Category: R Language

Assignment Operators in R – Which One to Use and Where

Whenever you start learning a new programming language, you must get accustomed to the language’s syntax. One of the first operators you’d expect to come across is the assignment operator for the language. Assignment operators are used to, well, assign values to variables. The R language has a few different ways to assign values. Let’s take a quick look and them and some of their differences and use cases.

Assignment Operators

R has five common assignment operators:

  • <-
  • ->
  • <<-
  • ->>
  • =

Many style guides and traditionalists prefer the left arrow operator, <-. Why use that when it’s an extra keystroke? <- always means assignment. The equal sign is overloaded a bit taking on the roles of an assignment operator, function argument binding, or depending on the context, case statement.

Equal or “arrow” as an Assignment Operator?

In R, both the equal and arrow symbols work to assign values. Therefore, the following statements have the same effect of assigning a value on the right to the variable on the left:

x = 42

x <- 42

There is also a right arrow, -> which assigns the value on the left, to a variable on the right:

42 -> x

All three assign the value of forty-two to the variable x.

So what’s the difference? Are these assignment operators interchangeable? Mostly, yes. The difference comes into play, however, when working with functions.

The equal sign can also work as an operator for function parameters.

x <- 42
y <- 18
function(value = x-y)

History of the <- Operator

Where did the arrow as an assignment operator originate? As you may know, the R language has its origins in the S language. S was originally influenced, in part, by APL. APL had its own keyboards which included arrow symbols.

APL Keyboard Layout - Assignment Operators next to the "P" key

The S language also didn’t have == for equality testing, so that was left to the single equal sign. Therefore, variable assignment needed to be accomplished with a different symbol, and the arrow was chosen.

Conclusion

There are some differences of opinion as to which assignment operator to use when it comes to = vs <-. Some believe that = is more clear. The <- operator maintains backward compatibility with S. Google’s R Style Guide recommends using the <- assignment operator, which seems to be a pretty decent reason as well. When all is said and done, though, it is like many things in programming, it depends on what your team does.

R is a great language for data analysis. If you’re interested in learning how to use R to explore data in a MongoDB database, please check out this blog post I wrote. There are many uses for it, and knowing a bit about the assignment operators and which one to use should help tremendously.

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New R Driver Option in MongoDB 3.6

In a previous post, I discussed some options for using MongoDB with the R Language. While the information in that post is still accurate, MongoDB 3.6 introduces a new R driver option. Unlike the drivers previously discussed, this new R driver is under active development.

New R Driver

The new R driver, mongolite, can be found on GitHub and is easily installed from the Comprehensive R Archive Network (CRAN) using the install.packages("mongolite") command on Windows or OS-X. The driver, or client, is authored by Jeroen Ooms who also has provided some nice documentation in PDF format. Let’s head into RStudio and take a look at using the new R driver.

Accessing MongoDB

Let’s use the same dataset as in the previous post and, with mongoimport, get the data into a MongoDB collection. The database again will be called kenblog and the collection is scores. Here again is a sample document in the collection:

{
   "_id" : ObjectId("5627207b33ff2cf40effc25e"),
   "student" : 2,
   "type" : "quiz",
   "score" : 74
}

After using the install.packages("mongolite") command, we can put the power of R to work. We establish a connection to our database:

> require("mongolite")
Loading required package: mongolite
> connection <- mongo(collection = "scores", db = "kenblog", url = "mongodb://localhost")

The mongo connection method accepts the following arguments:

  • collection
  • db
  • url
  • verbose
  • options

The collection and db arguments allow for the specification of the names of the respective database information. url is the mongo connection string in URI format. If you need additional output from the connection one can set the verbose boolean value to TRUE. Additional connection options, such as SSL information, can also be passed in.

With a connection established, let’s query our scores collection for exam data.

examQuery <- connection$find('{"type": "exam"}')

This brings in our 585 exam documents. We can then create a vector of the exam scores and have a look at their summary.

> exam_scores <- examQuery[c('score')] 
> summary(exam_scores)
     score       
 Min.   : 60.00  
 1st Qu.: 72.00  
 Median : 79.00  
 Mean   : 79.45  
 3rd Qu.: 86.00  
 Max.   :100.00

Personally, I’m already liking the syntax of this new R driver for doing queries and working with MongoDB in R.

Other methods that will be familiar to MongoDB users are drop() to drop a collection, aggregate for aggregation pipeline operations, and insert for creating information in the database. There are many additional methods that can be used that allow for map-reduce operations and importing or exporting JSON or BSON data, and many more.

Further, with support for features such as indexing, encryption, and authentication, this new R driver is much more robust than previous options.

Wrap Up

Connecting to MongoDB from R is pretty straightforward and simple using the new R driver. It is the new “official” and supported method to leverage the power of R with the flexibility and power of MongoDB.

R is a great statistical language and can definitely be used to query and analyze MongoDB collections. If you are using R in your work today, this new way of connecting with MongoDB is definitely worth a look. If R is still new to you, Learning R: A Step-by-Step Function Guide to Data Analysis is a great way to get started. Or the R For Dummies book may be of interest as well.

This post was updated on 5 Jan 2020.

Follow me on Twitter @kenwalger to get the latest updates on my postings.

There are a few MongoDB specific terms in this post. I created a MongoDB Dictionary skill for the Amazon Echo line of products. Check it out and you can say “Alexa, ask MongoDB for the definition of a document?” and get a helpful response.

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