Merge pull request #14 from tscnlab/comparisons

imports and improvements

.github/workflows/test-coverage.yaml

@@ -0,0 +1,51 @@
+# Workflow derived from https://github.com/r-lib/actions/tree/v2/examples
+# Need help debugging build failures? Start at https://github.com/r-lib/actions#where-to-find-help
+
+on:
+  push:
+    branches: [main, master]
+  pull_request:
+    branches: [main, master]
+
+name: Test coverage
+
+jobs:
+  test-coverage:
+    runs-on: ubuntu-latest
+    env:
+      GITHUB_PAT: ${{ secrets.GITHUB_TOKEN }}
+
+    steps:
+      - uses: actions/checkout@v3
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::covr
+          needs: coverage
+
+      - name: Test coverage
+        run: |
+          covr::codecov(
+            quiet = FALSE,
+            clean = FALSE,
+            install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package")
+          )
+        shell: Rscript {0}
+
+      - name: Show testthat output
+        if: always()
+        run: |
+          ## --------------------------------------------------------------------
+          find ${{ runner.temp }}/package -name 'testthat.Rout*' -exec cat '{}' \; || true
+        shell: bash
+
+      - name: Upload test results
+        if: failure()
+        uses: actions/upload-artifact@v3
+        with:
+          name: coverage-test-failures
+          path: ${{ runner.temp }}/package

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: LightLogR
 Title: Work With Data from Wearable Light Loggers and Optical Radiation Dosimeters
-Version: 0.3.0
+Version: 0.3.2
 Authors@R: c(
     person("Johannes", "Zauner", 
       email = "[email protected]", role = c("aut", "cre"),

NEWS.md

@@ -1,3 +1,9 @@
+# LightLogR 0.3.2
+
+* added import functions for `nanoLambda`and `LightWatcher` devices
+
+* new Logo!
+
 # LightLogR 0.3.1
 
 * fixed bug in `interval2state()` that would dismiss the first state if it starts before the actual data

R/data.r

@@ -38,7 +38,7 @@
 #' These expressions are used to import and prepare data from specific devices.
 #' The list is made explicit, so that a user, requiring slight changes to the
 #' import functions, (e.g., because a timestamp is formatted differently) can
-#' modify of add to the list.
+#' modify or add to the list. The list can be turned into a fully functional import function through `import_adjustment‚()`.
 #'
 #' @format `ll_import_expr` A list, with specific expressions for each supported device
 #' \describe{

R/gg_overview.R

@@ -1,18 +1,22 @@
 #' Plot an overview of dataset intervals with implicit missing data
 #'
+#'
+#'
 #' @inheritParams gg_day
 #' @param Datetime.colname column name that contains the datetime. Defaults to
 #'   `"Datetime"` which is automatically correct for data imported with
 #'   [LightLogR]. Expects a `symbol`. Needs to be part of the `dataset`.
-#' @param Id.colname The column name of the Id column (default is `Id`), needs to
-#'   be in the `dataset`. This is also used as the y-axis variable and is the
+#' @param Id.colname The column name of the Id column (default is `Id`), needs
+#'   to be in the `dataset`. This is also used as the y-axis variable and is the
 #'   minimum grouping variable.
 #' @param gap.data Optionally provide a `tibble` with `start` and `end`
 #'   `Datetimes` of gaps per group. If not provided, the function uses
 #'   [gap_finder()] to calculate implicit missing data. This might be
 #'   computationally intensive for large datasets and many missing data. In
 #'   these cases it can make sense to calculate those gaps beforehand and
-#'   provide them to the function.
+#'   provide them to the function. If an empty `tibble` ([tibble::tibble()]) is
+#'   provided, the function will just plot the start and end dates of the
+#'   dataset, which is computationally very fast at the cost of additional info.
 #' @param ... Additional arguments given to the main [ggplot2::aes()] used for
 #'   styling depending on data within the `dataset`
 #'