switch pipes from %>% to |>

DESCRIPTION

@@ -1,7 +1,7 @@
 Type: Package
 Package: serocalculator
 Title: Estimating Infection Rates from Serological Data
-Version: 1.2.0.9022
+Version: 1.2.0.9023
 Authors@R: c(
     person("Peter", "Teunis", , "[email protected]", role = c("aut", "cph"),
            comment = "Author of the method and original code."),

NAMESPACE

@@ -70,7 +70,6 @@ importFrom(ggplot2,aes)
 importFrom(ggplot2,autoplot)
 importFrom(ggplot2,ggplot)
 importFrom(lifecycle,deprecated)
-importFrom(magrittr,"%>%")
 importFrom(mixtools,normalmixEM)
 importFrom(rlang,.data)
 importFrom(rlang,.env)

NEWS.md

@@ -1,7 +1,6 @@
 # serocalculator (development version)
 
 ## New features
-
 * Added example datasets with documentation for examples and testing (#314)
 
 * Improved error messaging for `autoplot.pop_data()` (#234).
@@ -26,6 +25,8 @@
 (from <https://github.com/bcgov/ssdtools>) (#265)
 
 ## Internal changes
+* Replace pipes to base R pipes (#310)
+
 * Change default pipe setting (#312)
 
 * Add test for missing strata in `est.incidence.by` (#227)

R/add_point_to_graph.R

@@ -3,11 +3,11 @@ add_point_to_graph <- function(
     fit,
     name = "est.incidence",
     point_data =
-      tibble(
-        x = fit$estimate %>% exp(),
-        y = log_likelihood(.data$x, ...),
-        label = "est.incidence"
-      ),
+        tibble(
+          x = fit$estimate |> exp(),
+          y = log_likelihood(.data$x, ...),
+          label = "est.incidence"
+        ),
     ...) {
   graph <-
     graph +

R/as_curve_params.R

@@ -9,8 +9,8 @@
 #' @examples
 #' library(magrittr)
 #' curve_data <-
-#'   "https://osf.io/download/rtw5k/" %>%
-#'   readr::read_rds() %>%
+#'   "https://osf.io/download/rtw5k/" |>
+#'   readr::read_rds() |>
 #'   as_curve_params()
 #'
 #' print(curve_data)
@@ -29,7 +29,7 @@ as_curve_params <- function(data, antigen_isos = NULL) {
   }
 
   curve_data <-
-    data %>%
+    data |>
     tibble::as_tibble()
 
   # check if object has expected columns:
@@ -38,7 +38,7 @@ as_curve_params <- function(data, antigen_isos = NULL) {
   curve_cols <- c("antigen_iso", "y0", "y1", "t1", "alpha", "r")
 
   # get columns from provided data
-  data_cols <- data %>% names()
+  data_cols <- data |> names()
 
   # get any missing column(s)
   missing_cols <- setdiff(x = curve_cols, y = data_cols)
@@ -70,7 +70,7 @@ as_curve_params <- function(data, antigen_isos = NULL) {
   # assign antigen attribute
   attr(curve_data, "antigen_isos") <- antigen_isos
 
-  curve_data <- curve_data %>%
+  curve_data <- curve_data |>
     set_biomarker_var(biomarker = "antigen_iso", standardize = FALSE)
 
   return(curve_data)

R/as_noise_params.R

@@ -10,8 +10,8 @@
 #' @examples
 #' library(magrittr)
 #' noise_data <-
-#'   "https://osf.io/download//hqy4v/" %>%
-#'   readr::read_rds() %>%
+#'   "https://osf.io/download//hqy4v/" |>
+#'   readr::read_rds() |>
 #'   as_noise_params()
 #'
 #' print(noise_data)
@@ -31,7 +31,7 @@ as_noise_params <- function(data, antigen_isos = NULL) {
     )
   }
 
-  noise_data <- data %>% tibble::as_tibble()
+  noise_data <- data |> tibble::as_tibble()
 
   # Define noise columns
   noise_cols <- c("antigen_iso", "y.low", "eps", "nu", "y.high")

R/as_pop_data.R

@@ -1,18 +1,21 @@
 #' Load a cross-sectional antibody survey data set
 #'
 #' @param data a [data.frame()] or [tibble::tbl_df]
-#' @param antigen_isos [character()] vector of antigen isotypes to be used in analyses
+#' @param antigen_isos [character()] vector of antigen
+#' isotypes to be used in analyses
 #' @param age a [character()] identifying the age column
 #' @param id a [character()] identifying the id column
 #' @param value a [character()] identifying the value column
-#' @param standardize a [logical()] to determine standardization of columns
-#' @returns a `pop_data` object (a [tibble::tbl_df] with extra attribute `antigen_isos`)
+#' @param standardize a [logical()] to determine
+#' standardization of columns
+#' @returns a `pop_data` object (a [tibble::tbl_df]
+#' with extra attribute `antigen_isos`)
 #' @export
 #' @examples
 #' library(magrittr)
 #' xs_data <-
-#'   "https://osf.io/download//n6cp3/" %>%
-#'   readr::read_rds() %>%
+#'   "https://osf.io/download//n6cp3/" |>
+#'   readr::read_rds() |>
 #'   as_pop_data()
 #'
 #' print(xs_data)
@@ -25,7 +28,7 @@ as_pop_data <- function(data,
 
