R statistical programming related stuff
Discover the key to data manipulation in R by learning how to check and manage variable types. Uncover the nuances of data types and elevate your data analysis expertise with the comprehensive insights provided in this post. Get ready to enhance your data-handling skills and drive more precise analyses!
Learn how to calculate row means in R, whether you’re analyzing every 5th column, applying conditions, or using dplyr for precise numeric column calculations. Explore the power of R for tailored row averaging.
Unlock the power of Fisher’s Exact Test in R and uncover hidden associations in your categorical data. Dive into interpretation, post-hoc analysis, and data visualization. Discover how to go beyond statistics and turn insights into actions.
Learn to calculate Cronbach’s Alpha in R for assessing internal consistency. Explore manual methods and convenient packages like psych and performance.
Cross-tabulation in R: Explore diverse methods for analyzing categorical data. Learn basic and advanced techniques using functions like table(), dplyr, and sjPlot. Enhance your data analysis skills now!
Explore how to sum rows in R using dplyr’s powerful functions and enhance your data analysis. Sum across specific rows and based on conditions.
In this post, you will learn how to efficiently determine if a file is empty in R using the file.info() and file.size() functions. We will explore practical examples and scenarios, including reading multiple files. Mastering these techniques empowers streamlined file validation for effective data processing and analysis.
Unlock the potential of Modulo in R with practical applications. Explore data segmentation, odd/even checks, day filtering, and optimized iterations for streamlined coding efficiency.
Learn how to check the R version in RStudio and ensure reproducibility. Discover the best method for compatibility and smooth data analysis. Follow simple steps for quick access to version details.
Z test in R is a crucial statistical tool for hypothesis testing. This comprehensive guide covers one-sample and two-sample Z tests, providing practical examples utilizing base R and the BSDA package.
