In this online book, we went over information to make sure you had some basics to really start learning R. This journey began with the basics of R, emphasizing that while multiple methods exist to achieve the same results in R, it’s crucial to find the approach that works best for you. As we learn R, you will get used to doing things your way to be able to slice and evaluate the data to find rich information from the data sets we look at. As long as the data was handled properly, it does not matter how we reach our goal using R as long as we do it ourselves.
Mastering R allows you to effectively clean, analyze, and interpret data, unlocking valuable insights from various datasets. We explored the essential practice of data cleaning, learning various techniques and popular functions within the dplyr package under the tidyverse. Proper data cleaning ensures the integrity and accuracy of your analysis. We delved into skewness, kurtosis, variables, and scales of measurement, focusing on summarizing qualitative and quantitative data. Visualizations were introduced as powerful tools to describe variables and uncover patterns in the data.
We examined basic probability rules were covered alongside binomial and continuous distributions. We examined the normal distribution, its limitations, and methods for transforming non-normal variables. Central limit theorem is discussed with the Empirical rule alongside normal distributions.
This book has equipped you with the foundational skills needed to handle data before deeply analyzing it using R. Remember, the key to mastering these techniques lies in consistent practice and finding the methods that best suit your analytical style.