Fix plot issues and clean up code

Author: HzaCode

.gitignore

@@ -91,4 +91,8 @@ docs/
 
 # Temporary image files
 image.png
-image/
+image/
+preview_*.png
+reproduce_*.png
+reproduce_*.R
+temp_*.png

DESCRIPTION

@@ -12,7 +12,7 @@ Description: An open-source computational framework for longitudinal analysis of
     including Acute:Chronic Workload Ratio (ACWR), Efficiency Factor (EF), and 
     training load metrics. 
 License: MIT + file LICENSE
-URL: https://hezhiang.com/Athlytics/, https://github.com/HzaCode/Athlytics
+URL: https://hzacode.github.io/Athlytics/, https://github.com/HzaCode/Athlytics
 BugReports: https://github.com/HzaCode/Athlytics/issues 
 Encoding: UTF-8
 LazyData: true
@@ -25,6 +25,7 @@ Imports:
     purrr,
     readr,
     rlang (>= 0.4.0),
+    scales,
     tidyr,
     viridis,
     zoo,
@@ -38,7 +39,6 @@ Suggests:
     rmarkdown,
     testthat (>= 3.0.0),
     mockery,
-    XML,
     rStrava,
     xml2,
     FITfileR

NAMESPACE

@@ -93,6 +93,7 @@ importFrom(readr,cols)
 importFrom(readr,read_csv)
 importFrom(rlang,"%||%")
 importFrom(rlang,.data)
+importFrom(scales,pretty_breaks)
 importFrom(stats,median)
 importFrom(stats,na.omit)
 importFrom(stats,quantile)
@@ -102,6 +103,7 @@ importFrom(tidyr,drop_na)
 importFrom(tidyr,pivot_longer)
 importFrom(tidyr,pivot_wider)
 importFrom(tidyr,unnest)
+importFrom(tools,toTitleCase)
 importFrom(utils,read.csv)
 importFrom(viridis,scale_color_viridis)
 importFrom(zoo,rollapply)

R/calculate_acwr.R

@@ -150,25 +150,24 @@
 #' plot_acwr(run_acwr, highlight_zones = TRUE)
 #' 
 #' # Multi-athlete cohort analysis
-#' library(dplyr)
 #' 
 #' # Load data for multiple athletes and add athlete_id
 #' athlete1 <- load_local_activities("athlete1_export.zip") %>%
-#'   mutate(athlete_id = "athlete1")
+#'   dplyr::mutate(athlete_id = "athlete1")
 #' 
 #' athlete2 <- load_local_activities("athlete2_export.zip") %>%
-#'   mutate(athlete_id = "athlete2")
+#'   dplyr::mutate(athlete_id = "athlete2")
 #' 
 #' # Combine all athletes
-#' cohort_data <- bind_rows(athlete1, athlete2)
+#' cohort_data <- dplyr::bind_rows(athlete1, athlete2)
 #' 
 #' # Calculate ACWR for each athlete using group_modify()
 #' cohort_acwr <- cohort_data %>%
-#'   group_by(athlete_id) %>%
-#'   group_modify(~ calculate_acwr(.x, 
+#'   dplyr::group_by(athlete_id) %>%
+#'   dplyr::group_modify(~ calculate_acwr(.x, 
 #'                                  activity_type = "Run",
 #'                                  load_metric = "duration_mins")) %>%
-#'   ungroup()
+#'   dplyr::ungroup()
 #' 
 #' # View results
 #' print(cohort_acwr)

