APCtools (version 1.0.4)

plot_densityMatrix: Create a matrix of density plots

Description

This function creates a matrix of individual density plots (i.e., a ridgeline matrix) or boxplots (for metric variables) or of individual barplots (for categorical variables). The age, period or cohort information can each either be plotted on the x-axis or the y-axis.

Usage

plot_densityMatrix(
  dat,
  y_var,
  dimensions = c("period", "age"),
  age_groups = NULL,
  period_groups = NULL,
  cohort_groups = NULL,
  plot_type = "density",
  highlight_diagonals = NULL,
  y_var_cat_breaks = NULL,
  y_var_cat_labels = NULL,
  weights_var = NULL,
  log_scale = FALSE,
  legend_title = NULL,
  ...
)

Value

ggplot object

Arguments

dat

Dataset with columns period and age and the main variable specified through argument y_var.

y_var

Character name of the main variable to be plotted.

dimensions

Character vector specifying the two APC dimensions that should be visualized along the x-axis and y-axis. Defaults to c("period","age").

age_groups, period_groups, cohort_groups

Each a list. Either containing purely scalar values or with each element specifying the two borders of one row or column in the density matrix. E.g., if the period should be visualized in decade columns from 1980 to 2009, specify period_groups = list(c(1980,1989), c(1990,1999), c(2000,2009)). The list can be named to specify labels for the categories. Only the two arguments must be passed that were specified by the dimensions argument.

plot_type

One of c("density","boxplot"). Only used if the y_var column is metric.

highlight_diagonals

Optional list to define diagonals in the density that should be highlighted with different colors. Each list element should be a numeric vector stating the index of the diagonals (counted from the top left) that should be highlighted in the same color. If the list is named, the names are used as legend labels.

y_var_cat_breaks

Optional numeric vector of breaks to categorize y_var based on calling function cut. Only used to highlight the categories based on different colors. And only used if the y_var column is numeric.

y_var_cat_labels

Optional character vector for the names of the categories that were defined based on y_var_cat_breaks. The length of this vector must be one shorter than length(y_var_cat_breaks). Only used if the y_var column is numeric.

weights_var

Optional character name of a weights variable used to project the results in the sample to some population.

log_scale

Indicator if the main variable should be log10 transformed. Only used if the y_var column is numeric. Defaults to FALSE.

legend_title

Optional plot annotation.

...

Additional arguments passed to plot_density.

Author

Alexander Bauer , Maximilian Weigert

References

Weigert, M., Bauer, A., Gernert, J., Karl, M., Nalmpatian, A., Küchenhoff, H., and Schmude, J. (2021). Semiparametric APC analysis of destination choice patterns: Using generalized additive models to quantify the impact of age, period, and cohort on travel distances. Tourism Economics. doi:10.1177/1354816620987198.

Examples

library(APCtools)

# define categorizations for the main trip distance
dist_cat_breaks <- c(1,500,1000,2000,6000,100000)
dist_cat_labels <- c("< 500 km","500 - 1,000 km", "1,000 - 2,000 km",
                     "2,000 - 6,000 km", "> 6,000 km")

age_groups    <- list(c(80,89),c(70,79),c(60,69),c(50,59),c(40,49),c(30,39),c(20,29))
period_groups <- list(c(1970,1979),c(1980,1989),c(1990,1999),c(2000,2009),c(2010,2019))
cohort_groups <- list(c(1980,1989),c(1970,1979),c(1960,1969),c(1950,1959),c(1940,1949),
                      c(1930,1939),c(1920,1929))

plot_densityMatrix(dat              = travel,
                   y_var            = "mainTrip_distance",
                   age_groups       = age_groups,
                   period_groups    = period_groups,
                   log_scale        = TRUE)

# \donttest{
# highlight two cohorts
plot_densityMatrix(dat                 = travel,
                   y_var               = "mainTrip_distance",
                   age_groups          = age_groups,
                   period_groups       = period_groups,
                   highlight_diagonals = list(8, 10),
                   log_scale           = TRUE)

# also mark different distance categories
plot_densityMatrix(dat              = travel,
                   y_var            = "mainTrip_distance",
                   age_groups       = age_groups,
                   period_groups    = period_groups,
                   log_scale        = TRUE,
                   y_var_cat_breaks = dist_cat_breaks,
                   y_var_cat_labels = dist_cat_labels,
                   highlight_diagonals = list(8, 10),
                   legend_title     = "Distance category")

# flexibly assign the APC dimensions to the x-axis and y-axis
plot_densityMatrix(dat              = travel,
                   y_var            = "mainTrip_distance",
                   dimensions       = c("period","cohort"),
                   period_groups    = period_groups,
                   cohort_groups    = cohort_groups,
                   log_scale        = TRUE,
                   y_var_cat_breaks = dist_cat_breaks,
                   y_var_cat_labels = dist_cat_labels,
                   legend_title     = "Distance category")

# use boxplots instead of densities
plot_densityMatrix(dat           = travel,
                   y_var         = "mainTrip_distance",
                   plot_type     = "boxplot",
                   age_groups    = age_groups,
                   period_groups = period_groups,
                   log_scale     = TRUE,
                   highlight_diagonals = list(8, 10))

# plot categorical variables instead of metric ones
plot_densityMatrix(dat                 = travel,
                   y_var               = "household_size",
                   age_groups          = age_groups,
                   period_groups       = period_groups,
                   highlight_diagonals = list(8, 10))
# }