View source: R/tidyMS_plotting.R
plot_hierarchies_boxplot_df | R Documentation |
generates peptide level plots for all Proteins
plot_hierarchies_boxplot_df(
pdata,
config,
hierarchy = config$table$hierarchy_keys_depth(),
facet_grid_on = NULL
)
pdata |
data.frame |
config |
AnalysisConfiguration |
facet_grid_on |
default NULL |
hiearchy |
e.g. protein_Id default hierarchy_keys_depth |
Other plotting:
ContrastsPlotter
,
INTERNAL_FUNCTIONS_BY_FAMILY
,
UpSet_interaction_missing_stats()
,
UpSet_missing_stats()
,
medpolish_estimate_df()
,
missigness_histogram()
,
missingness_per_condition()
,
missingness_per_condition_cumsum()
,
plot_NA_heatmap()
,
plot_estimate()
,
plot_heatmap()
,
plot_heatmap_cor()
,
plot_hierarchies_add_quantline()
,
plot_hierarchies_line()
,
plot_hierarchies_line_df()
,
plot_intensity_distribution_violin()
,
plot_pca()
,
plot_raster()
,
plot_sample_correlation()
,
plot_screeplot()
istar <- sim_lfq_data_peptide_config(with_missing = FALSE)
res <- plot_hierarchies_boxplot_df(istar$data,istar$config)
istar <- sim_lfq_data_peptide_config()
config <- istar$config
analysis <- istar$data
analysis <- analysis |>
dplyr::filter(protein_Id %in% sample(protein_Id, 2))
res <- plot_hierarchies_boxplot_df(analysis,config)
res$boxplot[[1]]
res <- plot_hierarchies_boxplot_df(analysis,config,config$table$hierarchy_keys()[1])
res$boxplot[[1]]
res <- plot_hierarchies_boxplot_df(analysis,config,
config$table$hierarchy_keys()[1],
facet_grid_on = config$table$hierarchy_keys()[2])
res$boxplot[[1]]
res$boxplot[[2]]
iostar <- sim_lfq_data_protein_config()
iostar$data <- iostar$data |>
dplyr::filter(protein_Id %in% sample(protein_Id, 4))
unique(iostar$data$protein_Id)
res <- plot_hierarchies_boxplot_df(iostar$data,iostar$config)
res$boxplot[[1]]
res <- plot_hierarchies_boxplot_df(iostar$data,iostar$config,
iostar$config$table$hierarchy_keys()[1])
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