updates for olivroy

Merge branch 'master' of https://github.com/business-science/modeltime.resample

# Conflicts:
#	.github/workflows/R-CMD-check.yaml
#	.github/workflows/pkgdown.yaml
#	.github/workflows/test-coverage.yaml

.github/workflows/R-CMD-check.yaml

@@ -8,7 +8,6 @@ on:
   pull_request:
     branches: [main, master]
 
-
 name: R-CMD-check
 
 jobs:

.github/workflows/pkgdown.yaml

@@ -24,20 +24,6 @@ jobs:
     steps:
       - uses: actions/checkout@v3
 
-      - uses: actions/setup-python@v4
-        with:
-          python-version: '3.8'
-
-      - uses: actions/setup-java@v3
-        with:
-          java-version: '11'
-          distribution: 'temurin'
-
-      - uses: vemonet/setup-spark@v1
-        with:
-          spark-version: '3.1.2'
-          hadoop-version: '3.2'
-
       - uses: r-lib/actions/setup-pandoc@v2
 
       - uses: r-lib/actions/setup-r@v2
@@ -53,6 +39,7 @@ jobs:
         run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
         shell: Rscript {0}
 
+
       - name: Deploy to GitHub pages 🚀
         if: github.event_name != 'pull_request'
         uses: JamesIves/github-pages-deploy-action@v4.4.1

CRAN-SUBMISSION

@@ -1,3 +1,3 @@
 Version: 0.2.2
-Date: 2022-10-16 15:28:21 UTC
-SHA: 4c5bb0660261c3b0a543d44ffa25008488277a6b
+Date: 2022-10-18 00:28:29 UTC
+SHA: f1034b5de71bd1177110566d67b5f7687c136114

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: modeltime.resample
 Title: Resampling Tools for Time Series Forecasting
-Version: 0.2.2.9000
+Version: 0.2.3.9000
 Authors@R: c(
     person("Matt", "Dancho", email = "mdancho@business-science.io", role = c("aut", "cre")),
     person("Business Science", role = "cph")
@@ -12,7 +12,7 @@ Description:
 License: MIT + file LICENSE
 Encoding: UTF-8
 LazyData: true
-URL: https://github.com/business-science/modeltime.resample
+URL: https://business-science.github.io/modeltime.resample/, https://github.com/business-science/modeltime.resample
 BugReports: https://github.com/business-science/modeltime.resample/issues
 Depends:
     modeltime (>= 0.3.0),
@@ -27,35 +27,26 @@ Imports:
     yardstick,
     timetk (>= 2.5.0),
     tibble,
-    dplyr,
+    dplyr (>= 1.0.0),
     tidyr,
     purrr,
-    forcats,
-    glue,
     stringr,
-    ggplot2,
+    ggplot2 (>= 3.4.0),
     plotly,
     cli,
-    crayon,
     magrittr,
     rlang (>= 0.1.2),
     progressr,
     tictoc,
     hardhat
 Suggests: 
-    roxygen2,
     testthat,
     tidymodels,
-    tidyverse,
     tidyquant,
     glmnet,
     lubridate,
     knitr,
-    rmarkdown,
-    covr, 
-    remotes
+    rmarkdown
 Roxygen: list(markdown = TRUE)
-RoxygenNote: 7.2.1
+RoxygenNote: 7.2.3
 VignetteBuilder: knitr
-
-

NEWS.md

@@ -1,4 +1,12 @@
-# modeltime.resample 0.2.2.9000 (Development Version)
+# modeltime.resample (development version)
+
+- Update for better compatibility with dplyr 1.1.0 and ggplot2 3.4.4 (@olivroy, #16)
+
+- Remove dependency on tidyverse (@olivroy, #16)
+
+# modeltime.resample 0.2.3
+
+- Resubmit to CRAN (timetk issue)
 
