fix: add pre-built vignettes to inst/doc for R CMD check

Author: HzaCode

R/calculate_ef.R

@@ -210,15 +210,14 @@ calculate_ef <- function(activities_data,
   }
 
   # --- Process Activities & Calculate EF ---
+  safe_as_numeric <- function(x) { as.numeric(rlang::`%||%`(x, 0)) }
+
   ef_data <- purrr::map_dfr(1:nrow(activities_df_filtered), function(i) {
     activity <- activities_df_filtered[i, ]
     act_type <- activity$type %||% "Unknown"
     activity_date <- activity$date
-    duration_sec <- as.numeric(activity$moving_time %||% 0)
-    avg_hr <- as.numeric(activity$average_heartrate %||% 0)
-    distance_m <- as.numeric(activity$distance %||% 0)
-    avg_power <- as.numeric(activity$average_watts %||% 0)
-    weighted_power <- as.numeric(activity$weighted_average_watts %||% 0)
+    duration_sec <- safe_as_numeric(activity$moving_time)
+    avg_hr <- safe_as_numeric(activity$average_heartrate)
     distance_m <- safe_as_numeric(activity$distance)
     avg_power <- safe_as_numeric(activity$average_watts)
     weighted_power <- safe_as_numeric(activity$weighted_average_watts)

