Overview

Rbearcat provides nine plot functions that wrap ggplot2 with UC themes, the official UC expanded color palette, and a consistent interface. Every function returns a ggplot object that can be further customized with standard + layers.

Function Chart type
bcat_plt_bar() Bar chart (counts, identity, or summary stats)
bcat_plt_line() Line chart
bcat_plt_point() Scatter plot
bcat_plt_area() Stacked / filled area chart
bcat_plt_hist() Histogram with optional density curve
bcat_plt_box() Box plot or violin plot
bcat_plt_coef() Coefficient (forest) plot
bcat_plt_diag() Regression diagnostic dashboard
bcat_plt_ts() Time series with decomposition and ACF/PACF 
library(Rbearcat)
library(ggplot2)
set_UC_geoms()

Bar Charts — bcat_plt_bar()

Frequency Counts

bcat_plt_bar(
  df = mpg,
  x = class,
  order = TRUE,
  title = "Vehicle Count by Class",
  x_lab = NULL, y_lab = "Count"
)

Grouped Bars with Summary Statistic

Use stat to compute a summary (mean, median, sum) of y within each group:

bcat_plt_bar(
  df = mpg,
  x = class,
  y = hwy,
  fill = factor(year),
  stat = "mean",
  position = "dodge",
  order = TRUE,
  coord_flip = TRUE,
  x_lab = NULL, y_lab = "Highway MPG",
  title = "Mean Highway MPG by Class and Year"
)

Stacked Proportions

bcat_plt_bar(
  df = mpg,
  x = class,
  fill = drv,
  position = "fill",
  y_scale = scale_y_continuous(labels = scales::percent_format()),
  title = "Drive Type Proportions by Class",
  x_lab = NULL, y_lab = NULL,
  legend_lab = "Drive"
)

Line Charts — bcat_plt_line()

Basic Line Plot

bcat_plt_line(
  df = economics,
  x = date,
  y = unemploy,
  y_scale = scale_y_continuous(labels = scales::comma_format()),
  title = "US Unemployment Over Time",
  y_lab = "Number Unemployed"
)

Multiple Series with Highlight Regions

bcat_plt_line(
  df = economics_long,
  x = date,
  y = value,
  color = variable,
  facet = vars(variable),
  facet_scale = "free_y",
  ncol = 1,
  x_highlight_min = as.Date(c("2007-12-01")),
  x_highlight_max = as.Date(c("2009-06-01")),
  title = "Economic Indicators with Recession Shading",
  x_lab = NULL, y_lab = NULL,
  legend_lab = NULL
)

Scatter Plots — bcat_plt_point()

Basic Scatter

bcat_plt_point(
  df = iris,
  x = Sepal.Length,
  y = Sepal.Width,
  title = "Sepal Width vs Length",
  x_lab = "Length", y_lab = "Width"
)

Faceted with Fit Lines

bcat_plt_point(
  df = iris,
  x = Sepal.Length,
  y = Sepal.Width,
  color = Species,
  facet = vars(Species),
  smooth = TRUE,
  method = "lm",
  nrow = 1,
  title = "By Species with Linear Fit",
  x_lab = "Length", y_lab = "Width",
  legend_lab = NULL
)

Area Charts — bcat_plt_area()

set.seed(42)
d <- data.frame(
  t = rep(0:23, each = 4),
  category = rep(LETTERS[1:4], 24),
  value = round(runif(96, 10, 50))
)

bcat_plt_area(
  df = d, x = t, y = value, fill = category,
  position = "stack",
  title = "Stacked Area Chart",
  x_lab = "Hour", y_lab = "Value",
  legend_lab = "Category"
)

Filled (100%) Area

bcat_plt_area(
  df = d, x = t, y = value, fill = category,
  position = "fill",
  title = "Proportional Area Chart",
  x_lab = "Hour", y_lab = NULL,
  legend_lab = "Category"
)

Histograms — bcat_plt_hist()

Basic Histogram

A dashed vertical line at the mean is drawn by default.

bcat_plt_hist(
  mtcars, x = mpg,
  title = "Distribution of MPG",
  x_lab = "Miles per Gallon"
)

With Density Curve

bcat_plt_hist(
  mtcars, x = mpg,
  density = TRUE,
  bins = 15,
  title = "MPG with Density Overlay",
  x_lab = "Miles per Gallon"
)

