R dplyr 滚动总和 [英] R dplyr rolling sum

问题描述

我正在通过 dplyr 实现滚动总和计算，但在我的数据库中，我有许多变量只有一个或只有几个观察值，导致(k 小于 n)错误.我试图在 thisj 示例中使用过滤器和合并来解决这个问题，但想知道是否有一种方法可以在 dplyr 中更优雅和自动地做到这一点.请看下面的例子

i am implementing a rolling sum calculation through dplyr, but in my database i have a number of variables that have only one or only a few observations, causing an (k is smaller than n) error. i have tried to resolve this in thisj example with filter and merge, but wondering if there is a way to do this more elegantly and automatically within dplyr. please see the example below

    #create data
    dg = expand.grid(site = c("Boston","New York"),
                     year = 2000:2004)
    dg$animal="dog"
    dg$animal[10]="cat";dg$animal=as.factor(dg$animal)
    dg$count = rpois(dim(dg)[1], 5) 

如果我运行下面的代码，因为我只有一行带有cat"，就会得到(错误:k <= n is not true)错误

If i would run the code below, because i only have one row with "cat", one gets the (Error: k <= n is not true) error

#running average
dg2 = dg %>%
  arrange(site,year,animal) %>%
  group_by(site,animal) %>%
#   filter(animal=="dog") %>%
  mutate(roll_sum = rollsum(x = count, 2, align = "right", fill = NA))

我尝试使用以下代码来解决此问题，该代码过滤掉cat"值并进行后续合并，但我想知道是否可以直接在 dplyr 中执行此操作，尤其是在此解决方案中预先指定/知道每个变量的唯一行数并手动调整是否会更改滚动总和的范围等.

i have tried to solve this by using the following code, which filters out the "cat" value and does a subsequent merge, but I was wondering whether one can do this directly inside dplyr, especially as in this solution one would have to specify / know the number of unique rows for each variable in advance and adjust manually if one would change the range of the rolling sum, etc.

dg2 = dg %>%
  arrange(site,year,animal) %>%
  group_by(site,animal) %>%
  filter(animal=="dog") %>%
  mutate(roll_sum = rollsum(x = count, 2, align = "right", fill = NA))

merge(dg,dg2,c("site", "year","animal","count"),all.x=TRUE)

       site year animal count roll_sum
1    Boston 2000    dog     5       NA
2    Boston 2001    dog     6       11
3    Boston 2002    dog     6       12
4    Boston 2003    dog     5       11
5    Boston 2004    dog     3        8
6  New York 2000    dog     8       NA
7  New York 2001    dog     3       11
8  New York 2002    dog    12       15
9  New York 2003    dog     3       15
10 New York 2004    cat     3       NA

非常感谢 - W

推荐答案

您可以改用 RcppRoll::roll_sum 如果样本大小(n) 为小于窗口大小(k).

You can instead use RcppRoll::roll_sum which returns NA if the sample size(n) is less than the window size(k).

set.seed(1)
dg$count = rpois(dim(dg)[1], 5) 
library(RcppRoll)
library(dplyr)
dg %>%
     arrange(site,year,animal) %>%
     group_by(site, animal) %>%
     mutate(roll_sum = roll_sum(count, 2, align = "right", fill = NA))    
#       site year animal count roll_sum
#1    Boston 2000    dog     4       NA
#2    Boston 2001    dog     5        9
#3    Boston 2002    dog     3        8
#4    Boston 2003    dog     9       12
#5    Boston 2004    dog     6       15
#6  New York 2000    dog     4       NA
#7  New York 2001    dog     8       12
#8  New York 2002    dog     8       16
#9  New York 2003    dog     6       14
#10 New York 2004    cat     2       NA

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