Merge branch 'fix_pipes' into 'main'

Fix pipes

See merge request lpjml/lpjmlstats!30

Author: DavidhoPIK

.buildlibrary

@@ -1,4 +1,4 @@
-ValidationKey: '1905849'
+ValidationKey: '1926436'
 AutocreateReadme: yes
 AcceptedWarnings:
 - 'Warning: package ''.*'' was built under R version'

CITATION.cff

@@ -2,8 +2,8 @@ cff-version: 1.2.0
 message: If you use this software, please cite it using the metadata from this file.
 type: software
 title: 'lpjmlstats: Statistical tools for LPJmL data analysis'
-version: 0.9.3
-date-released: '2026-02-09'
+version: 0.9.4
+date-released: '2026-02-10'
 abstract: This package provides statistical tools for LPJmL data analysis to be used
   for benchmarking LPJmL outputs.
 authors:

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: lpjmlstats
 Title: Statistical tools for LPJmL data analysis
-Version: 0.9.3
+Version: 0.9.4
 Authors@R: c(
     person("David","Hötten", , "davidho@pik-potsdam.de", role = c("aut", "cre")),
     person("Jannes","Breier", , "jannes.breier@pik-potsdam.de", role = c("aut"), comment = c(ORCID = "0000-0002-9055-6904")),
@@ -46,5 +46,5 @@ Config/testthat/edition: 3
 VignetteBuilder: knitr
 Depends:
     R (>= 3.5.0)
-Date: 2026-02-09
+Date: 2026-02-10
 BinaryFiles: inst/bin2cdf

R/Metric_subclasses.R

@@ -16,8 +16,8 @@ GlobSumTimeAvgTable <- # nolint: object_name_linter.
       #' @param data LPJmLDataCalc object to be summarized
       #' @return A summarized \link{LPJmLDataCalc} object
       summarize = function(data) {
-        data %>%
-          aggregate(cell = list(to = "global", stat = "weighted_sum")) %>%
+        data |>
+          aggregate(cell = list(to = "global", stat = "weighted_sum")) |>
           aggregate(time = list(to = "sim_period", stat = "mean"))
       },
 
@@ -94,8 +94,8 @@ GlobAvgTimeAvgTable <- R6::R6Class( # nolint: object_name_linter.
     #' @param data LPJmLDataCalc object to be summarized
     #' @return A summarized \link{LPJmLDataCalc} object
     summarize = function(data) {
-      data %>%
-        aggregate(cell = list(to = "global", stat = "weighted_mean")) %>%
+      data |>
+        aggregate(cell = list(to = "global", stat = "weighted_mean")) |>
         aggregate(time = list(to = "sim_period", stat = "mean"))
     },
 
@@ -129,7 +129,7 @@ GlobSumTimeseries <- R6::R6Class( # nolint: object_name_linter.
     #' @param data LPJmLDataCalc object to be summarized
     #' @return A summarized \link{LPJmLDataCalc} object
     summarize = function(data) {
-      data %>%
+      data |>
         aggregate(cell = list(to = "global", stat = "weighted_sum"))
     },
 
@@ -198,7 +198,7 @@ GlobAvgTimeseries <- R6::R6Class( # nolint: object_name_linter.
     #' @param data LPJmLDataCalc object to be summarized
     #' @return A summarized \link{LPJmLDataCalc} object
     summarize = function(data) {
-      data %>%
+      data |>
         aggregate(cell = list(to = "global", stat = "weighted_mean"))
     },
 
@@ -229,8 +229,8 @@ GlobSumAnnAvgTimeseries <- # nolint: object_name_linter.
       #' @param data LPJmLDataCalc object to be summarized
       #' @return A summarized \link{LPJmLDataCalc} object
       summarize = function(data) {
-        data %>%
-          aggregate(time = list(to = "years", stat = "mean")) %>%
+        data |>
+          aggregate(time = list(to = "years", stat = "mean")) |>
           aggregate(cell = list(to = "global", stat = "weighted_sum"))
       },
 
@@ -261,8 +261,8 @@ GlobAvgAnnAvgTimeseries <- R6::R6Class( # nolint: object_name_linter.
     #' @param data LPJmLDataCalc object to be summarized
     #' @return A summarized \link{LPJmLDataCalc} object
     summarize = function(data) {
-      data %>%
-        aggregate(time = list(to = "years", stat = "mean")) %>%
+      data |>
+        aggregate(time = list(to = "years", stat = "mean")) |>
         aggregate(cell = list(to = "global", stat = "weighted_mean"))
     },
 
@@ -293,7 +293,7 @@ CellSubsetAnnAvgTimeseries <- # nolint: object_name_linter.
       #' @param lpjml_data LPJmLDataCalc object to be summarized
       #' @return A summarized \link{LPJmLDataCalc} object
       summarize = function(lpjml_data) {
-        subset(lpjml_data, cell = self$m_options$cell) %>%
+        subset(lpjml_data, cell = self$m_options$cell) |>
           aggregate(time = list(to = "years", stat = "mean"))
       },
 
@@ -380,7 +380,7 @@ TimeAvgMap <- # nolint: object_name_linter.
       #' @param data LPJmLDataCalc object to be summarized
       #' @return A summarized \link{LPJmLDataCalc} object
       summarize = function(data) {
-        data %>%
+        data |>
           aggregate(time = list(to = "sim_period", stat = "mean"))
       },
 

README.md

@@ -1,6 +1,6 @@
 # Statistical tools for LPJmL data analysis
 
-R package **lpjmlstats**, version **0.9.3**
+R package **lpjmlstats**, version **0.9.4**
 
