debugging r cmd check

Author: atyrell3

DESCRIPTION

@@ -15,8 +15,8 @@ BugReports: https://github.com/NEFSC/READ-EDAB-NEesp2/issues
 Depends: 
     R (>= 3.5.0)
 Imports:
-  magrittr,
-  rlang
+    magrittr,
+    rlang
 Suggests:
     assessmentdata,
     bookdown,
@@ -38,11 +38,13 @@ Suggests:
     knitr,
     lintr,
     lwgeom,
+    mockery,
     nmfspalette,
     pander,
     papeR,
     priceR,
     rmarkdown,
+    rvest,
     scales,
     sf,
     stringr,
@@ -54,7 +56,7 @@ Suggests:
     utils,
     viridis
 VignetteBuilder: 
-    rmarkdown
+    knitr
 Remotes:
     NEFSC/READ_EDAB_Utilities,
     nmfs-general-modeling-tools/nmfspalette,

R/create_recreational_indicators.R

@@ -4,7 +4,6 @@
 #' Input data is MRIP catch (A, B1, B2 catch combined)
 #'
 #' @param data The mrip data
-#' @param species The species common name
 #' @param var_name The variable name to use in the indicator name. Default is "catch".
 #' @param var_units The variable units to use in the indicator name. Default is "n".
 #' @param remove_non_standard Boolean, if TRUE will remove non-standard data ("Does Total Catch (A+B1+B2) Meet MRIP Standard" = NO)
@@ -76,7 +75,7 @@ create_total_mrip <- function(
 #' Choose year of interest, summarize by Annual, Calendar Year, Atlantic coast by state, species of interest, all modes and areas, Primary Target
 #' Download csv as output
 #' @param files A list of the full file names of annual directed trip data (.csv format). Must download for each year in MRIP query tool.
-#' @param states States in which to filter data, from MRIP query 'ATLANTIC COAST BY STATE'
+#' @param remove_non_standard Boolean, if TRUE will remove non-standard data ("Does Directed Trips Meet MRIP Standard" = NO)
 #' @importFrom magrittr %>%
 #' @return Saves R object `rec_trips`, returns directed recreational trips indicator
 #' @export

R/create_template.R

@@ -2,16 +2,16 @@
 #'
 #' This function creates an ESP two-pager template at the specified path.
 #' @param path Where to create the template. Defaults to the present working directory.
+#' @param over whether to overwrite existing files. Defaults to FALSE.
 #' @export
 
-
-create_template <- function(path = getwd(),
-                            over = FALSE) {
+create_template <- function(path = getwd(), over = FALSE) {
   file.copy(
-    from = list.files(path = system.file(c("esp_template"),
-                                         package = "NEesp2"), 
-                      # recursive = TRUE,
-                      full.names = TRUE),
+    from = list.files(
+      path = system.file(c("esp_template"), package = "NEesp2"),
+      # recursive = TRUE,
+      full.names = TRUE
+    ),
     to = path,
     recursive = TRUE,
     overwrite = over

R/get_mrip_data.R

@@ -285,7 +285,7 @@ save_trips <- function(
 #' @param out_folder where to save the data
 #' @param catch_type the type of catch to query. Can be "all" for all catch types (A, B1, B2), or "landings" for just the landings (A and B1). Default is "all".
 #' @param wait whether to pause after saving the data. Default is TRUE.
-#' @param return_fmane whether to return the file name of the saved data. Default is TRUE.
+#' @param return_fname whether to return the file name of the saved data. Default is TRUE.
 #' @return Saves list of the scraped data and metadata. Returns the file name.
 #' @export
 
@@ -298,7 +298,6 @@ save_catch <- function(
   wait = TRUE,
   return_fname = TRUE
 ) {
-
   fname <- paste0(
     out_folder,
     "/catch_",

R/plot_condition.R

@@ -1,58 +1,62 @@
-#' Plot Condition 
+#' Plot Condition
 #'
 #' @param data A data frame of species condition from `species_condition(..., output = "soe")`
 #' @param var species of interest to plot
 #' @return A ggplot
-#' 
+#'
 #' @export
 
