Package 'pubrplot' reference manual

Title:	Publication-Ready Plots and Statistical Visualizations
Description:	Provides functions to create high-quality, publication-ready plots for numeric and categorical data, including bar plots, violin plots, boxplots, line plots, error bars, correlation plots, linear model plots, odds ratio plots, and normality plots.
Authors:	Umar Hussain [aut, cre]
Maintainer:	Umar Hussain <[email protected]>
License:	MIT + file LICENSE
Version:	0.0.1
Built:	2026-05-15 09:38:28 UTC
Source:	https://github.com/umarhussain-git/pubrplot

Bar Plot for Categorical Data with Optional Grouping

Description

Creates a publication-quality bar plot for a categorical variable, with optional grouping by another variable. Automatically calculates counts and percentages and can display them on the bars. Also performs Chi-square or Fisher exact test if by is provided.

Usage

plot_bar(
  data,
  var,
  by = NULL,
  vjust = -0.3,
  hjust = 0.5,
  axis.label.angle = 45,
  label = NULL,
  border.color = NULL,
  label.color = "black",
  x.lab = "Group",
  y.lab = "Percentage (%)",
  fill.lab = "Variable",
  text.size = 3,
  color.bar = NULL,
  theme_fun = ggthemes::theme_stata,
  bar.width = 0.8,
  y.expand = 1.12
)
plot_bar(
  data,
  var,
  by = NULL,
  vjust = -0.3,
  hjust = 0.5,
  axis.label.angle = 45,
  label = NULL,
  border.color = NULL,
  label.color = "black",
  x.lab = "Group",
  y.lab = "Percentage (%)",
  fill.lab = "Variable",
  text.size = 3,
  color.bar = NULL,
  theme_fun = ggthemes::theme_stata,
  bar.width = 0.8,
  y.expand = 1.12
)

Arguments

data

A data frame containing the variables to plot.

var

The main categorical variable to display on the x-axis (unquoted or quoted).

by

Optional grouping variable for stacked/dodged bars (unquoted or quoted). Default is NULL.

vjust

Vertical adjustment for text labels. Default is -0.3.

hjust

Horizontal adjustment for text labels. Default is 0.5.

axis.label.angle

Angle of x-axis labels. Default is 45.

label

Optional custom labels for factor levels of var.

border.color

Optional color for bar borders. Default is NULL.

label.color

Color of the text labels on bars. Default is "black".

x.lab

Label for x-axis. Default is "Group".

y.lab

Label for y-axis. Default is "Percentage (%)".

fill.lab

Legend title for the fill variable. Default is "Variable".

text.size

Size of the text labels. Default is 3.

color.bar

Optional vector of colors for bars.

theme_fun

Theme function from ggthemes (or ggplot2) for styling. Default is ggthemes::theme_stata.

bar.width

Width of the bars. Default is 0.8.

y.expand

Factor to expand the y-axis for space above the highest bar. Default is 1.12.

Value

A ggplot2 object representing the bar plot.

Examples

# Example using CO2 dataset
plot_bar(
  CO2,
  var = "Type",
  by = "Treatment",
  fill.lab = "Plant Type",
  color.bar = c("lightblue","lightgreen"),
  border.color = "black",
  bar.width = 0.5,
  text.size = 3,
  label = c("Quebec","Mississippi")
)

# Example using diamonds dataset
plot_bar(
  ggplot2::diamonds,
  var = "cut",
  by = "color",
  y.lab = "Distribution (%)",
  fill.lab = "Cut",
  text.size = 2,
  bar.width = 0.9,
  color.bar = c("#a465db","steelblue","darkgreen","darkred","#fcba03")
)

# Simple bar plot without grouping
plot_bar(ggplot2::diamonds, var = "cut")

# Example using CO2 dataset
plot_bar(
  CO2,
  var = "Type",
  by = "Treatment",
  fill.lab = "Plant Type",
  color.bar = c("lightblue","lightgreen"),
  border.color = "black",
  bar.width = 0.5,
  text.size = 3,
  label = c("Quebec","Mississippi")
)

# Example using diamonds dataset
plot_bar(
  ggplot2::diamonds,
  var = "cut",
  by = "color",
  y.lab = "Distribution (%)",
  fill.lab = "Cut",
  text.size = 2,
  bar.width = 0.9,
  color.bar = c("#a465db","steelblue","darkgreen","darkred","#fcba03")
)

# Simple bar plot without grouping
plot_bar(ggplot2::diamonds, var = "cut")

Correlation Heatmap Plot

Description

Creates a publication-ready correlation heatmap for numeric variables in a data frame. Each tile shows the correlation coefficient, with optional significance stars.

