#Multiple series scatter plot ggplot2 full

You can issue colors() in R to see a full list of available color names. We have already used string names like “white” and “green” to specify colors. We can specify a specific color in R in several ways: Now we’ll use geom_line() to show the best fit line, and geom_ribbon() to show the confidence bands: ggplot(birthwt, aes(x=age, y=fit)) + # low age lwt race smoke ptl ht ui ftv bwt fit Preddata <- predict(m, interval='confidence') # get predicted values (fit) and confidence limits (lwr and upr) First, we’ll run the model and add the predicted values and the confidence limits to the original data set for plotting: # linear regression of birth weight on age

This time, we’ll create a plot of predicted values with confidence bands from a regression of birthweight on age. We’ll need values for the maximum and minium again for geom_ribbon(). Mean birthweight by race, narrower error barsĬonfidence bands work similarly. Geom_errorbar(aes(ymin=lower, ymax=upper)) Now we can plot the means by race with geom_point() and the confidence limits with geom_errorbar(): ggplot(bwt_by_race, aes(x=race, y=mean)) + Tapply(birthwt$bwt, birthwt$race, mean_cl_normal))īwt_by_race$race <- row.names(bwt_by_race) The means and confidence limits are stored in a new ame called bwt_bt_race.

#Multiple series scatter plot ggplot2 code

ymax: vertical position of upper error bar or bandįor example, the following code estimates the mean birth weight and 95% confidence interval for the mean for the three races in data set birthwt.ymin: vertical position of lower error bar or band.x: horizontal positioning of error bar or band.To use both geoms, the following aesthetics are required: To draw these, we’ll use geom_errorbar() and geom_ribbon(), repsectively. Dose: dose of phenylbiguanide in microgramsĮrror bars and confidence bands are both used to express ranges of statistics.BPchange: change in blood pressure relative to the start of the experiment.The data set contains 60 rows (5 rabbits measured 12 times) of the following 5 variables:

The purpose was to test whether blood pressure changes were dependent on activation of serotonin receptors.

Thus each rabbit’s blood pressure was measured 12 times, 6 each for treatment and control.

Change in blood pressure was measured as the outcome.

After injection of treatment or control drug, each rabbit was injected with 6 ascending doses of phenylbiguanide, which raises blood pressure as a function of its dose.

5 rabbits were each treated with both saline (control) and serotonin receptor antagonist (blocker) “MDL 72222”.

The Rabbit data set describes an experiment where: Run data(Rabbit) and then str(Rabbit) to look at the structure of Rabbit and to bring it into the RStudio Environment.

To practice using the elements of the grammar of graphics, we will begin with the idea of the what we want to display, and step-by-step, we will add to and adjust the graphic until we feel it is ready to share with an audience.įor our next graph, we will be visualizing the data in the Rabbit data set, also loaded with the MASS package. From idea to final graphic: graphing the Rabbit data set.Changing the overall look with complete themes.