Methods 1, Week 1

PGUD 5160 DUE Methods 1

URBAN DATA ANALYSIS, MAPPING AND VISUALIZATION Section A | Fall 2022

Sara Hodges (she/her/hers) | hodgess@newschool.edu

Slack workspace: duemethods1fall2022.slack.com

Class: Mondays, 9am - 11:40am

Building: Parsons 2 W 13th St

Room: 1108

TODAY

• Introduction
• Class Description
  • Learning Goals
  • Assignments
  • Projects
• Introductions
• LAB
  • Install R and R Studio
  • Intro to R

Learning Goals

• Develop research methods to analyze the urban world that acknowledge and moderate bias
• Define thoughtful data-driven research projects
• Combine qualitative and quantitative sources and methods
• Create compelling and truthful visualizations to accompany your research

Technical Learning Goals

• Learn R for data analysis and visualization.
• Use it to enhance your research and advocacy.
  • Write analysis R scripts that are easy to understand
    • Clear analysis
    • Well-documented
    • Reproducible
• Produce simple, effective data visualizations that make your analysis easy to understand and use for advocacy

Class Structure

Lecture and/or Reading Discussion
Homework Assignments Questions
Lab

• Technical skill development
• Mostly R

Canvas and Slack

• Between class discussion/Share interesting stuff
• Homework help

Weekly Assignments

Each week:

• Readings
  • Theory
  • Technical instruction
• Technical
  • Practice skills learned in the lab
  • Data analysis to enrich current or previous readings

Ongoing:

• Research Journal

Final Project

Quantitative Research Project using R, on the topic of your choosing

• Research Journal
• Project Proposal
• Publish results in a presentation, book, or e-book made with Quarto
• Presentation on last day of class

Introductions - Sara

Introductions - Sara

Introductions - Sara

Introductions - Sara

My favorite visualization tools

Group Introductions

• 7 Questions
• We’ll create our first class dataset
• 3 - 6 volunteers to collect the data
• Link to the dataset

Group Introductions

• Your name and preferred pronouns
• The last state or country you lived in
• When you moved to New York City area
• Any particular research interests that you hope to explore in this class?
• Have you used R before?
• Have you used any other data analysis or visualization tool?
• Name one small thing that made you happy in the last 6 months

LET’S TAKE A BREAK!

Intro to R and R Studio

Terms/Definitions

R, R Studio, and CRAN

A few things

Installation

Install R

Install RStudio

Lecture Format: Quarto

I use Quarto to make slides and an e-book for this course.

• It contains text and executable R code together

• Text in a box is a code chunk - something you can execute in R

• The output from the code chunk displays below the box

# this is a gray box
print("this is the output from the code chunk")

[1] "this is the output from the code chunk"

• You can follow along with all of the lectures in R Studio on your computer.

R Studio Layout

The Console pane

The Console is where you can type code that executes immediately, and where you view the output.

Type into your console, and then press enter:

4 + 5

[1] 9

14 - 7

[1] 7

The Console Pane

The Console Pane

Create new objects (variables):

variable_name <- value of the variable

<- is the ‘assignment operator’, it is like an equal sign

# create a variable to hold the value 9
calc1 <- 4 + 5

# create a variable to hold the value 7
calc2 <- 14 - 7

# add the two variables together
total <- calc1 + calc2

total 

[1] 16

The Console Pane

The Console Pane

calc3 <- 4 + calc2
calc3

[1] 11

# try this one too
# calc4 <- 4 + seven

The Source pane

Use the Source Pane to write scripts to save your work.

Or to open and run existing scripts.

In-class exercise: Create your first script

Projects

Projects are a good way to keep track of all of the files for a specific task or project. We’ll create projects for each class in this course.

In-class exercise: Create a project

• Create a new project
• File New Project
• Existing Directory navigate to your class1 folder
• Click Create Project

The Files Pane

There are lots of useful tabs in this pane

The Files Pane

The Files window is like file explorer

The Plots window display charts and maps you create

The Files Pane

The Packages window lists the packages you have installed and provides a user interface to search for other packages and install them.

Packages are collections of functions and datasets developed by the R community to expand the things you can do in R.

• Some, like the tidyverse, have become the backbone of analysis in R.

In-class exercise: Install your first package

• Let’s install the tidyverse:
• You can either click Install in the Packages pane and use the user interface to install the package. OR type into the Console:
• install.packages('tidyverse')

The Files Pane

The Help window is where you learn about packages and functions.

Two ways to open documentation:

• Help tab
  • In the Help Search bar, type readr and press enter
• Console
  • type directly into the console:
  • ??readr
    • opens documentation about a function or package

The Environment pane

The Environment shows all of the objects that you have in your workspace

If you are following along, you should have at least 4 objects in your Environment.

In-class exercise: Import a CSV

• Download the 2019 education dataset from EdBuild,
• Move it to the data folder in your class1 folder
• Load the tidyverse package into your environment (add this at the top of your script):

## Load packages necessary for this script
library(tidyverse)

Now we’ll import our first dataset into R using the read_csv function:

## import the education dataset for 2018 using read_csv from the readr package of the tidyverse
ed18 <- read_csv("data/full_data_19_geo_exc.csv")

Data frames

Data tables are called dataframes in R.

• When you read a table into your R Environment, all of changes that you make to the dataframe are not saved to your computer until you save the dataframe to your computer (in R we say write it out).

Let’s explore our first data frame by typing some functions in our script

## list all of the column names in the console
names(ed18)

# print information about each column 
glimpse(ed18)

View the dataframe by clicking on it in the Environment pane or typing View(ed18) in the Console.

Viewing a dataframe

Basic data types in R

• Numeric
  • Integers (whole numbers)
  • Doubles (fractions)
• Character (string)
• Logical (boolean) - TRUE or FALSE

In-class exercise: Create two new columns in our dataframe:

• County Number = numeric version of the county number
• Percent of students with Limited English Proficiency (LEP) = Number of students with LEP / Number of enrolled students

### Sometimes you want an id to be numeric instead of string
# Create a new numeric column for county number
ed18$county_num <- as.numeric(ed18$CONUM)

# Create a new numeric column - Percent of LEP students 
ed18$percent_lep <- ed18$dLEP/ed18$dEnroll_district

View the dataframe

Notice

Create a new dataframe of New York school districts

### Create a new dataframe for your New York
newyork18 <- subset(ed18, State == "New York")

# Calculate how many districts are there in New York?
ny_districts <- nrow(newyork18)
ny_districts

[1] 682

Assignments

See assignments for week 1 in Canvas.

• Assignment 1a: Readings
• Assignment 1b: Idea from Research Journals
• Assignment 1c: Technical: Import and explore data in R Studio