SITS: Satellite Image Time Series Analysis for Earth Observation Data Cubes (submit R package for review) #596
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Checks for sits (v1.4.1)git hash: 6eac9edf
Important: All failing checks above must be addressed prior to proceeding Package License: GPL-2 1. Package DependenciesDetails of Package Dependency Usage (click to open)
The table below tallies all function calls to all packages ('ncalls'), both internal (r-base + recommended, along with the package itself), and external (imported and suggested packages). 'NA' values indicate packages to which no identified calls to R functions could be found. Note that these results are generated by an automated code-tagging system which may not be entirely accurate.
Click below for tallies of functions used in each package. Locations of each call within this package may be generated locally by running 's <- pkgstats::pkgstats(<path/to/repo>)', and examining the 'external_calls' table. baselist (172), source (167), c (162), labels (100), nrow (72), length (62), file (55), scale (41), names (39), row (39), seq_len (38), unlist (37), col (32), date (31), unique (28), lapply (25), unname (25), q (23), sum (23), toupper (23), for (22), paste (22), return (19), as.Date (18), options (18), as.numeric (17), paste0 (17), seq_along (17), ceiling (15), class (15), apply (14), dim (14), environment (13), round (12), which (12), as.character (11), as.matrix (11), Sys.time (10), try (10), factor (9), rep (9), seq (9), sqrt (9), suppressMessages (9), url (9), array (8), do.call (8), format (8), matrix (8), ncol (8), rbind (8), sample (8), substitute (8), t (8), table (8), by (7), colnames (7), mean (7), Sys.getenv (7), tryCatch (7), character (6), data.frame (6), floor (6), formals (6), gamma (6), levels (6), strsplit (6), structure (6), version (6), which.min (6), as.integer (5), if (5), missing (5), replace (5), suppressWarnings (5), which.max (5), abs (4), any (4), as.list (4), choose (4), grepl (4), inherits (4), list2env (4), mode (4), rev (4), row.names (4), setdiff (4), tempdir (4), args (3), cbind (3), colSums (3), diag (3), eval (3), get (3), is.na (3), path.expand (3), plot (3), prop.table (3), raw (3), readRDS (3), signif (3), socketSelect (3), split (3), switch (3), system.file (3), vapply (3), as.factor (2), debug (2), deparse (2), double (2), exp (2), gsub (2), I (2), is.logical (2), log (2), logical (2), max.col (2), parent.env (2), pmin (2), quote (2), range (2), rawConnection (2), rowSums (2), sort (2), subset (2), substring (2), summary (2), tolower (2), write (2), all (1), all.vars (1), as.vector (1), basename (1), cat (1), cut (1), difftime (1), dir (1), dir.exists (1), dirname (1), drop (1), exists (1), file.exists (1), gc (1), integer (1), is.null (1), kappa (1), lengths (1), list.files (1), new.env (1), numeric (1), open (1), order (1), parent.frame (1), rbind.data.frame (1), remove (1), sample.int (1), srcfile (1), svd (1), sys.frames (1), tapply (1), tempfile (1), vector (1), within (1) utilsdata (249), txtProgressBar (3), head (2), write.csv (2), getTxtProgressBar (1), read.csv (1), tail (1) statsts (44), offset (36), formula (14), D (8), kernel (8), time (8), runif (6), predict (5), dt (4), na.action (4), quantile (4), sd (4), df (3), step (3), weights (3), as.formula (2), end (2), median (2), rbeta (2), rf (2), rlnorm (2), rnorm (2), start (2), addmargins (1), as.dendrogram (1), cutree (1), filter (1), reshape (1), smooth (1), var (1), window (1) sitssits_bands (25), sits_labels (21), sits_timeline (19), sits_select (10), train_fun (8), C_mask_na (3), mixture_fn (3), C_label_max_prob (2), C_max_sampling (2), comb_fn (2), download_fn (2), filter_call (2), label_fn (2), reclassify_fn (2), sits_train (2), sits_values (2), smooth_fn (2), .debug (1), C_entropy_probs (1), C_fill_na (1), C_kernel_max (1), C_kernel_mean (1), C_kernel_median (1), C_kernel_min (1), C_kernel_sd (1), C_least_probs (1), C_margin_probs (1), C_nnls_solver_batch (1), C_normalize_data (1), C_normalize_data_0 (1), create_iqr (1), impute_fun (1), plot_samples (1), replace_na (1), result_fun (1), seg_fun (1), sits_as_sf (1), sits_colors (1), sits_som_map (1), sits_validate (1), sits_view.sits (1), submit (1) purrrmap (63), map_chr (16), map_dfr (12), map_dfc (7), map2_dfr (7), pmap_dfr (6), map_lgl (5), pmap (4), is_character (2), map_dbl (2), imap_dfc (1), map_int (1), map2 (1), pmap_lgl (1), transpose (1) dplyrfilter (16), mutate (16), bind_rows (11), bind_cols (9), all_of (7), select (6), n (5), group_by (4), slice_sample (4), distinct (3), slice_head (3), left_join (2), select_if (2), starts_with (2), tibble (2), cur_group_id (1), inner_join (1), matches (1), summarise (1), transmute (1) ggplot2ggplot (12), aes (9), element_text (7), scale_fill_manual (5), element_rect (3), geom_line (3), labs (3), theme (3), .pt (2), element_blank (2), facet_grid (2), geom_point (2), geom_rect (2), guide_legend (2), position_dodge (2), scale_x_continuous (2), facet_wrap (1), geom_histogram (1), guides (1), scale_color_brewer (1), scale_x_date (1), scale_x_log10 (1) tibbletibble (49), as_tibble_row (10), as_tibble (7), lst (1) torchnn_module (20), torch_tensor (11), nn_cross_entropy_loss (5), nn_softmax (5), nn_batch_norm1d (4), torch_matmul (3), torch_zeros (3), nn_init_normal_ (2), torch_stack (2), torch_transpose (2), nn_dropout (1), torch_exp (1), torch_mean (1) graphicssegments (15), title (12), points (10), legend (9), plot (3), abline (2), grid (1) sfst_crs (8), st_sample (7), st_transform (7), st_as_sf (5), st_drop_geometry (3), st_intersects (3), st_coordinates (2), st_geometry (2), st_geometry_type (2), st_intersection (2), st_write (2), read_sf (1), st_bbox (1), st_convex_hull (1), st_distance (1), st_is_empty (1), st_polygon (1), st_sf (1), st_sfc (1), st_within (1) rstacpost_request (15), items_reap (11), items_fetch (9), items_matched (6), sign_planetary_computer (3), ext_query (2), stac (1), stac_search (1) terrarast (7), ext (4), spatSample (4), xFromCol (4), yFromRow (4), readValues (3), crop (2), crs (2), expanse (2), extract (2), freq (2), fileBlocksize (1), ncol (1), nlyr (1), nrow (1), readStart (1), values (1), xmax (1), xmin (1), xres (1), ymax (1), ymin (1), yres (1) sliderslide (18), slide_dfr (16), slide_dbl (4), slide_lgl (4), slide2_dfr (3), slide_chr (1), slide2_lgl (1) tidyrnest (14), unnest (11), expand_grid (9), pivot_longer (6), starts_with (2), everything (1) grDevicescolors (30), palette (9), hcl.colors (3) lubridateas_date (13), fast_strptime (1), mday (1), month (1), year (1), ymd (1) leafletlayersControlOptions (12), colorFactor (2), leaflet (1), providers$GeoportailFrance.orthos (1) digestdigest (15) tmaptm_borders (5), tm_shape (3), tm_layout (2), tm_facets (1), tm_raster (1), tmap_options (1) httradd_headers (4), write_disk (3), parse_url (2), build_url (1), content (1), GET (1) starsread_stars (8), st_warp (4) luzluz_metric_accuracy (5), setup (5), luz_callback_early_stopping (1) mgcvgam (3), predict.gam (2) yamlyaml.load_file (4), as.yaml (1) dtwclusthierarchical_control (2), cvi (1), tsclust (1) xgboostxgb.plot.tree (3), xgboost (1) caretconfusionMatrix (3) datasetstrees (3) FNNknnx.index (3) scalesdate_format (1), label_number (1), pretty_breaks (1) gdalcubesgdalcubes_options (1), image_collection (1) kohonensomgrid (1), supersom (1) methodsS3Part (2) openxlsxcreateWorkbook (1), saveWorkbook (1) e1071svm (1) exactextractrexact_extract (1) randomForestrandomForest (1) randomForestExplainerplot_min_depth_distribution (1) supercellssupercells (1) toolsfile_path_sans_ext (1) NOTE: Some imported packages appear to have no associated function calls; please ensure with author that these 'Imports' are listed appropriately. 2. Statistical PropertiesThis package features some noteworthy statistical properties which may need to be clarified by a handling editor prior to progressing. Details of statistical properties (click to open)
The package has:
Statistical properties of package structure as distributional percentiles in relation to all current CRAN packages
All parameters are explained as tooltips in the locally-rendered HTML version of this report generated by the The final measure (
2a. Network visualisationClick to see the interactive network visualisation of calls between objects in package 3.
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| id | name | conclusion | sha | run_number | date |
|---|---|---|---|---|---|
| 5438924419 | R-CMD-check | success | bc5d6c | 116 | 2023-07-02 |
3b. goodpractice results
R CMD check with rcmdcheck
R CMD check generated the following note:
- checking installed package size ... NOTE
installed size is 16.7Mb
sub-directories of 1Mb or more:
libs 14.1Mb
R CMD check generated the following check_fail:
- rcmdcheck_reasonable_installed_size
Test coverage with covr
Package coverage: 0.08
The following files are not completely covered by tests:
| file | coverage |
|---|---|
| R/api_accessors.R | 0% |
| R/api_accuracy.R | 0% |
| R/api_apply.R | 0% |
| R/api_band.R | 0% |
| R/api_bbox.R | 0% |
| R/api_block.R | 0% |