 # modeltime.resample 0.2.2
 

R/modeltime_fit_resamples.R

@@ -45,7 +45,7 @@
 #' library(tidymodels)
 #' library(modeltime)
 #' library(timetk)
-#' library(tidyverse)
+#' library(magrittr)
 #'
 #' # Make resamples
 #' resamples_tscv <- training(m750_splits) %>%
@@ -93,6 +93,8 @@ modeltime_fit_resamples.mdl_time_tbl <- function(object, resamples, control = co
 
     if (!control$save_pred) control$save_pred <- TRUE
 
+    # TODO Consider removing tictoc and progressr dep in favor
+    # of cli::cli_progress_step()
     if (control$verbose) {
         tictoc::tic()
         print(cli::rule("Fitting Resamples", width = 65))

R/modeltime_resample_accuracy.R

@@ -99,10 +99,9 @@ modeltime_resample_accuracy <- function(object, summary_fns = mean, metric_set =
             dplyr::mutate(n = dplyr::n()) %>%
             dplyr::group_by(.model_id, .model_desc, .type, n) %>%
             dplyr::summarise(
-                dplyr::across(.fns = summary_fns, ...),
+                dplyr::across(.cols = dplyr::everything(), .fns = summary_fns, ...),
                 .groups = "drop"
-            ) %>%
-            dplyr::ungroup()
+            )
 
     }
 

R/plot_modeltime_resamples.R

@@ -22,12 +22,12 @@
 #' @param .point_shape Controls the point shape. Default: 16.
 #' @param .point_alpha Controls the opacity of the points. Default: 1 (full opacity).
 #' @param .summary_line_show Whether or not to show the summary lines. Default: `TRUE`.
-#' @param .summary_line_size  Controls the summary line size. Default: 0.5.
+#' @param .summary_line_size  Controls the summary line width. Default: 0.5.
 #' @param .summary_line_type  Controls the summary line type. Default: 1.
 #' @param .summary_line_alpha Controls the summary line opacity. Default: 1 (full opacity).
 #' @param .x_intercept        Numeric. Adds an x-intercept at a location (e.g. 0). Default: NULL.
 #' @param .x_intercept_color  Controls the x-intercept color. Default: "red".
-#' @param .x_intercept_size   Controls the x-intercept size. Default: 0.5.
+#' @param .x_intercept_size   Controls the x-intercept linewidth. Default: 0.5.
 #'
 #' @details
 #'
@@ -110,17 +110,17 @@ plot_modeltime_resamples <- function(.data,
 
     # Prepare Data for Plot
     data_prepared <- resample_results_tbl  %>%
-        dplyr::rename(.value = !! target_var) %>%
+        dplyr::rename(.value = !!target_var) %>%
 
         dplyr::mutate(.model_desc = ifelse(!is.na(.model_id), stringr::str_c(.model_id, "_", .model_desc), .model_desc)) %>%
         dplyr::mutate(.model_desc = .model_desc %>% stringr::str_trunc(width = .legend_max_width)) %>%
-        dplyr::mutate(.model_desc = forcats::as_factor(.model_desc)) %>%
+        dplyr::mutate(.model_desc = as.factor(.model_desc)) %>%
 
         dplyr::group_by(.resample_id, .model_desc) %>%
         .metric_set(.value, .pred) %>%
         dplyr::ungroup() %>%
 
-        dplyr::mutate(.metric = forcats::as_factor(.metric)) %>%
+        dplyr::mutate(.metric = as.factor(.metric)) %>%
 
         dplyr::group_by(.model_desc, .metric) %>%
         dplyr::mutate(..summary_fn = summary_fn_partial(.estimate)) %>%
@@ -147,17 +147,17 @@ plot_modeltime_resamples <- function(.data,
     if (.summary_line_show) {
         g <- g +
             ggplot2::geom_vline(ggplot2::aes(xintercept = ..summary_fn, color = .model_desc),
-                                size     = .summary_line_size,
-                                alpha    = .summary_line_alpha,
-                                linetype = .summary_line_type)
+                                linewidth = .summary_line_size,
+                                alpha     = .summary_line_alpha,
+                                linetype  = .summary_line_type)
     }
 
