On 3/8/2017 6:14 AM, AbouEl-Makarim Aboueissa wrote:
Dear All: goods morning
Is there is a way to reverse the scoring of the first three columns x1, x2,
and x3 and keep the original scores for the fourth column x4.
*Here is an example of the data set:*
x1 x2 x3 x4
2 5 4 4
1 1 1 6
1
Perhaps
dta <- cbind( dta[ rev( seq.int( nrow( dta ) ) ), 1:3 ], dta[ , 4, drop=FALSE ]
)
?
--
Sent from my phone. Please excuse my brevity.
On March 8, 2017 6:14:25 AM PST, AbouEl-Makarim Aboueissa
wrote:
>Dear All: goods morning
>
>Is there is a way to reverse the scoring of the first thre
Dear All: goods morning
Is there is a way to reverse the scoring of the first three columns x1, x2,
and x3 and keep the original scores for the fourth column x4.
*Here is an example of the data set:*
x1 x2 x3 x4
2 5 4 4
1 1 1 6
1 2 1 6
2 3 2 4
1 2 1 6
1 3 1 6
2 2
thanks a lot.
发自网易邮箱大师
On 2017-03-06 18:19 , Jim Lemon Wrote:
Hi laomeng,
If you know how to plot the means and calculate the standard
deviations, perhaps look at the "dispersion" function in the plotrix
package.
Jim
On Sat, Mar 4, 2017 at 5:39 PM, laomeng_3 wrote:
> hi all
> I have a que
Dear all,
Thank you very much for all of your inputs. I finally managed to get a
table with the percentages using R. I'm definitely going to invest more
time to learn R. Thank you again for taking your valuable time to answer my
question. Really appreciate it.
*Thilini Jayasinghe*
PhD Candidate
L
Many thanks Paul,
That looks very good and certainly the end result is absolutely along the lines
I was hoping to find. I will read that article thoroughly and it will clearly
teach me a lot about the graphics you used there.
I was really hoping that someone might have wrapped something lik
I understood your answer.
The point is that sort = TRUE that doesn't sort is plain confusing.
Instead, the option should have been something like efficient = TRUE
or FALSE. At least then no one would stupidly expect sort = TRUE to
sort and sort = FALSE to NOT sort.
On Wed, Mar 8, 2017 at 12:51 PM,
If you are still wondering, try re-reading my answer. FALSE is more efficient,
TRUE is sorted. Lack of sorting has nothing to do with preserving order.
--
Sent from my phone. Please excuse my brevity.
On March 8, 2017 8:55:06 AM PST, Dimitri Liakhovitski
wrote:
>Thank you. I was just curious w
Hello,
If you need to preserve the order you can do it like this.
inx <- order(grades2$grade)
result <- merge(grades2, info, by = "grade", all.x = T, all.y = F, sort
= FALSE)
result[order(inx), ]
Hope this helps,
Rui Barradas
Em 08-03-2017 16:55, Dimitri Liakhovitski escreveu:
Thank you. I
Seems pretty normal except that your one-by-one lookup process usually gets old
eventually, and comparing results is much easier if you merge the study data
with the lookup data all at once and then use aggregate() (or any of numerous
equivalents from contributed packages) to collect results or
Thank you. I was just curious what sort=FALSE had no impact.
Wondering what it is there for then...
On Wed, Mar 8, 2017 at 11:43 AM, Jeff Newmiller
wrote:
> Merging is not necessarily an order-preserving operation, but sorting can
> make the operation more efficient. The sort=TRUE argument force
Merging is not necessarily an order-preserving operation, but sorting can make
the operation more efficient. The sort=TRUE argument forces the result to be
sorted, but sort=FALSE is in not a promise that order will be preserved. (I
think the imperfect sorting occurs when there are multiple keys
This does not appear to be a legitimate topic for r-help: it is are
not a consulting service. Please see the posting guide.
Of course, others may disagree and reply. Wouldn't be the first time I'm wrong.
-- Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along
Hello!
I have a vector 'grades' and a data frame 'info':
grades2 <- data.frame(grade = c(1,2,2,3,1))
info <- data.frame(
grade = 3:1,
desc = c("Excellent", "Good", "Poor"),
fail = c(F, F, T)
)
I want to get the info for all grades I have in info:
This solution resorts everything in the ord
Hi All,
today I have a more general question concerning the approach of storing
different values from the analysis of multiple variables.
My task is to compare distributions in a universe with distributions from
the respondents using a whole bunch of variables. Comparison shall be done
on rela
I have been on a fair old learning curve handling a fairly complex Access
database with my beloved, if sometimes tantaslising, R. I've been using RODBC
to do this and, despite the database not being all that well designed, the
power of R and RODBC has been fantastic (of course). Huge thanks to R
remindR is a simple, small package with essentially a single purpose:
to Insert/extract text "reminders" into/from function source code
comments or as the "comment" attribute of any object. The intent is
that the former could be handy in the course of program development to
remind one of e.g. argum
Dear Lorenzo,
No, the code does not do what you are after. R-package transport is for point
patterns and histograms in two and more dimensions. You have a distribution in
one dimension.
> 1) two distributions with the same bins (I identify each bin by the
> central point in the bin).
>
> n_bin
Hi All,
I got a late reply from RStudio Support concerning the question where
RStudio store options and configurations:
-- cut --
The post RStudio Config Files has a new comment.
. . .
Unfortunately, it's unlikely that we'll be able to provide a programmatic
R interface in the near future --
Hi All,
I got a late reply from RStudio Support concerning the question where
RStudio store options and configurations:
-- cut --
The post RStudio Config Files has a new comment.
. . .
Unfortunately, it's unlikely that we'll be able to provide a programmatic
R interface in the near future --
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