compare rolling mean functions

compare_rolls.R

suppressMessages({
  library(fromo)
  library(roll)
  library(RcppRoll)
  library(microbenchmark)
  library(RollingWindow)
})

set.seed(1234)
df <- data.frame(Adjusted=rnorm(10000))
matdf <- as.matrix(df$Adjusted)

wins <- 50

res <- microbenchmark(RcppRoll::roll_mean(df$Adjusted,n=wins,align='right',fill=NA),
                      roll::roll_mean(as.matrix(df$Adjusted), width=wins),
                      roll::roll_mean(matdf, width=wins),
                      RollingWindow::RollingMean(df$Adjusted, window=wins),
                      fromo::running_sd3(df$Adjusted,window=wins,restart_period=10000L)[,2]
                      )
print(res)

# runtime should be independent of window size:

testme <- function(wins) { RcppRoll::roll_mean(df$Adjusted,n=wins,align='right',fill=NA) }

res <- microbenchmark(testme(10),
                      testme(100),
                      testme(1000))
print(res)

testme <- function(wins) { roll::roll_mean(as.matrix(df$Adjusted), width=wins) }

res <- microbenchmark(testme(10),
                      testme(100),
                      testme(1000))
print(res)

testme <- function(wins) { RollingWindow::RollingMean(df$Adjusted, window=wins) }

res <- microbenchmark(testme(10),
                      testme(100),
                      testme(1000))
print(res)

testme <- function(wins) { fromo::running_sd3(df$Adjusted,window=wins,restart_period=10000L)[,2] }

res <- microbenchmark(testme(10),
                      testme(100),
                      testme(1000))
print(res)

output.txt

Unit: microseconds
                                                                              expr   min    lq  mean median    uq     max neval cld
            RcppRoll::roll_mean(df$Adjusted, n = wins, align = "right", fill = NA) 300.2 344.2 390.1  364.2 394.1   646.4   100   a
                             roll::roll_mean(as.matrix(df$Adjusted), width = wins) 384.3 430.2 514.0  445.3 473.9  1806.5   100   a
                                              roll::roll_mean(matdf, width = wins) 347.3 398.8 463.7  414.4 444.0  2113.0   100   a
                            RollingWindow::RollingMean(df$Adjusted, window = wins) 146.9 165.5 609.0  188.5 210.4 32298.9   100   a
 fromo::running_sd3(df$Adjusted, window = wins, restart_period = 10000L)[,      2] 430.6 496.5 638.4  520.9 556.6  1664.5   100   a
Unit: microseconds
         expr     min      lq   mean median     uq  max neval cld
   testme(10)   79.12   90.74  141.7  100.7  110.0 1476   100 a  
  testme(100)  606.34  619.62  695.8  672.3  705.1 1946   100  b 
 testme(1000) 6932.67 7046.28 7483.6 7361.4 7792.1 8824   100   c
> source('~/comparem.r') 
Unit: microseconds
                                                                              expr   min    lq  mean median    uq  max neval  cld
            RcppRoll::roll_mean(df$Adjusted, n = wins, align = "right", fill = NA) 301.5 342.9 377.3  352.5 381.4 1505   100  b  
                             roll::roll_mean(as.matrix(df$Adjusted), width = wins) 418.1 440.3 499.1  462.3 477.5 1691   100   c 
                                              roll::roll_mean(matdf, width = wins) 395.6 413.3 443.5  429.2 442.7 1603   100  bc 
                            RollingWindow::RollingMean(df$Adjusted, window = wins) 156.1 169.2 231.9  194.4 210.3 1684   100 a   
 fromo::running_sd3(df$Adjusted, window = wins, restart_period = 10000L)[,      2] 439.8 505.1 601.5  513.1 541.3 1836   100    d
Unit: microseconds
         expr    min      lq    mean median     uq    max neval cld
   testme(10)   77.1   89.69   98.64   96.9  103.3  182.4   100 a  
  testme(100)  606.9  618.49  655.01  634.0  670.0  928.0   100  b 
 testme(1000) 6932.7 6979.20 7251.09 7068.6 7505.0 8266.8   100   c
Unit: microseconds
         expr    min     lq   mean median     uq  max neval cld
   testme(10)  179.3  201.1  247.8  206.4  232.5 1577   100 a  
  testme(100)  617.8  652.1  705.2  671.9  691.3 1988   100  b 
 testme(1000) 4917.7 5026.7 5178.5 5063.1 5148.4 8206   100   c
Unit: microseconds
         expr   min    lq  mean median    uq  max neval cld
   testme(10) 150.4 153.9 226.0  165.3 169.4 1387   100   a
  testme(100) 142.1 152.8 242.4  156.4 172.0 1578   100   a
 testme(1000) 143.2 149.7 250.0  163.0 169.5 1556   100   a
Unit: microseconds
         expr   min    lq  mean median    uq  max neval cld
   testme(10) 438.2 493.3 524.2  496.5 501.7 1844   100   a
  testme(100) 446.6 490.9 564.9  494.9 499.3 1901   100   a
 testme(1000) 439.1 484.0 571.2  489.2 496.9 1839   100   a