Package works

AssumptionPlotter/AssumptionPlotter.Rproj

@@ -18,4 +18,4 @@ StripTrailingWhitespace: Yes
 BuildType: Package
 PackageUseDevtools: Yes
 PackageInstallArgs: --no-multiarch --with-keep.source
-PackageRoxygenize: rd,collate,namespace
+PackageRoxygenize: rd,collate,namespace,vignette

AssumptionPlotter/DESCRIPTION

@@ -1,28 +1,39 @@
 Package: AssumptionPlotter
 Type: Package
-Title: Assumption Plotter
+Title: AssumptionPlotter
 Version: 0.1.0
 Authors@R: c(
     person(
       "Emma", "Akrong",
-      email = "emma.akrong@student.uva.nl",
+      email = "emma.akrong@gmail.com",
       role = c("aut", "cre")
     )
   )
-Description: This package attempt to provide an intuitive visualization of whether
-    the user's dataset meets the assumptions of statistical models, as well as 
-    whether a statistical model is expected to capture what the user wants 
-    the model to capture.  
+Description: This package creates an app that allows for visual inspection of 
+    EMA/ESM data. The purpose is to give EMA/ESM researchers a nice overview of 
+    what their data looks like, and possibly guide decisions on what statistical 
+    model to use. It was developed with the datasets from openESM in mind, and 
+    can currently plot built-in datasets, the users own data, and data from 
+    openESM accessed through the link to download the tsv file in its dedicated 
+    Zenodo page.
 License: 
 Encoding: UTF-8
 LazyData: true
 RoxygenNote: 7.3.2
 Config/testthat/edition: 3
 Imports: 
+    bslib,
+    dplyr,
+    DT,
     ggplot2,
+    grDevices,
     knitr,
     quarto,
+    readr,
+    sass,
+    shiny,
     testthat,
+    tidyr,
     usethis
 VignetteBuilder: quarto
 Depends: 

AssumptionPlotter/NAMESPACE

@@ -1,9 +1,22 @@
 # Generated by roxygen2: do not edit by hand
 
-export(data_log_plot)
+export(assumption_plot)
+export(clean_df)
+export(pie_bar_chart)
+export(rank_participants)
 export(run_plotter)
-export(simulate_logistic)
+import(bslib)
+import(dplyr)
 import(ggplot2)
+import(grDevices)
+import(knitr)
+import(quarto)
+import(sass)
 import(shiny)
 import(testthat)
+import(tidyr)
 import(usethis)
+importFrom(DT,DTOutput)
+importFrom(DT,renderDT)
+importFrom(readr,read_csv)
+importFrom(readr,read_tsv)

