The documentation has an example for RNAseq data. You input your data, map to entrez id, correct multimapped entrez, generate the design and contrasts, load the gene sets, and run gsva. You also might want to check out EGSEA() as well.

#pipeline for RNAseq GSVA analysis

##install libraries
#source("https://bioconductor.org/biocLite.R")
#biocLite(c("limma","GSVA","GSVAdata","org.Hs.eg.db","GSEABase","snow","rlecuyer","edgeR"))
##

#load libraries
library(limma)
library(GSVA)
library(GSVAdata)
library(org.Hs.eg.db)
library(GSEABase)
library(snow)
library(rlecuyer)
library(edgeR)

##make_unique(df)
#usage: analysis_ready_data <- make_unique(data_frame)
#converts ensembl to entrez id and collapses redundant ids by greatest IQR 
make_unique <- function(input.data) {
 #annotate entrez id
 input.data <- data.frame(cbind(input.data, entrez=as.numeric(mapIds(org.Hs.eg.db, keys=gsub("\\..*","",rownames(input.data)), column="ENTREZID", keytype="ENSEMBL",multiVals="first"))))
 #remove NA
 input.data <- input.data[!is.na(input.data$entrez),]
 #parse duplicated entrez id
 id.dup <- input.data[duplicated(input.data$entrez) | duplicated(input.data$entrez, fromLast = TRUE),ncol(input.data)]
 data.dup <- as.matrix(input.data[duplicated(input.data$entrez) | duplicated(input.data$entrez, fromLast = TRUE),])
 ezid.dup <- unique(id.dup)
 data.unique <- input.data[!input.data$entrez %in% id.dup,]
 rownames(data.unique) <- data.unique$entrez
 data.unique$entrez = NULL
 #assess IQR for duplicates
 data.dup2 <- lapply(ezid.dup, function(x) { 
  expr <- data.dup[id.dup == x,]
  if(is.matrix(expr)){
   sd.values <- apply(expr[,1:ncol(input.data)-1], 1, sd)
   mean.values <- apply (expr[,1:ncol(input.data)-1], 1, mean)
   CV.values <- sd.values/mean.values
   CV.values <- gsub("NaN","0",CV.values)
   expr <- expr[which(CV.values == max(CV.values))[[1]],]
  } else {
   expr
  }
 })
 #merge data
 merger <- data.frame(do.call(rbind, data.dup2))
 rownames(merger) <- merger$entrez
 merger$entrez = NULL
 eset <- as.matrix(rbind(data.unique, merger))
 return(eset)
}

#load logCPM, convert ensembl id to entrez, and for multimaps retain entry with highest IQR
logCPM <- read.table("logCPM.csv",header=TRUE,row.names=1,sep="\t")
logCPM <- make_unique(logCPM) 

#load omics, e.g., factor levels for each sample
factor.table <- read.delim("omics.csv", header=TRUE,sep="\t")
groups <- factor(factor.table$group)
institution <- factor(factor.table$Institution)
lane <- factor(factor.table$Lane)
group.intercepts <- model.matrix(~0 + groups + institution + lane)
contrasts <- makeContrasts(
groups1 - (groups2 + groups3) / 2, 
groups2 - (groups1 + groups3) / 2, 
groups3 - (groups1 + groups2) / 2, levels=group.intercepts)

#C2 collection of curated gene sets that form part of the Molecular Signatures Database (MSigDB) version 3.0.
data(c2BroadSets)
gene.sets <- GeneSetCollection(c2BroadSets)

#calculate GSVA enrichment scores
enrichment.scores <- gsva(logCPM, gene.sets, method = "gsva", mx.diff = TRUE, verbose=TRUE, rnaseq=FALSE, parallel.sz=8)$es.obs
fit <- lmFit(enrichment.scores, group.intercepts)
fit2 <- contrasts.fit(fit, contrasts)
fit2 <- eBayes(fit2,trend = TRUE, robust = TRUE)
topTable(fit2, coef=1)
topTable(fit2, coef=2)
topTable(fit2, coef=3)