diff --git a/NAMESPACE b/NAMESPACE
index cc0068e0..b76432de 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -73,6 +73,8 @@ S3method(plot_acf_diagnostics,data.frame)
 S3method(plot_acf_diagnostics,grouped_df)
 S3method(plot_anomalies,data.frame)
 S3method(plot_anomalies,grouped_df)
+S3method(plot_anomalies_decomp,data.frame)
+S3method(plot_anomalies_decomp,grouped_df)
 S3method(plot_anomaly_diagnostics,data.frame)
 S3method(plot_anomaly_diagnostics,grouped_df)
 S3method(plot_seasonal_diagnostics,data.frame)
@@ -287,6 +289,7 @@ export(parse_date2)
 export(parse_datetime2)
 export(plot_acf_diagnostics)
 export(plot_anomalies)
+export(plot_anomalies_decomp)
 export(plot_anomaly_diagnostics)
 export(plot_seasonal_diagnostics)
 export(plot_stl_diagnostics)
diff --git a/R/anomalize-plotting.R b/R/anomalize-plotting.R
index 05ad8c0b..c8085ad7 100644
--- a/R/anomalize-plotting.R
+++ b/R/anomalize-plotting.R
@@ -5,7 +5,7 @@
 #' 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`
+#' @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
 #' @param .facet_vars One or more grouping columns that broken out into `ggplot2` facets.
 #'  These can be selected using `tidyselect()` helpers (e.g `contains()`).
@@ -356,3 +356,260 @@ 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.
+#'
+#' @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
+#' @param .facet_vars One or more grouping columns that broken out into `ggplot2` facets.
+#'  These can be selected using `tidyselect()` helpers (e.g `contains()`).
+#' @param .facet_ncol Number of facet columns.
+#' @param .facet_nrow Number of facet rows (only used for `.trelliscope = TRUE`)
+#' @param .facet_scales Control facet x & y-axis ranges. Options include "fixed", "free", "free_y", "free_x"
+#' @param .facet_dir The direction of faceting ("h" for horizontal, "v" for vertical). Default is "h".
+#' @param .facet_collapse Multiple facets included on one facet strip instead of
+#'  multiple facet strips.
+#' @param .facet_collapse_sep The separator used for collapsing facets.
+#' @param .facet_strip_remove Whether or not to remove the strip and text label for each facet.
+#' @param .line_color Line color.
+#' @param .line_size Line size.
+#' @param .line_type Line type.
+#' @param .line_alpha Line alpha (opacity). Range: (0, 1).
+#' @param .anom_color Color for the anomaly dots
+#' @param .anom_alpha Opacity for the anomaly dots. Range: (0, 1).
+#' @param .anom_size Size for the anomaly dots
+#' @param .ribbon_fill Fill color for the acceptable range
+#' @param .ribbon_alpha Fill opacity for the acceptable range. Range: (0, 1).
+#' @param .legend_show Toggles on/off the Legend
+#' @param .title Plot title.
+#' @param .x_lab Plot x-axis label
+#' @param .y_lab Plot y-axis label
+#' @param .color_lab Plot label for the color legend
+#' @param .interactive If TRUE, returns a `plotly` interactive plot.
+#'  If FALSE, returns a static `ggplot2` plot.
+#' @param .trelliscope Returns either a normal plot or a trelliscopejs plot (great for many time series)
+#'  Must have `trelliscopejs` installed.
+#' @param .trelliscope_params Pass parameters to the `trelliscopejs::facet_trelliscope()` function as a `list()`.
+#'  The only parameters that cannot be passed are:
+#'  - `ncol`: use `.facet_ncol`
+#'  - `nrow`: use `.facet_nrow`
+#'  - `scales`: use `facet_scales`
+#'  - `as_plotly`: use `.interactive`
+#'
+#'
+#' @return A `plotly` or `ggplot2` visualization
+#'
+#'
+#' @examples
+#' library(dplyr)
+#'
+#' walmart_sales_weekly %>%
+#'     filter(id %in% c("1_1", "1_3")) %>%
+#'     group_by(id) %>%
+#'     anomalize(Date, Weekly_Sales, .message = FALSE) %>%
+#'     plot_anomalies_decomp(Date, .interactive = FALSE)
+#'
+#' @name plot_anomalies
+#' @export
+plot_anomalies_decomp <- function(
+        .data,
+        .date_var,
+
+        .facet_vars = NULL,
+
+        .facet_scales = "free",
+
+        .line_color = "#2c3e50",
+        .line_size = 0.5,
+        .line_type = 1,
+        .line_alpha = 1,
+
+        .title = "Anomaly Decomposition Plot",
+        .x_lab = "",
+        .y_lab = "",
+        .interactive = TRUE
+) {
+
+    date_var_expr <- rlang::enquo(.date_var)
+
+    if (!is.data.frame(.data)) {
+        rlang::abort(".data is not a data-frame or tibble. Please supply a data.frame or tibble.")
