You can use the GenomiDataCommons package from Bioconductor. For example, in an old topic I found this:

install.packages("tidyverse")
library(tidyverse)
if (!require("BiocManager"))
    install.packages("BiocManager")
BiocManager::install('GenomicDataCommons')
library(GenomicDataCommons)

qfiles = files() %>% filter( ~ cases.project.project_id == 'CPTAC-3-kirc' &
                            type == 'mutation' &
                            access == "open" #&
                            #analysis.workflow_type == 'STAR - Counts'
                              )

My advice is to carefully read the documentation to adapt this script to your data and what you want to find. Alternatively, there is a Python library that you can find here: https://pypi.org/project/cptac/ where you can download your data. Use the reticulate package to convert data tables into data.frames in R.