Handling Large Data in R, a Naive Way

Siqi Zhang

2019/07/16

Categories: R

Recently I ran accross a problem: The program I’m writing reads from hundreds of large data sets totaling in tens of gigabytes, and the memory on my or any potential user’s machine simply won’t fit.

Ideally, for caching objects on the disk, I would have wanted to use more sophisticated solusions such as Roger Peng’s filehash. Instead, I just wanted to simply dump everything onto the disk, and read & process each as needed, with the following solution:

It goes as follows:

stash <- function(object, dir_path = tempdir()){
        file_name <- paste0(paste0(sample(c(letters, LETTERS, 0:9), 20, TRUE), collapse = ""), ".RStash")
        file_path <- file.path(dir_path, file_name)
        saveRDS(object, file_path)
        f <- function(){
                if (!file.exists(file_path)){
                        stop("stash file missing.")
                } else {
                        readRDS(file_path)
                }
        }

        structure(f,
                  class = c("stash_pointer", class(f)),
                  file_path = file_path,
                  obj_size = format(object.size(object), unit = "MB", digits = 2),
                  obj_class = class(object)
                  )
}

So calling stash() saves a binary of the object onto the disk, and returns a function that is going to read from this the file when called, with relevant metadata. I just call the cache on the disk “stash” and this function a “stash pointer”.

Let’s test it out.

mtcars2 <- stash(mtcars)
mtcars2() 
##                      mpg cyl  disp  hp drat    wt  qsec vs am gear carb
## Mazda RX4           21.0   6 160.0 110 3.90 2.620 16.46  0  1    4    4
## Mazda RX4 Wag       21.0   6 160.0 110 3.90 2.875 17.02  0  1    4    4
## Datsun 710          22.8   4 108.0  93 3.85 2.320 18.61  1  1    4    1
## Hornet 4 Drive      21.4   6 258.0 110 3.08 3.215 19.44  1  0    3    1
## Hornet Sportabout   18.7   8 360.0 175 3.15 3.440 17.02  0  0    3    2
## Valiant             18.1   6 225.0 105 2.76 3.460 20.22  1  0    3    1
## Duster 360          14.3   8 360.0 245 3.21 3.570 15.84  0  0    3    4
## Merc 240D           24.4   4 146.7  62 3.69 3.190 20.00  1  0    4    2
## Merc 230            22.8   4 140.8  95 3.92 3.150 22.90  1  0    4    2
## Merc 280            19.2   6 167.6 123 3.92 3.440 18.30  1  0    4    4
## Merc 280C           17.8   6 167.6 123 3.92 3.440 18.90  1  0    4    4
## Merc 450SE          16.4   8 275.8 180 3.07 4.070 17.40  0  0    3    3
## Merc 450SL          17.3   8 275.8 180 3.07 3.730 17.60  0  0    3    3
## Merc 450SLC         15.2   8 275.8 180 3.07 3.780 18.00  0  0    3    3
## Cadillac Fleetwood  10.4   8 472.0 205 2.93 5.250 17.98  0  0    3    4
## Lincoln Continental 10.4   8 460.0 215 3.00 5.424 17.82  0  0    3    4
## Chrysler Imperial   14.7   8 440.0 230 3.23 5.345 17.42  0  0    3    4
## Fiat 128            32.4   4  78.7  66 4.08 2.200 19.47  1  1    4    1
## Honda Civic         30.4   4  75.7  52 4.93 1.615 18.52  1  1    4    2
## Toyota Corolla      33.9   4  71.1  65 4.22 1.835 19.90  1  1    4    1
## Toyota Corona       21.5   4 120.1  97 3.70 2.465 20.01  1  0    3    1
## Dodge Challenger    15.5   8 318.0 150 2.76 3.520 16.87  0  0    3    2
## AMC Javelin         15.2   8 304.0 150 3.15 3.435 17.30  0  0    3    2
## Camaro Z28          13.3   8 350.0 245 3.73 3.840 15.41  0  0    3    4
## Pontiac Firebird    19.2   8 400.0 175 3.08 3.845 17.05  0  0    3    2
## Fiat X1-9           27.3   4  79.0  66 4.08 1.935 18.90  1  1    4    1
## Porsche 914-2       26.0   4 120.3  91 4.43 2.140 16.70  0  1    5    2
## Lotus Europa        30.4   4  95.1 113 3.77 1.513 16.90  1  1    5    2
## Ford Pantera L      15.8   8 351.0 264 4.22 3.170 14.50  0  1    5    4
## Ferrari Dino        19.7   6 145.0 175 3.62 2.770 15.50  0  1    5    6
## Maserati Bora       15.0   8 301.0 335 3.54 3.570 14.60  0  1    5    8
## Volvo 142E          21.4   4 121.0 109 4.11 2.780 18.60  1  1    4    2

