Fixing Data Issues in Tableau

And we applied it to a state selector, then we created another parameter, and it was a slider control that can display the number of values that we see, the number of sales, top five sales that we see, or top 10, or top eight, or 12, or whatever. Troubleshooting data sources. So for this exercise, we're gonna take a look at when you have problems with your data and ways that you can fix the problems with your data.

I'm going to save this file because I'm gonna open a new data source. So I'm saving my file. I'm gonna click File, Close.

I closed that one file. Now I'm ready to drag in new data. We have another data source that has issues.

This data source that has issues is not something that is going to be caught by the data interpreter. We're manually gonna have to make some changes. In the course of making these changes, we're going to create international cities.

So it's another way to troubleshoot your data, built-in tools that are available in Tableau. In a perfect world, your data sources would always be up-to-date, free from typos, formatted to work properly in Tableau. Errors in your data source when creating a visualization in Tableau can occur for various reasons and take various forms.

You may have incomplete or incorrect data. Your data might be missing information or have inaccuracies. Data type mismatch.

The data types of your columns might not match the requirements of Tableau, or you may have other issues. We wanna create an international map. This is related to soccer.

This is a sport that is definitely international. And so let's bring the information into Tableau so we can start working with it. So I'm gonna go back to Tableau.

This is my starting screen. I'm gonna do what I like to do, which is open up the folder with my datasets. Class Files Day, Tableau, Tableau Level 2 datasets.

We're looking for MLS salaries. MLS has nothing to do with real estate. It's Major League Soccer.

It took me a while for me to wrap my head around that. I thought, MLS, that sounds like real estate. No, it's Major League Soccer.

This contains salaries for players in Major League Soccer. You must've got this information from somewhere. I'm gonna take this, I'm gonna drag it into the blank area, and this is gonna import the information.

I'm gonna minimize this, and I'll open this up. When you have a single file, this is the first time where you'll see a situation where the file automatically comes in. I didn't have to drag anything in.

So, if you have a single file or a single sheet file, then that single sheet is automatically brought in. If you have multiple sheets, then you may have to make the connections. Tableau is not gonna assume the connections that need to be made.

This, for the most part, is gonna be our last exercise, and then I just take you through a quick overview of different types of chart types. And if you wanna stay and watch me create a donut pie chart, I can show you how to do that as well. But yes, we're starting to get close to wrapping up.

One of the things I can see right away that's wrong with this information that you might not notice just from looking at it is the season. The season is not $2,000. $2,007, that's weird.

It's pretty much $2,007 for every season here. For season, they're talking about years. So, we ran into this situation when we were bringing in the life expectancy data and the date was showing up as just the year.

We need to convert this to a date. Instead of 2007, this should be 1-1-2007. It's the 2007 season.

So, I'm gonna click on the hashtag and I'm gonna choose date. So, that is gonna make an improvement to our data because we will be able to use this as discrete or continuous fields. All right, so that's one example of fixing something.

It's not a major example, but it's one example of something to look out for. You can do minor things like splitting names. We don't have any examples here, but I now wanna go into the data.

So, I'll go to sheet one and I'll call this International Cities. State and city have already been automatically grouped here. What I'm gonna do is just double-click on cities.

Okay, we have a problem here. We should have a location for Atlanta and I'm not seeing it. Also, the thing you're gonna notice right away is over on the bottom right, there's five unknown issues.

If you click on them, I'll choose edit locations to see what's going on. These are the issues. BC is unrecognized, Null is unrecognized, ON and QC.

So, if you have international cities, but you don't have the country for those international cities, they won't be recognized. On the city level, we have at Atlanta. At Atlanta is not Atlanta.

Also, MLS is not a city. Monterio, Toronto and Vancouver are not recognized. So, this is the problem with the five cities that we need to correct.

This just tells us what the problem is. We're not necessarily gonna solve them here. So, I'm gonna click cancel.

As we solve each problem, the number of unknowns is gonna decrease until we have zero issues. Now, this is all accounted for in our PowerPoint. We wanna create a map.

I can simply double click on the city field to automatically create the map. That's what I did. I wanna assign one of our measures to control the size of the symbols.

In this case, we wanna use base salary. So, I'll do that. I didn't do that yet.

I'll take base salary and move it over into size. I'll increase the size so it stands out a little bit more. Okay, that looks good.

A warning appears at the bottom of the map. There are five unknowns present in the city field. Specifically, the unknowns are at Atlanta.

Atlanta is misspelled. It's at Atlanta. MLS is not a city.

And there are three international cities. Let's tackle them one by one. So, one of the ways you can discover the issue with the Atlanta city is if you go over to city, click the dropdown, and choose describe.

This will display the members in the city field. And the very first one says at Atlanta. And so, that's the misspelling.

All right, so that's just to figure out where the issue is. That doesn't necessarily solve it. So, this tells us there's 24 members in the city field, but there are some problems.

Atlanta is misspelled. MLS is not a city. None of the Canadian cities are included.

So, we close the dialog. One of the first things we wanna do is fix the misspelled city with an alias. If you use SQL, you're very familiar with an alias.

You may not be able to edit information from an SQL table unless you've been given permission by the Database Administrator. So, you may need to use an alias sometimes to accomplish what you need to accomplish or cast a certain field in a different data type. Here, we're gonna use an alias for at Atlanta.

So, you can do that by heading over to city, clicking or right-clicking the menu and looking for aliases. When you find aliases, you'll click on it. And here, you'll see the member and you'll see the value.

Right now, they're both the same. If the spelling's correct, there's nothing you need to fix. If it is not correct, then you're gonna click in where the alias is and put in the correct spelling.

And I'm gonna type in Atlanta. What is Tableau going to use? Tableau is gonna use the alias if it's different from the member. It's always gonna use the alias.

So, if the original member is misspelled, fix the alias so it can actually recognize the city. So, I'll do that. And then, I'll click OK.

Atlanta just showed up. So, I'm gonna undo. And you can see in Georgia, there was no Atlanta.

I'm gonna click Redo. It shows up. So, that is a very common mistake.

Maybe people aren't paying attention when they type in a certain state or city. And you don't have to edit the original data. You can correct it in Tableau.

So, one down, four to go.