Merge pull request #72 from olivroy/clean

.Rbuildignore

@@ -12,3 +12,4 @@
 ^CRAN-RELEASE$
 ^CRAN-SUBMISSION$
 ^\.github$
+^pkgdown$

DESCRIPTION

@@ -1,7 +1,7 @@
 Package: anomalize
 Type: Package
 Title: Tidy Anomaly Detection
-Version: 0.3.0
+Version: 0.3.0.9000
 Authors@R: c(
     person("Matt", "Dancho", email = "[email protected]", role = c("aut", "cre")),
     person("Davis", "Vaughan", email = "[email protected]", role = c("aut"))
@@ -18,7 +18,7 @@ Description:
     and generalized extreme studentized deviation ("gesd"). These methods are based on
     those used in the 'forecast' package and the Twitter 'AnomalyDetection' package. 
     Refer to the associated functions for specific references for these methods. 
-URL: https://github.com/business-science/anomalize
+URL: https://business-science.github.io/anomalize/, https://github.com/business-science/anomalize
 BugReports: https://github.com/business-science/anomalize/issues
 License: GPL (>= 3)
 Encoding: UTF-8
@@ -35,14 +35,14 @@ Imports:
     rlang,
     tibble,
     tidyr (>= 1.0.0),
-    ggplot2
+    ggplot2 (>= 3.4.0)
 RoxygenNote: 7.2.3
 Roxygen: list(markdown = TRUE)
 Suggests: 
-    tidyverse,
     tidyquant,
     stringr,
-    testthat (>= 2.1.0),
+    testthat (>= 3.0.0),
     knitr,
     rmarkdown
 VignetteBuilder: knitr
+Config/testthat/edition: 3

NAMESPACE

@@ -53,6 +53,7 @@ importFrom(ggplot2,"%+replace%")
 importFrom(rlang,"!!!")
 importFrom(rlang,"!!")
 importFrom(rlang,":=")
+importFrom(rlang,.data)
 importFrom(stats,as.formula)
 importFrom(stats,mad)
 importFrom(stats,median)

NEWS.md

@@ -1,3 +1,9 @@
+# anomalize (development version)
+
+* anomalize works better with ggplot2 3.4.0
+
+* anomalize no longer depends on tidyverse, devtools and roxygen2 (@olivroy, #70)
+
 # anomalize 0.3.0
 
 Prepare for supercession by `timetk`. Note that `anomalize` R package will be maintained for backwards compatibility. Users may wish to add these 2 lines of code to existing codebases that use the legacy anomalize R package:

R/anomalize-package.R

@@ -1,5 +1,4 @@
-#' anomalize: Tidy anomaly detection
-#'
+#' @description
 #' The 'anomalize' package enables a "tidy" workflow for detecting anomalies in data.
 #' The main functions are time_decompose(), anomalize(), and time_recompose().
 #' When combined, it's quite simple to decompose time series, detect anomalies,
@@ -14,14 +13,14 @@
 #'
 #' To learn more about `anomalize`, start with the vignettes:
 #'  `browseVignettes(package = "anomalize")`
-#'
-#'
-#' @docType package
-#' @name anomalize_package
-#'
 #' @aliases anomalize-package
-#' @importFrom rlang := !! !!!
+#' @keywords internal
+"_PACKAGE"
+
+## usethis namespace: start
+#' @importFrom rlang := !! !!! .data
 #' @importFrom dplyr %>% n row_number contains quo_name
 #' @importFrom stats median mad qt as.formula
 #' @import ggplot2
+## usethis namespace: end
 NULL

R/anomalize.R

@@ -82,8 +82,6 @@
 #' # Needed to pass CRAN check / This is loaded by default
 #' set_time_scale_template(time_scale_template())
 #'
-#' data(tidyverse_cran_downloads)
-#'
 #' tidyverse_cran_downloads %>%
 #'     time_decompose(count, method = "stl") %>%
 #'     anomalize(remainder, method = "iqr")
@@ -145,8 +143,8 @@ anomalize.tbl_df <- function(data, target, method = c("iqr", "gesd"),
 
