plot_anomalies_decomp

R/anomalize-plotting.R

@@ -2,7 +2,7 @@
 
 #' Visualize Anomalies for One or More Time Series
 #'
-#' An interactive and scalable function for visualizing anomalies in time series data.
+#' `plot_anomalies()` is an interactive and scalable function for visualizing anomalies in time series data.
 #' Plots are available in interactive `plotly` (default) and static `ggplot2` format.
 #'
 #' @param .data A `tibble` or `data.frame` that has been anomalized by `anomalize()`
@@ -47,6 +47,7 @@
 #'
 #'
 #' @examples
+#' # Plot Anomaly
 #' library(dplyr)
 #'
 #' walmart_sales_weekly %>%
@@ -360,8 +361,8 @@ plot_anomalies.grouped_df <- function(
 # 2.0 PLOT ANOMALIES DECOMP ----
 #' Visualize Anomaly Decomposition
 #'
-#' An interactive and scalable function for visualizing anomaly decomposition in time series data.
-#' Plots are available in interactive `plotly` (default) and static `ggplot2` format.
+#' `plot_anomalies_decomp` function takes in data from the `anomalize()`
+#' function, and returns a plot of the anomaly decomposition.
 #'
 #' @param .data A `tibble` or `data.frame` that has been anomalized by `anomalize()`
 #' @param .date_var A column containing either date or date-time values
@@ -400,11 +401,8 @@ plot_anomalies.grouped_df <- function(
 #'  - `scales`: use `facet_scales`
 #'  - `as_plotly`: use `.interactive`
 #'
-#'
-#' @return A `plotly` or `ggplot2` visualization
-#'
-#'
 #' @examples
+#' # Plot Anomaly Decomposition
 #' library(dplyr)
 #'
 #' walmart_sales_weekly %>%
@@ -610,6 +608,7 @@ plot_anomalies_decomp.grouped_df <- function(
 
     return(g)
 
-
 }
 
+
+

R/anomalize.R

@@ -101,11 +101,22 @@
 #' @name anomalize
 #' @export
 #'
-anomalize <- function(.data, .date_var, .value,
-                      .frequency = "auto", .trend = "auto",
-                      .method = "stl",
-                      .iqr_alpha = 0.05, .clean_alpha = 0.75, .max_anomalies = 0.2,
-                      .message = TRUE) {
+anomalize <- function(
+        .data,
+        .date_var,
+        .value,
+
+        .frequency = "auto",
+        .trend = "auto",
+
+        method = "stl",
+
+        .iqr_alpha = 0.05,
+        .clean_alpha = 0.75,
+        .max_anomalies = 0.2,
+
+        .message = TRUE
+) {
 
     # Tidyeval Setup
     date_var_expr <- rlang::enquo(.date_var)
@@ -132,11 +143,22 @@ anomalize <- function(.data, .date_var, .value,
 
 
 #' @export
-anomalize.data.frame <- function(.data, .date_var, .value,
-                                 .frequency = "auto", .trend = "auto",
-                                 .method = "stl",
-                                 .iqr_alpha = 0.05, .clean_alpha = 0.75, .max_anomalies = 0.2,
-                                 .message = TRUE) {
+anomalize.data.frame <- function(
+    .data,
+    .date_var,
+    .value,
+
+    .frequency = "auto",
+    .trend = "auto",
+
+    method = "stl",
+
+    .iqr_alpha = 0.05,
+    .clean_alpha = 0.75,
+    .max_anomalies = 0.2,
+
+    .message = TRUE
+) {
 
     # STL Decomposition (observed, season, trend, remainder, seasadj)
     ret <- .data %>%
@@ -181,12 +203,22 @@ anomalize.data.frame <- function(.data, .date_var, .value,
 }
 
 #' @export
-anomalize.grouped_df <- function(.data, .date_var, .value,
-                                 .frequency = "auto", .trend = "auto",
-                                 .method = "stl",
-                                 .iqr_alpha = 0.05, .clean_alpha = 0.75, .max_anomalies = 0.2,
-                                 .message = TRUE) {
+anomalize.grouped_df <- function(
+        .data,
+        .date_var,
+        .value,
+
+        .frequency = "auto",
+        .trend = "auto",
+
+        method = "stl",
+
+        .iqr_alpha = 0.05,
+        .clean_alpha = 0.75,
+        .max_anomalies = 0.2,
 
+        .message = TRUE
+) {
 
     # Tidy Eval Setup
     value_expr    <- rlang::enquo(.value)

_pkgdown.yml

@@ -114,7 +114,7 @@ reference:
 - subtitle: Anomaly Detection
   contents:
   - anomalize
-  - plot_anomalies
+  - contains("plot_anomalies")
 - subtitle: Function Operations
   contents: slidify
 - subtitle: Vector Operations