Packt – Harness the Power of Tidyverse for Data Preprocessing and Visualisation in R-ZH
Read in data into the R environment from different sources
Carry out basic data pre-processing & wrangling in R Studio
Use some of the most important R data wrangling & visualization packages such as dplyr and ggplot2
Gain proficiency in data preprocessing, data wrangling, and data visualization in R and put what you’ve learned into action straightaway
This is your roadmap to becoming highly proficient in data preprocessing, data wrangling, and data visualization using two of the most in-demand R data science packages
What this course will do for you:
It will take you from a basic level to a level where you’ll perform some of the most common data wrangling tasks in R—with two of the most well-known R data science packages: Tidyverse and dplyr.
It will equip you to use some of the most important R data wrangling and visualization packages such as dplyr and ggplot2.
It will Introduce you, in a practical way, to some of the most important data visualization concepts so that you can apply them to practical data analysis and interpretation.
You will also be able to decide which wrangling and visualization techniques are best suited to answering your research questions and most applicable to your data, so that you can interpret the results.
The course will mostly focus on helping you implement different techniques on real-life data
After each video, you will have learned a new concept or technique and will be able to apply it to your own projects immediately!
All the code and supporting files for this course are available at – https://github.com/PacktPublishing/Harness-the-Power-of-Tidyverse-for-Data-Preprocessing-and-Visualisation-in-R
Minimal mathematical jargon. The course focuses on teaching you basic time series concepts and hands-on applications for the most important concepts in R
People with no prior exposure to time series can use this course to get started
A thorough grounding in how to use both statistical and machine learning techniques on time series data
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