inst/doc/advanced_features.R

@@ -0,0 +1,210 @@
+## ----setup, include = FALSE---------------------------------------------------
+knitr::opts_chunk$set(
+  collapse = TRUE,
+  comment = "#>",
+  fig.width = 7,
+  fig.height = 5
+)
+
+## ----eval=FALSE---------------------------------------------------------------
+# library(Athlytics)
+# library(dplyr)
+# 
+# # Load a single activity with stream data
+# activities <- load_local_activities("export_12345678.zip")
+# 
+# # Parse stream data from a FIT/TCX file
+# # (Assuming you have stream data - this is a conceptual example)
+# stream_data <- parse_activity_file("path/to/activity.fit")
+# 
+# # Flag quality issues
+# flagged_data <- flag_quality(
+#   stream_data,
+#   sport = "Run",
+#   hr_range = c(30, 220),           # Valid HR range
+#   pw_range = c(0, 1500),            # Valid power range
+#   max_run_speed = 7.0,              # Max speed (m/s) ≈ 2:23/km pace
+#   max_hr_jump = 10,                 # Max HR change per second
+#   max_pw_jump = 300,                # Max power change per second
+#   min_steady_minutes = 20,          # Min duration for steady-state
+#   steady_cv_threshold = 8           # CV% threshold for steady-state
+# )
+# 
+# # View quality summary
+# summary_stats <- quality_summary(flagged_data)
+# print(summary_stats)
+
+## ----eval=FALSE---------------------------------------------------------------
+# # Load activities
+# activities <- load_local_activities("export_12345678.zip")
+# 
+# # Calculate ACWR using Rolling Average (traditional)
+# acwr_ra <- calculate_acwr_ewma(
+#   activities,
+#   activity_type = "Run",
+#   method = "ra",
+#   acute_period = 7,
+#   chronic_period = 28,
+#   load_metric = "duration_mins"
+# )
+# 
+# # Calculate ACWR using EWMA
+# acwr_ewma <- calculate_acwr_ewma(
+#   activities,
+#   activity_type = "Run",
+#   method = "ewma",
+#   half_life_acute = 3.5,    # Acute load half-life (days)
+#   half_life_chronic = 14,   # Chronic load half-life (days)
+#   load_metric = "duration_mins"
+# )
+# 
+# # Compare the two methods
+# library(ggplot2)
+# ggplot() +
+#   geom_line(data = acwr_ra, aes(x = date, y = acwr_smooth),
+#             color = "blue", size = 1, linetype = "solid") +
+#   geom_line(data = acwr_ewma, aes(x = date, y = acwr_smooth),
+#             color = "red", size = 1, linetype = "dashed") +
+#   labs(title = "ACWR: Rolling Average vs EWMA",
+#        subtitle = "Blue = RA (7:28d) | Red = EWMA (3.5:14d half-life)",
+#        x = "Date", y = "ACWR") +
+#   theme_minimal()
+
+## ----eval=FALSE---------------------------------------------------------------
+# # Calculate EWMA with 95% confidence bands
+# acwr_ewma_ci <- calculate_acwr_ewma(
+#   activities,
+#   activity_type = "Run",
+#   method = "ewma",
+#   half_life_acute = 3.5,
+#   half_life_chronic = 14,
+#   load_metric = "duration_mins",
+#   ci = TRUE,                # Enable confidence intervals
+#   B = 200,                  # Bootstrap iterations
+#   block_len = 7,            # Block length (days) for bootstrap
+#   conf_level = 0.95         # 95% CI
+# )
+# 
+# # Plot with confidence bands
+# ggplot(acwr_ewma_ci, aes(x = date)) +
+#   geom_ribbon(aes(ymin = acwr_lower, ymax = acwr_upper),
+#               fill = "gray70", alpha = 0.5) +
+#   geom_line(aes(y = acwr_smooth), color = "black", size = 1) +
+#   geom_hline(yintercept = c(0.8, 1.3), linetype = "dotted", color = "green") +
+#   geom_hline(yintercept = 1.5, linetype = "dotted", color = "red") +
+#   labs(title = "ACWR with 95% Confidence Bands",
+#        subtitle = "Gray band = bootstrap confidence interval",
+#        x = "Date", y = "ACWR") +
+#   theme_minimal()
+
+## ----eval=FALSE---------------------------------------------------------------
+# # Load data for multiple athletes
+# athlete1 <- load_local_activities("athlete1_export.zip") %>%
+#   mutate(athlete_id = "athlete1", sex = "M", age_band = "25-35")
+# 
+# athlete2 <- load_local_activities("athlete2_export.zip") %>%
+#   mutate(athlete_id = "athlete2", sex = "F", age_band = "25-35")
+# 
+# athlete3 <- load_local_activities("athlete3_export.zip") %>%
+#   mutate(athlete_id = "athlete3", sex = "M", age_band = "35-45")
+# 
+# # Combine
+# cohort_data <- bind_rows(athlete1, athlete2, athlete3)
+# 
+# # Calculate ACWR for each athlete
+# cohort_acwr <- cohort_data %>%
+#   group_by(athlete_id) %>%
+#   group_modify(~ calculate_acwr_ewma(.x, method = "ewma")) %>%
+#   ungroup() %>%
+#   left_join(
+#     cohort_data %>% select(athlete_id, sex, age_band) %>% distinct(),
+#     by = "athlete_id"
+#   )
+
+## ----eval=FALSE---------------------------------------------------------------
+# # Calculate cohort reference by sport and sex
+# reference <- cohort_reference(
+#   cohort_acwr,
+#   metric = "acwr_smooth",
+#   by = c("sport", "sex"),           # Group by sport and sex
+#   probs = c(0.05, 0.25, 0.5, 0.75, 0.95),  # Percentiles
+#   min_athletes = 3                  # Minimum athletes per group
+# )
+# 
+# # View reference structure
+# head(reference)
+
+## ----eval=FALSE---------------------------------------------------------------
+# # Extract one athlete's data
+# individual <- cohort_acwr %>%
+#   filter(athlete_id == "athlete1")
+# 
+# # Plot with cohort reference
+# p <- plot_with_reference(
+#   individual = individual,
+#   reference = reference %>% filter(sex == "M"),  # Match athlete's group
+#   metric = "acwr_smooth",
+#   bands = c("p25_p75", "p05_p95", "p50")
+# )
+# 
+# print(p)
+
+## ----eval=FALSE---------------------------------------------------------------
+# library(Athlytics)
+# library(dplyr)
+# library(ggplot2)
+# 
+# # --- Step 1: Load multi-athlete cohort ---
+# cohort_files <- c("athlete1_export.zip", "athlete2_export.zip", "athlete3_export.zip")
+# cohort_data <- lapply(cohort_files, function(file) {
+#   load_local_activities(file) %>%
+#     mutate(athlete_id = tools::file_path_sans_ext(basename(file)))
+# }) %>% bind_rows()
+# 
+# # --- Step 2: Quality control (conceptual - for stream data) ---
+# # If you have stream data, flag quality before calculating metrics
+# # cohort_data <- cohort_data %>%
+# #   group_by(athlete_id) %>%
+# #   mutate(quality_score = calculate_quality_score(streams))
+# 
+# # --- Step 3: Calculate ACWR with EWMA + CI ---
+# cohort_acwr <- cohort_data %>%
+#   group_by(athlete_id) %>%
+#   group_modify(~ calculate_acwr_ewma(
+#     .x,
+#     method = "ewma",
+#     half_life_acute = 3.5,
+#     half_life_chronic = 14,
+#     load_metric = "duration_mins",
+#     ci = TRUE,
+#     B = 100  # Reduced for speed in example
+#   )) %>%
+#   ungroup()
+# 
+# # --- Step 4: Calculate cohort reference ---
+# reference <- cohort_reference(
+#   cohort_acwr,
+#   metric = "acwr_smooth",
+#   by = c("sport"),
+#   probs = c(0.05, 0.25, 0.5, 0.75, 0.95),
+#   min_athletes = 3
+# )
+# 
+# # --- Step 5: Visualize individual with confidence + cohort ---
+# individual <- cohort_acwr %>% filter(athlete_id == "athlete1")
+# 
+# p <- plot_with_reference(individual, reference, metric = "acwr_smooth") +
+#   # Add individual's confidence bands
+#   geom_ribbon(data = individual,
+#               aes(x = date, ymin = acwr_lower, ymax = acwr_upper),
+#               fill = "blue", alpha = 0.2)
+# 
+# print(p)
+# 
+# # --- Step 6: Export for statistical analysis ---
+# write.csv(cohort_acwr, "cohort_acwr_with_ci.csv", row.names = FALSE)
+# write.csv(reference, "cohort_reference.csv", row.names = FALSE)
+
+## -----------------------------------------------------------------------------
+sessionInfo()
+