final commit

Author: emma-akrong

AssumptionPlotter/DESCRIPTION

@@ -1,29 +1,33 @@
 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,
-    datasets,
     dplyr,
     DT,
     ggplot2,
+    grDevices,
     knitr,
     quarto,
     readr,

AssumptionPlotter/NAMESPACE

@@ -2,12 +2,10 @@
 
 export(assumption_plot)
 export(clean_df)
-export(data_log_plot)
 export(pie_bar_chart)
+export(rank_participants)
 export(run_plotter)
-export(simulate_logistic)
 import(bslib)
-import(datasets)
 import(dplyr)
 import(ggplot2)
 import(grDevices)

AssumptionPlotter/R/plots.R AssumptionPlotter/R/assumption_plot.RAssumptionPlotter/R/plots.R renamed to AssumptionPlotter/R/assumption_plot.R

@@ -254,123 +254,3 @@ assumption_plot <- function(
 
 
 
-#' Pie or Bar Chart of Missing Data
-#'
-#' @details
-#' This function creates a piechart of all missing data points
-#' @param df Selected dataset.
-#' @param participant Participant being plotted
-#' @param type Whether it should return a pie or bar chart.
-#' @param plot_all Whether the function should generate a plot for everyone or only.
-#' @param text_font Font style.
-#' @param axis_text_size Text sixe of axes.
-#' @param legend_text_size Text size of legend.
-#' @param theme_choice The bar chart has the same theme as the assumption plot
-#' the participant
-#' @return A pie or bar chart showing the ratio of missing values for either a
-#' participant or the entire dataset.
-#' @import ggplot2
-#' @import dplyr
-#' @import tidyr
-#' @export
-#' @examples
-#' \dontrun{
-#' pie_bar_chart(
-#'   df,
-#'   participant,
-#'   type = c("pie", "bar"),
-#'   plot_all = FALSE,
-#'   text_font = c("sans", "serif", "mono"),
-#'   axis_text_size = 12,
-#'   legend_text_size = 12,
-#'   theme_choice = c("classic", "minimal", "bw", "void")
-#'   )
-#' }
-#'
-#'
-
-
-pie_bar_chart <- function(df,
-                      participant,
-                      type = "pie",
-                      plot_all = FALSE,
-                      text_font = "sans",
-                      axis_text_size = 12,
-                      legend_text_size = 12,
-                      theme_choice = "classic"){
-
-  # Subset data if we are not plotting everyone
-  if(!plot_all){
-    df <- df %>%
-      filter(id == participant)
-  }else{
-    df <- df
-  }
-
-
-  # Keep relevant variables
-  missing <- df$missing
-
-
-  # Make df
-  plot_df <- data.frame(
-    Missing = missing
-  )
-
-  plot_df <- df %>%
-    mutate(Status = ifelse(missing, "Missing", "Included")) %>%
-    dplyr::count(Status)
-
-
-  # Create different types of plots
-  if(type == "pie"){
-
-    p <-
-      ggplot(plot_df, aes(x = "", y = n, fill = Status)) +
-      geom_bar(stat = "identity", width = 1) +
-      coord_polar("y", start = 0)+
-      scale_fill_manual(values =
-                          c("Missing" = "red",
-                            "Included" = "blue"))+
-      theme_void(base_family = text_font)+
-      theme(
-        legend.title = element_text(size = legend_text_size+2),
-        legend.text = element_text(size = legend_text_size)
-      )
-  } else if(type == "bar"){
-    p <-
-      ggplot(plot_df, aes(x = Status, y = n, fill = Status)) +
-      geom_col() +
-      xlab("Data points")+
-      ylab("Count")+
-      scale_fill_manual(values =
-                          c("Missing" = "red",
-                            "Included" = "blue"))
-
-
-    # Change theme
-    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),
-                void = p + theme_void(base_family = text_font),
-                p)
-
-    # Edit font size
-    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(p)
-
-}
-
-

AssumptionPlotter/R/edit_datasets.R AssumptionPlotter/R/clean_df.RAssumptionPlotter/R/edit_datasets.R renamed to AssumptionPlotter/R/clean_df.R

@@ -4,9 +4,12 @@
 #' @import knitr
 #' @import quarto
 #' @import shiny
-#' @import datasets
 #' @import bslib
 #' @importFrom DT renderDT DTOutput
 #' @import sass
 #' @importFrom readr read_tsv read_csv
+#' @import dplyr
+#' @import tidyr
+#' @import grDevices
+
 NULL

