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subsets

10 messages · Den, Taras Zakharko, Keith Jewell +5 more

Den
# 
Dear R people
Could you please help.

Basically, there are two variables in my data set. Each patient ('id')
may have one or more diseases ('diagnosis'). It looks like 

id	diagnosis
1	ah
2	ah
2	ihd
2	im
3	ah
3	stroke
4	ah
4	ihd
4	angina
5	ihd
..............
Q: How to make three data sets:
	1. Patients with ah and ihd
 	2. Patients with ah but no ihd
	3. Patients with  ihd but no ah?

 If you have any ideas could just guide what should I look for. Is a
subset or aggregate, or loops, or something else??? I am a bit lost. (F1
F1 F1 !!!:)
Thank you
Ivan Calandra
# 
Hi!

I think you should read the intro to R, as well as ?"[" and ?subset. It 
should help you to understand.

Let's say your data is in a data.frame called df:
# 1. ah and ihd
df_ah_ihd <- df[df$diagnosis=="ah" | df$diagnosis=="ihd", ]  ## the "|" 
is the boolean OR (you want one OR the other). Note the last comma

#2. ah
df_ah <- df[df$diagnosis=="ah", ]

#3. ihd
df_ihd <- df[df$diagnosis=="ihd", ]

You could do the same using subset() if you feel better with this function.

HTH,
Ivan

Le 1/20/2011 09:53, Den a ?crit :

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      Taras Zakharko
    

    

      
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Hello Den,

your problem is not as it may seem so Ivan's suggestion is only a partial answer. I see that each patient can have
more then one diagnosis and I take that you want to isolate patients based on particular conditions. 
Thus, simply looking for "ah" or "idh" as Ivan suggests will yield patients which can have either of those but not 
necessarily patients that have both.

Instead, what one must do is apply the condition to the whole set of diagnosis associated with each patient.
I think that its done best with the aggregate function. This function splits the data according to some
factor (in our case it will be the patient id) and performs a routine on each subset (in our case it will be
a condition test):


ids <- aggregate(diagnosis ~ id, df, function(x) "ah" %in% x &&  "ihd" %in% x)
ids <- aggregate(diagnosis ~ id, df, function(x) "ah" %in% x &&  !"ihd" %in% x)
ids <- aggregate(diagnosis ~ id, df, function(x) ! "ah" %in% x &&  "ihd" %in% x)

Now, ids will contain a data frame like:

id	diagnosis
1	TRUE
2	FALSE
3	FALSE
...

which shows which patients have the set of diagnoses you asked for. You can then apply these
patients to the original data by something like:

subset(df, id %in% subset(ids, diagnosis == TRUE)$id)

this will extract only patients from the 'ids' data frame  for which  the diagnosis applies and then extract the associated
diagnosis sets from the original 'df' data frame. 

Hope it helps,

Taras

        
On Jan 20, 2011, at 9:53 , Den wrote:

        

            

      
      
      
    

        

      
      
    

  
  


  


      

    

    

      
      

        


  

        

      Keith Jewell
    

    

      
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I don't think Ivan's solution meets the OP's needs.

I think you could do it using %in% and the approriate logical operations 
e.g.

aDF <- data.frame(id=c(1,2,2,2,3,3,4,4,4,5),
     diagnosis=c("ah", "ah", "ihd", "im", "ah", "stroke", "ah", "ihd", 
"angina", "ihd"))
aDF[with(aDF,(id %in% id[diagnosis=="ah"]) & (id %in% 
id[diagnosis=="ihd"])),]
aDF[with(aDF,(id %in% id[diagnosis=="ah"]) & !(id %in% 
id[diagnosis=="ihd"])),]
aDF[with(aDF,!(id %in% id[diagnosis=="ah"]) & (id %in% 
id[diagnosis=="ihd"])),]

That starts to feel a bit fiddly for me. You might want to look at package 
sqldf.

HTH

Keith J
--------------------------
"Ivan Calandra" <ivan.calandra at uni-hamburg.de> wrote in message 
news:4D37FBEA.5070100 at uni-hamburg.de...
Hi!

I think you should read the intro to R, as well as ?"[" and ?subset. It
should help you to understand.

Let's say your data is in a data.frame called df:
# 1. ah and ihd
df_ah_ihd <- df[df$diagnosis=="ah" | df$diagnosis=="ihd", ]  ## the "|"
is the boolean OR (you want one OR the other). Note the last comma

#2. ah
df_ah <- df[df$diagnosis=="ah", ]

#3. ihd
df_ihd <- df[df$diagnosis=="ihd", ]

You could do the same using subset() if you feel better with this function.

