Suppose you’re a new data analyst, and you're wondering what language you need to learn, or your company is looking to put together a data analysis team and needs to decide what language to use. In that case, you'll need to know about the two most common data analysis languages. R and Python are some of the most common computer science and programming languages.
Both R and Python have been around since the 1990s and are extremely popular in data analysis and business intelligence. While both have useful qualities that can help make your day-to-day data analysis easier, they're also both complicated coding languages in their own right.
Python is often so complicated to learn that there's a running joke amongst programmers and developer communities that it's one of the worst coding languages out there. While we think this is a little harsh, particularly given the uses of Python, we agree that it involves a very large learning curve.
So what are R and Python, and are they useful in data analytics? Let's take a look.
R and Python at a Glance
What is R?
R was developed as an open-source language in 1995 to provide a more user-friendly way to analyze data, particularly in academics and research. Since then, it's spread outside of these fields into the business world, and it's one of the fastest-growing analysis languages in the business world.
What is Python?
Created in 1991, Python was designed to be a software development language that was easy to read and understand. It's relatively flexible compared to many other programming languages, making it more of a general-purpose language in modern tech stacks. However, Python has recently gained popularity in the data analysis industry.
When Should You Use R or Python?
Using R
R is at its most useful when you're doing data analysis that needs to be run on individual servers, and in particular, if you need to do some exploratory research. Because R has a huge number of ready-to-use tests and packages available, it's relatively easy to use in teams with little to no programming experience.
Statistical modeling research tends to be done using R. Some R's packages have been designed to help you create dashboards to represent your data without needing years of coding experience. R is also widely used in large data analysis teams to conduct statistical models and specialized exploratory work.
That being said, R is still a statistical programming language, so you will still need to take time to learn and become familiar with how it works.
Using Python
Python is very popular in teams that already have programming experience. Many developers find the transition from other popular languages like C++ simpler than if they were to learn R.
Because it's a more complicated language than R, it's often used in conjunction with other coding languages to deploy data analysis models into other software applications, as well as for deep learning research and analysis. Data analysts from a software development background are also frequently used to develop algorithms for data analysis that people can feed into production databases.
Python has the steepest learning curve of both of these languages, particularly if you don’t have any experience in software development or with other coding languages.
R and Python for Data Analysis and Business Intelligence
R for Data Analysis/BI: Pros and Cons
Pros
Easy to pick up: R tends to be easier to learn if you have very little coding experience, making it a popular choice for new data analysts or teams with no background in software development.
Flexible: R lets you code the same piece of functionality in multiple ways, so you don't have to remember exact phrases to get your code to work.
A variety of packages: Because R has been around for 15 years, there's a vast catalog of pre-written code packages available for download that can help you do everything from building dashboards to conducting tests.
Cons
Learning curve: R was designed by statisticians, so it can be a steep learning curve to understand how it works if you've never worked with code before.
A lack of deep learning: While people can use R for deep learning tasks, it doesn't have the same functionality as Python. So, very few data analysts can use R in isolation unless they're part of a big team that uses a variety of coding languages.
It's slow: If R is written poorly, it often doesn't run as optimally as programs written in other coding languages. As with any language, learning to write code that runs quickly is difficult, particularly if you're new to coding languages.
Python for Data Analysis/BI: Pros and Cons
It's an analyst coming from a software development back; Python often comes more naturally than R.
Easy to read and debug: Python was designed for readability, so it uses simple English syntax that's easy to understand. So, if your code goes wrong, you're more likely to be able to see where it's gone wrong than with R.
General-purpose language: Because Python was designed as a general-purpose programming language before it became popular in data analysis circles, it's easier to integrate Python programs with other software applications.
Cons
Lack of packages: While R has hundreds of packages considered essential amongst R users, Python often has no equivalent packages available. It also doesn't have as many package libraries as R, meaning that you'll have a steeper learning curve to write the more advanced applications.
Backward compatibility: Most coding languages can work with previous versions, but this isn't the case for Python. Major releases are often too different to integrate, and minor updates often mean that you need to rework your old code to get your application to work.
No compiler: If you're coming from a development background, you're probably used to using a compiler to make sure your code works. Python doesn't have this key bit of functionality, meaning that you'll only notice errors in your code when you come to run it.
How DashboardFox Can Help in R and Python
While DashboardFox doesn't have any in-built integrations with R and Python, the inclusion of DashboardFox in your data analytics tech stack can give your team superpowers.
The starting point for any data analysis is a data set. DashboardFox can make it very easy for data analysts to pull live data from one or more data sources and then export that data into Excel or CSV to be used with R and Python. So we can save a lot of time by pulling live data to analyze without requiring complicated SQL queries or ETL processes.
Conversely, after you have completed an analysis in R or Python, what do you want to do with it? Communicate it to the stakeholders that need it. Suppose you push that analysis into a database or data lake. In that case, DashboardFox can have a set of pre-built reports and dashboards, complete with data-level security, drill down, and filters that your end users can now use to gain meaningful insights from your analysis.
More importantly, you can save your valuable R and Python developers from having to build dashboards, reports, and visualizations.
DashboardFox is user-friendly that even those without any existing computer language proficiency can use it in viewing and creating informative and detailed dashboards and diagrams that can help them run their business smoothly.
Apart from that, you can benefit from its self-hosted setup, which means your data remains secure and safe from any outside intruders. Our dedicated team of experts is also ready to help you every step of the way.
Lastly, our affordable prices and no subscription (!) policy is the icing on top of the cake. We will only charge you once, and you can use everything to your heart's content.
What are you waiting for? Contact us or book a live demo to see what we can do for your business.