Each dataset looks something like this:
df1
season
win
FO
2019
0.586
0.512
There are 5 of those datasets, and I want to merge/join all of them, by all three variables. There is only 1 observation per dataset, so I want to make a datset with the 5 seasons and the relative statistics.
How would I go about merging them? I tried using left join but get the error message: suffix is a tbl_df/tbl/data.frame object of length 3.
Related
I am trying to merge the 2 csv files(in Azure data factory) which has different schema. Below is the scenario
CSV 1: 15 columns -> say 5 dimensions and 10 metrices(x1, x2,...x10)
CSV 2: 15 columns -> 5 dimensions(same as above) and 10 metrices(different from above, y1, y2...y10)
So my schema is different. Now I have to merge both CSV files so that only 5 dimensions comes with all 20 metrices.
I tried with Data Transformation using Select operation. That is giving me 2 rows in the merged file. One row with first 5 dimensions and 10 metrices and second row with next 5 dimensions and 10 metrices, which is incorrect as I am looking only for one row with 5 dimensions and all 20 metrics(x1,x2...x10, y1,y2...y10)
Any help is much appreciated on this issue
Thank you #sac for the update and thank you #Joel Cochran, for the suggestion. Posting it as an answer to help other community members.
Use Join transformation and Join type as Inner join. Use Key columns or common columns (dimension columns) from 2 Input files as your Join condition. This will output all columns from file1 and file2.
Use Select transformation to get the required select list from the join output.
Refer below process for implementation:
(i) Join 2 source files, with inner join and key columns in the join condition.
(ii) Output of Join transformation will list all the columns from source1 and all columns from source2 (includes duplicate key columns from both source files).
(iii) Use select transformation and remove duplicate (or not required in select list) columns from the Join output.
(iv) Output of Select transformation.
I am trying to compute a N_break that has to "satisfy" a condition. I have a variable which indicates 1 or 0. Lets call that variable "HT". Every lopnr is also labled in every row multiple times. So first 10 rows can be ID nr 1. And next 20 can be ID nr 2 and so on.
My question is: How do i create a N-break with lopnr as breakvariable that has to have HT=1? I am not allowed to select only 1s on variable HT before, since i need the 0s in the file.
A few simple ways to do this:
1 - USE FILTER
filter cases by HT.
aggregate ....
when you get back to original dataset, use:
filter off.
use all.
2 - COPY DATASET
dataset name orig.
dataset copy foragg.
dataset activate foragg.
select if HT.
aggregate....
3 - TEMPORARY SELECTION
temporary.
select if HT.
aggregate....
I am quite new to Tableau, so have patience with me :)
I have two tables,
Table one (T1) contains all my data with the first row being Year-Week, like 2014-01, 2014-02, and so on. Quick question regarding this, how do I make Tableau consider this as a date, and not as string?
T1 contains a lot of data that looks like this:
YearWeek Spend TV Movies
2014-01 5000 42 12
2014-02 4800 41 32
2014-03 2000 24 14
....
2015-24 7000 45 65
I have another table (T2) that contains information regarding some values I want to multiply with the T1 columns, T2 looks like:
NAME TV Movies
Weight 2 5
Response 6 3
Ad 7 2
Version 1 0
I want to create a calculated field (TVNEW) that takes the values from T1 of TV, and adds Response(TV) to it, and times it with the weight(TV),
So something like this:
(T1[TV]+T2[TV[Response]])*T2[TV[Weight]]
This looks like this for the rows:
(42+6)*2
(41+6)*2
(24+6)*2
...
(45+6)*2
So the calculation should take a specific value from T2, and do the calculation for each value in T1[TV]
Thanks in advance
The easy answer to your question will be: No, not natively.
What you want to do sounds like accessing a 2 dimensional array and that's not really the intention of Tableau. Additionally you have 2 completely independent tables without a common attribute to JOIN on. Tableau is just not meant to work that way.
I cannot think of a way to dynamically extract that value (I assume your example is just that, an example; and in your case you don't just use two values in the calculation, otherwise you could create 2 parameters that you can use in your calculated fields)
When I look at your tables it looks like you could transpose and join them that they ideally look like this: (Edit: Comment says transposing is not an option)
Medium Value YearWeek Spend
Movies 12 2014-01 5,000
Movies 32 2014-02 4,000
Movies 14 2014-03 2,000
Movies 65 2015-24 7,000
TV 42 2014-01 5,000
TV 41 2014-02 4,000
TV 24 2014-03 2,000
TV 45 2015-24 7,000
and
Medium Weight Response Ad Version
TV 2 6 7 1
Movies 5 3 2 0
Depending on the systems you work with you could already put it in one CSV or table so you wouldn't have to do a JOIN in Tableau.
