R for Data Science

With Sports Applications

2023-09-20

Course Philosophy

  • Hands on

  • Strong Fundamentals: run before you fly

  • Relevance to your job

Data Scientists task

A wide image with a diagram of branching in git.

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.

Documents

Informe URSEC

Slide Decks

  • This one!

Blogs

  • http://sportstatisticsrsweet.rbind.io/

  • https://www.mitchhenderson.org/

Visualization (1)

From sportstatisticsrsweet.rbind.io

Visualization (2)

Mitch Henderson

Visualization (3)

Basketball Data Science: With Applications in R

Themes

  • [Use theming to adhere to styleguides] (https://bbc.github.io/rcookbook/)

Interactive applications

https://shiny-eio.upc.edu/pubs/sport-R-packages/

Open Source, reproducibility, et all

  • R’s strength is its community

  • Workshops, blogs, Journals, etc.

Getting started

R & RStudio

R & RStudio

  • RStudio is a computing environment

  • R is a programming language

RStudio

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 .R

  • Opening 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    2

???

Ask 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

What the script does

  • Opens two rds files with data from NBA teams.

  • Merges on the Team column.

  • Calculates the mean assits, blocks, steals for the teams that went to the playoffs and the ones that did not.

  • Create an html table 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 df data frame have?

  • Which are the columns?