I'm sure this is an easy question, but I'm fairly new to using limma and R and I'd appreciate any help or guidance. I've run a paired limma analysis trying to look at differentially expressed genes in a pre-post experiment design. I'm trying to understand what direction my logFC values represent i.e. whether the gene is being up regulated or down regulated from pre to post or post to pre. My code is as follows: 

write.exprs(e,file="expressionDataHFNIH5-7-14.txt") 

d<-read.table("expressionDataHFNIH5-7-14.txt", header=T, row.names=1)
names(d)<-gsub(".CEL", "", gsub("X", "", names(d)))
d<-round(d, 5)

write.table(d, file="filepath.txt", quote=F, sep='\t')

#create a design matrix
#congestion 1=pre and 2=post

design <- cbind(ID = c(1,1,2,2,7,7,8,8,9,9,10,10,11,11,14,14,16,16,20,20,21,21,22,22,23,23,24,24,25,25,26,26,27,27,28,28,29,29,30,30,33,33,34,34,35,35,36,36,37,37,39,39,40,40,41,41), Congestion=c(1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2,1,2))

ID<-factor(design[,1])
Congestion<-factor(design[,2], levels=c("1","2"))

#create a design matrix
design<-model.matrix(~ID+Congestion)

fit <- lmFit(e, design)
fit <- eBayes(fit)

topTable(fit, coef = "Congestion2", adjust = "fdr")

topt2<-topTable(fit, coef = "Congestion2", adjust.method = "fdr", n=Inf, sort.by="p")
write.table(topt2,file="filepath.txt",quote=F,sep='\t'

Thank you! 

Your Congestion2 coefficient represents the log-fold change of your "2" samples over your "1" samples (i.e., post over pre). When you drop this coefficient during your DE comparison, the reported log-fold change corresponds to the value of this coefficient. Thus, you can interpret positive values as being upregulated in the post condition compared to pre, and negative values as being downregulated.

Incidentally, if you ever get confused, it's always a good idea to look at the expression values to figure out what's going on. In this case, you can pick a couple of the top DE genes and have a look at the direction of change within each patient for those genes. This is also a good way to check that you've set up the design and contrasts correctly for more complicated experiments; it never hurts to be on the safe side.