Merge pull request #1139 from OldLipe/master

Add rOpenSci badge in README

README.Rmd

@@ -24,6 +24,7 @@ torch::torch_manual_seed(1234)
 <!-- badges: start -->
 
 <!-- [![Build Status](https://drone.dpi.inpe.br/api/badges/e-sensing/sits/status.svg)](https://drone.dpi.inpe.br/e-sensing/sits) -->
+[![Status at rOpenSci Software Peer Review](https://badges.ropensci.org/596_status.svg)](https://github.com/ropensci/software-review/issues/596)
 [![CRAN status](https://www.r-pkg.org/badges/version/sits)](https://cran.r-project.org/package=sits)
 [![R-check-dev](https://github.com/e-sensing/sits/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/e-sensing/sits/actions/workflows/R-CMD-check.yaml)
 [![Codecov](https://codecov.io/gh/e-sensing/sits/branch/dev/graph/badge.svg?token=hZxdJgKGcE)](https://codecov.io/gh/e-sensing/sits)

README.md

@@ -9,6 +9,8 @@ Cubes
 <!-- badges: start -->
 <!-- [![Build Status](https://drone.dpi.inpe.br/api/badges/e-sensing/sits/status.svg)](https://drone.dpi.inpe.br/e-sensing/sits) -->
 
+[![Status at rOpenSci Software Peer
+Review](https://badges.ropensci.org/596_status.svg)](https://github.com/ropensci/software-review/issues/596)
 [![CRAN
 status](https://www.r-pkg.org/badges/version/sits)](https://cran.r-project.org/package=sits)
 [![R-check-dev](https://github.com/e-sensing/sits/actions/workflows/R-CMD-check.yaml/badge.svg)](https://github.com/e-sensing/sits/actions/workflows/R-CMD-check.yaml)
@@ -119,7 +121,7 @@ devtools::install_github("e-sensing/sits", dependencies = TRUE)
 # load the sits library
 library(sits)
 #> SITS - satellite image time series analysis.
-#> Loaded sits v1.4.2-1.
+#> Loaded sits v1.5.0.
 #> See ?sits for help, citation("sits") for use in publication.
 #> Documentation avaliable in https://e-sensing.github.io/sitsbook/.
 ```
@@ -137,8 +139,8 @@ more information on how to install the required drivers.
 ### Image Collections Accessible by `sits`
 
 Users create data cubes from analysis-ready data (ARD) image collections
-available in cloud services. The collections accessible in `sits`
-1.4.2.1 are:
+available in cloud services. The collections accessible in `sits` 1.5.0
+are:
 
 1. Brazil Data Cube
  ([BDC](http:https://brazildatacube.org/en/home-page-2/#dataproducts)):
@@ -174,13 +176,13 @@ similar ways.
 
 ``` r
 s2_cube <- sits_cube(
- source = "MPC",
- collection = "SENTINEL-2-L2A",
- tiles = c("20LKP", "20LLP"),
- bands = c("B03", "B08", "B11", "SCL"),
- start_date = as.Date("2018-07-01"),
- end_date = as.Date("2019-06-30"),
- progress = FALSE
+  source = "MPC",
+  collection = "SENTINEL-2-L2A",
+  tiles = c("20LKP", "20LLP"),
+  bands = c("B03", "B08", "B11", "SCL"),
+  start_date = as.Date("2018-07-01"),
+  end_date = as.Date("2019-06-30"),
+  progress = FALSE
 )
 ```
 
@@ -208,11 +210,11 @@ Pebesma, 2019](https://www.mdpi.com/2306-5729/4/3/92).
 
