I am building a chat application where I am using Firebase to send and receive messages. Once I send or receive a message, I am storing it to SQLite as follows. Now it the recent chats screen, I need the last message from all the unique chats, number of unread messages in those unique chats in one single query as I am observing the SQLite database.
Mid(STRING) | SentBy | SentTo | message | readTime | sentTime| Type
----------------+--------+--------+---------+----------+---------+------
A | AA | JD | M1 | 1 | 0 | S
B | JD | AA | M2 | 2 | 1 | s
C | AA | JD | M3 | 3 | 2 | s
D | AB | JD | m5 | null | 3 | s
E | AA | JC | M1 | 5 | 4 | s
F | JD | AB | M2 | 6 | 5 | s
G | AA | JD | M3 | 7 | 6 | s
H | AA | JC | m5 | 8 | 7 | s
I | AA | JD | M1 | null | 8 | s
J | JD | AA | M2 | 10 | 9 | s
K | AA | JD | M3 | 11 | 10 | s
L | AB | JC | m5 | 12 | 11 | s
M | AA | JD | M1 | 13 | 12 | s
N | JC | AA | M2 | 14 | 13 | s
O | AB | JD | M3 | 15 | 14 | s
P | JC | JD | m5 | 16 | 15 | s
I tried
SELECT *,COUNT() FROM messagesTable GROUP BY min ( sentBy, sentTo ), max( sentBy , sentTo ) ORDER BY sentTime desc
This query gives me the last messages from every combination of sentTo and sentBy. But I also need to know how many messages are unread for that combination. I want to run a query for every row like
SELECT COUNT() FROM messagesTable WHERE sentBy = message.sentBy, sentTo = message.sentTo, readTime = null
How can I run both queries in a single query?
You must group by the combination of (sentby, sentto) and with a straight count(*) get the total number of messages and with conditional aggregation you can get the number of unread mesages.
Then join to the result to the table to get also the last message:
select
g.user1, g.user2, g.lasttime, m.message lastmessage,
g.totalcounter, g.unreadcounter
from messagestable m inner join (
select
min(sentby, sentto) user1, max(sentby, sentto) user2,
max(senttime) lasttime, count(*) totalcounter,
sum(case when readtime is null then 1 else 0 end) unreadcounter
from messagestable
group by user1, user2
) g
on g.user1 = min(m.sentby, m.sentto) and g.user2 = max(m.sentby, m.sentto)
and g.lasttime = m.senttime
order by g.lasttime desc
See the demo.
Results:
| user1 | user2 | lasttime | lastmessage | totalcounter | unreadcounter |
| ----- | ----- | -------- | ----------- | ------------ | ------------- |
| JC | JD | 15 | m5 | 1 | 0 |
| AB | JD | 14 | M3 | 3 | 1 |
| AA | JC | 13 | M2 | 3 | 0 |
| AA | JD | 12 | M1 | 8 | 1 |
| AB | JC | 11 | m5 | 1 | 0 |
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'm using SQL Server 2012 and I have the following problem:
I have the this table (Category):
IDCategory| CategoryDesc | Father
1 | R1 | 0
2 | R1 - ST | 1
3 | R1 - CT | 1
4 | R1 - ST - SA | 2
5 | R1 - ST - CA 10 | 2
6 | R1 - ST - CA 20 | 2
7 | R1 - CT - CA | 3
8 | R1 - CT - SA | 3
9 | R2 | 0
10 | R2 ST | 9
.
.
until R9
And this one (CategoryDefinition):
IDCategory| First| Last
1 | 0 | 300
9 | 301 | 600
.
.
.
And I'm using the following query because I know there're only 3 levels:
SELECT
cat3.IDCategory,
cat.CategoryDesc AS title1,
cat2.CategoryDesc AS title2,
cat3.CategoryDesc AS title3,
catdef.First,
catdef.Last
FROM Category as cat
LEFT JOIN Category AS cat2 ON cat2.Father=cat.IDCategory
LEFT JOIN Category AS cat3 ON cat3.Father=cat2.IDCategory
INNER JOIN CategoryDefinition as catdef on cat.IDCategory = catdef.IDCategory
WHERE cat3.IDCategory = 7
Query result:
IDCategory| title1 | title2 |title3 |First|Last
7 | R1 | R1 - CT | R1 - CT - CA | 0 | 300
But how can I make this recursive? In the case that in the future could appear new levels (So I don't have to add a new left join for each new level that appears).
Thanks!