| R/api_check.R | 0% |
| R/api_chunks.R | 0% |
| R/api_classify.R | 0% |
| R/api_cluster.R | 0% |
| R/api_combine_predictions.R | 0% |
| R/api_comp.R | 0% |
| R/api_conf.R | 0% |
| R/api_csv.R | 0% |
| R/api_cube.R | 0% |
| R/api_data.R | 0% |
| R/api_debug.R | 0% |
| R/api_download.R | 0% |
| R/api_expressions.R | 0% |
| R/api_factory.R | 0% |
| R/api_file_info.R | 0% |
| R/api_file.R | 0% |
| R/api_gdal.R | 0% |
| R/api_gdalcubes.R | 0% |
| R/api_imputation.R | 0% |
| R/api_jobs.R | 0% |
| R/api_label_class.R | 0% |
| R/api_mixture_model.R | 0% |
| R/api_ml_model.R | 0% |
| R/api_mosaic.R | 0% |
| R/api_parallel.R | 0% |
| R/api_period.R | 0% |
| R/api_plot_raster.R | 0% |
| R/api_plot_time_series.R | 0% |
| R/api_point.R | 0% |
| R/api_predictors.R | 0% |
| R/api_raster_sub_image.R | 0% |
| R/api_raster_terra.R | 0% |
| R/api_raster.R | 0% |
| R/api_reclassify.R | 0% |
| R/api_roi.R | 0% |
| R/api_samples.R | 0% |
| R/api_segments.R | 0% |
| R/api_sf.R | 0% |
| R/api_shp.R | 0% |
| R/api_signal.R | 0% |
| R/api_smooth.R | 0% |
| R/api_smote.R | 0% |
| R/api_som.R | 0% |
| R/api_source_aws.R | 0% |
| R/api_source_bdc.R | 0% |
| R/api_source_deafrica.R | 0% |
| R/api_source_hls.R | 0% |
| R/api_source_local.R | 0% |
| R/api_source_mpc.R | 0% |
| R/api_source_sdc.R | 0% |
| R/api_source_stac.R | 0% |
| R/api_source_usgs.R | 0% |
| R/api_source.R | 0% |
| R/api_space_time_operations.R | 0% |
| R/api_stac.R | 0% |
| R/api_stats.R | 0% |
| R/api_summary.R | 0% |
| R/api_tibble.R | 0% |
| R/api_tile.R | 0% |
| R/api_timeline.R | 0% |
| R/api_torch.R | 0% |
| R/api_ts.R | 0% |
| R/api_tuning.R | 0% |
| R/api_uncertainty.R | 0% |
| R/api_utils.R | 0% |
| R/api_variance.R | 0% |
| R/api_view.R | 0% |
| R/sits_accuracy.R | 0% |
| R/sits_active_learning.R | 0% |
| R/sits_apply.R | 0% |
| R/sits_bands.R | 0% |
| R/sits_bbox.R | 0% |
| R/sits_classify.R | 0% |
| R/sits_cluster.R | 0% |
| R/sits_colors.R | 0% |
| R/sits_combine_predictions.R | 0% |
| R/sits_config.R | 0% |
| R/sits_csv.R | 0% |
| R/sits_cube_copy.R | 0% |
| R/sits_cube.R | 0% |
| R/sits_factory.R | 0% |
| R/sits_filters.R | 0% |
| R/sits_geo_dist.R | 0% |
| R/sits_get_data.R | 0% |
| R/sits_label_classification.R | 0% |
| R/sits_labels.R | 0% |
| R/sits_lighttae.R | 0% |
| R/sits_machine_learning.R | 0% |
| R/sits_merge.R | 0% |
| R/sits_mixture_model.R | 0% |
| R/sits_mlp.R | 0% |
| R/sits_model_export.R | 0% |
| R/sits_mosaic.R | 0% |
| R/sits_patterns.R | 0% |
| R/sits_plot.R | 0% |
| R/sits_predictors.R | 0% |
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| R/sits_resnet.R | 0% |
| R/sits_sample_functions.R | 0% |
| R/sits_segmentation.R | 0% |
| R/sits_select.R | 0% |
| R/sits_sf.R | 0% |
| R/sits_smooth.R | 0% |
| R/sits_som.R | 0% |
| R/sits_summary.R | 0% |
| R/sits_tae.R | 0% |
| R/sits_tempcnn.R | 0% |
| R/sits_temporal_segmentation.R | 0% |
| R/sits_timeline.R | 0% |
| R/sits_train.R | 0% |
| R/sits_tuning.R | 0% |
| R/sits_uncertainty.R | 0% |
| R/sits_utils.R | 12.5% |
| R/sits_validate.R | 0% |
| R/sits_values.R | 0% |
| R/sits_variance.R | 0% |
| R/sits_view.R | 0% |
| R/sits_xlsx.R | 0% |
| src/combine_data.cpp | 0% |
| src/kernel.cpp | 0% |
| src/label_class.cpp | 0% |
| src/linear_interp.cpp | 0% |
| src/nnls_solver.cpp | 0% |
| src/normalize_data_0.cpp | 0% |
| src/normalize_data.cpp | 0% |
| src/sampling_window.cpp | 0% |
| src/smooth_bayes.cpp | 0% |
| src/smooth_sgp.cpp | 0% |
| src/smooth_whit.cpp | 0% |
| src/smooth.cpp | 0% |
| src/uncertainty.cpp | 0% |
Cyclocomplexity with cyclocomp
The following function have cyclocomplexity >= 15:
| function | cyclocomplexity |
|---|---|
| sits_cube.stac_cube | 16 |
Static code analyses with lintr
lintr found the following 23 potential issues:
| message | number of times |
|---|---|
| Avoid library() and require() calls in packages | 16 |
| Lines should not be more than 80 characters. | 3 |
| Use <-, not =, for assignment. | 4 |
Package Versions
| package | version |
|---|---|
| pkgstats | 0.1.3.4 |
| pkgcheck | 0.1.1.26 |
Editor-in-Chief Instructions:
Processing may not proceed until the items marked with ✖️ have been resolved.
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Many thanks for your response. Please see below the following explanation, which was included as an "Important Note" in the submission, but maybe it has failed to catch the attention of the reviewers.
✖️ The following function has no documented return value: [sits_filter] ✖️ Package has no HTML vignettes ✖️ Package coverage is 0.1% (should be at least 75%).