     # Add a X-Intercept if desired
     if (!is.null(.x_intercept)) {
         g <- g +
             ggplot2::geom_vline(xintercept = .x_intercept,
                                 color      = .x_intercept_color,
-                                size       = .x_intercept_size)
+                                linewidth  = .x_intercept_size)
     }
 
     # Add theme & labs

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

README.Rmd

@@ -26,7 +26,7 @@ library(modeltime.resample)
 [![CRAN status](https://www.r-pkg.org/badges/version/modeltime.resample)](https://CRAN.R-project.org/package=modeltime.resample)
 ![](http://cranlogs.r-pkg.org/badges/modeltime.resample?color=brightgreen)
 ![](http://cranlogs.r-pkg.org/badges/grand-total/modeltime.resample?color=brightgreen)
-[![R-CMD-check](https://github.com/business-science/modeltime.resample/workflows/R-CMD-check/badge.svg)](https://github.com/business-science/modeltime.resample/actions)
+[![R-CMD-check](https://github.com/business-science/modeltime.resample/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/business-science/modeltime.resample/actions/workflows/R-CMD-check.yaml)
 [![Codecov test coverage](https://codecov.io/gh/business-science/modeltime.resample/branch/master/graph/badge.svg)](https://app.codecov.io/gh/business-science/modeltime.resample?branch=master)
 <!-- badges: end -->
 

README.md

@@ -9,7 +9,7 @@
 status](https://www.r-pkg.org/badges/version/modeltime.resample)](https://CRAN.R-project.org/package=modeltime.resample)
 ![](http://cranlogs.r-pkg.org/badges/modeltime.resample?color=brightgreen)
 ![](http://cranlogs.r-pkg.org/badges/grand-total/modeltime.resample?color=brightgreen)
-[![R-CMD-check](https://github.com/business-science/modeltime.resample/workflows/R-CMD-check/badge.svg)](https://github.com/business-science/modeltime.resample/actions)
+[![R-CMD-check](https://github.com/business-science/modeltime.resample/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/business-science/modeltime.resample/actions/workflows/R-CMD-check.yaml)
 [![Codecov test
 coverage](https://codecov.io/gh/business-science/modeltime.resample/branch/master/graph/badge.svg)](https://app.codecov.io/gh/business-science/modeltime.resample?branch=master)
 <!-- badges: end -->
@@ -58,9 +58,23 @@ where we can see that Prophet Boost and XGBoost Models outperform
 Prophet with Regressors for the Walmart Time Series Panel Dataset using
 the 6-Slice Time Series Cross Validation plan shown above.
 
-<img src="man/figures/plotly_resample_error_plot.jpg" title="Model Accuracy for 6 Time Series Resamples" alt="Model Accuracy for 6 Time Series Resamples" width="100%" style="display: block; margin: auto;" />
+<div class="figure" style="text-align: center">
 
-<img src="man/figures/gt_accuracy_table.jpg" title="Resampled Model Accuracy (3 Models, 6 Resamples, 7 Time Series Groups)" alt="Resampled Model Accuracy (3 Models, 6 Resamples, 7 Time Series Groups)" width="80%" style="display: block; margin: auto;" />
+<img src="man/figures/plotly_resample_error_plot.jpg" alt="Model Accuracy for 6 Time Series Resamples" width="100%" />
+<p class="caption">
+Model Accuracy for 6 Time Series Resamples
+</p>
+
+</div>
+
+<div class="figure" style="text-align: center">
+
+<img src="man/figures/gt_accuracy_table.jpg" alt="Resampled Model Accuracy (3 Models, 6 Resamples, 7 Time Series Groups)" width="80%" />
+<p class="caption">
+Resampled Model Accuracy (3 Models, 6 Resamples, 7 Time Series Groups)
+</p>
+
+</div>
 
 ## Getting Started
 
@@ -79,28 +93,35 @@ the 6-Slice Time Series Cross Validation plan shown above.
 
