-
Preliminaries & Appetizers Getting started ggplot2 &
aggregation Exercises @ggplot2 ggplotly
R: Basics, Data Management & Manipulation, Graphics
Univ.-Prof. Dr. Wolfgang Trutschnig
Research Group Statistics/StochasticsFB Mathematik
Universität [email protected]
www.trutschnig.net
Nawi, 2021-01
Wolfgang Trutschnig
Introduction to R (Day 1)
http://www.trutschnig.net
-
Preliminaries & Appetizers Getting started ggplot2 &
aggregation Exercises @ggplot2 ggplotly
Preliminaries
Plan for today: Learn R hands on
I R-appetizers/extensions: knitR & shiny.
I First steps in R and R-Studio: data types & objects, data
import/export, datamanipulation, loops, aggregation (and writing
functions1).
I ggplot2: powerful R-package based on grammar of graphics.
I Refresher basic tools descriptive statistics.
I ggplotly: Interactive version of ggplot2
I Exercises.
1If we have timeWolfgang Trutschnig
Introduction to R (Day 1)
http://ggplot2.tidyverse.org/reference/
-
Preliminaries & Appetizers Getting started ggplot2 &
aggregation Exercises @ggplot2 ggplotly
Preliminaries
Mini-CV:
I MSc & PhD in Mathematics at TU Wien.
I 2008-2013: Postdoc/Associate Researcher at the European Center
for SoftComputing in northern Spain (Asturias).
I Currently: Professor for Stochastics/Statistics in
Salzburg
I 2006-2013: Statistics-Consultant for various companies
(Allianz, Telekom, etc.).
I Co-founder of correlate OG (Statistical Consulting and
more).
I Project experience with: A1, Alimerka (Spain), Allianz
Versicherung, Cajastur(Spanien), Czech Telekom, Land Salzburg,
ÖBB, Porsche Informatik, SALK,
Servus-TV, Siemens Austria, T-Mobile and many more.
I In all projects R was used.
Wolfgang Trutschnig
Introduction to R (Day 1)
https://correlate.at
-
Preliminaries & Appetizers Getting started ggplot2 &
aggregation Exercises @ggplot2 ggplotly
Preliminaries
General idea of the course
I Learn R hands-on and refresh basic statistics knowledge.
I The course should be interactive - ask questions whenever
necessary.
I Most questions are relevant for all participants.
@Datasets for today: Mainly work with two datasets (in R):
1. ATM.txt
I The data set contains a 3-years time series of cash
withdrawals at an ATMin northern Spain.
I Allows for easy illustrations of standard descriptive
tools.
I
ymd weekday nr weekday sum out holiday2007-01-01 Mon 1 4040
1.002007-01-02 Tue 2 22760 1.502007-01-03 Wed 3 18810
0.002007-01-04 Thu 4 24910 0.00
Figure: First 4 rows of the ATM dataset
Wolfgang Trutschnig
Introduction to R (Day 1)
-
Preliminaries & Appetizers Getting started ggplot2 &
aggregation Exercises @ggplot2 ggplotly
Preliminaries
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2009
2008
2007
2009−01−01 2009−02−01 2009−03−01 2009−04−01 2009−05−01
2009−06−01 2009−07−01 2009−08−01 2009−09−01 2009−10−01 2009−11−01
2009−12−01
2008−01−01 2008−02−01 2008−03−01 2008−04−01 2008−05−01
2008−06−01 2008−07−01 2008−08−01 2008−09−01 2008−10−01 2008−11−01
2008−12−01 2009−01−01
2007−01−01 2007−02−01 2007−03−01 2007−04−01 2007−05−01
2007−06−01 2007−07−01 2007−08−01 2007−09−01 2007−10−01 2007−11−01
2007−12−01 2008−01−01
0
10000
20000
30000
0
10000
20000
30000
0
10000
20000
30000
ymd
with
draw
n
Figure: Full three years of the ATM dataset - what can be
seen?
Wolfgang Trutschnig
Introduction to R (Day 1)
-
Preliminaries & Appetizers Getting started ggplot2 &
aggregation Exercises @ggplot2 ggplotly
Preliminaries
2 RTR2015.RData
I 239.060 speed measurements of mobile devices in Austria.
I Allows for easy illustrations of standard descriptive tools
and practiceprogramming skills in R.
id mtime mymd mym op name nw cat device
Oadff95f3-880a-4cb3-9534-b85e932fb137 2014-01-01 00:10:00
2014-01-01 2014-01 Pear 3G Galaxy Note 2
Oc2852599-37bd-4c3c-8fe1-15c4884f5926 2014-01-01 00:16:00
2014-01-01 2014-01 Pear 3G Galaxy Note 2
O48a47c74-c7a6-4ac6-9c24-fc442d0674dd 2014-01-01 00:21:00
2014-01-01 2014-01 Kiwi 3G Galaxy Note 10.1 LTE
O69f25c23-5089-45d9-8407-3159f35f4a7e 2014-01-01 00:21:00
2014-01-01 2014-01 Kiwi 3G Galaxy S3
Oede0db57-1d56-432c-bf1d-73327add2591 2014-01-01 00:27:00
2014-01-01 2014-01 Kiwi 3G Galaxy Note 10.1 LTE
O383e9306-9612-4c39-af8f-1b018f6db871 2014-01-01 00:41:00
2014-01-01 2014-01 Apple 3G iPhone 5s
device platform device has lte longitude latitude iso adm2 rtr
speed dl rtr speed ul rtr ping
Android FALSE 13.20764 47.34299 at0504 3226 419 56.6295
Android FALSE 13.22863 47.34588 at0504 2068 572 41.7948
Android TRUE 15.75156 48.20457 at0319 11604 2881 63.5496
Android FALSE 16.39714 48.23074 at0900 1948 1039 45.0728
Android TRUE 15.63205 48.20800 at0302 6277 2484 59.1642
iOS TRUE 15.51962 47.12490 at0606 3577 2433 47.9287
Wolfgang Trutschnig
Introduction to R (Day 1)
-
Preliminaries & Appetizers Getting started ggplot2 &
aggregation Exercises @ggplot2 ggplotly
Preliminaries
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