; [[3]]
#&gt; x y
#&gt; 1 2 2
#&gt; 2 3 3
</code></pre>

The overhead of both slider and data.table's <code>frollapply()</code> should be pretty low (much faster than zoo). <code>frollapply()</code> looks to be a little faster for this simple example here, but note that it only takes numeric input, and the output must be a scalar numeric value. slider functions are completely generic, and you can return any data type.



<pre class="lang-r prettyprint-override"><code>library(slider)
library(zoo)
library(data.table)

x &lt;- 1:50000 + 0L

bench::mark(
 slider = slide_int(x, function(x) 1L, .before = 5, .complete = TRUE),
 zoo = rollapplyr(x, FUN = function(x) 1L, width = 6, fill = NA),
 datatable = frollapply(x, n = 6, FUN = function(x) 1L),
 iterations = 200
)
#&gt; # A tibble: 3 x 6
#&gt; expression min median `itr/sec` mem_alloc `gc/sec`
#&gt; &lt;bch:expr&gt; &lt;bch:tm&gt; &lt;bch:tm&gt; &lt;dbl&gt; &lt;bch:byt&gt; &lt;dbl&gt;
#&gt; 1 slider 19.82ms 26.4ms 38.4 829.8KB 19.0
#&gt; 2 zoo 177.92ms 211.1ms 4.71 17.9MB 24.8
#&gt; 3 datatable 7.78ms 10.9ms 87.9 807.1KB 38.7
</code></pre>

blocks|key|1368837|text|vector_avg+<-+function(x){
++sum_x+=+0
++for(i+in+1:length(x)){
++++if(!is.na(x[i]))
++++++sum_x+=+sum_x+%2B+x[i]
++}
++return(sum_x/length(x))
}|type|code-block|depth|inlineStyleRanges|entityRanges|data|syntax|javascript|1368838|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>vector_avg &lt;- function(x){
 sum_x = 0
 for(i in 1:length(x)){
 if(!is.na(x[i]))
 sum_x = sum_x + x[i]
 }
 return(sum_x/length(x))
}
</code></pre>

blocks|key|1584055|text|下面是一个使用filter的简单函数，演示了一种使用填充处理开始和结束NAs的方法，并使用自定义权重计算加权平均值(受filter支持)：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1584056|wma+<-+function(x)+{+
++wts+<-+c(seq(0.5,+4,+0.5),+seq(3.5,+0.5,+-0.5))
++nside+<-+(length(wts)-1)/2
++#+pad+x+with+begin+and+end+values+for+filter+to+avoid+NAs
++xp+<-+c(rep(first(x),+nside),+x,+rep(last(x),+nside))+
++z+<-+stats::filter(xp,+wts/sum(wts),+sides+=+2)+%25>%25+as.vector+
++z[(nside%2B1):(nside%2Blength(x))]
}|code-block|syntax|javascript|1584057|entityMap^0|7|6|1N|6|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 is a simple function with <code>filter</code> demonstrating one way to take care of beginning and ending NAs with padding, and computing a weighted average (supported by <code>filter</code>) using custom weights:
<pre><code>wma &lt;- function(x) { 
 wts &lt;- c(seq(0.5, 4, 0.5), seq(3.5, 0.5, -0.5))
 nside &lt;- (length(wts)-1)/2
 # pad x with begin and end values for filter to avoid NAs
 xp &lt;- c(rep(first(x), nside), x, rep(last(x), nside)) 
 z &lt;- stats::filter(xp, wts/sum(wts), sides = 2) %&gt;% as.vector 
 z[(nside+1):(nside+length(x))]
}
</code></pre>

blocks|key|1584119|text|您可以通过以下方法计算窗口宽度为k的向量x的移动平均值：|type|unstyled|depth|inlineStyleRanges|offset|length|style|CODE|entityRanges|data|1584120|apply(embed(x,+k),+1,+mean)|code-block|syntax|javascript|1584121|entityMap^0|G|1|K|1|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|$]]

You may calculate the moving average of a vector <code>x</code> with a window width of <code>k</code> by:
<pre><code>apply(embed(x, k), 1, mean)
</code></pre>

blocks|key|1369024|text|我将聚合与rep()创建的向量一起使用。这样做的好处是可以使用cbind()一次聚合数据帧中的多个列。以下是长度为1000的向量(v)的移动平均值为60的示例：|type|unstyled|depth|inlineStyleRanges|entityRanges|data|1369025|v=1:1000*0.002%2Brnorm(1000)
mrng=rep(1:round(length(v)/60%2B0.5),+length.out=length(v),+each=60)
aggregate(v~mrng,+FUN=mean,+na.rm=T)|code-block|syntax|javascript|1369026|注rep中的第一个参数只是根据向量的长度和要平均的量为移动范围获取足够的唯一值；第二个参数使长度与向量长度相等，最后一个参数将第一个参数的值重复与平均周期相同的次数。|1369027|总而言之，你可以使用几个函数(中位数，最大值，最小值)-均值，例如。同样，可以使用带有cbind的公式对数据帧中的多个(或所有)列执行此操作。|1369028|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]]|$1|G|3|H|5|6|7|O|8|@]|9|@]|A|$]]|$1|I|3|J|5|6|7|P|8|@]|9|@]|A|$]]|$1|K|3|-4|5|6|7|Q|8|@]|9|@]|A|$]]]|L|$]]

I use aggregate along with a vector created by rep(). This has the advantage of using cbind() to aggregate more than 1 column in your dataframe at time. Below is an example of a moving average of 60 for a vector (v) of length 1000:
<pre><code>v=1:1000*0.002+rnorm(1000)
mrng=rep(1:round(length(v)/60+0.5), length.out=length(v), each=60)
aggregate(v~mrng, FUN=mean, na.rm=T)
</code></pre>
Note the first argument in rep is to simply get enough unique values for the moving range, based on the length of the vector and the amount to be averaged; the second argument keeps the length equal to the vector length, and the last repeats the values of the first argument the same number of times as the averaging period.
In aggregate you could use several functions (median, max, min) - mean shown for example. Again, could could use a formula with cbind to do this on more than one (or all) columns in a dataframe.

I'm trying to use R to calculate the moving average over a series of values in a matrix. The normal R mailing list search hasn't been very helpful though. There doesn't seem to be a <a href="http://www.statmethods.net/management/functions.html" rel="noreferrer">built-in function</a> in R will allow me to calculate moving averages. Do any packages provide one? Or do I need to write my own?

Calculating moving average

我尝试使用R来计算矩阵中一系列值的移动平均值。不过，普通的R邮件列表搜索并不是很有帮助。在R中似乎没有可以让我计算移动平均线。有没有包提供这样的功能呢？或者我需要写我自己的？

问计算移动平均值
EN

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问计算移动平均值EN

社区

活动

资源

关于

腾讯云开发者

热门产品

热门推荐

更多推荐

问计算移动平均值
EN