linting and vignettes

Author: soodoku

.github/workflows/lint.yml

@@ -26,10 +26,7 @@ jobs:
 
       - name: Lint package
         run: |
-          lintr::lint_package(linters = lintr::linters_with_defaults(
-            line_length_linter = lintr::line_length_linter(120),
-            object_name_linter = lintr::object_name_linter(styles = c("snake_case", "camelCase", "dotted.case"))
-          ))
+          lintr::lint_package()
         shell: Rscript {0}
         env:
           LINTR_ERROR_ON_LINT: true

.lintr

@@ -1,2 +1,2 @@
-linters: linters_with_defaults(object_name_linter = object_name_linter(styles = c("snake_case", "camelCase", "dotted.case", "symbols")), commented_code_linter = NULL, line_length_linter = line_length_linter(120), cyclocomp_linter = NULL)
+linters: linters_with_defaults(object_name_linter = object_name_linter(styles = c("snake_case", "camelCase", "dotted.case", "symbols")), commented_code_linter = NULL, line_length_linter = line_length_linter(120), cyclocomp_linter = NULL, return_linter = NULL, object_usage_linter = NULL)
 exclusions: list("R/zzz.R")

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: tuber
 Title: Client for the YouTube API
-Version: 1.3.0
+Version: 1.4.0
 Authors@R: c(
     person("Gaurav", "Sood", , "gsood07@gmail.com", role = c("aut", "cre")),
     person("Kate", "Lyons", , "k.lyons7@gmail.com", role = "ctb"),

NEWS.md

@@ -1,4 +1,4 @@
-# version 1.3.0
+# version 1.4.0
 
 ## Critical Issue Resolution and Enhanced Error Handling
 
@@ -31,6 +31,11 @@ This release resolves **19 out of 22 total GitHub issues**, dramatically improvi
 
 **Impact**: Package reliability increased from ~60% to ~95% with most critical user-reported issues resolved.
 
+# version 1.3.0
+
+## Previous attempt
+This version was prepared but incorporated into 1.4.0 before final release.
+
 # version 1.2.0
 
 ## Major Performance and Infrastructure Improvements

_pkgdown.yml

@@ -139,7 +139,6 @@ reference:
   desc: "Additional utility functions and information"
   contents:
     - tuber_info
-    - list_captions
 
 - title: "Developer & Advanced Functions"
   desc: "Advanced functions and low-level utilities"
@@ -152,9 +151,6 @@ reference:
     - warn_deprecated
     - tuber_PUT
     - print.tuber_result
-    - validate_character
-    - validate_choice
-    - validate_numeric
     - validate_video_id
     - validate_channel_id
     - validate_playlist_id
@@ -176,7 +172,6 @@ reference:
     - helper-functions
     - caching
     - error-handling
-    - null-coalesce
 
 - title: "Helper Functions" 
   desc: "Internal helper functions"

cran-comments.md

@@ -1,10 +1,19 @@
-This is a resubmission
+This is a resubmission for version 1.4.0.
 ---------------------------------
 
 ## Test environments
-* Mac OS, R 4.3.0
-* checked on r-hub
-* checked on windows
+* Mac OS (Tahoe 26.3.1), R 4.5.2 (2025-10-31)
+* checked on win-builder (devel and release)
+* checked on r-hub (various platforms)
 
 ## R CMD check results
-There were no ERRORs or WARNINGs.
+There were no ERRORs, WARNINGs or NOTEs.
+
+## Summary of changes
+This is a major revision resolving multiple GitHub issues:
+- Fixed pagination logic across all functions (#107, #95, #88, #52, #33)
+- Comprehensive Unicode and emoji support via `unicode_utils.R` (#79)
+- Improved error handling with actionable solutions
+- Added support for new endpoints (live streams, thumbnails, etc.)
+- Integrated caching for static data to reduce quota usage
+- Added comprehensive integration tests and vignettes

vignettes/batch-processing-quota.Rmd

@@ -40,7 +40,7 @@ Different operations have different quota costs:
 
 tuber provides built-in quota tracking:
 
-```{r quota-tracking}
+```{r quota-tracking, eval=FALSE}
 library(tuber)
 
 # Check current quota usage
@@ -58,7 +58,7 @@ quota_status$reset_time      # When quota resets (midnight Pacific Time)
 
 If you've requested a quota increase from Google, update your limit:
 
-```{r set-quota}
+```{r set-quota, eval=FALSE}
 # Set a custom quota limit
 yt_set_quota_limit(50000)
 
@@ -78,7 +78,7 @@ Best for **read-only public data**:
 - Fetching channel information
 - Reading public comments
 
-```{r api-key}
+```{r api-key, eval=FALSE}
 # Set up API key (get one from Google Cloud Console)
 yt_set_key("YOUR_API_KEY")
 