-plot_condition <- function(data, 
-                           var,
-                           return = TRUE){
+plot_condition <- function(data, var) {
   condition <- data |>
-    dplyr::select(YEAR,
-                  Species,
-                  EPU,
-                  DATA_VALUE) |>
+    dplyr::select(YEAR, Species, EPU, DATA_VALUE) |>
     dplyr::group_by(Species) |>
-    dplyr::mutate(scaleCond = scale(DATA_VALUE,scale =T,center=T))
-  
-  xs <- quantile(condition$scaleCond, seq(0,1, length.out = 6), na.rm = TRUE)
-  
+    dplyr::mutate(scaleCond = scale(DATA_VALUE, scale = T, center = T))
+
+  xs <- quantile(condition$scaleCond, seq(0, 1, length.out = 6), na.rm = TRUE)
+
   condition <- condition |>
-    dplyr::mutate(category = cut(scaleCond,
-                                 breaks = xs,
-                                 labels = c( "Poor Condition",
-                                             "Below Average",
-                                             "Neutral",
-                                             "Above Average",
-                                             "Good Condition"),
-                                 include.lowest = TRUE))
-  
+    dplyr::mutate(
+      category = cut(
+        scaleCond,
+        breaks = xs,
+        labels = c(
+          "Poor Condition",
+          "Below Average",
+          "Neutral",
+          "Above Average",
+          "Good Condition"
+        ),
+        include.lowest = TRUE
+      )
+    )
+
   condition <- condition |>
     dplyr::filter(Species %in% var) |>
     dplyr::ungroup() |>
     dplyr::arrange(YEAR) |>
     dplyr::group_by(EPU) |>
-    dplyr::mutate(mean = mean(DATA_VALUE, na.rm = TRUE),
-                  sd = sd(DATA_VALUE, na.rm = TRUE)) |>
-    ggplot2::ggplot(ggplot2::aes(x = YEAR,
-                                 y = DATA_VALUE,
-                                 color = category,
-                                 shape = EPU
+    dplyr::mutate(
+      mean = mean(DATA_VALUE, na.rm = TRUE),
+      sd = sd(DATA_VALUE, na.rm = TRUE)
+    ) |>
+    ggplot2::ggplot(ggplot2::aes(
+      x = YEAR,
+      y = DATA_VALUE,
+      color = category,
+      shape = EPU
     )) +
     ggplot2::geom_path(color = "black", lty = 2, alpha = 0.5) +
     ggplot2::geom_point(cex = 3) +
     ggplot2::xlim(c(1989, 2024)) +
     ggplot2::theme_classic(base_size = 16) +
-    ggplot2::theme(strip.text = ggplot2::element_text(size = 16),
-                   axis.title.x = ggplot2::element_blank(),
-                   aspect.ratio = 0.4,
-                   legend.direction = "vertical",
-                   legend.box = "horizontal") +
+    ggplot2::theme(
+      strip.text = ggplot2::element_text(size = 16),
+      axis.title.x = ggplot2::element_blank(),
+      aspect.ratio = 0.4,
+      legend.direction = "vertical",
+      legend.box = "horizontal"
+    ) +
     viridis::scale_color_viridis(discrete = TRUE)
-  
+
   return(condition)
 }
-
-

R/plot_indicator_ts.R

@@ -1,58 +1,64 @@
-#' Plot indicator time series 
+#' Plot indicator time series
 #'
 #' This function plots an indicator time series with a year, indicator name, and data value with mean and standard deviation lines.
 #'
 #' @param data A data frame with columns of YEAR, INDICATOR_NAME, and DATA_VALUE
 #' @param ar Aspect Ratio for the resulting plot
+#' @param include_trends Boolean, if TRUE will include linear and GLS trend lines using ecodata package. Default is TRUE.
 #' @return A ggplot
 #' @importFrom magrittr %>%
 #' @importFrom rlang .data
 #' @export
-#' 
-plt_indicator <- function(data,
-                          ar = 1/4,
-                          include_trends = TRUE) {
+#'
+plt_indicator <- function(data, ar = 1 / 4, include_trends = TRUE) {
   plt <- data |>
     dplyr::group_by(INDICATOR_NAME) |>
-    dplyr::mutate(mean = mean(DATA_VALUE, na.rm = TRUE),
-                  sd = sd(DATA_VALUE, na.rm = TRUE)) |>
-    ggplot2::ggplot(ggplot2::aes(x = YEAR,
-                                 y = DATA_VALUE
-    )) +
-    ggplot2::geom_hline(ggplot2::aes(
-      yintercept = .data$mean + .data$sd
-    ),
-    color = "darkgreen",
-    linetype = "solid"
+    dplyr::mutate(
+      mean = mean(DATA_VALUE, na.rm = TRUE),
+      sd = sd(DATA_VALUE, na.rm = TRUE)
+    ) |>
+    ggplot2::ggplot(ggplot2::aes(x = YEAR, y = DATA_VALUE)) +
+    ggplot2::geom_hline(
+      ggplot2::aes(
+        yintercept = .data$mean + .data$sd
+      ),
+      color = "darkgreen",
+      linetype = "solid"
     ) +
-    ggplot2::geom_hline(ggplot2::aes(
-      yintercept = .data$mean - .data$sd
-    ),
-    color = "darkgreen",
-    linetype = "solid"
+    ggplot2::geom_hline(
+      ggplot2::aes(
+        yintercept = .data$mean - .data$sd
+      ),
+      color = "darkgreen",
+      linetype = "solid"
     ) +
-    ggplot2::geom_hline(ggplot2::aes(
-      yintercept = .data$mean
-    ),
-    color = "darkgreen",
-    linetype = "dotted"
+    ggplot2::geom_hline(
+      ggplot2::aes(
+        yintercept = .data$mean
+      ),
+      color = "darkgreen",
+      linetype = "dotted"
     ) +
     ggplot2::geom_point() +
     ggplot2::geom_line() +
     ggplot2::scale_y_continuous(labels = scales::comma) +
     # ecodata::theme_ts() +
     ggplot2::theme_classic(base_size = 16) +
-    ggplot2::theme(strip.text = ggplot2::element_text(size = 16),
-                   axis.title = ggplot2::element_blank(),
-                   aspect.ratio = ar,
-                   plot.background = ggplot2::element_rect(fill='transparent',
-                                                           color='transparent'))
-  
-  if(include_trends) {
+    ggplot2::theme(
+      strip.text = ggplot2::element_text(size = 16),
+      axis.title = ggplot2::element_blank(),
+      aspect.ratio = ar,
+      plot.background = ggplot2::element_rect(
+        fill = 'transparent',
+        color = 'transparent'
+      )
+    )
+
+  if (include_trends) {
     plt <- plt +
       ecodata::geom_gls() +
       ecodata::geom_lm()
   }
-  
+
   return(plt)
 }