Perfect!

Let’s add some housekeeping funcitions, too.

print.stash_pointer <- function(x){
        cat(paste0("<stash_pointer>", " `", attr(x, "obj_class")[1],"` ", attr(x, "obj_size"), "\n"))
        cat("- ", paste(attr(x, "file_path")))
}

# delete the cache on disk
clear_stash <- function(stash_pointer){
        file_path <- attr(stash_pointer, "file_path")
        if (file.exists(file_path)){
                file.remove(file_path)      
        } 
}

stash_exists <- function(stash_pointer){
        file.exists(attr(stash_pointer, "file_path"))
}

is_stash_pointer <- function(x){
        inherits(x, "stash_pointer")
}
mtcars2
## <stash_pointer> `data.frame` 0.01 Mb
## -  C:\Users\Siqi\AppData\Local\Temp\RtmpQvafGF/4IETGun86tbpeMdGmOy0.RStash
is_stash_pointer(mtcars2)
## [1] TRUE
clear_stash(mtcars2)
## [1] TRUE
stash_exists(mtcars2)
## [1] FALSE
df_list <- list(mtcars, iris, chickwts, PlantGrowth, USArrests)

Let’s utilize purrr’s functional programming interface and stash() everything in df_list:

require(purrr)
## Loading required package: purrr
stash_pointer_list <- map(df_list, stash)

The result is a list of stash_pointers:

stash_pointer_list
## [[1]]
## <stash_pointer> `data.frame` 0.01 Mb
## -  C:\Users\Siqi\AppData\Local\Temp\RtmpQvafGF/kkss3mmDmUw1JsAq2BgX.RStash
## [[2]]
## <stash_pointer> `data.frame` 0.01 Mb
## -  C:\Users\Siqi\AppData\Local\Temp\RtmpQvafGF/IdLKIRa0R9qooIhqnnLm.RStash
## [[3]]
## <stash_pointer> `data.frame` 0 Mb
## -  C:\Users\Siqi\AppData\Local\Temp\RtmpQvafGF/8EkrNmQVmLZFg1WXtkPM.RStash
## [[4]]
## <stash_pointer> `data.frame` 0 Mb
## -  C:\Users\Siqi\AppData\Local\Temp\RtmpQvafGF/PH8BuUoxJyrVXepp3g8K.RStash
## [[5]]
## <stash_pointer> `data.frame` 0.01 Mb
## -  C:\Users\Siqi\AppData\Local\Temp\RtmpQvafGF/drRGUIhaXoejjSJHZ0eG.RStash

See the column name of every data frame:

map(stash_pointer_list, ~ exec(.) %>% colnames())
## [[1]]
##  [1] "mpg"  "cyl"  "disp" "hp"   "drat" "wt"   "qsec" "vs"   "am"   "gear"
## [11] "carb"
## 
## [[2]]
## [1] "Sepal.Length" "Sepal.Width"  "Petal.Length" "Petal.Width" 
## [5] "Species"     
## 
## [[3]]
## [1] "weight" "feed"  
## 
## [[4]]
## [1] "weight" "group" 
## 
## [[5]]
## [1] "Murder"   "Assault"  "UrbanPop" "Rape"

Cheers!