removed dependency on magrittr from package to be replaced with native pipe

Author: andybeet

DESCRIPTION

@@ -5,7 +5,9 @@ Authors@R:
     person(given = "Andy",family = "Beet", role = c("aut", "cre"),
            email = "andrew.beet@noaa.gov",
            comment = c(ORCID = "0000-0001-8270-7090"))
-Description: A data package serving all data found on NOAAs stock SMART website
+Description: Provides access to data found on the NOAA Stock SMART website. 
+    The package serves as a data repository for fisheries assessment 
+    information and includes utilities for data retrieval.
 URL: https://github.com/NOAA-EDAB/stocksmart, https://noaa-edab.github.io/stocksmart/
 BugReports: https://github.com/NOAA-EDAB/stocksmart/issues
 License: file LICENSE
@@ -18,7 +20,6 @@ Depends:
 Imports: 
     dplyr,
     tidyr,
-    magrittr,
     rlang,
     ggplot2,
     tibble,

NAMESPACE

@@ -6,5 +6,4 @@ export(get_latest_metrics)
 export(get_species_itis)
 export(get_species_stock_data)
 export(plot_ts)
-importFrom(magrittr,"%>%")
 importFrom(rlang,.data)

R/get_available_ts.R

@@ -14,54 +14,56 @@
 #' Fmort). Some assessments have differing lengths of time series.  }
 #'
 #'
-#' @importFrom magrittr "%>%"
 #' @importFrom rlang .data
 #'
 #' @export
 
 get_available_ts <- function(itis = NULL, jurisdiction = NULL) {
-
-
   # Error check for metric names
   if (is.null(itis) || is.null(jurisdiction)) {
-    stop("If you do not know the ITIS code or jurisdiction then please
-         use get_species_itis function")
+    stop(
+      "If you do not know the ITIS code or jurisdiction then please
+         use get_species_itis function"
+    )
   }
 
   # Pull filter the data to determine if present
-  tsData <- stocksmart::stockAssessmentData %>%
-    dplyr::filter(.data$ITIS == itis,
-                  .data$Jurisdiction == jurisdiction)
+  tsData <- stocksmart::stockAssessmentData |>
+    dplyr::filter(.data$ITIS == itis, .data$Jurisdiction == jurisdiction)
 
   if (nrow(tsData) == 0) {
-    stop("No data found. Either the itis or jurisdiction is not entered
+    stop(
+      "No data found. Either the itis or jurisdiction is not entered
     correctly. Please check your inputs or use get_species_itis() to find the
-            correct ITIS code and jurisdiction.")
+            correct ITIS code and jurisdiction."
+    )
   }
 
-
   # find the first and last year of each assessment
-  res <- tsData %>%
-
-    dplyr::group_by(.data$StockName,
-                    .data$CommonName,
-                    .data$Jurisdiction,
-                    .data$StockArea,
-                    .data$ITIS,
-                    .data$Metric,
-                    .data$AssessmentYear) %>%
-    dplyr::summarise(FirstYear = min(.data$Year), LastYear = max(.data$Year),
-                     numYears = .data$LastYear - .data$FirstYear + 1,
-                     .groups = "drop")  %>%
-    dplyr::group_by(.data$StockName,
-                    .data$Jurisdiction,
-                    .data$ITIS,
-                    .data$AssessmentYear) %>%
-    dplyr::summarise(nYrs = mean(.data$numYears),
-                     .groups = "drop")
+  res <- tsData |>
 
+    dplyr::group_by(
+      .data$StockName,
+      .data$CommonName,
+      .data$Jurisdiction,
+      .data$StockArea,
+      .data$ITIS,
+      .data$Metric,
+      .data$AssessmentYear
+    ) |>
+    dplyr::summarise(
+      FirstYear = min(.data$Year),
+      LastYear = max(.data$Year),
+      numYears = .data$LastYear - .data$FirstYear + 1,
+      .groups = "drop"
+    ) |>
+    dplyr::group_by(
+      .data$StockName,
+      .data$Jurisdiction,
+      .data$ITIS,
+      .data$AssessmentYear
+    ) |>
+    dplyr::summarise(nYrs = mean(.data$numYears), .groups = "drop")
 
   return(res)
-
-
 }

R/get_latest_full_assessment.r

@@ -16,17 +16,15 @@
 #' AssessmentYear the data came from, the FirstYear and LastYear of the data
 #'  and the number of years (numYear) of data retrieved}
 #'
-#' @importFrom magrittr "%>%"
 #' @importFrom rlang .data
 #'
 #' @export
 
 get_latest_full_assessment <- function(itis = NULL) {
-
-  result <-  get_latest_metrics(itis = itis,
-                                metrics = c("Catch", "Abundance", "Fmort",
-                                            "Recruitment"))
+  result <- get_latest_metrics(
+    itis = itis,
+    metrics = c("Catch", "Abundance", "Fmort", "Recruitment")
+  )
 
   return(result)
-
 }

R/get_latest_metrics.r

@@ -31,7 +31,6 @@
 #' the data came from, the FirstYear and LastYear of the data and the number
 #'  of years (numYear) of data retrieved}
 #'
-#' @importFrom magrittr "%>%"
 #' @importFrom rlang .data
 #'
 #' @export

