In section 3.2, averaging popularity per year in the plots will enhance visibility and facilitate trend observation. The lack of data for unpopular artists in several years diminishes the credibility of the correlation line and discussion. Additionally, the rationale for selecting the US market for analysis in sections 3.2 and 3.3 should be explicitly explained. In section 3.3, exclude plots with no significant differences to streamline focus. Moreover, the discussion neglects unpopular artists, rendering their inclusion in the plots redundant. Furthermore, it would be more valuable to compare the popularity and audio features of tracks. The results can only indicate that popular singers have more songs with certain audio features, but this does not guarantee the popularity of those songs. Lastly, the loudness density plot contradicts the discussion; it suggests that the music of today's popular singers is louder, while the author asserts that it is quieter.
Regarding the code, the 'jsonlite' package is loaded but not utilized. When fetching Rolling Stones ranking data, using the 'rvest' package instead of 'Rselenium' is more efficient, as the website does not require dynamic scraping. Additionally, there's no need to extract rankings from the website since they are already in order from 100 to 1. To improve efficiency, consider employing the 'bind_rows' function from the 'dplyr' package instead of 'rbind' when merging datasets. 'rbind' is slower and demands more RAM, especially with larger datasets. Furthermore, when editing column values, such as modifying columns of 'merge_data_p3,' using 'mutate' with 'case_when' is more efficient.
In section 3.2, averaging popularity per year in the plots will enhance visibility and facilitate trend observation. The lack of data for unpopular artists in several years diminishes the credibility of the correlation line and discussion. Additionally, the rationale for selecting the US market for analysis in sections 3.2 and 3.3 should be explicitly explained. In section 3.3, exclude plots with no significant differences to streamline focus. Moreover, the discussion neglects unpopular artists, rendering their inclusion in the plots redundant. Furthermore, it would be more valuable to compare the popularity and audio features of tracks. The results can only indicate that popular singers have more songs with certain audio features, but this does not guarantee the popularity of those songs. Lastly, the loudness density plot contradicts the discussion; it suggests that the music of today's popular singers is louder, while the author asserts that it is quieter.
Regarding the code, the 'jsonlite' package is loaded but not utilized. When fetching Rolling Stones ranking data, using the 'rvest' package instead of 'Rselenium' is more efficient, as the website does not require dynamic scraping. Additionally, there's no need to extract rankings from the website since they are already in order from 100 to 1. To improve efficiency, consider employing the 'bind_rows' function from the 'dplyr' package instead of 'rbind' when merging datasets. 'rbind' is slower and demands more RAM, especially with larger datasets. Furthermore, when editing column values, such as modifying columns of 'merge_data_p3,' using 'mutate' with 'case_when' is more efficient.