Now you can create the first table natively in Tableau (from Version 9.0 onwards), if you open your data source, in the Data Source Preview choose the columns TV and Movies, click on the small triangle and then on Pivot. (At this point you can also choose the YearWeek column click on the triangle and Split to create a seperate field for Year and Week. You won't be able to assign the type date to it put that shouldn't give you any disadvantages.)
For the second table I can think of two possibilities:
you have access to a tool that can transpose your table (Excel can do that see: Convert matrix to 3-column table ('reverse pivot', 'unpivot', 'flatten', 'normalize') Once you have done that you can open it in Tableau and join the two tables on Medium
You could create calculated fields depending on the medium:
Field: Weight
CASE [Medium]
WHEN 'TV' THEN 2
WHEN 'Movies' THEN 5
END
And accordingly for Response, Ad and Version
Obviously that is only reasonable if you really just need a handfull of values.
Once this is done it's only a matter of creating a calculated field with
([Value]+[Response])*[Weight]
And this will calculate all the values for your table
I am currently working on Pentaho and I have the following problem:
I want to get a "rooling distinct count on a value, which ignores the "group by" performed by Business Analytics. For instance:
Date Field
2013-01-01 A
2013-02-05 B
2013-02-06 A
2013-02-07 A
2013-03-02 C
2013-04-03 B
When I use a classical "distinct count" aggregator in my schema, sum it, and then add "month" to column, I get:
Month Count Sum
2013-01 1 1
2013-02 2 3
2013-03 1 4
2013-04 1 5
What I would like to get would be:
Month Sum
2013-01 1
2013-02 2
2013-03 3
2013-04 3
which is the distinct count of all Fields so far. Does anyone has any idea on this topic?
my database is in Postgre, and I'm looking for any solution under PDI, PSW, PBA or PME.
Thank you!
A naive approach in PDI is the following:
Sort the rows by the Field column
Add a sequence for changing values in the Field column
Map all sequence values > 1 to zero
These first 3 effectively flag the first time a value was seen (no matter the date).
Sort the rows by year/month
Sum the mapped sequence values by year+month
Get a Cumulative Sum of all the previous sums
These 3 aggregate the distinct values per month, then keep a cumulative sum. In PDI this might look something like:
I posted a Gist of this transformation here.
A more efficient solution is to parallelize the two sorts, then join at the latest point possible. I posted this one as it is easier to explain, but it shouldn't be too difficult to take this transformation and make it more parallel.
Imagine I have a MSSQL 2005 table(bbstats) that updates weekly showing
various cumulative categories of baseball accomplishments for a team
week 1
Player H SO HR
Sammy 7 11 2
Ted 14 3 0
Arthur 2 15 0
Zach 9 14 3
week 2
Player H SO HR
Sammy 12 16 4
Ted 21 7 1
Arthur 3 18 0
Zach 12 18 3
I wish to highlight textually where there has been a change in leader for each category
so after week 2 there would be nothing to report on hits(H); Zach has joined Arthur with most strikeouts(SO) at
18; and Sammy is new leader in homeruns(HR) with 4
So I would want to set up a process something like
a) save the past data(week 1) as table bbstatsPrior,
b) updates the bbstats for the new results - I do not need assistance with this
c) compare between the tables for the player(s with ties) with max value for each column
and spits out only where they differ
d) move onto next column and repeat
In any real world example there would be significantly more columns to calculate for
Thanks
Responding to Brents comments, I am really after any changes in the leaders for each category
So I would have something like
select top 1 with ties player
from bbstatsPrior
order by H desc
and
select top 1 with ties player,H
from bbstats
order by H desc
I then want to compare the player from each query (do I need to do temp tables) . If they differ I want to output the second select statement. For the H category Ted is leader `from both tables but for other categories there are changes between the weeks
I can then loop through the columns using
select name from sys.all_columns sc
where sc.object_id=object_id('bbstats') and name <>'player'
If the number of stats doesn't change often, you could easily just write a single query to get this data. Join bbStats to bbStatsPrior where bbstatsprior.week < bbstats.week and bbstats.week=#weekNumber. Then just do a simple comparison between bbstats.Hits to bbstatsPrior.Hits to get your difference.
If the stats change often, you could use dynamic SQL to do this for all columns that match a certain pattern or are in a list of columns based on sys.columns for that table?
You could add a column for each stat column to designate the leader using a correlated subquery to find the max value for that column and see if it's equal to the current record.
This might get you started, but I'd recommend posting what you currently have to achieve this and the community can help you from there.