Is there a way to create several groups in Case When statement?
For example,
CASE [Sales Manager]
WHEN "Manager 1" THEN "Germany"
WHEN "Manager 1" THEN "Russia"
WHEN "Manager 2" THEN "Russia"
END
Such statement will assign Manager 1 only to Germany, while I need to have it for both countries. Any other possible ways to do that ?
One solution is to define a table in your database (or Excel) that maps managers to countries. You just need two columns, one for manager and for country, and a row in the table for each association between a manager and a country.
That way you can easily represent a manager that works with many countries, or a country that has many managers (a many-to-many relationship).
You can then combine that table with your other data using joins or data blending. Realize that when you join data that has a to-many type of relationship that you can in general cause duplicate values to arise in the query results (e.g. the sales quota for a manager can be repeated multiple times, once for each country the manager visits). Unless your filters and work flow eliminate that case, you need to make sure your calculations account for duplication and avoid double counting.
Bottom line -- sometimes it is alot easier to specify information as data than as code.
Related
I am really new to Tableau.
I have an "accident" table (excel) that describes each traffic accident in the past few years, including its "district" (location). I have another "district" table that describes each district of the city, including its population.
Now I want to join those two tables and create a graph of accidents per person for each district.
The problem I face is: The two excel files are from different databases, which means that the same district may appear to have different names in two tables. How do I let tableau know the matching between districts?
Could you tell me how I can join those two tables so that I can create my chart?
Please let me know if there are any problems with my approach or understanding. Thank you in advance!
Assuming I understand question correctly.
Your accident transactions might have..
District#1, Accident Date, ,
District#2, Accident Date, ....
District#3, Accident Date, ....
Your district dimension table has 1 unique record per district, but names don't match.
District1, CityName
District2, CityName
District#3, CityName
You want to combine the results correctly...
Excel is the preferred datasource?
I assume the actual sources of data do NOT have a singular "code" value for the district name that actually does match. This is where MDM is important for system integrations. Right answer IMO is to actually have source systems/dbms understand that they are integrated and create XREF in 1 system that is required when a district is setup or exists.
Options. I think you need to "clean" the data someplace. The question is where.
MDM in your source system integrations.
Are the excel sources created in automated fashion? You could create a manual XREF excel sheet and lookup/decode one of the district values to conform a single definition of the district. In addition to your graph. create an audit that looks for any new data that doesn't "lookup" correctly to know to maintain the manual XREF.
Probably could create calculated XREF is tableau calc itself and then blend based on that calculated field. I think that would require full desktop versus web editor.
Tableau Prep might help as well. (although my company doesn't use this)
I am trying to add relationships for multiple tables but Power Bi is only allowing ONE table to be with "Both" for the "Cross Filter Direction".
If I try to select other tables to be with "Both" cross filter, then an error comes up saying "Power BI Desktop allows only one filtering path between tables in a Data Model". Is there a way to go around that?
I have three data sets ( tables ), that are joined based on two non-unique columns. First dataset (table) has site, item numbers, orders, due dates. Second dataset (table) has site, item numbers, and available quantities. Third dataset has site, item numbers, and released orders to manufacture. The three non-unique columns are sites and item numbers.
Out of these three datasets (tables), I have created two more datasets with one unique columns (sites, and item numbers) to inforce unique relationships between the three datasets.
The problem that I am facing is when I have filtering in the models it doesn't flow back and forth between the datasets. I assume fixing the filtering direction will result in fixing that.
Thanks!
I would not use the Both cross filter direction for that scenario. As you are observing it comes with a lot of restrictions, so I only use it for "Many-to-Many" scenarios - not what you described.
I think you are on the right path with your sites and item number tables. Just be careful to use the copy of the fields in those tables (e.g. item number from item numbers) in your visuals, not the same fields in the 3 "data" tables.
I have a set of 100 “student records”, I want to have checkboxes for each "favorite_food_type" and "favorite_food", whichever is checked would filter a "bar graph" that counts number of reports that contain that specific "favorite_food"type" and "favorite_food" schema could be:
name
favorite_food_type (e.g. vegetable)
favorite_food (e.g. banana)
I would like to in the dashboard be able to select via checkboxes, “Give me all the COUNT OF DISTINCT students with favorite_food of banana, apple, pear“ and filter graphs for all records. My issue is for a single student record, maybe one student likes both banana and apple. How do I best capture that? Should I have:
CASE A: Duplicate Records (this captures the two different “favorite_food”, but now I have to figure out how many students there are (which is one student)
NAME, FAVORITE_FOOD_TYPE,FRUIT
Charlie, Fruit, Apple
Charlie, Fruit, Pear
CASE B: Single Records (this captures the two different “favorite_food”, but is there a way to pick out from delimiters?)
NAME, FAVORITE_FOOD_TYPE,FRUITS
Charlie, Fruit, Apple#Pear
CASE C: Column for Each Fruit (this captures one record per student, but need a loooot of columns for each fruit, many would be false)
NAME, FAVORITE_FOOD_TYPE, APPLE, BANANA, PINEAPPLE, PEAR
Charlie, Fruit, TRUE, FALSE, TRUE, FALSE
I want to do this as easy as possible.
