Python done the R (Markdown) way

Are you an R user flirting with the idea of doing Python? I wrote an article that may be of your interest! Starting to work with a new tool may not be easy, especially when that “tool” means a new programming language. At the same time, there might be an opportunity to build upon something already known and so make the transition smoother and less painful.

Introduction

Starting to work with a new tool may not be easy, especially when that “tool” means a new programming language. At the same time, there might be an opportunity to build upon something already known and so make the transition smoother and less painful.

In my case, it was the transition from R to Python. Unfortunately, my former colleagues, pythonians, did not share my genuine fondness of R. Also, whether R enthusiasts like it or not, Python is a widely used tool in data analysis/engineering/science and beyond. So, I concluded that learning at least some Python is a reasonable thing to do.

For me, the first steps were maybe the most difficult ones. Residing in the comfort of RStudio, IDEs like Pycharm or Atom did not feel familiar. This experience led to the decision to begin in the well-known environment and test its limits when it comes to working with Python.

To tell the truth, I did not end up using RStudio as the weapon of choice for using Python in a general setting. Hopefully, the following text will deliver the message why. However, I am convinced that for some use-cases, like integrating R and Python in an ad hoc analysis R Markdown way, RStudio still represents a viable way to go.

More importantly, it could be a convenient starting line for people with the primary background in R.

So, what did I find?

Analysis

Packages and environment

First of all, let us set the environment and load the required packages.

• Global environment:

## Globally round numbers at decimals
options(digits=2)

## Force R to use regular numbers instead of using the e+10-like notation
options(scipen = 999)

• R libraries:

## Load the required packages. 
## If these are not available, install them first.
ipak <- function(pkg){
  new.pkg <- pkg[!(pkg %in% installed.packages()[, "Package"])]
  if (length(new.pkg)) 
    install.packages(new.pkg, 
                     dependencies = TRUE)
  sapply(pkg, 
         require, 
         character.only = TRUE)
}
packages <- c("tidyverse", ## Data wrangling
              "gapminder", ## Data source
              "knitr", ## R Markdown styling
              "htmltools") ## .html files manipulation
ipak(packages)
tidyverse gapminder     knitr htmltools 
     TRUE      TRUE      TRUE      TRUE

pandas rstudio python plotly data-science

🔵 Intellipaat Data Science with Python course: https://intellipaat.com/python-for-data-science-training/In this Data Science With Python Training video, you...

🔥Intellipaat Python for Data Science Course: https://intellipaat.com/python-for-data-science-training/In this python for data science video you will learn e...

Master Applied Data Science with Python and get noticed by the top Hiring Companies with IgmGuru's Data Science with Python Certification Program. Enroll Now

Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.

Welcome to this course on Data Science For Beginners With Python Pandas. Learn how Perform a Many of data operations in Python's popular Pandas library.