Plot updates and fix notes

[karissawhiting]

The main purpose of this PR is to separate out the maf.summary functions into smaller, modularized plotting functions. The need for this arose when I was trying to plot things using API data, but the API data is missing HGVSc and therefore I was not able to get any of the plots. I think this change is beneficial in general, as it allows users to be able to run only what they need. Also, it allows for easy customization syntax like this:

ggvarclass(mut) + 
  scale_fill_pancan() +
  ggtitle("Overwrite Title")

A wrapper function called maf_viz() (feel free to change name of course) now exists that can call them all at once. It returns results in a list, as before.

I also added a utils.R file with some functions that may be called in several places (e.g. check_maf_input())

Here are a few outstanding questions/issues. Please let me know if you have any opinions:

• For now I removed all of the scale_fill_manual() statements from plotting functions and am just using out of the box ggplot defaults. As discussed with @arorarshi, we may want to consider adding default already loaded palettes, however since it is very simple to customize ggplot objects (see code above) we may not need this.
• The previous version of the plotting function had the following filtering. Currently, I only included this in the SNV plotting functions. Should all plotting functions have this filtering?

 maf <- maf %>%
    filter(.data$Variant_Type == "SNP",
           .data$HGVSc != "") %>%
    mutate(SNV_Class = substrRight(.data$HGVSc,3)

• Arshi and I discussed potentially removing p.variant.bp, p.variant.dist and p.variant.dist.bar and p.snv.dist to simplify the functions, as these may not be needed. We can discuss more in the meeting.
• I’m a little confused about p.corr versus p.comut. Is this just two ways to filter which genes are displayed? one shows top co-occuring genes and one shows correlations for overall top genes? If so, maybe we can put this in one function with an arg option? I may be misunderstanding this though.

@arorarshi @AxelitoMartin

[AxelitoMartin]

Let's go over this quickly at the meeting