R中的动态分组|根据应用功能的条件进行分组 [英] Dynamic Grouping in R | Grouping based on condition on applied function

问题描述

在R中的gregation（）函数中，如何在对变量的应用函数进行分组时指定停止条件？

例如，我有这样的数据框架： df
输入数据框架

For example, I have data-frame like this: "df" Input Data frame

注意：假设输入数据框中的每一行表示该比赛中一名球员打的单个球。因此，通过计数行数可以告诉我们所需的球数。

Note: Assuming each row in input data frame is denoting single ball played by a player in that match. So, by counting a number of rows can tell us the number of balls required.

而且，我想要这样的数据框：输出数据帧
我需要的是：得分10个奔跑需要多少个球？

And, I want my data frame like this one: Output data frame My need is: How many balls are required to score 10 runs?

当前，我正在使用以下R代码：
group_data<-聚合（df $ score，by = list（Category = df $ player， df $ match），FUN = sum，na.rm = TRUE）

Currently, I am using this R code: group_data <- aggregate(df$score, by=list(Category=df$player,df$match), FUN=sum,na.rm = TRUE)

使用此代码，我无法停止想要的分组，当它对所有行进行分组时，它将停止。我不希望所有行都考虑在内。

Using this code, I can not stop grouping as I want, it stops when it groups all rows. I don't want all rows to consider.

但是如何设置约束，例如得分> = 10时立即停止分组
通过施加此约束，我唯一的目的是计算满足此条件的行数。

But How to put constraint like "Stop grouping as soon as score >= 10" By putting this constraint, my sole purpose is to count the number of rows satisfying this condition.

预先感谢。

推荐答案

以下是使用 dplyr

library(dplyr)
df1 %>%
    group_by(match, player) %>% 
    filter(!lag(cumsum(score) > 10, default = FALSE)) %>% 
    summarise(score = sum(score), Count = n())
# A tibble: 2 x 4
# Groups:   match [?]
#   match player score Count
#   <int>  <int> <dbl> <int>
#1     1     30    12     2
#2     2     31    15     3

数据

data

df1 <- structure(list(match = c(1L, 1L, 1L, 2L, 2L, 2L), player = c(30L, 
30L, 30L, 31L, 31L, 31L), score = c(6, 6, 6, 3, 6, 6)), .Names = c("match", 
 "player", "score"), row.names = c(NA, -6L), class = "data.frame")

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