Create new_code.R

[nateschor]

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[nateschor]

@CodiumAI-Agent /review

[nateschor]

@CodiumAI-Agent /improve

[QodoAI-Agent]

PR Code Suggestions ✨

Category	Suggestion	Impact
General	Fix inconsistent object naming The naming is inconsistent with the other filtered datasets (*_filtered), increasing risk of referencing the wrong object later. Align the object name to maintain consistency and prevent accidental misuse. new_code.R [179] -df_snowball_filter <- Remove_Stopwords(df_snowball_stop_words) +df_snowball_filtered <- Remove_Stopwords(df_snowball_stop_words) Suggestion importance[1-10]: 7 __ Why: Correcting df_snowball_filter to df_snowball_filtered matches the established naming pattern and prevents likely downstream reference errors; small change with clear benefit.	Medium
	Stabilize label layer mapping geom_text_repel uses a filtered data frame without guaranteed matching aesthetics, which can cause label misplacement or errors if rows are missing. Explicitly map x and y for the label layer and guard against empty data to avoid failures. new_code.R [144-152] p_sentiment <- ggplot(df_sentiment, aes(x = song_number, y = Sentiment)) + geom_line() + geom_hline(color = "red", yintercept = 0) + geom_point() + facet_wrap(~ `Sentiment Source`, ncol = 1, scales = "free_y") + theme_minimal() + labs(x = "Song Number") + - geom_text_repel(data = df_sentiment %>% filter(song_number %in% c(10, 11, 23)), - aes(label = title)) + geom_text_repel( + data = df_sentiment %>% filter(song_number %in% c(10, 11, 23)), + aes(x = song_number, y = Sentiment, label = title), + na.rm = TRUE + ) Suggestion importance[1-10]: 6 __ Why: Explicitly mapping x and y in geom_text_repel and adding na.rm = TRUE reduces risks of aesthetic mismatch or empty data issues; it's a solid, low-risk improvement with modest impact.	Low
Possible issue	Preserve text and id alignment The regex stripping non-ASCII characters can mangle tokens and shrink text length, causing element_id misalignment with df_raw. Preserve character integrity and explicitly carry element_id through the pipeline to guarantee correct joins. new_code.R [53-62] df_sentimentr <- df_raw %>% - mutate( - replace = gsub('[^ -~]', '', lines) + transmute( + element_id = row_number(), + text = lines ) %>% - pull(replace) %>% + mutate(text = stringi::stri_trans_general(text, "Any-NFKC")) %>% + pull(text) %>% get_sentences() %>% sentiment() %>% as_tibble() %>% select(element_id, sentiment) Suggestion importance[1-10]: 5 __ Why: Highlighting potential ID misalignment due to regex stripping is reasonable, and carrying element_id explicitly improves robustness, though the proposed change alters preprocessing semantics and may not be strictly necessary; impact is moderate.	Low

[nateschor]

/ask suggest substitute packages

[nateschor]

@CodiumAI-Agent /ask suggest substitute packages

[nateschor]

@CodiumAI-Agent /ask suggest substitute packages here https://github.com/nateschor/R-Hamilton/pull/1/files#diff-2df9c9583bf9967b2a0eecdf15fce59d45dd358fd0303f6f815442423df1cd31R8

[QodoAI-Agent]

Ask❓

suggest substitute packages here https://github.com/nateschor/R-Hamilton/pull/1/files

Answer:

Here are package substitution suggestions for the newly added code, focusing on reducing dependencies, improving stability, and aligning with common R workflows.

General recommendation: avoid pacman in production scripts. Prefer base install/load or renv for reproducibility.

• pacman::p_load

  • Substitute with: base install/load logic or renv
    • Example:
      • if (!requireNamespace("tidyverse", quietly = TRUE)) install.packages("tidyverse")
      • library(tidyverse)
    • Or manage dependencies with renv::init() and renv::snapshot() for lockfile reproducibility.

• tidyverse

  • You’re using readr, dplyr, tidyr, stringr, ggplot2, purrr, tibble. You can import selectively to reduce footprint:
    • readr, dplyr, tidyr, stringr, ggplot2, purrr, tibble
  • If you want even lighter:
    • data.table for fast IO and manipulation (fread/fwrite, :=, setorder) replacing many dplyr verbs.
    • But given current tidytext/topicmodels usage, sticking with tidyverse components is fine.

• tidytext

  • Keep: you rely on unnest_tokens, get_sentiments, stop_words.
  • Alternative: quanteda + quanteda.textstats (tokens(), dfm(), stopwords()).
    • Pros: faster on large corpora, richer text infrastructure.
    • Cons: would require adapting pipelines (non-tidy verbs).

• sentimentr

  • Alternative 1: sentiment.ai (transformer-based), better accuracy but heavier dependency.
  • Alternative 2: syuzhet (NRC, Afinn, Bing, Syuzhet methods in one package), overlaps with tidytext lexicons and offers sentence extraction.
  • Alternative 3: textdata + text2vec to build custom models if you want ML-based sentiment.
  • If you only need lexicon-based methods (Afinn/Bing/NRC), you could drop sentimentr and rely solely on tidytext, unless you specifically need the sentence parsing that sentimentr provides.

• topicmodels

  • Alternatives:
    • stm (Structural Topic Model) for document-level covariates and better diagnostics.
    • textmineR (fast LDA, coherence, scoring utilities).
    • quanteda.textmodels::textmodel_lda if moving to quanteda ecosystem.
  • For simple LDA, topicmodels is fine; stm often provides more modern tooling.

• ggrepel

  • Keep: it’s lightweight and solves overplotting elegantly.
  • Alternative: directlabels or ggtext for annotations, but ggrepel is the standard.

• kableExtra

  • Alternatives:
    • gt for table rendering (HTML/LaTeX via gt + gtsave).
    • flextable (Word/PowerPoint) and HTML output.
    • knitr::kable + tinytex/xtable for simpler LaTeX tables if you want minimal deps.
  • If you only need a simple LaTeX table, knitr::kable with basic options may suffice.

Other ecosystem swaps if you want a coherent alternative stack:

• Quanteda stack: quanteda, quanteda.textstats, quanteda.textplots, quanteda.textmodels to replace tidytext + topicmodels. Add stopwords package for multilingual stopword lists.
• Visualization labels: ggrepel remains best-in-class within ggplot2.

Minimal-change recommendations for this PR:

• Replace pacman::p_load with explicit library() calls and recommend renv.
• Keep tidytext, ggrepel.
• Consider dropping sentimentr if sentence-level sentiment is not essential; replicate with tidytext lexicons you already use.
• Keep topicmodels unless you need covariate-aware topics; then consider stm.
• If LaTeX table needs are simple, switch kableExtra to knitr::kable; otherwise keep.

Example header refactor without pacman:

• needed <- c("readr","dplyr","tidyr","stringr","ggplot2","purrr","tibble","tidytext","sentimentr","topicmodels","ggrepel","kableExtra")
• to_install <- needed[!sapply(needed, requireNamespace, quietly = TRUE)]
• if (length(to_install)) install.packages(to_install)
• lapply(needed, library, character.only = TRUE)

• or use renv to manage versions and installation reproducibly.