     # Returns
     ret <- data %>%
-        dplyr::mutate(!! paste0(dplyr::quo_name(target_expr), "_l1") := limit_lower,
-                      !! paste0(dplyr::quo_name(target_expr), "_l2") := limit_upper) %>%
+        dplyr::mutate(!!paste0(dplyr::quo_name(target_expr), "_l1") := limit_lower,
+                      !!paste0(dplyr::quo_name(target_expr), "_l2") := limit_upper) %>%
         tibble::add_column(anomaly = outlier)
 
     if (verbose) {
@@ -181,7 +179,7 @@ anomalize.grouped_df <- function(data, target, method = c("iqr", "gesd"),
             method    = method[[1]],
             alpha     = alpha,
             max_anoms = max_anoms,
-            verbose   = F,
+            verbose   = FALSE,
             ...)
 
     return(ret)

R/plot_anomalies.R

@@ -27,7 +27,6 @@
 #' library(dplyr)
 #' library(ggplot2)
 #'
-#' data(tidyverse_cran_downloads)
 #'
 #' #### SINGLE TIME SERIES ####
 #' tidyverse_cran_downloads %>%
@@ -80,7 +79,7 @@ plot_anomalies.tbl_time <- function(data, time_recomposed = FALSE, ncol = 1,
     date_col   <- tibbletime::get_index_char(data)
 
     g <- data %>%
-        ggplot2::ggplot(ggplot2::aes_string(x = date_col, y = "observed"))
+        ggplot2::ggplot(ggplot2::aes(x = .data[[date_col]], y = .data[["observed"]]))
 
 
     if (time_recomposed) {
@@ -94,8 +93,8 @@ plot_anomalies.tbl_time <- function(data, time_recomposed = FALSE, ncol = 1,
     }
 
     g <- g +
-        ggplot2::geom_point(ggplot2::aes_string(color = "anomaly"), size = size_dots, alpha = alpha_dots) +
-        ggplot2::geom_point(ggplot2::aes_string(x = date_col, y = "observed", color = "anomaly"),
+        ggplot2::geom_point(ggplot2::aes(color = .data[["anomaly"]]), size = size_dots, alpha = alpha_dots) +
+        ggplot2::geom_point(ggplot2::aes(x = .data[[date_col]], y = .data[["observed"]], color = .data[["anomaly"]]),
                            size = size_circles, shape = 1, alpha = alpha_circles,
                            data = data %>% dplyr::filter(anomaly == "Yes"),
                            inherit.aes = FALSE) +

R/plot_anomaly_decomposition.R

@@ -27,8 +27,6 @@
 #' library(dplyr)
 #' library(ggplot2)
 #'
-#' data(tidyverse_cran_downloads)
-#'
 #' tidyverse_cran_downloads %>%
 #'     filter(package == "tidyquant") %>%
 #'     ungroup() %>%
@@ -78,11 +76,11 @@ plot_anomaly_decomposition.tbl_time <- function(data, ncol = 1, color_no = "#2c3
     date_col   <- tibbletime::get_index_char(data)
 
     data_anomaly_tbl <- data %>%
-        dplyr::select(!! date_expr, observed:remainder, anomaly) %>%
+        dplyr::select(!!date_expr, observed:remainder, anomaly) %>%
         tidyr::gather(key = key, value = value, -dplyr::one_of(c(!! date_col, 'anomaly')), factor_key = T)
 
     g <- data_anomaly_tbl  %>%
-        ggplot2::ggplot(ggplot2::aes_string(x = date_col, y = "value", color = "anomaly")) +
+        ggplot2::ggplot(ggplot2::aes(x = .data[[date_col]], y = .data$value, color = .data$anomaly)) +
         # Points
         ggplot2::geom_point(size = size_dots, alpha = alpha_dots) +
         # Circles

R/tidyquant_theme_compat.R

@@ -15,10 +15,10 @@ theme_tq <- function(base_size = 11, base_family = "") {
         ggplot2::theme(
 
             # Base Inherited Elements
-            line               =  ggplot2::element_line(colour = blue, size = 0.5, linetype = 1,
+            line               =  ggplot2::element_line(colour = blue, linewidth = 0.5, linetype = 1,
                                                         lineend = "butt"),
             rect               =  ggplot2::element_rect(fill = white, colour = blue,
-                                                        size = 0.5, linetype = 1),
+                                                        linewidth = 0.5, linetype = 1),
             text               =  ggplot2::element_text(family = base_family, face = "plain",
                                                         colour = blue, size = base_size,
                                                         lineheight = 0.9, hjust = 0.5, vjust = 0.5, angle = 0,
@@ -27,14 +27,14 @@ theme_tq <- function(base_size = 11, base_family = "") {
             # Axes
             axis.line          = ggplot2::element_blank(),
             axis.text          = ggplot2::element_text(size = ggplot2::rel(0.8)),
-            axis.ticks         = ggplot2::element_line(color = grey, size = ggplot2::rel(1/3)),
+            axis.ticks         = ggplot2::element_line(color = grey, linewidth = ggplot2::rel(1/3)),
             axis.title         = ggplot2::element_text(size = ggplot2::rel(1.0)),
 