We are confident that We would also like to respond to the The package imports directly 17 packages, which are required for most functions. It also suggests 33 packages, which are typically used only in a few function, and need to be included only in "as-is" basis. This is based on CRAN policies that restrict the number of imported packages. |
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Thank you for your submission @gilbertocamara! As well as your careful response to the automatic checks. We agree with all your responses above. However, as this a package that implements statistical and ML methods of geospatial data, rather than just the “accessing, manipulating, converting” and converting in our scope, it falls under under our newer statistical peer review program, which has its own time series and [geospatial standards](https://stats-devguide.ropensci.org/standards.html#standards-spatial. Submission requirements are different for this as authors need to document their standards compliance with our code annotation system. A note - SITS is a package that is very large in scope and code base, as exemplified by the fact that it has a whole book for its documentation. As such, we anticipate that it will be challenging to find reviewers and we will need to give them considerably longer than usual to review the code base and documentation in full. Most of our submissions are not as large or mature at the point of review and up for significant API or architecture changes in response to review. For something in an earlier stage we would likely have suggested breaking functionality up into smaller, more focused packages. Nonetheless, we are up for the challenge if you are up for the higher statistical submission requirements and potential changes. One last note regarding check results: As of now we can't set any environment variables when running the checks automatically, so they'd have to be set on your side, maybe using the withr::local_envvar() function or similar. Thanks again! We're happy to answer further questions. |
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Dear @maelle, many thanks for your response. Please see my comments below: Good! Looking at the specific requirements for ROpenSci statistical packages, the At a first glance, Allow me to propose an alternative: please consider that the information provided in "codecov.io" to be sufficient to assert that We will work on improving Thanks, |
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Thank you! 🎉 Here's a direct link to the author guide for stat submissions: https://stats-devguide.ropensci.org/pkgdev.html |
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@gilbertocamara just a clarification: your package will have to comply with one of the category standards of the statistical review system, probably spatial (because time series is for class-based manipulation of time-series data, which is not what your package does as far as I understand). Probability distributions should be considered an "additional' category that may be complied with in addition to the main categories. Thank you! |
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@gilbertocamara the CONTRIBUTING guide could link to the book chapter, as long as it's easy to find all information. I'm still waiting for R CMD check to finish, but examples ran without error (tests now running). |
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Thanks for the tips! |
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Tests passed! Now on to trying autotest... |
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Dear @maelle, autotest runs OK in "sits". Now, we have to work on the recommendations. Thanks! |
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Dear @maelle @mpadge I would like to ask for your help to understand how In the Consider the following function, which takes as input a set of spatially referenced time series and allows the user to select some of its members. Users can either select a number or a fraction of the series. The relevant part of the code is shown below: The output for autotest for this function is: In the above the We are failing to understand what is being tested by We are failing to see what we might be doing wrong. What are the expectations of We would appreciate your response. Best |
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Please, could you explain what appears to be an unexpected behaviour of Today, I ran I tried to fix some of these problems by considering the recommendations of the tidyverse design guide. In Section 26 ("Side-effect functions should return invisibly"), the guide states: "If a function is called primarily for its side-effects, it should invisibly return a useful output. If there’s no obvious output, return the first argument". See more at https://design.tidyverse.org/out-invisible.html. I am assuming that Could you please help me and explain why *** MWE *** Best regards |
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Hello! I'll get to this later this week, thanks for your patience! |
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Dear @maelle @mpadge Begging your indulgence for being insistent, I would like to ask if there is a detailed explanation of the types of diagnostics provided by Out of these 18 instances, Since the condition to proceed with the revision for statistical packages submitted to ROpenSci is that Please also explain why Many thanks for your help, |
res <- autotest::autotest_package("/home/maelle/Documents/ropensci/SOFTWARE-REVIEW/sits", test = TRUE)
#> Loading required namespace: devtools
#> ℹ Loading sits
#> SITS - satellite image time series analysis.
#>
#> Loaded sits v1.4.2.
#> See ?sits for help, citation("sits") for use in publication.
#> Documentation avaliable in https://e-sensing.github.io/sitsbook/.
#>
#> ★ Extracting example code from 107 .Rd files
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#> ★ Converting 47 examples to yaml
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#> ✔ Converted examples to yaml
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#> ── autotesting sits ──
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#> ✔ [1 / 19]: sits_clean
#> ✔ [2 / 19]: sits_cluster_clean
Created on 2023-07-18 with reprex v2.0.2 |
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@gilbertocamara regarding the output you mentioned in your comment #596 (comment), can you confirm it's gone? I don't see that exact error (I'm going through your comments chronologically). |
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Dear @maelle, above you have shown the |
Good question. To me the best answer is currently https://docs.ropensci.org/autotest/reference/autotest_types.html, does it help? I opened an issue in autotest because I agree the documentation could be improved on this front ropensci-review-tools/autotest#83 |
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currently actually running the tests 😅 sorry about that |
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Regarding the flagging of 2/18 functions, obviously I'll have a better idea once I have the results locally, but since autotest works by scraping examples, this might be due to different examples in these 2 functions? |
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I updated the results I get. Are they the same as on your machine @gilbertocamara? |
