As a novice R user, I face a similar challenge. I am almost afraid to
share
with this group how I solved it. About 65 labs in our proficiency program
submit data on individual Excel spreadsheets with triple replicates. There
always are a few labs that do not complete the full set of three
replicates,
and I do not want their data included in my analysis.
First, I combine all the individual spreadsheets into one large Excel
spreadsheet. The replicates are in three columns: rep1, rep2, and rep3. I
sort on each individual rep column in Excel. Then I go to both the top and
the bottom of the list.
For example, I sort on rep1 and go to the top of the list to delete any
rows
where a value for rep1 was not recorded. Then I go to the bottom of the
list
and delete any rows where rep1 is text instead of a number, for example,
<0.001. I should say that the labs are instructed that they must complete
all three replicates, and they must not enter results as text. Next I
repeat
the process for rep2 and rep3.
I'll do a little more work in Excel on the large, combined table with all
the lab data. I calculate in Excel the mean, standard deviation, and
coefficient of variation for each of the three reps. Finally, I filter all
the data and delete duplicate rows. This is necessary as I sometimes
accidentally copy the same spreadsheet two times from a lab into my large
table. Finally, I save the cleaned up table in *.csv format that is easily
read into R.
I know that R can do all of these things, but if you are just learning how
to use R it might be easier to do some initial work in Excel, or a similar
spreadsheet, before running your data through R.
I also use MS-Word's mail merge feature to generate my code. I'll get
three
or four pages of code doing what I want for a single analytical test, for
example, calcium. Then I'll use the mail merge feature to generate
hundreds
of pages of code with the other analytical tests (nitrogen, phosphorus,
potassium, etc.). I just copy and paste the large, merged Word document
into
R. R cranks away for 30 minutes and I end up with several large tables
(and
these get additional editing in Ecel) and hundreds of beautiful graphs
that
would take weeks to create in Excel.
I was amazed that Word would work. I expected all of Word's special print
control codes would mess things up. I just recently received a new laptop
computer, and now I have an occassional problem with Word's "pretty print
quotes," but if you know about that problem, it is easy to fix.
Jerry Floren
Minnesota Department of Agriculture
Matthew Dowle-3 wrote:
As can data.table (i.e. do 'having' in one statement) :
DT = data.table(DF)
DT[,list(n=length(NAME),mean(SCORE)),by="NAME"][n==3]
NAME n V2
[1,] James 3 64.00000
[2,] Tom 3 78.66667
but data.table isn't restricted to SQL functions (such as avg), any R
functions can be used, sometimes for their side effects (such as
plotting)
rather than just returning data.
Further data.table has a thing called 'join inherited scoping'. Say we
knew the specific groups, we can go directly to them (without even
looking
at the rest of the data in the table) in very short and convenient
syntax,
which also happens to run quickly on large data sets (but can be useful
just
for the syntax alone) :
setkey(DT,NAME)
DT[c("James","Tom"),mean(SCORE),mult="all"]
NAME V1
[1,] James 64.00000
[2,] Tom 78.66667
Notice there is no "group by" or even a "by" in the above. It inherits
the
scope from the join because mult="all" means that "James" matches to
multiple rows, as does "Tom", creating two groups. It does it by binary
search to the beginning of each group, binary search to the end of the
group, and runs the R expression inside the scope of that group.
An example of join inherited scoping for the side effects only :
pdf("out.pdf")
DT[c("James","Tom"),plot(SCORE),mult="all"]
# out.pdf now contains 2 plots
which you couldn't do in SQL because SQL has no plotting (or any of R's
other packages).
It aims to do this quickly. Where 'quickly' means 1) shorter code is
quicker to write, read, debug and maintain and also 2) quicker to
compute,
and its 1 that often dominates 2.
Finally, consider the following two statements which are both equivalent
:
sqldf("select NAME, avg(SCORE) from DF group by NAME having count(*) =
3")
NAME avg(SCORE)
1 James 64.00000
2 Tom 78.66667
DT[ J(DT[,length(NAME),by="NAME"][V1==3,NAME]), mean(SCORE), mult="all"]
NAME avg(SCORE)
1 James 64.00000
2 Tom 78.66667
Now ok I hear you groaning (!) that the 2nd looks (on first glance) ugly,
but bear with me ... in the SQL solution do you know for sure that
avg(SCORE) isn't computed wastefully for the all the groups that don't
have
count(*)=3 ? It might well do the 'group by' first for all the groups,
then
do the 'having' afterwards as a 'where' on the result. It might depend
on
the particular SQL database being used (mySQL, sqllite, etc) or the
installation parameters, any indexes etc. Some investigation would be
required (taking time) if someone doesn't already know. In the
data.table
however, the syntax explictly makes it clear than mean(SCORE) is only
computed for the particular groups. For certain, always. Maybe this
particular example is not a good one, but I'm trying to demonstrate an
overall syntax which is scalable (i.e. this syntax can do more
complicated
things that SQL can't, or can't do well). Notice that the method
earlier
on i.e. "DT[,list(n=length(NAME),mean(SCORE)),by="NAME"][n==3]" is
simpler
but wasteful as it does compute mean(SCORE) for all the groups. But the
syntax explicity conveys what is being done, and the user has the choice.
"Gabor Grothendieck" <ggrothendieck at gmail.com> wrote in message
news:971536df1001051122l58389037p4e16288aedfdeb07 at mail.gmail.com...
Here is the solution using sqldf which can do it in one statement:
# read in data
Lines <- "OBS NAME SCORE
+ 1 Tom 92
+ 2 Tom 88
+ 3 Tom 56
+ 4 James 85
+ 5 James 75
+ 6 James 32
+ 7 Dawn 56
+ 8 Dawn 91
+ 9 Clara 95
+ 10 Clara 84"
DF <- read.table(textConnection(Lines), header = TRUE)
# run
library(sqldf)
sqldf("select NAME, avg(SCORE) from DF group by NAME having count(*) =
3")
NAME avg(SCORE)
1 James 64.00000
2 Tom 78.66667
On Tue, Jan 5, 2010 at 2:03 PM, Gabor Grothendieck
<ggrothendieck at gmail.com> wrote:
Hello, does anyone know how to take the mean for a subset of
observations?
For example, suppose my data looks like this:
OBS NAME SCORE
1 Tom 92
2 Tom 88
3 Tom 56
4 James 85
5 James 75
6 James 32
7 Dawn 56
8 Dawn 91
9 Clara 95
10 Clara 84
Is there a way to get the mean of the SCORE variable by NAME but only
when
the number of observations is equal to 3? In other words, is there a
way
to
get the mean of the SCORE variable for Tom and James, but not for Dawn
and
Clara? Thank you.
--
Geoffrey Smith
Visiting Assistant Professor
Department of Finance
W. P. Carey School of Business
Arizona State University
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