使用dplyr组合并使用迭代过滤器进行汇总 [英] Group and summarize with iterative filter using dplyr

问题描述

如果已经提出了这个问题，我们一直在搜索，没有找到可以应用于我的问题的答案。

Upfront apology if this has been asked, I have been searching all day and have not found an answer I can apply to my problem.

我试图使用dplyr（和co。）解决这个问题，因为我以前的方法（for循环）太低效了。我有一个事件时间的数据集，在站点，分组。我想总结沿序列移动窗口中发生的事件的数量（和比例）。

I am trying to solve this issue using dplyr (and co.) because my previous method (for loops) was too inefficient. I have a dataset of event times, at sites, that are in groups. I want to summarize the number (and proportion) of events that occur in a moving window along a sequence.

# Example data
set.seed(1)
sites = rep(letters[1:10],10)
groups = c('red','blue','green','yellow')
times = round(runif(length(sites),1,100))

timePeriod = seq(1,100)

# Example dataframe
df = data.frame(site = sites,
                group = rep(groups,length(sites)/length(groups)),
                time = times)

这是我尝试总结在给定的移动时间窗口中包含时间（事件）的每个组中的站点数。
目标是移动向量 timePeriod 的每个元素，并总结每个组中的事件发生在 timePeriod [i] + / - 半窗口。最终将它们存储在例如每个组中的列的数据框中，并且每个时间步的行都是理想的。

This is my attempt to summarize the number of sites from each group that contain a time (event) within a given moving window of time. The goal is to move through each element of the vector timePeriod and summarize how many events in each group occurred at timePeriod[i] +/- half-window. Ultimately storing them in, e.g., a dataframe with a column for each group, and a row for each time step, is ideal.

df %>%
filter(time > timePeriod[i]-25 & time < timePeriod[i]+25) %>%
group_by(group) %>%
summarise(count = n())

如何循环遍历我的序列的时间并分别存储每个组的汇总表？谢谢！

How can I do this without looping through my sequence of time and storing the summary table for each group individually? Thanks!

推荐答案

结合 lapply 和 dplyr ，您可以执行以下操作，这与您迄今为止所做的工作相近。

Combining lapply and dplyr, you can do the following, which is close to what you had worked so far.

lapply(timePeriod, function(i){
  df %>%
    filter(time > (i - 25) & time < ( i + 25 ) )  %>%
    group_by(group) %>%
    summarise(count = n()) %>% 
    mutate(step = i)
}) %>% 
  bind_rows()

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