- PhillyR held 11 meetups from 2016-2018 with 9 presenters and 5 sponsors. - In 2018-2019, PhillyR is funded by the R Consortium and must follow its Code of Conduct. - PhillyR is run by volunteers and relies on sponsorships and donations. Meetup topics are decided by members, who are encouraged to host and present.

1. PhillyR
2018-2019 Kickoff
Sept 6, 2018

2. 2016 - 2018 PhillyR Summary
- 11 Meetups, 9 in the last
12 months
- 8 presenters
- Sean Davis, Greg Baldini,
Leon Kim, Scott Jackson,
George Kikuchi, Tan Chen,
Russ Lavery, Dror Walter
- 5 Sponsors
- Microsoft, RStudio,
PromtWorks, WeWork,
Optymyze, Jornaya

3. 2018 - 2019 PhillyR
- For 2018-2019 season, PhillyR is part of R User Support Program funded by R
Consortium.
- PhillyR and its members are expected to follow R Consortium’s R Community Code
of Conduct.
- If you have questions or concerns regarding PhillyR and RConsortium’s Code of
Conduct, please contact conduct@r-consortium.org for more details

4. 2018 - 2019 PhillyR
- PhillyR is run by volunteer organizers. If you like to become an organizer, please
contact me through the PhillyR meetup page
- PhillyR is entirely funded by sponsors and donations! Help us find sponsors
and/or contribute financially to PhillyR through meetup page
- PhillyR events are decided by YOU. If you have a topic that you would like to learn
and/or present, host a meetup. We will find the place and food to host your event!

5. Announcements

6. Data Structures in R
2018-2019 Kickoff
Sept 6, 2018

8. Your computer
R software
1. R code 2. R execution
3. R output
RStudio software

9. 4. Display R output
Your computer
R software
1. R code 2. R execution
3. R output
RStudio software

10. Write your R codes here if you want to save the R
codes you ran
This is just a text file with .R extension

11. Writing R code here is exactly the same thing
except you can’t save your code easily

12. R is a programming language
- This means it represents data in computers and operates some function on that
data.
- There are many ways you can represent data in computers
- The same data can be represented in different ways, with different functions
String (e.g. “Leon”) -> character and factor
Number (e.g. 1) -> integer and double
and this data is often stored as variable for later use so that you don’t have to
keep writing “Leon” or 1 everytime. e.g. myName <- “Leon” , myNumber <- 1

13. R doesn’t have scalar type
- In other programming languages, there is a big difference between storing “Leon” to
a variable vs storing both “Leon” and “Kim” a variable
- This is called a “list” or “array” of string in other languages
- A non-list or non-array is called scalar type
- This is 1-Dimensional data structure (Q: Why is it 1 not 2?)
- Because R is statistics-oriented, every variable can have 1 or many values
- If you want to apply some function to every value, you do not have to loop
through every element. Many operations are vectorized
- x <- c(1,2,3)
y <- x + 1
y now has values (2,3,4)
- Therefore in R, the most basic data structure is a vector

14. R doesn’t have scalar type
- In other programming languages, there is a big difference between storing “Leon” to
a variable vs storing both “Leon” and “Kim” a variable
- This is called a “list” or “array” of string in other languages
- A non-list or non-array is called scalar type
- This is 1-Dimensional data structure (Q: Why is it 1 not 2?)
- Because R is statistics-oriented, every variable can have 1 or many values
- If you want to apply some function to every value, you do not have to loop
through every element. Many operations are vectorized
- x <- c(1,2,3)
y <- x + 1
y now has values (2,3,4)
- Therefore in R, the most basic data structure is a atomic vector

15. - Combines multipe vectors into one vector
- c(1,2,3)
- c(c(1), c(2,3))
- c(x, y)
- Because a vector can be of different length, operations can depend on length
- c(1,2,3) + 1
- c(1,2,3) + c(1) exactly the same as above
- c(1,2,3) + c(1,2) error because this doesn’t make sense, even in real life!
Warning message:
In c(1, 2, 3) + c(1, 2) :
longer object length is not a multiple of shorter object length
- c(1,2,3,4) + c(1,2)
- 2 4 4 6

16. R list
- R does have a “list” but it is slightly different from other programming languages
- Since all values can be length of 1 or more, the difference between vector and list is
that list can have different types in each element
- i.e. vectors are homogeneous
- c(1,2,3) c(“leon”, “kim”, “loves”, “R”)
- i.e. vectors are heterogeneous
- list(“PhillyR”, 2018)
- list(c(1,2,3),
c(“leon”, “kim”, “loves”, “R”)
)
- list(list(“4”, 6),
c(“leon”, “kim”, “loves”, “R”)
)

17. - list: A default name is the index #
- List: names shown between [[ ]]
named vectors and list
- If no name is specified:
- vector: no names displayed,
only values
- If name is specified:
- vector: display name on top
- Each element in a vector and list have a “name”.

18. named vectors and list

19. Let’s get 2-Dimensional
- Vectors and lists are 1-Dimensional. They grow in length only.
- Most of data that we are familiar with are 2-Dimensional (i.e. is rectangular)
- In R, this is represented as a vector (or a list) with row and column lengths (i.e. 2D)
- i.e. matrix is homogeneous
- matrix(1:9, nrow = 1, ncol = 1)
- i.e. data.frame is heterogeneous
- data.frame(x, y, z)
- where x, y, z are vectors that must ...
- Have same length (or at least multiples of each other)
- but x, y , z can be of different type

20. Let’s get 2-Dimensional
- data.frame can have special columns
- This suspiciously behave similar to lists
- An element in a list can be a list
- This is because data.frame is a list
with constraints applied to them