Why programming

• Programming will make your academic journey better.

• Instead of remembering what buttons you clicked, what analyses you did, you write scripts with exact commands implementing your analysis and allow others to understand/reproduce it (or, spot mistakes).

• Programming forces you to have a deeper understanding of what you are doing, and facilitates your learning and comprehension of the methods you use.

Why R?

• R is a programming language designed for data analysis and statistics.

• Extremely powerful for statistical modeling, machine learning, data manipulation, and visualization.

  • Efficient data analysis on data of all shapes and sizes (big data including).
  • Optimized operations on vectors, matrices, lists.
  • Very sophisticated graphs and data visualizations.

• Not just scripts, but fully reproducible reports, papers, presentations, web applications.

• Free, cross-platform, and open-source.

https://www.r-project.org/

Why R?

• Thousands of packages that add extra functionality. Covering virtually all scientific disciplines and analytical frameworks.
  • Image analysis, geospatial, epidemiology, genetics, bioinformatics, and a lot more.

19,772 CRAN (The Comprehensive R Archive Network) packages and 2,230 Bioconductor packages, as of 08/21/2023

18,000 CRAN packages, tweet by Dirk Eddelbuettel, 2021-08-11

• R can connect to spreadsheets, databases, and many other data formats, on your computer or on the web.

• Large and welcoming user community.

RStudio

• RStudio is an IDE (integrated development environment) to work with R, with many features and functionalities for efficient work.

• Integrates file navigation, visualization, documentation, version control and project management.

• You write the same R code in RStudio as you would elsewhere, and it executes the same way. RStudio helps by keeping things nicely organized.

• Free, cross-platform, and open-source.

• Developed by Posit (formerly, Rstudio) company.

Why RStudio

• Project-centric work - scripts and data are organized in one folder (project), easily accessible.

• Work on multiple projects simultaneously in several instances of RStudio.

• Work on multiple (types of) scripts.

• After you install R and RStudio, you only need to run RStudio.

RStudio interface

• Single workspace with four (rearrangeable, zoomable) panels.

• See all variables in R environment, easily visualize them.

• Easy access to help, plots, packages.

• Simple integration with Git version control system.

RStudio interface

RStudio interface

RStudio is divided into 4 panes, by default:

• Source for scripts and documents (top-left).
• Environment/History (top-right).
• Files/Plots/Packages/Help/Viewer (bottom-right).
• R Console (bottom-left).

Additional goodies:

• Autocompletion.
• Highlightning.
• Color themes.
• Keyboard shortcuts.
• Many more.

RStudio help

Installing and loading packages

# install.packages('cowsay')
library(cowsay)
say(what = "fortune", by = "rabbit")

## 
##  ----- 
## If you don't go with R now, you will someday.
##  David Kane
##  on whether to use R or S-PLUS
##  R-SIG-Finance
##  November 2004 
##  ------ 
##     \   
##      \
##       ( )_( )
##       (='.'=)
##       (^)_(^) [nosig]
##

RStudio is more than IDE

Posit develops many now-gold-standard R packages.

• tidyverse - R packages for data science, including ggplot2, dplyr, tidyr, and purrr.
• rmarkdown - Insert R code into markdown documents.
• knitr - Dynamic reports combining R, TeX, Markdown & HTML.
• quarto - Publishing system for R, Python, Julia, Observable.
• shiny - An interactive web technology.
• tensorflow - R interface to Tensorflow deep learning library.
• reticulate - interface to Python.
• devtools, usethis - Package development tools.

Getting help

• Get an overview of all functions in a package: help(package = "dplyr").

• Use ?function_name to get help on a function from a loaded package. E.g., ?boxplot (same as help(boxplot)).

  • Use example(boxplot) to see how the function can be used.

• Use ??function_name to search for the function across all installed packages, even not loaded. E.g., ??ggplotly.

• Search engine is your best friend.

Getting data in and out of R

read.csv()
write.csv()
read.table()
write.table()
readr::read_csv()
readr::write_csv()
readr::read_tsv()
readr::write_tsv()
data.table::fread()
data.table::fwrite()
readxl::read_xlsx()
writexl::write_xlsx()

R datasets

R contains many datasets (stored as data frames) that are built-in to the software.

data() # All built-in datasets
# ?trees
data(trees) # Load a particular one
head(trees)

##   Girth Height Volume
## 1   8.3     70   10.3
## 2   8.6     65   10.3
## 3   8.8     63   10.2
## 4  10.5     72   16.4
## 5  10.7     81   18.8
## 6  10.8     83   19.7

Get started

• https://posit.cloud/ - Posit Cloud free computing.

• Introduction to bioinformatics, https://uclouvain-cbio.github.io/WSBIM1207/sec-rrstudio.html

• Orientation to programming, R, and RStudio, https://gge-ucd.github.io/R-DAVIS/lesson_01_intro_r_rstudio.html

install.packages("swirl")

library(swirl)

## 
## | Hi! I see that you have some variables saved in your workspace. To keep
## | things running smoothly, I recommend you clean up before starting swirl.
## 
## | Type ls() to see a list of the variables in your workspace. Then, type
## | rm(list=ls()) to clear your workspace.
## 
## | Type swirl() when you are ready to begin.

Conclusion

https://twitter.com/TanentzapfLab/status/1427720047431065601?s=20