I can at least help with the recusive cte of an adjacency list and building a materialized path:
For this table:
create table category (IDCategory int primary key,CategoryDesc varchar(32),Father int)
insert into category values
(1,'R1',0)
,(2,'ST',1)
,(3,'CT',1)
,(4,'SA',3)
,(5,'SA 10',2)
,(6,'SA 20',2)
,(7,'CA',2)
,(8,'SA',2)
,(9,'R2',0)
,(10,'ST',9)
using a recursive cte:
;with cte as (
-- anchor elements: where Father = 0
select
IDCategory
, categoryDesc
, Father
, parentName = convert(varchar(32),null)
, path = convert(varchar(128),categoryDesc)
from category
where Father = 0
-- recursion begins here
union all
select
c.IDCategory
, c.categoryDesc
, c.Father
, parentName = p.categoryDesc
, path = convert(varchar(128),p.path+' - '+c.categoryDesc)
from category c
inner join cte as p on c.Father= p.IDCategory
)
-- we select all the results
select cte.*
from cte
order by idCategory
returns:
+------------+--------------+--------+------------+-----------------+
| IDCategory | categoryDesc | Father | parentName | path |
+------------+--------------+--------+------------+-----------------+
| 1 | R1 | 0 | NULL | R1 |
| 2 | ST | 1 | R1 | R1 - ST |
| 3 | CT | 1 | R1 | R1 - CT |
| 4 | SA | 3 | CT | R1 - CT - SA |
| 5 | SA 10 | 2 | ST | R1 - ST - SA 10 |
| 6 | SA 20 | 2 | ST | R1 - ST - SA 20 |
| 7 | CA | 2 | ST | R1 - ST - CA |
| 8 | SA | 2 | ST | R1 - ST - SA |
| 9 | R2 | 0 | NULL | R2 |
| 10 | ST | 9 | R2 | R2 - ST |
+------------+--------------+--------+------------+-----------------+
Adding the join to the anchor of the recursive cte:
;with cte as (
-- anchor elements: where Father = 0
select
c.IDCategory
, c.categoryDesc
, c.Father
, parentName = convert(varchar(32),null)
, path = convert(varchar(128),c.categoryDesc)
, cd.First
, cd.Last
from category c
inner join CategoryDefinition cd
on c.IdCategory=cd.IdCategory
where Father = 0
-- recursion begins here
union all
select
c.IDCategory
, c.categoryDesc
, c.Father
, parentName = p.categoryDesc
, path = convert(varchar(128),p.path+' - '+c.categoryDesc)
, p.First
, p.Last
from category c
inner join cte as p on c.Father= p.IDCategory
)
select cte.*
from cte
--where IdCategory = 7
order by idCategory
rextester demo: http://rextester.com/POSVP81190
returns:
+------------+--------------+--------+------------+-----------------+-------+------+
| IDCategory | categoryDesc | Father | parentName | path | First | Last |
+------------+--------------+--------+------------+-----------------+-------+------+
| 1 | R1 | 0 | NULL | R1 | 0 | 300 |
| 2 | ST | 1 | R1 | R1 - ST | 0 | 300 |
| 3 | CT | 1 | R1 | R1 - CT | 0 | 300 |
| 4 | SA | 3 | CT | R1 - CT - SA | 0 | 300 |
| 5 | SA 10 | 2 | ST | R1 - ST - SA 10 | 0 | 300 |
| 6 | SA 20 | 2 | ST | R1 - ST - SA 20 | 0 | 300 |
| 7 | CA | 2 | ST | R1 - ST - CA | 0 | 300 |
| 8 | SA | 2 | ST | R1 - ST - SA | 0 | 300 |
| 9 | R2 | 0 | NULL | R2 | 301 | 600 |
| 10 | ST | 9 | R2 | R2 - ST | 301 | 600 |
+------------+--------------+--------+------------+-----------------+-------+------+
I have a sale data as below:
+------------+------+-------+
| Receipt ID | Item | Value |
+------------+------+-------+
| 1 | a | 2 |
| 1 | b | 3 |
| 1 | c | 2 |
| 1 | k | 4 |
| 2 | a | 2 |
| 2 | b | 5 |
| 2 | d | 6 |
| 2 | k | 7 |
| 3 | a | 8 |
| 3 | k | 1 |
| 3 | c | 2 |
| 3 | q | 3 |
| 4 | k | 4 |
| 4 | a | 5 |
| 5 | b | 6 |
| 5 | a | 7 |
| 6 | a | 8 |
| 6 | b | 3 |
| 6 | c | 4 |
+------------+------+-------+
