String Functions

Most values in the columns of GLAM data will be Strings, a series of text characters (vs. other Refine data types: numbers, booleans, dates, or null). GREL provides a variety of functions to manipulate and clean up string values.

Common String Functions

• value.replace("old","new") – Perhaps the most common function is replace which takes as arguments a string you want to find and what you want to replace it with.
• value.replaceChars("áéíóú", "aeiou") – Rather than a string, this replaces all the individual characters listed in the argument. For example, this is often used to clean up junk characters resulting from OCR.
• Change case:
  • value.toLowercase()
  • value.toTitlecase()
  • value.toUppercase()
• value.trim() – removes white space from beginning and end of string.
• value.slice(1,5) – returns characters starting from the first argument (including it) to the second number (no including it). Alternatively, you can use bracket notation for a short cut, e.g. value[1,5].

Examples

• Create a Custom text facet:
  • just beginning of the values: value.slice(0,5)
  • combining multiple columns: value.trim().toLowercase() + " " + cells["example"].value.trim().toLowercase()
• Create a new column as a unique id with leading zeros: "row_id_" + "0000"[0,4-length(row.index +1)] + (row.index +1)
• Apply title case with hyphenated names: value.replace("-"," || ").toTitlecase().replace(" || ","-") (notice the strategic replace, use a temporary string that will be unique in the data to avoid unintended consequences!)
• Create a URL to fetch data: "https://example.com/api?keyword=" + value.trim().escape('url') + "&date=" + cells['year'].value.escape('url') + "&format=json"