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Type 'q()' to quit R. > # Test script for gooseR essential functions > # Date: 2025-12-03 > > library(gooseR) > library(cli) > > cli::cli_h1("Testing gooseR Essential Functions") ── Testing gooseR Essential Functions ────────────────────────────────────────── > > # Test 1: goose_give_sample() > cli::cli_h2("Test 1: goose_give_sample()") ── Test 1: goose_give_sample() ── > test_data <- data.frame( + id = 1:10, + value = rnorm(10), + category = sample(letters[1:3], 10, replace = TRUE), + date = Sys.Date() + 1:10 + ) > > # Share the test data > goose_give_sample(test_data, "test_dataset") [Data] Object Shared with Goose -------------------------------------------------- Name: test_dataset Class: data.frame Dimensions: 10 x 4 Size: 1.9 Kb Columns: Column Type Missing id integer 0 value numeric 0 category character 0 date Date 0 Sample Data: id value category date 1 1 -2.49833587 b 2025-12-13 2 2 0.06876809 b 2025-12-14 3 3 1.02790735 c 2025-12-15 4 4 -0.11521859 b 2025-12-16 5 5 -1.23866059 c 2025-12-17 ℹ Created Goose memory directory: 'D:/temp/2025_12_12_00_30_16_734/RtmpAlYyPU/gooseR_memory' ✔ Saved "test_dataset" to Goose memory (category: "shared_objects") ℹ Tags: "structure" and "reference" ✔ Object structure saved to Goose memory > > # Test with mtcars > cli::cli_alert_info("Testing with mtcars dataset") ℹ Testing with mtcars dataset > goose_give_sample(mtcars, rows = 3) [Data] Object Shared with Goose -------------------------------------------------- Name: mtcars Class: data.frame Dimensions: 32 x 11 Size: 7 Kb Columns: Column Type Missing mpg numeric 0 cyl numeric 0 disp numeric 0 hp numeric 0 drat numeric 0 wt numeric 0 qsec numeric 0 vs numeric 0 am numeric 0 gear numeric 0 carb numeric 0 Sample Data: mpg cyl disp hp drat wt qsec vs am gear carb Mazda RX4 21.0 6 160 110 3.90 2.620 16.46 0 1 4 4 Mazda RX4 Wag 21.0 6 160 110 3.90 2.875 17.02 0 1 4 4 Datsun 710 22.8 4 108 93 3.85 2.320 18.61 1 1 4 1 ✔ Saved "mtcars" to Goose memory (category: "shared_objects") ℹ Tags: "structure" and "reference" ✔ Object structure saved to Goose memory > > # Test 2: goose_make_a_plan() > cli::cli_h2("Test 2: goose_make_a_plan()") ── Test 2: goose_make_a_plan() ── > plan <- goose_make_a_plan(focus = "predictive") ── [Goose] Goose Analysis Planner ────────────────────────────────────────────── ── Goose Memory Objects ── Found 2 objects name category class size tags 1 test_dataset shared_objects data.frame 1.9 Kb structur.... 2 mtcars shared_objects data.frame 7 Kb structur.... # [Data] Data Analysis Plan Generated: 2025-12-12 00:31:43 ## [Files] Available Objects - test_dataset - mtcars ## [Target] Phase 1: Data Understanding - [ ] Explore data structure and types - [ ] Check for missing values and outliers - [ ] Generate summary statistics - [ ] Create initial visualizations ## [Search] Phase 2: Data Quality - [ ] Identify data quality issues - [ ] Handle missing values - [ ] Detect and handle outliers - [ ] Validate data consistency ## [Chart] Phase 3: Exploratory Analysis - [ ] Univariate analysis - [ ] Bivariate relationships - [ ] Correlation analysis - [ ] Feature importance ## [AI] Phase 4: Predictive Modeling - [ ] Feature engineering - [ ] Train/test split - [ ] Model selection - [ ] Model evaluation - [ ] Hyperparameter tuning ## [List] Phase 5: Reporting - [ ] Create executive summary - [ ] Generate visualizations - [ ] Document methodology - [ ] Prepare recommendations ## [Idea] Suggested Next Steps 1. Run `goose_give_sample()` on additional datasets 2. Use `goose_ask()` for specific analysis questions 3. Use `goose_honk()` to review and improve your approach> > # Test 3: goose_loop_me() > cli::cli_h2("Test 3: goose_loop_me()") ── Test 3: goose_loop_me() ── > > # Simple code to loop > simple_code <- "df <- read.csv(file) + summary(df) + write.csv(df, paste0('processed_', file))" > > cli::cli_alert_info("Converting to file loop:") ℹ Converting to file loop: > goose_loop_me(simple_code, loop_over = "files") ── [Loop] Loop Generator ── Generated Loop Structure: -------------------------------------------------- # Loop Structure files <- list.files(pattern = '*.csv', full.names = TRUE) # Optional: Add