You can add some annotations to some coordinates or label data points. Conceptually, an annotation supplies metadata for the plot: that is, it provides additional information about the data being displayed. There is also a hjust which operates on the horizontal plane. The geomtext and geomlabel functions allows adding text or labels, respectively, to plots created with ggplot2. You can play around with the vjust value (eg try -1, 0, 1) to get a feel for what it is doing to the vertical position of the label.
We can now add a label for the special cause data point. We can now highlight this remarkable special cause data point which is clearly a false signal also known as special cause variation. For this, we have to specify the fontface argument within the annotate function to be equal to bold: ggp + Add bold text element to plot annotate ('text', x 4.5, y 2.2, size 5, label 'My Bold.
#Ggplot annotate how to
This example illustrates how to draw a bold text element to a ggplot2 graph in R. A nice reminder that control limits are guidelines not hard and fast tests of non-randomness. Example 1: Annotate Bold Text Element to ggplot2 Plot. Remarkably we see a point below the lower control limit even though the data are purely pseudo-random. You then add layers, scales, coords and facets with +. Geom_hline(yintercept = c(3,0,-3), linetype='dashed') + # adds the upper, mean and lower linesĪnnotate("label", x=c(35,35,35), y=c(3,0,-3), color='darkgrey', label= c("Upper control limit", "Average line", "Lower control limit"), size=3) # adds the annotations All ggplot2 plots begin with a call to ggplot (), supplying default data and aesthethic mappings, specified by aes (). Alternatively, you could use qicharts2 package to do SPC calculations and then use the generated ggplot2 object and keep following our steps. Although in practice the calculation of control limits differs from this demo, for simplicity we imply control limits and a mean as set numbers. We can now plot this as an SPC chart with lower and upper control limits set at 3 standard deviations from the mean. One of the main features of SPC charts are upper and lower control limits.
Ylim(-4,4) # increase the y-axis range to aid visualisation Now we can plot the data using ggplot function. Y <- c(y, rep(NA, 10)) # add 10 NA's to extend the plot (see later)ĭf <- tibble(x=x, y=y) # store as a tibble data frame for convenience Y <- rnorm(30, 0, 1) # generate 30 random numbers for the y-axis set.seed(2020) # set the random number seed to ensure you can replicate this example So let’s get started and generate some dummy data from a normal distribution with a mean of 0 and and a standard deviation of 1. Instead we see this as a short show and tell piece with signsposts at end of the blog. xmin, xmax, ymin and ymax can be used to specify the position of annotations and to represent rectangular areas. We won’t explain all the parameters in the annotate function.
Here is a simple example of annotting points and text on SPC plots using ggplot2 package. Christopher Reading has already published a blog on SPC Charts and you also can read more about SPC charts here or here. Statistical Process Control (SPC) charts are widely used in healthcare analytics to examine how the metric varies over time and whether this variation is abnormal. 5 ) # Geom_segment examples p + geom_segment ( aes (x = 2, y = 15, xend = 2, yend = 25 ), arrow = arrow (length = unit ( 0.5, "cm" ) ) ) p + geom_segment ( aes (x = 2, y = 15, xend = 3, yend = 15 ), arrow = arrow (length = unit ( 0.5, "cm" ) ) ) p + geom_segment ( aes (x = 5, y = 30, xend = 3.5, yend = 25 ), arrow = arrow (length = unit ( 0.5, "cm" ) ) ) # You can also use geom_segment() to recreate plot(type = "h") # from base R: counts <- as.ame ( table (x = rpois ( 100, 5 ) ) ) counts $ x <- as.numeric ( as.Annotating SPC plots using annotate with ggplot # Generate data: means and standard errors of means for prices # for each type of cut dmod <- lm ( price ~ cut, data = diamonds ) cut <- unique ( diamonds $ cut ) cuts_df <- ame ( cut, predict ( dmod, ame ( cut ), se = TRUE ) ) ggplot ( cuts_df ) + aes ( x = cut, y = fit, ymin = fit - se.fit, ymax = fit + se.fit, colour = cut ) + geom_pointrange ( ) # Using annotate p <- ggplot ( mtcars, aes (x = wt, y = mpg ) ) + geom_point ( ) p p + annotate ( "rect", xmin = 2, xmax = 3.5, ymin = 2, ymax = 25, fill = "dark grey", alpha =.
0 kommentar(er)
