Answer to rOpenSci second round of editor review

.github/workflows/pkgdown.yaml

@@ -23,7 +23,7 @@ jobs:
     permissions:
       contents: write
     steps:
-      - uses: actions/checkout@v4
+      - uses: actions/checkout@v7
 
       - uses: r-lib/actions/setup-pandoc@v2
 

DESCRIPTION

@@ -28,6 +28,8 @@ URL: https://hugogruson.fr/grumpy/, https://github.com/Bisaloo/grumpy
 BugReports: https://github.com/Bisaloo/grumpy/issues
 Imports: 
     jsonlite
+Config/Needs/website:
+    Huber-group-EMBL/Rarr
 Config/roxygen2/version: 8.0.0
 Depends: 
     R (>= 4.2.0)

README.md

@@ -23,7 +23,8 @@ is performant, flexible. Overall, it is designed to be used deep in the
 dependency graph of other packages.
 
 For more details on the motivation and design principles underpinning
-`{grumpy}`, see the dedicated vignette: .
+`{grumpy}`, see the dedicated vignette:
+`vignette("design", package = "grumpy")`.
 
 ## Installation
 

README.qmd

@@ -29,7 +29,7 @@ As a file format generated by a Python package, `.npy` files are prime candidate
 However, this comes with downsides in terms of performance, flexibility, and robustness of the R package infrastructure. 
 `{grumpy}`, on the other hand, is a pure R package with a single dependency (`{jsonlite}`), and is performant, flexible. Overall, it is designed to be used deep in the dependency graph of other packages.
 
-For more details on the motivation and design principles underpinning `{grumpy}`, see the dedicated vignette: `r vignette("design", package = "grumpy")`.
+For more details on the motivation and design principles underpinning `{grumpy}`, see the dedicated vignette: `vignette("design", package = "grumpy")`.
 
 ## Installation
 

vignettes/beyond.qmd

@@ -44,9 +44,10 @@ size <- list.files(f_zarr, full.names = TRUE, recursive = TRUE) |>
   file.info() |>
   subset(select = "size") |>
   sum()
+size
 ```
 
-**Without compression**, the equivalent Zarr data is thus 320.577 kB on disk, so `round(8e8 / size)` times smaller than the `.npy` file. We could also use compression to further reduce the size of the Zarr file on disk, but this is out of scope for this vignette.
+**Without compression**, the equivalent Zarr data is thus 320 kB on disk, so `round(8e8 / size)` times smaller than the `.npy` file. We could also use compression to further reduce the size of the Zarr file on disk, but this is out of scope for this vignette.
 
 ## Decoding speed comparison
 
@@ -61,11 +62,13 @@ np$save(f_npy, x)
 ```
 
 ```{r}
-bench::mark(
+bm <- bench::mark(
   grumpy = read_npy(f_npy),
   zarr = read_zarr_array(f_zarr),
-  iterations = 20
+  iterations = 50
 )
+bm
+summary(bm, relative = TRUE)
 ```
 
 There is a small time penalty for reading the Zarr file, since the various chunks need to be read and concatenated together, but the memory footprint is much smaller, and the Zarr file is much smaller on disk. This is particularly important for large datasets that do not fit into memory, as it allows for out-of-core processing of the data.