v0.2.14

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: MVPapp
 Title: Model Visualization Platform
-Version: 0.2.13
+Version: 0.2.14
 Authors@R: c(
     person("Steve", "Choy", email = "[email protected]", role = c("aut", "cre")),
     person("Jin Gyu", "Kim", role = "aut"))
@@ -52,9 +52,9 @@ Suggests: readr,
 Depends: R (>= 3.5.0)
 LazyData: true
 Collate: "utils-pipe.R"
-         "ui_settings_v_0_2_13.R"
+         "ui_settings_v_0_2_14.R"
          "code_templates_v_0_2_10.R"
-         "functions_v_0_2_13.R"
+         "functions_v_0_2_14.R"
          "config.R"
          "run_mvp.R"
          "data.R"

NAMESPACE

@@ -122,6 +122,7 @@ export(dp_checkbox_name)
 export(draw_correlation_plot)
 export(eval_parse)
 export(expand_addl_ii)
+export(exposures_table)
 export(extract_matrix)
 export(extract_model_params)
 export(facet_by_label)
@@ -219,6 +220,7 @@ export(pkpd_te)
 export(placeholder_password)
 export(plot_data_with_nm)
 export(plot_iiv_data_with_nm)
+export(plot_iiv_exp_data)
 export(plot_three_data_with_nm)
 export(plot_title_label)
 export(plot_title_placeholder)
@@ -322,12 +324,14 @@ importFrom(ggplot2,geom_count)
 importFrom(ggplot2,geom_line)
 importFrom(ggplot2,geom_point)
 importFrom(ggplot2,geom_rect)
+importFrom(ggplot2,geom_text)
 importFrom(ggplot2,ggplot)
 importFrom(ggplot2,ggplot_build)
 importFrom(ggplot2,ggtitle)
 importFrom(ggplot2,label_both)
 importFrom(ggplot2,labs)
 importFrom(ggplot2,scale_color_manual)
+importFrom(ggplot2,scale_fill_manual)
 importFrom(ggplot2,scale_size_area)
 importFrom(ggplot2,scale_x_log10)
 importFrom(ggplot2,scale_y_log10)

NEWS.md

@@ -1,3 +1,13 @@
+# MVPapp 0.2.14 (2025-02-07)
+
+## Features
+
+* Additional tab for Variability plots to display box plots of exposures (#22)
+
+## Bugfixes
+
+* Minor updates to some tooltips and button placements to increase clarity.
+
 # MVPapp 0.2.13 (2025-02-04)
 
 ## Features

R/functions_v_0_2_13.R → R/functions_v_0_2_14.R

@@ -4297,4 +4297,172 @@ trim_columns <- function(data,
 
   return(data_to_plot)
 