AssumptionPlotter/R/data_log_plot.R

@@ -0,0 +1,119 @@
+#' Pie or Bar Chart of Missing Data
+#'
+#' @details
+#' This function creates a piechart of all missing data points
+#' @param df Selected dataset.
+#' @param participant Participant being plotted
+#' @param type Whether it should return a pie or bar chart.
+#' @param plot_all Whether the function should generate a plot for everyone or only.
+#' @param text_font Font style.
+#' @param axis_text_size Text sixe of axes.
+#' @param legend_text_size Text size of legend.
+#' @param theme_choice The bar chart has the same theme as the assumption plot
+#' the participant
+#' @return A pie or bar chart showing the ratio of missing values for either a
+#' participant or the entire dataset.
+#' @import ggplot2
+#' @import dplyr
+#' @import tidyr
+#' @export
+#' @examples
+#' \dontrun{
+#' pie_bar_chart(
+#'   df,
+#'   participant,
+#'   type = c("pie", "bar"),
+#'   plot_all = FALSE,
+#'   text_font = c("sans", "serif", "mono"),
+#'   axis_text_size = 12,
+#'   legend_text_size = 12,
+#'   theme_choice = c("classic", "minimal", "bw", "void")
+#'   )
+#' }
+#'
+#'
+
+
+pie_bar_chart <- function(df,
+                          participant,
+                          type = "pie",
+                          plot_all = FALSE,
+                          text_font = "sans",
+                          axis_text_size = 12,
+                          legend_text_size = 12,
+                          theme_choice = "classic"){
+
+  # Subset data if we are not plotting everyone
+  if(!plot_all){
+    df <- df %>%
+      filter(id == participant)
+  }else{
+    df <- df
+  }
+
+
+  # Keep relevant variables
+  missing <- df$missing
+
+
+  # Make df
+  plot_df <- data.frame(
+    Missing = missing
+  )
+
+  plot_df <- df %>%
+    mutate(Status = ifelse(missing, "Missing", "Included")) %>%
+    dplyr::count(Status)
+
+
+  # Create different types of plots
+  if(type == "pie"){
+
+    p <-
+      ggplot(plot_df, aes(x = "", y = n, fill = Status)) +
+      geom_bar(stat = "identity", width = 1) +
+      coord_polar("y", start = 0)+
+      scale_fill_manual(values =
+                          c("Missing" = "red",
+                            "Included" = "blue"))+
+      theme_void(base_family = text_font)+
+      theme(
+        legend.title = element_text(size = legend_text_size+2),
+        legend.text = element_text(size = legend_text_size)
+      )
+  } else if(type == "bar"){
+    p <-
+      ggplot(plot_df, aes(x = Status, y = n, fill = Status)) +
+      geom_col() +
+      xlab("Data points")+
+      ylab("Count")+
+      scale_fill_manual(values =
+                          c("Missing" = "red",
+                            "Included" = "blue"))
+
+
+    # Change theme
+    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),
+                void = p + theme_void(base_family = text_font),
+                p)
+
+    # Edit font size
+    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(p)
+
+}
+

AssumptionPlotter/R/external_packages.R

@@ -1,4 +1,18 @@
-#' @importFrom dplyr group_by summarise desc slice_head slice_tail
+#' Create table that ranks participants after number of missing values
+#'
+#' @details
+#' This function creates the table in the summary tab. It shows the participants
+#' with the most and least missing values and the sum and proportion of these
+#' missing values.
+#' @param df Data frame that has been cleaned with clean_df().
+#' @param show How many rows the table should have.
+#' @return Table showing the participants with most and least missing values.
+#' @import dplyr
+#' @export
+#' @examples
+#' \dontrun{
+#' rank_participants(menghini_2013, 5)
+#' }
 
 
 rank_participants <- function(df, show = 5){
@@ -7,8 +21,8 @@ rank_participants <- function(df, show = 5){
   df <- df[,keep_cols]
 
   df <- df %>%
-    dplyr::group_by(id) %>%
-    dplyr::summarise(
+    group_by(id) %>%
+    summarise(
       missing_sum = sum(missing, na.rm = TRUE),
       missing_prop = mean(missing, na.rm = TRUE)
     ) %>%