change pipes

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)

R/add_point_to_graph.R

@@ -4,7 +4,7 @@ add_point_to_graph <- function(
     name = "est.incidence",
     point_data =
       tibble(
-        x = fit$estimate %>% exp(),
+        x = fit$estimate |> exp(),
         y = log_likelihood(.data$x, ...),
         label = "est.incidence"
       ),

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

@@ -11,8 +11,8 @@
 #' @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 +25,7 @@ as_pop_data <- function(data,
 
 
   pop_data <-
-    data %>%
+    data |>
     tibble::as_tibble()
 
   class(pop_data) <-
@@ -39,10 +39,10 @@ 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 @@ autoplot.curve_params <- function(
     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 @@ autoplot.pop_data <- function(
 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 @@ density_plot <- function(
     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 @@ density_plot <- function(
   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

@@ -13,16 +13,16 @@
 #'
 #' 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")) %>%
+#' 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
 #'
 #' 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
 #' )

R/autoplot.seroincidence.by.R

@@ -11,20 +11,20 @@
 #' 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")) %>%
+#' 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
 #'
 #' 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

R/autoplot.summary.seroincidence.by.R

@@ -16,17 +16,17 @@
 #'
 #' 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")) %>%
+#' 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
 #'
 #' 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
 #' )
@@ -42,7 +42,7 @@ autoplot.summary.seroincidence.by <- function(
     shape = 1,
     width = 0.001,
     ...) {
-  object %>%
+  object |>
     ggplot2::ggplot(
       ggplot2::aes(
         x = get(xvar),

R/check_parallel_cores.R

@@ -3,7 +3,7 @@ check_parallel_cores <- function(num_cores) {
 
   if (num_cores > (parallel::detectCores() - 1)) {
     num_cores <-
-      num_cores %>%
+      num_cores |>
       min(parallel::detectCores() - 1)
 
     warning(

R/check_pop_data.R

@@ -6,7 +6,7 @@
 #' @export
 #' @examples
 #' library(dplyr)
-#' xs_data <- readr::read_rds("https://osf.io/download//n6cp3/") %>%
+#' xs_data <- readr::read_rds("https://osf.io/download//n6cp3/") |>
 #'   as_pop_data()
 #' check_pop_data(xs_data, verbose = TRUE)
 #'
@@ -23,24 +23,24 @@ check_pop_data <- function(pop_data, verbose = FALSE) {
   }
 
   missing_age <- is.element(
-    pop_data %>% get_age_var(),
-    pop_data %>% names()
+    pop_data |> get_age_var(),
+    pop_data |> names()
   )
 
   if (!missing_age) {
     "Argument {.arg pop_data} is missing column
-    {.var {pop_data %>% get_age_var()}}(age, in years)" %>%
+    {.var {pop_data |> get_age_var()}}(age, in years)" |>
       cli::cli_abort(class = "missing-var")
   }
 
   missing_value <- is.element(
-    pop_data %>% get_value_var(),
-    pop_data %>% names()
+    pop_data |> get_value_var(),
+    pop_data |> names()
   )
 
   if (!missing_value) {
     "Argument {.arg pop_data} is missing column
-    {.var {pop_data %>% get_value_var()}} (antibody measurement)" %>%
+    {.var {pop_data |> get_value_var()}} (antibody measurement)" |>
       cli::cli_abort(class = "missing-var")
   }