获得组的 3 个最常见元素，连接关系，并忽略不太常见的值

Question

我正在尝试使用函数获取每组数据帧中最常见的 3 个数字，但忽略不太常见的值（每组），并允许使用唯一数字（如果存在）。接受的答案将具有最低的system.time

#my current function
library(plyr)
get.3modes.andcounts<- function(origtable,groupby,columnname) {
  data <- ddply (origtable, groupby, .fun = function(xx){
    c(m1 = paste(names(sort(table(xx[,columnname]),decreasing=TRUE)[1])),
      m2 = paste(names(sort(table(xx[,columnname]),decreasing=TRUE)[2])),
      m3 = paste(names(sort(table(xx[,columnname]),decreasing=TRUE)[3])),
      counts=length2(xx[[columnname]], na.rm=TRUE) #http://www.cookbook-r.com/Manipulating_data/Summarizing_data/
    ) } ) 
  return(data)
}
length2 <- function (x, na.rm=FALSE) {
  if (na.rm) sum(!is.na(x))
  else       length(x)
}
# example df
col2<-c(4, 4, 4, 4, 5, 3, 3, 3, 2, 2, # group1 "5" is the less common
        2, 2, 2, 4, 4, 3, 3, 2, 2, 2, # group2 "3" and "4" are equally less common, and there is 2 more frequent
        4, 4, 4, 4, 4, 4, 4, 4, 4, 4, # group3 "4" is unique
        4, 4, 4, 4, 5, 5, 5, 5, 2, 2, # group4 "2" is the less common, other ties more frequent
        4, 4, 4, 4, 4, 5, 5, 5, 5, 5) # group5 "4" and "5" are equally common and no value is less common (similar to unique)
col1<-paste(c(rep("group1",10),rep("group2",10),rep("group3",10),rep("group4",10),rep("group5",10)), sep=", ")
df<-data.frame(col1=col1,col2=col2)

get.3modes.andcounts(df,"col1","col2")

#CURRENT result 
col1 m1 m2 m3 counts
1 group1  4  3  2     10 # ok
2 group2  2  3  4     10 # no, 3 and 4 are the less common
3 group3  4 NA NA     10 # ok
4 group4  4  5  2     10 # no, 2 is less common
5 group5  4  5 NA     10 # ok

# desired
col1 m1 m2 m3 counts
1 group1  4  3  2     10
2 group2  2 NA NA     10
3 group3  4 NA NA     10
4 group4  4  5 NA     10
5 group5  4  5 NA     10

编辑：真实样本有几个联系，并且不希望有超过 3 列。仅当出现平局时才接受超过 3 个数字（在 3 列中）。这就是为什么，我决定要求另一种类型的输出。
编辑：组 7。只有三个最常见的通缉犯。例外，包括第 3 最常见的关系（如在其他组中）。

    # EXAMPLE 2
    # new proposal
col2<-c(4, 4, 4, 4, 5, 3, 3, 3, 2, 2, 6, 6, # group1 2 and 6 tied in the 3rd position, 5 less common
        2, 2, 2, 4, 4, 3, 3, 2, 2, 2, 6, 6, # group2 4, 3 and 6 tied in the less common, excluded.
        4, 4, 4, 7, 7, 7, 5, 5, 5, 4, 4, 6, # group3 4, 7 and 5 more common, 3 most common present, exclude everything else
        4, 4, 4, 4, 5, 5, 5, 5, 2, 2, 6, 6, # group4 2 and 6 less common, excluded (4 AND 5 tied)
        4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6, # group5 6 less common, excluded, (4 and 5 tied)
        4, 4, 4, 3, 3, 3, 2, 2, 2, 1, 1, 1, # group6 all tied
      14,14,14,16,16,16,16,34,34,42,42,42,80,80,84,92, #group7 16, 14, 42 are the three most freq.
      20,52,40,40,40,20,20,60,60,50) #group 8 20,40 tied, 60 next.
col1<-paste(c(rep("group1",12),rep("group2",12),rep("group3",12),rep("group4",12),rep("group5",12),
              rep("group6",12),rep("group7",16),rep("group8", 10)), sep=", ") 
df<-data.frame(col1=col1,col2=col2)

#desired output
    col1 m1       m2   m3 counts
1 group1  4        3  2,6     12 # 2 and 6 tied in the 3rd position, 5 less common
2 group2  2       NA   NA     12 # 4, 3 and 6 tied in the less common, excluded.
3 group3  4      7,5   NA     12 # three most common numbers present, exclude everything else 
4 group4  4,5     NA   NA     12 # 2 and 6 less common excluded (4 AND 5 tied)
5 group5  4,5     NA   NA     12 # 6 less common, excluded, (4 and 5 tied)
6 group6  4,3,2,1 NA   NA     12 # all tied
7 group7  16    14,42  NA     16 # three most frequent present, discard others
8 group8  20,40   60   NA     10 # three most frequent present

