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<!DOCTYPE html>
<html>
<head>
<title>Data Analysis with R | Datapolitan Training</title>
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<p style="display:block;text-align:left;color:gray;font-size:1em; position: fixed; top: 0px; left: 155px; height: 30px;vertical-align:middle;margin:7px 0 0 0; ">Data Analysis with R</p>
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<span xmlns:dct="http://purl.org/dc/terms/" property="dct:title">Data Analysis with R</span> by <a xmlns:cc="http://creativecommons.org/ns#" href="http://www.tinypanther.com" property="cc:attributionName" rel="cc:attributionURL">Julia Marden</a> and <a xmlns:cc="http://creativecommons.org/ns#" href="http://www.datapolitan.com" property="cc:attributionName" rel="cc:attributionURL">Richard Dunks</a> is licensed under a <a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/">Creative Commons Attribution-ShareAlike 4.0 International License</a>.<a rel="license" href="http://creativecommons.org/licenses/by-sa/4.0/"><img alt="Creative-Commons-License" style="border-width:0" src="https://i.creativecommons.org/l/by-sa/4.0/80x15.png" /></a>
</p>
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--
class:center, middle
# Data Analysis With R
- - -
## Instructors: Elizabeth DiLuzio and Mark Yarish
### Follow along at: http://bit.ly/data-analysis-r<br>See the code at: http://bit.ly/data-analysis-r-code
---
# A Few Ground Rules
???
+ Facilitators establish the intention we have for the culture of the classroom
--
+ Step up, step back
--
+ One mic
--
+ Be curious and ask questions
--
+ Assume noble regard and positive intent
--
+ Respect multiple perspectives
--
+ Listen to comprehend, not to respond
--
+ Be present (phone, email, social media, etc.)
---
# Introduce Yourself to Your Neighbor
+ Who you are
+ Where you work
+ What are you hoping to learn today?
+ What you've done with code (any code)
---
# What to Expect Today
--
+ Introduction to R
--
+ Using R in Data Analysis
--
+ Getting Familiar: R Syntax + R Studio
--
+ 311 Data Analysis
--
+ Presentations!
---
# Key Skills
--
+ R syntax and commands
--
+ [RStudio](https://www.rstudio.com/)
--
+ Load data
--
+ Explore data
--
+ Wrangle data
--
+ Visualize data
???
+ Students will review progress and give feedback on key takeaways
---
name:housekeeping
# Housekeeping
--
+ We’ll have one 15 minute break in the morning
--
+ We’ll have an hour for lunch
--
+ We’ll have a 15 minute break in the afternoon
--
+ Class will start promptly after breaks
--
+ Feel free to use the bathroom if you need during class
--
+ Please take any phone conversations into the hall to not disrupt the class
---
# What is Analysis?
--
>“Analysis is simply the pursuit of understanding, usually through detailed inspection or comparison”
## - [Carter Hewgley](https://www.linkedin.com/in/carterhewgley), Senior Advisor for Family & Homeless Services, Department of Human Services, District of Columbia
???
+ Orient students to key concept in analysis
+ Use R to uncover meaning in data
---
# The Analytical Process
???
+ Establish frame for the analytics process to be followed in class
+ Familiarize students with terminology (esp "data wrangling/data cleaning")
+ Demystify the process
+ Empower students to do analysis
---
# Exercise: Old Faithful
.caption[Image Credit: Astroval1, [CC BY-SA 4.0](http://creativecommons.org/licenses/by-sa/4.0) via [Wikimedia Commmons](https://commons.wikimedia.org/wiki/File%3ABig_Dipper_Ursa_Major_over_Old_Faithful_geyser_Yellowstone_National_Park_Wyoming_Astrophotography.jpg)]
???
+ Facilitator provides context for the exercise by describing Old Faithful
+ Students will download script with prepared code snippets to run
+ Students will learn the steps of running summary statistics in R
---
# Identify the Question
--
+ What's the minimum amount of time I should plan to spend at Old Faithful?
--
+ Is there a relationship between the amount of time I wait and the length of time it erupts?
--
???
+ Students will understand the problem we're seeking to solve in class
+ Students will learn by example the value of problem setting.
+ This will be done by writing out explicit problem statement for 311 Noise, possibly vision 0 db after we have exercise.
---
# Exercise: Old Faithful
#### [Click to download the `faithful_a.R` code for this exercise (already loaded in RStudio)](code/faithful_a.R)
???
+ Students will open and load a simple dataset.
+ They will inspect the data in the viewer and confirm it loaded properly.
+ This will be done by live demo of code
+ Students will be writing code themselves
+ Introduce basic commands and tab completion
+ Describe comments and their purpose
+ Emphasize cooperation between participants
---
# [RStudio Introduction](https://www.rstudio.com/)
### Find your student number and link in your workbook<br>.red[Username and Password: `rstudio`]
???
+ Introduce students to Console, Environment, and Help
+ Students will be familiar with the key features of the console for the exercises to come
+ This will be done by live demo and verbal discussion
+ Ctrl+L clear console
---
# What is Syntax?
--
.caption[Image Credit: AnonMoos, Public Domain via [Wikipedia](https://commons.wikimedia.org/wiki/File%3ABasic_constituent_structure_analysis_English_sentence.svg)]
???
+ Students will get vocabulary for accomplishing tasks in code
+ This will be done with an overview discussion
---
# R Syntax
```R
# basic command
command(dataset)
View(faithful)
```
???
+ Facilitator guides students through basic syntax in R for simple tasks
+ Instructor reinforces syntax idea and relation to regular sentence structure to convey meaning where appropriate
--
```R
# select a column
command(dataset$column)
mean(faithful$waiting)
```
--
```R
# get help
?help
?faithful
```
---
# Your Turn 1
--
+ Look through the code we just wrote
--
+ Make a change to one thing on the chart
--
+ If necessary, check out the help documentation
--
+ Be ready to describe what you did
---
# What is R?
--
+ Statistical programming language
--
+ [Open-source](https://opensource.com/resources/what-open-source)
--
+ Made for and by people who work with data
--
+ Used for data analysis
--
+ For the history of R, [see this video](https://www.coursera.org/lecture/r-programming/overview-and-history-of-r-pAbaE)
???
+ Familiarize students with basics of R and set context
+ "Created for and by the people" - Julia Marden
---
# R vs. Excel
???
+ Facilitator compares R directly to Excel for context (assuming most participants are well-acquainted with Excel)
--
+ R is a _programming language_ while Excel is an _application_
--
+ R can work with much larger datasets than Excel
--
+ R can perform more complex operations than Excel
--
+ R commands can be easily saved, re-run, and automated
--
+ R doesn't have the icons, animations, and wizards of Excel
---
name:nola
# New Orleans Distributes Smoke Alarms
.caption[Image Credit: Michael Barnett [CC BY-SA 2.5](http://creativecommons.org/licenses/by-sa/2.5), via Wikimedia Commons]
???
+ Students will be inspired to use their knowledge in practical applications
---
# Targeted Outreach Saves Lives
.caption[Image Credit: City of New Orleans, via [nola.gov](http://nola.gov/performance-and-accountability/nolalytics/files/full-report-on-analytics-informed-smoke-alarm-outr/)]
???
+ Students will be inspired to use their knowledge in practical applications
---
# Targeted Outreach Saves Lives
.caption[Image Credit: City of New Orleans, via [nola.gov](http://nola.gov/performance-and-accountability/nolalytics/files/full-report-on-analytics-informed-smoke-alarm-outr/)]
???
+ Students will be inspired to use their knowledge in practical applications
---
# And Here's the R Code for It
[](https://github.com/enigma-io/smoke-signals-model)
[Click here for the code](https://github.com/enigma-io/smoke-signals-model)
---
class:center,middle
# Wrap-Up
---
class:center, middle
# 15 Min Break
Source: https://xkcd.com/378/
---
# 5 Data Analytics Tasks
--
1. Sorting
--
2. Filtering
--
3. Aggregating (PivotTable)
--
4. Transforming
--
5. Visualizing
---
# 1. Sorting
--
+ Reorganize rows in a dataset based on the values in a column
--
+ Can sort on multiple columns
---
# Sorting in R
--
+ Use [`order()`](https://www.r-bloggers.com/r-sorting-a-data-frame-by-the-contents-of-a-column/)
--
+ Specify the column you want to sort by<br>(in our case `eruptions` or `waiting`)
--
```r
df[order(df$column_to_sort_by),]
```
--
## Your Turn 2
+ Sort the Old Faithful data to find the shortest waiting time
+ Sort the Old Faithful data to find the longest waiting time
???
+ Why the comma?
+ The syntax is `df[row specifier, column specifier]`.
+ If a specifier is absent, R returns all.
---
# 2. Filtering
--
+ Only show rows that contain some value
--
+ Can filter by multiple values
--
+ Can filter by values in multiple columns
---
# Filtering in R
--
+ Provide some logical test (`<`, `>`, `==`, etc.)
--
+ The format is
--
```r
df[df$column_to_filter_by <logical test>,]
```
--
## Your Turn 3
+ Filter the Old Faithful data for all eruptions longer than 4 minutes
---
# 3. Aggregating Data
--
+ Trends only become clear in aggregate
--
+ Often where you discover the "so what"
--
+ Aggregating data meaningfully can be tricky
--
+ We'll be showing how to do this with R later
---
# 4. Transforming Data
--
+ Sometimes available categories don't make sense
--
+ Values may not be in the format you need (or have mistakes)
--
+ You always want to have a clean copy of the data to go back to
--
+ Best to keep track of what you've done
--
+ We'll be showing how to do this with R later
---
# 5. Visualizing Data
--
+ Quickly communicate information
--
+ Tell a clearer story
--
+ A picture is worth a thousands words
--
+ We've already seen this with the Old Faithful data
```r
hist(faithful$waiting)
hist(faithful$eruptions)
plot(faithful, main="Eruptions of Old Faithful", xlab="Eruption Time in Minutes", ylab="Waiting Time to Next Eruption in Min")
abline(lm(faithful$waiting~faithful$eruptions), col="red")
```
---
# 5 Data Analytics Tasks
1. Sorting
2. Filtering
3. Aggregating (PivotTable)
4. Transforming
5. Visualizing
---
# Derelict Vehicles
.center[Derelict Vehicles Across NYC]
---
# The Analytical Process
---
# Identify the Question
--
+ How many people complain about derelict vehicles?
--
+ Do people complain more at a particular time of day?
--
+ Do people complain more in a particular neighborhood or borough?
--
???
+ Students will understand the problem we're seeking to solve in class
+ Students will learn by example the value of problem setting.
+ This will be done by writing out explicit problem statement for 311 Noise, possibly vision 0 db after we have exercise.
---
# Exercise: 311 Service Requests
--
+ Open the `311_a.R` script (already loaded in RStudio)
--
+ Follow along the code as we load the dataset
--
+ You can [download the code here](code/311_a.R)
--
+ The [data dictionary](https://data.cityofnewyork.us/api/views/erm2-nwe9/files/68b25fbb-9d30-486a-a571-7115f54911cd?download=true&filename=311_SR_Data_Dictionary_2018.xlsx)
explains each column
???
+ Students will conduct the same commands from Faithful with 311 exercise
+ Students will hit the roadblocks
+ Can't run summary statistics
+ Exercise will be run through script showing comments (not on slide)
+ Script will mirror the Faithful with intention of not working
---
# R Data Types
+ [Numeric](http://www.r-tutor.com/r-introduction/basic-data-types/numeric) vs [Factor](https://www.stat.berkeley.edu/~s133/factors.html)
--
???
+ Students will understand a few of the different data types in R
+ They will use the `str` and `summary` command
+ This will be done with a live demo of code
---
# R Data Structures
--
+ [Vectors](http://www.r-tutor.com/r-introduction/vector) and [matrices](http://www.r-tutor.com/r-introduction/matrix) (single data types)
--
+ [Lists](http://www.r-tutor.com/r-introduction/list) and [data frames](http://www.r-tutor.com/r-introduction/data-frame) (mixed data types)
--
--
+ You often need to restructure your data to make it usable
???
+ Students will review work done in simple data load
+ They will learn key elements of data structures based on Faithful data
+ This will be done with live demo and discussion
+ They will use the `str` and `summary` command
---
class:center, middle
# Wrap-Up
???
+ Facilitator reviews the learning in the morning with participants
+ Facilitator answers any questions
+ If there is time, facilitator has participants switch and review someone else's code, then has them reflect on what they learned looking at someone else's code
---
class:center, middle
# Lunch
Source: https://xkcd.com/1319/
---
class:center, middle
# Welcome Back!
---
# Data Wrangling (i.e. Cleaning)
--
+ Get data into right type or structure
--
+ Create subsets
--
+ Add packages to work with the data we have
???
+ start of section discussing manipulating data
+ picking up pieces from exercise where script failed
+ start of exercise 3
---
# Packages
--
+ Add-ons: extra functions, data viz, special features
--
+ Can help you load data, work with timestamps, create charts
--
+ If you need to do something, there's probably a package for it
--
+ To use: `install.packages()`
???
+ Students will understand the purpose and value of packages
+ This will be done with a discussion
---
# Exercise: 311 Service Requests
#### [Click to download the `311_b.R` code for this exercise (already loaded in RStudio)](code/311_b.R)
???
+ An example question of the 311 dataset
+ students will be walked through the exercise with a script
+ Prompts in the script with a more specific question
+ incidents per borough -> distribution of complaints
---
# Your Turn 4
--
+ Switch out derelict vehicles for another complaint type
--
+ Look at a different borough, ZIP, or community board
--
+ Look at day of the week instead of hour
--
+ Challenge yourself
--
+ We'll be around to help
---
class:center,middle
# It All Begins With a Question
???
+ Students will understand better the purpose of using code for analysis
+ Remind them we all have hypothesis -> need to be acknowledged
---
# Questions
--
+ How many?
--
+ Where?
--
+ When?
--
## What are some of your questions of this data?
???
+ Prompts for starting your investigation of the data
+ Students will have a way to start exploring data
+ Discussion leading into guided exercise
---
# Your Turn 5
--
+ Working in pairs or alone, start working on a question that interests you
--
+ Start with a new script and give it a name
--
+ Use the skills we've covered
--
+ Challenge yourself to do something new
--
+ Don't be afraid of not knowing
--
+ Use the documentation
--
+ Help each other out
--
+ We'll be around to help
---
class:center, middle
# 15 Min Break
Source: https://xkcd.com/1831/
---
# Debugging
--
+ Everyone gets errors all the time
--
+ It's just a matter of how complex they are<br>
--
_And fixing them_
--
+ **Syntax errors** -> using the wrong instructions
--
+ **Semantic errors** -> doing the wrong things
--
+ When in doubt, take a breath, try breaking things apart into smaller pieces, review the documentation, and search for help
???
+ Students will be introduced to key concepts in identifying and resolving errors
+ This will be done with a lecture/discussion leading into an exercise
+ Class exercise finding errors in code -> slide with code snippets in Markdown with errors
+ deal with issue of correctness
---
# Exercise
+ Debug your neighbor's R Script and verify results
???
+ Students will examine another student's code, run the code, and fix any errors
+ Students will have a better understanding of how to think in code
+ Goal is to get students talking to each other about their code
+ have documentation at end of slides
---
class:center,middle
# <a href="https://script.google.com/a/macros/datapolitan.com/s/AKfycbwMcE5pcJwZHdbSCN_2epwaXnRLSRPaLbHCAhxaZJ79UXaRpQ_l/exec" target="_blank">Click to submit your work</a>
---
class:middle,center
# Code Review
???
+ Students will review select code examples
+ Goal is to model a collaborative process for data analysis
+ Time buffer for end of class
---
class:center,middle
# Wrap Up
---
# Key Skills
--
+ R syntax and commands
--
+ [RStudio](https://www.rstudio.com/)
--
+ Load data
--
+ Explore data
--
+ Wrangle data
--
+ Visualize data
--
+ Anything else?
???
+ Students will review progress and give feedback on key takeaways
---
# Taking This Out of the Classroom
???
+ Facilitators reinforce key learning points with participants for integrating into their workflow
--
+ R is a powerful tool for cleaning, analyzing, and visualizing data
--
+ Integrating it into your workflow takes practice and a commitment to not giving up (Google is your friend)
--
+ [RStudio](https://www.rstudio.com/) makes it easy to get started
--
+ You should be able to [download R and RStudio on your work computer](https://ayeimanol-r.net/2014/01/16/getting-started-installation-of-rstudio-and-some-packages-using-ggplot-to-make-a-simple-plot/) (Use the zip/tarball option)
---
name:resources
# Key Links
--
+ [Download R](https://cloud.r-project.org/)
--
+ [Download RStudio](https://www.rstudio.com/products/rstudio/download/)
--
+ [Download exercise files from this class](http://bit.ly/data-analysis-r-code)
---
# Learning and Practicing More with R
--
+ [Hands-On Programming with R](https://rstudio-education.github.io/hopr/) - Free online book with code examples meant for non-programmers
--
+ [R for Data Science](https://r4ds.had.co.nz/)- Free online book covering basic topics in data science with R
--
+ [R Cookbook](http://www.cookbook-r.com) - Free online walkthrough of the basics
--
+ [R Programming Coursera Course](https://www.coursera.org/learn/r-programming) - Free course in R that runs regularly
--
+ [Swirl](http://swirlstats.com) - Interactive learning inside of R `install.packages(“swirl”)`
---
# Other Useful Resources
--
+ [NYC Open Statistical Programming Meetup](https://www.meetup.com/nyhackr) - Monthly talks about R and sponsor of the [NYC R Conference](http://www.rstats.nyc/)
--
+ [Tidyverse](https://www.tidyverse.org) - R packages for Data Science
--
+ [Stat Methods](http://statmethods.net) - Great documentation for doing data analysis in R
--
+ [UCLA Stats](https://stats.idre.ucla.edu/other/dae/) - Many examples of statistical analysis with comparisons between R, Stata, SPSS, etc.
--
+ [Stack Overflow](https://stackoverflow.com/questions) - One of the best Q&A sites for technology
--
+ [Class handout](workbook.pdf)
--
+ [Datapolitan training classes](https://www.datapolitan.com/) - The online home of our training materials
???
+ Students will have key resources for moving forward in their learning
---
## .center[Contact Information]
## Julia Marden
+ Email: julia[at]tinypanther[dot]com
+ Website: http://tinypanther.com
+ Twitter: [@juliaem](https://twitter.com/juliaem)
-->
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