plot_coverage()
function can be used to plot the average coverage across
a given grouping variable (#46).label_mutations()
has been renamed from
ans_der_indel
to mutation_label
(#44).label_mutations()
more clearly signals when the computation can not be
completed. This can occur because of missing variables or an incorrect input
table (#44).{cli}
. Messages are now properly
pluralized and display the erroring function (#42).{dplyr}
version to 1.0.0.{chromoMap}
.read_tbl_genotype()
function can be used to read in the
genotypes_AA_table.csv
and genotypes_AN_table.csv
generated by MIPTools
(#34).chromosome_map()
in favor of plot_chromoMap()
and
plot_karyoploteR()
(#25).read_tbl_*()
family of functions provides a consistent function call
to read data tables generated by MIPTools (#17).read_tbl_haplotype()
can be used to read the haplotypes_count.csv
file
generated by MIPTools (#15).vignette("faq")
answers some frequently asked questions (#32).mutation_frequency()
and plot_mutation_frequency()
functions can be
used to determine the frequency of mutations (#29).mutation_prevalence()
and plot_mutation_prevalence()
functions can be
used to determine the prevalence of mutations (#23).label_mutations()
labels a mutation as "ref"
, "alt"
, "ins"
, or
"del"
(#3).theme_rainbow()
creates a custom theme.plot_karyoploteR()
no longer prints plot noisily.read_tbl_*()
functions no longer change sample IDs (#20).{rlang}
instead of {cli}
to display messages, warnings, and
errors (#16).plot_karyoploteR()
now works for arbitrary column names (#14).chromosome_map()
can now create an additional type of map (#11).chromosome_map()
creates a chromosome map annotated with probe
locations.theme_miplicorn()
specifies a custom theme for creating figures.read()
now allows for filtering on multiple conditions (#7).arrange_natural()
naturally sorts a data frame (#5).convert_single()
and convert_three
convert amino acid abbreviations
(#1).read()
(#2).NEWS.md
file to track changes to the package.Add the following code to your website.
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