Importing and recoding data

Lecture 8

Dr. Benjamin Soltoff

Cornell University
INFO 5001 - Fall 2025

September 18, 2025

Announcements

Announcements

Homework 03
Quiz 01

Popstars’ height comparison

Learning objectives

Identify common methods for reading data from a file
Clean and wrangle data frames to facilitate analysis tasks

Data “wrangling”

A screenshot of a New York Times article.

A screenshot of 'Data Carpentry' by David Mimno.

Reading data into R

Local data files
Databases
Web scraping
Application programming interfaces (APIs)

Reading rectangular data

{readr}:
- Most commonly: read_csv()
- Maybe also: read_tsv(), read_delim(), etc.
{readxl}: read_excel()
{arrow}: read_arrow(), read_parquet()
{haven}: read_sas(), read_sav(), read_dta()
{googlesheets4}: read_sheet()
{data.table}: fread()¹

Avoid read.csv(), read.tsv(), etc. (base R functions)

Application exercise

Powerball Lottery

Powerball Lottery

Powerball Lottery

`ae-06`

Note

Go to the course GitHub org and find your ae-06 (repo name will be suffixed with your GitHub name).
Clone the repo in Positron, run renv::restore() to install the required packages, open the Quarto document in the repo, and follow along and complete the exercises.
Render, commit, and push your edits by the AE deadline – end of the day

Wrap up

Recap

Simplify your life – get the data in as simple a format as possible
Ensure all data cleaning is reproducible. Do not replace your raw data files.