This week we continued our fundamental challenges by focussing on a common problem in data preparation. That problem involves including data about something that doesn't happen. Our example is we're trying to identify the days when there were no sales at our stores. Let's see how we solved the challenge.
Step 1 - Days with no sales
First up we want to convert our date field from a datetime to just a date. This can be done by pressing on the calendar and clock icon in the top left of the field in the profile pane, and then changing to a Date option.
We can now go and add some new rows for days when there were no sales. For this use the 'New Rows' step to help us.
After bringing the New Rows into our workflow, we need to set this up so that we are creating a new row from our minimum to maximum sale date:
This will allow us to fill in the missing days and will create a new row for each of them. Our table should look something like this, where if there are no sales on that day, the row should have 0s and nulls:
We are only interested in the days where there were no sales, so we can filter the Sale Value and keep only 0:
Step 2 - Days of the Week
Now we have identified all of the days where there are no sales, we can now do some further analysis based on which days of the week this occurs most regularly.
First we can remove any fields that we no longer require. This includes OrderID, Sale Value, Product Name, Store Name, Region, and Scent Name. We should be left with just the Sale Date field.
Using the Sale Date we can duplicate this by clicking on the three dots and then choosing duplicate. Once duplicated, we can use the Convert Dates feature to convert one of the date fields in days on the week:
Now we should have two fields, Day of the Week and Sale Date
The final steps for the challenge are to total the numbers by each day. For this we can use an aggregate step where we group by Day of the Week and CountD Sale Date
The final task is to rename Sale Date to Number of Days and then we are ready to output our table:
You can also post your solution on the Tableau Forum where we have a Preppin' Data community page. Post your solutions and ask questions if you need any help!
Created by: Carl Allchin Welcome to a New Year of Preppin' Data challenges. For anyone new to the challenges then let us give you an overview how the weekly challenge works. Each Wednesday the Preppin' crew (Jenny, myself or a guest contributor) drop a data set(s) that requires some reshaping and/or cleaning to get it ready for analysis. You can use any tool or language you want to do the reshaping (we build the challenges in Tableau Prep but love seeing different tools being learnt / tried). Share your solution on LinkedIn, Twitter/X, GitHub or the Tableau Forums Fill out our tracker so you can monitor your progress and involvement The following Tuesday we will post a written solution in Tableau Prep (thanks Tom) and a video walkthrough too (thanks Jenny) As with each January for the last few years, we'll set a number of challenges aimed at beginners. This is a great way to learn a number of fundamental data preparation skills or a chance to learn a new tool — New Year...
Created by: Carl Allchin Welcome to a New Year of Preppin' Data. These are weekly exercises to help you learn and develop data preparation skills. We publish the challenges on a Wednesday and share a solution the following Tuesday. You can take the challenges whenever you want and we love to see your solutions. With data preparation, there is never just one way to complete the tasks so sharing your solutions will help others learn too. Share on Twitter, LinkedIn, the Tableau Forums or wherever you want to too. Tag Jenny Martin, Tom Prowse or myself or just use the #PreppinData to share your solutions. The challenges are designed for learning Tableau Prep but we have a broad community who complete the challenges in R, Python, SQL, DBT, EasyMorph and many other tools. We love seeing people learn new tools so feel free to use whatever tools you want to complete the challenges. A New Year means we start afresh so January's challenges will be focused on beginners. We will u...
Free isn't always a good thing. In data, Free text is the example to state when proving that statements correct. However, lots of benefit can be gained from understanding data that has been entered in Free Text fields. What do we mean by Free Text? Free Text is the string based data that comes from allowing people to type answers in to systems and forms. The resulting data is normally stored within one column, with one answer per cell. As Free Text means the answer could be anything, this is what you get - absolutely anything. From expletives to slang, the words you will find in the data may be a challenge to interpret but the text is the closest way to collect the voice of your customer / employee. The Free Text field is likely to contain long, rambling sentences that can simply be analysed. If you count these fields, you are likely to have one of each entry each. Therefore, simply counting the entries will not provide anything meaningful to your analysis. The value is in ...
