The question is a bit confusing to me, at it's core I understand you want non-overlapping windows across chromosomes.

One way to achieve is to use GenomicRanges::tileGenome function, which needs the chromosome lengths as input, e.g.

library(Biostrings)
mygenome <- readDNAStringSet(list.files(mypath,"mygenome.fa$",full=TRUE))
chrSizes <- width(mygenome)
names(chrSizes) <- names(mygenome)
chrSizes
#     Chr1     Chr2     Chr3     Chr4     Chr5 
# 18585056 19698289 23459830 26975502 30427671 

This can than be fed into tileGenomes

library(GenomicRanges)
bins   <- tileGenome(chrSizes, tilewidth=5e5, cut.last.tile.in.chrom=T)

This creates a GenomicRange object which gives you a whole repertoire of post processing options, i.e. overlapping with

bins
# GRanges object with 240 ranges and 0 metadata columns:
#         seqnames               ranges strand
#            <Rle>            <IRanges>  <Rle>
#     [1]     Chr1   [      1,  500000]      *
#     [2]     Chr1   [ 500001, 1000000]      *
#     [3]     Chr1   [1000001, 1500000]      *
#     [4]     Chr1   [1500001, 2000000]      *
#     [5]     Chr1   [2000001, 2500000]      *
#     ...      ...                  ...    ...
#   [236]     Chr5 [28000001, 28500000]      *
#   [237]     Chr5 [28500001, 29000000]      *
#   [238]     Chr5 [29000001, 29500000]      *
#   [239]     Chr5 [29500001, 30000000]      *
#   [240]     Chr5 [30000001, 30427671]      *
#   -------
#   seqinfo: 5 sequences from an unspecified genome

The question is a bit confusing to me, at it's core I understand you want non-overlapping windows across chromosomes.

One way to achieve is to use GenomicRanges::tileGenome function, which needs the chromosome lengths as input, e.g.

library(Biostrings)
mygenome <- readDNAStringSet(list.files(mypath,"mygenome.fa$",full=TRUE))
chrSizes <- width(mygenome)
names(chrSizes) <- names(mygenome)
print(chrSizes)
#     Chr1     Chr2     Chr3     Chr4     Chr5 
# 18585056 19698289 23459830 26975502 30427671 

This can than be fed into tileGenomes:

library(GenomicRanges)
bins   <- tileGenome(chrSizes, tilewidth=5e5, cut.last.tile.in.chrom=T)

This creates a GRange object which gives you a whole repertoire of post processing options, i.e. overlapping with SNPs or NGS reads (I use the latter for genome wide coverage plots):

print(bins)
# GRanges object with 240 ranges and 0 metadata columns:
#         seqnames               ranges strand
#            <Rle>            <IRanges>  <Rle>
#     [1]     Chr1   [      1,  500000]      *
#     [2]     Chr1   [ 500001, 1000000]      *
#     [3]     Chr1   [1000001, 1500000]      *
#     [4]     Chr1   [1500001, 2000000]      *
#     [5]     Chr1   [2000001, 2500000]      *
#     ...      ...                  ...    ...
#   [236]     Chr5 [28000001, 28500000]      *
#   [237]     Chr5 [28500001, 29000000]      *
#   [238]     Chr5 [29000001, 29500000]      *
#   [239]     Chr5 [29500001, 30000000]      *
#   [240]     Chr5 [30000001, 30427671]      *
#   -------
#   seqinfo: 5 sequences from an unspecified genome