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!
How to randomly select rows in R? Learn the sample() and slice_sample() functions to take random samples from dataframes. Explore practical examples and synthetic datasets for hands-on learning. Enhance your data analysis skills and unlock new possibilities with random row selection in R.
How to Randomly Select Rows in R – Sample from Dataframe Read More »
In this blog post, we will learn how to extract P-values from the regression models in R. We will explore the process of fitting a regression model, and then dive into the methods of extracting P-values using the lm() function. Additionally, we will demonstrate how to extract P-values from all predictors and use the tidy() function for a tidy output. Unlock the power of statistical inference with the ability to extract P-values from lm() in R.
Extract P-Values from lm() in R: Empower Your Data Analysis Read More »
Unlock the potential of or in R! Explore advanced data manipulation techniques and learn how to filter, select, mutate, and summarize data based on multiple conditions. Boost your coding skills and streamline your data analysis workflows with ease.
Master or in R: A Comprehensive Guide to the Operator Read More »
Keeping your software tools up-to-date is essential for a seamless and efficient workflow, and the R programming language is no exception. In this blog post, we will explore the importance of updating R, discuss the circumstances that may necessitate an update, address the possibility of updating R within RStudio, and explore different methods for upgrading
Update R: Keeping Your RStudio Environment Up-to-Date Read More »
Learn to calculate and interpret SSE/SSR and SST in R. Understand their significance, generate fake data, fit a linear model, and calculate SST and SSR using different methods, including ANOVA. Gain insights into evaluating model performance and enhance your statistical analysis skills. A comprehensive guide for data analysts and researchers.
Mastering SST & SSE in R: A Complete Guide for Analysts Read More »
In this post, you will learn how to report correlation according to APA. Adhering to APA (American Psychological Association) guidelines is important wen you report your analysis and write up your results . Whether you are conducting research in psychology, cognitive hearing science, or cognitive science, APA style is often required by journals and conferences.
Report Correlation in APA Style using R: Text & Tables Read More »
In this post, we will learn how to transform data from wide to long in R. Wide-to-long format conversion is often an important data manipulation technique in data analysis. In R, we can use many packages and their functions to transform data from a wide to long format. These functions include the tidyr package’s pivot_longer()
Wide to Long in R using the pivot_longer & melt functions Read More »
In this blog post, you will learn how to carry out a countif function using base R and dplyr. Countif is a powerful function that allows you to count the number of times a certain condition is met in a dataset. Countif is particularly useful in cognitive hearing science, where researchers often need to analyze
Countif function in R with Base and dplyr Read More »
Discover how to analyze non-parametric data using the Wilcoxon Signed-Rank Test in Python. Learn how to interpret the results and compare different Python packages for running the test. Get started now!
Wilcoxon Signed-Rank test in Python Read More »
In this blog post, you will learn how to test for the normality of residuals in R. Testing the normality of residuals is a step in data analysis. It helps determine if the residuals follow a normal distribution, which is important in many fields, including data science and psychology. Approximately normal residuals allow for powerful
Test for Normality in R: Three Different Methods & Interpretation Read More »