 > Learn a growing ecosystem of forecasting packages
 
-<img src="man/figures/modeltime_ecosystem.jpg" title="The modeltime ecosystem is growing" alt="The modeltime ecosystem is growing" width="100%" style="display: block; margin: auto;" />
+<div class="figure" style="text-align: center">
+
+<img src="man/figures/modeltime_ecosystem.jpg" alt="The modeltime ecosystem is growing" width="100%" />
+<p class="caption">
+The modeltime ecosystem is growing
+</p>
+
+</div>
 
 Modeltime is part of a **growing ecosystem** of Modeltime forecasting
 packages.
 
--   [Modeltime (Machine
-    Learning)](https://business-science.github.io/modeltime/)
+- [Modeltime (Machine
+  Learning)](https://business-science.github.io/modeltime/)
 
--   [Modeltime H2O
-    (AutoML)](https://business-science.github.io/modeltime.h2o/)
+- [Modeltime H2O
+  (AutoML)](https://business-science.github.io/modeltime.h2o/)
 
--   [Modeltime GluonTS (Deep
-    Learning)](https://business-science.github.io/modeltime.gluonts/)
+- [Modeltime GluonTS (Deep
+  Learning)](https://business-science.github.io/modeltime.gluonts/)
 
--   [Modeltime Ensemble (Blending
-    Forecasts)](https://business-science.github.io/modeltime.ensemble/)
+- [Modeltime Ensemble (Blending
+  Forecasts)](https://business-science.github.io/modeltime.ensemble/)
 
--   [Modeltime Resample
-    (Backtesting)](https://business-science.github.io/modeltime.resample/)
+- [Modeltime Resample
+  (Backtesting)](https://business-science.github.io/modeltime.resample/)
 
--   [Timetk (Feature Engineering, Data Wrangling, Time Series
-    Visualization)](https://business-science.github.io/timetk/)
+- [Timetk (Feature Engineering, Data Wrangling, Time Series
+  Visualization)](https://business-science.github.io/timetk/)
 
 ## Take the High-Performance Forecasting Course
 
@@ -130,17 +151,17 @@ Series Forecasting
 Course**](https://university.business-science.io/p/ds4b-203-r-high-performance-time-series-forecasting).
 You will learn:
 
--   **Time Series Machine Learning** (cutting-edge) with `Modeltime` -
-    30+ Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)
--   **Deep Learning** with `GluonTS` (Competition Winners)
--   **Time Series Preprocessing**, Noise Reduction, & Anomaly Detection
--   **Feature engineering** using lagged variables & external regressors
--   **Hyperparameter Tuning**
--   **Time series cross-validation**
--   **Ensembling** Multiple Machine Learning & Univariate Modeling
-    Techniques (Competition Winner)
--   **Scalable Forecasting** - Forecast 1000+ time series in parallel
--   and more.
+- **Time Series Machine Learning** (cutting-edge) with `Modeltime` - 30+
+  Models (Prophet, ARIMA, XGBoost, Random Forest, & many more)
+- **Deep Learning** with `GluonTS` (Competition Winners)
+- **Time Series Preprocessing**, Noise Reduction, & Anomaly Detection
+- **Feature engineering** using lagged variables & external regressors
+- **Hyperparameter Tuning**
+- **Time series cross-validation**
+- **Ensembling** Multiple Machine Learning & Univariate Modeling
+  Techniques (Competition Winner)
+- **Scalable Forecasting** - Forecast 1000+ time series in parallel
+- and more.
 
 <p class="text-center" style="font-size:24px;">
 Become the Time Series Expert for your organization.