Trying to understand cut

rx <- range(values)
dx <- diff(rx)
dx

nb <- 11
breaks <- seq.int(rx[1L], rx[2L], length.out = nb)
breaks[c(1L, nb)] <- c(rx[1L] - dx/1000, rx[2L] + 

 breaks

On 17 Apr 2016, at 06:12 , John Sorkin <jsorkin at grecc.umaryland.edu> wrote:

Jeff,
Perhaps I was sloppy with my notation:
I want groups

=0 <10
=10 <20
=20<30

......

=90 <100

In any event, my question remains, why did the four different versions of cut give me the same results? I hope someone can explain to me the function of 
include.lowest and right in the call to cut. As demonstrated in my example below, the parameters do not seem to alter the results of using cut.
Thank you,
John


P.S. How do I find FAQ 7.31?
Thank you,
John

I 



John David Sorkin M.D., Ph.D.
Professor of Medicine
Chief, Biostatistics and Informatics
University of Maryland School of Medicine Division of Gerontology and Geriatric Medicine
Baltimore VA Medical Center
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524
(Phone) 410-605-7119
(Fax) 410-605-7913 (Please call phone number above prior to faxing) 

Jeff Newmiller <jdnewmil at dcn.davis.ca.us> 04/16/16 11:07 PM >>>

Have you read FAQ 7.31 recently, John? Your whole premise is flawed. You should be thinking of ranges [0,10), [10,20), and so on because numbers ending in 0.9 are never going to be exact. 
-- 
Sent from my phone. Please excuse my brevity.


On April 16, 2016 7:38:50 PM PDT, John Sorkin <jsorkin at grecc.umaryland.edu> wrote:
I am trying to understand cut so I can divide a list of numbers into 10 group:
 0-9.0
10-10.9
20-20.9
30-30.9,
40-40.9,
50-50.9
60-60.9
70-70.9
80-80.9
90-90.9

As I try to do this, I have been playing with the cut function. Surprising the following for applications of cut give me the exact same groups. This surprises me given that I have varied parameters include.lowest and right. Can someone help me understand what include.lowest and right do? I have looked at the help page, but I don't seem to understand what I am being told!
Thank you,
John

values <- c((0:99),c(0.9:99.9))
sort(values)
c1<-cut(values,10,include.lowest=FALSE,right=TRUE)
c2<-cut(values,10,include.lowest=FALSE,right=FALSE)
c3<-cut(values,10,include.lowest=TRUE,right=TRUE)
c4<-cut(values,10,include.lowest=TRUE,right=FALSE)
cbind(min=aggregate(values,list(c1),min),max=aggregate(values,list(c1),max))
cbind(min=aggregate(values,list(c2),min),max=aggregate(values,list(c2),max))
cbind(min=aggregate(values,list(c3),min),max=aggregate(values,list(c3),max))
cbind(min=aggregate(values,list(c4),min),max=aggregate(values,list(c4),max))

You can run the code below, or inspect the results I got which are reproduced below:

cbind(min=aggregate(values,list(c1),min),max=aggregate(values,list(c1),max))

     min.Group.1 min.x    max.Group.1 max.x
1  (-0.0999,9.91]     0 (-0.0999,9.91]   9.9
2     (9.91,19.9]    10    (9.91,19.9]  19.9
3     (19.9,29.9]    20    (19.9,29.9]  29.9
4     (29.9,39.9]    30    (29.9,39.9]  39.9
5       (39.9,50]    40      (39.9,50]  49.9
6         (50,60]    50        (50,60]  59.9
7         (60,70]    60        (60,70]  69.9
8         (70,80]    70        (70,80]  79.9
9         (80,90]    80        (80,90]  89.9
10       (90,100]    90       (90,100]  99.9
cbind(min=aggregate(values,list(c2),min),max=aggregate(values,list(c2),max))

     min.Group.1 min.x    max.Group.1 max.x
1  [-0.0999,9.91)     0 [-0.0999,9.91)   9.9
2     [9.91,19.9)    10    [9.91,19.9)  19.9
3     [19.9,29.9)    20    [19.9,29.9)  29.9
4     [29.9,39.9)    30    [29.9,39.9)  39.9
5       [39.9,50)    40      [39.9,50)  49.9
6         [50,60)    50        [50,60)  59.9
7         [60,70)    60        [60,70)  69.9
8         [70,80)    70        [70,80)  79.9
9         [80,90)    80        [80,90)  89.9
10       [90,100)    90       [90,100)  99.9
cbind(min=aggregate(values,list(c3),min),max=aggregate(values,list(c3),max))

     min.Group.1 min.x    max.Group.1 max.x
1  [-0.0999,9.91]     0 [-0.0999,9.91]   9.9
2     (9.91,19.9]    10    (9.91,19.9]  19.9
3     (19.9,29.9]    20    (19.9,29.9]  29.9
4     (29.9,39.9]    30    (29.9,39.9]  39.9
5       (39.9,50]    40      (39.9,50]  49.9
6         (50,60]    50        (50,60]  59.9
7         (60,70]    60        (60,70]  69.9
8         (70,80]    70        (70,80]  79.9
9         (80,90]    80        (80,90]  89.9
10       (90,100]    90       (90,100]  99.9
cbind(min=aggregate(values,list(c4),min),max=aggregate(values,list(c4),max))

     min.Group.1 min.x    max.Group.1 max.x
1 [-0.0999,9.91)     0 [-0.0999,9.91)   9.9
2     [9.91,19.9)    10    [9.91,19.9)  19.9
3     [19.9,29.9)    20    [19.9,29.9)  29.9
4     [29.9,39.9)    30    [29.9,39.9)  39.9
5       [39.9,50)    40      [39.9,50)  49.9
6         [50,60)    50        [50,60)  59.9
7         [60,70)    60        [60,70)  69.9
8         [70,80)    70        [70,80)  79.9
9         [80,90)    80        [80,90)  89.9
10       [90,100]    90       [90,100]  99.9
John David Sorkin M.D., Ph.D.
Professor of Medicine
Chief, Biostatistics and Informatics
University of Maryland School of Medicine Division of Gerontology and Geriatric Medicine
Baltimore VA Medical Center
10 North Greene Street
GRECC (BT/18/GR)
Baltimore, MD 21201-1524
(Phone) 410-605-7119
(Fax) 410-605-7913 (Please call phone number above prior to faxing) 

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