I have 2 tables:
table 1 (let it be 'products'):
----------------
| id | product |
----------------
| 1 | Apple |
| 2 | Grape |
| 3 | Orange |
table 2 (let it be 'tags'):
------------------------------
| id | product_id | tag |
------------------------------
| 1 | 1 | tag1 |
| 2 | 1 | tag2 |
| 3 | 2 | tag2 |
| 4 | 2 | tag3 |
| 5 | 3 | tag4 |
I want to make a request to SQLite database which will generate such table as result:
---------------------------
| product | tags |
---------------------------
| Apple | tag1, tag2 |
| Grape | tag2, tag3 |
| Orange | tag4 |
How can I achieve this? How can I combine tags into one column using only SQLite query language?
I think what you're looking for is "group by" and "group_concat()":
select products.product,group_concat(tags.tag) from products join tags on tags.product_id = products.rowid group by tags.product_id;
Related
I would like to assign groups to larger groups in order to assign them to cores for processing. I have 16 cores.This is what I have so far
test<-data_extract%>%group_by(group_id)%>%sample_n(16,replace = TRUE)
This takes staples OF 16 from each group.
This is an example of what I would like the final product to look like (with two clusters),all I really want is for the same group id to belong to the same cluster as a set number of clusters
________________________________
balance | group_id | cluster|
454452 | a | 1 |
5450441 | a | 1 |
5444531 | b | 1 |
5404051 | b | 1 |
5404501 | b | 1 |
5404041 | b | 1 |
544251 | b | 1 |
254252 | b | 1 |
541254 | c | 2 |
54123254 | d | 1 |
542541 | d | 1 |
5442341 | e | 2 |
541 | f | 1 |
________________________________
test<-data%>%group_by(group_id)%>% mutate(group = sample(1:16,1))
I'm working with SQL Server and I have this 3 tables
STUDENTS
| id | student |
-------------
| 1 | Ronald |
| 2 | Jenny |
SCORES
| id | score | period | student |
| 1 | 8 | 1 | 1 |
| 2 | 9 | 2 | 1 |
PERIODS
| id | period |
| 1 | 1 |
| 2 | 2 |
| 3 | 3 |
| 4 | 4 |
And I want a query that returns this result:
| student | score1 | score2 | score3 | score4 |
| Ronald | 8 | 9 | null | null |
| Jenny | null | null | null | null |
As you can see, the number of scores depends of the periods because sometimes it can be 4 o 3 periods.
I don't know if I have the wrong idea or should I make this in the application, but I want some help.
You need to PIVOT your data e.g.
select Y.Student, [1], [2], [3], [4]
from (
select T.Student, P.[Period], S.Score
from Students T
cross join [Periods] P
left join Scores S on S.[Period] = P.id and S.Student = T.id
) X
pivot
(
sum(Score)
for [Period] in ([1],[2],[3],[4])
) Y
Reference: https://learn.microsoft.com/en-us/sql/t-sql/queries/from-using-pivot-and-unpivot?view=sql-server-20
I have a table, that looks roughly like this:
| variable | observer1 | observer2 | observer3 | final |
| -------- | --------- | --------- | --------- | ----- |
| case1 | | | | |
| var1 | 1 | 1 | | |
| var2 | 3 | 3 | | |
| var3 | 4 | 5 | | 5 |
| case2 | | | | |
| var1 | 2 | | 2 | |
| var2 | 5 | | 5 | |
| var3 | 1 | | 1 | |
| case3 | | | | |
| var1 | | 2 | 3 | 2 |
| var2 | | 2 | 2 | |
| var3 | | 1 | 1 | |
| case4 | | | | |
| var1 | 1 | | 1 | |
| var2 | 5 | | 5 | |
| var3 | 3 | | 3 | |
Three colums for the observers, but only two are filled.
First I want to compute the IRR, so I need a table that has two columns without the empty cells like this:
| variable | observer1 | observer2 |
| -------- | --------- | --------- |
| case1 | | |
| var1 | 1 | 1 |
| var2 | 3 | 3 |
| var3 | 4 | 5 |
| case2 | | |
| var1 | 2 | 2 |
| var2 | 5 | 5 |
| var3 | 1 | 1 |
| case3 | | |
| var1 | 2 | 3 |
| var2 | 2 | 2 |
| var3 | 1 | 1 |
| case4 | | |
| var1 | 1 | 1 |
| var2 | 5 | 5 |
| var3 | 3 | 3 |
I try to use the tidyverse packages, but I'm not sure. Some 'ifelse()' magic may be easier.
