Summary Barplot - Data Completeness

Plot number of identifications per missing values as stacked barplot.

Usage

plot_DC_stacked_barplot(
  input_list,
  level = c("Precursor.IDs", "Peptide.IDs", "Protein.IDs", "ProteinGroup.IDs"),
  label = c("absolute", "percentage")
)

Arguments

input_list

A list with data frames and respective level information.

level

Character string. Choose between "Precursor.IDs", "Peptide.IDs", "Protein.IDs" or "ProteinGroup.IDs" for corresponding level. Default is "Precursor.IDs".

label

Character string. Choose between "absolute" or "percentage". Default is "absolute".

Value

This function returns a stacked barplot.

Details

The analyses are summarized in a stacked barplot displaying information about the number of achieved identifications per missing values.

Author

Oliver Kardell

Examples

# Load libraries
library(magrittr)
library(dplyr)
library(tibble)

# Example data
data <- list(
 "A" = tibble::tibble(
   Analysis = c("A", "A", "A"),
   Nr.Missing.Values = c(2, 1, 0),
   Precursor.IDs = c(50, 200, 4500),
   Peptide.IDs = c(30, 190, 3000),
   Protein.IDs = c(20, 40, 600),
   ProteinGroup.IDs = c(15, 30, 450),
   Profile = c("unique", "shared with at least 50%", "complete")
 ),
 "B" = tibble::tibble(
   Analysis = c("B", "B", "B"),
   Nr.Missing.Values = c(2, 1, 0),
   Precursor.IDs = c(50, 180, 4600),
   Peptide.IDs = c(50, 170, 3200),
   Protein.IDs = c(20, 40, 500),
   ProteinGroup.IDs = c(15, 30, 400),
   Profile = c("unique", "shared with at least 50%", "complete")
 )
)

# Plot
plot_DC_stacked_barplot(
  input_list = data,
  level = "Precursor.IDs",
  label = "absolute"
)