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  3. Introduction to Spark with sparklyr in R

Exercise

Mother's little helper (2)

A more general way of matching columns is to check if their names contain a value anywhere within them (rather than starting or ending with a value). As you may be able to guess, you can do this using a helper named contains().

Even more generally, you can match columns using regular expressions. Regular expressions ("regexes" for short) are a powerful language used for matching text. If you want to learn how to use regular expressions, take the String Manipulation in R with stringr course. For now, you only need to know three things.

  1. a: A letter means "match that letter".
  2. .: A dot means "match any character, including letters, numbers, punctuation, etc.".
  3. ?: A question mark means "the previous character is optional".

You can find columns that match a particular regex using the matches() select helper.

Instructions

100 XP

A Spark connection has been created for you as spark_conn. A tibble attached to the track metadata stored in Spark has been pre-defined as track_metadata_tbl.

  • Select all columns from track_metadata_tbl containing "ti".
  • Select all columns from track_metadata_tbl matching the regular expression "ti.?t".