rolling-computation

My imported data contains 7 variables: Y and X1 , X2 , X3 , X4 , X5 , X6 . I tried applying the rollapply function in zoo in order to run a rolling regression within an in-sample with a window of 262 obs. (work days in a year). date Y X1 X2 1 10/1/07 -0.0080321720 4.690734e-03 3.333770e-03 2 10/2/07 0.0000000000 -2.818413e-03 5.418223e-03 3 10/3/07 0.0023158650 -4.178744e-03 -3.821100e-04 4 10/4/07 -0.0057491710 -5.071030e-03 -8.321550e-04 5 10/5/07 0.0073570500 3.065045e-03 5.179574e-03 6 10/8/07 0.0127708010 -7.278513e-03 1.145395e-03 7 10/9/07 0.0032661980 9.692267e-03 6.514035e-03 8 10/10

Lets say I have a data frame with the following structure: DF <- data.frame(x = 0:4, y = 5:9) > DF x y 1 0 5 2 1 6 3 2 7 4 3 8 5 4 9 what is the most efficient way to turn 'DF' into a data frame with the following structure: w x y 1 0 5 1 1 6 2 1 6 2 2 7 3 2 7 3 3 8 4 3 8 4 4 9 Where w is a length 2 window rolling through the dataframe 'DF.' The length of the window should be arbitrary, i.e a length of 3 yields w x y 1 0 5 1 1 6 1 2 7 2 1 6 2 2 7 2 3 8 3 2 7 3 3 8 3 4 9 I am a bit stumped by this problem, because the data frame can also contain an arbitrary number of columns, i.e. w,x,y,z etc.

There are many questions about rolling regression in R, but here I am specifically looking for something that uses dplyr , broom and (if needed) purrr . This is what makes this question different. I want to be tidyverse consistent. Is is possible to do a proper running regression with tidy tools such as purrr:map and dplyr ? Please consider this simple example: library(dplyr) library(purrr) library(broom) library(zoo) library(lubridate) mydata = data_frame('group' = c('a','a', 'a','a','b', 'b', 'b', 'b'), 'y' = c(1,2,3,4,2,3,4,5), 'x' = c(2,4,6,8,6,9,12,15), 'date' = c(ymd('2016-06-01', '2016

I have a time series object grouped of the type <pandas.core.groupby.SeriesGroupBy object at 0x03F1A9F0> . grouped.sum() gives the desired result but I cannot get rolling_sum to work with the groupby object. Is there any way to apply rolling functions to groupby objects? For example: x = range(0, 6) id = ['a', 'a', 'a', 'b', 'b', 'b'] df = DataFrame(zip(id, x), columns = ['id', 'x']) df.groupby('id').sum() id x a 3 b 12 However, I would like to have something like: id x 0 a 0 1 a 1 2 a 3 3 b 3 4 b 7 5 b 12 Note: as identified by @kekert, the following pandas pattern has been deprecated. See