This is a very ^2 basic introduction to R. The purpose of this presentation is to prepare you with all that you have to know about fundamentals of using R to operate data frames, which you can easily get by importing data from relational database table or csv/text file.

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…