Extracting information in genomic regions of interest can be done using the following steps:

1. Load in packages and define some functions that create GRanges from common files:

library(GenomicRanges)
library(rtracklayer)
library(SummarizedExperiment)

get_granges_from_gff <- function(file_name) {
  gff <- rtracklayer::import.gff(file_name)
  as(gff, "GRanges")
}

get_granges_from_bed <- function(file_name){
  bed <- rtracklayer::import.bed(file_name)
  as(bed, "GRanges")
}

get_granges_from_text <- function(file_name){
  df <- readr::read_tsv(file_name, col_names = TRUE ) 
  GenomicRanges::makeGRangesFromDataFrame(df, keep.extra.columns = TRUE)
}

2. Actually create some GRanges objects using those functions:

gr_from_gff <- get_annotated_regions_from_gff(file.path(getwd(), "datasets", "ch2", "arabidopsis_chr4.gff"))
gr_from_txt <- get_granges_from_text(file.path(getwd(), "datasets", "ch2", "arabidopsis_chr4.txt"))

3. Extract a region by filtering on attributes; in this case—the seqnames and metadata columns:

genes_on_chr4 <- gr_from_gff[ gr_from_gff$type == "gene" & seqnames(gr_from_gff) %in% c("Chr4") ]  

4. Manually create a region of interest:

region_of_interest_gr <- GRanges(
    seqnames = c("Chr4"), 
    IRanges(c(10000), width= c(1000))
)

5. Use the region of interest to subset the larger object:

overlap_hits <- findOverlaps(region_of_interest_gr, gr_from_gff)
features_in_region <- gr_from_gff[subjectHits(overlap_hits) ]
features_in_region