Usage

plot_cor(
  data,
  method = "pearson",
  conf.level = 0.95,
  stars = TRUE,
  plot.title = NULL,
  var.labels = NULL
)
plot_cor(
  data,
  method = "pearson",
  conf.level = 0.95,
  stars = TRUE,
  plot.title = NULL,
  var.labels = NULL
)

Arguments

data

A data frame containing numeric variables to correlate.

method

Correlation method: "pearson", "spearman", or "kendall". Default is "pearson".

conf.level

Confidence level for correlation confidence intervals. Default is 0.95.

stars

Logical. If TRUE, adds significance stars based on p-values. Default is TRUE.

plot.title

Character string specifying the plot title. If NULL, a default title is used.

var.labels

Optional character vector of variable labels to replace column names in the plot. Must match number of numeric columns.

Value

A ggplot object showing the correlation heatmap with correlation coefficients and significance stars.

Examples

plot_cor(mtcars)
plot_cor(mtcars, var.labels = colnames(mtcars))
plot_cor(mtcars, method = "spearman", stars = FALSE)
plot_cor(mtcars)
plot_cor(mtcars, var.labels = colnames(mtcars))
plot_cor(mtcars, method = "spearman", stars = FALSE)

Plot Mean with Error Bars

Description

This function creates a line plot with points and customizable error bars (standard deviation, standard error, or confidence interval) for a numeric variable grouped by a categorical variable. Mean values can optionally be displayed above the points.

Usage

plot_errorbar(
  data,
  var,
  by,
  error = c("sd", "se", "ci"),
  err.mult = 1.5,
  point.shape = 19,
  point.size = 3,
  line.color = "blue",
  line.size = 1,
  color.point = "black",
  color.error = "black",
  show.mean = TRUE,
  text.size = 3.5,
  err.width = 0.05,
  x.lab = "Group",
  y.lab = NULL,
  title = NULL,
  rotate = FALSE,
  theme_fun = ggthemes::theme_stata
)
plot_errorbar(
  data,
  var,
  by,
  error = c("sd", "se", "ci"),
  err.mult = 1.5,
  point.shape = 19,
  point.size = 3,
  line.color = "blue",
  line.size = 1,
  color.point = "black",
  color.error = "black",
  show.mean = TRUE,
  text.size = 3.5,
  err.width = 0.05,
  x.lab = "Group",
  y.lab = NULL,
  title = NULL,
  rotate = FALSE,
  theme_fun = ggthemes::theme_stata
)

Arguments

data

A data frame containing the variables to plot.

var

A numeric variable to be summarized and plotted.

by

A grouping (categorical) variable to calculate summary statistics by.

error

Type of error to display: "sd" (standard deviation), "se" (standard error), or "ci" (95% confidence interval). Default is "sd".

err.mult

Numeric multiplier for the error bars. Useful to extend or shrink error bars. Default is 1.5.

point.shape

Shape of the points. Default is 19 (solid circle).

point.size

Size of the points. Default is 3.

line.color

Color of the connecting line. Default is "blue".

line.size

Thickness of the connecting line. Default is 1.

color.point

Color of the points. Default is "black".

color.error

Color of the error bars. Default is "black".

show.mean

Logical; if TRUE, mean values are displayed above points. Default is TRUE.

text.size

Size of the mean value text labels. Default is 3.5.

err.width

Width of the error bars (horizontal whiskers). Default is 0.05.

x.lab

Label for the x-axis. Default is "Group".

y.lab

Label for the y-axis. If NULL, uses the name of var.

title

Plot title. Default is NULL.

rotate

Logical; if TRUE, rotates x-axis labels by 45 degrees. Default is FALSE.

theme_fun

ggplot2 theme function to customize the plot appearance. Default is ggthemes::theme_stata.

