View source: R/plot_feature_persistence.R
plot_feature_persistence | R Documentation |
Create a bar plot to visualize how likely features are to persist into
the future under a solution to a project prioritization problem()
.
plot_feature_persistence( x, solution, n = 1, symbol_hjust = 0.007, return_data = FALSE )
x |
project prioritization |
solution |
|
n |
|
symbol_hjust |
|
return_data |
|
In this plot, each bar corresponds to a different feature. The length of each bar indicates the probability that a given feature will persist into the future, and the color of each bar indicates the weight for a given feature. Features that directly benefit from at least a single completely funded project with a non-zero cost are depicted with an asterisk symbol. Additionally, features that indirectly benefit from funded projects—because they are associated with partially funded projects that have non-zero costs and share actions with at least one completely funded project—are depicted with an open circle symbol.
A ggplot()
object, or a
tibble::tbl_df()
object if return_data
is
TRUE
.
# set seed for reproducibility set.seed(500) # load the ggplot2 R package to customize plots library(ggplot2) # load data data(sim_projects, sim_features, sim_actions) # build problem p <- problem(sim_projects, sim_actions, sim_features, "name", "success", "name", "cost", "name") %>% add_max_richness_objective(budget = 400) %>% add_feature_weights("weight") %>% add_binary_decisions() %>% add_heuristic_solver(n = 10) ## Not run: # solve problem s <- solve(p) # plot the first solution plot(p, s) # plot the second solution plot(p, s, n = 2) # since this function returns a ggplot2 plot object, we can customize the # appearance of the plot using standard ggplot2 commands! # for example, we can add a title plot(p, s) + ggtitle("solution") # we can also obtain the raw plotting data using return_data=TRUE plot_data <- plot(p, s, return_data = TRUE) print(plot_data) ## End(Not run)
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