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如何使用 R 找到数据中最常见的序列？

2024-03-13

我正在尝试弄清楚如何使用rollapply函数（从Zoo包）来查找数据集中最常见字符串的序列，但我还需要对某些变量（例如日期、行等）进行分组

在进一步讨论之前，值得注意的是，该查询建立在我之前在此发布的一个问题的基础上：如何使用 Tableau 找到数据中最常见的（字符串）序列？ https://stackoverflow.com/questions/67064314/how-can-i-find-most-common-sequences-of-strings-in-my-data-using-tableau

那里提供的解决方案效果非常好，但我现在想将其应用到不同的数据集，这带来了一些新的挑战！以下是我在这个新数据集中使用的数据示例：

structure(list(Title = c("Dragons' Den", "One Hot Summer", "Keeping Faith", 
"Cuckoo", "Match of the Day", "Sportscene", "Sportscene", "The Irish League Show", 
"Match of the Day", "EastEnders", "Dragons' Den", "Fake or Fortune?", 
"Asian Provocateur", "In The Flesh", "Two Pints of Lager and a Packet of Crisps", 
"Travels in Trumpland with Ed Balls", "Hidden", "Train Surfing Wars: A Matter of Life and Death", 
"Bollywood: The World's Biggest Film Industry", "One Hot Summer", 
"Asian Provocateur", "In The Flesh", "Two Pints of Lager and a Packet of Crisps", 
"Travels in Trumpland with Ed Balls", "EastEnders", "Match of the Day", 
"Dragons' Den", "The Next Step", "Doctor Who Series 11 Trailer", 
"Doctor Who", "Doctor Who", "Doctor Who", "Picnic at Hanging Rock", 
"Sylvia", "Keeping Faith", "Cardinal: Blackfly Season", "Picnic at Hanging Rock", 
"Age Before Beauty", "One Hot Summer", "Stewart Lee's Comedy Vehicle", 
"Asian Provocateur", "In The Flesh", "Two Pints of Lager and a Packet of Crisps", 
"Travels in Trumpland with Ed Balls", "EastEnders", "Age Before Beauty", 
"Holby City", "Who Do You Think You Are?", "Louis Theroux: Dark States", 
"Louis Theroux: Dark States", "Louis Theroux", "Louis Theroux's Weird Weekends", 
"Picnic at Hanging Rock", "Sylvia", "Keeping Faith", "Cardinal: Blackfly Season"
), Programme_Genre = c("Entertainment", "Documentary", "Drama", 
"New SeriesComedy", "Sport", "Sport", "Sport", "Sport", "Sport", 
"Drama", "Entertainment", "Documentary", "Comedy", "Drama", "Comedy", 
"Documentary", "Crime Drama", "Documentary", "Documentary", "Documentary", 
"Comedy", "Drama", "Comedy", "Documentary", "Drama", "Sport", 
"Entertainment", "CBBC", "Sci-Fi", "Sci-Fi", "Sci-Fi", "Sci-Fi", 
"Drama", "Film", "Drama", "Crime Drama", "On Now", "Drama", "Documentary", 
"Comedy", "Comedy", "Drama", "Comedy", "Documentary", "Drama", 
"Drama", "Drama", "History", "Documentary", "Documentary", "Documentary", 
"Archive", "Drama", "Film", "Drama", "Crime Drama"), Programme_Category = c("Featured", 
"Featured", "Featured", "Featured", "This Weekend's Football", 
"This Weekend's Football", "This Weekend's Football", "This Weekend's Football", 
"Most Popular", "Most Popular", "Most Popular", "Most Popular", 
"Box Sets", "Box Sets", "Box Sets", "Box Sets", "Featured", "Featured", 
"Featured", "Featured", "Box Sets", "Box Sets", "Box Sets", "Box Sets", 
"Most Popular", "Most Popular", "Most Popular", "Most Popular", 
"Doctor Who S1-S10", "Doctor Who S1-S10", "Doctor Who S1-S10", 
"Doctor Who S1-S10", "Drama", "Drama", "Drama", "Drama", "Featured", 
"Featured", "Featured", "Featured", "Box Sets", "Box Sets", "Box Sets", 
"Box Sets", "Most Popular", "Most Popular", "Most Popular", "Most Popular", 
"Louis Theroux", "Louis Theroux", "Louis Theroux", "Louis Theroux", 
"Drama", "Drama", "Drama", "Drama"), date = c("13/08/2018", "13/08/2018", 
"13/08/2018", "13/08/2018", "13/08/2018", "13/08/2018", "13/08/2018", 
"13/08/2018", "13/08/2018", "13/08/2018", "13/08/2018", "13/08/2018", 
"13/08/2018", "13/08/2018", "13/08/2018", "13/08/2018", "14/08/2018", 
"14/08/2018", "14/08/2018", "14/08/2018", "14/08/2018", "14/08/2018", 
"14/08/2018", "14/08/2018", "14/08/2018", "14/08/2018", "14/08/2018", 
"14/08/2018", "14/08/2018", "14/08/2018", "14/08/2018", "14/08/2018", 
"14/08/2018", "14/08/2018", "14/08/2018", "14/08/2018", "15/08/2018", 
"15/08/2018", "15/08/2018", "15/08/2018", "15/08/2018", "15/08/2018", 
"15/08/2018", "15/08/2018", "15/08/2018", "15/08/2018", "15/08/2018", 
"15/08/2018", "15/08/2018", "15/08/2018", "15/08/2018", "15/08/2018", 
"15/08/2018", "15/08/2018", "15/08/2018", "15/08/2018"), column = c("1", 
"2", "3", "4", "1", "2", "3", "4", "1", "2", "3", "4", "1", "2", 
"3", "4", "1", "2", "3", "4", "1", "2", "3", "4", "1", "2", "3", 
"4", "1", "2", "3", "4", "1", "2", "3", "4", "1", "2", "3", "4", 
"1", "2", "3", "4", "1", "2", "3", "4", "1", "2", "3", "4", "1", 
"2", "3", "4"), row = c("1", "1", "1", "1", "2", "2", "2", "2", 
"3", "3", "3", "3", "4", "4", "4", "4", "1", "1", "1", "1", "2", 
"2", "2", "2", "3", "3", "3", "3", "4", "4", "4", "4", "5", "5", 
"5", "5", "1", "1", "1", "1", "2", "2", "2", "2", "3", "3", "3", 
"3", "4", "4", "4", "4", "5", "5", "5", "5")), row.names = c(NA, 
-56L), class = "data.frame")