Faceted

bcat_plt_hist(
  mtcars, x = mpg,
  facet = vars(cyl),
  facet_scale = "free_x",
  title = "MPG Distribution by Cylinder Count"
)

Box Plots and Violin Plots — bcat_plt_box()

Box Plot with Jittered Points

Points are overlaid by default to show the raw data.

bcat_plt_box(
  mtcars,
  x = factor(cyl),
  y = mpg,
  title = "MPG by Cylinder Count",
  x_lab = "Cylinders", y_lab = "MPG"
)

Violin Plot

bcat_plt_box(
  mtcars,
  x = factor(cyl),
  y = mpg,
  violin = TRUE,
  title = "MPG Distribution (Violin)",
  x_lab = "Cylinders", y_lab = "MPG"
)

Ordered and Flipped

bcat_plt_box(
  mtcars,
  x = factor(gear),
  y = mpg,
  order = TRUE,
  coord_flip = TRUE,
  title = "MPG by Gear Count (Ordered)",
  x_lab = "Gears", y_lab = "MPG"
)

Coefficient Plots — bcat_plt_coef()

Visualize regression coefficients with confidence intervals.

Single Model

m1 <- lm(mpg ~ wt + hp + cyl + disp, data = mtcars)
bcat_plt_coef(m1, title = "OLS Coefficient Estimates")

Comparing Models

m2 <- lm(mpg ~ wt + hp, data = mtcars)
bcat_plt_coef(
  list("Full" = m1, "Base" = m2),
  title = "Coefficient Comparison",
  subtitle = "95% Confidence Intervals"
)

Highlighting a Coefficient

bcat_plt_coef(
  m1,
  highlight = "Wt",
  title = "Highlighting Weight"
)

Regression Diagnostics — bcat_plt_diag()

A 4-panel dashboard: Residuals vs Fitted, Q-Q, Scale-Location, and Residuals vs Leverage. Prints Breusch-Pagan, Shapiro-Wilk, and Durbin-Watson test results to the console.

m <- lm(mpg ~ wt + hp + cyl, data = mtcars)
bcat_plt_diag(m)
#> 
#> --- Diagnostic Tests ---
#> 
#> Breusch-Pagan (heteroskedasticity): stat=2.935, p=0.4017  [PASS - no evidence of heteroskedasticity]
#> Shapiro-Wilk (normality):           stat=0.935, p=0.0525  [PASS - residuals appear normal]
#> Durbin-Watson (autocorrelation):     stat=1.644, p=0.1002  [PASS - no evidence of autocorrelation]

Select Specific Panels

bcat_plt_diag(m, which = c(1, 2), tests = FALSE)

Time Series — bcat_plt_ts()

Basic Time Series

bcat_plt_ts(
  economics,
  x = date, y = unemploy,
  y_scale = scale_y_continuous(labels = scales::comma_format()),
  title = "US Unemployment",
  y_lab = "Persons Unemployed"
)

With Highlight Regions

bcat_plt_ts(
  economics,
  x = date, y = unemploy,
  y_scale = scale_y_continuous(labels = scales::comma_format()),
  x_highlight_min = as.Date("2007-12-01"),
  x_highlight_max = as.Date("2009-06-01"),
  title = "US Unemployment with Great Recession Shading"
)

STL Decomposition

bcat_plt_ts(economics, x = date, y = unemploy, decompose = TRUE)

ACF / PACF

bcat_plt_ts(economics, x = date, y = unemploy, acf = TRUE)

Common Parameters

All bcat_plt_* functions share a consistent parameter interface:

Parameter Description
df Data frame
x, y Variables mapped to axes
color / fill Grouping aesthetic
facet Facetting variable(s) wrapped in vars()
title, subtitle, caption Plot text
x_lab, y_lab Axis labels
legend_lab, legend_position, legend_hide Legend control
x_scale, y_scale Custom axis scales
x_refline, y_refline Reference lines
facet_scale "fixed", "free", "free_x", "free_y"

Every function returns a standard ggplot object, so you can add more layers:

bcat_plt_point(iris, Sepal.Length, Sepal.Width,
               title = "Adding a Custom Annotation") +
  annotate("text", x = 7, y = 4.2, label = "Outlier region",
           color = "red", fontface = "italic")