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# 1.0 PLOT ANOMALIES ----
#' Visualize Anomalies for One or More Time Series
#'
#' `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()`
#' @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
#' # Plot Anomalies
#' library(dplyr)
#'
#' walmart_sales_weekly %>%
#' filter(id %in% c("1_1", "1_3")) %>%
#' group_by(id) %>%
#' anomalize(Date, Weekly_Sales) %>%
#' plot_anomalies(Date, .facet_ncol = 2, .ribbon_alpha = 0.25, .interactive = FALSE)
#'
#' @name plot_anomalies
#' @export
plot_anomalies <- function(
.data,
.date_var,
.facet_vars = NULL,
.facet_ncol = 1,
.facet_nrow = 1,
.facet_scales = "free",
.facet_dir = "h",
.facet_collapse = FALSE,
.facet_collapse_sep = " ",
.facet_strip_remove = FALSE,
.line_color = "#2c3e50",
.line_size = 0.5,
.line_type = 1,
.line_alpha = 1,
.anom_color = "#e31a1c",
.anom_alpha = 1,
.anom_size = 1.5,
.ribbon_fill = "grey20",
.ribbon_alpha = 0.20,
.legend_show = TRUE,
.title = "Anomaly Plot",
.x_lab = "",
.y_lab = "",
.color_lab = "Anomaly",
.interactive = TRUE,
.trelliscope = FALSE,
.trelliscope_params = list()
) {
# Checks
date_var_expr <- rlang::enquo(.date_var)
if (!is.data.frame(.data)) {
rlang::abort("`.data` must be a 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", "anomaly") %in% column_names
if (!all(check_names)) stop('Error in plot_anomalies(): column names are missing. Run `anomalize()` and make sure: observed, remainder, anomaly, recomposed_l1, and recomposed_l2 are present', call. = FALSE)
UseMethod("plot_anomalies", .data)
}
#' @export
plot_anomalies.data.frame <- function(
.data,
.date_var,
.facet_vars = NULL,
.facet_ncol = 1,
.facet_nrow = 1,
.facet_scales = "free",
.facet_dir = "h",
.facet_collapse = FALSE,
.facet_collapse_sep = " ",
.facet_strip_remove = FALSE,
.line_color = "#2c3e50",
.line_size = 0.5,
.line_type = 1,
.line_alpha = 1,
.anom_color = "#e31a1c",
.anom_alpha = 1,
.anom_size = 1.5,
.ribbon_fill = "grey20",
.ribbon_alpha = 0.20,
.legend_show = TRUE,
.title = "Anomaly Plot",
.x_lab = "",
.y_lab = "",
.color_lab = "Anomaly",
.interactive = TRUE,
.trelliscope = FALSE,
.trelliscope_params = list()
) {
# Tidy Eval Setup
date_var_expr <- rlang::enquo(.date_var)
facets_expr <- rlang::enquo(.facet_vars)
# Facet Names
facets_expr <- rlang::syms(names(tidyselect::eval_select(facets_expr, .data)))
data_formatted <- tibble::as_tibble(.data)
# FACET SETUP ----
facet_names <- data_formatted %>% dplyr::select(!!! facets_expr) %>% colnames()
if (length(facet_names) > 0) {
if (.facet_collapse) {
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::group_by(.facets_collapsed)
facet_names <- ".facets_collapsed"
} else {
data_formatted <- data_formatted %>%
dplyr::group_by(!!! rlang::syms(facet_names))
}
}
# ---- VISUALIZATION ----
g <- data_formatted %>%
ggplot2::ggplot(ggplot2::aes(!! date_var_expr, observed)) +
ggplot2::labs(x = .x_lab, y = .y_lab, title = .title, color = .color_lab) +
theme_tq()
# Add facets
if (length(facet_names) > 0) {
g <- g +
ggplot2::facet_wrap(
ggplot2::vars(!!! rlang::syms(facet_names)),
ncol = .facet_ncol,
scales = .facet_scales,
dir = .facet_dir
)
}
# Add Ribbon
g <- g +
ggplot2::geom_ribbon(ggplot2::aes(ymin = recomposed_l1, ymax = recomposed_l2),
fill = .ribbon_fill, alpha = .ribbon_alpha)
# Add line
g <- g +
ggplot2::geom_line(
color = .line_color,
linewidth = .line_size,
linetype = .line_type,
alpha = .line_alpha
)
# Add Outliers
g <- g +
ggplot2::geom_point(ggplot2::aes_string(color = "anomaly"), size = .anom_size, alpha = .anom_alpha,
data = . %>% dplyr::filter(anomaly == "Yes")) +
ggplot2::scale_color_manual(values = c("Yes" = .anom_color))
# Show Legend?
if (!.legend_show) {
g <- g +
ggplot2::theme(legend.position = "none")
}
# Remove the facet strip?
if (.facet_strip_remove) {
g <- g +
ggplot2::theme(
strip.background = ggplot2::element_blank(),
strip.text.x = ggplot2::element_blank()
)
