Dear R users,
I generated a boxplots in combination of dotplots using the R code below for
the attached test file.
boxplot(value~score, data = test, outpch = NA, xlab="",ylab="",xaxt='n',
cex.lab=1.2, cex.axis=1.2, main="Correlation of SCNV score and SCNV
value ")
mtext(side=2, "SCNV val
Hi R users,
I thought the follow R code should work, but I got error, Can you fix my code?
Thank you,
Ding
outlier_tcga_MAD3 <- outlier_tcga %>% filter(n_two >0) %>%
mutate(freqMAD3_gain2ratio = N_MAD3_gain2/n_two )%>%
if (N_MAD3 < 9) {filter(freqMAD3_gain >=1)} else if (N_MAD3 > n_t
Hi R users,
The following code worked well to summarize four data groups in a dataframe for
three variables (t_depth, t_alt_count, t_alt_ratio), 12 columns of summary, see
attached.
However, after running another 2000 lines of R codes using functions from more
than 10 other R libraries, then i
t1 and
dataset2 ?
* Is there a structural difference in the datasets - i.e. numbers, characters
or factors as columns. Often import functions guess a column type by reading
the first 500/1000 lines. If the data has numbers in column 1 for 1-1000 but on
line 1999 has a letter... The data type may
: Rui Barradas
Sent: Wednesday, June 12, 2024 11:29 AM
To: Yuan Chun Ding ; CALUM POLWART
Cc: r-help@r-project.org
Subject: Re: [R] my R code worked well when running the first 1000 lines of R
code
Hello, Inline. Às 19: 03 de 12/06/2024, Yuan Chun Ding via R-help escreveu: > I
am sorry that I know
Hi R users,
I generated a square correlation matrix for the dat dataframe below;
dat<-data.frame(g1=c(1,0,0,1,1,1,0,0,0),
g2=c(0,1,0,1,0,1,1,0,0),
g3=c(1,1,0,0,0,1,0,0,0),
g4=c(0,1,0,1,1,1,1,1,0))
library("Hmisc")
dat.rcorr = rcorr(as.matrix(dat))
da
25/07/2024, Yuan Chun Ding via R-help escreveu: > Hi R users, > >
I generated a square correlation matrix for the dat dataframe below; >
dat<-data. frame(g1=c(1,0,0,1,1,1,0,0,0), > g2=c(0,1,0,1,0,1,1,0,0), >
g3=c(1,1,0,0,0,1,0,0,0),
Às 17:39 de 25/07/2024, Yuan Chun Din
.test(tem2[,1],tem2[,2])
r[i, j] <- tmp3$estimate
P[i, j] <- tmp3$p.value
}
}
}
r<-as.data.frame(r)
P<-as.data.frame(P)
From: R-help On Behalf Of Yuan Chun Ding via
R-help
Sent: Thursday, July 25, 2024 11:26 AM
To: Rui Barradas ; r-help@r-project.org
Subject: Re: [
ng via R-help
> > > > Sent: Thursday, July 25, 2024 11:26 AM
> > > > To: Rui Barradas mailto:ruipbarra...@sapo.pt>>;
> > > > r-help@r-project.org<mailto:r-help@r-project.org>
> > > > Subject: Re: [R] please help generate a square correlation
zero
>
> > > in the denominator of the cor() calculation -- again, assuming I have
>
> > > correctly understood your request. If so, this might be something you
>
> > > need to worry about.
>
> > >
>
> > > Again, feel f
Dear R users,
I am running the following code below, the gem751be.rpkm is a dataframe with
dim of 751 samples by 35164 variables, 73 phenotypic variables in the furst to
73rd column and 35091 genomic variables or genes in the 74th to 35164th
columns. What I need to do is to calculate the res
the residuals from each of the
> > regressions.
> > I assume it will be considerably faster than all the overhead you are
> > carrying in your current code, but of course you'll have to try it and
> > see. ... Assuming that I have interpreted your reque
ll the overhead you are
> > carrying in your current code, but of course you'll have to try it and
> > see. ... Assuming that I have interpreted your request correctly.
> > Ignore if not.
> >
> > Cheers,
> > Bert
> >
> > On Fri, Aug 9, 2024 a
> variable, which should be even faster (not sure off the top of my head
> >>
> >> > how to get the residuals and reshape them to the dimensions you want)
> >>
> >> >
> >>
> >> > On Fri, Aug 9, 20
e.rpkm$purity2)
then running result2 <- residuals(lm.fit( x= pur2.1, y = dat));
now I am thinking whether an intercept is required or not.
Ding
From: R-help On Behalf Of Yuan Chun Ding via
R-help
Sent: Saturday, August 10, 2024 12:30 PM
To: Bert Gunter ; Ben Bolker
Cc: r-help@r-project.org
Subjec
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