SQL-Like Joins in Base R: Livestream Recording

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Links/Timestamps from Video:
00:50 What do I mean by “SQL-like joins” in base R? Compared to “merge” in SAS.
05:18 Register for Monika’s free online workshop, “Application Basics!”: buff.ly/43SfASM
7:48 Explanation of “join” terminology vs. “merge” terminology
9:01 Video on NHANES data: • Working With NHANES Data: Livestream ...
10:12 Explanation of scenario: Joining the NHANES demographic dataset with other NHANES datasets
11:00 Using foreign package to unpack SAS XPT file into R
12:40 Using colnames() and nrow() for column names and number of rows
15:44 Trimming off only the variables needed using a “keep” vector and brackets
18:56 Executing the join
21:30 Strategy: Adding flags along with joins to keep track of source data
24:04 Referring to variables using column numbers rather than names makes it easier to automate R code
27:04 Examples of calling up columns and rows using numerical reference
29:56 Using all.y instead of all.x to execute a right join
31:08 Syntax errors by using all.x or all.y incorrectly, or putting the dataframes in an incorrect order
31:30 Definition of inner join, and how to do it in base R
32:39 Big picture: Joins in public health data
34:00 Other uses of SQL-like joins are in data warehousing
37:52 More discussion on “which” and “grep” commands: u