the data format i have is as follows:
When i use s2<- fill_(s1,c("Time")), it would use the last seen value..
however i would like all values of time listed below to repeat for each value of Animal
Group Animal Sex Time
1 1001 M 0
4
8
24
48
1 1002 M
1 1003 M
Related
i'm currently solving a problem that states:
A company filed for bankruptcy and decided to pay the employees with the last remaining valuable items in the company only if it can be distributed evenly among them so that all of them have at least received 1 item and that the difference between the employee carrying the most valuable items and the employee carrying the least valuable items can not exceed a certain value x;
Input:
First row contains number of employee;
Second row contains the x value so that the the difference between the employee carrying the most valuable items and the employee carrying the least valuable items can not exceed;
Third row contains all the items with their value;
Output:
First number is the least valuable basket of items value and the second is the most valuable basket;
Example:
Input:
5
4
2 5 3 11 4 3 1 15 7 8 10
Output:
13 15
Input:
5
4
1 1 1 11 1 3 1 2 7 8
Output:
NO (It's impossible to distribute evenly)
Input:
5
10
1 1 1 1
Output:
NO (It's impossible to distribute evenly)
My solution to resolve this problem taking the first input is to, sort the items in ascending or descending order so from
2 5 3 11 4 3 1 15 7 8 10 --> 1 2 3 3 4 5 7 8 10 11 15
then create an adjacency list or just store it in simple variables where we add the biggest number to the lowest basket while iterating the item values array
Element 0: 15
Element 1: 11 <- 3 (sum 14)
Element 2: 10 <- 3 (sum 13)
Element 3: 8 <- 4 <- 1 (sum 13)
Element 4: 7 <- 5 <- 2 (sum 14)
So that my solution will have O(nlogN + 2n), first part using merge sort and then finding max e min value, what do you guys think about this solution?
Suppose I have a table as follows:
id=`A`B`A`B`B`B`A
item= 10 1 1 3 5 10 6
t=table(id,item)
id item
-- ----
A 10
B 1
A 1
B 3
B 5
B 10
A 6
For example, I want to sort the table with two conditions: first, by the most commonly occurring item in column item, then by the highest number in column item.
How can I sort like this:
id item
--- ----
A 10
B 10
A 1
B 1
A 6
B 5
B 3
Is there any way to go about this? Thanks!
Try this:
t1=table(id,item);
update t1 set count=count(item) context by item;
select * from t1 order by count desc, item desc;
I want to filter a pivot table in the following set up:
My Table:
Key Value1 Value2
1 23 a
2 33 b
3 1 c
4 5 d
My pivot table (simplified):
Key SUM of Value1 COUNTA of Value2
1 23 1
2 33 1
3 1 1
4 5 1
Grand Total 62 4
I now want to filter the pivot table by the values in this list:
Keys
1
2
4
So the resulting pivot table should look like this:
Key SUM of Value1 COUNTA of Value2
1 23 1
2 33 1
4 5 1
Grand Total 61 3
I thought this should be possible by using a custom formula in the pivot filter but it seems I have no way of using the current cell in the pivot e.g. to make a lookup.
I created a simple example of this setup here: https://docs.google.com/spreadsheets/d/1GlQDYtW8v8ri5L68RhryTZxwTikV_NXZQlccSI6_7pU/edit?usp=sharing
paste this formula in Filters!B1:
=ARRAYFORMULA(IFERROR(VLOOKUP(A1:A, Table!A1:C, {2,3}, 0), ))
and create a resulting pivot table from there:
demo spreadsheet
I have the following mock up table
#n a b group
1 1 1 1
2 1 2 1
3 2 2 1
4 2 3 1
5 3 4 2
6 3 5 2
7 4 5 2
I am using SAS for this problem. In column group, the rows that are interconnected through a and b are grouped. I will try to explain why these rows are in the same group
row 1 to 2 are in group 2 since they both have a = 1
row 3 is in group 2 since b = 2 in row 2 and 3 and row 2 is in group 1
row 3 and 4 are in group 1 since a = 2 in both rows and row 3 is in group 1
The overall logic is that if a row x contains the same value of a or b as row y, row x also belongs to the same group as y is a part of.
Following the same logic, row 5,6 and 7 are in group 2.
Is there any way to make an algorithm to find these groups?
Case I:
Grouping defined as to be item linkage within contiguous rows.
Use the LAG function to examine both variables prior values. Increase the group value if both have changed. For example
group + ( a ne lag(a) and b ne lag(b) );
Case II:
Grouping determined from pair item slot value linkages over all data.
From grouping pairs by either key
General statement of problem:
-----------------------------
Given: P = p{i} = (p{i,1},p{i,2}), a set of pairs (key1, key2).
Find: The distinct groups, G = g{x}, of P,
such that each pair p in a group g has this property:
key1 matches key1 of any other pair in g.
-or-
key2 matches key2 of any other pair in g.
Demonstrates
… an iterative way using hashes.
Two hashes maintain the groupId assigned to each key value.
Two additional hashes are used to maintain group mapping paths.
When the data can be passed without causing a mapping, then the groups have
been fully determined.
A final pass is done, at which point the groupIds are assigned to each
pair and the data is output to a table.
I have a crosstab and create custom grand total for the row level in each column dimension, by using a data element expression.
Crosstab Example:
Cat 1 Cat 2 GT
ITEM C F % VALUE C F % VALUE
A 101 0 0.9 10 112 105 93.8 10 20
B 294 8 2.7 6 69 66 95.7 10 16
C 211 7 3.3 4 212 161 75.9 6 10
------------------------------------------------------------------
GT 606 15 2.47 6 393 332 84.5 8 **14**
Explanation for GT row:
Those C and F column is summarized from the above. But the
% column is division result of F/C.
Create a data element to fill the VALUE column, which comes from range of value definition, varies for each Cat (category). For instance... in Cat 1, if the value is between 0 - 1 the value will be 10, or between 1 - 2 = 8, etc. And condition for Cat 2, between 85 - 100 = 10, and 80 - 85 = 8, etc.
The GT row (with the value of 14), is gathered by adding VALUE of Cat 1 + Cat 2.
I am able to work on point 1 and 2 above, but I can't seem to make it working for GT row. I don't know the code/expression to sum up the VALUE data element for this 2 categories. Because those VALUE field comes from one data element in design mode.
I have found the solution for my problem. I can show the result by using a report variable. I am assigning 2 report variables in % field expression, based on the category in data cube dimension (by using if statement). And then in data element expression, I am calling both of the expressions and add them.