Merge pull request #394 from bjohnso005/master

[v2.0.4] skip_on_cran & spanish vignette

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: wallace
-Version: 2.0.3
-Date: 2023-01-16
+Version: 2.0.4
+Date: 2023-03-11
 Title: A Modular Platform for Reproducible Modeling of Species Niches 
     and Distributions
 Description: The 'shiny' application Wallace is a modular platform for 

NEWS.md

@@ -1,3 +1,9 @@
+wallace 2.0.4
+=============
+- Added Spanish translation of vignette
+- Updates to v2 vignette
+- `skip_on_cran()` added to several tests
+
 wallace 2.0.3
 =============
 - Fix conditional in test for occs_queryDB

README.md

@@ -1,6 +1,6 @@
 [![R-CMD-check](https://github.com/wallaceEcoMod/wallace/workflows/R-CMD-check/badge.svg)](https://github.com/wallaceEcoMod/wallace/actions) [![License: GPL v3](https://img.shields.io/badge/License-GPL%20v3-blue.svg)](https://www.gnu.org/licenses/gpl-3.0) [![CRAN version](http://www.r-pkg.org/badges/version/wallace)](https://CRAN.R-project.org/package=wallace) [![downloads](https://cranlogs.r-pkg.org:443/badges/grand-total/wallace?color=orange)](https://cranlogs.r-pkg.org:443/badges/grand-total/wallace?color=orange)
 
-# Wallace (v2.0.3)
+# Wallace (v2.0.4)
 
 *Wallace* is a modular platform for reproducible modeling of species niches and distributions, written in R. The application guides users through a complete analysis, from the acquisition of data to visualizing model predictions on an interactive map, thus bundling complex workflows into a single, streamlined interface.
 
@@ -27,7 +27,7 @@ run_wallace()
 Please make sure you have installed the latest versions of both R (<a href= "https://cran.r-project.org/bin/macosx/" target="_blank">Mac OS</a>, <a href= "https://cran.r-project.org/bin/windows/base/" target="_blank">Windows</a>) and RStudio (<a href= "https://posit.co/download/rstudio-desktop/" target="_blank">Mac OS /  Windows</a>: choose the free version).
 
 #### How to run Maxent with maxent.jar
-*Wallace* v2.0.3 includes two options to run Maxent models: maxnet and maxent.jar. The former, which is an R implementation and fits the model with the package `glmnet`, is now the default and does not require the package `rJava` (see Phillips et al. 2017). The latter, which is the Java implementation, runs the `maxent()` function in the package `dismo`. This function requires the user to place the `maxent.jar` file in the `/java` directory of the `dismo` package root folder. You can download Maxent <a href="https://biodiversityinformatics.amnh.org/open_source/maxent/" target="_blank">here</a>, and locate `maxent.jar`, which is the Maxent program itself, in the downloaded folder. You can find the directory path to `dismo/java` by running `system.file('java', package="dismo")` at the R console. Simply copy `maxent.jar` and paste it into this folder. If you try to run Maxent in *Wallace* without the file in place, you will get a warning message in the log window and Maxent will not run.
+*Wallace* v2.0.4 includes two options to run Maxent models: maxnet and maxent.jar. The former, which is an R implementation and fits the model with the package `glmnet`, is now the default and does not require the package `rJava` (see Phillips et al. 2017). The latter, which is the Java implementation, runs the `maxent()` function in the package `dismo`. This function requires the user to place the `maxent.jar` file in the `/java` directory of the `dismo` package root folder. You can download Maxent <a href="https://biodiversityinformatics.amnh.org/open_source/maxent/" target="_blank">here</a>, and locate `maxent.jar`, which is the Maxent program itself, in the downloaded folder. You can find the directory path to `dismo/java` by running `system.file('java', package="dismo")` at the R console. Simply copy `maxent.jar` and paste it into this folder. If you try to run Maxent in *Wallace* without the file in place, you will get a warning message in the log window and Maxent will not run.
 
 ### Potential Issues
 

inst/shiny/Rmd/text_about.Rmd

@@ -7,7 +7,7 @@ output: html_document
 
 <img src="logo.png" alt="logo" style="width: 150px; float:right; padding:10px;"/>
 
-Welcome to *Wallace*, a flexible application for reproducible ecological modeling, built for community expansion. The current version of *Wallace* (v2.0.3) steps the user through a full niche/distribution modeling analysis, from data acquisition to visualizing results.
+Welcome to *Wallace*, a flexible application for reproducible ecological modeling, built for community expansion. The current version of *Wallace* (v2.0.4) steps the user through a full niche/distribution modeling analysis, from data acquisition to visualizing results.
 
 The application is written in `R` with the web app development package `shiny`. Please find the stable version of *Wallace* on <a href="https://CRAN.R-project.org/package=wallace" target="_blank">CRAN</a>, and the development version on <a href="https://github.com/wallaceEcoMod/wallace" target="_blank">Github</a>. We also maintain a *Wallace* <a href="https://wallaceecomod.github.io/" target="_blank">website</a> that has some basic info, links, and will be updated with tutorial materials in the near future.
 

inst/shiny/Rmd/text_intro_tab.Rmd

@@ -5,7 +5,7 @@ output: html_document
 
 #### WORKFLOW
 
-*Wallace* (v2.0.3) currently includes ten components, or steps of a possible workflow. Each component includes two or more modules, which are possible analyses for that step.  
+*Wallace* (v2.0.4) currently includes ten components, or steps of a possible workflow. Each component includes two or more modules, which are possible analyses for that step.  
 
