AddMaple automatically detects the following data types.
Numbers
Detecting numeric columns is important as AddMaple can use these columns to allow instant aggregations, e.g. of totals, mean or median values. AddMaple can deal with empty rows, "N/A" values, numbers formatted as strings, etc.
Multiple Choice (Category)
Multiple choice columns are columns where there are a limited set of text results in the column. This could be the results of a multiple choice question in a survey or it could represent a category in another type of dataset.
Multiple Choice + (Tag)
Multiple Choice Plus columns are columns where there are multiple results per column. In a survey this is where respondents are allowed to select multiple answers to a single question. In other datasets this could be tags, where a data element could be assigned multiple tags. AddMaple can deal with empty rows, and a variety of techniques for denoting that there are multiple answers - including commas, pipes and semicolons.
Dates
Date columns allow us to summarise data with special time based charts. If your date format is not recognised, please let us know.
Opinion Scale
AddMaple has special detection for opinion scales. This includes both numeric scales, i.e. 1 to 10, and text based answers, e.g. Very Important, Somewhat Important, etc.
These columns allow us to display special Likert charts.
Text
If AddMaple detects free text answers in a column it is classed as text. Future improvements to AddMaple will allow full text search for these columns and automated tagging. You can view data in this column in the table or raw data view.
Unique
These columns have a different value for each row. Typically they are identifiers. AddMaple can't filter or pivot by these columns, but you can view this data in the table or raw data view.
Percent
AddMaple automatically detects percentage columns that contain a number followed by a "%" sign. They can be filtered and viewed in the same way as numeric columns.
Currency
AddMaple automatically detects currency columns. They can be filtered and viewed in the same way as numeric columns.