Answer 1

使用dplyr 和tidyr（plyr 的更新版本）：

library(dplyr)
library(tidyr)

df %>% 
  group_by(col1, col2) %>% 
  summarise(n = n()) %>% 
  mutate(m = min_rank(desc(n)), count = sum(n)) %>% 
  filter(m <= 3 & (m != max(m) | m == 1)) %>% 
  group_by(col1, m, count) %>% 
  summarize(a = paste(col2, collapse = ',')) %>% 
  spread(m, a, sep = '') %>% 
  ungroup
# # A tibble: 7 × 5
#     col1 count      m1    m2    m3
# * <fctr> <int>   <chr> <chr> <chr>
# 1 group1    12       4     3   2,6
# 2 group2    12       2  <NA>  <NA>
# 3 group3    12       4   5,7  <NA>
# 4 group4    12     4,5  <NA>  <NA>
# 5 group5    12     4,5  <NA>  <NA>
# 6 group6    12 1,2,3,4  <NA>  <NA>
# 7 group7    16      16 14,42  <NA>

如果你在函数中需要它：

get.3modes.andcounts <- function(origtable, groupby, columnname) {
  origtable %>% 
    group_by_(groupby, columnname) %>% 
    summarise(n = n()) %>% 
    mutate(r = min_rank(desc(n)), count = sum(n)) %>% 
    filter(r <= 3 & (r != max(r) | r == 1)) %>% 
    group_by_(groupby, 'r', 'count') %>% 
    summarize_(a = paste0('paste(',columnname, ', collapse = ",")')) %>% 
    spread(r, a, sep = '') %>% 
    ungroup
}

get.3modes.andcounts(df, 'col1', 'col2')
# # A tibble: 7 × 5
#     col1 count      m1    m2    m3
# * <fctr> <int>   <chr> <chr> <chr>
# 1 group1    12       4     3   2,6
# 2 group2    12       2  <NA>  <NA>
# 3 group3    12       4   5,7  <NA>
# 4 group4    12     4,5  <NA>  <NA>
# 5 group5    12     4,5  <NA>  <NA>
# 6 group6    12 1,2,3,4  <NA>  <NA>
# 7 group7    16      16 14,42  <NA>

系统时间

system.time(get.3modes.andcounts(df, 'col1', 'col2'))
#    user  system elapsed 
#   0.012   0.000   0.011 
benchmark(get.3modes.andcounts(df, 'col1', 'col2'), replications = 10, columns = c("test", "replications", "elapsed"))
#                                       test replications elapsed
# 1 get.3modes.andcounts(df, "col1", "col2")           10    0.08
benchmark(get.3modes.andcounts(df, 'col1', 'col2'), replications = 100, columns = c("test", "replications", "elapsed"))
#                                       test replications elapsed
# 1 get.3modes.andcounts(df, "col1", "col2")          100   0.684
benchmark(get.3modes.andcounts(df, 'col1', 'col2'), replications = 1000, columns = c("test", "replications", "elapsed"))
#                                       test replications elapsed
# 1 get.3modes.andcounts(df, "col1", "col2")         1000   6.796

数据：

col2 <- c(4, 4, 4, 4, 5, 3, 3, 3, 2, 2, 6, 6, # group1 2 and 6 tied in the 3rd position, 5 less common
        2, 2, 2, 4, 4, 3, 3, 2, 2, 2, 6, 6, # group2 4, 3 and 6 tied in the less common, excluded.
        4, 4, 4, 7, 7, 7, 5, 5, 5, 4, 4, 6, # group3 4, 7 and 5 more common, 3 most common present, exclude everything else
        4, 4, 4, 4, 5, 5, 5, 5, 2, 2, 6, 6, # group4 2 and 6 less common, excluded (4 AND 5 tied)
        4, 4, 4, 4, 4, 5, 5, 5, 5, 5, 6, 6, # group5 6 less common, excluded, (4 and 5 tied)
        4, 4, 4, 3, 3, 3, 2, 2, 2, 1, 1, 1, # group6 all tied
        14,14,14,16,16,16,16,34,34,42,42,42,80,80,84,92) #group7 16, 14, 42 are the three most freq.
col1 <- paste(c(rep("group1", 12), rep("group2", 12), rep("group3", 12), rep("group4", 12), rep("group5", 12),
              rep("group6", 12), rep("group7", 16)), sep = ", ") 
df <- data.frame(col1=col1,col2=col2)