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Unfortunately, no. Please wait a little bit. Yesterday, we made some changes to |
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Ok. My answer might have a few days delay depending on my availability but I'll do my best. I'll re-read autotest docs. 😁 |
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@gilbertocamara Please accept my apologies as lead developer of autotest for issues here. I am currently away on holidays until start of August. Thank you for taking the autotest procedures and results so seriously, but please note that autotest is currently recommended and not required infrastructure. As such, its output should currently be considered (nothing more than) a useful guide to increase general robustness and documentation of packages prior to submission. It is not necessary for packages to completely pass autotest in order for submissions to proceed. In short: Please use autotest to help improve your package as much as possible. Once you are satisfied, feel free to ignore any remaining autotest issues and proceed on to documentation of statistical standards compliance. That said, please also feel free to open any issues in the autotest repository, or to ask any further questions there. The package will undergo a major revision hopefully sometime later this year, which will include numerous improvements in functionality, documentation, and general useability. Again, thank you for engaging so sincerely with these results, and apologies for any confusion during the process. |
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Dear @mpadge Many thanks for your response, even during your holidays. We will follow your recommendations and proceed to SRR once we consider we have followed all the relevant recommendations of |
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Any updates @gilbertocamara? As said, don't worry too much (or indeed at all) about |
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Dear @mpadge Apologies for the long delay in responding. First of all, kudos to ROpenSci for your work! In the last two months, we have been working on release 1.5.0 of the A key point here is that arguably The Overall, we found the guidelines to be very useful. To avoid lengthy issues, I will post my thoughts on the SRR guidelines in a set of comments below |
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Dear @mpadge Some comments on the SRR Generic Guidelines. Generic guidelines are quite good points, especially regarding documentation and error messages associated with variable checking. They encouraged us to: (a) improve documentation of internal functions (G1.4a); We could not fully understand the scope of G5.6 (parameter recovery) and G5.9 (noise susceptibility tests) so we consider that they do not apply to `sits. We missed explicit support in the SRR Guidelines regarding the |
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Dear @mpadge comments on SRR Guidelines on Machine Learning From the perspective of the Guidelines ML1.6 to ML1.8 deal with missing values. They were useful for us as reminders, taking in account that missing values in EO data arise in a different context than tabular data. We consider guideline ML2.0 quite important. In We also agree with ML2.2, although we have not yet implemented it. That said, we did not understand the difference between guidelines ML2.2, ML2.3, and ML2.4, ML2.5, and ML2.6. Perhaps they could be grouped together for brevity's sake. We did not understand the context of ML3.0 and its subpoints. We fail to see in which context such separation between specification and training might be useful. This is probably due to the specific nature of EO data analysis. As for item ML3.4 and subitems, we consider that requiring developers to provide functions for tuning hyperparameters might be a better approach, especially with deep learning. Although we understand the rationale for ML6.0, we advise against using training and test data for model assessment in the case of EO data. The community has developed a specific set of best practices of quality assessment. See Olofsson et al., (2014) doi:10.1016/j.rse.2014.02.015. In short, the specific case of applying ML/DL for EO data has issues which are impossible to cover in the scope of generic guidelines as provided in SSR for ML. |
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Dear @mpadge Comments on SRR Guidelines on Spatial and Time Series. The SSR Spatial Guidelines are very good and generally applicable. The emphasis on the We suggest the inclusion of a guideline regarding the installation of GDAL and PROJ, following the instructions associated with the As for time series, we fully agree with guidelines TS1.0 to T2.1c and have implemented them in |
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Dear @mpadge Final considerations and a plea for support. Overall, the SRR Guidelines deserve high praise. The ROpenSci team has provided an excellent service to the community by working hard to develop them. While the guidelines are aimed at small, focused R packages, they are also relevant to larger packages such as We thus would like your advice on how to proceed. We still consider that a software review of Whathever the case, warm congratulations and thanks to the ROpenSci community! |
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@gilbertocamara Thank you so much for your considered and very deep engagement with our statistical standards. The first thing I would like to ask would be for you to copy the above comments into separate issues within the repository for our Statistical Standards Book - one for the Machine Learning and one for Spatial standards. We'll then incorporate your excellent feedback there via updates to our standards. We are definitely keen to progress with peer-review here. The Our current Editor-in-Chief @jooolia will take it from here. Thank you for all of your work! |
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@ropensci-review-bot check srr |
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@ropensci-review-bot check package |
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Thanks, about to send the query. |
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🚀 Editor check started 👋 |
Checks for sits (v1.4.2-3)git hash: 06ab1b32
Important: All failing checks above must be addressed prior to proceeding Package License: GPL-2 1. Package DependenciesDetails of Package Dependency Usage (click to open)
The table below tallies all function calls to all packages ('ncalls'), both internal (r-base + recommended, along with the package itself), and external (imported and suggested packages). 'NA' values indicate packages to which no identified calls to R functions could be found. Note that these results are generated by an automated code-tagging system which may not be entirely accurate.