Value

A ggplot2 object displaying the line plot with points and error bars.

Examples

plot_errorbar(
  data = iris,
  var = Sepal.Length,
  by = Species,
  error = "se",
  err.mult = 1,
  point.shape = 19,
  point.size = 3,
  line.color = "red",
  line.size = 0.5,
  color.point = "blue",
  color.error = "blue",
  show.mean = TRUE,
  text.size = 3,
  err.width = 0.05,
  title = "Mean Sepal Length by Species",
  rotate = TRUE
)
plot_errorbar(
  data = iris,
  var = Sepal.Length,
  by = Species,
  error = "se",
  err.mult = 1,
  point.shape = 19,
  point.size = 3,
  line.color = "red",
  line.size = 0.5,
  color.point = "blue",
  color.error = "blue",
  show.mean = TRUE,
  text.size = 3,
  err.width = 0.05,
  title = "Mean Sepal Length by Species",
  rotate = TRUE
)

Line Plot with Error Bars by Group and Time

Description

This function creates a line plot showing the mean of a numeric variable over time for different groups, with optional error bars (standard deviation, standard error, or 95% confidence interval). Multiple groups are displayed on the same plot with customizable colors, point shapes, and line thickness.

Usage

plot_line(
  data,
  var,
  time,
  group,
  error = c("sd", "se", "ci"),
  err.mult = 1.5,
  point.shape = 19,
  point.size = 3,
  line.size = 1,
  color.lines = c("red", "blue"),
  show.mean = FALSE,
  text.size = 3.5,
  err.width = 0.05,
  x.lab = "Time",
  y.lab = NULL,
  title = NULL,
  theme_fun = ggthemes::theme_stata
)
plot_line(
  data,
  var,
  time,
  group,
  error = c("sd", "se", "ci"),
  err.mult = 1.5,
  point.shape = 19,
  point.size = 3,
  line.size = 1,
  color.lines = c("red", "blue"),
  show.mean = FALSE,
  text.size = 3.5,
  err.width = 0.05,
  x.lab = "Time",
  y.lab = NULL,
  title = NULL,
  theme_fun = ggthemes::theme_stata
)

Arguments

data

A data frame containing the variables to plot.

var

A numeric variable to summarize and plot.

time

A variable representing time points (x-axis). Converted to factor if not already.

group

A grouping variable (color/line grouping) for the plot.

error

Type of error to display: "sd" (standard deviation), "se" (standard error), or "ci" (95% confidence interval). Default is "sd".

err.mult

Numeric multiplier for the error bars. Default is 1.5.

point.shape

Shape of the points. Default is 19 (solid circle).

point.size

Size of the points. Default is 3.

line.size

Thickness of the lines. Default is 1.

color.lines

Vector of colors for the lines/groups. Default is c("red", "blue").

show.mean

Logical; if TRUE, mean values can optionally be displayed above points. Default is FALSE.

text.size

Size of mean value text labels (if show.mean = TRUE). Default is 3.5.

err.width

Width of the error bars. Default is 0.05.

x.lab

Label for the x-axis. Default is "Time".

y.lab

Label for the y-axis. If NULL, uses the name of var.

title

Plot title. Default is NULL.

theme_fun

ggplot2 theme function to customize plot appearance. Default is ggthemes::theme_stata.

Value

A ggplot object displaying the line plot with optional error bars for multiple groups.

