I am trying to create a heat map in R using the package(pheatmap). I am to do this using my normalized data for ALL genes with hierarchical clustering per 100's of genes. However, I am confused because my heat map still includes the control data. It was communicated to me that if the treatment was normalized to the control then the control data would appear as having zero expression. When I create my heat map I still see the control data. Is this right?

I have provided some of my code, I am following Analyzing RNA-Seq Data with DESEQ2

coldata <- read.csv("coldata.csv", row.names=1) 
cts <- read.csv("countmatrix.csv", row.names=1)

coldata <- coldata[,c("condition","type")]
coldata$condition <- factor(coldata$condition)
coldata$type <- factor(coldata$type)    

library("DESeq2")
dds <- DESeqDataSetFromMatrix(countData = cts, colData = coldata, design = ~ condition)
keep <- rowSums(counts(dds)) >= 10
dds <- dds[keep,]
dds <- DESeq(dds)
vsd <- vst(dds, blind=FALSE)

library("pheatmap")
select_test <- order(rowMeans(counts(dds,normalized=TRUE)), decreasing=TRUE)
df <- as.data.frame(colData(dds)[,c("condition","type")])
pheatmap(assay(vsd)[select_test,], cluster_rows=TRUE, cutree_rows=4, show_rownames=FALSE, 
              cluster_cols=FALSE, annotation_col=df)