Merge pull request #389 from spsanderson/development

Update to native pipe

DESCRIPTION

@@ -18,6 +18,8 @@ Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.2.3
 URL: https://github.com/spsanderson/TidyDensity
 BugReports: https://github.com/spsanderson/TidyDensity/issues
+Depends:
+    R (>= 4.1.0)
 Imports: 
     magrittr,
     rlang (>= 0.4.11),

R/augment-bootstrap-density.R

@@ -22,11 +22,11 @@
 #' @examples
 #' x <- mtcars$mpg
 #'
-#' tidy_bootstrap(x) %>%
+#' tidy_bootstrap(x) |>
 #'   bootstrap_density_augment()
 #'
-#' tidy_bootstrap(x) %>%
-#'   bootstrap_unnest_tbl() %>%
+#' tidy_bootstrap(x) |>
+#'   bootstrap_unnest_tbl() |>
 #'   bootstrap_density_augment()
 #'
 #' @return
@@ -56,26 +56,26 @@ bootstrap_density_augment <- function(.data) {
 
   # Add density data
   if (atb$tibble_type == "tidy_bootstrap_nested") {
-    df_tbl <- dplyr::as_tibble(.data) %>%
+    df_tbl <- dplyr::as_tibble(.data) |>
       TidyDensity::bootstrap_unnest_tbl()
   }
 
   if (atb$tibble_type == "tidy_bootstrap") {
     df_tbl <- dplyr::as_tibble(.data)
   }
 
-  df_tbl <- df_tbl %>%
-    dplyr::nest_by(sim_number) %>%
+  df_tbl <- df_tbl |>
+    dplyr::nest_by(sim_number) |>
     dplyr::mutate(dens_tbl = list(
       stats::density(unlist(data),
         n = nrow(data)
-      )[c("x", "y")] %>%
-        purrr::set_names("dx", "dy") %>%
+      )[c("x", "y")] |>
+        purrr::set_names("dx", "dy") |>
         dplyr::as_tibble()
-    )) %>%
-    tidyr::unnest(cols = c(data, dens_tbl)) %>%
-    dplyr::mutate(x = dplyr::row_number()) %>%
-    dplyr::ungroup() %>%
+    )) |>
+    tidyr::unnest(cols = c(data, dens_tbl)) |>
+    dplyr::mutate(x = dplyr::row_number()) |>
+    dplyr::ungroup() |>
     dplyr::select(sim_number, x, y, dx, dy, dplyr::everything())
 
   # Return

R/augment-bootstrap-p.R

@@ -20,8 +20,8 @@
 #'
 #' @examples
 #' x <- mtcars$mpg
-#' tidy_bootstrap(x) %>%
-#'   bootstrap_unnest_tbl() %>%
+#' tidy_bootstrap(x) |>
+#'   bootstrap_unnest_tbl() |>
 #'   bootstrap_p_augment(y)
 #'
 #' @return

R/augment-bootstrap-q.R

@@ -21,8 +21,8 @@
 #' @examples
 #' x <- mtcars$mpg
 #'
-#' tidy_bootstrap(x) %>%
-#'   bootstrap_unnest_tbl() %>%
+#' tidy_bootstrap(x) |>
+#'   bootstrap_unnest_tbl() |>
 #'   bootstrap_q_augment(y)
 #'
 #' @return

R/autoplot-combined-dist.R

@@ -47,10 +47,10 @@
 #'
 #' combined_tbl
 #'
-#' combined_tbl %>%
+#' combined_tbl |>
 #'   tidy_combined_autoplot()
 #'
-#' combined_tbl %>%
+#' combined_tbl |>
 #'   tidy_combined_autoplot(.plot_type = "qq")
 #'
 #' @return
@@ -126,7 +126,7 @@ tidy_combined_autoplot <- function(.data, .plot_type = "density", .line_size = .
   }
 