Examples

set.seed(123)
n_subj <- 10
time_points <- c("T1","T2","T3")
groups <- c("DrugA","DrugB")

df <- expand.grid(
  id = 1:n_subj,
  time = time_points,
  group = groups
)

# Arrange by group, id, time
df <- dplyr::arrange(df, group, id, time)

# Add BMI column
df <- dplyr::mutate(df,
  BMI = dplyr::case_when(
    time == "T1" & group == "DrugA" ~ 29 + stats::rnorm(dplyr::n(), 0, 0.3),
    time == "T2" & group == "DrugA" ~ 26 + stats::rnorm(dplyr::n(), 0, 0.3),
    time == "T3" & group == "DrugA" ~ 22 + stats::rnorm(dplyr::n(), 0, 0.3),
    time == "T1" & group == "DrugB" ~ 28 + stats::rnorm(dplyr::n(), 0, 0.3),
    time == "T2" & group == "DrugB" ~ 25 + stats::rnorm(dplyr::n(), 0, 0.2),
    time == "T3" & group == "DrugB" ~ 21 + stats::rnorm(dplyr::n(), 0, 0.2)
  )
)
set.seed(123)
n_subj <- 10
time_points <- c("T1","T2","T3")
groups <- c("DrugA","DrugB")

df <- expand.grid(
  id = 1:n_subj,
  time = time_points,
  group = groups
)

# Arrange by group, id, time
df <- dplyr::arrange(df, group, id, time)

# Add BMI column
df <- dplyr::mutate(df,
  BMI = dplyr::case_when(
    time == "T1" & group == "DrugA" ~ 29 + stats::rnorm(dplyr::n(), 0, 0.3),
    time == "T2" & group == "DrugA" ~ 26 + stats::rnorm(dplyr::n(), 0, 0.3),
    time == "T3" & group == "DrugA" ~ 22 + stats::rnorm(dplyr::n(), 0, 0.3),
    time == "T1" & group == "DrugB" ~ 28 + stats::rnorm(dplyr::n(), 0, 0.3),
    time == "T2" & group == "DrugB" ~ 25 + stats::rnorm(dplyr::n(), 0, 0.2),
    time == "T3" & group == "DrugB" ~ 21 + stats::rnorm(dplyr::n(), 0, 0.2)
  )
)

Plot Linear Regression Estimates with Confidence Intervals

Description

This function fits univariate and multivariate linear regression models for a given outcome and a set of predictors. It returns a ggplot showing point estimates and 95% confidence intervals for each predictor. Reference levels of factors can optionally be added, and univariate and multivariate results are plotted side by side.

Usage

plot_lm(
  data,
  outcome,
  predictors,
  label_vjust = -0.8,
  label_hjust = 0.4,
  label_size = 3.5,
  label_color = "black",
  point_color = c("steelblue", "firebrick"),
  point_shape = 15,
  ref = TRUE
)
plot_lm(
  data,
  outcome,
  predictors,
  label_vjust = -0.8,
  label_hjust = 0.4,
  label_size = 3.5,
  label_color = "black",
  point_color = c("steelblue", "firebrick"),
  point_shape = 15,
  ref = TRUE
)

Arguments

data

A data frame containing the outcome and predictor variables.

outcome

A string specifying the outcome (dependent) variable.

predictors

A character vector of predictor (independent) variables.

label_vjust

Vertical adjustment for text labels. Default is -0.8.

label_hjust

Horizontal adjustment for text labels. Default is 0.4.

label_size

Size of text labels. Default is 3.5.

label_color

Color of text labels. Default is "black".

point_color

Vector of colors for the points. Default is c("steelblue", "firebrick").

point_shape

Shape of the points. Default is 15.

ref

Logical; if TRUE, adds reference levels for factor variables. Default is TRUE.

Value

A ggplot object showing regression estimates with 95% confidence intervals for both univariate and multivariate models. @import broom

Examples

mtcars2 <- dplyr::mutate(
  mtcars,
  cyl = factor(cyl),
  am = factor(am, labels = c("Automatic", "Manual")),
  gear = factor(gear)
)

plot_lm(
  data = mtcars2,
  outcome = "mpg",
  predictors = c("cyl", "hp", "wt", "am", "gear"),
  point_shape = 18
)

plot_lm(
  data = mtcars2,
  outcome = "mpg",
  predictors = c("cyl", "hp", "wt", "am", "gear"),
  point_shape = 18
)
mtcars2 <- dplyr::mutate(
  mtcars,
  cyl = factor(cyl),
  am = factor(am, labels = c("Automatic", "Manual")),
  gear = factor(gear)
)

plot_lm(
  data = mtcars2,
  outcome = "mpg",
  predictors = c("cyl", "hp", "wt", "am", "gear"),
  point_shape = 18
)

plot_lm(
  data = mtcars2,
  outcome = "mpg",
  predictors = c("cyl", "hp", "wt", "am", "gear"),
  point_shape = 18
)

