R for Data Science
With Sports Applications
Rafael La Buonora
2023-09-20
Course Philosophy
Hands on
Strong Fundamentals: run before you fly
Relevance to your job
Leading introductory textbook by Hadley Wickham and coauthors.
Examples from books and blogs.
Data Scientists task
Data Import
Excel, text files.
Databases
Internet: APIS, data scraping
Devices (GPS, Imaging, etc.)
Data Wrangling
80% of data analysis is spent on the process of cleaning and preparing the data (Dasu and Johnson 2003)
Modeling
- Stats stuff
Visualize
- Exploratory, interactive visualization.
Communicate
Communicating your findings effectively to stakeholders is crucial for success of data science.
R supports multiple ways of presenting your results: blogs, written documents, data visualization and interactive applications.
Slide Decks
- This one!
Visualization (3)
Basketball Data Science: With Applications in R
Open Source, reproducibility, et all
R's strength is its community
Workshops, blogs, Journals, etc.
Getting started
R & RStudio
RStudio is a computing environment
R is a programming language
RStudio
You do not use a lot of the windows.
You need to use well the ones you use.
RStudio (2)
Working Directory
getwd(),setwd(),list.files().install.packages()Sourcing scripts
.ROpening data files
(.rds)
R
Functions and objects
Variables and assignment
Side effects
Objects
x <- c(1, 2, 3)Objects (2) The Data Frame
Similar to an Excel spreadsheet (?)
It has rows and columns
Each row is an observation
Each column is a feature of the data
mtcars## mpg cyl disp hp drat wt qsec vs am gear carb## Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4## Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4## Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1## Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1## Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2## Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1## Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4## Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2## Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2## Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4## Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4## Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3## Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3## Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3## Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4## Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4## Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4## Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1## Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2## Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1## Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1## Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2## AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2## Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4## Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2## Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1## Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2## Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2## Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4## Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6## Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8## Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2Ask for examples of data sets.
Functions
The take arguments and return objects and/or produce side effects.
View(mtcatrs)str(mtcars)## 'data.frame': 32 obs. of 11 variables:## $ mpg : num 21 21 22.8 21.4 18.7 18.1 14.3 24.4 22.8 19.2 ...## $ cyl : num 6 6 4 6 8 6 8 4 4 6 ...## $ disp: num 160 160 108 258 360 ...## $ hp : num 110 110 93 110 175 105 245 62 95 123 ...## $ drat: num 3.9 3.9 3.85 3.08 3.15 2.76 3.21 3.69 3.92 3.92 ...## $ wt : num 2.62 2.88 2.32 3.21 3.44 ...## $ qsec: num 16.5 17 18.6 19.4 17 ...## $ vs : num 0 0 1 1 0 1 0 1 1 1 ...## $ am : num 1 1 1 0 0 0 0 0 0 0 ...## $ gear: num 4 4 4 3 3 3 3 4 4 4 ...## $ carb: num 4 4 1 1 2 1 4 2 2 4 ...Your turn
Lab: a complete R script
- Available on the course website
- Download the script (
.R) and datasets (.rds).
What the script does
Opens two rds files with data from NBA teams.
Merges on the
Teamcolumn.Calculates the mean assits, blocks, steals for the teams that went to the playoffs and the ones that did not.
Create an
htmltable with the results.
TODO
Open the html file. Do Playoff teams have more points on average?
Open the script file in RStudio and run it. Make sure that it works correctly.
What functions does it use?
Can you identify which tasks each function supports (import, wrangling, model, communication)?
What objects does it create?
How many rows does the
dfdata frame have?Which are the columns?