如何从 R 中的分类变量更改所有期望值（按频率）

Question

I have a data frame in R which looks similar to the one below, with the factor variable "Genre":

|Genre|Listening Time|
|Rock |1:05          |
|Pop  |3:10          |
|RnB  |4:12          |
|Rock |2:34          |
|Pop  |5:01          |
|RnB  |4:01          |
|Rock |1:34          |
|Pop  |2:04          |

I want leave the top 15 genres (by count) as they are and only rename all other genres that are not among the top 15. Those should be renamed into the word "Other".

In other words - if for example the Genre "RnB" is not among the top 15 Genres, then it should be replaced by the word "Other".

The table I would like to get would look like this then:

|Genre|Listening Time|
|Rock |1:05          |
|Pop  |3:10          |
|Other|4:12          |
|Rock |2:34          |
|Pop  |5:01          |
|Other|4:01          |
|Rock |1:34          |
|Pop  |2:04          |

How would I approach this? Thank you!

Answer 1

If you want to look into tidyverse you may do something like this. I have tried to mimic your data frame but added some more rows.

You start with data > group_by Genre > order > chose top 5

library(tidyverse)

set.seed(1)
Data <- data.frame(
  listen = format(as.POSIXlt(paste0(
      as.character(sample(1:5)),
      ':',
      as.character(sample(0:59))), format = '%H:%M'),format = '%H:%M'),
  Genre = sample(c("Rock", "Pop", 'RnB'), 120, replace = TRUE)
)


Data %>%
  group_by(Genre ) %>%
  arrange(desc(listen)) %>% 
  select(listen) %>% 
  top_n(5) %>% 
  arrange(Genre)
#> Adding missing grouping variables: `Genre`
#> Selecting by listen
#> # A tibble: 15 x 2
#> # Groups:   Genre [3]
#>    Genre listen
#>    <chr> <chr> 
#>  1 Pop   05:47 
#>  2 Pop   05:47 
#>  3 Pop   05:43 
#>  4 Pop   05:41 
#>  5 Pop   05:28 
#>  6 RnB   05:54 
#>  7 RnB   05:44 
#>  8 RnB   05:43 
#>  9 RnB   05:29 
#> 10 RnB   05:28 
#> 11 Rock  05:54 
#> 12 Rock  05:44 
#> 13 Rock  05:41 
#> 14 Rock  05:29 
#> 15 Rock  05:26

---

Sorry, if I have misunderstood what you wanted. If you assign the code to a new data.frame and make an anti_join to the original DF and then mutate Genre to others it should be what you want - I guess.

df <- Data %>%
  group_by(Genre ) %>%
  arrange(desc(listen)) %>% 
  select(listen) %>% 
  top_n(5) %>% 
  arrange(Genre) 

# make an anti_join and assign 'other' to Genre

anti_join(Data, df) %>% 
  mutate(Genre = 'others')

---

Next Edit

Hopefully I have now understood your question. You want just to count how often the Genres occure in your data and give those which do not belong to the top 15 the name Others . Maybe I was mislead by the data frame you offered which shows only 3 Genres. So I looked up in Wikipedia and added a few, invented some own Genres and used LETTERS to build up a DF with sufficient numbers of Genre.

With count(Genre) the occurences of Genres are counted, and then arranged in descending order. I have then introduced a new column with the row numbers. You can delete this if you want, as it is only there to do the next step which is introducing another column - I have chosen to make a new column, instead of renaming all the names in Genre - with the name Top15 an giving every Genre which is on place(in row) 16 or later the name Others and keeping the rest unchanged.

head(20) just prints the first 20 rows of this DF.

library(tidyverse)

set.seed(1)
Data <- data.frame(
  listen = format(as.POSIXlt(paste0(
      as.character(sample(1:5)),
      ':',
      as.character(sample(0:59))), format = '%H:%M'),format = '%H:%M'),
  Genre = sample(c("Rock", "Pop", 'RnB', 'Opera',
                   'Birthday Songs', 'HipHop',
                   'Chinese Songs', 'Napoli Lovesongs',
                   'Benga', 'Bongo', 'Kawito', 'Noise',
                   'County Blues','Mambo', 'Reggae',
                   LETTERS[0:24]), 300, replace = TRUE)
)