R/get_species_stock_data.R

@@ -1,4 +1,4 @@
-#' Pull species data directly
+#' Pull real-time species stock data directly from StockSMART API
 #'
 #' Pulls data defined by stock directly to obtain current data. For those who
 #' don't want to reinstall the package to get most recent data

R/plot_ts.R

@@ -24,34 +24,36 @@
 #' \item{plot}{ggplot object}
 #' \item{data}{dataframe used in the plotting}
 #'
-#' @importFrom magrittr "%>%"
 #' @importFrom rlang .data
 #'
 #' @export
 
-plot_ts <- function(itis = NULL, stock = NULL, metric = "Catch",
-                    facetplot = FALSE, printfig = TRUE) {
-
-
+plot_ts <- function(
+  itis = NULL,
+  stock = NULL,
+  metric = "Catch",
+  facetplot = FALSE,
+  printfig = TRUE
+) {
   #error check for metric names
   if (is.null(itis)) {
     stop("If you do not know the ITIS code then please use get_species_itis()")
   }
   if (!metric %in% c("Catch", "Fmort", "Recruitment", "Abundance", "Index")) {
-    stop("Please use of the defined metrics to plot. Catch, Fmort, Recruitment,
-         Abundance, Index")
+    stop(
+      "Please use of the defined metrics to plot. Catch, Fmort, Recruitment,
+         Abundance, Index"
+    )
   }
 
-
-
   # filter by ITIS and Metric
-  dataToPlot <- stocksmart::stockAssessmentData %>%
-    dplyr::filter(.data$ITIS == itis,
-                  .data$Metric == metric) %>%
+  dataToPlot <- stocksmart::stockAssessmentData |>
+    dplyr::filter(.data$ITIS == itis, .data$Metric == metric) |>
     dplyr::mutate(AssessmentYear = as.factor(.data$AssessmentYear))
 
-  if (!is.null(stock)) {     # filter by stock
-    dataToPlot <- dataToPlot %>%
+  if (!is.null(stock)) {
+    # filter by stock
+    dataToPlot <- dataToPlot |>
       dplyr::filter(.data$StockName == stock)
   }
 
@@ -61,83 +63,103 @@ plot_ts <- function(itis = NULL, stock = NULL, metric = "Catch",
   }
 
   # select stocks for this ITIS
-  stocks <- dataToPlot %>%
+  stocks <- dataToPlot |>
     dplyr::distinct(.data$StockName)
 
   # if multiple stocks and user argument - NULL
   if ((nrow(stocks) > 1) && (is.null(stock))) {
-    message(paste0("There are multiple stocks for ITIS = ",
-                   itis,
-                   ". Please specify which stock you'd like to plot"))
+    message(paste0(
+      "There are multiple stocks for ITIS = ",
+      itis,
+      ". Please specify which stock you'd like to plot"
+    ))
     stop(stocks)
   }
   # if multiple stocks and user argument doesn't match
-  if ((nrow(stocks) > 1) && (!stock %in% (stocks %>% dplyr::pull()))) {
+  if ((nrow(stocks) > 1) && (!stock %in% (stocks |> dplyr::pull()))) {
     message(paste0("Please specify a valid stock to plot "))
     stop(stocks)
   }
 
   # if only a single stock and user argument doesn't match
   if (nrow(stocks) == 1) {
-    if (is.null(stock)) { # user left blank
+    if (is.null(stock)) {
+      # user left blank
       stock <- stocks
-    } else { # user entered a string
-      if (!stock %in% (stocks %>% dplyr::pull())) {
+    } else {
+      # user entered a string
+      if (!stock %in% (stocks |> dplyr::pull())) {
         message(paste0("Please specify a valid stock to plot "))
         stop(stocks)
       }
     }
-
   }
 
   # filter out stock to plot
   if (length(unique(dataToPlot$StockArea)) > 1) {
     # filter out stock
-    stockToPlot <- dataToPlot %>%
-      dplyr::filter(.data$StockName == stock) %>%
-      dplyr::mutate(AssmtYrUnits = as.factor(paste0(.data$AssessmentYear,
-                                                    " (", .data$Units, ")")))
+    stockToPlot <- dataToPlot |>
+      dplyr::filter(.data$StockName == stock) |>
+      dplyr::mutate(
+        AssmtYrUnits = as.factor(paste0(
+          .data$AssessmentYear,
+          " (",
+          .data$Units,
+          ")"
+        ))
+      )
   } else {
     # no need to filter out stock (else causes error in dplyr 1.1.0)
-    stockToPlot <- dataToPlot %>%
-      dplyr::mutate(AssmtYrUnits = as.factor(paste0(.data$AssessmentYear,
-                                                    " (", .data$Units, ")")))
+    stockToPlot <- dataToPlot |>
+      dplyr::mutate(
+        AssmtYrUnits = as.factor(paste0(
+          .data$AssessmentYear,
+          " (",
+          .data$Units,
+          ")"
+        ))
+      )
   }
 