progress bar # library(progress) # pb <- progress_bar$new(total = length(items)) for (file in files) { # Your code here: df <- read.csv(file) summary(df) write.csv(df, paste0('processed_', file)) } # pb$tick() # Update progress -------------------------------------------------- > > cli::cli_alert_info("\nConverting to parallel processing:") ℹ Converting to parallel processing: > goose_loop_me(simple_code, loop_over = "files", parallel = TRUE) ── [Loop] Loop Generator ── Generated Loop Structure: -------------------------------------------------- # Parallel Loop Structure library(parallel) files <- list.files(pattern = '*.csv', full.names = TRUE) results <- mclapply(files, function(file) { # Your code here: df <- read.csv(file) summary(df) write.csv(df, paste0('processed_', file)) }, mc.cores = detectCores() - 1) -------------------------------------------------- > > # Test 4: goose_honk() > cli::cli_h2("Test 4: goose_honk()") ── Test 4: goose_honk() ── > > # Gentle review > cli::cli_alert_info("Gentle review:") ℹ Gentle review: > goose_honk(severity = "gentle", focus = "statistics") ── [Goose] HONK! Code Review ─────────────────────────────────────────────────── ℹ Reviewing current directory: 'd:/RCompile/CRANincoming/R-devel/gooseR.Rcheck/tests' ── Goose Memory Objects ── Found 2 objects name category class size tags 1 test_dataset shared_objects data.frame 1.9 Kb structur.... 2 mtcars shared_objects data.frame 7 Kb structur.... [Star] Great work! Here are some friendly suggestions: ── Specific Code Analysis ── [Warning] No error handling detected in 76 lines of code. ── Data-Specific Suggestions ── [Data] Found 2 shared data objects: - test_dataset - data.frame - mtcars - data.frame [Chart] Found 1 data frames in environment: - test_data: 10 rows x 4 columns ── Focus Area: Statistics ── No statistical models found. Consider adding inferential statistics. ── Next Level Techniques for Your Code ── [Rainbow] Keep up the excellent work! Every iteration makes it better. > > # Harsh review > cli::cli_alert_info("\nHarsh review:") ℹ Harsh review: > goose_honk(severity = "harsh", focus = "performance") ── [Goose] HONK! Code Review ─────────────────────────────────────────────────── ℹ Reviewing current directory: 'd:/RCompile/CRANincoming/R-devel/gooseR.Rcheck/tests' ── Goose Memory Objects ── Found 2 objects name category class size tags 1 test_dataset shared_objects data.frame 1.9 Kb structur.... 2 mtcars shared_objects data.frame 7 Kb structur.... [Warning] Time for tough love! Critical improvements needed: ── Specific Code Analysis ── [Warning] No error handling detected in 76 lines of code. When this breaks (not if, WHEN), you'll have no idea why! ── Data-Specific Suggestions ── [Data] Found 2 shared data objects: - test_dataset - data.frame - mtcars - data.frame [Chart] Found 1 data frames in environment: - test_data: 10 rows x 4 columns ── Focus Area: Performance ── Code seems reasonably efficient. Profile to find bottlenecks. ── Next Level Techniques for Your Code ── [Fire] No excuses! Implement these changes and become a data science legend! > > # Test 5: goose_continuation_prompt() > cli::cli_h2("Test 5: goose_continuation_prompt()") ── Test 5: goose_continuation_prompt() ── > > # Generate continuation prompt > prompt_file <- goose_continuation_prompt( + include_files = TRUE, + include_todos = TRUE + ) ── [Note] Generating Continuation Prompt ─────────────────────────────────────── ℹ Mapping project files... ℹ Scanning for TODOs... ℹ Checking Goose memory... ! No memories found matching your criteria. ✔ Continuation prompt saved to: d:/RCompile/CRANincoming/R-devel/gooseR.Rcheck/tests/CONTINUATION_PROMPT_20251212_003143.md Summary: - Project: tests - Files tracked: 7 - TODOs found: 2 - Timestamp: 20251212_003143 ℹ Copy this prompt to start your next session exactly where you left off! > > cli::cli_alert_success("All essential functions tested successfully!") ✔ All essential functions tested successfully! > cli::cli_alert_info("Continuation prompt saved to: {.file {prompt_file}}") ℹ Continuation prompt saved to: 'd:/RCompile/CRANincoming/R-devel/gooseR.Rcheck/tests/CONTINUATION_PROMPT_20251212_003143.md' > > proc.time() user system elapsed 1.81 0.28 2.09