 ``` r
 gc_cube <- sits_regularize(
- cube = s2_cube,
- output_dir = tempdir(),
- period = "P15D",
- res = 60,
- multicores = 4
+  cube = s2_cube,
+  output_dir = tempdir(),
+  period = "P15D",
+  res = 60,
+  multicores = 4
 )
 ```
 
@@ -247,16 +249,16 @@ library(sits)
 data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
 # create a cube from downloaded files
 raster_cube <- sits_cube(
- source = "BDC",
- collection = "MOD13Q1-6",
- data_dir = data_dir,
- delim = "_",
- parse_info = c("X1", "X2", "tile", "band", "date"),
- progress = FALSE
+  source = "BDC",
+  collection = "MOD13Q1-6",
+  data_dir = data_dir,
+  delim = "_",
+  parse_info = c("X1", "X2", "tile", "band", "date"),
+  progress = FALSE
 )
 # obtain a set of samples defined by a CSV file
 csv_file <- system.file("extdata/samples/samples_sinop_crop.csv",
- package = "sits"
+  package = "sits"
 )
 # retrieve the time series associated with the samples from the data cube
 points <- sits_get_data(raster_cube, samples = csv_file)
@@ -311,16 +313,16 @@ data("samples_modis_ndvi")
 data("point_mt_6bands")
 # Train a deep learning model
 tempcnn_model <- sits_train(
- samples = samples_modis_ndvi,
- ml_method = sits_tempcnn()
+  samples = samples_modis_ndvi,
+  ml_method = sits_tempcnn()
 )
 # Select NDVI band of the point to be classified
 # Classify using TempCNN model
 # Plot the result
-point_mt_6bands |>
- sits_select(bands = "NDVI") |>
- sits_classify(tempcnn_model) |>
- plot()
+point_mt_6bands |> 
+  sits_select(bands = "NDVI") |> 
+  sits_classify(tempcnn_model) |> 
+  plot()
 #> | | | 0% | |=================================== | 50% | |======================================================================| 100%
 ```
 
@@ -342,44 +344,36 @@ using `sits_view()`.
 # Cube is composed of MOD13Q1 images from the Sinop region in Mato Grosso (Brazil)
 data_dir <- system.file("extdata/raster/mod13q1", package = "sits")
 sinop <- sits_cube(
- source = "BDC",
- collection = "MOD13Q1-6",
- data_dir = data_dir,
- delim = "_",
- parse_info = c("X1", "X2", "tile", "band", "date"),
- progress = FALSE
+  source = "BDC",
+  collection = "MOD13Q1-6",
+  data_dir = data_dir,
+  delim = "_",
+  parse_info = c("X1", "X2", "tile", "band", "date"),
+  progress = FALSE
 )
 # Classify the raster cube, generating a probability file
 # Filter the pixels in the cube to remove noise
 probs_cube <- sits_classify(
- data = sinop,
- ml_model = tempcnn_model,
- output_dir = tempdir()
+  data = sinop,
+  ml_model = tempcnn_model,
+  output_dir = tempdir()
 )
 #> | | | 0% | |======================================================================| 100%
 # apply a bayesian smoothing to remove outliers
 bayes_cube <- sits_smooth(
- cube = probs_cube,
- output_dir = tempdir()
+  cube = probs_cube,
+  output_dir = tempdir()
 )
 # generate a thematic map
 label_cube <- sits_label_classification(
- cube = bayes_cube,
- output_dir = tempdir()
+  cube = bayes_cube,
+  output_dir = tempdir()
 )
 #> | | | 0% | |======================================================================| 100%
 # plot the the labelled cube
 plot(label_cube,
- title = "Land use and Land cover in Sinop, MT, Brazil in 2018"
+  title = "Land use and Land cover in Sinop, MT, Brazil in 2018"
 )
-#> The legacy packages maptools, rgdal, and rgeos, underpinning the sp package,
-#> which was just loaded, will retire in October 2023.
-#> Please refer to R-spatial evolution reports for details, especially
-#> https://r-spatial.org/r/2023/05/15/evolution4.html.
-#> It may be desirable to make the sf package available;
-#> package maintainers should consider adding sf to Suggests:.
-#> The sp package is now running under evolution status 2
-#> (status 2 uses the sf package in place of rgdal)
 ```
 
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