R statistical programming related stuff
Discover the four simple steps to change R version in RStudio effortlessly. Navigate to Tools, click on Global Options, choose the specific R version needed, and restart RStudio. Check your R version with ease.
Change R Version in RStudio: A Quick How-To Read More »
Master techniques to disable scientific notation in R. This blogpost guides you through practical methods, empowering precise and readable data analysis. Say goodbye to default settings and take control.
Turn Off Scientific Notation in R Read More »
In data analysis using R, the need to convert character columns to factors is common. Character columns often contain categorical data, and converting them to factors enables R to interpret and analyze the data more effectively. Factors represent categorical variables with distinct levels, aiding in statistical modeling (e.g., ANOVA, MANOVA) and visualization. Data type conversion is
Convert All Character Columns to Factor in R: A Guide Read More »
Here we get into R’s correlation matrix creation, seamlessly transitioning between base R and the corrr package. Learn to visualize correlations, save APA 7 tables, and discover the pros and cons of each method. Empower your data analysis journey with this comprehensive guide.
Correlation Matrix in R: A Hands-On Guide for Practical Analysis Read More »
Discover versatile methods to convert multiple columns to numeric in R. From base R’s simplicity to dplyr’s efficiency, learn essential techniques. Enhance your data manipulation skills and tackle real-world challenges with confidence. Dive into this comprehensive guide and elevate your R programming expertise.
Convert Multiple Columns to Numeric in R with dplyr Read More »
Unlock the power of MANOVA in R for one-way and two-way analyses. This tutorial guides you through the process, from assumptions to interpretation, bolstering your statistical toolkit. Elevate your data analysis skills today!
Master MANOVA in R: One-Way, Two-Way, & Interpretation Read More »
In R, enhancing your data matrix is a breeze. Adding columns is simple, and with proper column names, your data organization gains clarity and power. Learn how to seamlessly expand and name columns for effective data manipulation in R. Elevate your data skills and unlock new possibilities.
How to Add a Column to a Matrix in R: A Guide Incl. Adding Names Read More »
Discover how to filter data in R using the %in% operator’s counterpart, ! (NOT) with filter(). This powerful technique allows you to exclude specific values from your dataset, providing fine-grained control over your data filtering process. Streamline your data manipulation with this essential skill. Explore more in our comprehensive guide.
Not in R: Elevating Data Filtering & Selection Skills with dplyr Read More »
Introduction Running R in Jupyter Notebook allows us to use notebooks’ power and simplicity for data analysis and research tasks. This post will cover the benefits and capabilities of using R in a Jupyter Notebook. Firstly, let us briefly introduce the concept of a notebook. A notebook is an interactive document that combines code, visualizations,
Running R in Jupyter: Unleash the Simplicity of Notebooks Read More »
Discover the key to data manipulation in R by learning how to check and manage variable types. Learn the nuances of data types and take your data analysis expertise to the next level with the comprehensive insights provided here. Get ready to enhance your data-handling skills and drive more precise analyses!
Check Variable Type in R: How to Use typeof() & str() Read More »