+    }
+    if (rlang::quo_is_missing(date_var_expr)) {
+        rlang::abort(".date_var is missing. Please supply a date or date-time column.")
+    }
+
+    column_names <- names(.data)
+    check_names  <- c("observed", "season", "trend", "remainder") %in% column_names
+    if (!all(check_names)) stop('Error in plot_anomalies_decomp(): column names are missing. Run `anomalize()` and make sure: observed, remainder, anomaly, recomposed_l1, and recomposed_l2 are present', call. = FALSE)
+
+    UseMethod("plot_anomalies_decomp", .data)
+
+}
+
+#' @export
+plot_anomalies_decomp.data.frame <- function(
+    .data,
+    .date_var,
+
+    .facet_vars = NULL,
+
+    .facet_scales = "free",
+
+    .line_color = "#2c3e50",
+    .line_size = 0.5,
+    .line_type = 1,
+    .line_alpha = 1,
+
+    .title = "Anomaly Decomposition Plot",
+    .x_lab = "",
+    .y_lab = "",
+    .interactive = TRUE
+) {
+
+    # ---- FORMAT DATA ----
+
+    date_var_expr <- rlang::enquo(.date_var)
+
+    data_formatted <- .data
+    feature_set <- c("observed", "season", "trend", "remainder")
+
+    date_var_expr <- rlang::enquo(.date_var)
+    facets_expr   <- rlang::enquo(.facet_vars)
+
+    data_formatted      <- tibble::as_tibble(.data)
+    .facet_collapse     <- TRUE
+    .facet_collapse_sep <- " "
+
+    # Facet Names
+    facets_expr <- rlang::syms(names(tidyselect::eval_select(facets_expr, .data)))
+
+    # FACET SETUP ----
+    facet_names <- data_formatted %>% dplyr::select(!!! facets_expr) %>% colnames()
+
+    if (length(facet_names) > 0) {
+        # Handle facets
+        data_formatted <- data_formatted %>%
+            dplyr::ungroup() %>%
+            dplyr::mutate(.facets_collapsed = stringr::str_c(!!! rlang::syms(facet_names),
+                                                             sep = .facet_collapse_sep)) %>%
+            dplyr::mutate(.facets_collapsed = forcats::as_factor(.facets_collapsed)) %>%
+            dplyr::select(-(!!! rlang::syms(facet_names))) %>%
+            dplyr::group_by(.facets_collapsed)
+
+        facet_names <- ".facets_collapsed"
+    }
+
+    data_formatted <- data_formatted %>%
+        dplyr::ungroup() %>%
+        tidyr::pivot_longer(cols = c(!!! rlang::syms(feature_set)),
+                            names_to = ".group", values_to = ".group_value") %>%
+        dplyr::mutate(.group = factor(.group, levels = feature_set))
+
+    # data_formatted
+
+    # ---- VISUALIZATION ----
+
+    g <- data_formatted %>%
+        ggplot2::ggplot(ggplot2::aes(!! date_var_expr, .group_value)) +
+        ggplot2::labs(x = .x_lab, y = .y_lab, title = .title)
+
+    # Add line
+    g <- g +
+        ggplot2::geom_line(
+            color     = .line_color,
+            linewidth = .line_size,
+            linetype  = .line_type,
+            alpha     = .line_alpha
+        )
+
+    # Add facets
+    if (length(facet_names) == 0) {
+        facet_ncol <- 1
+    } else {
+        facet_ncol <- data_formatted %>%
+            dplyr::distinct(dplyr::pick(dplyr::all_of(facet_names))) %>%
+            nrow()
+    }
+
+    facet_groups <- stringr::str_c(facet_names, collapse = " + ")
+    if (facet_groups == "") facet_groups <- "."