Click below for tallies of functions used in each package. Locations of each call within this package may be generated locally by running 's <- pkgstats::pkgstats(<path/to/repo>)', and examining the 'external_calls' table. baselist (201), source (190), c (185), labels (122), nrow (71), file (68), length (67), scale (45), unlist (45), names (42), row (41), seq_len (37), sum (36), unique (36), date (35), unname (31), col (30), lapply (27), as.numeric (26), paste (26), q (26), toupper (24), for (23), paste0 (23), class (19), options (18), return (18), seq_along (18), as.Date (16), ceiling (16), apply (15), dim (15), round (15), matrix (14), ncol (14), as.character (13), environment (13), by (12), format (12), rep (12), Sys.getenv (12), which (12), colnames (11), try (11), as.matrix (10), do.call (10), mean (10), sqrt (10), suppressMessages (10), tryCatch (10), factor (9), url (9), array (8), floor (8), rbind (8), sample (8), setdiff (8), substitute (8), t (8), table (8), seq (7), strsplit (7), version (7), as.integer (6), cbind (6), character (6), data.frame (6), formals (6), gamma (6), if (6), levels (6), structure (6), system.file (6), which.max (6), which.min (6), any (5), replace (5), suppressWarnings (5), switch (5), Sys.time (5), abs (4), as.list (4), choose (4), get (4), grepl (4), inherits (4), is.na (4), list2env (4), mode (4), readRDS (4), rev (4), row.names (4), split (4), tempdir (4), vapply (4), args (3), colSums (3), diag (3), eval (3), missing (3), path.expand (3), prop.table (3), raw (3), socketSelect (3), tolower (3), as.factor (2), cut (2), debug (2), deparse (2), double (2), gsub (2), I (2), is.logical (2), list.files (2), log (2), logical (2), max.col (2), parent.env (2), pmin (2), quote (2), range (2), rawConnection (2), readLines (2), rowSums (2), signif (2), subset (2), substring (2), tapply (2), within (2), write (2), all (1), all.vars (1), as.vector (1), basename (1), cat (1), difftime (1), dir (1), dir.exists (1), dirname (1), drop (1), exists (1), exp (1), file.exists (1), gc (1), integer (1), is.null (1), kappa (1), lengths (1), new.env (1), numeric (1), open (1), order (1), parent.frame (1), rbind.data.frame (1), remove (1), sample.int (1), sort (1), srcfile (1), summary (1), svd (1), sys.frames (1), tempfile (1), vector (1) utilsdata (267), vi (4), txtProgressBar (3), head (2), write.csv (2), getTxtProgressBar (1), read.csv (1), read.delim (1), tail (1) statsts (44), offset (33), formula (14), D (8), kernel (8), time (8), predict (5), df (4), dt (4), na.action (4), quantile (4), runif (4), sd (4), weights (3), as.formula (2), end (2), median (2), rbeta (2), rf (2), rlnorm (2), rnorm (2), start (2), addmargins (1), as.dendrogram (1), cutree (1), family (1), filter (1), reshape (1), smooth (1), step (1), var (1), window (1) sitssits_labels (27), sits_bands (26), sits_timeline (19), sits_select (10), train_fun (8), sits_bbox (4), C_mask_na (3), mixture_fn (3), uncert_fn (3), C_label_max_prob (2), C_max_sampling (2), comb_fn (2), download_fn (2), filter_call (2), label_fn (2), reclassify_fn (2), sits_accuracy (2), sits_train (2), smooth_fn (2), .debug (1), C_entropy_probs (1), C_fill_na (1), C_kernel_max (1), C_kernel_mean (1), C_kernel_median (1), C_kernel_min (1), C_kernel_modal (1), C_kernel_sd (1), C_kernel_var (1), C_least_probs (1), C_margin_probs (1), C_nnls_solver_batch (1), C_normalize_data (1), C_normalize_data_0 (1), choice (1), create_iqr (1), impute_fun (1), plot_samples (1), replace_na (1), result_fun (1), sits_apply (1), sits_as_sf (1), sits_classify (1), sits_clean (1), sits_label_classification (1), sits_som_map (1), sits_validate (1), sits_variance (1), sits_view.sits (1), submit (1) purrrmap (71), map_chr (20), map_dfr (18), map_lgl (9), map2_dfr (6), pmap (6), map_dfc (5), pmap_dfr (5), is_character (2), map_dbl (2), map_int (2), imap_dfc (1), map2 (1), pmap_chr (1), pmap_lgl (1), transpose (1) dplyrmutate (26), filter (18), all_of (14), bind_rows (12), bind_cols (9), select (6), n (5), across (4), left_join (4), slice_sample (4), group_by (3), slice_head (3), summarise (3), c_across (2), distinct (2), full_join (2), select_if (2), starts_with (2), tibble (2), cur_group_id (1), group_split (1), inner_join (1), matches (1), pull (1), relocate (1), rename (1), transmute (1) sfst_crs (10), st_transform (9), st_sample (7), st_as_sf (5), st_write (5), st_read (4), read_sf (3), st_drop_geometry (3), st_intersects (3), st_bbox (2), st_geometry (2), st_geometry_type (2), st_intersection (2), st_sf (2), st_sfc (2), st_as_sfc (1), st_contains (1), st_convex_hull (1), st_coordinates (1), st_distance (1), st_is_empty (1), st_multipoint (1), st_polygon (1), st_within (1) tibbletibble (49), as_tibble_row (10), as_tibble (6), lst (1) sliderslide (25), slide_dfr (23), slide_dbl (4), slide_lgl (4), slide2_dfr (3), slide_chr (1), slide2_lgl (1) torchnn_module (20), torch_tensor (11), nn_cross_entropy_loss (5), nn_softmax (5), nn_batch_norm1d (4), torch_matmul (3), torch_zeros (3), nn_init_normal_ (2), torch_stack (2), torch_transpose (2), nn_dropout (1), torch_exp (1), torch_mean (1) rstacpost_request (19), items_fetch (13), items_reap (11), items_matched (6), sign_planetary_computer (5), ext_query (2), stac (1), stac_search (1) ggplot2ggplot (11), aes (8), element_text (7), scale_fill_manual (5), geom_line (3), labs (3), theme (3), element_blank (2), element_rect (2), facet_grid (2), guide_legend (2), position_dodge (2), facet_wrap (1), guides (1), scale_color_brewer (1), scale_x_date (1), scale_x_log10 (1) graphicssegments (14), title (13), points (11), legend (9), plot (3), abline (2), grid (1) grDevicescolors (30), palette (10), hcl.colors (2) terraext (4), rast (4), xFromCol (4), yFromRow (4), readValues (3), crop (2), crs (2), extract (2), freq (2), spatSample (2), fileBlocksize (1), NAflag (1), ncol (1), nlyr (1), nrow (1), readStart (1), values (1), xmax (1), xmin (1), xres (1), ymax (1), ymin (1), yres (1) tidyrnest (15), unnest (14), pivot_longer (6), expand_grid (3), starts_with (2), everything (1) lubridateas_date (16), fast_strptime (1), mday (1), month (1), year (1), ymd (1) digestdigest (20) leafletlayersControlOptions (12), colorFactor (2), leaflet (1), providers$GeoportailFrance.orthos (1) httradd_headers (6), write_disk (3), parse_url (2), build_url (1), content (1), GET (1) tmaptm_borders (7), tm_shape (5), tm_facets (1), tmap_options (1) starsread_stars (8), st_warp (4) luzluz_metric_accuracy (5), setup (5), luz_callback_early_stopping (1) datasetstrees (6) mgcvgam (3), predict.gam (2) xgboostxgb.plot.tree (3), xgb.DMatrix (1), xgb.train (1) yamlyaml.load_file (4), as.yaml (1) dtwclusthierarchical_control (2), cvi (1), tsclust (1) stringrstr_wrap (3), str_count (1) caretconfusionMatrix (3) FNNknnx.index (3) scalesdate_format (1), label_number (1), pretty_breaks (1) kohonensomgrid (1), supersom (1) methodsS3Part (2) openxlsxcreateWorkbook (1), saveWorkbook (1) toolsfile_path_sans_ext (2) DiagrammeRrender_graph (1) e1071svm (1) exactextractrexact_extract (1) gdalcubesimage_collection (1) randomForestrandomForest (1) randomForestExplainerplot_min_depth_distribution (1) spdeppoly2nb (1) sysfontsfont_add_google (1) NOTE: Some imported packages appear to have no associated function calls; please ensure with author that these 'Imports' are listed appropriately. 2. Statistical PropertiesThis package features some noteworthy statistical properties which may need to be clarified by a handling editor prior to progressing. Details of statistical properties (click to open)
The package has:
Statistical properties of package structure as distributional percentiles in relation to all current CRAN packages
All parameters are explained as tooltips in the locally-rendered HTML version of this report generated by the The final measure (
2a. Network visualisationClick to see the interactive network visualisation of calls between objects in package 3.