   if (plot_type == "density") {
-    plt <- data_tbl %>%
+    plt <- data_tbl |>
       ggplot2::ggplot(
         ggplot2::aes(
           x = dx, y = dy,
@@ -144,16 +144,16 @@ tidy_combined_autoplot <- function(.data, .plot_type = "density", .line_size = .
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "quantile") {
     ## EDIT
-    data_tbl <- data_tbl %>%
-      dplyr::select(sim_number, dist_type, q) %>%
-      dplyr::group_by(sim_number, dist_type) %>%
-      dplyr::arrange(q) %>%
-      dplyr::mutate(x = 1:dplyr::n() %>%
-        tidy_scale_zero_one_vec()) %>%
+    data_tbl <- data_tbl |>
+      dplyr::select(sim_number, dist_type, q) |>
+      dplyr::group_by(sim_number, dist_type) |>
+      dplyr::arrange(q) |>
+      dplyr::mutate(x = 1:dplyr::n() |>
+        tidy_scale_zero_one_vec()) |>
       dplyr::ungroup()
     ## End EDIT
-    plt <- data_tbl %>%
-      dplyr::filter(q > -Inf, q < Inf) %>%
+    plt <- data_tbl |>
+      dplyr::filter(q > -Inf, q < Inf) |>
       ggplot2::ggplot(
         ggplot2::aes(
           # x = tidy_scale_zero_one_vec(dx),
@@ -174,7 +174,7 @@ tidy_combined_autoplot <- function(.data, .plot_type = "density", .line_size = .
       ) +
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "probability") {
-    plt <- data_tbl %>%
+    plt <- data_tbl |>
       ggplot2::ggplot(
         ggplot2::aes(
           x = y,
@@ -192,7 +192,7 @@ tidy_combined_autoplot <- function(.data, .plot_type = "density", .line_size = .
       ) +
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "qq") {
-    plt <- data_tbl %>%
+    plt <- data_tbl |>
       ggplot2::ggplot(
         ggplot2::aes(
           sample = y,
@@ -210,10 +210,10 @@ tidy_combined_autoplot <- function(.data, .plot_type = "density", .line_size = .
       ) +
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "mcmc") {
-    plt <- data_tbl %>%
-      dplyr::group_by(sim_number, dist_type) %>%
-      dplyr::mutate(cmy = dplyr::cummean(y)) %>%
-      dplyr::ungroup() %>%
+    plt <- data_tbl |>
+      dplyr::group_by(sim_number, dist_type) |>
+      dplyr::mutate(cmy = dplyr::cummean(y)) |>
+      dplyr::ungroup() |>
       ggplot2::ggplot(ggplot2::aes(
         x = x, y = cmy, group = interaction(dist_type, sim_number), color = sim_number
       )) +

R/autoplot-four-graphs.R

@@ -39,7 +39,7 @@
 #' will return an interactive `plotly` plot.
 #'
 #' @examples
-#' tidy_normal(.num_sims = 5) %>%
+#' tidy_normal(.num_sims = 5) |>
 #'   tidy_four_autoplot()
 #'
 #' @return

R/autoplot-multi-dist.R

@@ -50,10 +50,10 @@
 #'   )
 #' )
 #'
-#' tn %>%
+#' tn |>
 #'   tidy_multi_dist_autoplot()
 #'
-#' tn %>%
+#' tn |>
 #'   tidy_multi_dist_autoplot(.plot_type = "qq")
 #'
 #' @return
@@ -223,7 +223,7 @@ tidy_multi_dist_autoplot <- function(.data, .plot_type = "density", .line_size =
   }
 