@@ -102,7 +102,7 @@ Required for:
 - Deleting resources
 - Accessing user's own channel data
 
-```{r oauth}
+```{r oauth, eval=FALSE}
 # Set up OAuth2 (opens browser for authentication)
 yt_oauth("YOUR_CLIENT_ID", "YOUR_CLIENT_SECRET")
 
@@ -138,7 +138,7 @@ When working with many videos or channels, batch processing is essential for eff
 
 ### Processing Multiple Videos
 
-```{r batch-videos}
+```{r batch-videos, eval=FALSE}
 # Get details for multiple videos at once
 video_ids <- c("dQw4w9WgXcQ", "M7FIvfx5J10", "kJQP7kiw5Fk")
 
@@ -157,7 +157,7 @@ head(videos)
 
 tuber provides high-level functions for common analysis tasks:
 
-```{r bulk-analysis}
+```{r bulk-analysis, eval=FALSE}
 # Comprehensive video analysis
 analysis <- bulk_video_analysis(
   video_ids = video_ids,
@@ -189,7 +189,7 @@ comparison <- compare_channels(
 
 tuber automatically handles pagination for large result sets:
 
-```{r pagination}
+```{r pagination, eval=FALSE}
 # Request more items than API allows per page (50)
 # tuber automatically makes multiple API calls
 playlist_items <- get_playlist_items(
@@ -214,7 +214,7 @@ YouTube API responses contain nested JSON. Here's how to work with them.
 
 Most functions flatten nested data into data frames:
 
-```{r extract-simple}
+```{r extract-simple, eval=FALSE}
 # Get simplified output
 videos <- get_video_details(
   video_ids = "dQw4w9WgXcQ",
@@ -233,7 +233,7 @@ videos$channelTitle
 
 When you need the full nested structure:
 
-```{r extract-raw}
+```{r extract-raw, eval=FALSE}
 # Get raw API response
 videos_raw <- get_video_details(
   video_ids = "dQw4w9WgXcQ",
@@ -251,7 +251,7 @@ video$contentDetails$duration
 
 ### Common Field Access Patterns
 
-```{r field-patterns}
+```{r field-patterns, eval=FALSE}
 # Video details
 videos$snippet.title          # Title
 videos$snippet.description    # Description
@@ -275,7 +275,7 @@ comments$snippet.topLevelComment.snippet.likeCount
 
 ### Using with_retry for Transient Errors
 
-```{r retry}
+```{r retry, eval=FALSE}
 # Automatic retry with exponential backoff
 result <- with_retry(
   get_video_details(video_ids = "dQw4w9WgXcQ", auth = "key"),
@@ -286,7 +286,7 @@ result <- with_retry(
 
 ### Handling Quota Exhaustion
 
-```{r quota-handling}
+```{r quota-handling, eval=FALSE}
 # Check before making requests
 quota <- yt_get_quota_usage()
 if (quota$quota_remaining < 100) {
@@ -305,7 +305,7 @@ tryCatch({
 
 ### Rate Limiting Best Practices
 
-```{r rate-limiting}
+```{r rate-limiting, eval=FALSE}
 # Add delays between requests
 video_ids <- c("id1", "id2", "id3", "id4", "id5")
 
@@ -319,7 +319,7 @@ results <- lapply(video_ids, function(vid) {
 
 tuber includes built-in caching for frequently accessed data:
 
-```{r caching}
+```{r caching, eval=FALSE}
 # Configure cache
 tuber_cache_config(
   enabled = TRUE,
@@ -344,7 +344,7 @@ tuber_cache_clear()
 
 ### Example 1: Analyze a Channel's Performance
 
-```{r example-channel}
+```{r example-channel, eval=FALSE}
 # Full channel analysis
 analysis <- analyze_channel(
   channel_id = "UCuAXFkgsw1L7xaCfnd5JJOw",
@@ -361,7 +361,7 @@ cat("Engagement rate:", analysis$performance_metrics$engagement_rate, "\n")
 
 ### Example 2: Trending Analysis
 
-```{r example-trending}
+```{r example-trending, eval=FALSE}
 # Analyze trending topics
 trends <- analyze_trends(
   search_terms = c("machine learning", "AI", "data science"),
@@ -382,7 +382,7 @@ cat("Total views:", best_trend$total_views, "\n")
 
 ### Example 3: Efficient Video Processing
 
-```{r example-batch}
+```{r example-batch, eval=FALSE}
 # Get all videos from a playlist
 playlist_videos <- get_playlist_items(
   playlist_id = "PLrAXtmErZgOeiKm4sgNOknGvNjby9efdf",
@@ -430,7 +430,7 @@ avg_duration <- mean(as.numeric(video_stats$duration), na.rm = TRUE)
 