+}
+
+#-------------------------------------------------------------------------------
+#' @name exposures_table
+#'
+#' @title Function to calculate exposures from IIV output
+#'
+#' @param input_simulated_table  a dataframe (usually created from mrgsim)
+#' @param output_conc            y variable of interest to perform summary stats calc 
+#' @param start_time             start time interval for metrics
+#' @param end_time               end time interval for metrics
+#' @param debug                  show debugging messages
+#'
+#' @returns a dataframe with additional columns of summary stats
+#' @importFrom dplyr mutate if_else ungroup select filter rename first group_by distinct summarise
+#' @export
+#-------------------------------------------------------------------------------
+
+exposures_table <- function(input_simulated_table,
+                            output_conc,
+                            start_time = NULL,
+                            end_time   = NULL,
+                            debug      = FALSE
+) {
+
+  input_simulated_table$YVARNAME <- input_simulated_table[[output_conc]]
+
+  # if (debug) {
+  #   message(start_time)
+  #   message(end_time)
+  #   tmp <- input_simulated_table %>% dplyr::filter(TIME >= start_time)
+  #   message(dplyr::glimpse(tmp))
+  #   tmp2 <- input_simulated_table %>% dplyr::filter(TIME <= start_time)
+  #   message(dplyr::glimpse(tmp2))
+  # }
+
+  metrics_table <- input_simulated_table %>% dplyr::filter(TIME >= start_time, TIME <= end_time) %>%
+    group_by(ID) %>%
+    dplyr::mutate(CMIN = min(YVARNAME, na.rm = TRUE)[1],
+                  CMAX = max(YVARNAME, na.rm = TRUE)[1], ### First element if multiple values found
+                  CAVG = mean(YVARNAME, na.rm = TRUE)
+    ) %>%
+    ungroup()
+
+  # Calculate CMAX and TMAX in a separate table
+  tmax_table <- metrics_table %>%
+    group_by(ID) %>%
+    summarise(TMIN = TIME[which.min(YVARNAME)[1]],
+              TMAX = TIME[which.max(YVARNAME)[1]]) %>% # First element if multiple values found
+    ungroup()
+
+  metrics_table <- left_join(metrics_table, tmax_table, by = "ID")
+
+  metrics_table  <- metrics_table %>%
+    group_by(ID) %>%
+    dplyr::mutate(YLAG          = dplyr::lag(YVARNAME),
+                  XLAG          = dplyr::lag(TIME),
+                  dYVAR         = (YVARNAME + YLAG) * (TIME - XLAG) * 0.5, # Area for trapezoid
+                  dYVAR         = dplyr::if_else(is.na(dYVAR), 0, dYVAR),
+                  AUC           = sum(dYVAR)) %>%
+    dplyr::ungroup()
+
+  metrics_table_id <- metrics_table %>%
+    dplyr::select(ID, CMIN, CMAX, CAVG, AUC, TMAX, TMIN) %>%
+    dplyr::distinct(ID, .keep_all = TRUE)
+  return(metrics_table_id)
+}
+
+#-------------------------------------------------------------------------------
+#' @name plot_iiv_exp_data
+#'
+#' @title Function to plot variability exposure data
+#'
+#' @param input_dataset        Input dataset of Model 1 and/or Model 2 that contains MODEL ID CMIN CAVG CMAX AUC
+#' @param yvar                 Name of Y-variable (exposure metric) to be plotted, string 
+#' @param ylab                 Optional name for yvar
+#' @param model_1_name         Optional name for Model 1
+#' @param model_2_name         Optional name for Model 2
+#' @param model_1_color        Color for Model 1
+#' @param model_2_color        Color for Model 2
+#' @param show_stats           Display texts of stats for each box plot
+#' @param title                Title for plot
+#'
+#' @returns a ggplot object
+#' @importFrom dplyr select mutate all_of summarise
+#' @importFrom ggplot2 theme_bw geom_boxplot scale_fill_manual geom_text
+#' @importFrom forcats fct_inorder
+#' @importFrom ggrepel geom_text_repel
+#' @export
+#-------------------------------------------------------------------------------
+
+plot_iiv_exp_data <- function(input_dataset,
+                              yvar = 'yvar',