             # Panel
             panel.background   = ggplot2::element_rect(fill = white, color = NA),
-            panel.border       = ggplot2::element_rect(fill = NA, size = ggplot2::rel(1/2), color = blue),
-            panel.grid.major   = ggplot2::element_line(color = grey, size = ggplot2::rel(1/3)),
-            panel.grid.minor   = ggplot2::element_line(color = grey, size = ggplot2::rel(1/3)),
+            panel.border       = ggplot2::element_rect(fill = NA, linewidth = ggplot2::rel(1/2), color = blue),
+            panel.grid.major   = ggplot2::element_line(color = grey, linewidth = ggplot2::rel(1/3)),
+            panel.grid.minor   = ggplot2::element_line(color = grey, linewidth = ggplot2::rel(1/3)),
             panel.grid.minor.x = ggplot2::element_blank(),
             panel.spacing      = ggplot2::unit(.75, "cm"),
 
@@ -70,12 +70,12 @@ theme_tq_dark <- function(base_size = 11, base_family = "") {
         ggplot2::theme(
 
             # Axes
-            axis.ticks         = ggplot2::element_line(color = blue, size = ggplot2::rel(1/3)),
+            axis.ticks         = ggplot2::element_line(color = blue, linewidth = ggplot2::rel(1/3)),
 
             # Panel
             panel.background   = ggplot2::element_rect(fill = grey, color = NA),
-            panel.grid.major   = ggplot2::element_line(color = white, size = ggplot2::rel(1/3)),
-            panel.grid.minor   = ggplot2::element_line(color = white, size = ggplot2::rel(1/3)),
+            panel.grid.major   = ggplot2::element_line(color = white, linewidth = ggplot2::rel(1/3)),
+            panel.grid.minor   = ggplot2::element_line(color = white, linewidth = ggplot2::rel(1/3)),
 
             # Complete theme
             complete = TRUE
@@ -95,12 +95,12 @@ theme_tq_green <- function(base_size = 11, base_family = "") {
         ggplot2::theme(
 
             # Axes
-            axis.ticks         = ggplot2::element_line(color = blue, size = ggplot2::rel(1/3)),
+            axis.ticks         = ggplot2::element_line(color = blue, linewidth = ggplot2::rel(1/3)),
 
             # Panel
             panel.background   = ggplot2::element_rect(fill = green, color = NA),
-            panel.grid.major   = ggplot2::element_line(color = white, size = ggplot2::rel(1/3)),
-            panel.grid.minor   = ggplot2::element_line(color = white, size = ggplot2::rel(1/3)),
+            panel.grid.major   = ggplot2::element_line(color = white, linewidth = ggplot2::rel(1/3)),
+            panel.grid.minor   = ggplot2::element_line(color = white, linewidth = ggplot2::rel(1/3)),
 
             # Complete theme
             complete = TRUE

R/time_apply.R

@@ -24,8 +24,6 @@
 #'
 #' library(dplyr)
 #'
-#' data(tidyverse_cran_downloads)
-#'
 #' # Basic Usage
 #' tidyverse_cran_downloads %>%
 #'     time_apply(count, period = "1 week", .fun = mean, na.rm = TRUE)

R/time_decompose.R

@@ -81,8 +81,6 @@
 #'
 #' library(dplyr)
 #'
-#' data(tidyverse_cran_downloads)
-#'
 #' # Basic Usage
 #' tidyverse_cran_downloads %>%
 #'     time_decompose(count, method = "stl")

R/time_decompose_methods.R

@@ -55,7 +55,7 @@ decompose_twitter <- function(data, target, frequency = "auto", trend = "auto",
         tibble::add_column(!! date_col_name := date_col_vals, .after = 0) %>%
         purrr::set_names(c(date_col_name, "observed", "season", "trend", "remainder")) %>%
         dplyr::mutate(seasadj = observed - season) %>%
-        dplyr::select(!! date_col_expr, observed, season, seasadj, trend, remainder)
+        dplyr::select(!!date_col_expr, observed, season, seasadj, trend, remainder)
 
