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Thanks to @josliber's help (Rolling mean lag function), I was able to speed up a rolling mean function for different groups and rollmean lengths.

Now I'd like to add to this function the ability to loop through different variables and bind everything together.

Minimum reproducible example

library(zoo)
dat <- data.frame(fips = rep(c(1001, 1003), each = 100),
                  x = rnorm(200),
                  x2 = rnorm(200),
                  x3 = rnorm(200))


allFipsRM3 = function(dat, varName, len){
  do.call(rbind, lapply(split(dat, dat$fips), function(x) {
    all.rm <- as.data.frame(sapply(len, function(l) c(rollmean(x[,varName], l), rep(NA, l-1))))
    colnames(all.rm) <- paste0(varName, "_rm", len)
    cbind(data.frame(fips=x$fips[1]), all.rm, data.frame(year=seq_len(nrow(x))-1))
  }))
}

outdat3 <- allFipsRM3(dat, "x", c(1, 2))

       fips      x_rm1      x_rm2 year
1001.1 1001  1.3482892  1.3043620    0
1001.2 1001  1.2604348  0.2990267    1
1001.3 1001 -0.6623813 -0.4243813    2
1001.4 1001 -0.1863812  0.2806624    3
1001.5 1001  0.7477061 -0.5111745    4
1001.6 1001 -1.7700551 -0.8463731    5
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Answering my own question here, but certainly open to suggestions.

library(RcppRoll)

# Loop through n = 10
for (i in 1:10){

  # Create custom col labels
  lab1 <- paste0("x_", i)
  lab2 <- paste0("x2_", i)
  lab3 <- paste0("x3_", i)

  # Loop through each fips and calculate rollingmean
  dat <- dat %>%
      group_by(fips) %>%
      mutate(!!lab1 := roll_mean(x, i, align = "left", fill = "NA"),
             !!lab2 := roll_mean(x2, i, align = "left", fill = "NA"),
             !!lab3 := roll_mean(x3, i, align = "left", fill = "NA")) %>% 
    ungroup()

  # Progress bar for loop
  print(i)
}


> names(dat)
 [1] "fips"  "x"     "x2"    "x3"    "x_1"   "x2_1"  "x3_1"  "x_2"   "x2_2"  "x3_2"  "x_3"   "x2_3" 
[13] "x3_3"  "x_4"   "x2_4"  "x3_4"  "x_5"   "x2_5"  "x3_5"  "x_6"   "x2_6"  "x3_6"  "x_7"   "x2_7" 
[25] "x3_7"  "x_8"   "x2_8"  "x3_8"  "x_9"   "x2_9"  "x3_9"  "x_10"  "x2_10" "x3_10"
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