+
+    facet_formula <- stats::as.formula(paste0(".group ~ ", facet_groups))
+
+    g <- g + ggplot2::facet_wrap(facet_formula, ncol = facet_ncol, scales = .facet_scales)
+
+    # Add theme
+    g <- g + theme_tq()
+
+    # Convert to interactive if selected
+    if (.interactive) {
+        p <- plotly::ggplotly(g)
+        return(p)
+    } else {
+        return(g)
+    }
+}
+
+#' @export
+plot_anomalies_decomp.grouped_df <- function(
+    .data,
+    .date_var,
+
+    .facet_vars = NULL,
+
+    .facet_scales = "free",
+
+    .line_color = "#2c3e50",
+    .line_size = 0.5,
+    .line_type = 1,
+    .line_alpha = 1,
+
+    .title = "Anomaly Decomposition Plot",
+    .x_lab = "",
+    .y_lab = "",
+    .interactive = TRUE
+) {
+
+    # Tidy Eval Setup
+    group_names   <- dplyr::group_vars(.data)
+    facets_expr   <- rlang::enquos(.facet_vars)
+
+    # Checks
+    facet_names <- .data %>% dplyr::ungroup() %>% dplyr::select(!!! facets_expr) %>% colnames()
+    if (length(facet_names) > 0) message("plot_anomalies_decomp(...): Groups are previously detected. Grouping by: ",
+                                         stringr::str_c(group_names, collapse = ", "))
+
+    # ---- DATA SETUP ----
+
+    # Ungroup Data
+    data_formatted <- .data %>% dplyr::ungroup()
+
+    # ---- PLOT SETUP ----
+    g <- plot_anomalies_decomp.data.frame(
+        .data               = data_formatted,
+        .date_var           = !! rlang::enquo(.date_var),
+
+        .facet_vars         = !! enquo(group_names),
+
+        .facet_scales       = .facet_scales,
+        .line_color         = .line_color,
+        .line_size          = .line_size,
+        .line_type          = .line_type,
+        .line_alpha         = .line_alpha,
+
+        .title              = .title,
+        .x_lab              = .x_lab,
+        .y_lab              = .y_lab,
+        .interactive        = .interactive
+    )
+
+    return(g)
+
+
+}
+
diff --git a/man/plot_anomalies.Rd b/man/plot_anomalies.Rd
index 3de5ec5e..1871ef1f 100644
--- a/man/plot_anomalies.Rd
+++ b/man/plot_anomalies.Rd
@@ -2,6 +2,7 @@
 % Please edit documentation in R/anomalize-plotting.R
 \name{plot_anomalies}
 \alias{plot_anomalies}
+\alias{plot_anomalies_decomp}
 \title{Visualize Anomalies for One or More Time Series}
 \usage{
 plot_anomalies(
@@ -33,9 +34,24 @@ plot_anomalies(
   .trelliscope = FALSE,
   .trelliscope_params = list()
 )
+
+plot_anomalies_decomp(
+  .data,
+  .date_var,
+  .facet_vars = NULL,
+  .facet_scales = "free",
+  .line_color = "#2c3e50",
+  .line_size = 0.5,
+  .line_type = 1,
+  .line_alpha = 1,
+  .title = "Anomaly Decomposition Plot",
+  .x_lab = "",
+  .y_lab = "",
+  .interactive = TRUE
+)
 }
 \arguments{
-\item{.data}{A \code{tibble} or \code{data.frame} that has been anomalized by \code{anomalize}}
+\item{.data}{A \code{tibble} or \code{data.frame} that has been anomalized by \code{anomalize()}}
 
 \item{.date_var}{A column containing either date or date-time values}
 
@@ -101,11 +117,16 @@ The only parameters that cannot be passed are:
 }}
 }
 \value{
+A \code{plotly} or \code{ggplot2} visualization
+
 A \code{plotly} or \code{ggplot2} visualization
 }
 \description{
 An interactive and scalable function for visualizing anomalies in time series data.
 Plots are available in interactive \code{plotly} (default) and static \code{ggplot2} format.
+
+An interactive and scalable function for visualizing anomaly decomposition in time series data.
+Plots are available in interactive \code{plotly} (default) and static \code{ggplot2} format.
 }
 \examples{
 library(dplyr)
@@ -116,4 +137,12 @@ walmart_sales_weekly \%>\%
     anomalize(Date, Weekly_Sales) \%>\%
     plot_anomalies(Date, .facet_ncol = 2, .ribbon_alpha = 0.25, .interactive = FALSE)
 
+library(dplyr)
+
+walmart_sales_weekly \%>\%
+    filter(id \%in\% c("1_1", "1_3")) \%>\%
+    group_by(id) \%>\%
+    anomalize(Date, Weekly_Sales) \%>\%
+    plot_anomalies_decomp(Date, .interactive = FALSE)
+
 }