healthyR.ai (version 0.1.0)

hai_control_chart: Create a control chart

Description

Create a control chart, aka Shewhart chart: https://en.wikipedia.org/wiki/Control_chart.

Usage

hai_control_chart(
  .data,
  .value_col,
  .x_col,
  .center_line = mean,
  .std_dev = 3,
  .plt_title = NULL,
  .plt_catpion = NULL,
  .plt_font_size = 11,
  .print_plot = TRUE
)

Value

Generally called for the side effect of printing the control chart. Invisibly, returns a ggplot object for further customization.

Arguments

.data

data frame or a path to a csv file that will be read in

.value_col

variable of interest mapped to y-axis (quoted, ie as a string)

.x_col

variable to go on the x-axis, often a time variable. If unspecified row indices will be used (quoted)

.center_line

Function used to calculate central tendency. Defaults to mean

.std_dev

Number of standard deviations above and below the central tendency to call a point influenced by "special cause variation." Defaults to 3

.plt_title

Plot title

.plt_catpion

Plot caption

.plt_font_size

Font size; text elements will be scaled to this

.print_plot

Print the plot? Default = TRUE. Set to FALSE if you want to assign the plot to a variable for further modification, as in the last example.

Author

Steven P. Sanderson II, MPH

Details

Control charts, also known as Shewhart charts (after Walter A. Shewhart) or process-behavior charts, are a statistical process control tool used to determine if a manufacturing or business process is in a state of control. It is more appropriate to say that the control charts are the graphical device for Statistical Process Monitoring (SPM). Traditional control charts are mostly designed to monitor process parameters when underlying form of the process distributions are known. However, more advanced techniques are available in the 21st century where incoming data streaming can-be monitored even without any knowledge of the underlying process distributions. Distribution-free control charts are becoming increasingly popular.

Examples

data_tbl <- tibble::tibble(
  day = sample(
    c("Monday", "Tuesday", "Wednesday", "Thursday", "Friday"),
    100, TRUE
  ),
  person = sample(c("Tom", "Jane", "Alex"), 100, TRUE),
  count = rbinom(100, 20, ifelse(day == "Friday", .5, .2)),
  date = Sys.Date() - sample.int(100)
)

hai_control_chart(.data = data_tbl, .value_col = count, .x_col = date)

# In addition to printing or writing the plot to file, hai_control_chart
# returns the plot as a ggplot2 object, which you can then further customize

library(ggplot2)
my_chart <- hai_control_chart(data_tbl, count, date)
my_chart +
  ylab("Number of Adverse Events") +
  scale_x_date(name = "Week of ... ", date_breaks = "week") +
  theme(axis.text.x = element_text(angle = -90, vjust = 0.5, hjust = 1))