R/cohort_reference.R

@@ -265,9 +265,29 @@ add_reference_bands <- function(p,
 #' @importFrom ggplot2 ggplot aes geom_line labs theme_minimal scale_x_date
 #' @importFrom dplyr %>%
 #' @importFrom rlang %||% .data
+#' @importFrom tidyr pivot_wider
+#' @importFrom tools toTitleCase
 #' @export
 #'
 #' @examples
+#' # Simple example with fixed data
+#' individual_data <- data.frame(
+#'   date = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01", "2023-10-01")),
+#'   acwr_smooth = c(1.0, 1.2, 0.9, 1.1)
+#' )
+#' reference_data <- data.frame(
+#'   date = as.Date(c("2023-01-01", "2023-04-01", "2023-07-01", "2023-10-01")),
+#'   percentile = rep(c("p05", "p25", "p50", "p75", "p95"), 4),
+#'   value = c(0.7, 0.9, 1.1, 1.3, 1.5, 0.7, 0.9, 1.1, 1.3, 1.5, 0.7, 0.9, 1.1, 1.3, 1.5, 0.7, 0.9, 1.1, 1.3, 1.5)
+#' )
+#' 
+#' p <- plot_with_reference(
+#'   individual = individual_data,
+#'   reference = reference_data,
+#'   metric = "acwr_smooth"
+#' )
+#' print(p)
+#'
 #' \dontrun{
 #' plot_with_reference(
 #'   individual = athlete_acwr,
@@ -298,40 +318,36 @@ plot_with_reference <- function(individual,
   # Create base plot with reference bands
   p <- ggplot2::ggplot()
 
-  # Add reference bands (outermost first)
+  # Add reference bands (outermost first) - using enhanced colors
   if ("p05_p95" %in% bands && all(c("p05", "p95") %in% colnames(ref_wide))) {
     p <- p + ggplot2::geom_ribbon(
       data = ref_wide,
       ggplot2::aes(x = .data$date, ymin = .data$p05, ymax = .data$p95),
-      fill = "#3B528BFF", alpha = 0.15
-    ) +
-    ggplot2::annotate("text", x = min(ref_wide$date), y = max(ref_wide$p95, na.rm = TRUE),
-                     label = "P5-P95", hjust = 0, vjust = 1, size = 3, color = "#3B528BFF", alpha = 0.7)
+      fill = "#FFB6C1", alpha = 0.3
+    )
   }
 
   if ("p25_p75" %in% bands && all(c("p25", "p75") %in% colnames(ref_wide))) {
     p <- p + ggplot2::geom_ribbon(
       data = ref_wide,
       ggplot2::aes(x = .data$date, ymin = .data$p25, ymax = .data$p75),
-      fill = "#440154FF", alpha = 0.25
-    ) +
-    ggplot2::annotate("text", x = min(ref_wide$date), y = max(ref_wide$p75, na.rm = TRUE),
-                     label = "P25-P75", hjust = 0, vjust = 1, size = 3, color = "#440154FF", alpha = 0.7)
+      fill = "#87CEEB", alpha = 0.4
+    )
   }
 
   if ("p50" %in% bands && "p50" %in% colnames(ref_wide)) {
     p <- p + ggplot2::geom_line(
       data = ref_wide,
       ggplot2::aes(x = .data$date, y = .data$p50),
-      color = "#21908CFF", linetype = "dashed", linewidth = 0.8
+      color = "#4682B4", linetype = "dashed", linewidth = 1.5
     )
   }
 
-  # Add individual line on top
+  # Add individual line on top with enhanced visibility
   p <- p + ggplot2::geom_line(
     data = individual,
     ggplot2::aes(x = .data[[date_col]], y = .data[[metric]]),
-    color = "black", linewidth = 1.2
+    color = "#DC143C", linewidth = 2.5, alpha = 1.0
   )
 
   # Formatting
@@ -340,15 +356,23 @@ plot_with_reference <- function(individual,
   p <- p +
     ggplot2::labs(
       title = plot_title,
-      subtitle = "Black line = Individual | Shaded areas = Cohort percentiles",
+      subtitle = "Individual athlete metrics compared to cohort reference bands",
       x = "Date",
-      y = tools::toTitleCase(metric)
+      y = tools::toTitleCase(metric),
+      caption = "Red line: Individual athlete | Shaded bands: Cohort percentile ranges"
+    ) +
+    ggplot2::scale_x_date(
+      date_breaks = "3 months", 
+      labels = function(x) {
+        months <- c("Jan", "Feb", "Mar", "Apr", "May", "Jun", 
+                   "Jul", "Aug", "Sep", "Oct", "Nov", "Dec")
+        paste(months[as.integer(format(x, "%m"))], format(x, "%Y"))
+      }
     ) +
-    ggplot2::scale_x_date(date_breaks = "3 months", date_labels = "%b %Y") +
-    ggplot2::theme_minimal() +
+    theme_athlytics() +
     ggplot2::theme(
       axis.text.x = ggplot2::element_text(angle = 45, hjust = 1),
-      plot.title = ggplot2::element_text(face = "bold")
+      plot.margin = ggplot2::margin(20, 20, 20, 20)
     )
 
   return(p)

