The data to be visualized is from an experiment (T1-T8 represents different sections of the brain) and is as follows:
[[Block1]]
sum
[T1,] 6
[T2,] 6
[T3,] 4
[T4,] 5
[T5,] 8
[T6,] 9
[T7,] 8
[T8,] 6
[[Block2]]
sum
[T1,] 3
[T2,] 3
[T3,] 4
[T4,] 5
[T5,] 4
[T6,] 2
[T7,] 1
[T8,] 5
[[Block3]]
sum
[T1,] 3
[T2,] 3
[T3,] 4
[T4,] 2
[T5,] 4
[T6,] 8
[T7,] 3
[T8,] 1
[[Block4]]
sum
[T1,] 6
[T2,] 5
[T3,] 4
[T4,] 3
[T5,] 9
[T6,] 8
[T7,] 2
[T8,] 6
[[Block5]]
sum
[T1,] 8
[T2,] 3
[T3,] 4
[T4,] 5
[T5,] 7
[T6,] 6
[T7,] 2
[T8,] 2
[[Block6]]
sum
[T1,] 10
[T2,] 9
[T3,] 6
[T4,] 8
[T5,] 9
[T6,] 4
[T7,] 6
[T8,] 7
and so on.. For more than 100 blocks..
I would like to visualize the data in the following way to see the overall value in each region for very block..
For one block I get a line plot as shown below:
But it is tedious to visualize the same for 100 blocks.. What would be the best method to view it as a single plot using R..I tried doing it with heat maps but I would rather visualize them as a graph..
In the end it should be something like ( I have a rough figure of it).. Iam not sure how to do this in R for several blocks in a single plot or some other better way to visualize it:
•
written
by
deschisandy •
This is a difficult question, because the answer depends on what you want to show.
The ENCODE researchers faced similar problems, when they had to find a way to visualize expression levels from a lot of tissues. One solution they have found is the ENCODE-RNA-dashboard:
This basically is not much different from an heatmap, but it is interactive, so you can click and get more details about a specific tissue.
Another solution, as suggested bu Suhkdeep, is to use faceting. This is a reproducible example of how to use faceting to generate multiple plots:
> library(reshape)
> d = data.frame("B1"=rnorm(8),"B2"=rnorm(8), "B3"=rnorm(8), "B4"=rnorm(8), "B5"=rnorm(8), "B6"=rnorm(8), "B7"=rnorm(8), "B8"=rnorm(8))
> d
B1 B2 B3 B4 B5 B6
1 -0.6581045 -1.093919442 0.94578457 -0.1805976 0.41888201 -0.7794898
2 -1.1092189 -0.091769948 0.09502878 -1.4822778 -0.04307963 -0.5021063
3 0.8569934 -0.388095230 -1.06364075 0.2441476 -0.23060652 -1.4263023
4 -0.9435040 -0.791272225 -1.20657936 0.6161711 1.18477321 -0.3969317
5 0.1589559 -0.007835382 0.66982203 0.3872096 -0.88920083 -0.2209863
6 1.4952576 0.549350763 -0.61693139 -1.5470285 0.21826829 -0.7205813
7 -1.5454067 -0.785411708 0.48277576 -1.8998326 0.78415025 -0.8832751
8 1.0721951 0.490277322 1.17584947 0.1968467 1.74636297 1.8247020
B7 B8
1 -1.7490352 -0.3638126
2 1.1485487 0.7564457
3 0.9900087 -1.1589263
4 -1.2926119 1.4203860
5 3.4772243 0.8022015
6 0.5892454 0.3348479
7 -0.4157224 0.6205421
8 -0.6170603 1.5733568
> d$id = row.names(d)
> d.long = melt.data.frame(d, id.var='id')
# facets with ggplot2
> library(ggplot2)
> qplot(as.integer(id), value, facets=~variable, data=d.long, geom='line')
# facets with lattice
> library(lattice)
> xyplot(value~id2|variable, d.long, type=c('l','p'), layout=c(3,3)) # note: with the layout option you can split the plot into multiple page. This is actually not easy to do in ggplot2.
•
written
by
Giovanni M Dall'Olio
This sort of very simple, yet efficient way to obtain plot similar to yours at once, you will need to add proper breaks and axis labels
require(ggplot2)
require(reshape)
# make data matrix 10x100, step 5 for each next series
data = data.frame(
matrix(rnorm(1000)+rep(seq(1,50,5),100), nrow=10, byrow=F))
# numeric variables
colnames(data) = paste(seq(1:100))
# attach series names and do the melt
data = cbind(series=factor(seq(1:10), levels=seq(1:10),
labels=paste("series_",seq(1:10),sep="")), data)
dm = melt(data)
# plot
(p = ggplot(dm, aes(x=variable, y=value, group=series, colour=series)) +
geom_line() + theme_bw() + ggtitle("A rough figure of it...") +
scale_y_continuous("Blocks",breaks=seq(1,50,5), labels=paste("block_",seq(1:10),sep="")) +
scale_x_discrete("T-s",breaks=seq(1,100,10), labels=paste("T",seq(1:10),sep="")))
•
written
by
Pavel Senin
nice idea, you should add a screenshot of the plot to this discussion.
Alright. Sample added.
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Edit your question to a better title plus check facet_grid of ggplot!!
Cross-posted on stackoverflow, where several good answers have been offered. http://stackoverflow.com/questions/17280468/data-visualization-in-r