R Studio - A Crash Course

The ultimate guide for importing, editing & analyzing real-world data files

What you'll learn

Work fast and accurately with R Studio

Perform essential data editing and analysis skills in R

Screen data files for common issues and correct these if necessary

Create nicely detailed tables for frequencies, descriptives, correlations and more

Create decent bar charts, histogram, scatterplots and more

Requirements

You don't need any prior knowledge. However, minimal statistics (measurement levels, standard deviation, …) is helpful for some of the lectures.

Description

If you start analyzing real-world data, which steps should you take in which order?And what's a simple but solid way to perform these in R Studio?This course teaches you exactly that with a minimal time investment.We start off with a quick tour through the R Studio interface. Next up, we jump straight into a real-world data file. You'll learn a minimal, step-by-step data screening routine that includesinspecting variable distributions with bar charts and histograms,checking for undesired Chr (string) variables,counting NA (missing) valuesand way more…We'll then walk you through some fundamental data analyses such as frequency tables with frequencies & column percentages,descriptive statistics over all observations & subgroups separately,contingency tables with frequencies and column percentages &Pearson correlations with listwise & pairwise exclusion of missing values.Next up, you'll learn how to import & export various file types into & from R Studio such as .R, .RData, .RDS, Excel, .CSV, .SAV & .PNG.Finally, we'll round off with some extra data editing skills. These include reordering and removing variables (columns) or observations (rows) and counting NA (missing) values within observations. Last but not least, we'll cover computing means and sums over variables with & without NA values.In short, you'll learn exactly what you need for working with real-life data in R Studio. Just do it.Happy coding ;-)

Overview

Section 1: Getting Started

Lecture 1 InstallIing R & R Studio

Lecture 2 R Studio - Absolute Basics

Lecture 3 Packages in R Studio

Section 2: Minimal Data Screening

Lecture 4 Setting Up an R Project Folder

Lecture 5 Importing CSV Files into R Studio

Lecture 6 Visually Inspecting Dataframes in R

Lecture 7 Inspecting Variable Types in R

Lecture 8 Checking if ID Values Are Unique

Lecture 9 Creating Basic Bar Plots in R

Lecture 10 Creating Basic Histograms in R

Lecture 11 Counting NA Values Per Variable in R

Section 3: Importing & Exporting Files

Lecture 12 Saving and Opening R Files

Lecture 13 Importing Excel (.xlsx) Data Files into R

Lecture 14 Importing SPSS (.sav) Data Files into R

Lecture 15 Exporting R Tables to Excel

Lecture 16 Exporting R Plots as .PNG Files

Section 4: Univariate Data Analysis

Lecture 17 Creating APA Style Frequency Tables in R Studio

Lecture 18 Creating APA Style Descriptives Tables in R Studio

Section 5: Bivariate Data Analysis

Lecture 19 Creating Contingency Tables in R Studio

Lecture 20 Descriptive Statistics for Separate Groups in R Studio

Lecture 21 Creating Scatterplots in R Studio

Lecture 22 Run & Interpret Pearson Correlations with NA Values in R Studio

Section 6: Basic Data Editing

Lecture 23 Find Number of NA Values for Each Observation in R

Lecture 24 R Studio - Removing Observations from Dataframes

Lecture 25 Removing & Reordering Variables in R

Lecture 26 Computing Means over Variables in R Studio

Lecture 27 Computing Sums over Variables in R Studio

This course is for professionals who want to thoroughly master practical data analysis in R with a minimal time investment.