# [R] Computing means of multiple variables based on a condition

Jeff Newmiller jdnewmil at dcn.davis.ca.us
Thu May 26 08:34:31 CEST 2016

```Thank you for including some sample data, but I have to ask that you
please invest some time in learning how to edit your code in a text editor
and to post in plain text. The quote marks in your example were "curly",
which R does not understand. There are other ways in which HTML email
leads to corruption on this mailing list as well, so you will save
everyone numerous headaches by investing this time sooner rather than
later.

The type of operation you are looking for is referred to as an "outer
join" in SQL nomenclature, and it is intrinsically slow because the only
way to accomplish it is computationally equivalent to a for loop that
successively applies each minimum "d" value to your whole data set.

Having said that, you can accomplish this in the "dplyr" syntax instead of
using a for loop, if that makes you happy, but it is not really any
"better" than a for loop (and some people might consider it misleading
to drape a for loop in such fancy syntax):

DF <- data.frame( a = c( "A", "B", "A", "B", "A", "B", "A", "B", "A", "B" )
, b = c( 15, 35, 20,  99, 75, 64, 33, 78, 45, 20 )
, c = c( 111, 234, 456, 876, 246, 662, 345, 480, 512, 179 )
, d = c( 1.1, 3.2, 14.2, 8.7, 12.5, 5.9, 8.3, 6.0, 2.9, 9.3 )
, stringsAsFactors = FALSE
)
passes <- data.frame( dmin = c( 2, 4, 6 ) )

library(dplyr)

DF2 <- (   passes
%>% rowwise
%>% do({ # run once for each row in "passes"
dmin <- .\$dmin # dot here refers to row of
# "passes" data frame
(   DF
%>% filter( d >= dmin )
%>% group_by( a )
%>% summarise( meanb = mean( b )
, meanc = mean( c )
)
%>% mutate( condition = paste0( "d>=", dmin ) )
)
})
%>% select( a, condition, meanb, meanc )
%>% as.data.frame
)

On Wed, 25 May 2016, KMNanus wrote:

> These will be overlapping subgroups from the same data frame.  For example, d<=2 will have length=9, d<=4 will have length=7, etc.
>
>
> Ken
> kmnanus at gmail.com
> 914-450-0816 (tel)
> 347-730-4813 (fax)
>
>
>
>> On May 25, 2016, at 9:06 PM, William Dunlap <wdunlap at tibco.com> wrote:
>>
>> Just to be clear, do you really want your 'condition' groups to be be subsets
>> of one another?  Most (all?) of the *ply functions assume you want
>> non-overlapping groups so they do a split-summarize-combine sequence.
>> You would have to replace the split part of that.
>>
>> Bill Dunlap
>> TIBCO Software
>> wdunlap tibco.com <http://tibco.com/>
>> On Wed, May 25, 2016 at 3:37 PM, KMNanus <kmnanus at gmail.com <mailto:kmnanus at gmail.com>> wrote:
>> I have a large dataset, a sample of which is:
>>
>> a<- c(?A?, ?B?,?A?, ?B?,?A?, ?B?,?A?, ?B?,?A?, ?B?)
>> b <-c(15, 35, 20,  99, 75, 64, 33, 78, 45, 20)
>> c<- c( 111, 234, 456, 876, 246, 662, 345, 480, 512, 179)
>> d<- c(1.1, 3.2, 14.2, 8.7, 12.5, 5.9, 8.3, 6.0, 2.9, 9.3)
>>
>> df <- data.frame(a,b,c,d)
>>
>> I?m trying to construct a data frame that shows the means of c & b based on the condition of d and grouped by a.
>>
>> I want to create the data frame below, then use ggplot2 to create a line plot of b at various conditions of d.
>>
>> I can compute the grouped means (d>=2, d>=4, etc.) one at a time using dplyr but haven?t figured out how to put them all together or put them in one data frame.
>>
>> I?d rather not use a loop and am relatively new to R.  Is there a way i can use tapply and set it to the conditions above so that I can create the df below?
>>
>>
>>         condition    mean(b)     mean(c)
>> A        d>=2          ____         _____
>> B        d>=2          ____         _____
>> A        d>=4          ____         _____
>> B        d>=4         ____         _____
>> A        d>=6         ____         _____
>> B       d>=6         ____         _____
>>
>>
>>
>> Ken
>> kmnanus at gmail.com <mailto:kmnanus at gmail.com>
>> 914-450-0816 <tel:914-450-0816> (tel)
>> 347-730-4813 <tel:347-730-4813> (fax)
>>
>>
>>
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>> and provide commented, minimal, self-contained, reproducible code.
>>
>
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