Data %>% count(Genre) %>% 
  arrange(desc(n)) %>% 
  mutate(place = row_number()) %>% 
  mutate(Top15 = ifelse(place > 15, 'Others', Genre)) %>% 
  head(20)
#> # A tibble: 20 x 4
#>    Genre            n place Top15       
#>    <chr>        <int> <int> <chr>       
#>  1 N               15     1 N           
#>  2 T               13     2 T           
#>  3 V               13     3 V           
#>  4 K               12     4 K           
#>  5 Rock            11     5 Rock        
#>  6 X               11     6 X           
#>  7 E               10     7 E           
#>  8 W               10     8 W           
#>  9 Benga            9     9 Benga       
#> 10 County Blues     9    10 County Blues
#> 11 G                9    11 G           
#> 12 J                9    12 J           
#> 13 M                9    13 M           
#> 14 Reggae           9    14 Reggae      
#> 15 B                8    15 B           
#> 16 D                8    16 Others      
#> 17 I                8    17 Others      
#> 18 P                8    18 Others      
#> 19 R                8    19 Others      
#> 20 S                8    20 Others

I hope this was what you were looking for

Answer 2

Try replacing df with your data.frame to check if you get the desired output:

df <- data.frame(Genre=sample(letters, 1000, replace=TRUE),
                 ListeningTime=runif(1000, 3, 5))

 > head(df) Genre ListeningTime 1 j 3.437013 2 n 4.151121 3 p 3.109044 4 z 4.529619 5 h 4.043982 6 i 3.590463

freq <- table(df$Genre)
sorted <- sort(freq, decreasing=TRUE)  # Sorted by frequency of df$Genre

 > sorted dxoq r ugijfapbevnw c kmzlhtys 53 50 46 45 45 42 41 41 40 39 38 38 37 37 37 36 36 35 35 35 35 34 33 33 30 29

not_top_15 <- names(sorted[-1*1:15])  # The Genres not in the top 15
pos <- which(df$Genre %in% not_top_15)  # Their position in df

 > head(df[pos, ]) # The original data, without the top 15 Genres Genre ListeningTime 2 n 4.151121 4 z 4.529619 5 h 4.043982 7 s 3.521054 16 w 3.528091 18 h 4.588815

Answer 3

library(dplyr)

set.seed(123)
compute_listen_time <- function(n.songs) {
  min <- sample(1:15, n.songs, replace = TRUE)
  sec <- sample(0:59, n.songs, replace = TRUE)
  sec <- ifelse(sec > 10, sec, paste0("0", sec))
  paste0(min, ":", sec)
}



df <- data.frame(
  Genre = sample(c("Rock", "Pop", "RnB", "Rock", "Pop"), 100, replace = TRUE),
  Listen_Time = compute_listen_time(100)
)


df <- add_count(df, Genre, name = "count") %>%
  mutate(
    rank = dense_rank(desc(count)),
    group = ifelse(rank <= 15, Genre, "other")
  )
df

Answer 4

I can think of a data.table solution. Let's assume your data.frame is called music , then:

library(data.table)
setDT(music)

other_genres <- music[, .N, by = genre][order(-N)][16:.N, genre]

music[genre %chin% other_genres, genre := "other"]

The first line of effective code counts the appearances by genre, sorts it from largest to smallest and selects from the 16 down to the last one, assigning the result to a variable called other_genres . The second line will check which genres are in that list, and update their name to "other" .

Answer 5

There is a pretty neat solution with the forcats package applied here to the diamonds dataset to only name the top 5 clarity values and bundle the rest as "Other"

library(dplyr)
library(forcats)

diamonds %>%
  mutate(clarity2 = fct_lump(fct_infreq(clarity), n = 5))

Result:

# A tibble: 53,940 x 11
   carat cut       color clarity depth table price     x     y     z clarity2
   <dbl> <ord>     <ord> <ord>   <dbl> <dbl> <int> <dbl> <dbl> <dbl> <ord>   
 1 0.23  Ideal     E     SI2      61.5    55   326  3.95  3.98  2.43 SI2     
 2 0.21  Premium   E     SI1      59.8    61   326  3.89  3.84  2.31 SI1     
 3 0.23  Good      E     VS1      56.9    65   327  4.05  4.07  2.31 VS1     
 4 0.290 Premium   I     VS2      62.4    58   334  4.2   4.23  2.63 VS2     
 5 0.31  Good      J     SI2      63.3    58   335  4.34  4.35  2.75 SI2     
 6 0.24  Very Good J     VVS2     62.8    57   336  3.94  3.96  2.48 VVS2    
 7 0.24  Very Good I     VVS1     62.3    57   336  3.95  3.98  2.47 Other   
 8 0.26  Very Good H     SI1      61.9    55   337  4.07  4.11  2.53 SI1     
 9 0.22  Fair      E     VS2      65.1    61   337  3.87  3.78  2.49 VS2     
10 0.23  Very Good H     VS1      59.4    61   338  4     4.05  2.39 VS1     
# … with 53,930 more rows