+                              ylab = yvar,
+                              model_1_name = '',
+                              model_2_name = '',
+                              model_1_color = "#F8766D",
+                              model_2_color = "#7570B3",
+                              show_stats = TRUE,
+                              xlab = '',
+                              title = "") {
+
+  input_dataset$MODEL <- gsub("Model 1", model_1_name, input_dataset$MODEL)
+  input_dataset$MODEL <- gsub("Model 2", model_2_name, input_dataset$MODEL)
+  input_dataset$MODEL <- as.factor(input_dataset$MODEL) %>%
+    forcats::fct_inorder()
+
+  # Define the colors for the models
+  model_colors <- c()
+
+  # Check if "Model 1" exists in the data
+  if (model_1_name %in% input_dataset$MODEL) {
+    model_colors[model_1_name] <- model_1_color
+  }
+
+  # Check if "Model 2" exists in the data
+  if (model_2_name %in% input_dataset$MODEL) {
+    model_colors[model_2_name] <- model_2_color
+  }
+
+  p <- ggplot2::ggplot(data = input_dataset, aes(x = MODEL, y = .data[[yvar]], group = MODEL, fill = MODEL)) +
+    ggplot2::theme_bw() +
+    ggplot2::geom_boxplot(alpha = 0.5) +
+    ggplot2::scale_fill_manual(values = model_colors)
+
+  if(show_stats) {
+    # Calculate statistics for each group
+    stats_df <- input_dataset %>%
+      dplyr::group_by(MODEL) %>%
+      dplyr::summarise(maximum = max(.data[[yvar]], na.rm = TRUE) %>% round(digits = 2),
+                       upper975 = quantile(.data[[yvar]], probs = 0.975, na.rm = TRUE) %>% round(digits = 2),
+                       upper95  = quantile(.data[[yvar]], probs = 0.95, na.rm = TRUE) %>% round(digits = 2),
+                       mean     = mean(.data[[yvar]], na.rm = TRUE) %>% round(digits = 2),
+                       median   = median(.data[[yvar]], na.rm = TRUE) %>% round(digits = 2),
+                       lower025 = quantile(.data[[yvar]], probs = 0.025, na.rm = TRUE) %>% round(digits = 2),
+                       lower05  = quantile(.data[[yvar]], probs = 0.05, na.rm = TRUE) %>% round(digits = 2),
+                       minimum  = min(.data[[yvar]], na.rm = TRUE) %>% round(digits = 2)) %>%
+      dplyr::ungroup()
+
+    p <- p +
+      ggplot2::geom_text(data = stats_df, aes(x = MODEL, y = maximum, label = paste("Max:", maximum)), vjust = -1) +
+      ggplot2::geom_text(data = stats_df, aes(x = MODEL, y = upper95, label = paste("95%:", upper95)), vjust = -1) +
+      ggplot2::geom_text(data = stats_df, aes(x = MODEL, y = mean,    label = paste("Mean:", mean)), vjust = -1) +
+      ggplot2::geom_text(data = stats_df, aes(x = MODEL, y = median,  label = paste("Median:", median)), vjust = -1) +
+      ggplot2::geom_text(data = stats_df, aes(x = MODEL, y = lower05, label = paste("5%:", lower05)), vjust = -1) +
+      ggplot2::geom_text(data = stats_df, aes(x = MODEL, y = minimum, label = paste("Min:", minimum)), vjust = 1)
+
+    # p <- p + 
+    #   ggrepel::geom_text_repel(data = stats_df, aes(x = MODEL, y = maximum, label = paste("Max:", maximum))) +
+    #   ggrepel::geom_text_repel(data = stats_df, aes(x = MODEL, y = upper95, label = paste("95%:", upper95))) +
+    #   ggrepel::geom_text_repel(data = stats_df, aes(x = MODEL, y = mean,    label = paste("Mean:", mean))) +
+    #   ggrepel::geom_text_repel(data = stats_df, aes(x = MODEL, y = median,  label = paste("Median:", median))) +
+    #   ggrepel::geom_text_repel(data = stats_df, aes(x = MODEL, y = lower05, label = paste("5%:", lower05))) +
+    #   ggrepel::geom_text_repel(data = stats_df, aes(x = MODEL, y = minimum, label = paste("Min:", minimum)))      
+
+  }
+
+  if(ylab != '') {
+    p <- p + ggplot2::labs(x = "", y = ylab)
+  } else {
+    p <- p + ggplot2::labs(x = "", y = yvar)
+  }
+
+  p <- p +
+    ggplot2::ggtitle(title) +
+    ggplot2::theme(legend.position = "none")
+
+  return(p)
 }

