Introduction to Data Analytics in R

Megha Joshi

Introduction to This Course

• Sharpen your data analytic skills in R

• Prepare you for career paths including Data Analyst, Quantitative User Researcher, Data Scientist

• Practice problems

• Job search resources

Instructors

Megha Joshi, PhD

Expectations

• Recommended readings

  • R4DS and R Programming for DS
  • For each class, we will list out relevant chapters

• Practice problems - practice, practice, practice

• Collaboration - Slack channel

Overview of Course

• Introduction

• Data visualization

• Data querying and wrangling

• Relational data

• Strings and factors

• Functions and iteration

• …advanced topics to be added in the future

Introduction to R

Artwork by @allison_horst

• Programming language - based on S
• Open source and FREE
• base R plus other R packages ~ over 20,000
• User community

  • Tidy Tuesday

  • Data Science Learning Community

  • Stack Overflow

Downloading

• Download R

• Download R Studio / Download Positron

• Download Quarto

R Studio - R Projects and Relative Path

• Keep all materials related to a project (or a class) together in one place

• The working directory is set to the base folder

• You can set relative paths so other users are able to run code without messing too much with the working directory

• Read more about how to set up projects here

• If you are switching to Positron, the set up is a bit different ~ https://github.com/posit-dev/positron/discussions/5425

# absolute path - this won't work on someone else's computer
dat <- read.csv("/Users/meghajoshi/Desktop/R_course_2025/data/fifa_world_cup/women_wc/matches_1991_2023.csv")

# example of relative path
dat <- read.csv("data/fifa_world_cup_kaggle/women_wc/matches_1991_2023.csv")

Basics of R: Objects and Assignment

x <- 5
x

[1] 5

y <- 2
x + y

[1] 7

my_children <- c("Yori", "Moni", "Rumi")
my_children

[1] "Yori" "Moni" "Rumi"

R Packages

Please install the following packages:

install.packages("tidyverse") # data wrangling and viz
install.packages("readxl") # read excel files
install.packages("janitor") # clean messy data
install.packages("estimatr") # robust estimators

Coding Best Practices

Write Clean Code

• Write code that is easy to read

• Add spaces between things

  a<-c(1,2,3,4,5) # don't do this
  a <- c(1, 2, 3, 4, 5) # do this

• In R, please use <- to assign

  x = 5 # don't do this
  x <- 5 # do this

• Use _ instead of . as separation when naming objects

  my.color <- "blue" # don't do this
  my_color <- "blue" # do this

• Some packages to help : lintr

Write Good Documentation

• Helps with reproducibility

• For yourself and for other people who may use your code

• Write comments, chunk codes out into sections

Conduct Code Review

• Process of systematically checking code

• On your own or by someone else

• Catch errors or suggest better ways to code

• Check out the Code Check Club by Lisa De Bruine et al.

Use Version Control

Artwork by @allison_horst

• git and GitHub

• Track changes, merge conflicts, branching to review code before merging

• Track issues

• A portfolio of your work

• Please read Vuorre & Curley (2018) to learn more about how to set up git to work with RStudio

Quarto

Introduction to Quarto

• Integrate text, code, graphs, tables

• Develop slides, websites, books

• Use R, Python

• The slides, practice problems, and the website for this course have been created using Quarto

• For more information, please visit https://quarto.org/

Thank You!