Individual Barplots - Data Completeness — plot_DC

Plot number of identifications per missing values for each analysis.

Usage

plot_DC_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 list with a barplot for each analysis.

Details

For each submitted individual analysis a detailed barplot is generated with information about the number of achieved identifications per missing values.

Author

Oliver Kardell

Examples

# Load libraries
library(magrittr)
library(comprehenr)
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_barplot(
  input_list = data,
  level = "Precursor.IDs",
  label = "absolute"
)
#> $A

#> 
#> $B

#>