As the title reads, few more R-related tips and tricks. I hope you have not seen those before.

Some utilities

Methods are functions which are specifically written for particular class. In the post Show yourself (look “under the hood” of a function in R) we saw how to get the methods that go with a particular class. Now there are more modern, less clunky ways for this.

See which methods are available per class

Have a look at the sloop package, maintained by Hadley Wickham (that alone is a reason). Use the function s3_methods_generic to get a nice table with some relevant information:

# install.packages("sloop")
library(sloop)
citation("sloop")
s3_methods_generic("mean")
# s3_methods_generic("as.data.frame")
# A tibble: 10 x 4
   generic class      visible source             
                             
 1 mean    Date       TRUE    base               
 2 mean    default    TRUE    base               
 3 mean    difftime   TRUE    base               
 4 mean    POSIXct    TRUE    base               
 5 mean    POSIXlt    TRUE    base               
 6 mean    quosure    FALSE   registered S3method
 7 mean    vctrs_vctr FALSE   registered S3method
 8 mean    yearmon    FALSE   registered S3method
 9 mean    yearqtr    FALSE   registered S3method
10 mean    zoo        FALSE   registered S3method

You can use the above to check if there exists a method for the class you are working with. If there is, you can help R by specifying that method directly. Do that and you gain, sometimes meaningfully so, a speed advantage. Let’s see how it works in couple of toy cases. One with a Date class and one with a numeric class.

library(magrittr) # for the piping operator
# install.packages("scales") # we talk about this shortly
library(scales)
# install.packages("microbenchmark")
library(microbenchmark)
citation("microbenchmark")
# Create a sequence of dates
some_dates % percent(digits = 2))
Save 23%

# Now something more standard
x % percent(digits = 2))
Save 56%

Specifying the exact method (if it is there) also reduces the variance around computational time, which is important for simulation exercises:

Percent formatting

In the code snippet above I used the scales package’s percent function, which spares the formatting annoyance.

Get your object’s size

When I load a data, I often want to know how big is it. There is the basic object.size function but it’s ummm, ugly. Use the aptly named object_size function from the pryr package.

library(pryr)
citation("pryr")
> x  object_size(x)
8.05 kB
> object.size(x)
8048 bytes
> x  object_size(x)
8 MB
> object.size(x)
8000048 bytes
> x  object_size(x)
800 MB
> object.size(x)
800000048 bytes # is this Mega or Giga?
> x  object_size(x)
8 GB
> object.size(x)
8000000048 bytes # is this Mega or Giga?

Memory management

Use the gc function; gc stands for garbage collection. It frees up memory by, well, collecting garbage objects from your workspace and trashing them. I at least, need to do this often.

heta function

I use the head and tail functions a lot, often as the first thing I do. Just eyeballing few lines helps to get a feel for the data. Default printing parameter for those function is 6 (lines) which is too much in my opinion. Also, especially with time series data you have a bunch of missing values at the start, or at the end of the time frame. So that I don’t need to run each time two function separately, I combined them into one:

heta 

Sound the alarm

If you stretch your model enough, you will have to wait until computation is done with. It is nice to get a sound notification for when you can continue working. A terrific way to do that is using the beepr package.

Click play to play:

Enjoy!

A Survivor’s Guide to R