library(cBioPortalData)
 library(dplyr)
 
 cbio <- cBioPortal()
 studies <- getStudies(cbio)
 
 #get studyIds
 study_names <-
   data.frame(cbind(gsub(" \\(TCGA, PanCancer Atlas\\)", "",studies$name[grepl("tcga_pan_can_atlas", studies$studyId)]),
                    studies$studyId[grepl("tcga_pan_can_atlas", studies$studyId)]))
 colnames(study_names) <- c("Cancer", "studyId")
 
 #loop through studyIds to get all_tcga2018_data
 all_tcga2018_data <- data.frame()
 for(study in study_names$studyId){
   cbio_table <- getDataByGenePanel(cbio, study, genePanelId = "IMPACT468",
                           molecularProfileId = paste0(study, "_gistic"),
                      sampleListId = paste0(study, "_cna"))
   cbio_dat <- data.frame(cbio_table[[1]], stringsAsFactors = FALSE)
   cbio_summ <- cbio_dat %>% group_by(hugoGeneSymbol) %>%
     summarise(Gain = sum(value ==1)/n(),
               Amplification = sum(value == 2)/n(),
               ShallowDeletion = sum(value == -1)/n(),
               DeepDeletion = sum(value == -2)/n())
   cbio_summ$sample_size <- rep(paste0(" (N = ", length(unique(cbio_dat$uniquePatientKey)), ")"), nrow(cbio_summ))
   cbio_summ$studyId <- rep(study, nrow(cbio_summ))
   all_tcga2018_data <- rbind(all_tcga2018_data, cbio_summ)
 }
 
 #add sample sizes to get cbio_studies
 ss <- all_tcga2018_data %>% group_by(studyId, sample_size) %>% summarise()
 cbio_studies <- inner_join(study_names, ss, by = c("studyId"))
 cbio_studies$Cancer <- paste0(cbio_studies$Cancer, cbio_studies$sample_size)
 cbio_studies <- dplyr::select(cbio_studies, Cancer, studyId)