   if (plot_type == "density" & atb$all$distribution_family_type == "continuous") {
-    plt <- data_tbl %>%
+    plt <- data_tbl |>
       ggplot2::ggplot(
         ggplot2::aes(x = dx, y = dy, group = interaction(dist_name, sim_number), color = dist_name)
       ) +
@@ -236,7 +236,7 @@ tidy_multi_dist_autoplot <- function(.data, .plot_type = "density", .line_size =
       ) +
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "density" & atb$all$distribution_family_type == "discrete") {
-    plt <- data_tbl %>%
+    plt <- data_tbl |>
       ggplot2::ggplot(
         ggplot2::aes(x = y, group = interaction(dist_name, sim_number), fill = dist_name)
       ) +
@@ -253,14 +253,14 @@ tidy_multi_dist_autoplot <- function(.data, .plot_type = "density", .line_size =
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "quantile") {
     ## EDIT
-    data_tbl <- data_tbl %>%
-      dplyr::select(sim_number, dist_name, q) %>%
-      dplyr::group_by(sim_number, dist_name) %>%
-      dplyr::arrange(q) %>%
-      dplyr::mutate(x = 1:dplyr::n()) %>%
+    data_tbl <- data_tbl |>
+      dplyr::select(sim_number, dist_name, q) |>
+      dplyr::group_by(sim_number, dist_name) |>
+      dplyr::arrange(q) |>
+      dplyr::mutate(x = 1:dplyr::n()) |>
       dplyr::ungroup()
     ## End EDIT
-    plt <- data_tbl %>%
+    plt <- data_tbl |>
       ggplot2::ggplot(
         ggplot2::aes(
           x = x, y = q, group = interaction(dist_name, sim_number), color = dist_name
@@ -275,7 +275,7 @@ tidy_multi_dist_autoplot <- function(.data, .plot_type = "density", .line_size =
       ) +
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "probability") {
-    plt <- data_tbl %>%
+    plt <- data_tbl |>
       ggplot2::ggplot(
         ggplot2::aes(
           x = y, group = interaction(dist_name, sim_number), color = dist_name
@@ -290,7 +290,7 @@ tidy_multi_dist_autoplot <- function(.data, .plot_type = "density", .line_size =
       ) +
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "qq") {
-    plt <- data_tbl %>%
+    plt <- data_tbl |>
       ggplot2::ggplot(
         ggplot2::aes(
           sample = y, group = interaction(dist_name, sim_number), color = dist_name
@@ -306,10 +306,10 @@ tidy_multi_dist_autoplot <- function(.data, .plot_type = "density", .line_size =
       ) +
       ggplot2::theme(legend.position = leg_pos)
   } else if (plot_type == "mcmc") {
-    plt <- data_tbl %>%
-      dplyr::group_by(sim_number, dist_name) %>%
-      dplyr::mutate(cmy = dplyr::cummean(y)) %>%
-      dplyr::ungroup() %>%
+    plt <- data_tbl |>
+      dplyr::group_by(sim_number, dist_name) |>
+      dplyr::mutate(cmy = dplyr::cummean(y)) |>
+      dplyr::ungroup() |>
       ggplot2::ggplot(ggplot2::aes(
         x = x, y = cmy, group = interaction(dist_name, sim_number), color = sim_number
       )) +

R/autoplot-randomwalk.R

@@ -27,12 +27,12 @@
 #' will return an interactive `plotly` plot.
 #'
 #' @examples
-#' tidy_normal(.sd = .1, .num_sims = 5) %>%
-#'   tidy_random_walk(.value_type = "cum_sum") %>%
+#' tidy_normal(.sd = .1, .num_sims = 5) |>
+#'   tidy_random_walk(.value_type = "cum_sum") |>
 #'   tidy_random_walk_autoplot()
 #'
-#' tidy_normal(.sd = .1, .num_sims = 20) %>%
-#'   tidy_random_walk(.value_type = "cum_sum", .sample = TRUE, .replace = TRUE) %>%
+#' tidy_normal(.sd = .1, .num_sims = 20) |>
+#'   tidy_random_walk(.value_type = "cum_sum", .sample = TRUE, .replace = TRUE) |>
 #'   tidy_random_walk_autoplot()
 #'
 #' @return
@@ -51,8 +51,8 @@ tidy_random_walk_autoplot <- function(.data, .line_size = .5, .geom_rug = FALSE,
   atb <- attributes(.data)
   n <- atb$all$.n
   sims <- atb$all$.num_sims
-  # dist_type <- stringr::str_remove(atb$all$tibble_type, "tidy_") %>%
-  #   stringr::str_replace_all(pattern = "_", " ") %>%
+  # dist_type <- stringr::str_remove(atb$all$tibble_type, "tidy_") |>
+  #   stringr::str_replace_all(pattern = "_", " ") |>
   #   stringr::str_to_title()
   dist_type <- dist_type_extractor(atb$all$tibble_type)
 
@@ -160,7 +160,7 @@ tidy_random_walk_autoplot <- function(.data, .line_size = .5, .geom_rug = FALSE,
   data_tbl <- dplyr::as_tibble(.data)
 
   # Plot ----
-  plt <- data_tbl %>%
+  plt <- data_tbl |>
     ggplot2::ggplot(ggplot2::aes(
       x = x, y = random_walk_value,
       group = sim_number, color = sim_number

