cumulative-sum

I tried asking this question before but was it was poorly stated. This is a new attempt cause I haven't solved it yet. I have a dataset with winners, losers, date, winner_points and loser_points. For each row, I want two new columns, one for the winner and one for the loser that shows how many points they have scored so far (as both winners and losers). Example data: winner <- c(1,2,3,1,2,3,1,2,3) loser <- c(3,1,1,2,1,1,3,1,2) date <- c("2017-10-01","2017-10-02","2017-10-03","2017-10-04","2017-10-05","2017-10-06","2017-10-07","2017-10-08","2017-10-09") winner_points <- c(2,1,2,1,2,1,2,1,2)

I have a table containing donations, and I am now creating a page to view statistics. I would like to fetch monthly data from the database with gross and cumulative gross. mysql> describe donations; +------------------+------------------+------+-----+---------+----------------+ | Field | Type | Null | Key | Default | Extra | +------------------+------------------+------+-----+---------+----------------+ | id | int(10) unsigned | NO | PRI | NULL | auto_increment | | transaction_id | varchar(64) | NO | UNI | | | | donor_email | varchar(255) | NO | | | | | net | double | NO | | 0 | | | gross |

As per Wikipedia: A summed area table is a data structure and algorithm for quickly and efficiently generating the sum of values in a rectangular subset of a grid. For a 2D space a summed area table can be generated by iterating x,y over the desired range, I(x,y) = i(x,y) + I(x-1,y) + I(x,y-1) - I(x-1,y-1) And the query function for a rectangle corners A(top-left) , B(top-right) , C(bottom-right) , D can be given by:- I(C) + I(A) - I(B) - I(D) I want to convert the above to 3D. Also please tell if any other method/data structure available for calculating partial sums in 3D space. I'm not sure

I make this simple cumulative and histogram plot of a uniform random distribution of real numbers (n=1000): http://www.filedropper.com/random1_1 : random1.dat And the macro is: unset key clear reset n=120 #number of intervals max=4. #max value min=1. #min value width=(max-min)/n #interval width #function used to map a value to the intervals bin(x,width)=width*floor(x/width)+width/2.0 # cosi viene centrato in mezzo set xtics min,(max-min)/10,max set boxwidth width set style fill solid 0.5 border set ylabel 'Frequency' set y2label 'Cumulative frequency' set y2tics 0,100,1000 set ytics nomirror

I have a simple binary vector a which I try to translate into vector b using the R function cumsum . However, cumsum does not exactly return vector b . Here is an example: a <- c(1,0,0,0,1,1,1,1,0,0,1,0,0,0,1,1) b <- c(1,2,2,2,3,4,5,6,7,7,8,9,9,9,10,11) > cumsum(a) [1] 1 1 1 1 2 3 4 5 5 5 6 6 6 6 7 8 The problem is that whenever a 0 appears in vector a then the previous number should be increased by 1 but only for the first 0. The remaining ones are given the same value. Any advise would be great! :-) The trick is to use diff to mark the transitions: cumsum(as.logical(a+c(0,abs(diff(a))))) [1]

I have an igraph object g made from dataframe df : df <- data.frame(c(0,1,2,2,4), c(1,2,3,4,5), c(0.01, 0.03, 0.05, 0.01, 0.02)) colnames(df) <- c('parent_id', 'id', 'dt') g <- graph_from_data_frame(df) Edges are made between parent_id and id . > g IGRAPH DN-- 6 5 -- + attr: name (v/c), dt (e/n) + edges (vertex names): [1] 0->1 1->2 2->3 2->4 4->5 Change in thickness dt is the edge attribute. This can be thought of as the change in thickness between a 'parent' and 'child' iceberg (this is my problem/project). list.edge.attributes(g) [1] "dt" to visualize: plot(g, edge.label=E(g)$dt) Example of

I have this dataframe +---+----+---+ | A| B| C| +---+----+---+ | 0|null| 1| | 1| 3.0| 0| | 2| 7.0| 0| | 3|null| 1| | 4| 4.0| 0| | 5| 3.0| 0| | 6|null| 1| | 7|null| 1| | 8|null| 1| | 9| 5.0| 0| | 10| 2.0| 0| | 11|null| 1| +---+----+---+ What I need do is a cumulative sum of values from column C until the next value is zero, then reset the cumulative sum, doing this until finish all rows. Expected output: +---+----+---+----+ | A| B| C| D| +---+----+---+----+ | 0|null| 1| 1| | 1| 3.0| 0| 0| | 2| 7.0| 0| 0| | 3|null| 1| 1| | 4| 4.0| 0| 0| | 5| 3.0| 0| 0| | 6|null| 1| 1| | 7|null| 1| 2| | 8|null| 1

I have some transactional records, like the following: library(data.table) customers <- 1:75 purchase_dates <- seq( as.Date('2016-01-01'), as.Date('2018-12-31'), by=1 ) n <- 500L set.seed(1) # Assume the data are already ordered and 1 row per cust_id/purch_dt df <- data.table( cust_id = sample(customers, n, replace=TRUE), purch_dt = sample(purchase_dates, n, replace=TRUE), purch_amt = sample(500:50000, n, replace=TRUE)/100 )[, .(purch_amt = sum(purch_amt)), keyby=.(cust_id, purch_dt) ] df # cust_id purch_dt purch_amt # 1 2016-03-20 69.65 # 1 2016-05-17 413.60 # 1 2016-12-25 357.18 # 1 2017-03