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If you only wanted one column then instead of switching the values in the column, you could instead use "sheet swapping" and create different sheets for each value you would show. In the case of "none", the sheet wouldn't have a column at all. This would get increasingly complicated if you wanted to not show a column at all but wanted more than one column in the table.

It was my pleasure. Thanks for the comment.

@@theanalystsplaybook welcome

Awesome! So happy to hear.

Welcome 😊

Only desktop.

Hey Hem. I'm not sure what you mean exactly. What do you mean by "does not work properly"? The process will fail if the query does not successfully run. If you notice that you are not receiving a message to Slack, then you would revisit the scheduled query and troubleshoot the failing query. BigQuery gives you a lot of indicators for why a query is failing.

I'm glad you found it useful João. Thanks for the comment. -Justin

@@theanalystsplaybook Just joined Nomad List! 🇧🇷✅

@ Great decision:) -Justin

I'm really glad to hear that the video was helpful to you Maximilliano. Thanks for your comment and support for the channel. -Justin

I believe Tableau has a limit on the number of columns you can have in a table. A quick Google search would be able to check that. Also, a table with 100 columns wouldn't be readable so you'd be better off building it using SQL as a view or using something like dbt, and then making it accessible within the database to end-users. Yes, you could go with 1 parameter which allows you to switch between dimensions. This is the classic approach. My method is a bit more advanced. Thanks for the questions. -Justin

Thanks for the comment! I'm glad you found the video helpful. -Justin

Hey Kateryna. Yes, you could do this by working backwards and starting with a retention curve which includes all available data, and then segmenting the lines by the relevant cohort grouping (signup month for example). This will result in a multi-line graph where each line represents a cohort. The issue is that such views can be difficult to read sometimes, and may create confusion since not all cohorts will be equal in age.

I'm glad you found it useful Scott. Thanks for the comment. -Justin

Hey Irene. Thanks for the nice comment, it made my day. Good luck with your career switch. Justin

Yes, this automation flow will work with Asana's free plan.

I'm glad you found the video helpful Gregori. Thanks for the comment.

You're welcome!

Hey Aviv. I'm glad you found the video helpful. Thanks for the support.

@@theanalystsplaybook I am looking for some more technical help in implementing a segment on a client's site (server side) Can you help me for a fee?

@@AVIVRO2 I'm afraid I'm no longer providing consulting services in that area. I recommend checking out www.measurematch.com. I'm confident you'll find the needed support through their marketplace. James, the co-founder of MeasureMatch knows me well.

Hey Jessica. I think you're using the percentage of total table calculation instead of dividing by window_max. You want to use the window max function to divide the number of users that are left at each point by the total number you started with. I hope that helps. Thanks for watching.

Hey Vivica. I'm not sure I fully understand your question. The window sum function is essentially calculated as "table across" since it takes the last X data points. Since it's a table calculation, you aren't going to be able to do any additional manipulation like running sum etc on top of it. If you expand on your question, I'll try and help.

cohort i mean

@@devanshbhardwaj5955 To build such a cohort table you need to create a calculated field which uses the datediff and IF functions in Tableau. You create custom buckets of X days (between 0 and 6 equals 1 would be the first bucket of 7 days, 7 - 13 would be the second bucket etc) and then set the calculated field as a dimension. When you place the field on the columns you will get columns for each bucket like shown in my video. You then need a date field for the rows, and then can use either countd for absolute values, or countd/window_max to calculate percentages.

@@theanalystsplaybook Would it be possible to be more specific on the exact formula you used?

@@stayriddi5094 The formula I used to create the columns in the table is made up of datediff and fixed. Calculated field #1: {fixed user_id: min(event_created_at)} --> This gives you the first event performed by the user. Calculated field #2: datediff('week',CALCULATED FIELD #1,event_created_at) --> This gives you the number of weeks between the first event and the current event. You'll then turn this into a dimension and add it to the columns shelf. You then use countd(user_id)/window_max(countd(user_id) for your values. This will give you the percentage of the users which did activity in the given cell, compared to the total # of users. I'll just add that this is a tough concept to grasp and you're better off trying to understand the approach first before trying to build it. Try it first on paper or in excel and once you grasp it, building it in Tableau isn't too difficult. Good luck. -Justin

Thanks Ayoub.

Yes, I believe this is possible using javascript. It's not part of the scope of my video though. I suggest using RUclips or Google to track down a tutorial that can help you with that.

Hey Kasper. If you want to show all your data points in the viz, then you can simply use the window_sum function on it's own without including the IIF piece.

Hey Hanna. Unfortunately you can't do that using the fixed function. You'll first need to rank the orders per customer which you'll need to do outside of Tableau. Once you've added the rank as a new column, you could then use fixed, rank and datediff to calculate time between first order and 2nd, 3rd, 4th order etc. This is a tricky one. The hardest part is adding the rank. If you're using the super store data set then you may need to import it into a database so you could then run some SQL on it. Might be able to do it in Excel but I'm not familiar with ranking in Excel, it's been a long time since I did that.

I'm glad you liked it Carlos, thanks for the comment.

I'm glad you found the content helpful Mellany.

@@theanalystsplaybook :))

I'm really glad you found it helpful.

@@theanalystsplaybook I will update you if i pass the job interview for a data analyst position. If I do get in a big part of it is due to you!

@@jasperb6191 Good luck! Keep us updated.

@@theanalystsplaybook I got the job man! Ive been coding Python since August and I got accepted in the best Data Science company here in the Philippines

@@jasperb6191 I'm really happy to hear that Jasper, congrats! Keep on pushing!

You are most welcome

Hey Doron, I'm glad you liked the video. You can't use Mixpanel to know how much time a user spends on a specific page. To do that I'd recommend Google Analytics or a tool like Hotjar (video record your sessions and view them). -Justin