抱歉，但我不太确定共享数据的最佳实践。希望以上方法有效。它应该看起来像这样：

   Title            Programme_Genre     Programme_Category  date         column row
1   Dragons Den     Entertainment       Featured            13/08/2018      1   1
2  One Hot Summer   Documentary         Featured            13/08/2018      2   1
3  Keeping Faith    Drama               Featured            13/08/2018      3   1
4  Cuckoo           New Series Comedy   Featured            13/08/2018      4   1
5  Match of the Day Sport               This Weekends...    13/08/2018      1   2
6  Sportscene       Sport               This Weekends...    13/08/2018      2   2

我想做的是使用rollapply功能类似于我在上一个问题中建议的方式（请参阅上面的链接），但仅查找出现在同一日期和特定列范围内的序列。例如，我想知道最常见的流派序列（“Programme_Genre”）是什么，但我只想要rollapply函数在每个日期的每一行的第 1-4 列中执行此操作。我确信我没有很好地解释这一点（如果你没有猜到，我没有数据科学背景）所以如果有必要的话我非常乐意详细说明。提前致谢！


使用 tidyverse、zoo 和 lubridate，尝试：

library(tidyverse)
library(zoo)
library(lubridate)

df %>% 
  mutate(date = lubridate::dmy(date)) %>% # Optional. Properly parses date as Date class. Makes sorting easier.
  filter(column <= 4) %>% # Step 1. Exclude observations with `column` values above 4.
  group_split(row, date) %>% # Step 2. Splits the DF into smaller DFs representing row and date groups.
  # Step 3 (below). Loops the solution to the previous question, gets a DF, and assigns the date and row signals to each observation.
  map_df(.x = . ,
         .f = ~(rollapply(data = .x$Programme_Genre , 3, c) %>% 
                  as_tibble() %>% 
                  mutate(date = unique(.x$date), row = unique(.x$row)))) %>% 
  group_by_all() %>% 
  tally() %>% 
  arrange(date, row, n)

    # A tibble: 26 x 6
# Groups:   V1, V2, V3, date [26]
   V1            V2            V3               date       row       n
   <chr>         <chr>         <chr>            <date>     <chr> <int>
 1 Documentary   Drama         New SeriesComedy 2018-08-13 1         1
 2 Entertainment Documentary   Drama            2018-08-13 1         1
 3 Sport         Sport         Sport            2018-08-13 2         2
 4 Drama         Entertainment Documentary      2018-08-13 3         1
 5 Sport         Drama         Entertainment    2018-08-13 3         1
 6 Comedy        Drama         Comedy           2018-08-13 4         1
 7 Drama         Comedy        Documentary      2018-08-13 4         1
 8 Crime Drama   Documentary   Documentary      2018-08-14 1         1
 9 Documentary   Documentary   Documentary      2018-08-14 1         1
10 Comedy        Drama         Comedy           2018-08-14 2         1
# ... with 16 more rows
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