The goal of methylONT is to porvide utility functions to differential methylation analysis for lonfg read sequencing
You can install the development version of methylONT from GitHub with:
# install.packages("devtools")
devtools::install_github("sahuno/methylONT")
This is a basic example which shows you how to solve a common problem:
library(methylONT)
#> Warning: replacing previous import 'IRanges::shift' by 'data.table::shift' when
#> loading 'methylONT'
#> Warning: replacing previous import 'Biostrings::tail' by 'utils::tail' when
#> loading 'methylONT'
#> Warning: replacing previous import 'IRanges::stack' by 'utils::stack' when
#> loading 'methylONT'
#> Warning: replacing previous import 'IRanges::relist' by 'utils::relist' when
#> loading 'methylONT'
#> Warning: replacing previous import 'Biostrings::head' by 'utils::head' when
#> loading 'methylONT'
## basic example code
What is special about using README.Rmd
instead of just README.md
?
You can include R chunks like so:
summary(cars)
#> speed dist
#> Min. : 4.0 Min. : 2.00
#> 1st Qu.:12.0 1st Qu.: 26.00
#> Median :15.0 Median : 36.00
#> Mean :15.4 Mean : 42.98
#> 3rd Qu.:19.0 3rd Qu.: 56.00
#> Max. :25.0 Max. :120.00
You’ll still need to render README.Rmd
regularly, to keep README.md
up-to-date. devtools::build_readme()
is handy for this. You could also
use GitHub Actions to re-render README.Rmd
every time you push. An
example workflow can be found here:
https://github.com/r-lib/actions/tree/v1/examples.
You can also embed plots, for example:
In that case, don’t forget to commit and push the resulting figure files, so they display on GitHub and CRAN.