In this session, a organizer from the SAS Users Group Japan explores comprehensive strategies for key-based data integration with R, emphasizing practical comparisons between methods. Attendees will gain insights into the differences and similarities between SAS Merge, SQL Joins, Base R merging , dplyr joins, as well as advanced R tools such as the hash package and rtables. Additionally, the presentation includes specific discussions on data merging practices relevant to CDISC standards, including examples from ADaM and SDTM datasets.
Yutaka Morioka | Yuki Nakagawa
EPS corporation | EPS corporation
Side-by-side by Design: Pharma Data Handling with Merge, Join, Match, and Hash in R
In this session, a organizer from the SAS Users Group Japan explores comprehensive strategies for key-based data integration with R, emphasizing practical comparisons between methods. Attendees will gain insights into the differences and similarities between SAS Merge, SQL Joins, Base R merging , dplyr joins, as well as advanced R tools such as the hash package and rtables. Additionally, the presentation includes specific discussions on data merging practices relevant to CDISC standards, including examples from ADaM and SDTM datasets.