Normality Assessment Plot with Shapiro-Wilk and Kolmogorov–Smirnov Tests

Description

This function visualizes the distribution of multiple numeric variables using boxplots or histograms with overlaid normal distribution curves. It automatically selects the appropriate normality test based on sample size: the Shapiro–Wilk test is applied when sample size is ≤ 5000, while the Kolmogorov–Smirnov test is used for larger samples (> 5000). The resulting p-values are displayed directly on the plots.

Usage

plot_norm(
  data,
  vars,
  geom = c("box", "hist"),
  color_bar = "#377eb8",
  color_line = "darkred",
  xlab = NULL,
  ylab = NULL,
  bins = 20,
  label_color = "black",
  label_size = 3.5,
  label_vjust = 0,
  label_hjust = 0,
  alpha_bar = 0.5,
  sample_size = 5000,
  label_fraction = 0.05,
  position = NULL,
  p.ypos = NULL
)
plot_norm(
  data,
  vars,
  geom = c("box", "hist"),
  color_bar = "#377eb8",
  color_line = "darkred",
  xlab = NULL,
  ylab = NULL,
  bins = 20,
  label_color = "black",
  label_size = 3.5,
  label_vjust = 0,
  label_hjust = 0,
  alpha_bar = 0.5,
  sample_size = 5000,
  label_fraction = 0.05,
  position = NULL,
  p.ypos = NULL
)

Arguments

data

A data frame containing the variables to be tested and plotted.

vars

A character vector of column names (numeric variables) to be assessed for normality.

geom

Character string specifying the plot type. Options are "box" for boxplots and "hist" for histograms with normal curves.

color_bar

Fill color for boxplots or histograms.

color_line

Color of the normal distribution curve (only used for histograms).

xlab

X-axis label.

ylab

Y-axis label.

bins

Number of bins used in histograms.

label_color

Color of the normality test p-value text labels.

label_size

Numeric size of the p-value text labels.

label_vjust

Vertical justification of the p-value labels.

label_hjust

Horizontal justification of the p-value labels.

alpha_bar

Transparency level for boxplots or histogram bars.

sample_size

Maximum sample size used for the normality test. When the total sample size exceeds 5000, the Kolmogorov–Smirnov test is applied automatically.

label_fraction

Fraction of plot height used to automatically position p-value labels.

position

Optional named list of manual ⁠(x, y)⁠ positions for p-value placement per variable.

p.ypos

Optional numeric value or named list to override automatic y-positions for p-values.

Value

A ggplot object displaying the selected normality plots with test p-values.

Examples

## Load example dataset safely
data(diamonds, package = "ggplot2")
## Example 1: Boxplots with Shapiro-Wilk test (n <= 5000)
plot_norm(
  data = diamonds[1:4000, ],
  vars = c("carat", "x", "y"),
  geom = "box"
)

## Example 2: Histograms with Shapiro-Wilk test (n <= 5000)
plot_norm(
  data = diamonds[1:4000, ],
  vars = c("carat", "x", "y"),
  geom = "hist",
  bins = 20,
  p.ypos = 0.6
)

## Example 3: Kolmogorov-Smirnov test automatically applied (n > 5000)
plot_norm(
  data = diamonds[1:6000, ],
  vars = c("carat", "x"),
  geom = "hist",
  bins = 25
)

## Example 4: CO2 dataset (base R)
plot_norm(
  data = CO2,
  vars = c("uptake", "conc"),
  geom = "hist",
  bins = 3
)
## Load example dataset safely
data(diamonds, package = "ggplot2")
## Example 1: Boxplots with Shapiro-Wilk test (n <= 5000)
plot_norm(
  data = diamonds[1:4000, ],
  vars = c("carat", "x", "y"),
  geom = "box"
)

## Example 2: Histograms with Shapiro-Wilk test (n <= 5000)
plot_norm(
  data = diamonds[1:4000, ],
  vars = c("carat", "x", "y"),
  geom = "hist",
  bins = 20,
  p.ypos = 0.6
)

