Understanding Data

Sources and Structure

June 22, 2025

Preliminaries

Where Does Data Come From?


Thoughts? 😎 💭

  • Your boss or a client sends you a file
  • Survey data collected by you or someone else
  • You can download it from a website
  • You can scrape it from a website
  • A data package (e.g. unvotes)
  • You can access it through an API

Getting Started with Data


  • Tabular data is data that is organized into rows and columns
    • a.k.a. rectangular data
  • A data frame is a special kind of tabular data used in data science
  • A variable is something you can measure
  • An observation is a single unit or case in your data set
  • The unit of analysis is the level at which you are measuring
    • In a cross-section: country, state, county, city, individual, etc.
    • In a time-series: year, month, day, etc.

Adjectives for Your Data

The Concept of “Tidy Data”


  • Each column represents a single variable
  • Each row represents a single observation
  • Each cell represents a single value

Tidy Data Example

What are Clean Data?

  • Column names are easy to work with and are not duplicated
  • Missing values have been dealt with
  • There are no repeated observations or columns
  • There are no blank observations or columns
  • The data are in the proper format, for example dates should be formatted as dates

Messy Data Example

Which of These is Likely Tidy/Clean?


  • Your boss or a client sends you a file
  • Survey data collected by you or someone else
  • You can download it from a website
  • You can scrape it from a website
  • A curated collection (e.g. unvotes)
  • You can access it through an API

How Do We Get Tidy/Clean Data?


  • Get lucky and find it
  • Wrangle it ourselves
  • Use a package where it has been wrangled for us
  • Download via an API

Reading Data

Read Data into R


  • Use read_csv() function from readr package
  • readr package is part of the tidyverse
  • Can do more with it than base R functions

R Code Review


  • <- is the assignment operator
    • Use it to assign values to objects
  • # is the comment operator
    • Use it to comment out code or add comments
    • Different function than in markdown text
  • To call a library, use library() and name of library
    • name of library does not have to be in quotes, e.g. library(readr)
    • only when you install it, e.g. install.packages("readr")

Read Data into R


# load libraries
library(readr)
library(dplry)

dem_summary <- read_csv("data/dem_summary.csv") #notice file path

glimpse(dem_summary)

Viewing the Data in R


Use glimpse() to see the columns and data types:

# load libraries
library(readr)
library(dplyr)

dem_summary <- read_csv("data/dem_summary.csv")

glimpse(dem_summary)
Rows: 6
Columns: 5
$ region    <chr> "The West", "Latin America", "Eastern Europe", "Asia", "Afri…
$ polyarchy <dbl> 0.8709230, 0.6371358, 0.5387451, 0.4076602, 0.3934166, 0.245…
$ gdp_pc    <dbl> 37.913054, 9.610284, 12.176554, 9.746391, 4.410484, 21.134319
$ flfp      <dbl> 52.99082, 48.12645, 50.45894, 50.32171, 56.69530, 26.57872
$ women_rep <dbl> 28.12921, 21.32548, 17.99728, 14.45225, 17.44296, 10.21568

Or use View() or click on the name of the object in your Environment tab to see the data in a spreadsheet:

Try It Yourself!

  • Open the CSV file to see what it looks like
  • Then use this code to read it into R and view it
# load libraries
library(readr)
library(dplyr)

dem_summary <- read_csv("data/dem_summary.csv")

glimpse(dem_summary)
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Write a New CSV File


Now try writing the same data to a file with a different name

write_csv(dem_summary, "data/your_new_file_name.csv")
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Excel Files

Read in Excel File


library(readxl)

dem_summary <- read_excel("data/dem_summary.xlsx")

glimpse(dem_summary)

Try With Excel


  • Read in the Excel file
  • Follow same steps as with CSV file
    • use read_excel() to read in the data
    • install and experiment with writexl
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Google Sheets

Import Data from Google Sheets


  • Can use googlesheets4
  • Have a look at these Gapminder data
  • Use gs4_deauth() to authenticate
  • Then use read_sheet() to read in the data

Example Code


library(googlesheets4)

# Deauthorize to access public sheets without credentials
gs4_deauth()

# Read in the gapminder Africa data
gapminder_data <- read_sheet("1U6Cf_qEOhiR9AZqTqS3mbMF3zt2db48ZP5v3rkrAEJY")

Or…


library(googlesheets4)

# Deauthorize to access public sheets without credentials
gs4_deauth()

# Read in the gapminder Africa data
gapminder_data <- read_sheet("1U6Cf_qEOhiR9AZqTqS3mbMF3zt2db48ZP5v3rkrAEJY")

Or…


library(googlesheets4)

# Deauthorize to access public sheets without credentials
gs4_deauth()

# Read in the gapminder Africa data
gapminder_data <- googledrive::drive_get("gapminder") |>
  read_sheet()

Try It Yourself!


  • Use the code above to read in the data
  • Try reading in Gapminder data for a different country
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Find Your Own Data


  • Visit kaggle.com
  • Find a dataset you like
  • Download it as a CSV
  • Upload to your Posit Cloud project
  • Read it into R
  • Explore with glimpse() and View()
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