Hi Rasmus,
thank you for getting back to be, the command your provided seems to
add all 11 NAs to 2s
> b$pheno <-
+ ifelse(b$PLASER==2 |
+ b$FLASER==2 |
+ is.na(b$PLASER) |
+ is.na(b$PLASER) & b$FLASER %in% 1:2 |
+ is.na(b$FLASER) & b$PLASER == 2,
+ 2, 1)
> table(b$pheno, exclude = NULL)
1 2
859 839
Once again my desired results is to keep these 7 NAs as NAs
> table(b$PLASER,b$FLASER, exclude = NULL)
1 2 3 <NA>
1 836 14 0 0
2 691 70 43 2
3 2 7 21 0
<NA> 4 1 0 7
and have
825 2s (825=691+14+70+7+43)
and the rest would be 1s (866=1698-7-825)
On Fri, Jun 12, 2020 at 9:29 PM Rasmus Liland <j...@posteo.no> wrote:
>
> On 2020-06-13 11:30 +1000, Jim Lemon wrote:
> > On Fri, Jun 12, 2020 at 8:06 PM Jim Lemon wrote:
> > > On Sat, Jun 13, 2020 at 10:46 AM Ana Marija wrote:
> > > >
> > > > I am trying to make a new column
> > > > "pheno" so that I reduce the number
> > > > of NAs
> > >
> > > it looks like those two NA values in
> > > PLASER are the ones you want to drop.
> >
> > From just your summary table, it's hard to
> > guess the distribution of NA values.
>
> Dear Ana,
>
> This small sample
>
> b <- read.table(text="FLASER;PLASER
> 1;2
> ;2
> ;
> 1;
> 2;
> 2;2
> 3;2
> 3;3
> 1;1", sep=";", header=TRUE)
>
> table(b$PLASER,b$FLASER, exclude = NULL)
>
> yields the same combinations you showed
> earlier:
>
> 1 2 3 <NA>
> 1 1 0 0 0
> 2 1 1 1 1
> 3 0 0 1 0
> <NA> 1 1 0 1
>
> If you want to eliminate the four <NA>-based
> combinations completely, this line
>
> b$pheno <-
> ifelse(b$PLASER==2 |
> b$FLASER==2 |
> is.na(b$PLASER) |
> is.na(b$PLASER) & b$FLASER %in% 1:2 |
> is.na(b$FLASER) & b$PLASER == 2,
> 2, 1)
> table(b$pheno, exclude = NULL)
>
> will do it:
>
> 1 2
> 2 7
>
> Best,
> Rasmus
> ______________________________________________
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