The document provides an introduction to setting up R locally, detailing its data types and structures like vectors, matrices, lists, and data frames. It contains examples of basic operations, such as creating and indexing these structures, along with methods for data manipulation similar to SQL. Additionally, it invites participation in a study group focused on further exploration of R, including plotting with ggplot.

1. R
and our platform
Winston Chen @ Fliptop

2. Going to Cover
Setting it up locally
R very basics
Our Current R Infrastructure
MongoDB and MySql Integration
The Follow-ups?

3. Installation On Your Local
Come On! We are developers.
http://cran.csie.ntu.edu.tw/
Just download it and install yourself.

4. Play with your RStudio

5. R Very Basics
A bunch of functions and data types to work with data.
- Not like C/java/scala/python. Don’t built products with it.
- It works with Matrices, or super/sub types of it:
- Vectors, Matrices, Data Frames… etc
- So, Think in tabular ways.
- All the operations based upon matrices.

6. R Data Types
Atomic
Vectors
Matrix
Lists
Data Frame

7. Atomic
Numeric: x = 10.55; class(x)
Integer: y = as.integer(x); is.integer(y)
Complex: z = 1 + 2i
Logical: TRUE/FALSE
Character: “nothing more than string”
String concanation: paste(“Adam”, ”Smith”)
[http://www.r-tutor.com/r-introduction/basic-data-types]

8. Vectors
mylovelyvector <- c(2, 3, 5)
Combining vectors:
combined_vectors <- c(n, s)
Indexing:
mylovelyvector[1]
Multiple Indexing:
mylovelyvector[c(2,3)]
Ranging:
mylovelyvector[1:2]
[http://www.r-tutor.com/r-introduction/vector]

9. Matrix
> A = matrix(
c(2, 4, 3, 1, 5, 7), # the data elements
nrow=2,
# number of rows
ncol=3,
# number of columns
byrow = TRUE)
>A
# fill matrix by rows
# print the matrix
[,1] [,2] [,3]
[1,]
2
4
3
[2,]
1
5
7

10. Matrix 2
Transpose:
tA <- t(A)
[http://www.r-tutor.com/r-introduction/matrix/matrixconstruction]

11. List
A list is a generic vector containing other objects.
n = c(2, 3, 5)
s = c("aa", "bb", "cc", "dd", "ee")
b = c(TRUE, FALSE, TRUE, FALSE, FALSE)
x = list(n, s, b, 3) # x contains copies of n, s, b
[http://www.r-tutor.com/r-introduction/list]

12. Data Frame
Basically like a mysql table.
Building one:
n = c(2, 3, 5)
s = c("aa", "bb", "cc")
b = c(TRUE, FALSE, TRUE)
df = data.frame(n, s, b)

13. Data Frame 2
head(df, n=10)
names(df)
df[2,3]
df[2,]
df[,1]
df$s
#df[,”s”]

14. Data Frame 3
Subsetting data frames:
df[1]
df[“s”]
df[c("s", "b")]
subset(df, n<=3)
Adding two data frames together:
combined <- rbind(df, df2)
data <- read.csv("/Path/To/Ur/CSV/R_Example.csv",
header=TRUE)
[http://www.r-bloggers.com/select-operations-on-r-dataframes/]

15. Data Frame 4
We wanna explore data:
select * from df where b=true
can be translated into
df_sub1 <- df[df$b==TRUE,]
Then, the holy grail: something like sql join
k = c('the','X','Men')
s = c("aa", "bb", "cc")
z = c(3.1415961, 124235243, 2309)
df2 = data.frame(k, s, z)
merged <- merge(df, df2, 's') #tada!!!

16. QA and Onward
Who is up to a R study group?
- Plotting (ggplot)
- etc…