# Author name: Erik Marsja

PhD in Psychology, Linköping University. Main interest is experimental and cognitive psychology. Enjoy programming in Python and R.

## Deskriptiva Analyser: Exempel med Jamovi och R Statistik

Upptäck hur man gör deskriptiva analyser i denna bloggpost. Från centralmått till verktyg som Jamovi och R, vår steg-för-steg guide hjälper dig navigera genom beskrivande statistik.

## Convert Multiple Columns to Numeric in R with dplyr

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.

## Pandas Convert All Columns to String: A Comprehensive Guide

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.

## Master MANOVA in R: One-Way, Two-Way, & Interpretation

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!

## Variance Inflation Factor in Python: Ace Multicollinearity Easily

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.

## Combine Year and Month Columns in Pandas

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!

## How to Add a Column to a Matrix in R: A Guide Incl. Adding Names

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.

## Not in R: Elevating Data Filtering & Selection Skills with dplyr

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.

## Running R in Jupyter: Unleash the Simplicity of Notebooks

Introduction Running R in Jupyter Notebook allows users to harness the power and simplicity of notebooks for their data analysis and research tasks. This post will explore the benefits and capabilities of using R in Jupyter Notebook. Firstly, let’s briefly introduce the concept of a notebook. A notebook is an interactive document that combines code,

## Check Variable Type in R: How to Use typeof() & str()

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!

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