The Shortest Most Powerful Line of Code in Exploratory Data Analysis Is in R

Okunola Musbaudeen
Apr 4 · 7 min read

Coding is simply the rearrangement of key words to form a coherent statement that machines can understand, without the understanding, we fail to instruct machines to do that analysis for us, or output that result we so want to present. Many a time, newbees get overwhelmed with so many functions they come across, a function that calls many functions within itself, some even call outside functions. How can newbies dissect these functions easily, what are the variables the function works with, what does one even use the function for? Today I’d like to talk about a simple yet powerful function in R. The str() function.

First, let’s talk about str(), the idea behind this function is to return the internal structure of an object in a compact form, hence its name, str(ucture) function. It’s a simple diagnostic tool that is very versatile such that it can work with any function and object. Once called, it aims to return a compact output detailing what is contained in the object or function we call with it, even if it is nested over several layers.

Let’s see what it does exactly, For simplicity in case you’re practicing along, let’s use a dataset available in R already. My dataset of choice is the infert dataset because I’ve never explored the dataset before. So…

head(infert, 5)

infert is a dataset about infertility after spontaneous and induced abortion, so if we want a snapshot of the data, we can check the structure…


…now we know that the dataset has 248 observations and 8 variables, we can also see the names of the variables, the first rows of the data, and the datatype with str(). The data type that holds more details, education being a factor gets expatiated, we see it has 3 levels and the first levels are also listed. This gives us a quick sense of what the data looks like.

Structure can also work on functions to show a snapshot of the arguments the function works on, let’s try it on a function


we can see that lsis a function, and most arguments are displayed already, reading function documentations is great but most documentation readings can be avoided if we use str more often.

Let’s try it on a nested dataset, a dataframe of dataframes. We’ll use the EuStockMarkets here..


After checking the structure, we see it’s a time series dataset, so it isn’t a great dataset to illustrate what I’m about to show you. So let’s return to our first data because the data still makes sense after splitting.

Infertile <- split(infert, infert$education)

We’ve split our data frame into 3 dataframes, all packed into a dataframe named Infertile, let’s see the data frames differently.

lapply(Infertile, head)

Then let’s go ahead and check Infertile structure


This lists the data frames then goes ahead to detail the structure of the objects in each data frame, commonly referred to as element, contained in Infertile. We can see from the first lines of these elements that 0–5yrs has 12 observations, 6–11yrs has 120 observations, and 12+ yrs has 116 observations. Do with that piece of info what you wish.

We can now see the power of str(), it gives a nice as-compact-as-it-can-get view of data so you can get a quick understanding of what’s missing and the next step you need to take in your EDA. So anytime you have an R object and you don’t know what is in it, I implore you to throw a str() at it.

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Okunola Musbaudeen

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A solution driven data analyst, domain expertise cut across procurement planning, inventory management, Supply chain network design, and operations research.

Geek Culture

A new tech publication by Start it up (

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