Thread by @mdancho84

People are shocked when they learn that I picked #R over Python. It’s a true story.

I began learning data science with Python. This decision actually cost me 2-months and caused me to quit data science for a little bit.

Why? (a 🧵)
#rstats

2/n Python was too much like coding. And I came from a business background. My main tool was Excel. Python was unnatural.

A friend of mine recommended me to try #R.

I was instantly surprised at how much more intuitive it was for me given my Excel background. Here's what R had:

3/n
👉#R has functions just like #Excel. I could quickly summarize my data using mean(), sd(), sum(), and friends. These functions were very similar to AVERAGE(), STDEV(), and SUM() from Excel.

4/n
👉#R has analytics built-in. I could do correlation and make trendlines with linear regression very easily.

5/n
👉#R had the #tidyverse. The tidyverse blew my mind. This toolkit includes data wrangling and visualization libraries that effortlessly worked together.

6/n
👉 #R had the pipe! If you’ve never tried it, the pipe %>% is this amazing operation that allows you to flow your data transformations from one operation to another.

7/n
👉#R has reporting! I was able to make a simple PDF report in minutes versus struggling with Jupyter.

8/n
So I ended up picking #R and to this day I’m so happy I did.

Want more information on how to become a data scientist with R?

I’ve consolidated my learnings into 10 secrets that I wished I knew going into data science.

learn.business-science.io/free-rtrack-ma…