## Example 3: Kolmogorov-Smirnov test automatically applied (n > 5000)
plot_norm(
  data = diamonds[1:6000, ],
  vars = c("carat", "x"),
  geom = "hist",
  bins = 25
)

## Example 4: CO2 dataset (base R)
plot_norm(
  data = CO2,
  vars = c("uptake", "conc"),
  geom = "hist",
  bins = 3
)

Publication-Quality Numeric Plot with Optional Grouping and Statistical Tests

Description

Creates a publication-ready plot for numeric variables, including bar plots, violin plots, boxplots, and combinations (violin + box, violin + jitter, box + jitter). Supports error bars (SD, SE, CI), group comparisons, and automatic or specified statistical tests with optional post-hoc annotations.

Usage

plot_numeric(
  data,
  var,
  by,
  geom_type = c("bar", "violin", "box", "violin_box", "violin_jitter", "box_jitter"),
  error = c("sd", "se", "ci"),
  test.type = c("auto", "parametric", "nonparametric"),
  vjust = 0,
  rotate = FALSE,
  x.lab = "Group",
  y.lab = NULL,
  text.size = 3.5,
  color.violin = NULL,
  color.box = NULL,
  box.color = "black",
  color.jitter = "black",
  jitter.size = 1.5,
  ptext.size = 3,
  theme_fun = ggthemes::theme_stata,
  bar.width = 0.85,
  box.width = 0.2,
  show.posthoc = TRUE,
  err.mult = 1.5,
  position.p = NULL,
  jitter.width = 0.1
)
plot_numeric(
  data,
  var,
  by,
  geom_type = c("bar", "violin", "box", "violin_box", "violin_jitter", "box_jitter"),
  error = c("sd", "se", "ci"),
  test.type = c("auto", "parametric", "nonparametric"),
  vjust = 0,
  rotate = FALSE,
  x.lab = "Group",
  y.lab = NULL,
  text.size = 3.5,
  color.violin = NULL,
  color.box = NULL,
  box.color = "black",
  color.jitter = "black",
  jitter.size = 1.5,
  ptext.size = 3,
  theme_fun = ggthemes::theme_stata,
  bar.width = 0.85,
  box.width = 0.2,
  show.posthoc = TRUE,
  err.mult = 1.5,
  position.p = NULL,
  jitter.width = 0.1
)

Arguments

data

A data frame containing the variables to plot.

var

Numeric variable to plot (unquoted).

by

Optional grouping variable (unquoted) to create separate groups.

geom_type

Type of plot: "bar", "violin", "box", "violin_box", "violin_jitter", "box_jitter".

error

Type of error to display for bar plots: "sd", "se", or "ci".

test.type

Statistical test type: "auto", "parametric", or "nonparametric".

vjust

Vertical adjustment for text labels. Default is 0.

rotate

Logical, whether to rotate x-axis labels. Default is FALSE.

x.lab

Label for x-axis. Default is "Group".

y.lab

Label for y-axis. Defaults to variable name.

text.size

Size of labels above bars or violin/box plots. Default is 3.5.

color.violin

Fill color for violin plots. Can be a vector of colors per group.

color.box

Fill color for boxplots inside violins. Can be a vector of colors per group.

box.color

Outline color for boxplots. Default is "black".

color.jitter

Color of jittered points. Default is "black".

jitter.size

Size of jittered points. Default is 1.5.

ptext.size

Size of text for post-hoc annotations. Default is 3.

theme_fun

Theme function from ggthemes or ggplot2 for styling. Default is ggthemes::theme_stata.

bar.width

Width of bars for bar plots. Default is 0.85.

box.width

Width of boxplots inside violin. Default is 0.2.

show.posthoc

Logical, whether to display post-hoc test results. Default is TRUE.

err.mult

Multiplier for error bars (SD/SE/CI). Default is 1.5.

position.p

Optional vector c(x, y) to place post-hoc text manually.

jitter.width

Width of jitter for points in violin_jitter or box_jitter plots. Default is 0.1.