AssumptionPlotter/R/assumption_plot.R

@@ -0,0 +1,256 @@
+#' AssumptionPlotter Plot
+#'
+#' @details
+#' This function creates the plots in the Plots tab of AssumptionPlotter
+#' @param df Selected dataset.
+#' @param participant Participant to be plotted.
+#' @param variables All variables to be plotted.
+#' @param expected_days Expected days of the study
+#' @param beeps_per_day Expected beeps per day
+#' @param include_day Option to include day labels.
+#' @param include_day_line Option to include day lines.
+#' @param impute Method for imputing NA's.
+#' @param add_trend Include a regression line showing the trend in the data.
+#' @param trend_type Type of trend line to be passed to geom_smooth().
+#' @param theme_choice Decide what theme the plot should have.
+#' @param palette Chooses either no palette or custom.
+#' @param palette_options Palette options. Any of grDevices::hcl.pals().
+#' @param text_font What text font the plot should have.
+#' @param axis_text_size Adjust the text size of the plot's axes.
+#' @param legend_text_size Adjust the text size of the legend.
+#' @return Interactive plot in AssumptionPlotter.
+#' @import ggplot2
+#' @import dplyr
+#' @import tidyr
+#' @import grDevices
+#' @export
+#' @examples
+#' \dontrun{
+#' assumption_plot(
+#'   df,
+#'   participant,
+#'   variables,
+#'   expected_days,
+#'   beeps_per_day,
+#'   include_day = TRUE,
+#'   include_day_line = TRUE,
+#'   impute = c("none", "mean", "mode"),
+#'   add_trend = FALSE,
+#'   trend_type = c("lm", "loess"),
+#'   theme_choice = c("classic", "minimal", "bw", "dark"),
+#'   palette = c("none", "custom"),
+#'   palette_option = "Reds",
+#'   text_font = c("sans", "serif", "mono"),
+#'   axis_text_size = 12,
+#'   legend_text_size = 12
+#' )
+#' }
+#'
+
+assumption_plot <- function(
+    df,
+    participant,
+    variables,
+    expected_days,
+    beeps_per_day,
+    include_day = TRUE,
+    include_day_line = TRUE,
+    impute = "none",
+    add_trend = FALSE,
+    trend_type = "lm",
+    theme_choice = "classic",
+    palette = "none",
+    palette_option = "Reds",
+    text_font = "sans",
+    axis_text_size = 12,
+    legend_text_size = 12){
+
+  # Create error message for when all variables are deselected
+
+  if(length(variables)==0){
+    stop("The plot cannot render when no variables are selected")
+  }
+
+  # Subset data
+  ## In the future it would be cool to implement the option to plot multiple
+  ## participants at once (so you can compare them).
+  df <- df %>%
+    filter(id == participant)
+
+
+  # Keep relevant variables
+  keep_cols <- c(
+    "id",
+    "day",
+    "beep",
+    "missing",
+    variables
+  )
+
+  df <- df[,keep_cols]
+
+
+  # Specify imputation method (per variable)
+  if(impute!="none"){
+
+    for(v in variables){
+
+      if(impute=="mean"){
+
+        df[[v]][is.na(df[[v]])] <-mean(df[[v]],na.rm=TRUE)
+      }
+
+      if(impute == "mode"){
+
+        mode_val <- names(sort(table(df[[v]]), decreasing = TRUE))[1]
+
+        if(is.numeric(df[[v]])) {
+          mode_val <- as.numeric(mode_val)
+        }
+
+        df[[v]][is.na(df[[v]])] <- mode_val
+      }
+
+    }
+
+  }
+
+
+  # Ensure that all variables are numeric
+  df[variables] <- lapply(df[variables], as.numeric)
+
+
+  # Create plotting index
+  df <- df %>%
+    mutate(
+      plot_x = (day - 1) * beeps_per_day + beep
+    ) %>%
+    arrange(plot_x)
+
+
+  # Make df long format (required for plotting with ggplot)
+  long_df <- df %>%
+    pivot_longer(
+      cols = -c(id, day, beep, missing, plot_x),
+      names_to = "Variables", # Risky name since name is same as argument
+      values_to = "value"
+    )
+
+
+  # Create location of vertical day lines on the x-axis
+  day_lines <- seq(beeps_per_day+.5, # Make line appear between last and first day
+                  max(long_df$plot_x),
+                  by = beeps_per_day)
+
+  # Create location of days
+  ## Note that this is not ideal currently. Might make sense to define max days
+  ## and beeps by participant. Whether to plot predetermined expected days and
+  ## beeps for all participants or a specific one could be plot argument.
+
+  day_labels <- data.frame(
+    day = 1:expected_days,
+    x = (0:(expected_days - 1)) * beeps_per_day +
+      (beeps_per_day + 1) / 2,
+    label = paste("Day", 1:expected_days)
+  )
+
+
+  # Initialize plot
+  p <- ggplot2::ggplot(long_df, aes(x = plot_x, y = value, color = Variables))+
+    geom_line()+
+    geom_point()+
+    ylab("Value")+
+    xlab("Time")
+
+
+  # Option to include day labels
+  if(include_day){
+    p <- p +
+      geom_label(
+        data = day_labels,
+        aes(x = x, y = Inf, label = label),
+        inherit.aes = FALSE,
+        vjust = 1.5,
+        size = 4,
+        alpha = .8
+      )
+  }
+
+
+  # Option to include lines separating days
+  if(include_day_line){
+    p <- p +
+      geom_vline(xintercept = day_lines, alpha = .15, linetype = "dashed")
+  }
+
+  # Option to add a trend line
+  if(add_trend){
+
+    p <- p +
+      geom_smooth(method = trend_type,
+                  se = FALSE)
+  }
+
+
+  # Create x-axis tick labels
+  breaks <- sort(unique(long_df$plot_x))
+
+  labels <-rep(1:beeps_per_day, length.out = length(breaks))
+
+  ## Edit x-axis tick labels
+  p <- p +
+    scale_x_continuous(breaks = breaks, labels = labels)
+
+
+  # Ensure that y-lim is large enough to show all data points of all variables
+  p <- p +
+    coord_cartesian(ylim = c(
+      min(long_df$value, na.rm = TRUE),
+      max(long_df$value, na.rm = TRUE)
+    ))
+
+
+  # Switch themes
+  p <- switch(theme_choice,
+              minimal = p + theme_minimal(base_family = text_font),
+              classic = p + theme_classic(base_family = text_font),
+              bw = p + theme_bw(base_family = text_font),
+              dark = p + theme_dark(base_family = text_font),
+              p)
+
+
+  # Switch the colors of the plot
+
+  ## Get number of variables
+  nr_var <- length(variables)
+
+  p <- switch(palette,
+              none = p + scale_color_hue(),
+              custom = p + scale_color_manual(values =
+                                                grDevices::hcl.colors(nr_var, palette_option,
+                                                                      rev=TRUE)),
+              p)
+
+
+  # Edit font sizes
+
+  p <- p +
+    theme(
+      text = element_text(size = axis_text_size),
+      axis.title = element_text(size = axis_text_size+2),
+      axis.text = element_text(size = axis_text_size),
+      legend.title = element_text(size = legend_text_size+2),
+      legend.text = element_text(size = legend_text_size)
+    )
+
+
+
+  # Return plot
+  return(p)
+
+}
+
+
+
+
+