 [![CRAN status](https://www.r-pkg.org/badges/version/lpjmlstats)](https://cran.r-project.org/package=lpjmlstats) [![R build status](https://github.com/PIK-LPJmL/lpjmlstats/workflows/check/badge.svg)](https://github.com/PIK-LPJmL/lpjmlstats/actions) [![codecov](https://codecov.io/gh/PIK-LPJmL/lpjmlstats/branch/master/graph/badge.svg)](https://app.codecov.io/gh/PIK-LPJmL/lpjmlstats) [![r-universe](https://pik-piam.r-universe.dev/badges/lpjmlstats)](https://pik-piam.r-universe.dev/builds)
 
@@ -48,17 +48,17 @@ In case of questions / problems please contact David Hötten <davidho@pik-potsda
 
 To cite package **lpjmlstats** in publications use:
 
-Hötten D, Breier J, Müller C, Wirth S (2026). "lpjmlstats: Statistical tools for LPJmL data analysis." Version: 0.9.3, <https://github.com/PIK-LPJmL/lpjmlstats>.
+Hötten D, Breier J, Müller C, Wirth S (2026). "lpjmlstats: Statistical tools for LPJmL data analysis." Version: 0.9.4, <https://github.com/PIK-LPJmL/lpjmlstats>.
 
 A BibTeX entry for LaTeX users is
 
  ```latex
 @Misc{,
   title = {lpjmlstats: Statistical tools for LPJmL data analysis},
   author = {David Hötten and Jannes Breier and Christoph Müller and Stephen B. Wirth},
-  date = {2026-02-09},
+  date = {2026-02-10},
   year = {2026},
   url = {https://github.com/PIK-LPJmL/lpjmlstats},
-  note = {Version: 0.9.3},
+  note = {Version: 0.9.4},
 }
 ```

inst/Benchmark_markdown.Rmd

@@ -52,14 +52,14 @@ sim_table <- attr(bench_data, "sim_table")
 
 # manually escape special character ":" and "_"
 sim_table <-
-  sim_table %>%
+  sim_table |>
   dplyr::mutate_all(escape_latex_special_chars)
 
 # reorder columns as they should be displayed
-tabl <- sim_table %>% dplyr::select(sim_names,
-                                    sim_ident,
-                                    sim_type,
-                                    lpjml_version)
+tabl <- sim_table |> dplyr::select(sim_names,
+                                   sim_ident,
+                                   sim_type,
+                                   lpjml_version)
 
 # create and style table
 knitr::kable(
@@ -70,16 +70,16 @@ knitr::kable(
   escape = FALSE,
   # allow latex commands but depends on manual escaping
   col.names = c("Sim Name", "Sim Identifier", "Sim Type", "Version")
-)  %>%
+)  |>
   kableExtra::kable_styling(font_size = 8,
                             latex_options = c("HOLD_position"))
 ```
 \vspace{2cm}
 \textbf{Simulation Paths}
 ```{r print-simulation-paths, results='asis'}
 # select relevant columns and make font size of paths very small
-tabl <- sim_table %>%
-  dplyr::mutate(sim_paths = paste0("\\tiny ", sim_paths)) %>%
+tabl <- sim_table |>
+  dplyr::mutate(sim_paths = paste0("\\tiny ", sim_paths)) |>
   dplyr::select(sim_ident, sim_paths)
 
 # create and style table
@@ -89,7 +89,7 @@ knitr::kable(
   booktabs = TRUE,
   escape = FALSE,
   col.names = c("Sim Identifier", "Sim Path")
-) %>%
+) |>
   kableExtra::kable_styling(font_size = 8,
                             latex_options = c("HOLD_position"))
 ```

tests/testthat/testLPJmLDataCalc_aggregate.R

@@ -138,13 +138,13 @@ test_that("usecases of aggregation with cow and plotting runs error free", {
   soiln$add_grid()
 
   # usecase 1: subset and plot
-  expect_no_error(soiln %>% subset(time = 1) %>% plot())
+  expect_no_error(soiln |> subset(time = 1) |> plot())
 
   # usecase 2: aggregate, subset and plot
   expect_no_error(
-    soiln %>%
-      aggregate(cell = list(to = "countries", stat = "sum")) %>%
-      subset(time = 1) %>%
+    soiln |>
+      aggregate(cell = list(to = "countries", stat = "sum")) |>
+      subset(time = 1) |>
       plot()
   )
 })
@@ -158,7 +158,7 @@ test_that("usecases of aggregation with cow integral on multiple timesteps ",
             soiln$add_grid()
 
             # aggregate multiple time steps #nolint
-            expect_no_error(soiln %>%
+            expect_no_error(soiln |>
                               aggregate(cell =
                                           list(to = "countries", stat = "sum")))
 
@@ -334,7 +334,7 @@ test_that("grid for aggregation is loaded automatically", {
   path_to_soil_n_json <-
     test_path("../testdata/path1", "soiln.bin.json")
   soiln <- read_io_calc(path_to_soil_n_json)
-  expect_no_error(soiln %>% aggregate(cell = list(to = "global", stat = "sum")))
+  expect_no_error(soiln |> aggregate(cell = list(to = "global", stat = "sum")))
 })
 
 

vignettes/.lintr

@@ -2,3 +2,6 @@ linters: lucode2::lintrRules(allowUndesirable = TRUE,
                              modification = list(object_name_linter = lintr::object_name_linter(styles = "snake_case"),
                              absolute_path_linter = NULL))
 encoding: "UTF-8"
+exclusions: [
+  "\\.R$"
+]