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Visual adjustments in controlcharts are handled through
dedicated settings arguments. Each group of settings (e.g., canvas,
axes, lines) has its own argument (e.g., canvas_settings,
x_axis_settings).
To see the full list of available options for a group, you can use
the helper functions spc_default_settings('{group_name}')
or funnel_default_settings('{group_name}') (e.g.,
spc_default_settings('x_axis')).
We will use the following synthetic datasets for the examples below:
set.seed(42)
# SPC Data (Time Series)
spc_data <- data.frame(
date = seq(as.Date("2023-01-01"), by = "month", length.out = 12),
value = c(10, 12, 11, 15, 12, 11, 25, 12, 11, 10, 11, 12),
group = "A"
)
spc_data$label_text <- paste0("Val: ", spc_data$value)
# Funnel Data (Categorical)
funnel_data <- data.frame(
id = LETTERS[1:10],
num = sample(10:50, 10),
denom = sample(50:100, 10)
)The title argument allows you to add a plot title and
customize its font and position. Note that title takes a
named list of parameters.
spc(
spc_data,
keys = date,
numerators = value,
title = list(
text = "Monthly Performance",
font_size = "20px",
font_family = "Courier New",
font_weight = "bold"
)
)$static_plotThe canvas_settings argument controls the overall plot
area, such as padding. This is useful if labels or titles are being
clipped.
You can customize the X and Y axes using x_axis_settings
and y_axis_settings. Common options include toggling
visibility, rotating ticks, and forcing limits.
For SPC charts with date keys, date_settings controls
how dates are formatted on the axis and tooltips.
spc(
spc_data,
keys = date,
numerators = value,
date_settings = list(
date_format_day = "DD",
date_format_month = "Mon", # Abbreviated month
date_format_year = "YY",
date_format_delim = "-"
)
)$static_plotThe line_settings argument allows customization of the
main data lines, including targets and limits lines.
spc(
spc_data,
keys = date,
numerators = value,
line_settings = list(
colour_main = "purple",
width_main = 3,
type_main = "2 5" # Dashed line pattern
)
)$static_plotFunnel plots rely heavily on control limits. You can customize the 95% and 99% limits independently.
funnel(
funnel_data,
keys = id,
numerators = num,
denominators = denom,
line_settings = list(
colour_95 = "orange",
width_95 = 2,
type_95 = "10 0", # Solid
colour_99 = "red",
width_99 = 3
)
)$static_plotControl the appearance of data points using
scatter_settings.
funnel(
funnel_data,
keys = id,
numerators = num,
denominators = denom,
scatter_settings = list(
shape = "Diamond",
size = 8,
colour = "orange",
opacity = 0.8
)
)$static_plotFunnel charts allow displaying the group key directly on the scatter
points (instead of floating tooltips). You can customize this text using
scatter_text_* options.
funnel(
funnel_data,
keys = id,
numerators = num,
denominators = denom,
scatter_settings = list(
use_group_text = TRUE, # Enable text labels on points
scatter_text_colour = "darkblue",
scatter_text_size = 14,
scatter_text_font = "Georgia"
)
)$static_plotData labels can be customized via label_settings. You
can pass a column to the labels argument to display custom
text.
A powerful feature of controlcharts is vectorised
settings. While most settings take a single value (e.g.,
colour = "blue"), specific settings accept a vector
matching the length of your data.
This allows you to apply conditional formatting logic directly from R.
Currently, this is supported for: * scatter_settings
(Both SPC and Funnel) * line_settings (SPC Only)
In this SPC example, we color data points red if they exceed a value of 15.
# Create a color vector matching the data logic
point_colors <- ifelse(spc_data$value > 15, "red", "#E69F00")
spc(
spc_data,
keys = date,
numerators = value,
scatter_settings = list(
colour = point_colors,
size = 5
)
)$static_plotIn this Funnel example, we highlight points with a high denominator (large population).
# Logic: Highlight large denominators
funnel_cols <- ifelse(funnel_data$denom > 80, "green", "gray")
funnel(
funnel_data,
keys = id,
numerators = num,
denominators = denom,
scatter_settings = list(
colour = funnel_cols,
size = 6,
opacity = 1
)
)$static_plotYou can also color different segments of the main line. This is useful for highlighting specific time periods or phases.
# Define colors for line segments
# Note: The vector length corresponds to the points; segments connect points.
line_colors <- rep("gray", nrow(spc_data))
line_colors[5:8] <- "orange" # Highlight a middle section
spc(
spc_data,
keys = date,
numerators = value,
line_settings = list(
colour_main = line_colors,
width_main = 3
)
)$static_plotThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.