How to quickly form groups quartiles deciles etc by ordering column s in a data frame

Question

I see a lot of questions and answers re order and sort  Is there anything that sorts vectors or data frames into groupings  like quartiles or deciles   I have a  manual  solution  but there s likely a better solution that has been group-tested   Here s my attempt   temp  lt - data frame name letters 1 12   value rnorm 12   quartile rep NA  12   temp      name       value quartile   1     a  2 55118169       NA   2     b  0 79755259       NA   3     c  0 16918905       NA   4     d  1 73359245       NA   5     e  0 41027113       NA   6     f  0 73012966       NA   7     g -1 35901658       NA   8     h -0 80591167       NA   9     i  0 48966739       NA   10    j  0 88856758       NA   11    k  0 05146856       NA   12    l -0 12310229       NA temp sorted  lt - temp order temp value     temp sorted quartile  lt - rep 1 4  each 12 4  temp  lt - temp sorted order as numeric rownames temp sorted       temp      name       value quartile   1     a  2 55118169        4   2     b  0 79755259        3   3     c  0 16918905        2   4     d  1 73359245        4   5     e  0 41027113        2   6     f  0 73012966        3   7     g -1 35901658        1   8     h -0 80591167        1   9     i  0 48966739        3   10    j  0 88856758        4   11    k  0 05146856        2   12    l -0 12310229        1   Is there a better  cleaner faster one-line  approach  Thanks

User · Answer

I ll add the data table version for anyone else Googling it  i e    BondedDust s solution translated to data table and pared down a tad    library data table  setDT temp  temp    quartile    cut value                          breaks   quantile value  probs   0 4 4                           labels   1 4  right   FALSE     Which is much better  cleaner  faster  than what I had been doing   temp    quartile             as factor ifelse value  lt  quantile value   25   1                           ifelse value  lt  quantile value   5   2                                  ifelse value  lt  quantile value   75   3  4      Note  however  that this approach requires the quantiles to be distinct   e g  it will fail on rep 0 1  c 100  1    what to do in this case is open ended so I leave it up to you

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There s a handy ntile function in package dplyr  It s flexible in the sense that you can very easily define the number of  tiles or  bins  you want to create    Load the package  install first if you haven t  and add the quartile column   library dplyr  temp quartile  lt - ntile temp value  4      Or  if you want to use dplyr syntax   temp  lt - temp   gt   mutate quartile   ntile value  4     Result in both cases is   temp     name       value quartile  1     a -0 56047565        1  2     b -0 23017749        2  3     c  1 55870831        4  4     d  0 07050839        2  5     e  0 12928774        3  6     f  1 71506499        4  7     g  0 46091621        3  8     h -1 26506123        1  9     i -0 68685285        1  10    j -0 44566197        2  11    k  1 22408180        4  12    l  0 35981383        3   data   Note that you don t need to create the  quartile  column in advance and use set seed to make the randomization reproducible   set seed 123  temp  lt - data frame name letters 1 12   value rnorm 12

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Try this function  getQuantileGroupNum  lt - function vec  group num  decreasing FALSE      if decreasing        abs cut vec  quantile vec  probs seq 0  1  1   group num   type 8  na rm TRUE   labels FALSE  include lowest T  - group num - 1      else       cut vec  quantile vec  probs seq 0  1  1   group num   type 8  na rm TRUE   labels FALSE  include lowest T           gt  t1  lt - runif 7   gt  t1  1  0 4336094 0 2842928 0 5578876 0 2678694 0 6495285 0 3706474 0 5976223  gt  getQuantileGroupNum t1  4   1  2 1 3 1 4 2 4  gt  getQuantileGroupNum t1  4  decreasing T   1  3 4 2 4 1 3 1

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You can use the quantile   function  but you need to handle rounding precision when using cut    So  set seed 123  temp  lt - data frame name letters 1 12   value rnorm 12   quartile rep NA  12   brks  lt - with temp  quantile value  probs   c 0  0 25  0 5  0 75  1    temp  lt - within temp  quartile  lt - cut value  breaks   brks  labels   1 4                                        include lowest   TRUE     Giving    gt  head temp    name       value quartile 1    a -0 56047565        1 2    b -0 23017749        2 3    c  1 55870831        4 4    d  0 07050839        2 5    e  0 12928774        3 6    f  1 71506499        4