|
| id | name | conclusion | sha | run_number | date |
|---|---|---|---|---|---|
| 8933087450 | R-CMD-check | success | 1ccc1c | 358 | 2024-05-03 |
3b. goodpractice results
R CMD check with rcmdcheck
R CMD check generated the following note:
- checking installed package size ... NOTE
installed size is 18.4Mb
sub-directories of 1Mb or more:
extdata 1.7Mb
libs 14.8Mb
R 1.1Mb
R CMD check generated the following check_fail:
- rcmdcheck_reasonable_installed_size
Test coverage with covr
Package coverage: 0.06
The following files are not completely covered by tests:
| file | coverage |
|---|---|
| R/api_accessors.R | 0% |
| R/api_accuracy.R | 0% |
| R/api_apply.R | 0% |
| R/api_band.R | 0% |
| R/api_bbox.R | 0% |
| R/api_block.R | 0% |
| R/api_check.R | 0% |
| R/api_chunks.R | 0% |
| R/api_classify.R | 0% |
| R/api_clean.R | 0% |
| R/api_cluster.R | 0% |
| R/api_colors.R | 0% |
| R/api_combine_predictions.R | 0% |
| R/api_comp.R | 0% |
| R/api_conf.R | 0% |
| R/api_csv.R | 0% |
| R/api_cube.R | 0% |
| R/api_data.R | 0% |
| R/api_debug.R | 0% |
| R/api_download.R | 0% |
| R/api_factory.R | 0% |
| R/api_file_info.R | 0% |
| R/api_file.R | 0% |
| R/api_gdal.R | 0% |
| R/api_gdalcubes.R | 0% |
| R/api_imputation.R | 0% |
| R/api_jobs.R | 0% |
| R/api_label_class.R | 0% |
| R/api_mixture_model.R | 0% |
| R/api_ml_model.R | 0% |
| R/api_mosaic.R | 0% |
| R/api_parallel.R | 0% |
| R/api_period.R | 0% |
| R/api_plot_raster.R | 0% |
| R/api_plot_time_series.R | 0% |
| R/api_plot_vector.R | 0% |
| R/api_point.R | 0% |
| R/api_predictors.R | 0% |
| R/api_raster_sub_image.R | 0% |
| R/api_raster_terra.R | 0% |
| R/api_raster.R | 0% |
| R/api_reclassify.R | 0% |
| R/api_regularize.R | 0% |
| R/api_roi.R | 0% |
| R/api_s2tile.R | 0% |
| R/api_samples.R | 0% |
| R/api_segments.R | 0% |
| R/api_sf.R | 0% |
| R/api_shp.R | 0% |
| R/api_signal.R | 0% |
| R/api_smooth.R | 0% |
| R/api_smote.R | 0% |
| R/api_som.R | 0% |
| R/api_source_aws.R | 0% |
| R/api_source_bdc.R | 0% |
| R/api_source_deafrica.R | 0% |
| R/api_source_hls.R | 0% |
| R/api_source_local.R | 0% |
| R/api_source_mpc.R | 0% |
| R/api_source_sdc.R | 0% |
| R/api_source_stac.R | 0% |
| R/api_source_usgs.R | 0% |
| R/api_source.R | 0% |
| R/api_space_time_operations.R | 0% |
| R/api_stac.R | 0% |
| R/api_stats.R | 0% |
| R/api_summary.R | 0% |
| R/api_tibble.R | 0% |
| R/api_tile.R | 0% |
| R/api_timeline.R | 0% |
| R/api_torch.R | 0% |
| R/api_ts.R | 0% |
| R/api_tuning.R | 0% |
| R/api_uncertainty.R | 0% |
| R/api_utils.R | 0% |
| R/api_values.R | 0% |
| R/api_variance.R | 0% |
| R/api_vector_info.R | 0% |
| R/api_vector.R | 0% |
| R/api_view.R | 0% |
| R/sits_accuracy.R | 0% |
| R/sits_active_learning.R | 0% |
| R/sits_apply.R | 0% |
| R/sits_bands.R | 0% |
| R/sits_bbox.R | 0% |
| R/sits_classify.R | 0% |
| R/sits_clean.R | 0% |
| R/sits_cluster.R | 0% |
| R/sits_colors.R | 0% |
| R/sits_combine_predictions.R | 0% |
| R/sits_config.R | 0% |
| R/sits_csv.R | 0% |
| R/sits_cube_copy.R | 0% |
| R/sits_cube.R | 0% |
| R/sits_factory.R | 0% |
| R/sits_filters.R | 0% |
| R/sits_geo_dist.R | 0% |
| R/sits_get_data.R | 0% |
| R/sits_label_classification.R | 0% |
| R/sits_labels.R | 0% |
| R/sits_lighttae.R | 0% |
| R/sits_machine_learning.R | 0% |
| R/sits_merge.R | 0% |
| R/sits_mixture_model.R | 0% |
| R/sits_mlp.R | 0% |
| R/sits_model_export.R | 0% |
| R/sits_mosaic.R | 0% |
| R/sits_patterns.R | 0% |
| R/sits_plot.R | 0% |
| R/sits_predictors.R | 0% |
| R/sits_reclassify.R | 0% |
| R/sits_regularize.R | 0% |
| R/sits_resnet.R | 0% |
| R/sits_sample_functions.R | 0% |
| R/sits_segmentation.R | 0% |
| R/sits_select.R | 0% |
| R/sits_sf.R | 0% |
| R/sits_smooth.R | 0% |
| R/sits_som.R | 0% |
| R/sits_summary.R | 0% |
| R/sits_tae.R | 0% |
| R/sits_tempcnn.R | 0% |
| R/sits_timeline.R | 0% |
| R/sits_train.R | 0% |
| R/sits_tuning.R | 0% |
| R/sits_uncertainty.R | 0% |
| R/sits_utils.R | 12.5% |
| R/sits_validate.R | 0% |
| R/sits_variance.R | 0% |
| R/sits_view.R | 0% |
| R/sits_xlsx.R | 0% |
| src/combine_data.cpp | 0% |
| src/kernel.cpp | 0% |
| src/label_class.cpp | 0% |
| src/linear_interp.cpp | 0% |
