I have a table in Access database as below;
Name | Range | X | Y | Z
------------------------------
A | 100-200 | 1 | 2 | 3
A | 200-300 | 4 | 5 | 6
B | 100-200 | 10 | 11 | 12
B | 200-300 | 13 | 14 | 15
C | 200-300 | 16 | 17 | 18
C | 300-400 | 19 | 20 | 21
I have trying write a query that convert this into the following format.
Name | X_100_200 | Y_100_200 | Z_100_200 | X_200_300 | Y_200_300 | Z_200_300 | X_300_400 | Y_300_400 | Z_300_400
A | 1 | 2 | 3 | 4 | 5 | 6 | | |
B | 10 | 11 | 12 | 13 | 14 | 15 | | |
C | | | | 16 | 17 | 18 | 19 | 20 | 21
After trying for a while the best method I could come-up with is to write bunch of short queries that selects the data for each Range and then put them together again using a Union query. The problem is that for this example I have shown 3 columns (X, Y and Z), but I actually have much more. Access is starting to strain with the amount of SQL I have come up with.
Is there a better way to achieve this?
The answer was simple. Just use Access Pivotview. Finding it hard to export the results to Excel though.
Related
I have a data source table in Google Sheets, looking like:
+------------+--------------+--------+
| Date | Search query | Clicks |
+------------+--------------+--------+
| 09.11.2020 | keyword 1 | 20 |
+------------+--------------+--------+
| 16.11.2020 | keyword 1 | 24 |
+------------+--------------+--------+
| 16.11.2020 | keyword 2 | 23 |
+------------+--------------+--------+
| 09.11.2020 | keyword 2 | 18 |
+------------+--------------+--------+
| 09.11.2020 | keyword 3 | 19 |
+------------+--------------+--------+
| 16.11.2020 | keyword 3 | 17 |
+------------+--------------+--------+
With this data source i have a Data Studio pivot, looking like:
+--------------+------------+------------+
| Search query | 09.11.2020 | 16.11.2020 |
+--------------+------------+------------+
| keyword 1 | 20 | 24 |
+--------------+------------+------------+
| keyword 2 | 18 | 23 |
+--------------+------------+------------+
| keyword 3 | 19 | 17 |
+--------------+------------+------------+
How can i create an additional column in Data Studio with calculation of procentual clicks difference between dates? So the Data Studio table will look like:
+--------------+------------+------------+---------------------------------+-----------------------------+
| Search query | 09.11.2020 | 16.11.2020 | Difference between B and C in % | Formula for Difference in % |
+--------------+------------+------------+---------------------------------+-----------------------------+
| keyword 1 | 20 | 24 | 17 | =100-((B2*100)/C2) |
+--------------+------------+------------+---------------------------------+-----------------------------+
| keyword 2 | 18 | 23 | 22 | =100-((B3*100)/C3) |
+--------------+------------+------------+---------------------------------+-----------------------------+
| keyword 3 | 19 | 17 | -12 | =100-((B4*100)/C4) |
+--------------+------------+------------+---------------------------------+-----------------------------+
Last column contains just formula example.
I tried all available possibilities in Data Studio, but failed. The cause of my fail is maybe a bug i've experienced.
My other try was to build a pivot in Google Sheet directly and calculate the difference there - but this doesn't work for me too - because my Google Sheet breaks pivot when it renews its data.
The key is calculated field. For each pair of columns, where you need a difference, you create a calculated field. Then you add this field to your table and see the calculated difference. Like on the following screenshot.
I have a concern about data organisation and the best approach to simplify some multi-layered data. Simply, I have a 10 replicates of small wood beams (BeamID, ~10) subjected to a 10 different treatment (TreatID, ~10), and each beam is load tested which produces a series data of a Load with consequent Displacement (ranging from 10 to 50 rows per test; I have code that corrects for disparities in row length). Each wood beam is tested multiple times (Rep, ~10).
