Plot & Summarize Group Stats via MakeComparisonTable (BH q from p; SHAPE by p; COLOR by Category (vector or data frame); stable point size; palette via paletteer)

Usage

Plot2GroupStats(
  data,
  variables,
  VariableCategories = NULL,
  impClust,
  normalClust,
  group_var,
  missing_threshold = 0.8,
  max_levels = 10,
  label_q = 0.05,
  x_axis = c("signed_logp", "signed_effect", "effect", "logp"),
  sort_by = c("q", "p", "effect", "signed_logp", "signed_effect", "none"),
  mct_args = list(),
  palette = NULL,
  point_size = 3.5,
  Data = lifecycle::deprecated(),
  Variables = lifecycle::deprecated(),
  GroupVar = lifecycle::deprecated()
)

Arguments

data

data.frame

variables

character vector of variables to analyze

VariableCategories

optional:

data frame with columns Variable, Category; OR
vector of categories (named by variable OR unnamed aligned to Variables)

impClust, normalClust

labels for the two groups (impClust plotted to the RIGHT for signed axes)

group_var

column name in Data holding the group labels

missing_threshold

drop vars with > this fraction missing (default 0.80)

max_levels

drop factors with > this many levels (default 10)

label_q

label threshold using q (default 0.05)

x_axis

one of c("signed_logp","signed_effect","effect","logp")

sort_by

one of c("q","p","effect","signed_logp","signed_effect","none")

mct_args

list of extra args to SciDataReportR::MakeComparisonTable(); e.g., AddEffectSize=TRUE

palette

Optional paletteer palette string for category colors. When NULL (the default), the SciDataReportR palette is used. Passing a paletteer string such as "pals::alphabet" still works as before.

point_size

numeric constant for point size (default 3.5)

Data

Deprecated (since 19.15.0). Use data instead.

Variables

Deprecated (since 19.15.0). Use variables instead.

GroupVar

Deprecated (since 19.15.0). Use group_var instead.

Value

list(plot=ggplot, table=gtsummary, pvaltable=data.frame, data_used=tibble)

Examples

# \donttest{
data(SampleData)
data(SampleVariableTypes)

# Attach labels and factor levels for readable output
Labelled <- RevalueData(SampleData, SampleVariableTypes)$RevaluedData

# A broad biomarker panel compared between Diagnosis groups
vars <- c(
  "age", "ACE_CD143_Angiotensin_Converti", "ACTH_Adrenocorticotropic_Hormon",
  "AXL", "Adiponectin", "Alpha_1_Antichymotrypsin", "Alpha_1_Antitrypsin",
  "Alpha_1_Microglobulin", "Alpha_2_Macroglobulin", "Angiopoietin_2_ANG_2",
  "Angiotensinogen", "Apolipoprotein_A_IV", "Apolipoprotein_A1",
  "Apolipoprotein_A2", "Apolipoprotein_B", "Apolipoprotein_CI",
  "Apolipoprotein_CIII", "Apolipoprotein_D", "Apolipoprotein_E",
  "Apolipoprotein_H", "B_Lymphocyte_Chemoattractant_BL", "BMP_6",
  "Beta_2_Microglobulin", "Betacellulin", "C_Reactive_Protein", "CD40",
  "CD5L", "Calbindin", "Calcitonin", "CgA", "GRO_alpha", "MMP10", "MMP7",
  "NT_proBNP", "PAI_1", "TRAIL_R3", "VEGF", "Ab_42", "p_tau", "tau"
)

result <- Plot2GroupStats(
  Labelled,
  variables = vars,
  group_var = "Diagnosis",
  impClust = "Impaired",
  normalClust = "Control",
  label_q = 0.0001
)

# Compact y-axis labels; full results stay in result$pvaltable
result$plot + ggplot2::theme(
  axis.text.y = ggplot2::element_text(size = 6),
  plot.margin = ggplot2::margin(t = 20, r = 10, b = 10, l = 10)
)

# }