🤓 Best Productivity Tips in R for Data Analysts and Scientists

Описание: Hi data analysts and scientists! This is the first video of the programming series for data analysis on my channel, where I make tutorials and share tips and tricks for working in R and Python for data analysis. In today's video, I'm going to share with you the best practices and most important tips for data analysis in R that I've learned in the past 5 years. Check out the timestamp below. Hope you enjoy it! 🤗

💻 Link to a nice comparison of data.table vs dplyr (one of the main packages used in Tidyverse)
https://atrebas.github.io/post/2019-0...

My Medium article about data wrangling with data.table 👉https://bit.ly/3AuAhXk

👩🏻‍💻 COURSES & RESOURCES
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📖 Google Advanced Data Analytics Certificate 👉 https://imp.i384100.net/anK9zZ
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📖 Learn SQL Basics for Data Science Specialization 👉 https://imp.i384100.net/AovPnJ
📖 Excel Skills for Business 👉 https://coursera.pxf.io/doPaoy
📖 Machine Learning Specialization 👉 https://imp.i384100.net/RyjykN
📖 Data Visualization with Tableau Specialization 👉https://imp.i384100.net/n15XWR
📖 Deep Learning Specialization 👉 https://imp.i384100.net/zavBA0
📖 Mathematics for Machine Learning and Data Science Specialization 👉 https://imp.i384100.net/LXK0gj
📖 Applied Data Science with Python 👉 https://imp.i384100.net/gbxOqv

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🔑 TIMESTAMPS
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0:00 - Intro
0:24 - Pro tip #1: Using R projects for better workflow
2:27 - Pro tip #2: Structuring analysis folder
4:26 - Pro tip #3: Base R vs Tidyverse vs data.table: What to use?
6:33 - Pro tip #4: Debugging code in R studio
9:56 - Outro

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