My plan was to lump all this data into a 5-D array:
Array[Load, Deflection, BeamID, TreatID, Rep]
This way, I should be able to plot the load~deflection curves for a given BeamID, TreatID, for all Reps by using Array[ , ,1,1, ], right? So the hypothetical output for Array[ , ,1,1,1], would be:
+------------+--------+-----+
| Deflection | Load | Rep |
+------------+--------+-----+
| 0 | 0 | 1 |
| 6.35 | 10.5 | 1 |
| 12.7 | 20.8 | 1 |
| 19.05 | 45.3 | 1 |
| 25.4 | 75.2 | 1 |
+------------+--------+-----+
And Array[ , ,1,1,2] would be:
+------------+--------+-----+
| Deflection | Load | Rep |
+------------+--------+-----+
| 0 | 0 | 2 |
| 7.3025 | 12.075 | 2 |
| 14.605 | 23.92 | 2 |
| 21.9075 | 52.095 | 2 |
| 29.21 | 86.48 | 2 |
+------------+--------+-----+
Or I think I could keep it as a simpler, 'melted' dataframe, which would have columns for Load and Deflection, and BeamID, TreatID, and Rep would be repeated for each row of the test output.
+------------+--------+-----+--------+---------+
| Deflection | Load | Rep | BeamID | TreatID |
+------------+--------+-----+--------+---------+
| 0 | 0 | 1 | 1 | 1 |
| 6.35 | 10.5 | 1 | 1 | 1 |
| 12.7 | 20.8 | 1 | 1 | 1 |
| 19.05 | 45.3 | 1 | 1 | 1 |
| 25.4 | 75.2 | 1 | 1 | 1 |
| 0 | 0 | 2 | 1 | 1 |
| 7.3025 | 12.075 | 2 | 1 | 1 |
| 14.605 | 23.92 | 2 | 1 | 1 |
| 21.9075 | 52.095 | 2 | 1 | 1 |
| 29.21 | 86.48 | 2 | 1 | 1 |
+------------+--------+-----+--------+---------+
However, with the latter, I'm not sure how I could easily and discretely pull out all the Rep test values for a specific BeamID and TreatID, especially since I use a linear model to fit a 3rd order polynomial for an specific test to extract the slope of the curves. Having it as a continuous dataframe means I'd have to specify starting and stopping points to start the linear model, correct?
Thoughts, suggestions? Am I headed in the right direction in using a 5-D array? R is a new programming language for me, so please pardon my misunderstandings.
I want to sum the 7 preceding values of a row as a measure like so:
| Wk_number | Value A | Measure | Array |
-------------------------------------------
| 01 | 1 | N/A# | N/A# |
| 02 | 1 | 1 | {01} |
| 03 | 10 | 2 | {01-02} |
| 04 | 3 | 12 | {01-03} |
| 05 | 5 | 15 | {01-04} |
| 06 | 10 | 20 | {01-05} |
| 07 | 1 | 30 | {01-06} |
| 08 | 4 | 31 | {01-07} |
| 09 | -10 | 34 | {02-08} |
| 10 | 3 | 26 | {03-09} |
| 11 | 2 | 18 | {04-10} |
etc...
I added the array column just to clarify the example how of the sum is comprised, notice that from wk09 it's not simply a running total.
How to do this using DAX statements?
Two ways to do this: you can either create a calculated column, or a measure.
For the column:
=CALCULATE(SUM([Value A]),FILTER(Table,Table[Wk_number]<EARLIER(Table[Wk_number]) && Table[Wk_number] >= (EARLIER(Table[Wk_number])-7)))
For the measure, it's a very similar formula but instead of using EARLIER(), we use MAX():
=CALCULATE(SUM([Value A]),FILTER(ALL(Table3),Table3[Wk_number]<MAX(Table3[Wk_number]) && Table3[Wk_number] >= (MAX(Table3[Wk_number])-7)))
Below is a screenshot of the results. A few of the numbers in your example table seem to be off based on the math:
I need to make 2 database constraints that connect two different tables at one time.
