FlowerViz

One of other keyword of LoD is the function EXCLUDE. If you have already read my article about INCLUDE than you will easier understand the function EXCLUDE. Remember! By the function INCLUDE we included some numbers into the calculation even though we didn't visualize this number. By the function EXCLUDE we exclude some numbers from the calculation even through we visualize them. However, let's us go step by step... With the function EXCLUDE you can find answers to such questions like: - What is the total slaes, as well as the total sales by region. Its means: [(1) We need to exclude Region from our calculation of the monthly Total Sales (2) And then include Region when calculating the regional Sales breakdowns.] Here is the structure of this function: With other words: with this formula we said to Tableau: "Hey, Tableau, please ignore (EXCLUDE) "Region" if you calculate sales (SUM[Sales])" Let's have a look to the practice:...

I think LoD is the most popular calculation method if you are using tableau. Thus, you can find a lot of good blogs, whitepappers etc., to this topic. There are three keywords in LoD: INCLUDE, EXCLUDE and FIXED. However, why LoD is so popular? Why everybody discussed about this keywords? Where is the magic? As you know, Tableau shows you data if you put this data to the row or to the column. However, what if you would like to count into some numbers, but you don't want to vizualize them?... The answer is LoD :) Let's have a look to the first magic function: INCLUDE With this function you can find an answer to such questions like: - Which region has the highest average of order size? - Which country in the sales database has sales reps who close the “biggest deals,” on average? Here is the structure of this function: If we modify the formula to : AVG({INCLUDE [Order ID]:SUM([Sales])}) we would find an answer to the question: Which region has the highes...

There are a lot of blog posts about this function. However, for me the best one was written by Tim Costello. You can find the post here. ATTR comes from Attribute. An attribute is a specification that defines a property of an object, element, or file. It may also refer to or set the specific value for a given instance of such. If you take a look for Tableau Online Help , then you can find this definition: Tableau computes Attribute using the following formula: IF MIN([dimension]) = MAX([dimension]) THEN MIN([dimension]) ELSE “*” END I would say this is the main logic of ATTR function. With this formula you can understand how the ATTR function works. In simple words ATTR returns a value if it is unique, else it returns * Check out this links for mor examples: Drawing with numbers Powerful &Misunderstood: Tableau's ATTR Function Using ATTR Function It would be gerate if you share your examples of using of ATTR's function. Thank you!

ASCII ASCII stay for American Standard Code for Information Interchange. ASCII is a character encoding to display text on computers and electronic devices. All ASCII codes you can find here . ASCII function returns the ASCII value of the leftmost character of a given string. Usually these functions are using when you work with HTML. Unfortunately I do not work with HTML and I cannot say more about the way of using of this function. Anyway let us take a look at the example in tableau and let's understand what happened by using of this function. I dropped "Category" from the example of Superstore to the row and created a calculation field If we take a look at the table: ASCII - Code , we can see that for the upper "F" we have value 70, for "O" - 79 and for "T" - 84. So why do we have 148,470 for the "Furniture"; 476,054 for the "Office Supplies" and 155,148 for the "Technology"? Because tableau...

I have been working with Tableau since 2014, but I have still a feeling that my knowledge about this software not good enough. When I rebuild dashboards from tableau.public I am fascinating how some people can create amazing formulas and thus it makes the calculation of data sets easier. Sometimes I got stuck by creating of graphs and I do some calculations with data in Excel before I visualize them. Otherwise, you can find the definition of every function by creating of calculation field. But, honestly, are this definitions always clearly enough? For me not and I am done by doing long calculations with Excel. 😠 I like my work and I want to have more fun and not do such long calculation with Excel! I took up a challenge and decided to go through every function in tableau in order to understand how they work.  I would like to begin with ABS function.  ABS function is very easy one, as it used to get an absolute value of a number from a negative one. As an examp...

It is evening and already dark. I feel forced to stay at home because of the cold October weather. In order not to be bored I decided to make a post. This post will be the special one because today I'm not going to describe how to do any graphics with tableau. Today I'll share with you my approach by data visualization. As a data analyst with 1,5 year of work experience I have to create a lot of dashboards. My challenge is to create a reasonable dashboard with unknown data. So this is my approach: The first thing I do is ask the questions Who is my audience? (Management, Marketing, HR etc. -with this information you can decide what kind of KPI are important) What is relevant for my audience? (strategically, analytic or operative KPIs) Which data I need in order to create desired dashboards? Which data I miss? What is my deadline? In the second step I begin to study the data. While my first year as a data analyst I was always overdone with data, when I op...

I tried to improve some graph what I found online on the page of NewYork Times. The article is about the relationship between driving habits and gasoline prices in the period from 1956 until 2010. In this article I found some interesting graph: It is informative graph was created 2010. I wonder how it will look like if I created this graph today with tableau? And here is my result: What become better?  - at first sight you can recognize deepest and highest prices as the color red signaled something negative. The blue means something positive in this case. - I added the average line, so you can estimate if the prices are over or under the average - I made x aches a bit longer in order not to be deceived with price development. If you let data talk it is important not to overdone with visualization. Some changes look positive (e.g. 1990) but prices are still in the negative area. On the original graph it is difficult to recognize this. There are some hi...