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.

```
# Create a vector of variable names
x_var_list <- c("x1", "x2", "x3", "x4", "x5", "x6")
# Or simply use the paste function and R might work too
paste0("x", 1:6)
#> [1] "x1" "x2" "x3" "x4" "x5" "x6"
```

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: `w1`

, `w2`

, `w3`

,
`w10`

. R will get this order wrong and thinks that
`w1`

is followed by `w10`

and then `w2`

and `w3`

. To avoid this there are a couple options, I usually
do one of the two:

- Create a factor with the original variable names using the in build
`factor()`

function from base R - Extracting the number from the string and then creating a factor (see example below)

```
# 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()`).
```

Similar visualisations that communicate the data in a more open way have been developed for example by Allen et al. (2019) and Langen (2020).

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 containing missing values (`geom_line()`).
#> 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 containing missing values (`geom_line()`).
#> 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/https://doi.org/10.5281/zenodo.3715576.

Wickham, Hadley. 2016. *Ggplot2: Elegant Graphics for Data
Analysis*. Springer-Verlag New York. https://ggplot2.tidyverse.org.