 
   pop_data <-
-    data %>%
+    data |>
     tibble::as_tibble()
 
   class(pop_data) <-
@@ -39,10 +42,10 @@ as_pop_data <- function(data,
 
   attr(pop_data, "antigen_isos") <- antigen_isos
 
-  pop_data <- pop_data %>%
-    set_age(age = age, standardize = standardize) %>%
-    set_value(value = value, standardize = standardize) %>%
-    set_id(id = id, standardize = standardize)  %>%
+  pop_data <- pop_data |>
+    set_age(age = age, standardize = standardize) |>
+    set_value(value = value, standardize = standardize) |>
+    set_id(id = id, standardize = standardize)  |>
     set_biomarker_var(biomarker = "antigen_iso", standardize = standardize)
 
   return(pop_data)

R/autoplot.curve_params.R

@@ -23,9 +23,9 @@
 #' library(ggplot2)
 #' library(magrittr)
 #'
-#' curve = load_curve_params("https://osf.io/download/rtw5k/") %>%
-#'   filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) %>%
-#'   slice(1:100, .by = antigen_iso)  %>% # Reduce dataset for this example
+#' curve = load_curve_params("https://osf.io/download/rtw5k/") |>
+#'   filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) |>
+#'   slice(1:100, .by = antigen_iso)  |> # Reduce dataset for this example
 #'   autoplot()
 #'
 #' curve
@@ -35,13 +35,13 @@
     antigen_isos = unique(object$antigen_iso),
     ncol = min(3, length(antigen_isos)),
     ...) {
-  split_data <- object %>%
-    filter(.data$antigen_iso %in% antigen_isos) %>%
-    droplevels() %>%
+  split_data <- object |>
+    filter(.data$antigen_iso %in% antigen_isos) |>
+    droplevels() |>
     split(~antigen_iso)
 
   labels <- names(split_data)
-  figs <- split_data %>%
+  figs <- split_data |>
     lapply(FUN = plot_curve_params_one_ab, ...)
 
   for (i in seq_along(figs)) {

R/autoplot.pop_data.R

@@ -24,8 +24,8 @@
 #'   standardize = TRUE
 #' )
 #'
-#' xs_data %>% autoplot(strata = "Country", type = "density")
-#' xs_data %>% autoplot(strata = "Country", type = "age-scatter")
+#' xs_data |> autoplot(strata = "Country", type = "density")
+#' xs_data |> autoplot(strata = "Country", type = "age-scatter")
 #' @export
 autoplot.pop_data <- function(
     object,
@@ -65,21 +65,21 @@
 age_scatter <- function(
     object,
     strata = NULL,
-    age_var = object %>% get_age_var(),
-    value_var = object %>% get_value_var()) {
+    age_var = object |> get_age_var(),
+    value_var = object |> get_value_var()) {
   # create default plotting
 
   if (is.null(strata)) {
     plot1 <-
-      object %>%
+      object |>
       ggplot2::ggplot() +
       ggplot2::aes(
         x = .data[[age_var]],
         y = .data[[value_var]]
       )
   } else {
     plot1 <-
-      object %>%
+      object |>
       ggplot2::ggplot() +
       ggplot2::aes(
         col = .data[[strata]],
@@ -120,9 +120,9 @@
     object,
     strata = NULL,
     log = FALSE,
-    value_var = object %>% get_value_var()) {
+    value_var = object |> get_value_var()) {
   plot1 <-
-    object %>%
+    object |>
     ggplot2::ggplot() +
     ggplot2::aes(x = .data[[value_var]]) +
     ggplot2::theme_linedraw() +
@@ -146,19 +146,19 @@
   if (log) {
 
     min_nonzero_val <-
-      object %>%
-      get_value() %>%
-      purrr::keep(~ . > 0) %>%
+      object |>
+      get_value() |>
+      purrr::keep(~ . > 0) |>
       min()
 
     max_val <-
-      object %>%
-      get_value() %>%
+      object |>
+      get_value() |>
       max()
 
     breaks1 <- c(0, 10^seq(
-      min_nonzero_val %>% log10() %>% floor(),
-      max_val %>% log10() %>% ceiling()
+      min_nonzero_val |> log10() |> floor(),
+      max_val |> log10() |> ceiling()
     ))
 
