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Group Rows in a variable


Group Rows in a variable

By : user2948048
Date : November 14 2020, 04:48 PM
should help you out You can use .add to add elements to a jQuery collection:
code :
var combined = $("#Grp1").add($("#Grp1").nextUntil("#Grp2"));
var combined = $("#Grp1").nextUntil("#Grp2").addBack();


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select rows with largest value of variable within a group in r

select rows with largest value of variable within a group in r


By : Peaches
Date : March 29 2020, 07:55 AM
like below fixes the issue
code :
a.2<-sample(1:10,100,replace=T)
b.2<-sample(1:100,100,replace=T)
a.3<-data.frame(a.2,b.2)
sel <- ave(b.2, a.2, FUN = max) == b.2
a.3[sel,]
parse CSV, Group all rows containing string at 5th field, export each group of rows to file with filename <group>_

parse CSV, Group all rows containing string at 5th field, export each group of rows to file with filename <group>_


By : Chris Munro
Date : March 29 2020, 07:55 AM
will be helpful for those in need If I have understood well your problem this is very easy...
You can just:
code :
$ awk -F, '{fifth=substr($5, 1, 4) ; print > (fifth "_mysuffix.csv")}' file.cv
$ awk -F, '{print > (substr($5, 1, 4) "_mysuffix.csv")}' file.csv
$ cat 3100_mysuffix.csv 
1,2,3,4,3100_group2,6,7,8
1,2,3,4,3100_group2,6,7,8
$ cat 5400_mysuffix.csv 
1,2,3,4,5400_group3,6,7,8
1,2,3,4,5400_group3,6,7,8
T-SQL update top n rows for each group with n variable for each group (cross apply alternative)

T-SQL update top n rows for each group with n variable for each group (cross apply alternative)


By : Diego Kfuri
Date : March 29 2020, 07:55 AM
around this issue I have two tables - SourceTable and DestTable - Source contains columns RowNum and Col. I want to join the two tables on Col and then update top 'n' rows of DestTable where 'n' is the value of RowNum in SourceTable. I'm able to do this with cross apply but it takes hours. I'm looking for another way of writing this query. , You could try to use simple JOIN instead of correlated subquery:
code :
WITH cte AS
(
 SELECT d.id, d.col, d.dest, s.source
 FROM (SELECT *,
       rn = ROW_NUMBER() OVER(PARTITION BY col ORDER BY id) FROM #desttable) d
 JOIN #sourcetable s
   ON d.col = s.col
  AND d.rn <= s.rownum 
)
UPDATE cte
SET dest = source;

SELECT *
FROM #desttable;
How to take first 5 rows by a group without replacement in another variable value

How to take first 5 rows by a group without replacement in another variable value


By : Matthew
Date : March 29 2020, 07:55 AM
Hope that helps I am trying to figure out how I can take only first 5 rows by a group without replacement in another variable value. For example, if the existing data table (or frame) looks like this: , Here's an option in three steps:
code :
# create a vector to store set values
x <- numeric()
# compute the values by id and update x in the process
res <- lapply(split(df$V1, df$id), function(y) {
     y <- head(setdiff(y, x), 5)
     x <<- union(x, y)
     if(!length(y)) NA else y
})
# combine the result to data.frame
stack(res)
#   values ind
#1     101   1
#2     102   1
#3     103   1
#4     104   1
#5     105   1
#6     107   2
#7     108   2
#8     109   2
#9     110   2
#10    111   2
#11     NA   3
R: How can I group rows in a dataframe, ID rows meeting a condition, then delete prior rows for the group?

R: How can I group rows in a dataframe, ID rows meeting a condition, then delete prior rows for the group?


By : mindentropy
Date : March 29 2020, 07:55 AM
With these it helps As a first step I am marking existing customers (customer 3 in this case) in the key column -
code :
df %>% 
  group_by(customer) %>% 
  mutate(
    key = as.character(key), # can be avoided if key is a character to begin with
    key = ifelse(row_number() == 1 & (!is.na(C) | !is.na(D)), "existing", key)
  ) %>% 
  filter(cumsum(!is.na(key)) > 0) %>% 
  ungroup()

# A tibble: 7 x 5
  customer  year     C     D key     
     <dbl> <dbl> <dbl> <dbl> <chr>   
1        1  2002     1     5 new     
2        1  2003     2     6 NA      
3        2  2003     2     7 new     
4        3  2000     2     4 existing
5        3  2001     6     4 NA      
6        3  2002     2     5 NA      
7        3  2003     1     8 NA 
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