R/color_palettes.R

@@ -16,10 +16,9 @@ NULL
 #' @export
 #' @examples
 #' \dontrun{
-#' library(ggplot2)
-#' ggplot(data, aes(x, y, color = group)) +
-#'   geom_line() +
-#'   scale_color_manual(values = athlytics_palette_nature())
+#' ggplot2::ggplot(data, ggplot2::aes(x, y, color = group)) +
+#'   ggplot2::geom_line() +
+#'   ggplot2::scale_color_manual(values = athlytics_palette_nature())
 #' }
 athlytics_palette_nature <- function() {
   c(
@@ -45,10 +44,9 @@ athlytics_palette_nature <- function() {
 #' @export
 #' @examples
 #' \dontrun{
-#' library(ggplot2)
-#' ggplot(data, aes(x, y, color = group)) +
-#'   geom_line() +
-#'   scale_color_manual(values = athlytics_palette_academic())
+#' ggplot2::ggplot(data, ggplot2::aes(x, y, color = group)) +
+#'   ggplot2::geom_line() +
+#'   ggplot2::scale_color_manual(values = athlytics_palette_academic())
 #' }
 athlytics_palette_academic <- function() {
   c(
@@ -73,10 +71,9 @@ athlytics_palette_academic <- function() {
 #' @export
 #' @examples
 #' \dontrun{
-#' library(ggplot2)
-#' ggplot(data, aes(x, y, fill = category)) +
-#'   geom_bar(stat = "identity") +
-#'   scale_fill_manual(values = athlytics_palette_vibrant())
+#' ggplot2::ggplot(data, ggplot2::aes(x, y, fill = category)) +
+#'   ggplot2::geom_bar(stat = "identity") +
+#'   ggplot2::scale_fill_manual(values = athlytics_palette_vibrant())
 #' }
 athlytics_palette_vibrant <- function() {
   c(
@@ -226,62 +223,109 @@ athlytics_colors_ef <- function() {
 #' @return A ggplot2 theme object that can be added to plots
 #'
 #' @examples
-#' library(ggplot2)
 #' # Apply theme to a plot
-#' ggplot(mtcars, aes(mpg, wt)) +
-#'   geom_point() +
+#' ggplot2::ggplot(mtcars, ggplot2::aes(mpg, wt)) +
+#'   ggplot2::geom_point() +
 #'   theme_athlytics()
 #'
 #' @export
 #' @import ggplot2
-theme_athlytics <- function(base_size = 12, base_family = "") {
+theme_athlytics <- function(base_size = 13, base_family = "") {
   ggplot2::theme_minimal(base_size = base_size, base_family = base_family) +
     ggplot2::theme(
-      # Plot
+      # Plot background - clean white
+      plot.background = ggplot2::element_rect(fill = "white", color = NA),
+      panel.background = ggplot2::element_rect(fill = "white", color = NA),
+      
+      # Plot titles - modern, bold, left-aligned
       plot.title = ggplot2::element_text(
         face = "bold", 
-        size = base_size * 1.2,
+        size = base_size * 1.4,
         hjust = 0,
-        margin = ggplot2::margin(b = base_size * 0.5)
+        color = "#2c3e50",
+        margin = ggplot2::margin(b = base_size * 0.8)
       ),
       plot.subtitle = ggplot2::element_text(
-        size = base_size * 0.9,
-        color = "gray40",
+        size = base_size * 0.95,
+        color = "#7f8c8d",
         hjust = 0,
-        margin = ggplot2::margin(b = base_size * 0.5)
+        lineheight = 1.2,
+        margin = ggplot2::margin(b = base_size * 1)
       ),
       plot.caption = ggplot2::element_text(
-        size = base_size * 0.8,
-        color = "gray50",
+        size = base_size * 0.75,
+        color = "#95a5a6",
         hjust = 1,
-        margin = ggplot2::margin(t = base_size * 0.5)
+        margin = ggplot2::margin(t = base_size * 0.8)
       ),
+      plot.margin = ggplot2::margin(t = 15, r = 15, b = 15, l = 15),
 
