rank

All: I have a data frame like the follow.I know I can do a global rank order like this: dt <- data.frame( ID = c('A1','A2','A4','A2','A1','A4','A3','A2','A1','A3'), Value = c(4,3,1,3,4,6,6,1,8,4) ); > dt ID Value 1 A1 4 2 A2 3 3 A4 1 4 A2 3 5 A1 4 6 A4 6 7 A3 6 8 A2 1 9 A1 8 10 A3 4 dt$Order <- rank(dt$Value,ties.method= "first") > dt ID Value Order 1 A1 4 5 2 A2 3 3 3 A4 1 1 4 A2 3 4 5 A1 4 6 6 A4 6 8 7 A3 6 9 8 A2 1 2 9 A1 8 10 10 A3 4 7 But how can I set a rank order for a particular ID instead of a global rank order. How can I get this done? In T-SQL, we can get this done as the following

I am new to Python and the Pandas library, so apologies if this is a trivial question. I am trying to rank a Timeseries over a rolling window of N days. I know there is a rank function but this function ranks the data over the entire timeseries. I don't seem to be able to find a rolling rank function. Here is an example of what I am trying to do: A 01-01-2013 100 02-01-2013 85 03-01-2013 110 04-01-2013 60 05-01-2013 20 06-01-2013 40 If I wanted to rank the data over a rolling window of 3 days, the answer should be: Ranked_A 01-01-2013 NaN 02-01-2013 Nan 03-01-2013 1 04-01-2013 3 05-01-2013 3

I am searching for a method to utilize within Microsoft PowerPivot 2010 that will allow me to perform dynamic ranking that will automatically update the associated rank value based on filters and slicer values that are applied. Thusfar, all examples I have seen utilize the Calculate() DAX function that overrides existing filters within the PowerPivot table via the All() function which causes predefined filters that users may apply to be disregarded. To illustrate my requirements, please reference the example below: (Source Data within PowerPivot Window:) ---------------------------------------

I am trying to write a program where I want the allocatable array A to be of either rank 1, 2, or 3, depending on my input at run-time. I want to do this since the subsequent operations on A are similar, and I have defined in a module an interface work with module procedures that when acted on A , gives the desired result. What I am doing currently is this: program main implicit none integer :: rank,n=10 real*8, allocatable :: A1(:) real*8, allocatable :: A2(:,:) read (*,*) rank if (rank.eq.1) then allocate (A1(n)) else if (rank.eq.2) then allocate (A2(n,n)) end if ! operate on the array if

If I want to rank a set of numbers using the minimum rank for shared cases (aka ties): dat <- c(13,13,14,15,15,15,15,15,15,16,17,22,45,46,112) rank(dat, ties = 'min') I get the results: 1 1 3 4 4 4 4 4 4 10 11 12 13 14 15 However, I want the rank to be a continuous series consisting of 1,2,3,... n , where n is the number of unique ranks . Is there a way to make rank (or a similar function) rank a series of numbers by assigning ties to the lowest rank as above but instead of skipping subsequent rank values by the number of previous ties to instead continue ranking from the previous rank ? For

I have a table with one column as follows: name ------- Michael Michael Michael Michael John John John Alex Alex I need to rank them to give: name | rank --------|------ Michael |1 Michael |2 Michael |3 Michael |4 John |1 John |2 John |3 Alex |1 Alex |2 How can I perform that? There's nothing in mysql that lets you do this directly, but you can hack it in: SET @prev := null; SET @cnt := 1; SELECT name, IF(@prev <> name, @cnt := 1, @cnt := @cnt + 1) AS rank, @prev := name FROM yourtable ORDER BY name This sort of thing is easier done in your client app, using the same basic logic. Simple,

I'm using R for the analysis of my masterthesis. Unfortunately, I got stuck with this problem: I would like to compute a new variable which calculates the rank of one variable per row within many variables. Example: V1 V2 V3 NewVariable_V1 NewVariable_V2 NewVariable_V3 11 21 35 3 2 1 22 12 66 2 3 1 44 22 12 1 2 3 Does anyone know how to do that? I'd be glad for help. You're looking for rank. To get decreasing order, first negate the data.frame. data.frame(d, t(apply(-d, 1, rank, ties.method='min'))) # V1 V2 V3 V1.1 V2.1 V3.1 # 1 11 21 35 3 2 1 # 2 22 12 66 2 3 1 # 3 44 22 12 1 2 3 ?rank and

OK, check out this data frame... customer_name order_dates order_values 1 John 2010-11-01 15 2 Bob 2008-03-25 12 3 Alex 2009-11-15 5 4 John 2012-08-06 15 5 John 2015-05-07 20 Lets say I want to add an order variable that Ranks the highest order value, by name, by max order date, using the last order date at the tie breaker. So, ultimately the data should look like this: customer_name order_dates order_values ranked_order_values_by_max_value_date 1 John 2010-11-01 15 3 2 Bob 2008-03-25 12 1 3 Alex 2009-11-15 5 1 4 John 2012-08-06 15 2 5 John 2015-05-07 20 1 Where everyone's single order gets 1,