[DOC] Split

source/widgets/data/split.md

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+Split
+=====
+
+Split text or categorical variables into indicator variables.
+
+**Inputs**
+
+- Data: data table
+
+**Outputs**
+
+- Data: data table with added columns
+
+The widget splits the chosen text or categorical variable into separate variables. The newly-created variables can be categorical (Yes or No) or numeric (0 or 1) indicators, or appearance counts. The widget is typically used with survey data.
+
+![](images/Split-stamped.png){width=40%}
+
+1. Select the variable to split. Define the delimiter.
+2. Set the type of output variables. Categorical produces categorical indicator with values Yes and No, Numerical produces a dummy numerical variable with values 0 and 1, while Counts creates a variable that contains the number of appearances of each term. The latter two options are equivalent if each value can appear only once.
+3. If *Apply Automatically* is ticked, changes are communicated automatically. Alternatively, press *Apply*.
+
+Example
+-------
+
+The workflow uses data from the Orange survey taken in 2020. The survey was done in Google Forms, which joins responses for multiple choice questions.
+
+To use this data in the analysis, we have to split on a delimiter, a semicolon in this case. We do this using **Split**. We pass the data from the [File](../data/file.md) widget to Split and set the parameters.
+
+Say, we wish to observe and count the reasons users like Orange. We split on *Reason* and set the delimiter to semicolon. The output will be an array of categorical variables. We can observe the results in a [Data Table](../data/datatable.md).
+
+![](images/Split-Example.png)
+
+The advantage of Split is that now, we can count each response individually. Say, we observe how the responses differ on the position of the respondent in [Box Plot](../visualize/boxplot.md). It seems the professors especially appreciate Orange for its visual programming approach.
+
+![](images/Split-BoxPlot.png){width=500px}