}
# Convert to trelliscope and/or plotly?
if (!.trelliscope) {
if (.interactive) {
g <- plotly::ggplotly(g)
}
} else {
trell <- do.call(trelliscopejs::facet_trelliscope, c(
list(
facets = ggplot2::vars(!!! rlang::syms(facet_names)),
ncol = .facet_ncol,
nrow = .facet_nrow,
scales = .facet_scales,
as_plotly = .interactive
),
.trelliscope_params
))
g <- g + trell
}
return(g)
}
#' @export
plot_anomalies.grouped_df <- function(
.data,
.date_var,
.facet_vars = NULL,
.facet_ncol = 1,
.facet_nrow = 1,
.facet_scales = "free",
.facet_dir = "h",
.facet_collapse = FALSE,
.facet_collapse_sep = " ",
.facet_strip_remove = FALSE,
.line_color = "#2c3e50",
.line_size = 0.5,
.line_type = 1,
.line_alpha = 1,
.anom_color = "#e31a1c",
.anom_alpha = 1,
.anom_size = 1.5,
.ribbon_fill = "grey20",
.ribbon_alpha = 0.20,
.legend_show = TRUE,
.title = "Anomaly Plot",
.x_lab = "",
.y_lab = "",
.color_lab = "Anomaly",
.interactive = TRUE,
.trelliscope = FALSE,
.trelliscope_params = list()
) {
# 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(...): Groups are previously detected. Grouping by: ",
stringr::str_c(group_names, collapse = ", "))
# ---- DATA SETUP ----
# Ungroup Data
data_formatted <- .data %>% dplyr::ungroup()
# ---- PLOT SETUP ----
plot_anomalies.data.frame(
.data = data_formatted,
.date_var = !! rlang::enquo(.date_var),
.facet_vars = !! enquo(group_names),
.facet_ncol = .facet_ncol,
.facet_nrow = .facet_nrow,
.facet_scales = .facet_scales,
.facet_dir = .facet_dir,
.facet_strip_remove = .facet_strip_remove,
.line_color = .line_color,
.line_size = .line_size,
.line_type = .line_type,
.line_alpha = .line_alpha,
.anom_color = .anom_color,
.anom_alpha = .anom_alpha,
.anom_size = .anom_size,
.ribbon_fill = .ribbon_fill,
.ribbon_alpha = .ribbon_alpha,
.legend_show = .legend_show,
.title = .title,
.x_lab = .x_lab,
.y_lab = .y_lab,
.color_lab = .color_lab,
.interactive = .interactive,
.trelliscope = .trelliscope,
.trelliscope_params = .trelliscope_params
)
}
# 2.0 PLOT ANOMALIES DECOMP ----
#' Visualize Anomaly Decomposition
#'
#' `plot_anomalies_decomp()`: Takes in data from the `anomalize()`
#' function, and returns a plot of the anomaly decomposition. Useful for interpeting
#' how the `anomalize()` function is determining outliers from "remainder".
#'
#' @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`
#'
#' @examples
#' # Plot Anomalies Decomposition
#' 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)
}
# 3.0 PLOT ANOMALIES CLEANED -----
#' Visualize Anomalies for One or More Time Series
#'
#' `plot_anomalies_cleaned()` helps users visualize the before/after of
#' cleaning anomalies.
#'
#' @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 .cleaned_line_color Line color.
#' @param .cleaned_line_size Line size.
#' @param .cleaned_line_type Line type.
#' @param .cleaned_line_alpha Line alpha (opacity). 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`
#'
#'
#' @examples
#' # Plot Anomalies Cleaned
#' library(dplyr)
#'
#' walmart_sales_weekly %>%
#' filter(id %in% c("1_1", "1_3")) %>%
#' group_by(id) %>%
#' anomalize(Date, Weekly_Sales, .message = FALSE) %>%
#' plot_anomalies_cleaned(Date, .facet_ncol = 2, .interactive = FALSE)
#'
#' @name plot_anomalies
#' @export
plot_anomalies_cleaned <- function(
.data,
.date_var,
.facet_vars = NULL,
.facet_ncol = 1,
.facet_nrow = 1,
.facet_scales = "free",
.facet_dir = "h",
.facet_collapse = FALSE,
.facet_collapse_sep = " ",
.facet_strip_remove = FALSE,
.line_color = "#2c3e50",
.line_size = 0.5,
.line_type = 1,
.line_alpha = 1,
.cleaned_line_color = "#e31a1c",
.cleaned_line_size = 0.5,
.cleaned_line_type = 1,
.cleaned_line_alpha = 1,
.legend_show = TRUE,
.title = "Anomalies Cleaned Plot",
.x_lab = "",
.y_lab = "",
.color_lab = "Legend",
.interactive = TRUE,
.trelliscope = FALSE,
.trelliscope_params = list()
) {
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", "observed_clean") %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_cleaned", .data)
}
#' @export
plot_anomalies_cleaned.data.frame <- function(
.data,
.date_var,
.facet_vars = NULL,
.facet_ncol = 1,
.facet_nrow = 1,
.facet_scales = "free",
.facet_dir = "h",
