ggplot.spaghetti

The goal of ggplot.spaghetti is to aid preliminary data investigation into longitudinal/time-series data through visualization. By being able to create plots using different grouping variable, the investigator can have a better idea which variables may be worthwhile to control or include in a hypothesis test. Also these images can be used to help rely to other non-statistical collaborators the trends from a mixed-effects or other type of longitudinal model.

Installation

You can install ggplot.spaghetti from github via the devtools package with:

install.packages("devtools")
devtools::install_github("williazo/ggplot.spaghetti")

Technical Notes

• Data needs to be in long format with each row representing one observation per ID
• Variable used to specify time in the function must be numeric
• To control the visibility of individual trajectories use alpha=[0, 1]
• Plots can be slow for large data with many individuals

Example

This is an example using the Orthodont data set from the nlme package. Children were measured at 8, 10, 12, and 14 years to determine the distance from the pituitary to the pterygomaxillary fissure in millimeters. We can group these time measurements by Gender, and I have also created another binary variable, Race, to highlight the ability to look at multiple grouping variables at the same time.

Data

Reading in the dataset and creating a random variable to represent a binary racial cateogry as White vs. Non-White.

library(ggplot.spaghetti)
library(nlme)
data("Orthodont")
Orthodont = data.frame(Orthodont, Race = rep(ifelse(rbinom(n = 27, size = 1, prob = 0.5)==0, "White", "Non-White"), each = 4))
attach(Orthodont)

Graphing group trends in a single plot

In order to look at trends for a categorical variable with overall trends plotted within a single image specify only the group option as shown below.

ortho_plot_group <- ggplot_spaghetti(y = distance, id = Subject, time = age,
                               alpha = 0.3, group = Sex, method = "lm")+
  xlab("Age (yrs.)")+
  ylab("Distance")+
  scale_color_grey(name = "Gender", start = 0.0, end = 0.5)+
  scale_linetype_manual(name = "Gender", values = c("dashed", "solid"))
ortho_plot_group

Graphing group trends across facetted display

In order to look at trends for categorical variables with overall trends plotted in separate plots specify only the wrap option as shown below.

#specifying just wrap
ortho_plot_wrap <- ggplot_spaghetti(y = distance, id = Subject, time = age,
                               alpha = 0.3, wrap = Race, method = "loess")+
  xlab("Age (yrs.)")+
  ylab("Distance")+
  scale_color_grey(name = "Race", start = 0.0, end = 0.5)+
  scale_linetype_manual(name = "Race", values = c("dashed", "solid"))
ortho_plot_wrap

Graphing two-way group trends

In order to look at trends for a potential interaction between two categorical variables with overall trends specify both the group and wrap option as shown below.

#specifying both group and wrap
ortho_plot <- ggplot_spaghetti(y = distance, id = Subject, time = age,
                               alpha = 0.3, group = Sex, wrap = Race,
                               method = "glm")+
  xlab("Age (yrs.)")+
  ylab("Distance")+
  scale_color_grey(name = "Gender", start = 0.0, end = 0.5)+
  scale_linetype_manual(name = "Race", values = c("dashed", "solid"))
ortho_plot