# [R] plot question

Tue Oct 2 22:13:01 CEST 2007

```The last example does connect the points of average intensity, doesn't it?

On 10/2/07, Tiandao Li <Tiandao.Li at usm.edu> wrote:
>
> Thanks for your help, Hadley. I want to treat concentration as factor, and
> the 2nd and 3rd part of codes are what I wanted. However, how to draw the
> lines to connect the points of average intensity of each gene at different
> concentrations?
>
> On Tue, 2 Oct 2007, hadley wickham wrote:
>
> On 10/2/07, Tiandao Li <Tiandao.Li at usm.edu> wrote:
> > Hello,
> >
> > I have a question about how to plot a series of data. The folloqing is my
> > data matrix of n
> > > n
> >              25p    5p  2.5p 0.5p
> > 16B-E06.g 45379  4383  5123   45
> > 16B-E06.g 45138  4028  6249   52
> > 16B-E06.g 48457  4267  5470   54
> >
> > colnames(n) is concentrations, rownames(n) is gene IDs, and the rest is
> > Intensity. I want to plot the data this way.
> > x-axis is colnames(n) in the order of 0.5p, 2.5p,5p,and 25p.
> > y-axis is Intensity
> > Inside of plot is the points of intensity over 4 concentrations, points
> > from different genes have different color or shape. A regression line of
> > each genes crosss different concetrations, and at the end of line is gene
> > IDs.
>
> I might do it something like this:
>
> df <- structure(list(gene = structure(c(1L, 1L, 1L, 1L, 6L, 3L, 3L,
> 3L, 3L, 7L, 7L, 7L, 7L, 2L, 5L, 5L, 5L, 5L, 4L, 4L), .Label = c("16B-E06.g",
> "35A-G04.g", "35B-A02.g", "35B-A04.g", "35B-D01.g", "37B-B02.g",
> "45B-C12.g"), class = "factor"), X25p = c(45379L, 45138L, 48457L,
> 47740L, 42860L, 48325L, 48410L, 48417L, 51403L, 50939L, 52356L,
> 49338L, 51567L, 40365L, 54217L, 55283L, 55041L, 54058L, 42745L,
> 41055L), X5p = c(4383L, 4028L, 4267L, 4676L, 6152L, 12863L, 12806L,
> 9057L, 13865L, 3656L, 5524L, 5141L, 3915L, 5513L, 12607L, 11441L,
> 9626L, 9465L, 12080L, 12423L), X2.5p = c(5123L, 6249L, 5470L,
> 6769L, 19276L, 38274L, 39013L, 40923L, 43338L, 5783L, 6041L,
> 5266L, 5677L, 6971L, 13067L, 14964L, 14928L, 14912L, 34271L,
> 34874L), X0.5p = c(45L, 52L, 54L, 48L, 72L, 143L, 175L, 176L,
> 161L, 43L, 55L, 41L, 43L, 32L, 93L, 101L, 94L, 88L, 105L, 126L
> )), .Names = c("gene", "X25p", "X5p", "X2.5p", "X0.5p"),
> class = "data.frame", row.names = c(NA, -20L))
>
> library(reshape)
> library(ggplot2)
>
> dfm <- melt(df, id=1)
> names(dfm) <- c("gene", "conc", "intensity")
> dfm\$conc <- as.numeric(gsub("[Xp]", "", as.character(dfm\$conc)))
>
> qplot(conc, intensity, data=dfm, colour=gene, log="xy") + geom_smooth(method=lm)
>
> Note that I've converted the concentrations to numeric values and
> plotted them on a log scale.  If you want to treat concentration as a
> factor, then you'll need the following code:
>
> dfm\$conc <- factor(dfm\$conc)
> qplot(conc, intensity, data=dfm, colour=gene, group=gene, log="y") +
> geom_smooth(method=lm, xseq=levels(dfm\$conc))
>
> But in that case, fitting a linear model seems a bit dubious.
>
> Note that you can also use this format of data with lattice:
>
> library(lattice)
> xyplot(intensity ~ conc, data=dfm, type=c("p","r"), group=gene, auto.key=T)
>