     # Median Span Logic
     trnd <- time_trend(data, period = trend, message = FALSE)
@@ -74,7 +74,7 @@ decompose_twitter <- function(data, target, frequency = "auto", trend = "auto",
         med_span <- decomp_tbl %>%
             dplyr::count(median_spans) %>%
             dplyr::pull(n) %>%
-            median(na.rm = TRUE)
+            stats::median(na.rm = TRUE)
 
         med_scale <- decomp_tbl %>%
             timetk::tk_index() %>%

R/utils.R

@@ -4,7 +4,7 @@
 
 grouped_mapper <- function(data, target, .f, ...) {
 
-    data            <- prep_tbl_time(data, message = F)
+    data            <- prep_tbl_time(data, message = FALSE)
 
     target_expr     <- dplyr::enquo(target)
 

README.Rmd

@@ -28,6 +28,7 @@ knitr::opts_chunk$set(
   warning = F
 )
 library(anomalize)
+library(dplyr) # for pipe 
 ```
 
 
@@ -79,12 +80,11 @@ install.packages("anomalize")
 
 ## Getting Started
 
-Load the `tidyverse` and `anomalize` packages.
+Load the `anomalize` package. Usually, you will also load the tidyverse as well!
 
 ```{r, eval = F}
-library(tidyverse)
 library(anomalize)
-
+library(tidyverse)
 # NOTE: timetk now has anomaly detection built in, which 
 #  will get the new functionality going forward.
 #  Use this script to prevent overwriting legacy anomalize:
@@ -99,7 +99,6 @@ Next, let's get some data.  `anomalize` ships with a data set called `tidyverse_
 Suppose we want to determine which daily download "counts" are anomalous. It's as easy as using the three main functions (`time_decompose()`, `anomalize()`, and `time_recompose()`) along with a visualization function, `plot_anomalies()`.
 
 ```{r tidyverse_anoms_1, fig.height=8}
-library(magrittr)
 tidyverse_cran_downloads %>%
     # Data Manipulation / Anomaly Detection
     time_decompose(count, method = "stl") %>%

README.md

@@ -78,12 +78,12 @@ install.packages("anomalize")
 
 ## Getting Started
 
-Load the `tidyverse` and `anomalize` packages.
+Load the `anomalize` package. Usually, you will also load the tidyverse
+as well!
 
 ``` r
-library(tidyverse)
 library(anomalize)
-
+library(tidyverse)
 # NOTE: timetk now has anomaly detection built in, which 
 #  will get the new functionality going forward.
 #  Use this script to prevent overwriting legacy anomalize:
@@ -102,7 +102,6 @@ anomalous. It’s as easy as using the three main functions
 visualization function, `plot_anomalies()`.
 
 ``` r
-library(magrittr)
 tidyverse_cran_downloads %>%
     # Data Manipulation / Anomaly Detection
     time_decompose(count, method = "stl") %>%

_pkgdown.yml

@@ -1,35 +1,12 @@
 template:
+  bootstrap: 5
+  bootswatch: flatly
   params:
-    bootswatch: flatly
     ganalytics: UA-76139189-1
 
-navbar:
-  title: "anomalize"
-  left:
-    - text: "Home"
-      href: index.html
-    - text: "Function Reference"
-      href: reference/index.html
-    - text: "Vignettes"
-      href: articles/index.html
-      menu:
-        - text: "Anomalize Quick Start Guide"
-          href: articles/anomalize_quick_start_guide.html
-        - text: "Anomalize Methods"
-          href: articles/anomalize_methods.html
-        - text: "Reduce Forecast Error by Cleaning Anomalies"
-          href: articles/forecasting_with_cleaned_anomalies.html
-    - text: "News"
-      href: news/index.html
-
-  right:
-    - icon: fa-github
-      href: https://github.com/business-science/anomalize
-
 reference:
   - title: General
     contents:
-    - anomalize_package
     - tidyverse_cran_downloads
   - title: Anomalize workflow
     desc: __The main functions used to anomalize time series data.__

anomalize.Rproj

@@ -19,3 +19,7 @@ BuildType: Package
 PackageUseDevtools: Yes
 PackageInstallArgs: --no-multiarch --with-keep.source
 PackageRoxygenize: rd,collate,namespace
+
+UseNativePipeOperator: No
+
+SpellingDictionary: en_US