Avoid Case B if at all possible. Repeating information is almost always best handled by repeating rows -- not by cramming multiple values into a single table cell, nor by creating multiple columns such as Favorite_1 and Favorite_2
If you are provided data with multiple values in a field, Tableau does have functions and data connection features that can be used to split a single field into its constituent parts to form multiple fields. That works well with fixed number of different kinds of information -- say splitting a City, State field into separate fields for City and State.
Avoid Case C if at all possible. That cross tab structure makes it hard to analyze the data and make useful visualizations. Each value is treated as a separated field.
If you are provided data in crosstab format, Tableau allows you to pivot the data in the data connection pane to reshape into a form with fewer columns and many rows.
Case A is usually the best approach. You can simplify it further by factoring out repeating information into separated tables -- a process known as normalization. Then you can use a join to recombine the tables and see the repeating information when desired.
A normalized approach to your example would have two tables (or tabs in excel). The first table would have exactly one row per student with 2 columns: name and favorite_food_type. The second table would have a row per student/favorite food combination, with 2 columns: name and favorite_food. Now each student can have as many favorite foods as you like or none at all. Since both columns have a name field, that would be the key used to join (combine) the tables when needed.
Given that table design, you could have 2 data sources in Tableau. The first one just pointed to the student table and could be used to create visualizations that only involved students and favorite_food_types. The second data source would use a (left) join to read from both tables and could be used to look at favorite foods. When working with the second data source, you would have to be careful about reporting information about student names and favorite food types to account for the duplicate information. So use the first data source when possible. Finally, you could put both kinds of visualizations on a dashboard and use filter and highlight actions to make interaction seamless despite the two sources -- getting the best of both worlds.
I am making an application for a restaurant.
For some food items, there are some add-ons available - e.g. Toppings for Pizza.
My current design for Order Table-
FoodId || AddOnId
If a customer opts for multiple addons for a single food item (say Topping and Cheese Dip for a Pizza), how am I gonna manage?
Solutions I thought of -
Ids separated by commas in AddOnId column (Bad idea i guess)
Saving Combinations of all addon as a different addon in Addon Master Table.
Making another Trans table for only Addon for ordered food item.
Please suggest.
PS - I searched a lot for a similar question but cudnt find one.
Your relationship works like this:
(1 Order) has (1 or more Food Items) which have (0 or more toppings).
The most detailed structure for this will be 3 tables (in addition to Food Item and Topping):
Order
Order to Food Item
Order to Food Item to Topping
Now, for some additional details. Let's start flushing out the tables with some fields...
Order
OrderId
Cashier
Server
OrderTime
Order to Food Item
OrderToFoodItemId
OrderId
FoodItemId
Size
BaseCost
Order to Food Item to Topping
OrderToFoodItemId
ToppingId
LeftRightOrWhole
Notice how much information you can now store about an order that is not dependent on anything except that particular order?
While it may appear to be more work to maintain more tables, the truth is that it structures your data, allowing you many added advantages... not the least of which is being able to more easily compose sophisticated reports.
You want to model two many-to-many realtionships by the sound of it.
i.e. Many products (food items) can belong to many orders, and many addons can belong to many products:
Orders
Id
Products
Id
OrderLines
Id
OrderId
ProductId
Addons
Id
ProductAddons
Id
ProductId
AddonId
Option 1 is certainly a bad idea as it breaks even first normal form.
why dont you go for many-to-many relationship.
situation: one food can have many toppings, and one toppings can be in many food.
you have a food table and a toppings table and another FoodToppings bridge table.
this is just a brief idea. expand the database with your requirement
You're right, first one is a bad idea, because it is not compliant with normal form of tables and it would be hard to maintain it (e.g. if you remove some addon you would need to parse strings to remove ids from each row - really slow).
Having table you have already there is nothing wrong, but the primary key of that table will be (foodId, addonId) and not foodId itself.
Alternatively you can add another "id" not to use compound primary key.
I have two tables: "Stock Master" and "Stock In", how do I create a many-to-one relationship between them? "Stock In" records many different stocks by different dates and quantities, but "Stock Master" must show and combine the same stocks with their quantities into one, and must function as first-in first-out.
It doesn't sound like a many-to-one is what you really need.
If I understand correctly, you have inventory coming in at different times of different types. You want to record what has come in, you want to see how much of a specific type you have, and you want to be able to identify oldest received batch so you can prioritise that for shipping.
Vastly simplified, you'd just have that one table recording received shipments with a time and date received column which you can call WHERE clauses on to determine which entry is the oldest and should therefore be shipped.
You don't need a table as such for aggregating inventory (ignoring options like materialized views and such for now). Just sum the quantity column; group by product type.
If you want to create a view in Postgresql (as it appears you do from your comment to JosefAssad's advice), as in just about any other SQL db, use something like:
CREATE VIEW Stockmaster (prodid, total)
AS SELECT prodid, SUM(quantity)
FROM Stockin
GROUP BY prodid
Unless I'm missing something here, you would handle this by using the appropriate primary/foreign key relationships.