test coverage

.github/workflows/test-coverage.yaml

@@ -0,0 +1,50 @@
+# 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: ${{ secrets.GITHUB_TOKEN }}
+
+    steps:
+      - uses: actions/checkout@v4
+
+      - uses: r-lib/actions/setup-r@v2
+        with:
+          use-public-rspm: true
+
+      - uses: r-lib/actions/setup-r-dependencies@v2
+        with:
+          extra-packages: any::covr
+          needs: coverage
+
+      - name: Test coverage
+        run: |
+          covr::codecov(
+            quiet = FALSE,
+            clean = FALSE,
+            install_path = file.path(normalizePath(Sys.getenv("RUNNER_TEMP"), winslash = "/"), "package")
+          )
+        shell: Rscript {0}
+
+      - name: Show testthat output
+        if: always()
+        run: |
+          ## --------------------------------------------------------------------
+          find '${{ runner.temp }}/package' -name 'testthat.Rout*' -exec cat '{}' \; || true
+        shell: bash
+
+      - name: Upload test results
+        if: failure()
+        uses: actions/upload-artifact@v4
+        with:
+          name: coverage-test-failures
+          path: ${{ runner.temp }}/package

README.Rmd

@@ -43,15 +43,15 @@ library(responsesR)
 ### Simulating survey data
 The following sample code creates a simulated survey data. The hypothetical survey simulation is roughly based on the actual [comparative study](https://arxiv.org/abs/2201.12960) on teaching and learning R in a pair of introductory statistics labs.
 
-Imagine a situation in which 10 participants from course A and 20 participants from course B have completed the survey. Suppose that the initial question was: 
+Consider a scenario where 10 participants who completed Course A and 20 participants who completed Course B have taken the survey. Let's assume the initial question was:
 
 > "How would you rate your experience with the course?" 
 
 with four possible answers: 
 
 > Poor, Fair, Good, and Excellent. 
 
-Let's assume that the participants in course A were neutral regarding the question and participants in Course B had a more positive experience on average.
+Let's suppose that participants in Course A had a neutral opinion regarding the question, while those in Course B, on average, had a more positive experience.
 
 By choosing appropriate parameters for the latent distributions and setting number of categories `K = 4`, we can generate hypothetical responses (standard deviation `sd = 1` and skewness `gamma1 = 0`, by default):
 ```{r}
@@ -116,15 +116,15 @@ knitr::include_graphics("./figures/articles_courses_grouped_bar_chart-1.png")
 
 Suppose that the survey also asked the participants to rate their skills on a 5-point Likert scale, ranging from 1 (very poor) to 5 (very good) in:
 
-* programming, 
-* searching online, 
-* solving problems. 
+* Programming, 
+* Searching Online, 
+* Solving Problems. 
 
 The survey was completed by the participants both before and after taking the course for a pre and post-comparison. Suppose that participants' assessments of:
 
-* programming skills on average increased, 
-* searching online stayed about the same,
-* solving problems increased in course A, but decreased for participants in course B.
+* Programming skills on average increased, 
+* Searching Online stayed about the same,
+* Solving Problems increased in Course A, but decreased for participants in Course B.
 
 Let's simulate the survey data for this scenario (number of categories is `K = 5` by default):
 ```{r}
@@ -139,7 +139,7 @@ pre_B <- get_responses(n = 20, mu = c(-1, 0, 1))
 post_B <- get_responses(n = 20, mu = c(0, 0, 0)) # <-- decrease for skill 3
 ```
 
-Pre- and post-survey responses to Likert-scale questions visualized using a grouped bar chart:
+The grouped bar chart below displays the responses to Likert-scale questions before and after the survey:
 <details>
   <summary><b><a style="cursor: pointer;">Click here to expand </a></b> </summary>
 

README.md

@@ -48,19 +48,19 @@ hypothetical survey simulation is roughly based on the actual
 [comparative study](https://arxiv.org/abs/2201.12960) on teaching and
 learning R in a pair of introductory statistics labs.
 
-Imagine a situation in which 10 participants from course A and 20
-participants from course B have completed the survey. Suppose that the
-initial question was:
+Consider a scenario where 10 participants who completed Course A and 20
+participants who completed Course B have taken the survey. Let’s assume
+the initial question was:
 
 > “How would you rate your experience with the course?”
 
 with four possible answers:
 
 > Poor, Fair, Good, and Excellent.
 
-Let’s assume that the participants in course A were neutral regarding
-the question and participants in Course B had a more positive experience
-on average.
+Let’s suppose that participants in Course A had a neutral opinion
+regarding the question, while those in Course B, on average, had a more
+positive experience.
 
 By choosing appropriate parameters for the latent distributions and
 setting number of categories `K = 4`, we can generate hypothetical
@@ -133,18 +133,18 @@ Suppose that the survey also asked the participants to rate their skills
 on a 5-point Likert scale, ranging from 1 (very poor) to 5 (very good)
 in:
 
-- programming,
-- searching online,
-- solving problems.
+- Programming,
+- Searching Online,
+- Solving Problems.
 
 The survey was completed by the participants both before and after
 taking the course for a pre and post-comparison. Suppose that
 participants’ assessments of:
 
-- programming skills on average increased,
-- searching online stayed about the same,
-- solving problems increased in course A, but decreased for participants
-  in course B.
+- Programming skills on average increased,
+- Searching Online stayed about the same,
+- Solving Problems increased in Course A, but decreased for participants
+  in Course B.
 
 Let’s simulate the survey data for this scenario (number of categories
 is `K = 5` by default):
@@ -161,8 +161,8 @@ pre_B <- get_responses(n = 20, mu = c(-1, 0, 1))
 post_B <- get_responses(n = 20, mu = c(0, 0, 0)) # <-- decrease for skill 3
 ```
 
-Pre- and post-survey responses to Likert-scale questions visualized
-using a grouped bar chart:
+The grouped bar chart below displays the responses to Likert-scale
+questions before and after the survey:
 <details>
 <summary>
 <b><a style="cursor: pointer;">Click here to expand </a></b>

tests/helper_functions_testthat.R

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+library("testthat")
+library("responsesR")
+context("Testing helper functions")
+
+test_that("prop_table gives the correct result", {
+  data <- rep(c(1,2,3,4), each=2)
+  prob_tab <- get_prop_table(data, K=4)
+  correct_prob_tab <- rep(0.25, 4)
+  names(correct_prob_tab) <- 1:4
+  expect_that( identical(prob_tab, correct_prob_tab), equals(TRUE) )
+})
+
+test_that("pad_levels gives the correct result", {
+  pk <- rep(0.25, 4)
+  names(pk) <- 1:4
+  padded_pk <- pad_levels(pk = pk, K = 5)
+
+  correct_pk <- c(rep(0.25, 4), 0)
+  names(correct_pk) <- 1:5
+  expect_that( identical(padded_pk, correct_pk), equals(TRUE) )
+})
+
+test_that("percentify gives the correct result", {
+  xbreaks <- seq(from = 0, to = 1, length.out = 6)
+  xlabs <- sapply(xbreaks, percentify)
+  correct_xlabs <- c("0%", "20%", "40%", "60%", "80%", "100%")
+  expect_that( identical(xlabs, correct_xlabs), equals(TRUE) )
+})
+
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