I have a matrix of 8 rows and 12 columns, and randomly distributed 10 different treatments with 9 replicates and a final treatment only with 6 replicates in the matrix. The code might be redundant, but it was the first think that came to mind and worked. I just wanted to have a scheme so that I could follow easily afterwards in the lab, to avoid mistakes:
library(ggplot2)
library(RColorBrewer)
library(reshape2)
library(scales)
replicates<-c(rep(seq(1:11),c(rep(9,10),6)));replicates
dimna<-list(c("A","B","C","D","E","F","G","H"),seq(1,12,1))
plate<-array(sample(replicates),dim=c(8,12),dimnames=dimna);plate
platec<-melt(plate);platec
guide<-ggplot(platec,aes(Var2,Var1,fill=factor(value))) + geom_tile()+geom_text(aes(fill=factor(value),label=value)) + ylim(rev(levels(platec$Var1))) + theme_bw() + theme(panel.grid.major.y=element_blank(),panel.grid.minor.y=element_blank(),panel.grid.major.x=element_blank(), axis.text.x=element_text(size=10), axis.title.y=element_blank(), axis.text.y=element_text(size=12)) + scale_fill_brewer(name="",palette="Spectral") + scale_x_continuous("",labels=c(seq(1,12,1)),breaks=c(seq(1,12,1)));guide
However, now imagine that I take measurements for the randomized matrix multiple times. And for the data processing I need to identify the treatment and replicates in the matrix. I can either have the data at the end in a columnwise:
A1 A2 A3 A4 A5 A6 A7 A8
0.12 0.2 0.124 0.14 0.4 0.18 0.46 0.47
0.13 0.21 0.6 0 0 0.58 0.4 0.2
0.15 0.248 0.58 0.4 0.2 0.248 0.2 0.18
0.18 0.46 0.47 0.3 0.21 0.2 0.21 0.58
0.1784 0.14 0.95 0.7 0.248 0.21 0.248 0.248
. . .
Or rowwise fashion:
A1 0.12 0.13 0.15 0.18 0.1784
A2 0.2 0.21 0.248 0.46 0.14
A3 0.124 0.6 0.58 0.47 0.95
A4 0.14 0 0.4 0.3 0.7
A5 0.4 0 0.2 0.21 0.248
A6 0.18 0.58 0.248 0.2 0.21
A7 0.46 0.4 0.2 0.21 0.248
A8 0.47 0.2 0.18 0.58 0.248
...
Is there a way in R in which I can relate the random matrix to the data I have collected, I have no clue on how to begin even. I'm really sorry for not having an attempt even, but I honestly wouldn't know on how to start
I think I know what you're asking... let me know if this doesn't make sense. You need to have a design dataframe first - let's make a dummy plate:
Wells <- paste0(rep(LETTERS[1:8],each=12), rep(1:12, times = 8))
design <- data.frame(Wells, ID = sample(letters[1:10], 96, replace = TRUE))
Then when you get your result, assuming it's in a dataframe (your 'rowwise fashion?'), you can merge them together:
#dummy result data
result <- data.frame(Wells, measure = rnorm(96, 0.5))
result_whole <- merge(design, result)
head(result_whole)
# Wells ID measure
#1 A1 j -0.4408472
#2 A10 d -0.5852285
#3 A11 d 1.0379943
#4 A12 e 0.6917493
#5 A2 g 0.8126982
#6 A3 b 2.0218953
If you keep your designs neatly, this is very straightforward. You can then label the results (measure in this case) however you want to keep track of it all.
I hope that addresses your problem...