Using APriori algorithm, I modified the Rules into different columns:
For eg, I got output as below, I trimmed support, confidence, Lift value.. I am only considering rules which mapped into different columns into Target Item, Item1, Items ({Item1,Item2} -> {Target Item})
Output is as below:
+-------------+-------+-------+
| Target Item | Item1 | Item2 |
+-------------+-------+-------+
| a | b | |
| a | b | c |
| a | k | |
+-------------+-------+-------+
I am looking to calculate the all the receipts having the rules combination and identify the Target item Sale value only in those receipts and also Combined sale value of Item 1 and Item 2 in the combination receipts:
Output should be something like below (I dont need receipt ID's from below)
+-------------+-------+-------+--------------+----------------------+------------------------------+
| Target Item | Item1 | Item2 | Receipt ID's | Value of Target Item | Remaining value(Item1+item2) |
+-------------+-------+-------+--------------+----------------------+------------------------------+
| a | b | | 1,2,5,6 | 2+2+7+8 | 3+5+6+3 |
| a | b | c | 1,6 | 2 | (3+3) + (2+4) |
| a | k | | 1,2,3,4 | 2+2+8+5 | 4+7+1+4 |
+-------------+-------+-------+--------------+----------------------+------------------------------+
To replicate the Apriori:
library(arules)
Data <- data.frame(
Receipt_ID = c(1,1,1,1,2,2,2,2,3,3,3,3,4,4,5,5,6,6,6),
item = c('a','b','c','k','a','b','d','k','a','k','c','q','k', 'a','b','a','a', 'b', 'c'
)
,
value = c(2,3,2,4,2,5,6,7,8,1,2,3,4,5,6,7,8,3,4
)
)
write.table(Data,"item.csv",sep=',',row.names = F)
data_frame = read.transactions(
file = "item.csv",
format = "single",
sep = ",",
cols = c("Receipt_ID","item"),
rm.duplicates = T
)
rules_apriori <- apriori(data_frame)
rules_apriori
rules_tab <- as(rules_apriori, "data.frame")
rules_tab
out <- strsplit(as.character(rules_tab$rules),'=>')
rules_tab$rhs <- do.call(rbind, out)[,2]
rules_tab$lhs <- do.call(rbind, out)[,1]
rules_tab$rhs <- gsub("\\{", "", rules_tab$rhs)
rules_tab$rhs <- gsub("}", "", rules_tab$rhs)
rules_tab$lhs = gsub("}", "", rules_tab$lhs)
rules_tab$lhs = gsub("\\{", "", rules_tab$lhs)
rules_final <- data.frame (target_item = character(),item_combination = character() )
rules_final <- cbind(target_item = rules_tab$rhs,item_Combination = rules_tab$lhs)
rules_final
I have 2 dataframes
Dataframe1:
| Cue | Ass_word | Condition | Freq | Cue_Ass_word |
1 | ACCENDERE | ACCENDINO | A | 1 | ACCENDERE_ACCENDINO
2 | ACCENDERE | ALLETTARE | A | 0 | ACCENDERE_ALLETTARE
3 | ACCENDERE | APRIRE | A | 1 | ACCENDERE_APRIRE
4 | ACCENDERE | ASCENDERE | A | 1 | ACCENDERE_ASCENDERE
5 | ACCENDERE | ATTIVARE | A | 0 | ACCENDERE_ATTIVARE
6 | ACCENDERE | AUTO | A | 0 | ACCENDERE_AUTO
7 | ACCENDERE | ACCENDINO | B | 2 | ACCENDERE_ACCENDINO
8 | ACCENDERE| ALLETTARE | B | 3 | ACCENDERE_ALLETTARE
9 | ACCENDERE| ACCENDINO | C | 2 | ACCENDERE_ACCENDINO
10 | ACCENDERE| ALLETTARE | C | 0 | ACCENDERE_ALLETTARE
Dataframe2:
| Group.1 | x
1 | ACCENDERE_ACCENDINO | 5
13 | ACCENDERE_FUOCO | 22
16 | ACCENDERE_LUCE | 10
24 | ACCENDERE_SIGARETTA | 6
....
I want to exclude from Dataframe1 all the rows that contain words (Cue_Ass_word) that are not reported in the column Group.1 in Dataframe2.
In other words, how can I subset Dataframe1 using the strings reported in Dataframe2$Group.1?
It's not quite clear what you mean, but is this what you need?
Dataframe1[!(Dataframe1$Cue_Ass_word %in% Dataframe2$Group1),]