## Moving Averages on multiple columns - Grouped Data

Apologies if this has been answered. I've gone through numerous examples today but I can't find any that match what I am trying to do.

I have a data set which I need to calculate a 3 point moving average on. I've generated some dummy data below:

set.seed(1234) data.frame(Week = rep(seq(1:5), 3), Section = c(rep("a", 5), rep("b", 5), rep("c", 5)), Qty = runif(15, min = 100, max = 500), To = runif(15, min = 40, max = 80))

I want to calculate the MA for each group based on the 'Section' column for both the 'Qty' and the 'To' columns. Ideally the output would be a data table. The moving average would start at Week 3 so would be the average of wks 1:3

I am trying to master the data.table package so a solution using that would be great but otherwise any will be much appreciated.

Just for reference my actual data set will have approx. 70 sections with c.1M rows in total. I've found the data.table to be extremely fast at crunching these kind of volumes so far.

We could use `rollmean`

from the `zoo`

package, in combination with `data.table`

.

library(data.table) library(zoo) setDT(df)[, c("Qty.mean","To.mean") := lapply(.SD, rollmean, k = 3, fill = NA, align = "right"), .SDcols = c("Qty","To"), by = Section] > df # Week Section Qty To Qty.mean To.mean #1: 1 a 145.4814 73.49183 NA NA #2: 2 a 348.9198 51.44893 NA NA #3: 3 a 343.7099 50.67283 279.3703 58.53786 #4: 4 a 349.3518 47.46891 347.3271 49.86356 #5: 5 a 444.3662 49.28904 379.1426 49.14359 #6: 1 b 356.1242 52.66450 NA NA #7: 2 b 103.7983 52.10773 NA NA #8: 3 b 193.0202 46.36184 217.6476 50.37802 #9: 4 b 366.4335 41.59984 221.0840 46.68980 #10: 5 b 305.7005 48.75198 288.3847 45.57122 #11: 1 c 377.4365 72.42394 NA NA #12: 2 c 317.9899 61.02790 NA NA #13: 3 c 213.0934 76.58633 302.8400 70.01272 #14: 4 c 469.3734 73.25380 333.4856 70.28934 #15: 5 c 216.9263 41.83081 299.7977 63.89031

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A solution using dplyr:

library(dplyr); library(zoo) myfun = function(x) rollmean(x, k = 3, fill = NA, align = "right") df %>% group_by(Section) %>% mutate_each(funs(myfun), Qty, To) #### Week Section Qty To #### (int) (fctr) (dbl) (dbl) #### 1 1 a NA NA #### 2 2 a NA NA #### 3 3 a 279.3703 58.53786 #### 4 4 a 347.3271 49.86356

**Calculate Moving Average over X days across multip,** Hi All, I've been working with some data for COVID-19 and had begun Calculate Moving Average over X days across multiple columns and Now the groupby method first splits the data by Type, and data within each Type group is further split into subgroups by the values in the Test_1_Grade column. The averages may look a little funny in our Test_2 column because of how we composed our DataFrame, but the groupby methods successfully performs its job.

There is currently faster approach using new `frollmean`

function in data.table 1.12.0.

setDT(df)[, c("Qty.mean","To.mean") := frollmean(.SD, 3), .SDcols = c("Qty","To"), by = Section]

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##### Comments

- See also here for some more options
- did you use the same seed as the OP?
- @mtoto thanks for the quick reply, it's exactly what I needed!!
- thank you! It's good to see how you can you solve the same problem multiple ways.