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

control_chart(d, measure, x, group1, group2, center_line = mean, sigmas = 3,
  title = NULL, catpion = NULL, font_size = 11, print = TRUE)

Arguments

d

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

measure

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

x

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

group1

Optional grouping variable to be panelled horizontally (quoted)

group2

Optional grouping variable to be panelled vertically (quoted)

center_line

Function used to calculate central tendency. Defaults to mean

sigmas

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

title

Title in upper-left

catpion

Caption in lower-right

font_size

Base font size; text elements will be scaled to this

print

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.

Value

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

Examples

d <- tibble::data_frame( 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)) # Minimal arguments are the data and the column to put on the y-axis. # If x is not provided, observations will be plotted in order of the rows control_chart(d, "count")
# Specify categorical variables for group1 and/or group2 to get a separate # panel for each category control_chart(d, "count", group1 = "day", group2 = "person")
# In addition to printing or writing the plot to file, control_chart # returns the plot as a ggplot2 obejct, which you can then further customize library(ggplot2) my_chart <- control_chart(d, "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))