R/ui_settings_v_0_2_13.R → R/ui_settings_v_0_2_14.R

@@ -118,7 +118,7 @@ label_lloq_colname    <- 'LLOQ Column'
 bspop_lloq_colname    <- '(Optional) Provide LLOQ/BLQ column to be plotted as dashed horizontal lines.'
 
 #' @export
-bspop_download_plot   <- 'Adjust output settings in the "Download Options" box below.'
+bspop_download_plot   <- 'Adjust output settings in the "Download Options" box below.<br><br>This button will be disabled when the plot is interactive, instead please download by using the Camera icon on the top right of the plot.'
 #' @export
 plotly_filename_label <- "Plot name"
 #' @export

README.Rmd

@@ -94,7 +94,7 @@ Uploading NONMEM-formatted datasets (.csv) and tab-delimited text files (.txt) i
 
 4.  Quick data exploration are provided by the "General Plot", "Ind. Plot", and "Corr. Plot" tabs (tip: clicking on the legend can toggle each subgroup on/off when the plot is interactive):  
 
-General plots are flexible plots that can handle continuous/continuous, continuous/discrete (Box plot option), and discrete/discrete (Box plot option) types of data. Settings from General Plots will be carried over to the "Ind. Plot", where users can explore individual profiles in more detail. Dosing information from the "EVID", "ADDL", "II", "RATE" can be inserted on each plot when the Dose Column is provided. LLOQ information can also be inserted.
+General plots are flexible plots that can handle continuous/continuous, continuous/discrete (Box plot option), and discrete/discrete (Box plot option) types of data. Settings from General Plots will be carried over to the "Ind. Plot", where users can explore individual profiles in more detail. Dosing information ("EVID", "ADDL", "II", "RATE" columns) can be inserted on each plot when the Dose Column is provided. LLOQ information can also be inserted.
 
 ![](www/facet_plot.png)
 
@@ -160,6 +160,17 @@ Both OMEGAs and SIGMAs, if it exists in the model, will be available for the use
 
 ![](www/varplot.png)
 
+#### Comparing Exposure Differences
+
+Summary of exposure metrics are readily available to be viewed, which is handy when assessing differences in models, dosing regimens, and/or demographics.\  
+
+1. Navigate to the "Exposure Box Plots" on the top right side of the page.\
+2. Select the time intervals of interest to derive metrics, which could be relevant when deriving AUCtau,ss, for example. By default the entire time range is used.
+3. Pick an exposure metric in the "Select Metric" option in the "Plotting Options" box. Currently, Cmax, Cmin, Cavg, AUC, Tmax and Tmin are supported.
+4. Labels, titles, and the option to insert more summary statistics are available to further customize the plot, allowing great "off-the-shelf" graphics to be downloaded for presentations.
+
+![](www/expplot.png)
+
 ## Extended Features and Vignettes
 
 ### Providing external models and changing default settings

README.md

@@ -165,7 +165,7 @@ General plots are flexible plots that can handle continuous/continuous,
 continuous/discrete (Box plot option), and discrete/discrete (Box plot
 option) types of data. Settings from General Plots will be carried over
 to the “Ind. Plot”, where users can explore individual profiles in more
-detail. Dosing information from the “EVID”, “ADDL”, “II”, “RATE” can be
+detail. Dosing information (“EVID”, “ADDL”, “II”, “RATE” columns) can be
 inserted on each plot when the Dose Column is provided. LLOQ information
 can also be inserted.
 
@@ -285,6 +285,26 @@ matrices has reverted to its last known state.
 
 ![](www/varplot.png)
 
+#### Comparing Exposure Differences
+
+Summary of exposure metrics are readily available to be viewed, which is
+handy when assessing differences in models, dosing regimens, and/or
+demographics. 
+
+1.  Navigate to the “Exposure Box Plots” on the top right side of the
+    page.  
+2.  Select the time intervals of interest to derive metrics, which could
+    be relevant when deriving AUCtau,ss, for example. By default the
+    entire time range is used.
+3.  Pick an exposure metric in the “Select Metric” option in the
+    “Plotting Options” box. Currently, Cmax, Cmin, Cavg, AUC, Tmax and
+    Tmin are supported.
+4.  Labels, titles, and the option to insert more summary statistics are
+    available to further customize the plot, allowing great
+    “off-the-shelf” graphics to be downloaded for presentations.
+
+![](www/expplot.png)
+
 ## Extended Features and Vignettes
 
 ### Providing external models and changing default settings