.facet_collapse = FALSE,
.facet_collapse_sep = " ",
.facet_strip_remove = FALSE,
.line_color = "#2c3e50",
.line_size = 0.5,
.line_type = 1,
.line_alpha = 1,
.cleaned_line_color = "#e31a1c",
.cleaned_line_size = 0.5,
.cleaned_line_type = 1,
.cleaned_line_alpha = 1,
.legend_show = TRUE,
.title = "Anomalies Cleaned Plot",
.x_lab = "",
.y_lab = "",
.color_lab = "Legend",
.interactive = TRUE,
.trelliscope = FALSE,
.trelliscope_params = list()
) {
# Tidy Eval Setup
date_var_expr <- rlang::enquo(.date_var)
facets_expr <- rlang::enquo(.facet_vars)
# Facet Names
facets_expr <- rlang::syms(names(tidyselect::eval_select(facets_expr, .data)))
data_formatted <- tibble::as_tibble(.data)
# FACET SETUP ----
facet_names <- data_formatted %>% dplyr::select(!!! facets_expr) %>% colnames()
if (length(facet_names) > 0) {
if (.facet_collapse) {
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::group_by(.facets_collapsed)
facet_names <- ".facets_collapsed"
} else {
data_formatted <- data_formatted %>%
dplyr::group_by(!!! rlang::syms(facet_names))
}
}
# ---- VISUALIZATION ----
g <- data_formatted %>%
ggplot2::ggplot(ggplot2::aes(!! date_var_expr, observed)) +
ggplot2::labs(x = .x_lab, y = .y_lab, title = .title, color = .color_lab) +
theme_tq()
# Add facets
if (length(facet_names) > 0) {
g <- g +
ggplot2::facet_wrap(
ggplot2::vars(!!! rlang::syms(facet_names)),
ncol = .facet_ncol,
scales = .facet_scales,
dir = .facet_dir
)
}
# Add line - observed
g <- g +
ggplot2::geom_line(
ggplot2::aes(color = "Observed"),
# color = .line_color,
linewidth = .line_size,
linetype = .line_type,
alpha = .line_alpha
)
# Add color scale
g <- g +
ggplot2::scale_color_manual(values = c(.line_color, .cleaned_line_color))
# Add line - observed_clean
g <- g +
ggplot2::geom_line(
ggplot2::aes(y = observed_clean, color = "Observed Cleaned"),
# color = .cleaned_line_color,
linewidth = .cleaned_line_size,
linetype = .cleaned_line_type,
alpha = .cleaned_line_alpha
)
# Show Legend?
if (!.legend_show) {
g <- g +
ggplot2::theme(legend.position = "none")
}
# Remove the facet strip?
if (.facet_strip_remove) {
g <- g +
ggplot2::theme(
strip.background = ggplot2::element_blank(),
strip.text.x = ggplot2::element_blank()
)
}
# Convert to trelliscope and/or plotly?
if (!.trelliscope) {
if (.interactive) {
g <- plotly::ggplotly(g)
}
} else {
trell <- do.call(trelliscopejs::facet_trelliscope, c(
list(
facets = ggplot2::vars(!!! rlang::syms(facet_names)),
ncol = .facet_ncol,
nrow = .facet_nrow,
scales = .facet_scales,
as_plotly = .interactive
),
.trelliscope_params
))
g <- g + trell
}
return(g)
}
#' @export
plot_anomalies_cleaned.grouped_df <- function(
.data,
.date_var,
.facet_vars = NULL,
.facet_ncol = 1,
.facet_nrow = 1,
.facet_scales = "free",
.facet_dir = "h",
.facet_collapse = FALSE,
.facet_collapse_sep = " ",
.facet_strip_remove = FALSE,
.line_color = "#2c3e50",
.line_size = 0.5,
.line_type = 1,
.line_alpha = 1,
.cleaned_line_color = "#e31a1c",
.cleaned_line_size = 0.5,
.cleaned_line_type = 1,
.cleaned_line_alpha = 1,
.legend_show = TRUE,
.title = "Anomalies Cleaned Plot",
.x_lab = "",
.y_lab = "",
.color_lab = "Legend",
.interactive = TRUE,
.trelliscope = FALSE,
.trelliscope_params = list()
) {
# 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_cleaned(...): 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_cleaned.data.frame(
.data = data_formatted,
.date_var = !! rlang::enquo(.date_var),
.facet_vars = !! enquo(group_names),
.facet_ncol = .facet_ncol,
.facet_nrow = .facet_nrow,
.facet_scales = .facet_scales,
.facet_dir = .facet_dir,
.facet_strip_remove = .facet_strip_remove,
.line_color = .line_color,
.line_size = .line_size,
.line_type = .line_type,
.line_alpha = .line_alpha,
.cleaned_line_color = .cleaned_line_color,
.cleaned_line_size = .cleaned_line_size,
.cleaned_line_type = .cleaned_line_type,
.cleaned_line_alpha = .cleaned_line_alpha,
.legend_show = .legend_show,
.title = .title,
.x_lab = .x_lab,
.y_lab = .y_lab,
.color_lab = .color_lab,
.interactive = .interactive,
.trelliscope = .trelliscope,
.trelliscope_params = .trelliscope_params
)
return(g)
}
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