Value

A ggplot2 object representing the numeric variable plot.

Examples

# Violin + Box plot for iris dataset
plot_numeric(
  data = iris,
  var = Sepal.Length,
  by = Species,
  geom_type = "violin_box",
  box.width = 0.1,
  color.violin = c("#377eb8", "#ff7f00", "#4daf4a"),
  color.box = c("darkgreen", "#a65628", "#f781bf"),
  box.color = "black",
  color.jitter = "red",
  position.p = c(1,9),
  jitter.size = 2,
  ptext.size = 4,
  show.posthoc = TRUE
)

# Simple bar plot with error bars
plot_numeric(
  data = iris,
  var = Sepal.Length,
  by = Species,
  geom_type = "bar",
  error = "se"
)

# Violin plot with jitter points
plot_numeric(
  data = iris,
  var = Sepal.Length,
  by = Species,
  geom_type = "violin_jitter"
)
# Violin + Box plot for iris dataset
plot_numeric(
  data = iris,
  var = Sepal.Length,
  by = Species,
  geom_type = "violin_box",
  box.width = 0.1,
  color.violin = c("#377eb8", "#ff7f00", "#4daf4a"),
  color.box = c("darkgreen", "#a65628", "#f781bf"),
  box.color = "black",
  color.jitter = "red",
  position.p = c(1,9),
  jitter.size = 2,
  ptext.size = 4,
  show.posthoc = TRUE
)

# Simple bar plot with error bars
plot_numeric(
  data = iris,
  var = Sepal.Length,
  by = Species,
  geom_type = "bar",
  error = "se"
)

# Violin plot with jitter points
plot_numeric(
  data = iris,
  var = Sepal.Length,
  by = Species,
  geom_type = "violin_jitter"
)

Plot Odds Ratios from Logistic Regression

Description

This function fits univariate and multivariate logistic regression models and plots odds ratios with 95% confidence intervals. Reference levels can optionally be displayed.

Usage

plot_or(
  data,
  outcome,
  predictors,
  label_vjust = -0.8,
  label_hjust = 0.5,
  label_size = 3.5,
  label_color = "black",
  point_color = c("steelblue", "firebrick"),
  ref = TRUE
)
plot_or(
  data,
  outcome,
  predictors,
  label_vjust = -0.8,
  label_hjust = 0.5,
  label_size = 3.5,
  label_color = "black",
  point_color = c("steelblue", "firebrick"),
  ref = TRUE
)

Arguments

data

A data frame containing the outcome and predictors.

outcome

Name of the binary outcome variable (as string).

predictors

Vector of predictor variable names (as strings).

label_vjust

Vertical adjustment for labels (default -0.8).

label_hjust

Horizontal adjustment for labels (default 0.5).

label_size

Size of the text labels (default 3.5).

label_color

Color of the text labels (default "black").

point_color

Colors for points corresponding to univariate and multivariate models (default c("steelblue", "firebrick")).

ref

Logical, whether to show reference levels (default TRUE).

Value

A ggplot object showing odds ratios with confidence intervals.

Examples

# Load built-in infertility dataset
infert1 <- datasets::infert
infert$case <- factor(infert$case, levels = c(0,1), labels = c("Control","Infertile"))
infert$induced <- factor(infert$induced, levels = c(0,1), labels = c("No","Yes"))
infert$spontaneous <- factor(infert$spontaneous, levels = c(0,1), labels = c("No","Yes"))

# Plot with reference levels
plot_or(
  data = infert1,
  outcome = "case",
  predictors = c("parity","induced","spontaneous","age"),
  ref = TRUE
)

# Plot without reference levels
plot_or(
  data = infert1,
  outcome = "case",
  predictors = c("parity","induced","spontaneous","age"),
  ref = FALSE
)
# Load built-in infertility dataset
infert1 <- datasets::infert
infert$case <- factor(infert$case, levels = c(0,1), labels = c("Control","Infertile"))
infert$induced <- factor(infert$induced, levels = c(0,1), labels = c("No","Yes"))
infert$spontaneous <- factor(infert$spontaneous, levels = c(0,1), labels = c("No","Yes"))

# Plot with reference levels
plot_or(
  data = infert1,
  outcome = "case",
  predictors = c("parity","induced","spontaneous","age"),
  ref = TRUE
)

# Plot without reference levels
plot_or(
  data = infert1,
  outcome = "case",
  predictors = c("parity","induced","spontaneous","age"),
  ref = FALSE
)

Scatter Plot with Linear Regression and Equation Annotation

Description

This function creates a scatter plot of a numeric outcome against a numeric predictor, optionally grouped by a factor (by). A linear regression line is added with optional standard error (SE) shading, and the regression equation and R² value are displayed on the plot.