| src/nnls_solver.cpp | 0% |
| src/normalize_data_0.cpp | 0% |
| src/normalize_data.cpp | 0% |
| src/sample_points.cpp | 0% |
| src/sampling_window.cpp | 0% |
| src/smooth_bayes.cpp | 0% |
| src/smooth_sgp.cpp | 0% |
| src/smooth_whit.cpp | 0% |
| src/smooth.cpp | 0% |
| src/uncertainty.cpp | 0% |
Cyclocomplexity with cyclocomp
The following functions have cyclocomplexity >= 15:
| function | cyclocomplexity |
|---|---|
| sits_select.raster_cube | 18 |
| sits_classify.raster_cube | 15 |
Static code analyses with lintr
lintr found the following 45 potential issues:
| message | number of times |
|---|---|
| Avoid library() and require() calls in packages | 17 |
| Lines should not be more than 80 characters. | 23 |
| Use <-, not =, for assignment. | 5 |
Package Versions
| package | version |
|---|---|
| pkgstats | 0.1.3.13 |
| pkgcheck | 0.1.2.21 |
Editor-in-Chief Instructions:
Processing may not proceed until the items marked with ✖️ have been resolved.
|
Dear @jooolia Many thanks for starting the review process of R package Regarding the items mentioned: ✖️ Package name is not available (on CRAN): ✖️ Package has no HTML vignettes: ✖️ These functions do not have examples: [sits_run_examples, sits_run_tests]. ✖️ Package coverage is 0.1% (should be at least 75%). The issues raised by |
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Dear @jooola, version 1.5.0 of |
Submitting Author Name: Gilberto Camara
Submitting Author Github Handle: @gilbertocamara
Other Package Authors Github handles: @rolfsimoes, @OldLipe, @pedro-andrade-inpe
Repository: https://github.com/e-sensing/sits
Version submitted: 1.4.2
Submission type: Standard
Editor: TBD
Reviewers: TBD
Archive: TBD
Version accepted: TBD
Language: en
Scope
Please indicate which category or categories from our package fit policies this package falls under:
Explain how and why the package falls under these categories (briefly, 1-2 sentences):
sitsis a package for satellite image time series analysis, that works with big Earth observation data sets.Who is the target audience and what are scientific applications of this package?
The target audience is made of remote sensing and environmental experts that want to classify remote sensing images for applications such as deforestation detection, agricultural and land use/land cover mapping, biodiversity conservation, and land degradation monitoring.,
Are there other R packages that accomplish the same thing? If so, how does yours differ or meet our criteria for best-in-category?
There are currently no other open source software packages that have the same capabilities.
(If applicable) Does your package comply with our guidance around Ethics, Data Privacy and Human Subjects Research?
Not applicable
If you made a pre-submission inquiry, please paste the link to the corresponding issue, forum post, or other discussion, or @tag the editor you contacted.
Not applicable
Explain reasons for any
pkgcheckitems which your package is unable to pass.(a) Vignettes: instead of preparing vignettes, the authors have written an on-line book that describes the contents of the package in detail. The book is available at the URL https://e-sensing.github.io/sitsbook/
Important notes:
(1) To run the tests, examples, and code coverage, please make
sure the following environment variables are set in the R session:
Sys.setenv("SITS_RUN_TESTS" = "YES")
Sys.setenv("SITS_RUN_EXAMPLES" = "YES")
sitsis a fairly large package, and the tests take a long time to run, since they access cloud services. For this reason, testing needs to be manually enabled.(2) Please review version 1.4.2, not yet on CRAN, which is available in the "dev" branch in the github repository.
Technical checks
Confirm each of the following by checking the box.
This package:
Publication options
Do you intend for this package to go on CRAN?
The package is already on CRAN.
Do you intend for this package to go on Bioconductor?
[ x] Do you wish to submit an Applications Article about your package to Methods in Ecology and Evolution? If so:
MEE Options
Code of conduct
The text was updated successfully, but these errors were encountered: