Math 335
Syllabus
Tools
R/R-Studio Details
Slack & Stackoverflow Details
R-Lab
RStudio Connect
Python Details
Tasks
Hathaway
Assignment Schedule
Weekly Reading List
Palmer
Assignment Schedule
Weekly Reading List
General
Semester Task List
Team Lead Guidance
Task Backgrounds
Project
R-Lab
Git/GitHub
Start Here
One Image
Additional Links
Book
Course Textbook
R for Data Science
Git/GitHub and R
R Markdown: The Definitive Guide
Geocomputation with R
Supplemental Material
Happy Git and Github for the useR
plotly for R
Computing in R for Social Sciences
Statistical Concepts in Presenting Data:
Advanced R
R Packages
Tidy evaluation
Fundamentals of Data Visualization
Geocomputation with R
Crosstalk: htmlwidgets add-on
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Weekly Readings Palmer
Case Study 1
Being Readings
o
John Rauser Talk
o
Being a better online reader
Doing Readings
o
Making your first R Markdown File
o
Chapter 27: R for Data Science - R Markdown
o
Chapter 30: R for Data Science - R Markdown workflow
o
Chapter 1: R for Data Scientists
o
Getting used to R, Rstudio, and R Markdown (R basics)
o
R and R-Studio Practice
o
Chapter 4: R for Data Scientists - Workflow Basics
Case Study 2
Being Readings
o
Computational Thinking
o
Optional Reading for new programmers
o
Effectively Communicating Numbers (pg 1-13)
o
Hans Rosling: The best stats you’ve ever seen
o
Questions and data science
Doing Readings
o
Modern Drive: Chapter 1 Getting Started with Data in R
o
Using the geom_col function
o
Using the geom_line function
o
Chapter 3: R for Data Science - Data visualization
o
Creating Questions for your project (watch the videos that are free)
o
Chapter 5: R for Data Science - Data transformation
o
MCS 335, Git, and Github use
o
Why Git, Git, and GitHub setup (optional)
o
Getting Github and Git connected to R-Studio (optional)
o
Chapter 8: R for Data Science - Projects (optional)
o
R-Studio and the Git GUI (optional)
Case Study 3
Being Readings
o
Hans Rosling: The River of Myths
o
Data Scientist Florence Nightengale
o
How to Become a Data Scientist, The Self-Starter Way
o
What’s The Best Path To Becoming A Data Scientist?
Doing Readings
o
Example of GitHub Issue conversation
o
Posting Issues on GitHub
o
reprex R Package
o
Chapter 28: R for Data Science - Graphics for communication
o
Chapter 6: R for Data Science - Scripts
o
Chapter 11: R for Data Science - Data Import
o
Chapter 28: R for Data Science - Graphics for communication
Case Study 4
Being Readings
o
What do people do with new data
o
Data Visualization (Chapter 1 - Look at data)
o
Being a good critiquer
Doing Readings
o
Finding data to answer your question
o
Find a post from the functional art
o
Chapter 20: R for Data Science - Vectors
o
Chapter 18: R for Data Science - Pipes
o
Chapter 7: R for Data Science - Exploratory Data Analysis
o
devtoools R Package
Case Study 5
Being Readings
o
Hadley on Tidy Data (skim read)
o
The art of structured thinking and analyzing
o
Tools for improving structured thinking (for analysts)
o
Quartz Reference for How to deal with data issues (optional)
Doing Readings
o
foreign R Package and
read.dbf()
o
Chapter 11: R for Data Science - Data Import
o
haven R Package
o
readxl R Package
o
downloader R Package
o
Chapter 12: R for Data Science - Tidy Data
o
tidy R Package functions
o
openxlsx R package
Case Study 6
Being Readings
o
Effectively Communicating Numbers (pg 13-20)
Doing Readings
o
Regular Expressions in R
o
Chapter 14: R for Data Science - Strings
o
RVerbalExpressions package
o
regexr.com (optional)
o
Regular Expression examples (optional)
o
Regular Expression support applet (optional)
o
Regular Expression for R (optional)
o
Chapter 13: R for Data Science - Relational Data
Case Study 7
Being Readings
o
Statistical Concepts in Presenting Data (pgs 72 - 85)
Doing Readings
o
The Book of Mormon
o
Populating missing values
o
Chapter 15: R for Data Science - Factors
o
forcats R package
o
Chapter 21: Iteration (21.1-21.5 is all you will need)
o
Hadley Wickham - Cupcakes to teach for loops
o
stringi R package and the
stri_stats_latex()
function
Case Study 8
Being Readings
o
What charts say
o
Chapter 4: The Truthful Art: Data, Charts, and Maps for Communication
o
Plotly: Time Series Blog Post
Doing Readings
o
lubridate R package
o
Chapter 16: R for Data Science - Dates and Times
o
Time Series Visualization Gallery
o
lubridate Vignette
o
Tips for timed tests
o
Completing timed work
Case Study 9
Being Readings
o
Five principles of effective data visualizations
o
What charts do
o
10 Myths about Data Science
Doing Readings
o
See task 16
o
tidyquants R package
o
dygraphs for R
o
DT: An R interface to the Data Tables library
o
timetk R package
Case Study 10
Being Readings
o
Josh Wills on big data (tech data scientist)
o
Issues with Spatial Aggregation
Doing Readings
o
Tidy Spatial Data (Blog Post)
o
rnaturalearth R Package
o
geofacet for ggplot2 in R
o
Using SF package with tidyverse
o
SF R package
o
USAboundaries R Package
o
Video on spatial datums
o
Video 2 on spatial datums
o
Using
library(sf)
to read in spatial data
Case Study 11
Being Readings
o
Divide and Recombine (plus a history of data science)
o
Background reading for D&R article (optional)
Doing Readings
o
Leaflet for R: Introduction
o
Leaflet for R: Introduction
o
Leaflet for R: The Map Widget
o
Leaflet for R: Basemaps
o
Leaflet for R: Markers
o
Leaflet for R: Popups and Labels
o
Leaflet for R: Lines and Shapes
o
Leaflet for R: Colors
o
What is Spark?
o
What is Hadoop?
Case Study 12
Being Readings
o
Tips for timed tests
o
Completing timed work
o
Ethics of a Data Scientist
Doing Readings
o
Semester Project
o
Chapter 29: R for Data Science - R Markdown format
o
Chapter 19: R for Data Science - Functions
