## conditional rolling average in R

r rolling sum by group

dplyr rolling average

rolling sum in r

rollapply r

rolling mean in r

cumulative sum in r

rolling window in r

dat <- structure(list(yearRef = c(1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018), value = c(0.761253538863966, 0.778365700864592, 0.748473422160476, 0.790408287413012, 0.726707786670043, 0.80587461240495, 0.81582881742434, 0.914998995290579, 0.903241004636529, 0.883446087736501, 0.878399385374308, 0.790239960507709, 0.853841173129717, 0.972923769177295, 0.899133969911117, 0.865840008976815, 0.85942147306247, 0.9471790327507, 0.905362802563981, 0.91644169495142, 0.985789564141214, 0.978212191208007, 0.885157529562834, 1.01638026873823, 1.02702020472382, 0.944421276774342, 0.979439113456467, 0.951183598644539, 1.12054063623421, 1.00767230122493, 1.02132151007705, 0.95649988168142, 0.928385199359045, 1.05071183719421, 1.11654102944792, 0.910601547182633, 0.936460862711605, 1.2398210426787, 0.979036947391532, 1.09931214756341, 1.12206830109171, 0.997384903912461, 1.07413151131128, 0.967026290186151, 1.04921352764649, 1.08746580600605, 1.02444885186573, 1.14604631626466, 1.06449109417896)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -49L))

For each year, I want to calculate the mean of top 5 values from the previous 7 values. For e.g. the first mean value will be for 1977 and will consist of mean of best 5 years from 1970 till 1976. Similarly, for 1978, mean value will be the top 5 values from 1971-1977. Similarly, for 2018, the mean value will be top 5 values from 2011 - 2017

I have the following code from SO which sort of does the job.

library(data.table) library(zoo) setDT(dat) dat[, mean.val:= if (.N > 6) rollapplyr(value, 7,function(x) mean(tail(sort(x), 5)), fill = NA) else mean(value)]

though the first value in the new column `mean.val`

is correct, it should be assigned to the row which has
1977 but has been assigned to 1976.

You want to process the *PRIOR* 7 points rather than the 7 points that end at the current point. To do that use a width of `list(-(1:7))`

. That says to use offsets -1 through -7 when processing the data. See `?rollapply`

for more information on specifying the `width`

argument.

This (1) more directly specifies the intention making it easier to comprehend than approaches which require ignoring the required offsets and then fixing it up later and (2) uses only the packages you are already using (3) expresses the solution compactly and (4) preserves your solution changing only one argument.

dat[, mean.val:= if (.N > 6) rollapply(value, list(-(1:7)), function(x) mean(tail(sort(x), 5)), fill = NA) else mean(value)]

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If the only issue is that the values should be shifted down 1 row, you can use `shift`

to fix this.

dat[, mean.val := shift(mean.val)]

FYI if you're on version >= 1.12.4 data.table you don't need zoo and can use `data.table::frollapply`

.

dat[, mean.val2 := shift(frollapply(value, 7, function(x) mean(tail(sort(x), 5))))] dat[, all.equal(mean.val, mean.val2)] #TRUE

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This simple for loop solve the problem:

dat$mean.val = NA for(i in 8:nrow(dat)) { dat$mean.val[i] = mean(sort(dat$value[(i-7):(i-1)],decreasing = TRUE)[1:5]) }

**conditional rolling average,** Re: Conditional moving average � Yuri Fal Ambassador Sep 30, 2018 11:46 PM ( in response to A few more steps and a little bit repetitive base R solution: I'm very new to R (and coding in general), and I'm using R Studio. I had a question about how create a new variable, that is an average value of another variable (but based on the level of a third variable). I am doing a meta-analysis with my dataset, metacomplete_, and I'm trying to average effect-sizes (variable: *_selectedES.prepost_*) into one value per paper (variable Paper#). Basically

I think you can use the excellent tsibble package for an amazing rolling function and then you can use the lead function to displace the results

library(tidyverse) dat <- structure(list(yearRef = c(1970, 1971, 1972, 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989, 1990, 1991, 1992, 1993, 1994, 1995, 1996, 1997, 1998, 1999, 2000, 2001, 2002, 2003, 2004, 2005, 2006, 2007, 2008, 2009, 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018), value = c(0.761253538863966, 0.778365700864592, 0.748473422160476, 0.790408287413012, 0.726707786670043, 0.80587461240495, 0.81582881742434, 0.914998995290579, 0.903241004636529, 0.883446087736501, 0.878399385374308, 0.790239960507709, 0.853841173129717, 0.972923769177295, 0.899133969911117, 0.865840008976815, 0.85942147306247, 0.9471790327507, 0.905362802563981, 0.91644169495142, 0.985789564141214, 0.978212191208007, 0.885157529562834, 1.01638026873823, 1.02702020472382, 0.944421276774342, 0.979439113456467, 0.951183598644539, 1.12054063623421, 1.00767230122493, 1.02132151007705, 0.95649988168142, 0.928385199359045, 1.05071183719421, 1.11654102944792, 0.910601547182633, 0.936460862711605, 1.2398210426787, 0.979036947391532, 1.09931214756341, 1.12206830109171, 0.997384903912461, 1.07413151131128, 0.967026290186151, 1.04921352764649, 1.08746580600605, 1.02444885186573, 1.14604631626466, 1.06449109417896)), class = c("tbl_df", "tbl", "data.frame"), row.names = c(NA, -49L)) complex_function <- . %>% sort %>% tail(.,5) %>% mean dat %>% mutate(roll_avg = tsibble::slide_dbl(.x = value,.f = complex_function,.size = 7), roll_avg2 = lag(roll_avg)) #> # A tibble: 49 x 4 #> yearRef value roll_avg roll_avg2 #> <dbl> <dbl> <dbl> <dbl> #> 1 1970 0.761 NA NA #> 2 1971 0.778 NA NA #> 3 1972 0.748 NA NA #> 4 1973 0.790 NA NA #> 5 1974 0.727 NA NA #> 6 1975 0.806 NA NA #> 7 1976 0.816 0.790 NA #> 8 1977 0.915 0.821 0.790 #> 9 1978 0.903 0.846 0.821 #> 10 1979 0.883 0.865 0.846 #> # … with 39 more rows

Created on 2020-01-14 by the reprex package (v0.3.0)

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##### Comments

- Is the issue only that the results should be shifted down 1 row? If so, you can fix that with the shift function, i.e.
`dat[, mean.val := shift(mean.val)]`

- Yeah I think the problem is that 1970 until 1976 consists of 7 values (rows). Therefore the value gets assigned at 1976. Shifting the data one row is the easiest solution.
- In your
`complext_function`

, do you mean to say`mean`

instead of`sum`

? - Yes well spotted fixing it
- @89_Simple Does it work for you now? I would throw away the year 1975 for correctness but still
- not really. Working by hand, the mean value for 1977 should be 0.7902 (mean of 0.761, 0.778, 0.790, 0.806, 0.816)
- Oh I guess you need lag