It’s important to understand the data. For longitudinal analyses it may be helpful visualise the individual trajectories. A more detailed overview of the rational behind this be found in Ghisletta and McArdle (2012) or Chapter 2 in Grimm, Ram, and Estabrook (2017). Most basic (and advanced) plots can be done relatively easily using already existing R packages. THe plotting functions of the
lcsm package actually build on function from other packages. I’ll show some examples illustrating how to visualise longitudinal data using the R package
ggplot2 (Wickham 2016). The function
plot_trajectories() of the
lcsm R package builds on
ggplot2 to make it a little easier to visualize individual trajectories.
I’ll show how to visualise 6 repeated measurements from the data set
data_bi_lcsm from this package.
When working with repeated measures I like to create a vector of the variables so I don’t have to type the names again and again, but this really needed and everything would also work without doing this.
The repeated measurements need to be in “long” format when using
ggplot2 for plotting. Note that this is different to the data structure needed for
lavaan, so the same data can’t just be used for plotting without restructuring -
lavaan expects the data to be in “wide” format.
I’ll show how to transform the data anyway because the plotting function
plot_trajectories() is limited and other plots may be more appropriate. Fortunately, this is a relatively straightforward to do, my favourite function to do this is
pivot_longer() from the
tidyr package. Next, to get the correct order of repeated measures the time variable needs to be ordered in the correct order. R know how to order numbers but only if the variable is actually numeric. If you’re using variable names that also include letters R might get confused and get the order wrong. Imagine you have four repeated measurements, week 1 to 3 and week 10 and the following variable names:
w10. R will get this order wrong and thinks that
w1 is followed by
w10 and then
w3. To avoid this there are a couple options, I usually do one of the two:
factor()function from base R
# Create long data set data_long <- data_bi_lcsm %>% select("id", all_of(x_var_list)) %>% # Pivot data long pivot_longer(cols = all_of(x_var_list), names_to = "time", values_to = "value") %>% mutate( # Extract number from time variable time = str_extract(time, "\\d+"), # At the moment the numbers in the time are 'characters' # So here it gets transformed to a numeric value time = factor(as.numeric(time)) )
These data manipulations are necessary for plotting longitudinal data in R and the
plot_trajectories() function from this package is doing this transformation automatically in the background.
# Create violin plot with outliers in colour blue # Also add boxplot ggplot(data_long, aes(time, value)) + geom_violin() + geom_boxplot(width = 0.1, outlier.colour = "blue") + theme_classic() #> Warning: Removed 154 rows containing non-finite values (stat_ydensity). #> Warning: Removed 154 rows containing non-finite values (stat_boxplot).
While the violin plot focuses on more on the overall distribution, the following plots highlight the individual trajectories for each case in the data. Longitudinal data can be visualised using the
plot_trajectories() function from the
lcsm package. Here only 1.8% of the data is visualised using the argument
random_sample_frac = 0.018. Only consecutive measures are connected by lines as specified in
connect_missing = FALSE.
# Create longitudinal plot for construct x # Select ransom 1.8% of the sample plot_trajectories(data = data_bi_lcsm, id_var = "id", var_list = x_var_list, xlab = "Time", ylab = "Value", connect_missing = FALSE, random_sample_frac = 0.018, title_n = TRUE) #> Warning: Removed 1 row(s) containing missing values (geom_path). #> Warning: Removed 4 rows containing missing values (geom_point).
# Create plot for construct x # Add facet_wrap() function from ggplot2 plot_trajectories(data = data_bi_lcsm, id_var = "id", var_list = x_var_list, xlab = "Time", ylab = "Value", connect_missing = F, random_sample_frac = 0.018, title_n = T) + facet_wrap(~id) #> Warning: Removed 1 row(s) containing missing values (geom_path). #> Warning: Removed 4 rows containing missing values (geom_point).
Allen, Micah, Davide Poggiali, Kirstie Whitaker, Tom Rhys Marshall, and Rogier A. Kievit. 2019. “Raincloud Plots: A Multi-Platform Tool for Robust Data Visualization.” Wellcome Open Research 4 (April): 63. https://doi.org/10.12688/wellcomeopenres.15191.1.
Ghisletta, Paolo, and John J. McArdle. 2012. “Latent Curve Models and Latent Change Score Models Estimated in R.” Structural Equation Modeling: A Multidisciplinary Journal 19 (4): 651–82. https://doi.org/10.1080/10705511.2012.713275.
Grimm, Kevin J., Nilam Ram, and Ryne Estabrook. 2017. Growth Modeling - Structural Equation and Multilevel Modeling Approaches. New York: The Guilford Press.
Langen, J. van. 2020. Open-Visualizations in R and Python. https://doi.org/http://doi.org/10.5281/zenodo.3715576.
Wickham, Hadley. 2016. Ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York. https://ggplot2.tidyverse.org.