This document discusses Martin Schneider's experiences teaching R. It notes that students come from diverse backgrounds in business, academics, statistics, and goals. Recurring problems include steep learning curves, technical hurdles, and different ways of solving problems in R. The author advocates for a course structure with exercises of appropriate difficulty, example datasets, and workshop elements to break training into smaller chunks.

1. © 2010 – 2016 eoda GmbHMartin Schneider
I have to teach R
Experiences in the teaching of R
eRum 2016
Martin Schneider
Data Scientist

2. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
About eoda
Interdisciplinary Team
Statisticians | Engineers | Economists | Sociologist | …
Based in Kassel - Germany
Data Science Consulting, Training, Support,
Software and Analytic Services with a focus on R

3. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
Teaching R since
2013
Motivation and Qualification
Number of courses
0025
Course Participants
0125I

4. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
Motivation and Qualification
Teaching R since
2010
Number of courses
0150
Course Participants
1000WE

5. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
For who?
BusinessAcademics
Wide differing Background

6. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
For who?
Business
Wide differing Priorities
We want to do everything perfect, every
prerequisite for analysis has to be met.
Academics

7. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
For who?
BusinessAcademics
Wide differing Priorities
How much money can we save?
We want to see results

8. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
For who?
? Excel
Wide differing preknowledge in Analytic Tools

9. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
For who?
Wide differing Statistical Background
NONECOOL
NONE

10. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
For who?
Wide differing Statistical Background
A LOT
COOL
A LOT

11. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
For who?
Wide differing Statistical Background
SOME
Dangerous
half knowledge
SOME

12. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
For who?
Wide differing Personal Goals
I want to start with
statistical analysis
I‘m already using SPSS
but I heard a lot of R so I
wanted to take a look

13. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
For who?
BusinessAcademics
Wide differing Personal Goals?
I want to start with
statistical analysis
I‘m already using SPSS
but I heard a lot of R so I
wanted to take a look
I‘m already using Excel and
everything is working fine
but my boss said…

14. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
Package updates
Recurring Problems

15. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
Recurring Problems
diverse classes

16. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
Recurring Problems
In R there are usually lots of different ways
to solve a problem
We did this already this way, why
are we doing it another way?

17. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
Recurring Problems
Steep learning curve
What does this Error Message
mean? I‘m having problems
solving this exercise.
This is in SPSS much easier
I will never
under-
stand R

18. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
Recurring Problems
Technical hurdles
I´m missing the permission
to install packages
I have to use external
software

19. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
Writing code
in front
Excersises
Usual Course
Small Part of Presentation
Presentation

20. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
Usual Course
Writing code in front
cars <- read.csv(file = "Data/carsMO.csv", stringsAsFactors = FALSE)
select(cars, Manufacturer, Modell, contains("sales", ignore.case = TRUE))
select(cars, Manufacturer, Modell, Sales:Sales_grouped)
mutate(cars, weight_to_hp = HP/Weight) %>%
filter(weight_to_hp > mean(weight_to_hp, na.rm = TRUE)) %>%
arrange(desc(weight_to_hp))%>%
select(Modell, weight_to_hp)
abs_loss_data <- cars %>%
mutate(absolute_loss = Purchase_price - Resaleprice) %>%
arrange(desc(absolute_loss)) %>%
select(Modell, contains("price"), absolute_loss) %>%
filter(!is.na(absolute_loss))
Slowdown
when writing
code

21. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
Usual Course
Excercises
Don‘t make it
to difficult
Now that you know, what a list is
please load this dataset, apply a
cluster analysis on it and create
an appealing report

22. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
Usual Course
Excercises
Don‘t make it
to easy
Now that you know how to
extract the first element from a
list, please extract the second

23. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
Example Data
Iris
NYC Taxi Data
MT Cars
Usual Course

24. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
Wishfull Thinking
workshop element attached to the course
smaller chunks of training sessions

25. © 2010 – 2016 eoda GmbHMartin Schneider www.eoda.de
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