Plot individual trajectories

Plot individual trajectories of selected cases using ggplot. This function can be combined with a filter command to explore the trajectories of individual or groups of cases. Further ggplot2 components can be added using + following this function.

Usage

plot_sg_trajectories(
  data,
  id_var,
  var_list,
  select_id_list = NULL,
  select_n = NULL,
  show_id = TRUE,
  show_legend = TRUE,
  legend_title = "ID",
  id_label_size = 2,
  connect_missing = TRUE,
  colour = c("viridis", "ggplot", "grey"),
  viridis_option = c("D", "A", "B", "C"),
  viridis_begin = 0,
  viridis_end = 1,
  line_alpha = 1,
  point_alpha = 1,
  xlab = "X",
  ylab = "Y",
  scale_x_num = FALSE,
  scale_x_num_start = 1,
  apaish = TRUE,
  ...
)

Arguments

data

Dataset in wide format.

id_var

String, specifying ID variable.

var_list

Vector, specifying variable names to be plotted in sequential order.

select_id_list

Vector, specifying case IDs to be plotted.

select_n

Numeric, specifying number of randomly selected cases to be plotted.

show_id

Logical, specifying whether or not to show ID variables inside the plot near the first measurement point.

show_legend

Logical, specifying whether or not a legend of all IDs.

legend_title

String, specifying the title of legend, by default the variable name of id_var will be shown.

id_label_size

Numeric, specifying the size of the ID label, if show_id = TRUE.

connect_missing

Logical, specifying whether to connect points across missing values.

colour

String, specifying the discrete colour palette to be used.

viridis_option

String, specifying the colour option for discrete viridis palette, if colour = "viridis". See scale_fill_viridis_d for more details.

viridis_begin

Numeric, specifying hue between 0 and 1 at which the viridis colormap begins, if colour = "viridis". See scale_fill_viridis_d for more details.

viridis_end

Numeric, specifying hue between 0 and 1 at which the viridis colormap ends, if colour = "viridis". See scale_fill_viridis_d for more details.

line_alpha

Numeric, specifying alpha (transparency) of lines.

point_alpha

Numeric, specifying alpha (transparency) of points.

xlab

String for x axis label.

ylab

String for y axis label.

scale_x_num

Logical, if TRUE print sequential numbers starting from 1 as x axis labels, if FALSE use variable names.

scale_x_num_start

Numeric, specifying the starting value of the x axis, if scale_x_num = TRUE.

apaish

Logical, if TRUE aligns plot with APA guidelines.

...

Further arguments to be passed on to geom_label_repel.

Value

ggplot2 object

Examples

# Plot individual trajectories of IDs 2, 4, 5, and 9
plot_sg_trajectories(data = sgdata,
                     id_var = "id",
                     select_id_list = c("2", "4", "5", "9"),
                     var_list = c("bdi_s1", "bdi_s2", "bdi_s3", "bdi_s4",
                                  "bdi_s5", "bdi_s6", "bdi_s7", "bdi_s8",
                                  "bdi_s9", "bdi_s10", "bdi_s11", "bdi_s12"),
                     show_id = TRUE,
                     id_label_size = 4,
                     label.padding = .2,
                     show_legend = FALSE,
                     colour = "viridis",
                     viridis_option = "D",
                     viridis_begin = 0,
                     viridis_end = .8,
                     connect_missing = FALSE,
                     scale_x_num = TRUE,
                     scale_x_num_start = 1,
                     apaish = TRUE,
                     xlab = "Session",
                     ylab = "BDI")
#> Warning: Removed 3 rows containing missing values (`geom_point()`).
#> Warning: Removed 3 rows containing missing values (`geom_label_repel()`).


# Create byperson dataset to use for plotting
byperson <- create_byperson(data = sgdata,
                            sg_crit1_cutoff = 7,
                            id_var_name = "id",
                            tx_start_var_name = "bdi_s1",
                            tx_end_var_name = "bdi_s12",
                            sg_var_list = c("bdi_s1", "bdi_s2", "bdi_s3",
                                            "bdi_s4", "bdi_s5", "bdi_s6",
                                            "bdi_s7", "bdi_s8", "bdi_s9",
                                            "bdi_s10", "bdi_s11", "bdi_s12"),
                            sg_measure_name = "bdi")
#> First, second, and third sudden gains criteria were applied.
#> The critical value for the third criterion was adjusted for missingness.
#> The first gain/loss was selected in case of multiple gains/losses.

# First, filter byperson dataset to only include cases with more than one sudden gain
# Next, plot BDI trajectory of 3 randomly selected cases with with more than one sudden gain
byperson %>%
  dplyr::filter(sg_freq_byperson > 1) %>%
    plot_sg_trajectories(id_var = "id_sg",
                         var_list = c("bdi_s1", "bdi_s2", "bdi_s3", "bdi_s4",
                                      "bdi_s5", "bdi_s6", "bdi_s7", "bdi_s8",
                                      "bdi_s9", "bdi_s10", "bdi_s11", "bdi_s12"),
                         select_n = 3,
                         show_id = TRUE,
                         show_legend = TRUE,
                         scale_x_num = TRUE,
                         scale_x_num_start = 1,
                         xlab = "Session",
                         ylab = "BDI")