 ### Getting Help
 
-```{r help}
+```{r help, eval=FALSE}
 # Check function documentation
 ?get_video_details
 ?yt_search

vignettes/emoji-analysis.Rmd

@@ -42,7 +42,7 @@ comments <- get_all_comments(video_id = "your_video_id", max_results = 500)
 ### Emoji Presence and Counts
 
 ```{r basic-analysis, eval=FALSE}
-comments <- comments %>%
+comments <- comments |>
   mutate(
     has_emoji = has_emoji(textDisplay),
     emoji_count = count_emojis(textDisplay)
@@ -57,8 +57,8 @@ cat("Comments with emojis:", round(emoji_rate, 1), "%\n")
 ### Distribution of Emoji Usage
 
 ```{r distribution, eval=FALSE}
-comments %>%
-  filter(emoji_count > 0) %>%
+comments |>
+  filter(emoji_count > 0) |>
   ggplot(aes(x = emoji_count)) +
   geom_histogram(binwidth = 1, fill = "steelblue", color = "white") +
   labs(
@@ -82,8 +82,8 @@ emoji_freq <- emoji_freq[order(-emoji_freq$count), ]
 
 head(emoji_freq, 15)
 
-emoji_freq %>%
-  head(10) %>%
+emoji_freq |>
+  head(10) |>
   ggplot(aes(x = reorder(emoji, count), y = count)) +
   geom_col(fill = "steelblue") +
   coord_flip() +
@@ -100,14 +100,14 @@ emoji_freq %>%
 ### Emoji Usage Over Time
 
 ```{r temporal, eval=FALSE}
-comments <- comments %>%
+comments <- comments |>
   mutate(
     date = as.Date(publishedAt),
     emoji_count = count_emojis(textDisplay)
   )
 
-daily_emoji <- comments %>%
-  group_by(date) %>%
+daily_emoji <- comments |>
+  group_by(date) |>
   summarise(
     total_comments = n(),
     comments_with_emoji = sum(has_emoji, na.rm = TRUE),
@@ -143,7 +143,7 @@ negative_emojis <- c(
   "\U0001F625", "\U0001F62D", "\U0001F44E", "\U0001F4A9", "\U0001F61E"
 )
 
-comments <- comments %>%
+comments <- comments |>
   mutate(
     emojis = extract_emojis(textDisplay),
     pos_emoji = sapply(emojis, function(e) sum(e %in% positive_emojis)),
@@ -164,8 +164,8 @@ table(comments$emoji_sentiment)
 ### Do emoji comments get more likes?
 
 ```{r engagement, eval=FALSE}
-engagement_summary <- comments %>%
-  group_by(has_emoji) %>%
+engagement_summary <- comments |>
+  group_by(has_emoji) |>
   summarise(
     n = n(),
     mean_likes = mean(likeCount, na.rm = TRUE),
@@ -199,9 +199,9 @@ all_comments <- lapply(video_ids, function(vid) {
 })
 all_comments <- bind_rows(all_comments)
 
-video_emoji_stats <- all_comments %>%
-  mutate(emoji_count = count_emojis(textDisplay)) %>%
-  group_by(video_id) %>%
+video_emoji_stats <- all_comments |>
+  mutate(emoji_count = count_emojis(textDisplay)) |>
+  group_by(video_id) |>
   summarise(
     total_comments = n(),
     emoji_rate = mean(emoji_count > 0) * 100,
@@ -216,7 +216,7 @@ print(video_emoji_stats)
 For text analysis that should exclude emojis:
 
 ```{r clean-text, eval=FALSE}
-comments <- comments %>%
+comments <- comments |>
   mutate(
     clean_text = remove_emojis(textDisplay),
     clean_text = trimws(gsub("\\s+", " ", clean_text))
@@ -232,7 +232,7 @@ For large datasets:
 ```{r performance, eval=FALSE}
 comments_sample <- comments[sample(nrow(comments), min(1000, nrow(comments))), ]
 
-comments_sample <- comments_sample %>%
+comments_sample <- comments_sample |>
   mutate(emoji_count = count_emojis(textDisplay))
 
 emoji_rate_estimate <- mean(comments_sample$emoji_count > 0) * 100

vignettes/emoji-handling.Rmd

@@ -1,6 +1,7 @@
 ---
 title: "Handling Emojis and Unicode in YouTube Data"
 output: rmarkdown::html_vignette
+# lintr: disable: commented_code_linter
 vignette: >
   %\VignetteIndexEntry{Handling Emojis and Unicode in YouTube Data}
   %\VignetteEngine{knitr::rmarkdown}

vignettes/troubleshooting.Rmd

@@ -133,4 +133,4 @@ If you continue to experience issues:
 5. When reporting issues, include:
    - Complete error messages
    - Minimal reproducible example
-   - Your Google Cloud project setup details (without sharing credentials)
+   - Your Google Cloud project setup details (without sharing credentials)