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I would like to propose a version  which seems to be more robust  since I ran into a lot of problems using quantile   in the breaks option cut   on my dataset  I am using the ntile function of plyr  but it also works with ecdf as input   temp        quartile    bincode x   ntile value  100   breaks   seq 0 100 25   right   TRUE  include lowest   TRUE              decile    bincode x   ntile value  100   breaks   seq 0 100 10   right   TRUE  include lowest   TRUE      temp        quartile    bincode x   ecdf value  value   breaks   seq 0 1 0 25   right   TRUE  include lowest   TRUE              decile    bincode x   ecdf value  value   breaks   seq 0 1 0 1   right   TRUE  include lowest   TRUE       Is that correct

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The method I use is one of these or Hmisc  cut2 value  g 4    temp quartile  lt - with temp  cut value                                   breaks quantile value  probs seq 0 1  by 0 25   na rm TRUE                                    include lowest TRUE     An alternate might be   temp quartile  lt - with temp  factor                              findInterval  val  c -Inf                                 quantile val  probs c 0 25   5   75    Inf    na rm TRUE                                labels c  Q1   Q2   Q3   Q4              The first one has the side-effect of labeling the quartiles with the values  which I consider a  good thing   but if it were not  good for you   or the valid problems raised in the comments were a concern you could go with version 2  You can use labels  in cut  or  you could add this line to your code   temp quartile  lt - factor temp quartile  levels c  1   2   3   4       Or even quicker but slightly more obscure in how it works  although it is no longer a factor  but rather a numeric vector   temp quartile  lt - as numeric temp quartile

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temp quartile  lt - ceiling sapply temp value function x  sum x-temp value gt  0    length temp value  4

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Adapting dplyr  ntile to take advantage of data table optimizations provides a faster solution   library data table  setDT temp  temp order value    quartile    floor  1   4     I-1     N     Probably doesn t qualify as cleaner  but it s faster and one-line   Timing on bigger data set  Comparing this solution to ntile and cut for data table as proposed by  docendo discimus and  MichaelChirico   library microbenchmark  library dplyr   set seed 123   n  lt - 1e6 temp  lt - data frame name sample letters  size n  replace TRUE   value rnorm n   setDT temp   microbenchmark       ntile    temp   quartile ntile    ntile value  4         cut    temp   quartile cut    cut value                                         breaks   quantile value  probs   seq 0  1  by 1 4                                           labels   1 4  right FALSE         dt ntile    temp order value   quartile ntile dt    floor  1   4     I-1   N       Gives   Unit  milliseconds      expr      min       lq     mean   median       uq      max neval     ntile 608 1126 647 4994 670 3160 686 5103 691 4846 712 4267   100       cut 369 5391 373 3457 375 0913 374 3107 376 5512 385 8142   100  dt ntile 117 5736 119 5802 124 5397 120 5043 124 5902 145 7894   100

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There is possibly a quicker way  but I would do   a  lt - rnorm 100    Our data q  lt - quantile a    You can supply your own breaks  see  quantile    Define a simple function that checks in which quantile a number falls getQuant  lt - function x          for  i in 1  length q -1                   if  x gt  q i   amp  amp  x lt q i 1             break              i         Apply the function to the data res  lt - unlist lapply as matrix a   getQuant

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Sorry for being a bit late to the party  I wanted to add my one liner using cut2 as I didn t know max min for my data and wanted the groups to be identically large  I read about cut2 in an issue which was marked as duplicate  link below    library Hmisc     For cut2 set seed 123      To keep answers below identical to my random run  temp  lt - data frame name letters 1 12   value rnorm 12   quartile rep NA  12    temp quartile  lt - as numeric cut2 temp value  g 4      as numeric to number the factors temp quartileBounds  lt - cut2 temp value  g 4   temp   Result    gt  temp    name       value quartile  quartileBounds 1     a -0 56047565        1  -1 265 -0 446  2     b -0 23017749        2  -0 446  0 129  3     c  1 55870831        4   1 224  1 715  4     d  0 07050839        2  -0 446  0 129  5     e  0 12928774        3   0 129  1 224  6     f  1 71506499        4   1 224  1 715  7     g  0 46091621        3   0 129  1 224  8     h -1 26506123        1  -1 265 -0 446  9     i -0 68685285        1  -1 265 -0 446  10    j -0 44566197        2  -0 446  0 129  11    k  1 22408180        4   1 224  1 715  12    l  0 35981383        3   0 129  1 224    Similar issue where I read about cut2 in detail

[r] How to quickly form groups (quartiles, deciles, etc) by ordering column(s) in a data frame

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