Intro to This Course;
Getting Started with R

Enrico Toffalini

Why becoming an R user?

• Mainstream in academia for statistical computing and data science, increasingly used in business. Job market advantage!

• Free & open-source: wherever you go, R will be with you at no costs (unlike MATLAB, MPLUS, SPSS, etc.)

• Real programming language: difficult at the beginning, but: 1) gives you lots of flexibility; 2) has transfer on other programming languages (e.g., Python).

• Vast community support thanks to a large and active community (plus chatGPT, Gemini, Lucrez-IA, etc., know it pretty well!).

• Huge ecosystem, >23,000 packages on CRAN, more from other sources (e.g., GitHub), to do amazing stuff with statistical data analysis, machine learning, data visualization, developing webapps [shiny], writing reports and even entire books [bookdown, rmarkdown]); also, can integrate with Quarto, GitHub.

• Facilitates reproducible scientific research by sharing code and workflows.

What you may expect to learn in this course:

• Executing fundamental operations and using basic functions;

• Working with essential data types and structures;

• Gaining some proficiency in managing and manipulating data with vectors and dataframes;

• Understanding some fundamental concepts of programming.

Over the next two years, following this PhD program, you will or may have the opportunity to use R to perform at least some fundamentals about:

• Core statistical inference methods;

• LM/LMM/GLMM: (Generalized) linear (mixed-effects) models;

• Data visualization using ggplot2;

• Power analysis & more via data simulation;

• SEM: Structural Equation Modeling;

• Conducting meta-analysis.

you may even create greeting cards

you may even create greeting cards

or like fancy infographics

or like fancy infographics

perform fancy moderated meta-analyses

you may create interactive webapps with Shiny

see Shiny gallery

here’s a couple of recent real examples from Psicostat members:

• this game-like shiny app developed for the science4all event in Padova; see here some explanation in Italian

• practical ad-hoc shiny app for scoring experimental data collected by students

you may create interactive webapps with Shiny

or entire websites and books

examples of other resources that can be created within the R ecosystem, integrating other tools such as GitHub and Quarto:

• this very course support material is a website in its own right

• this very course textbook is a book/website

• this book by Daniël Lakens explaining Statistical Inference

R + Integrated Development Environment(s)

Make sure you install:

• R as the programming language interpreter and the basic environment and packages
• RStudio is the IDE of election to make writing R code easy

Interesting alternatives to installing RStudio:

• Positron (based on MS Visual Studio Code)
• Posit.cloud (fully online, actually RStudio)
• Google Colab (fully online, make sure to set R runtime type; actually a Jupyter notebook)

R Console (just base R)

R Studio (full IDE)

Google Colab (online notebook)

Let’s Test the Environment!

Let’s run a few commands in RStudio to familiarize with its console and see if the installation works properly

rnorm(10) # draw 10 random values from a Standard Normal distribution

 [1]  0.63285715 -0.09089180  0.05989258 -1.98495842 -1.39771385 -1.76343919
 [7] -1.37670755 -0.97808745  0.72135449  0.59570175

?rnorm # open the help tab for the "rnorm" function

round( rnorm(10, mean=100, sd=15) ) # draw 10 values from IQ distribution, round them

 [1] 108 103  81  89 101 121 100 104  97 101

install.packages("psych") # install a package from CRAN

library(psych) # load the newly installed package

fisherz(rho=0.5) # use it to transform a correlation into a Fisher's z

[1] 0.5493061