-      # Axes
-      axis.title = ggplot2::element_text(size = base_size, face = "bold"),
-      axis.text = ggplot2::element_text(size = base_size * 0.9, color = "gray20"),
-      axis.line = ggplot2::element_line(color = "gray40", linewidth = 0.5),
+      # Axes - clean and minimal
+      axis.title.x = ggplot2::element_text(
+        size = base_size * 1.05, 
+        face = "bold",
+        color = "#34495e",
+        margin = ggplot2::margin(t = base_size * 0.6)
+      ),
+      axis.title.y = ggplot2::element_text(
+        size = base_size * 1.05, 
+        face = "bold",
+        color = "#34495e",
+        margin = ggplot2::margin(r = base_size * 0.6)
+      ),
+      axis.text.x = ggplot2::element_text(
+        size = base_size * 0.9, 
+        color = "#7f8c8d",
+        margin = ggplot2::margin(t = base_size * 0.3)
+      ),
+      axis.text.y = ggplot2::element_text(
+        size = base_size * 0.9, 
+        color = "#7f8c8d",
+        margin = ggplot2::margin(r = base_size * 0.3)
+      ),
+      axis.line = ggplot2::element_blank(),
+      axis.ticks = ggplot2::element_line(color = "#bdc3c7", linewidth = 0.3),
+      axis.ticks.length = ggplot2::unit(4, "pt"),
 
-      # Legend
+      # Legend - modern and spacious
       legend.position = "bottom",
-      legend.title = ggplot2::element_text(size = base_size, face = "bold"),
-      legend.text = ggplot2::element_text(size = base_size * 0.9),
-      legend.key.size = ggplot2::unit(1, "lines"),
+      legend.direction = "horizontal",
+      legend.title = ggplot2::element_text(
+        size = base_size * 0.95, 
+        face = "bold",
+        color = "#34495e"
+      ),
+      legend.text = ggplot2::element_text(
+        size = base_size * 0.85,
+        color = "#7f8c8d"
+      ),
+      legend.key.size = ggplot2::unit(1.2, "lines"),
+      legend.key = ggplot2::element_blank(),
+      legend.spacing.x = ggplot2::unit(0.5, "lines"),
+      legend.box.spacing = ggplot2::unit(0.5, "lines"),
+      legend.margin = ggplot2::margin(t = base_size),
 
-      # Panel
-      panel.grid.major = ggplot2::element_line(color = "gray90", linewidth = 0.3),
+      # Panel grid - subtle and elegant
+      panel.grid.major = ggplot2::element_line(
+        color = "#ecf0f1", 
+        linewidth = 0.5,
+        linetype = "solid"
+      ),
       panel.grid.minor = ggplot2::element_blank(),
       panel.border = ggplot2::element_blank(),
+      panel.spacing = ggplot2::unit(1.5, "lines"),
 
-      # Strip (for facets)
+      # Strip (for facets) - clean and modern
       strip.text = ggplot2::element_text(
-        size = base_size,
+        size = base_size * 1.05,
         face = "bold",
-        color = "gray20"
+        color = "#2c3e50",
+        margin = ggplot2::margin(b = base_size * 0.5, t = base_size * 0.5)
       ),
       strip.background = ggplot2::element_rect(
-        fill = "gray95",
-        color = NA
+        fill = "#f8f9fa",
+        color = "#dee2e6",
+        linewidth = 0.5
       )
     )
 }
@@ -296,10 +340,9 @@ theme_athlytics <- function(base_size = 12, base_family = "") {
 #' @return A ggplot2 scale object (scale_color_manual or scale_fill_manual)
 #'
 #' @examples
-#' library(ggplot2)
 #' # Apply nature palette to plot
-#' ggplot(iris, aes(Sepal.Length, Sepal.Width, color = Species)) +
-#'   geom_point() +
+#' ggplot2::ggplot(iris, ggplot2::aes(Sepal.Length, Sepal.Width, color = Species)) +
+#'   ggplot2::geom_point() +
 #'   scale_athlytics("nature", "color")
 #'
 #' @export