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!
When working with data in Python, using Pandas to read JSON from URL is an excellent tool that lets you directly load JSON data from a web source into a Pandas dataframe. This tutorial will teach you the steps to accomplish this task, building upon our previous discussions on reading JSON with Python more generally.
Using Pandas to Read JSON from URL Read More »
In this volcano plot in R tutorial, we will use ggplot2, a popular package for creating beautiful and customizable graphics in R. A volcano plot in R is a scatter plot showing the relationship between the fold change and the statistical significance in certain data types. Outline In this data visualization tutorial, we will start
How to Make a Volcano Plot in R using ggplot2 Read More »
A random intercept model in R is useful for analyzing data from experiments that involve repeated measurements of the same participants or items. It allows you to control for the variability in the outcome variable due to random effects, such as individual differences or item difficulty while estimating the fixed effects of your predictors of
Random Intercept Model in R: Interpretation and Visualization Read More »
In this post, we will look at how to use the unique function in R, which is a powerful and versatile tool for identifying and removing duplicate values in different types of data structures. We will first have a look at what the unique() function is and how it works. Next, we will demonstrate its
The Unique Function in R: How to Use it in 4 Ways Read More »
Mann whitney u test in R with ggstatsplot
Mann Whitney U Test in R: A Comprehensive Guide Read More »
Sometimes, when we use data frames in R, we need to remove rows with specific values. For example, we might want to exclude rows that have missing values, outliers, or errors. Or we might want to subset our data based on some criteria, such as a range of values, a category, or a pattern. In
R: Remove Rows with Certain Values using dplyr Read More »
Here you will learn by examples how to use Pandas to calculate cumulative sum by group.
Pandas: Cumulative Sum by Group Read More »
Discover effective methods for removing specific rows in R using both base functions and dplyr. Improve your data manipulation skills and streamline your analysis process. Explore examples and enhance your coding proficiency.
Remove Specific Row in R: How to & Examples with dplyr Read More »
Learn all you need about variance calculation in R with base R and dplyr. From single-column insights to multi-variable analyses, this tutorial equips you for robust data exploration. Discover efficiency with dplyr’s simplicity or opt for the reliability of base R. Your journey to mastering variance starts here.
Variance in R: How to Find & Calculate Read More »
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 »