我有一个显示人的“活动链”的df，看起来像这样（问题底部的片段）：
head(agents)
id leg_activity
1 9 home, adpt, shop, car_passenger, home, adpt, work, adpt, home, work, outside, pt, home
2 10 home, pt, outside, pt, home, car, leisure, car, other, car, leisure, car, leisure, car, other, car, leisure, car, other, car, leisure, car, home, adpt, leisure, adpt, home
3 11 home, work, adpt, home
4 96 home, car, work, car, home, work, adpt, home
5 97 home, adpt, work, car_passenger, leisure, car_passenger, work, adpt, home, car_passenger, outside, car_passenger, outside, car_passenger, home
6 101 home, bike, outside, car_passenger, outside, car_passenger, outside, bike, home, adpt, leisure, adpt, home, bike, leisure, bike, home
我感兴趣的是检测adpt发生的模式。最简单的方法是使用count()函数，该函数给了我一个频率表作为输出。不幸的是，这种结果将产生误导。
外观如下：
x freq
home, adpt, work, adpt, home 2071
home, adpt, shop, adpt, home 653
home, adpt, education, adpt, home 545
home, pt, work, adpt, home 492
home, adpt, work, pt, home 468
home, adpt, work, home 283
这种方法的问题是，我无法在更长的活动链中检测到模式；例如：
home, adpt, education, adpt, education, adpt, home, car, work, car, home, shop, adpt, home
此案例在开始时就有一个活动链，该活动链非常频繁，但是随着后续活动的进行，它不会计入count函数。
是否有一种使用count函数的方法，该方法也考虑了单元格内部发生了什么？因此，有一个表格可以显示所有可能的组合及其频率，这将很有趣，例如：
x freq
home, adpt, home 10
home, adpt, home, pt, work, home 4
home, pt, work, home 2
非常感谢您的帮助！
数据：
structure(list(id = c(9L, 10L, 11L, 96L, 97L, 101L, 103L, 248L,
499L, 1044L, 1215L, 1238L, 1458L, 1569L, 1615L, 1626L, 1734L,
1735L, 1790L, 1912L, 9040L, 14858L, 14859L, 14967L, 15011L, 15012L,
15015L, 15045L, 15050L, 15058L, 15060L, 15086L, 15088L, 15094L,
15109L, 15113L, 15152L, 15157L, 15192L, 15193L, 15222L, 15230L,
15231L, 15234L, 15235L, 15237L, 15256L, 15257L, 15258L, 15269L,
15270L, 15318L, 15319L, 15338L, 15369L, 15371L, 15396L, 15397L,
15399L, 15404L, 15505L, 15506L, 15515L, 15516L, 15525L, 15542L,
15593L, 15602L, 15608L, 15643L, 15667L, 15727L, 15728L, 15729L,
15752L, 15775L, 15808L, 15851L, 15869L, 15881L, 15882L, 15960L,
15962L, 15966L, 16058L, 16107L, 16174L, 16229L, 16237L, 16238L,
16291L, 16333L, 16416L, 16418L, 16449L, 16450L, 16451L, 16491L,
16506L, 16508L), leg_activity = c("home, adpt, shop, car_passenger, home, adpt, work, adpt, home, work, outside, pt, home",
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"home, work, adpt, home", "home, car, work, car, home, work, adpt, home",
"home, adpt, work, car_passenger, leisure, car_passenger, work, adpt, home, car_passenger, outside, car_passenger, outside, car_passenger, home",
"home, bike, outside, car_passenger, outside, car_passenger, outside, bike, home, adpt, leisure, adpt, home, bike, leisure, bike, home",
"home, adpt, work, adpt, home, walk, other, pt, home", "home, adpt, work, walk, home, adpt, work, walk, home",
"home, adpt, leisure, adpt, home, bike, outside, bike, home",
"home, pt, work, adpt, home, adpt, work, adpt, home", "home, adpt, work, adpt, home, car, outside, car, work, car, work, car, home",
"home, work, leisure, adpt, home", "home, outside, pt, home, adpt, leisure, adpt, home",
"home, car_passenger, leisure, walk, work, walk, leisure, walk, work, adpt, home, walk, home",
"home, adpt, work, walk, work, walk, work, pt, home", "home, car, work, pt, leisure, adpt, work, car, home, car, home",
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"home, adpt, work, adpt, home, car_passenger, outside, car_passenger, leisure, car_passenger, home, car_passenger, home",
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"home, car, other, car, home, adpt, shop, adpt, home", "home, pt, work, adpt, home",
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"home, walk, education, adpt, home, walk, education, walk, home, bike, leisure, bike, home",
"home, adpt, shop, adpt, home, car, home", "home, adpt, leisure, walk, leisure, walk, leisure, adpt, home",
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"home, adpt, education, walk, home, walk, education, walk, home, walk, home",
"home, bike, education, bike, home, adpt, education, adpt, home, walk, home"
)), row.names = c(NA, 100L), class = "data.frame")
我不太确定您到底想做什么，但我知道您有兴趣检测活动adpt的发生方式。这通常是在NLP中完成的，下面是使用tidytext包的解决方案。我将leg_activity列拆分为n-grams，即按连续的单词序列拆分文本。两个连续单词的序列称为bi-gram，三个连续单词tri-gram等等。当我们对这些n-grams进行计数时，我们将了解哪些活动最常发生于适应，哪些活动最常发生于适应。