Your Essential Guide to Descriptive Statistics is an all-encompassing resource meticulously crafted to empower data analysts and statisticians. This guide serves as a detailed roadmap, illuminating the path to unraveling the full potential of RStudio for conducting descriptive statistical analysis. Brace yourself for a captivating expedition as we delve into the labyrinthine complexities of RStudio's formidable arsenal: the formidable tidyverse, dplyr, psych, describe, and summary functions.
Descriptive Statistics
Functions
- The 'sapply' function documentation in the base package provides information on applying a function to each element of a list or vector: Read More
The 'sd' function documentation in the stats package offers details on calculating the standard deviation of a numeric vector or matrix: Read More - Learn about the 'median' function in the base package, which calculates the median of a numeric vector or matrix: Read More
- Discover the 'mean' function in the base package, which allows you to compute the arithmetic mean of a numeric vector or matrix: The 'summary' function documentation in the base package provides insights into generating summary statistics for objects in R: Read More
- Learn about the 'table' function in the base package, used to create frequency tables and cross-tabulations in R: Read More
- The 'Normal' function documentation in the stats package provides information on generating random samples from a normal distribution. Read More
- The 'sample' function documentation in the base package explains how to generate random samples from specified vectors. Read More
Packages
- Explore the 'psyh' package manual, a comprehensive resource for performing psychological data analysis in R: Download
- The 'tidyverse' package offers powerful and user-friendly R packages for data manipulation, visualization, and analysis. Learn more about it here: Download
- Discover the 'dplyr' package, a key component of the tidyverse, providing a grammar of data manipulation to help you work with data frames efficiently: Download
- The 'plyr' package documentation outlines a set of tools for splitting, applying, and combining data in R: Download
