The charts and graphs can be sorted, but the rows of data won’t be shown in the order they are imported into the tool, so you don’t need to worry about their order in the data source you are building. Order of the rows You will analyze the rows through the visualizations you build. Therefore, there is no need to order the columns. There are a few aspects that do not matter: Order of columns Tableau Desktop, Server, and Online will import a data set and order the fields shown in the Data grid in alphabetical order. Measures must be either an integer or float data type for analysis. Is there a single data type for each data field? A data field in Tableau (and most other tools) requires a single data type. Is the data field a dimension or measure? Desktop will divide all data fields into dimensions (category) and measures (the numerical values to analyze). Is there a single column for each data field? These columns will form the data fields that are then dragged and dropped in Desktop. 1 Here are the key aspects to consider when structuring data for Desktop: When you load data into Tableau Desktop, the software sets the first row of data as the headers for the columns and all subsequent rows as the data points for those headers. What Shape Is Best for Analysis in Tableau? Second column values: Headers for the measure columnĮverything else: Values for the measures columns The data set from Figure 4-1 has been colored in Figure 4-2 to highlight the structure:įirst two headers: Header for dimension columnįirst column values and monthly data headers: Categorical values Likewise, you need to assess the measures to ensure each has a separate column in the data set. If the dimensions are all in individual columns, you can move on to measures without having to think about any structural changes. ![]() Measures refers to the numeric values of the data set that are being analyzed (such as the number of students in a college class or the tuition they are paying). Dimensions is the term Tableau Desktop uses to refer to the columns of data that describe the records example, the regions a product is sold, in or the category that product belongs to). When assessing an incoming data set, it’s important to identify both the dimensions (or categorical values) of the data and the measures.
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