Visualizing distributions of peptide hit counts to find thresholds can be done using the following steps:

1. Load the libraries and data:

library(MSnID)
library(data.table)
library(dplyr)
library(ggplot2)
msnid <- MSnID() 
msnid <- read_mzIDs(msnid, file.path(getwd(), "datasets", "ch6", "HeLa_180123_m43_r2_CAM.mzid.gz")) 
peptide_info <- as(msnid, "data.table")

2. Filter out decoy data rows and get a count of every time a peptide appears:

per_peptide_counts <- peptide_info %>% 
 filter(isDecoy == FALSE) %>%
 group_by(pepSeq) %>%
 summarise(count = n() ) %>% 
 mutate(sample = rep("peptide_counts", length(counts) ) )

3. Create a violin and jitter plot of the hit counts:

per_peptide_counts %>% 
 ggplot() + aes( sample, count) + geom_jitter() + geom_violin() + scale_y_log10()

4. Create a plot of cumulative hit counts for peptides sorted by hit count:

per_peptide_counts %>%
 arrange(count) %>%
 mutate(cumulative_hits = cumsum(count), peptide = 1:length(count)) %>%
 ggplot() + aes(peptide, cumulative_hits) + geom_line()

5. Filter out very low and very high peptide hits and then replot them:

filtered_per_peptide_counts <- per_peptide_counts %>%
  filter(count >= 5, count <= 2500) 

filtered_per_peptide_counts %>% 
 ggplot() + aes( sample, count) + geom_jitter() + geom_violin() + scale_y_log10()