Is there a clean and easy method to do something like this? Can anybody point me to the right function to use? Or just to a keyword to search for on stackoverflow? I found a lot of methods to remove whole empty columns or rows.
Edit: I removed the link to the original data. It was unnecessary. Thanks to Lamia for his working answer.
Out of your 3 columns observer1, observer2 and observer3, you sometimes have 2 non-NA values, 1 non-NA value, or 3 NA values.
If you want to merge your 3 columns, you could do:
res = data.frame(df$coding,t(apply(df[paste0("observer",1:3)],1,function(x) x[!is.na(x)][1:2])))
The apply function will return for each row the 2 non-NA values if there are 2, one non-NA value and one NA if there is only one value, and two NAs if there is no data in the row.
We then put this result in a dataframe with the first column (coding).
I'm trying to work out how, in Azure ML (and therefore R solutions are acceptable), to randomly split data based on a column, such that all records with any given value in that column wind up in one side of the split or another. For example:
+------------+------+--------------------+------+
| Student ID | pass | some_other_feature | week |
+------------+------+--------------------+------+
| 1234 | 1 | Foo | 1 |
| 5678 | 0 | Bar | 1 |
| 9101112 | 1 | Quack | 1 |
| 13141516 | 1 | Meep | 1 |
| 1234 | 0 | Boop | 2 |
| 5678 | 0 | Baa | 2 |
| 9101112 | 0 | Bleat | 2 |
| 13141516 | 1 | Maaaa | 2 |
| 1234 | 0 | Foo | 3 |
| 5678 | 0 | Bar | 3 |
| 9101112 | 1 | Quack | 3 |
| 13141516 | 1 | Meep | 3 |
| 1234 | 1 | Boop | 4 |
| 5678 | 1 | Baa | 4 |
| 9101112 | 0 | Bleat | 4 |
| 13141516 | 1 | Maaaa | 4 |
+------------+------+--------------------+------+
Acceptable output from that if I chose, say, a 50/50 split and to be grouped based on the Student ID column would be two new datasets:
+------------+------+--------------------+------+
| Student ID | pass | some_other_feature | week |
+------------+------+--------------------+------+
| 1234 | 1 | Foo | 1 |
| 1234 | 0 | Boop | 2 |
| 1234 | 0 | Foo | 3 |
| 1234 | 1 | Boop | 4 |
| 9101112 | 1 | Quack | 1 |
| 9101112 | 0 | Bleat | 2 |
| 9101112 | 1 | Quack | 3 |
| 9101112 | 0 | Bleat | 4 |
+------------+------+--------------------+------+
and
+------------+------+--------------------+------+
| Student ID | pass | some_other_feature | week |
+------------+------+--------------------+------+
| 5678 | 0 | Bar | 1 |
| 5678 | 0 | Baa | 2 |
| 5678 | 0 | Bar | 3 |
| 5678 | 1 | Baa | 4 |
| 13141516 | 1 | Meep | 1 |
| 13141516 | 1 | Maaaa | 2 |
| 13141516 | 1 | Meep | 3 |
| 13141516 | 1 | Maaaa | 4 |
+------------+------+--------------------+------+
Now, from what I can tell this is basically the opposite of stratified split, where it would get a random sample with every student represented on both sides.
I would prefer an Azure ML function that did this, but I think that's unlikely so is there an R function or library that gives this kind of functionality? All I could find was questions about stratification which obviously don't help me much.
You can use te following command:
data.fold <- mutate(df, fold = sample(rep_len(1:2, n_distinct(Student ID)))[Student ID])
It returns the original dataframe with an new column that indicates the fold that the student is in. If you want more folds, just adjust the '1:2' part.
I've tried the 'sample unique' way but it did not always work for me in the past.
how can i delete the whole rows of gridview using code behind c sharp like
+-----+-----+------+
|Col1 | Col2| Col3 |
| | | |
| a | 1 | 5 |
| | | |
| a | 2 | 6 |
| | | |
| a | 3 | 7 |
| | | |
| a | 4 | 8 |
+-----+-----+------+
and I want to delete the whole rows in the gridview so that it becomes
+-----+-----+------+
|Col1 | Col2| Col3 |
| | | |
| a | 4 | 8 |
+-----+-----+------+
only last duplicate row left and all the rest is deleted (rows are deleted from all the columns of gridview)
Anyone knows how to achieve this?
Have a look at GridView.DeleteRow(). MSDN documentation is here: GridView.DeleteRow Method