What’s going on, everybody? Welcome back to another article. Today we are going to be comparing Python versus R. We’re going to see which one is better. Before I start this article, yes, it took me entire week to research for this battle of Python vs. R for Data Analysis.
Overview
Some of the things that we’re going to be discussing today in our Python versus R includes:
- Descriptions, different libraries,
- Code syntax,
- Pros and Cons of both,
- And my final answer.
I’m not trying to go super in-depth. I tried to make it as user-friendly as possible. If you guys want a more in-depth presentation on just one of these, I can absolutely do that. This is going to be high-level and more about my thoughts and feelings. Let’s get into the description of both, keeping it high-level and getting to some specifics and then my conclusion.
Description
Starting with R, it is a programming language developed for statistical analysis. Statisticians mostly used it for a long time, and just recently, within the past five to ten years, it has been used for data science, data analysis, visualizations, and all those things. It was developed in 1993, primarily for statisticians, data miners, and analysts. It’s used by a ton of very large companies like Uber, Facebook, and Google, and even small companies. If your company does any type of statistical analysis, there’s a good chance they use or have used R.
Python is a general-purpose programming language used for almost anything you can imagine. It may not be the best for every single thing it can do, but it can do almost anything. It is quickly becoming the most popular programming language in the world and is used by companies like Google, Facebook, and Netflix. Large companies use both programming languages for what they’re good for, which we’ll discuss later.
Libraries and Packages
If I did not highlight your favorite library or package, I’m sorry. There are so many, especially with R. Here are some of the more popular ones I’ve used.
For R:
- Data Collection: rCrawler, readxl, readr, RCurl
- Data Wrangling/Exploration: dplyr, SQLDF, data.table, readr, tidyr
- Data Visualization: ggplot2, ggvis, plotly, shiny
For Python:
- Data Collection: pandas, requests, Beautiful Soup
- Data Wrangling/Exploration: pandas, NumPy, SciPy
- Data Visualization: matplotlib, seaborn, plotly
If you have never used R or Python, these packages are a good place to start.
Code Difficulty
For the code and the syntax, I tried to stay neutral. For R, it’s easy-medium difficulty to pick up and start working from scratch. It can be difficult to maintain your code as you start to scale. With Python, it’s also easy-medium difficulty to pick up and learn, and it’s easier to write and maintain larger-scale code. As you start building larger projects or join larger teams, it’s easier to scale up.
Syntax Examples
I 100% cherry-picked these, but they are fairly representative. We’re reading in a CSV file and then finding the mean on a column.
For R:
data <- read.csv("file.csv")
mean(data$column)
For Python:
import pandas as pd
data = pd.read_csv("file.csv")
data['column'].mean()
Hence R is a bit more complicated, while Python is cleaner and easier to read.
R: Pros and Cons
Pros:
- Open source
- Fantastic for statistical analysis
- Hundreds of packages and libraries for analytics
- Easy to build visualizations
Cons:
- Can’t be embedded in web applications
- Requires knowledge of multiple packages and libraries
- Can run slow due to data storage methods
Python: Pros and Cons
Pros:
- Open source
- Easy to read and learn
- Can be embedded into web applications
- Growing number of libraries for data analysis
Cons:
- Processing speed can be slow
- Uses a large amount of memory
- Simplicity can be a drawback for complex tasks
- Libraries for all analytics needs are still being developed
Final Answer
Which is better, Python or R? It really depends on what you’re using it for. For purely statistical work, R is the better choice. For machine learning, Python is arguably much better. R is harder to learn but has more features, while Python is easier to learn but isn’t as developed yet. You should try both and determine for yourself. For me, Python is better suited for my job. However, for other positions, R may be the programming language of choice. I have nothing against R; I’ve used it and taken courses on it, but I mostly stick with Python.
Also read:
- 5 Must-know VS Code Shortcuts
- BEST monitor for programming?
- 10 Programming Projects That Will Make You A God At Coding
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