In this category you will find posts that are related to programming and should be interesting for psychologists, cognitive scientists, and neuroscientists. Well, almost every researcher would probably find some of the information useful at some time!
Every research psychologist, cognitive scientist, and neuroscientist, should know how to program.. Knowing how to program and write scripts will make many of a researchers everyday tasks much easier. For instance, instead of going through line after line of raw data you can write a Python script that runs through each cell in each column. Furthermore, you get the possibility to use more advanced, and cutting edge, statistical techniques by using R statistical programming environment.
Another example might be to create experiments using PsychoPy (either by coding using Python or using the drag-and-drop interface) and the cheap and open-source Arduino microcontroller. Also, coding is fun and relaxing!
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 »
In this comprehensive tutorial, explore the powerful methods to convert all columns to strings in Pandas, ensuring data consistency and optimal analysis. Learn to harness the versatility of Pandas with ease.
Pandas Convert All Columns to String: A Comprehensive Guide 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 »
Learn all about multicollinearity with Python! This guide explores Variance Inflation Factor (VIF) using statsmodels and scikit-learn. Break down the complexity of real-world data analysis, and elevate your regression skills to the next level.
Variance Inflation Factor in Python: Ace Multicollinearity Easily Read More »
Unlock the power of Pandas! Discover the art of combining year and month columns in your data. Seamlessly organize, analyze, and visualize your time-based datasets. Elevate your data manipulation skills and supercharge your insights. Dive into our Pandas tutorial to become a data wizard!
Combine Year and Month Columns in Pandas 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 »