HTH,
Ivan

Le 1/20/2011 09:53, Den a ?crit :

            

      
      
      
    

        

      
      
    

        

  
    

      
      
    

  


  


      

    

    

      
      

        
      

    

    

      
      

        


  

        

      Petr Savicky
    

    

      
      #
    

  


  

      
On Thu, Jan 20, 2011 at 10:53:01AM +0200, Den wrote:

            

      
      
      
    

        
This may be understood as a two step procedure:
1. Split the id into disjoint groups according the above criteria.
2. Split the data cases into the groups from step 1.

If this is what you want, then function table() may be used to
collect information on each id.

  df <- structure(list(id = c(1L, 2L, 2L, 2L, 3L, 3L, 4L, 4L, 4L, 5L),
      diagnosis = structure(c(1L, 1L, 3L, 4L, 1L, 5L, 1L, 3L, 2L, 3L),
      .Label = c("ah", "angina", "ihd", "im", "stroke"), class = "factor")),
      .Names = c("id", "diagnosis"), class = "data.frame", row.names = c(NA, -10L))

  tab <- table(df$id, df$diag)

Then, for example, the data cases for "2. Patients with ah but no ihd"
may be obtained

  sel <- tab[, "ah"] != 0 & tab[, "ihd"] == 0
  ah.noihd <- dimnames(tab)[[1]][sel] # [1] "1" "3"
  df[df$id %in% ah.noihd, ]
  #   id diagnosis
  # 1  1        ah
  # 5  3        ah
  # 6  3    stroke

I hope, this helps.

Petr Savicky.

  
  


  


      

    

    

      
      

        


  

        

      Keith Jewell
    

    

      
      #
    

  


  

      
I did try it. It gave me
[[1]]
   id diagnosis
1   1        ah
5   3        ah
7   4        ah
8   4       ihd
10  5       ihd

[[2]]
  id diagnosis
1  1        ah
2  2        ah
5  3        ah
7  4        ah

[[3]]
   id diagnosis
3   2       ihd
8   4       ihd
10  5       ihd

Which isn't what the OP asked for

            

      
      
      
    

        
id diagnosis
2  2        ah
3  2       ihd
4  2        im
7  4        ah
8  4       ihd
9  4    angina

            

      
      
      
    

        
id diagnosis
1  1        ah
5  3        ah
6  3    stroke

            

      
      
      
    

        
id diagnosis
10  5       ihd

Regards,

KJ
---------------------------------
"Henrique Dallazuanna" <wwwhsd at gmail.com> wrote in message 
news:AANLkTikQnw_hNtDyXdrJ+yTyqf6tGHLmH0qsLEoufTdJ at mail.gmail.com...
Try this:

lapply(list(c('ah', 'ihd'), 'ah', 'ihd'), function(x)subset(aDF, diagnosis
== x))

        
On Thu, Jan 20, 2011 at 6:53 AM, Den <d.kazakiewicz at gmail.com> wrote:

        

            

      
      
      
    

        

      
      
    

        

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      Ivan Calandra
    

    

      
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Hi Taras,

Indeed, I've overlooked the problem. Anyway, I'm not sure I would have 
been able to give a complete answer like you did!

Ivan

Le 1/20/2011 11:05, Taras Zakharko a ?crit :

            

      
      
      
    

        

      
      
    

        

      
      
    

        

  
    

      
      
    

  


  


      

    

    

      
      

        


  

        

      Peter Ehlers
    

    

      
      #
    

  


  

      
On 2011-01-20 02:05, Taras Zakharko wrote:

            

      
      
      
    

        
Here's a tidy version using the plyr package:

require(plyr)
df1 <- ddply(df, .(id), summarize,
      has.both = ("ah" %in% diagnosis) & ("ihd" %in% diagnosis),
      has.only.ah = ("ah" %in% diagnosis) & !("ihd" %in% diagnosis),
      has.only.ihd = !("ah" %in% diagnosis) & ("ihd" %in% diagnosis)
)

Further processing on the columns of df1 is straightforward.

Peter Ehlers

            

      
      
      
    

        

      
      
    

            

      
      
      
    

        

      
      
    

  
  


  


      

    

    
3 days later

    

      
      

        


  

        

      Matt Dowle
    

    

      
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require(data.table)
DT = as.data.table(df)

# 1. Patients with ah and ihd
DT[,.SD["ah"%in%diagnosis && "ihd"%in%diagnosis],by=id]

     id diagnosis
[1,]  2        ah
[2,]  2       ihd
[3,]  2        im
[4,]  4        ah
[5,]  4       ihd
[6,]  4    angina

# 2. Patients with ah but no ihd
DT[,.SD["ah"%in%diagnosis && !"ihd"%in%diagnosis],by=id]

     id diagnosis
[1,]  1        ah
[2,]  3        ah
[3,]  3    stroke


# 3. Patients with  ihd but no ah?
DT[,.SD[!"ah"%in%diagnosis && "ihd"%in%diagnosis],by=id]

     id diagnosis
[1,]  5       ihd