add input data plot

DESCRIPTION

@@ -13,6 +13,7 @@ Imports:
     data.table,
     forcats,
     fs,
+    ggplot2,
     instantiate,
     logger,
     mosaic,
@@ -26,11 +27,11 @@ Additional_repositories:
 SystemRequirements: CmdStan (https://mc-stan.org/users/interfaces/cmdstan)
 Suggests:
     dplyr,
-    ggplot2,
     knitr,
     lubridate,
     rmarkdown,
     testthat
+    vdiffr
 VignetteBuilder: knitr
 LinkingTo: 
     cpp11

R/biokinetics.R

@@ -276,6 +276,11 @@ biokinetics <- R6::R6Class(
                             n_draws = n_draws,
                             data = private$data)
     },
+    #' @description Plot model input data with a smoothing function.
+    #' @return A ggplot2 object.
+    plot_data = function() {
+      plot_data(private$data)
+    },
     #' @description View the data that is passed to the stan model, for debugging purposes.
     #' @return A list of arguments that will be passed to the stan model.
     get_stan_data = function() {

R/plot.R

@@ -8,6 +8,7 @@
 #' @param tmax Integer. The number of time points in each simulated trajectory. Default 150.
 #' @param n_draws Integer. The number of trajectories to simulate. Default 2000.
 #' @param data Optional data.frame with columns t_since_last_exp and value. The raw data to compare to.
+#' @importFrom ggplot2 ggplot aes geom_point geom_ribbon geom_line
 plot_prior_predictive <- function(priors,
                                   tmax = 150,
                                   n_draws = 2000,
@@ -31,11 +32,9 @@ plot_prior_predictive <- function(priors,
   params_and_times[, mu := biokinetics_simulate_trajectory(t, t0, tp, ts, m1, m2, m3),
                      by = c("t", "t0", "tp", "ts", "m1", "m2", "m3")]
 
-  summary <- params_and_times %>%
-    group_by(t) %>%
-    summarise(me = quantile(mu, 0.5, names = FALSE),
-              lo = quantile(mu, 0.025, names = FALSE),
-              hi = quantile(mu, 0.975, names = FALSE))
+  summary <- params_and_times[, .(me = stats::quantile(mu, 0.5, names = FALSE),
+                                  lo = stats::quantile(mu, 0.025, names = FALSE),
+                                  hi = stats::quantile(mu, 0.975, names = FALSE)), by = t]
 
   plot <- ggplot(summary) +
     geom_line(aes(x = t, y = me)) +
@@ -46,3 +45,13 @@ plot_prior_predictive <- function(priors,
   }
   plot
 }
+
+#' @importFrom ggplot2 ggplot aes facet_wrap geom_point geom_smooth guides guide_legend
+plot_data <- function(data) {
+  validate_required_cols(data, c("t_since_last_exp", "value", "titre_type"))
+  ggplot(data) +
+    geom_point(aes(x = t_since_last_exp, y = value, colour = titre_type)) +
+    geom_smooth(aes(x = t_since_last_exp, y = value, colour = titre_type)) +
+    facet_wrap(~titre_type) +
+    guides(colour = guide_legend(title = "Titre type"))
+}