 **Components:**  
 

inst/shiny/Rmd/userReport_intro.Rmd

@@ -10,7 +10,7 @@ knit_engines$set(asis = function(options) {
 knitr::opts_chunk$set(message = FALSE, warning = FALSE, eval = FALSE)
 ```
 
-Please find below the R code history from your *Wallace* v2.0.3 session. 
+Please find below the R code history from your *Wallace* v2.0.4 session. 
 
 You can reproduce your session results by running this R Markdown file in RStudio. 
 

tests/testthat/test_envs_ecoClimate.R

@@ -19,6 +19,8 @@ bcScenarioFail='Present'
 
 ##start testing
 test_that('error_checks', {
+  # skip on CRAN
+  skip_on_cran()
   # Skip if cannot download
   skip_if(class(varsEcoClimate) == "try-error")
   ##wrong AOGCM
@@ -40,6 +42,8 @@ test_that('error_checks', {
   })
 
 test_that('output_checks', {
+  # skip on CRAN
+  skip_on_cran()
   # Skip if cannot download
   skip_if(class(varsEcoClimate) == "try-error")
   #output is a rasterstack

tests/testthat/test_envs_worldclim.R

@@ -21,6 +21,8 @@ test_that("error checks", {
 
 ### test output features
 test_that("output type checks", {
+  # skip on CRAN
+  skip_on_cran()
   # the output is a RasterBrick
   expect_is(arcmin10, "RasterStack")
   # the number of layer is the same as specified in the selected variables list

tests/testthat/test_occs_paleoDb.R

@@ -8,7 +8,7 @@ context("paleoDb")
 spName <- "Didelphis virginiana"
 spNameError <- "Panthera onc"
 genus <- "panthera"
-#maximum number of occurences to download
+#maximum number of occurrences to download
 occNum <- 100
 # For PaleobioDB only Holocene is allowed.
 timeInterval <- "Holocene"
@@ -19,7 +19,7 @@ if (Sys.info()['sysname'] != "Windows") {
 }
 
 
-###Test that erorrs check
+###Test that errors check
   ### test if the error messages appear when they are supposed to
 test_that("error checks", {
   # paleobioDB failing in Windows
@@ -56,6 +56,8 @@ test_that("error checks", {
 test_that("output type checks", {
   # paleobioDB failing in Windows
   skip_on_os("windows")
+  # skip on CRAN
+  skip_on_cran()
   #output is a list
   expect_is(occsPaleo, "list")
   #List has two elements
@@ -64,6 +66,8 @@ test_that("output type checks", {
 test_that("output data checks", {
   # paleobioDB failing in Windows
   skip_on_os("windows")
+  # skip on CRAN
+  skip_on_cran()
   # if the original database has records without coordinates OR duplicates:
   if ((TRUE %in% duplicated(occsPaleo$orig[,c('longitude','latitude')]) == TRUE) |
       (NA %in% occsPaleo$orig[,c('longitude','latitude')]) == TRUE){
@@ -93,7 +97,7 @@ test_that("headers check", {
   skip_on_os("windows")
   #
   expect_false('FALSE' %in%  (keyPaleoHeaders %in% names(occsPaleo$orig)))
-  # the headers in the claned table are the ones specified in the function
+  # the headers in the cleaned table are the ones specified in the function
   expect_equal(names(occsPaleo$cleaned),c(keyPaleoHeaders,"pop"))
 })
 

tests/testthat/test_occs_queryDb.R

@@ -124,7 +124,7 @@ test_that("GBIF headers", {
   skip_if(class(out.gbif) == "try-error")
   # the original headers haven't changed
   expect_false('FALSE' %in% (headersGBIF %in% names(out.gbif[[i]]$orig)))
-  # the headers in the claned table are the ones specified in the function
+  # the headers in the cleaned table are the ones specified in the function
   expect_equal(names(out.gbif[[i]]$cleaned),
                c("occID", "scientific_name", "longitude", "latitude", "country",
                  "state_province", "locality", "year", "record_type",
@@ -242,7 +242,7 @@ for (i in 1:length(spNames)) {
 # test_that("Bison headers", {
 #   # the original headers haven't changed
 #   expect_false('FALSE' %in%  (headersBison %in% names(out.bison[[i]]$orig)))
-#   # the headers in the claned table are the ones specified in the function
+#   # the headers in the cleaned table are the ones specified in the function
 #   expect_equal(names(out.bison[[i]]$cleaned),
 #                c("occID", "scientific_name", "longitude", "latitude", "country",
 #                  "state_province", "locality", "year", "record_type","catalog_number",
@@ -295,7 +295,7 @@ headersBien <- c("scrubbed_species_binomial", "longitude", "latitude",
 # test_that("Bien headers", {
 #   # the original headers haven't changed
 #   expect_false('FALSE' %in%  (headersBien %in% names(out.bien[[i]]$orig)))
-#   # the headers in the claned table are the ones specified in the function
+#   # the headers in the cleaned table are the ones specified in the function
 #   expect_equal(names(out.bien[[i]]$cleaned),
 #                c("occID", "scientific_name", "longitude", "latitude", "country",
 #           "state_province", "locality", "year", "record_type", "catalog_number",