1. The total score of the four quarters equals the total score of the game the quarters belong to.
2. The total point of all the players equals to the score of the game of that team.
Here is what my tables look like.
quarter table
+------+--------+--------+--------+
| gNum | Period | hScore | aScore |
+------+--------+--------+--------+
| 1 | 1 | 13 | 18 |
| 1 | 2 | 12 | 19 |
| 1 | 3 | 23 | 31 |
| 1 | 4 | 32 | 18 |
| | | Total | Total |
| | | 80 | 86 |
+------+--------+--------+--------+
Game Table
+-----+--------+--------+--------+
| gID | hScore | lScore | tScore |
+-----+--------+--------+--------+
| 1 | 86 | 80 | 166 |
+-----+--------+--------+--------+
Player Table
+-----+------+--------+--------+
| pID | gNum | Period | Points |
+-----+------+--------+--------+
| 1 | 1 | 1 | 20 |
| | | 2 | 20 |
| | | 3 | 20 |
| | | 4 | 20 |
+-----+------+--------+--------+
So Virtually I need to use CHECK I think to make sure that players points = score of their team ie (hScore, aScore) and also make sure that the hScore and aScore = the total score in the Game table.
I was thinking of creating a foreign key variable on one of the tables and setting up constraints on that would this be the best way of going about it?
Thanks
I have "components" which can be assembled in different ways into a "system". I want my database to hold all these "components", their type specific data and define how they are connected to each other to form a "system".
The systems are typically gearboxes and they can have rather complex branched designs. Let's start with an easy example:
This system is built up out of Masses (horizontal lines) and Stiffnesses (vertical lines). Gears and clutches are types of masses and come in pairs. Colors represent different branch speeds due to gear ratios. Here's a (bad) example of how I could store everything from this particular illustration:
ID | Type | Clutch | Ends | DrivenBy | NoOfTeeth| Mass | Stiffness
--- | ---- | ------ | ---- | --------- | -------- | ---- | ---------
1 | Mass | | Input1 | | | 5 |
2 | Stiffness | | | | | | 15
3 | Mass | 1.1 | | | | 2 |
4 | Mass | 1.2 | | | | 3 |
5 | Stiffness | | | | | | 20
6 | Gear | | | | 10 | 4 |
7 | Stiffness | | | | | | 30
8 | Gear | | | | 4 | 5 |
9 | Gear | | | 8 | 7 | 2 |
10 | Stiffness | | | | | | 40
11 | Mass | | | | | 4 |
12 | Stiffness | | Output1 | | | | 10
13 | Gear | | | 6 | 5 | 4 |
14 | Stiffness | | | | | | 20
15 | Mass | 2.1 | | | | 4 |
16 | Mass | 2.2 | | | | 3
17 | Stiffness | | | | | | 30
18 | Mass | | Output2 | | | 2 |
Obviously, this is not a very good way to store the data. This design pattern resembles somewhat of a "Repeated attributes" since each component type has a different attribute to be filled. I could create a table for each type of component, but things become more complex when looking at other examples, such as this 2-stage gearbox:
There are also examples with more than 1 input and several outputs, but I can't post more links due to low reputation.
Eitherway, you will see that the usual hierarchical data storage doesn't apply here because the data is not purely "tree-shaped" where everything branches off from 1 main branch.
I think that even though I could store data in the above mentioned way, I will get huge difficulties when it comes to the programming stage.
To add to the complexity, these gearboxes are actually sub-systems to a much bigger system.
So, any suggestions on a good way to store this type of data?*
Perhaps this is a possible way of doing it?
Here you will see that there is a "main" table called GearboxBranch, keeping track of all elements in the gearbox, giving them an id and to identify in which branch the element exists.
Then for the elements themselves, masses are defined in their dedicated table, so are stiffnesses. Gears and Clutches (which are types of masses) are then defined in their perspective tables. A recursive relationship is existing in the gear table, since one gear has to be driven by at least one other gear.
Furthermore, the table with Shaft Ends defines which of the elements in the gearbox are input or output and what number they have.
I can't seem to see any problems with this method, but I'm a little unsure how to get data out of the database. There will be considerable coding involved I'm afraid.