Question

I am trying the create a loop to extract data from an array created previously, so that I can then use the extracted data to generate a line plot.

So far I have been doing it manually by using:

allweek1<-(data.frame(t_weekmean[,,1])) #which selects the date and generates the data frame I want to later format the date using
week1<-stack(allweek1) #and then plot it using
plot(week1$values,type="n", xlim=c(0,2),xlab="Weight (gr)",ylab="Rate (umol/L*gr)",main="All individuals and Treatments at all times")
lines(week1$values[week1$ind=="X9"]~x,type="o",col="red")
lines(week1$values[week1$ind=="X12"]~x,type="o",col="blue")
lines(week1$values[week1$ind=="X15"]~x,type="o",col="green")
lines(week1$values[week1$ind=="X18"]~x,type="o",col="purple").

I know there has to be a way of making this into a loop, for this example I am giving just two weeks but my data goes up to 30, and doing it manually will be messy and easy to have mistakes.

This is the starting array that I have:

, , Week = 1

        Temp
variable       9      12      15      18
    X0   100.000 100.000 100.000 100.000
    X0.5  98.855  98.591  98.357  99.003
    X1    98.004  97.804  97.638  98.299
    X1.5  95.953  96.999  96.810  97.555
    X2    95.235  96.078  95.346  96.665

, , Week = 2

        Temp
variable       9      12      15      18
    X0   100.000 100.000 100.000 100.000
    X0.5  99.137  99.035  97.883  99.055
    X1    98.420  98.298  96.459  97.765
    X1.5  97.939  97.181  94.406  96.546
    X2    96.998  96.237  91.906  95.263

The following data frame which is then converted to a stack version:

          X9     X12     X15     X18
X0   100.000 100.000 100.000 100.000
X0.5  98.855  98.591  98.357  99.003
X1    98.004  97.804  97.638  98.299
X1.5  95.953  96.999  96.810  97.555
X2    95.235  96.078  95.346  96.665

and then the code for plotting is used.

Answer 1

Sounds like task for lattice:

X <- as.data.frame(as.table(t_weekmean), stringsAsFactors=FALSE, responseName="values")
X$variable <- as.numeric(gsub("^X","",X$variable))
X$Temp <- as.numeric(X$Temp)

require(lattice)
xyplot(values~variable|Week, groups=Temp, X, type="o", as.table=TRUE,
    xlab="Weight (gr)", ylab="Rate (umol/L*gr)", main="All individuals and Treatments at all times"
)

I recreate your data as:

t_weekmean <- structure(c(100, 98.855, 98.004, 95.953, 95.235, 100, 98.591, 97.804, 96.999, 96.078, 100, 98.357, 97.638, 96.81, 95.346, 100, 99.003, 98.299, 97.555, 96.665, 100, 99.137, 98.42, 97.939, 96.998, 
100, 99.035, 98.298, 97.181, 96.237, 100, 97.883, 96.459, 94.406, 91.906, 100, 99.055, 97.765, 96.546, 95.263, 99.9889679441867, 
98.8470416045204, 98.010997102523, 95.9636806506725, 95.235986063534, 100.00797414162, 98.5968712619705, 97.7984016535804, 96.9904933552904, 
96.0816877686208, 99.9946318131395, 98.3568674165109, 97.6357767063124, 96.8119443900658, 95.3441814383421, 99.989633272252, 99.0037062049508, 
98.3034580102509, 97.5568340624981, 96.6615796074679, 100.000379644977, 99.1375077671092, 98.4187321210541, 97.9350205929782, 97.0006243532971, 
100.003971157774, 99.0316462150477, 98.298322594611, 97.1782003010139, 96.239865449585, 100.002464797458, 97.8810655647218, 96.4592857614756, 
94.4099917372801, 91.9025173998885, 100.003642400375, 99.0529984607268, 97.76302246443, 96.5426428484451, 95.2658935513329),
.Dim = c(5L, 4L, 4L), .Dimnames = structure(list(variable = c("X0", "X0.5", "X1", "X1.5", "X2"),
Temp = c("9", "12", "15", "18"), Week = c("1", "2", "3", "4")), .Names = c("variable", "Temp", "Week"))
)