   # units check and standardize
   # eg if units change over time from say mt to thousands mt
 
-
   # plot data
   if (facetplot == TRUE) {
     p <- ggplot2::ggplot(stockToPlot) +
-      ggplot2::geom_line(ggplot2::aes(x = .data$Year,
-                                      y = .data$Value)) +
-      ggplot2::facet_wrap(~.data$AssmtYrUnits, scales = "free_y") +
+      ggplot2::geom_line(ggplot2::aes(x = .data$Year, y = .data$Value)) +
+      ggplot2::facet_wrap(~ .data$AssmtYrUnits, scales = "free_y") +
       ggplot2::ylab(metric)
   } else {
-    if ((stockToPlot %>% dplyr::distinct(.data$Units) %>% nrow()) > 1) {
-      message("The units change over time. Plotting as a facet plot
-              will be better")
+    if ((stockToPlot |> dplyr::distinct(.data$Units) |> nrow()) > 1) {
+      message(
+        "The units change over time. Plotting as a facet plot
+              will be better"
+      )
     }
     p <- ggplot2::ggplot(stockToPlot) +
-      ggplot2::geom_line(ggplot2::aes(x = .data$Year,
-                                      y = .data$Value,
-                                      color = .data$AssessmentYear)) +
-      ggplot2::ylab(paste0(metric, " (", stockToPlot %>%
-                             dplyr::distinct(.data$Units), ")")) +
+      ggplot2::geom_line(ggplot2::aes(
+        x = .data$Year,
+        y = .data$Value,
+        color = .data$AssessmentYear
+      )) +
+      ggplot2::ylab(paste0(
+        metric,
+        " (",
+        stockToPlot |>
+          dplyr::distinct(.data$Units),
+        ")"
+      )) +
       ggplot2::labs(color = "Assessment Year")
   }
 
   p <- p +
-    ggplot2::ggtitle(stockToPlot %>% dplyr::distinct(.data$StockName))
+    ggplot2::ggtitle(stockToPlot |> dplyr::distinct(.data$StockName))
 
   # print figure to window
   if (printfig) {
     print(p)
   }
 
   return(list(plot = p, data = stockToPlot))
-
-
 }

man/get_species_stock_data.Rd

@@ -19,10 +19,7 @@ reference:
 - title: "Filtering functions"
   desc: "Functions used to help user extract information from the bundled data"
 - contents:
-  - get_latest_full_assessment
-  - get_latest_metrics
-  - get_available_ts
-  - get_species_itis
+  - starts_with("get_")
 - title: "Plotting functions"
   desc: "Functions used to help user visualize the bundled data"
 - contents:

pkgdown/_pkgdown.yml

@@ -16,7 +16,6 @@ knitr::opts_chunk$set(
 
 ```{r setup}
 library(stocksmart)
-library(magrittr)
 ```
 
 
@@ -27,8 +26,8 @@ Time series data for all federally managed stocks are bundled with the package
 The [stockAssessmentData](../reference/stockAssessmentData.html) looks like this:
 
 ```{r sad, echo = FALSE}
-stockAssessmentData %>%
-  tibble::as_tibble()
+stockAssessmentData  |> 
+  dplyr::as_tibble()
 
 ```
 
@@ -104,12 +103,13 @@ cod$data
 We can then filter the the data by the Georges Bank stock and plot it.
 
 ``` {r GB, echo = TRUE, fig.alt = "Catch data for Atlantic cod in Georges Bank from the latest operational assessment"}
-cod$data %>%
-  dplyr::filter(StockArea == "Georges Bank") %>%
-  { . ->> filteredData } %>%
-  ggplot2::ggplot(.) +
+filteredData <- cod$data  |> 
+  dplyr::filter(StockArea == "Georges Bank") 
+cod$data |> 
+  dplyr::filter(StockArea == "Georges Bank") |> 
+  ggplot2::ggplot() +
   ggplot2::geom_line(ggplot2::aes(x = Year, y = Value)) +
-  ggplot2::ylab(filteredData %>% dplyr::distinct(Units)) +
-  ggplot2::ggtitle(paste0("Assessment Year = ", filteredData %>%
+  ggplot2::ylab(filteredData  |>  dplyr::distinct(Units)) +
+  ggplot2::ggtitle(paste0("Assessment Year = ", filteredData  |> 
                             dplyr::distinct(AssessmentYear)))
 ```