     plot1 <- plot1 +

R/autoplot.seroincidence.R

@@ -1,7 +1,8 @@
 #' Plot the log-likelihood curve for the incidence rate estimate
 #'
 #' @param object a `seroincidence` object (from [est.incidence()])
-#' @param log_x should the x-axis be on a logarithmic scale (`TRUE`) or linear scale (`FALSE`, default)?
+#' @param log_x should the x-axis be on a logarithmic scale
+#'(`TRUE`) or linear scale (`FALSE`, default)?
 #' @param ... unused
 #'
 #' @return a [ggplot2::ggplot()]
@@ -13,34 +14,34 @@
 #'
 #' xs_data <- load_pop_data("https://osf.io/download//n6cp3/")
 #'
-#' curve <- load_curve_params("https://osf.io/download/rtw5k/") %>%
-#'   filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) %>%
-#'   slice(1:100, .by = antigen_iso) # Reduce dataset for the purposes of this example
+#' curve <- load_curve_params("https://osf.io/download/rtw5k/") |>
+#'   filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG
+#'   # Reduce dataset for the purposes of this example
+#'   slice(1:100, .by = antigen_iso)
 #'
 #' noise <- load_noise_params("https://osf.io/download//hqy4v/")
 #'
 #' est1 <- est.incidence(
-#'   pop_data = xs_data %>% filter(Country == "Pakistan"),
+#'   pop_data = xs_data |> filter(Country == "Pakistan"),
 #'   curve_param = curve,
-#'   noise_param = noise %>% filter(Country == "Pakistan"),
+#'   noise_param = noise |> filter(Country == "Pakistan"),
 #'   antigen_isos = c("HlyE_IgG", "HlyE_IgA"),
 #'   build_graph = TRUE
 #' )
 #'
 #' # Plot the log-likelihood curve
 #' autoplot(est1)
 #'
-autoplot.seroincidence =
-  function(object, log_x = FALSE, ...)
-{
-  to_return = attr(object, "ll_graph")
+autoplot.seroincidence <-
+  function(object, log_x = FALSE, ...) {
+    to_return <- attr(object, "ll_graph")
 
     if (is.null(to_return)) {
       stop(
         "Graphs cannot be extracted; ",
         "`build_graph` was not `TRUE` in the call to `est.incidence()`"
       )
       figure <- NULL
     }
 
     if (log_x) {

R/autoplot.seroincidence.by.R

@@ -4,27 +4,29 @@
 #' @param object a '"seroincidence.by"' object (from [est.incidence.by()])
 #' @param ncol number of columns to use for panel of plots
 #' @inheritDotParams autoplot.seroincidence
-#' @return an object of class `"ggarrange"`, which is a [ggplot2::ggplot()] or a [list()] of [ggplot2::ggplot()]s.
+#' @return an object of class `"ggarrange"`,
+#' which is a [ggplot2::ggplot()] or a [list()] of [ggplot2::ggplot()]s.
 #' @export
 #' @examples
 #'
 #' library(dplyr)
 #' library(ggplot2)
 #'
-#' xs_data <- "https://osf.io/download//n6cp3/" %>%
+#' xs_data <- "https://osf.io/download//n6cp3/" |>
 #'   load_pop_data()
 #'
-#' curve <- load_curve_params("https://osf.io/download/rtw5k/") %>%
-#'   filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) %>%
-#'   slice(1:100, .by = antigen_iso) # Reduce dataset for the purposes of this example
+#' curve <- load_curve_params("https://osf.io/download/rtw5k/") |>
+#'   filter(antigen_iso %in% c("HlyE_IgA", "HlyE_IgG")) |>
+#'   # Reduce dataset for the purposes of this example
+#'   slice(1:100, .by = antigen_iso)
 #'
 #' noise <- load_noise_params("https://osf.io/download//hqy4v/")
 #'
 #' est2 <- est.incidence.by(
 #'   strata = c("catchment"),
-#'   pop_data = xs_data %>% filter(Country == "Pakistan"),
+#'   pop_data = xs_data |> filter(Country == "Pakistan"),
 #'   curve_params = curve,
-#'   noise_params = noise %>% filter(Country == "Pakistan"),
+#'   noise_params = noise |> filter(Country == "Pakistan"),
 #'   antigen_isos = c("HlyE_IgG", "HlyE_IgA"),
 #'   #num_cores = 8, #Allow for parallel processing to decrease run time
 #'   build_graph = TRUE
@@ -33,7 +35,7 @@
 #' # Plot the log-likelihood curve
 #' autoplot(est2)
 #'
-autoplot.seroincidence.by = function(
+autoplot.seroincidence.by <- function(
     object,
     ncol = min(3, length(object)),
     ...) {
@@ -52,8 +54,7 @@
   labels <- names(object)
   figs <- lapply(object, FUN = autoplot.seroincidence, ...)
 
-  for (i in 1:length(figs))
-  {
+  for (i in 1:length(figs)) {
     figs[[i]] <- figs[[i]] + ggplot2::ggtitle(labels[i])
   }