Usage

plot_scatter(
  data,
  outcome,
  predictor,
  by = NULL,
  point_color = "#377eb8",
  line_color = "#e41a1c",
  se_fill = "#e41a1c55",
  line_size = 1,
  se = TRUE,
  facet_scales = "free",
  eq_position = c(0.05, 0.95),
  ncol_by = NULL
)
plot_scatter(
  data,
  outcome,
  predictor,
  by = NULL,
  point_color = "#377eb8",
  line_color = "#e41a1c",
  se_fill = "#e41a1c55",
  line_size = 1,
  se = TRUE,
  facet_scales = "free",
  eq_position = c(0.05, 0.95),
  ncol_by = NULL
)

Arguments

data

A data frame containing the variables to plot.

outcome

Character string. Name of the numeric outcome variable.

predictor

Character string. Name of the numeric predictor variable.

by

Character string, optional. Name of a factor variable for grouping/faceting.

point_color

Color for the scatter plot points (default: "#377eb8").

line_color

Color for the regression line (default: "#e41a1c").

se_fill

Fill color for the confidence interval shading around the regression line (default: "#e41a1c55").

line_size

Numeric. Line width for the regression line (default: 1).

se

Logical. Whether to display the standard error shading around the regression line (default: TRUE).

Character. Scales argument for facet_wrap ("free", "fixed", "free_x", "free_y") (default: "free").

eq_position

Numeric vector of length 2. Relative position of regression equation on the plot: c(x_pos, y_pos) (default: c(0.05, 0.95)).

ncol_by

Numeric. Number of columns for faceting (passed to facet_wrap) (default: NULL, automatic).

Value

A ggplot2 object of the scatter plot with regression line and annotated equation.

Examples

# Basic scatter plot with regression line and equation
plot_scatter(mtcars, "mpg", "wt")

# Scatter plot grouped by cylinder
plot_scatter(mtcars, "mpg", "wt", by = "cyl",
           point_color = "blue",
           line_color = "red",
           se_fill = "#ff000055",
           line_size = 0.9,
           se = TRUE,
           eq_position = c(0.5, 0.95),
           ncol_by = 2)
# Basic scatter plot with regression line and equation
plot_scatter(mtcars, "mpg", "wt")

# Scatter plot grouped by cylinder
plot_scatter(mtcars, "mpg", "wt", by = "cyl",
           point_color = "blue",
           line_color = "red",
           se_fill = "#ff000055",
           line_size = 0.9,
           se = TRUE,
           eq_position = c(0.5, 0.95),
           ncol_by = 2)

Package 'pubrplot'

Help Index

Bar Plot for Categorical Data with Optional Grouping

Description

Usage

Arguments

Value

Examples

Correlation Heatmap Plot

Description

Usage

Arguments

Value

Examples

Plot Mean with Error Bars

Description

Usage

Arguments

Value

Examples

Line Plot with Error Bars by Group and Time

Description

Usage

Arguments

Value

Examples

Plot Linear Regression Estimates with Confidence Intervals

Description

Usage

Arguments

Value

Examples

Normality Assessment Plot with Shapiro-Wilk and Kolmogorov–Smirnov Tests

Description

Usage

Arguments

Value

Examples

Publication-Quality Numeric Plot with Optional Grouping and Statistical Tests

Description

Usage

Arguments

Value

Examples

Plot Odds Ratios from Logistic Regression

Description

Usage

Arguments

Value

Examples

Scatter Plot with Linear Regression and Equation Annotation

Description

Usage

Arguments

Value

Examples