This is shown in the screenshot.Ĭhanges, on the other hand, is a concept which is similar to the system audit log in the sense that they are generated automatically by Tableau to capture what changes you applied manually to the data as part of the flow. When you perform changes to the field cards, annotations are automatically added to them to show something has changed. Select the Extortion value, select the edit values and change it to Cyber Extortion. Select the Round() function to round off the exchange rate to decimal places. You will notice a library of available functions to use. Go to the Australian dollar card and select the calculated field option. Now let’s go back to our original data set of foreign exchange data from Bank of Canada. Please also refer to the section “Grouping” in this article for additional information. ![]() Notice that identifies Fraud and Identity Theft are grouped together now as an example. Select the Member Name field, group and replace by Pronunciation. Select Extortion and notice that all values are now filtered out except this one. Using the Member Name field card select manual filtering. Also, remove all the punctuations from the data by selecting the remove punctuations options. From the clean options, convert all the member name to Upper Case. Once loaded, add a Clean step after Input. ![]() Download the Excel file and use the data interpreter in the input step to select the correct data table. Cleanįor this step, we will use another data file from. You will notice that Tableau Prep has automatically created 3 new fields. Next, split the field by using the automatic split option. In order to do this, lets first convert the type of the field to a string field. To demonstrate the split function, let’s assume we want to split the date fields into the year, month and date. Rename the field FXAUDCAD to Australian dollar by selecting the rename option. Remove all the other fields by clicking on the 3 dots on the field cards and selecting remove. For the purpose of this article, let’s assume we are only interested in looking at the date field and the conversion rates for AUD and CAD. Now let’s remove a few fields that we don’t want and rename a few that we want to keep in our data. Select the second table on the bottom Remove and Rename You will notice that the Tableau Prep has suggested 2 tables to you after an automated analysis of the data in the Excel file. Once you have it connected the file, select cleaned with data interpreter. In order to do that, let’s open Tableau Prep and connect the Excel file. Hence, these rows have to be removed from the data. While it is useful to be aware of this information it is unlikely that this information will be used directly as part of the visualization process by Tableau or any other visualization tool. These rows contain information about the data itself. You will notice that the first 39 date rows in the Excel file do not contain any data. Look at the screenshot below showing the Excel file. Using Data Interpreter to Remove Extra Rows in Source Data ![]() As I mentioned, this is an Excel file that contains foreign exchange data. ![]() But before we get into the details of Tableau Prep, first let’s have a quick look at the sample data that we are going to use. In this section, we will use the cleaning step available as part of the Tableau Prep data flow. Additionally, I will also use a data file that was downloaded from the Statistics Canada Website. this file was downloaded from the Bank of Canada website. As a case study, I will use an Excel file that contains foreign exchange rates against the Canadian dollar. Having read the previous articles is recommended but not a pre-requisite. It assumes that the reader has fundamental knowledge about data visualization and has some basic level of exposure two one or more tools for data visualization and analytics. This article is part of a series of 6 articles on Tableau Prep and focused on Cleaning, Grouping and Replace. To ensure that this data can be aggregated in a meaningful and consistent manner for visualization and analytical purposes, data must be cleaned as part of the data preparation step. Because data is coming from different sources it could be in different formats, may have different meanings for the same data values, may have different purpose for the same data field, and may have different levels of data quality. In real life, data that is used for visualization is typically sourced from a variety of different sources. Data is like blood flowing through an organization and, just like blood quality is directly linked with the health of the body, organizations only function effectively when the quality of their data meets the minimum quality standards.
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