R for Beginners

Embark on a comprehensive journey through R programming with this 10-hour tutorial series designed for beginners. Learn essential skills from downloading and installing R and RStudio to advanced topics like multiple linear regression for selection decisions. Master fundamental concepts including data manipulation, statistical analysis, and visualization techniques. Follow along with self-contained, long-form tutorials covering topics such as setting up your environment, reading and writing data, merging datasets, subsetting cases, computing turnover rates, and conducting various statistical tests. Download the accompanying data files to practice hands-on exercises and gain practical experience in R programming for data analysis and decision-making.

Downloading & Installing R & RStudio.
Orientation to RStudio.
Gentle Introduction to R.
Creating & Saving an R Script.
Orientation to Written Tutorials.
Setting a Working Directory in R.
Creating a Project in RStudio.
Reading Data into R | Using the read.csv Function.
Merging Data Horizontally in R | Using the merge Function.
Subsetting Cases From a Data Frame in R | Using the subset Function.
Selecting & Removing Variables from a Data Frame in R | Using the subset Function.
Writing Data Frames from R | Using the write.csv Function.
Computing Turnover Rates in R | Using the sum & mean Functions.
Counts & Frequencies in R | Using the table & barplot Functions.
Measures of Central Tendency & Dispersion in R | Summarizing & Visualizing Distributions.
Cross Tabulation in R | Using the xtabs Function.
Estimating Internal Consistency Reliability (Using Cronbach's alpha) in R | Using the alpha Function.
Creating a Composite Variable in R | Using the rowMeans Function.
Independent-Samples t-test in R | Using the ttest Function from lessR.
Paired-Samples t-test in R | Using the ttest Function from lessR.
Estimating Criterion-Related Validity Using a Correlation in R.
Predicting Criterion Scores Using Simple Linear Regression in R.
Applying a Compensatory Approach to Selection Decisions Using Multiple Linear Regression in R.