How to programmatically create binary columns based on a categorical variable in data.table?

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無奈伤痛
無奈伤痛 2020-12-18 11:00

I have a big (12 million rows) data.table which looks like this:

library(data.table)
set.seed(123)
dt <         


        
3条回答
  •  -上瘾入骨i
    2020-12-18 11:33

    For small data sets the table function seems to be more efficient, but on large datasets dcast seems to be the most efficient and convenient option.

    TableFunction <- function(){
        df <- as.data.frame.matrix(table(dt$id, dt$y))
        df[df > 1] <- 1
        df <- cbind(id = as.numeric(row.names(df)), df)
        setDT(df)
    }
    
    
    AnsFunction <- function(){
        ans = data.table(id = unique(dt$id))[, unique(dt$y) := 0L][]
        dt[, {set(ans, i=id, j=unique(y), value=1L); NULL}, by=id]
    }
    
    dcastFunction <- function(){
        df <-dcast.data.table(dt, id ~ y, fun.aggregate = function(x) 1L, fill=0L,value.var = "y")
    
    }
    
    library(data.table)
    library(microbenchmark)
    set.seed(123)
    N = 10000
    dt <- data.table(id=rep(1:N, each=5),y=sample(letters[1 : 5], N*5, replace = T)) 
    
    
    microbenchmark(
        "dcast" = dcastFunction(),
        "Table" = TableFunction(),
        "Ans"   = AnsFunction()
        )
    
    
     Unit: milliseconds
      expr       min        lq      mean    median        uq       max neval cld
     dcast  42.48367  45.39793  47.56898  46.83755  49.33388  60.72327   100  b 
     Table  28.32704  28.74579  29.14043  29.00010  29.23320  35.16723   100 a  
       Ans 120.80609 123.95895 127.35880 126.85018 130.12491 156.53289   100   c
    
    > all(test1 == test2)
    [1] TRUE
    > all(test1 == test3)
    [1] TRUE
    
    y = apply(matrix(sample(letters, 10L*20L, TRUE), ncol=20L), 1L, paste, collapse="")
    dt = data.table(id=sample(1e5,1e7,TRUE), y=sample(y,1e7,TRUE))
    
    microbenchmark(
        "dcast" = dcastFunction(),
        "Table" = TableFunction(),
        "Ans"   = AnsFunction()
    )
    Unit: seconds
      expr      min       lq     mean   median       uq      max neval cld
     dcast 1.985969 2.064964 2.189764 2.216138 2.266959 2.643231   100 a  
     Table 5.022388 5.403263 5.605012 5.580228 5.830414 6.318729   100   c
       Ans 2.234636 2.414224 2.586727 2.599156 2.645717 2.982311   100  b 
    

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