R/bootstrap-unnest-tbl.R

@@ -18,7 +18,7 @@
 #' tb <- tidy_bootstrap(.x = mtcars$mpg)
 #' bootstrap_unnest_tbl(tb)
 #'
-#' bootstrap_unnest_tbl(tb) %>%
+#' bootstrap_unnest_tbl(tb) |>
 #'   tidy_distribution_summary_tbl(sim_number)
 #'
 #' @return
@@ -41,7 +41,7 @@ bootstrap_unnest_tbl <- function(.data) {
   }
 
   # Data ----
-  df <- tidyr::unnest(.data, bootstrap_samples) %>%
+  df <- tidyr::unnest(.data, bootstrap_samples) |>
     purrr::set_names("sim_number", "y")
 
   # Return ----

R/combine-multi-single-dist-tbl.R

@@ -68,22 +68,22 @@ tidy_multi_single_dist <- function(.tidy_dist = NULL,
   names(param_grid) <- methods::formalArgs(td)
 
   # Run call on the grouped df ----
-  dff <- param_grid %>%
+  dff <- param_grid |>
     dplyr::mutate(results = purrr::pmap(dplyr::pick(dplyr::everything()), match.fun(td)))
     #dplyr::mutate(results = purrr::pmap(dplyr::cur_data(), match.fun(td)))
 
   # Get the attributes to be used later on ----
-  atb <- dff$results[[1]] %>% attributes()
+  atb <- dff$results[[1]] |> attributes()
 
   # Make Dist Type for column ----
-  # dist_type <- stringr::str_remove(atb$tibble_type, "tidy_") %>%
-  #   stringr::str_replace_all(pattern = "_", " ") %>%
+  # dist_type <- stringr::str_remove(atb$tibble_type, "tidy_") |>
+  #   stringr::str_replace_all(pattern = "_", " ") |>
   #   stringr::str_to_title()
 
   dist_type <- dist_type_extractor(atb$tibble_type)
 
   # Get column names from the param_grid in order to make teh dist_type column ----
-  cols <- names(param_grid %>% dplyr::select(-c(.n, .num_sims)))
+  cols <- names(param_grid |> dplyr::select(-c(.n, .num_sims)))
 
   if (length(cols) == 1) {
     dff$dist_name <- paste0(dist_type, " c(", dff[, cols], ")")
@@ -101,11 +101,11 @@ tidy_multi_single_dist <- function(.tidy_dist = NULL,
   #   ")"
   # )
 
-  df_unnested_tbl <- dff %>%
-    tidyr::unnest(results) %>%
-    dplyr::ungroup() %>%
-    dplyr::select(sim_number, dist_name, x:q) %>%
-    dplyr::mutate(dist_name = as.factor(dist_name)) %>%
+  df_unnested_tbl <- dff |>
+    tidyr::unnest(results) |>
+    dplyr::ungroup() |>
+    dplyr::select(sim_number, dist_name, x:q) |>
+    dplyr::mutate(dist_name = as.factor(dist_name)) |>
     dplyr::arrange(sim_number, dist_name)
 
   # Attach attributes ----

R/combine-tidy-distributions-tbl.R

@@ -57,10 +57,10 @@ tidy_combine_distributions <- function(...) {
   # Get the distribution type
   dist_final_tbl <- purrr::map(
     .x = dist_list,
-    .f = ~ .x %>%
-      dplyr::mutate(dist_type = attributes(.x)[["dist_with_params"]]) %>%
+    .f = ~ .x |>
+      dplyr::mutate(dist_type = attributes(.x)[["dist_with_params"]]) |>
       dplyr::mutate(dist_type = as.factor(dist_type))
-  ) %>%
+  ) |>
     purrr::map_dfr(dplyr::as_tibble)
 
   attr(dist_final_tbl, "tibble_type") <- "tidy_multi_dist_combine"

R/empirical-tidy-bootstrap.R

@@ -74,11 +74,11 @@ tidy_bootstrap <- function(.x, .num_sims = 2000, .proportion = 0.8,
   }
 
   # Data ----
-  df <- dplyr::tibble(sim_number = as.factor(1:num_sims)) %>%
-    dplyr::group_by(sim_number) %>%
+  df <- dplyr::tibble(sim_number = as.factor(1:num_sims)) |>
+    dplyr::group_by(sim_number) |>
     dplyr::mutate(bootstrap_samples = list(
       sample(x = x_term, size = floor(prop * n), replace = TRUE)
-    )) %>%
+    )) |>
     dplyr::ungroup()
 
   # Attach descriptive attributes to tibble