Answer 2

您可以更改n > 0，它会起作用。您的问题要求 3，但通过接受任何正整数，我的回答将更加通用。

使用基础 R：

myfun <- function( data, n = 3, col1, col2 )
{
  ## n: numeric: total number of most common elements per group
  stopifnot( n > 0 )

  a1 <- lapply( split( data, data[[col1]] ), function( x ) { # split data by col1
    # browser()
    val  <- factor( x[[col2]] )                     # factor of data values
    z1   <- tabulate( val )                         # frequency table of levels of val
    z2   <- sort( z1[ z1 > 0 ], decreasing = TRUE ) # sorted frequency table with >0
    lenx <- length( unique( z2 ) )                  # length of unique of z2

    if ( lenx == 1 ) {  # lenx == 1
      return( c( paste( ( levels(val)[ which( z1 %in% z2 ) ] ), collapse = ','), rep(NA_character_, n - 1 ), sum( z1 ) ) )
    } else if ( lenx > 1 ) { # lenx > 1
      # remove the minimum, and and extract values by using levels of val with indices from the match of z1 and z2
      z2 <- setdiff( z2, min( z2 ) )
      z2 <- sapply( z2, function( y ) paste( levels(val)[ which( z1 %in% y ) ], collapse = ',') )      

      # count the length of z2 and get indices of length >= n
      z2_ind <- which( cumsum( lengths(unlist( lapply(z2, strsplit, split = "," ), 
                                               recursive = F ) ) ) >= n )
      if( length( z2_ind ) > 0 ) {
        z2 <- z2[ seq_len( z2_ind[1] ) ]
      }
      # adjust length by assigning NA
      if( length(z2) != n ) { z2[ (length(z2)+1):n ] <- NA_character_ }

      return( c( z2, sum( z1 ) ) )
    } else { # lenx < 1
      return( as.list( rep(NA_character_, n ), NA_character_ ) )
    }})  

  a1 <- do.call('rbind', a1)  # row bind values of a1
  a1 <- data.frame( group = rownames( a1 ), a1, stringsAsFactors = FALSE )
  colnames( a1 ) <- c( 'group', paste( 'm', 1:n, sep = '' ), 'count' )
  rownames( a1 ) <- NULL   # remove row names
  return( a1 )
}

输出：

# example1:
myfun(df, 3, 'col1', 'col2')
#    group   m1 m2 m3 count
# 1 group1    4  3  2    10
# 2 group2    2 NA NA    10
# 3 group3    4 NA NA    10
# 4 group4 4, 5 NA NA    10
# 5 group5 4, 5 NA NA    10

# example 2
myfun(df3, 3, 'col1', 'col2')
#    group         m1     m2   m3 count
# 1 group1          4      3 2, 6    12
# 2 group2          2     NA   NA    12
# 3 group3          4   5, 7   NA    12
# 4 group4       4, 5     NA   NA    12
# 5 group5       4, 5     NA   NA    12
# 6 group6 4, 3, 2, 1     NA   NA    12
# 7 group7         16 14, 42   NA    16

通过将字母分配给 example 1 data df 的第 3 列来创建字符数据而不是数字数据。

set.seed(1L)
df$col3 <- sample( letters, 50, TRUE )
myfun(df, 3, 'col1', 'col3')
#    group                  m1   m2   m3 count
# 1 group1                   x <NA> <NA>    10
# 2 group2                 j,u <NA> <NA>    10
# 3 group3 a,d,f,g,i,j,k,q,w,y <NA> <NA>    10
# 4 group4                   m <NA> <NA>    10
# 5 group5                   u <NA> <NA>    10

Answer 3

不需要额外的包。试试这个：

count <- function(df) {
  count_n <- function(vec, n) {
    fac <- factor(table(vec), levels = sort(unique(table(vec)), decreasing = T))
    top3 <- na.omit(names(sort(fac)[1:3]))
    min <- names(fac[fac == min(levels(fac))])
    if(length(levels(fac))==1){min <- 'NA'}
    top3 <- setdiff(top3,min)
    nums <- na.omit(names(fac[fac == levels(fac)[n]]))
    ifelse(length(intersect(nums, top3))>0,  paste(nums, collapse = ','),'NA')
  } ##Get the number of rank n. 
  group <- unique(as.character(df$col1))
  m1 <- aggregate(df, list(df$col1), count_n, 1)$col2
  m2 <- aggregate(df, list(df$col1), count_n, 2)$col2
  m3 <- aggregate(df, list(df$col1), count_n, 3)$col2
  count <- aggregate(df, list(df$col1), length)$col2

  res <- data.frame(col1 = group, m1, m2, m3, count)
  res
}