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!
Filtering data is a a common step in data analysis. In this post, we explore how to filter in data.table in R, including subsetting by conditions and selecting specific groups. We also show how to save the filtered data as a new data frame for further analysis.
How to Filter in data.table in R Read More »
This post explores summing multiple columns in data.table, including group-wise summation and dynamic column selection with %in%. We also compare this approach with dplyr and base R. Whether you’re working with large datasets or need flexible column selection, these techniques will help you get the job done. Read on to learn more!
How to Sum Multiple Columns in data.table Read More »
Learn how to select columns in data.table using names, character vectors, and indices. This guide covers selecting single and multiple columns, using .SDcols, and indexing. With practical examples, you’ll see how data.table offers a fast and flexible way to manipulate large datasets in R.
How to Select Columns from data.table in R Read More »
This blog post focuses on the practical aspects of conducting mediation analysis in R. First, we will look at how to do it using the mediation package. Then, we will walk through an example of using the Lavaan package to perform mediation analysis. Both mediation examples will include R code chunks with explanations. This blog
Mediation Analysis in R: How to Run it with two Packages Read More »
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 focuses on 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 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 This data visualization tutorial will start by learning the
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 good tool for identifying and removing duplicate values in different data structures. We will start by having a look at what the unique() function is to learn how it works. Next, we will look at examples of
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 »