R Script

Introduction and Practice -1

Shawn Chen  2016.8.13

R語言簡介

面對撲面而來的資料浪潮，包含 Google、Facebook、Intel、Pfizer、Bank of America 等國際級企業，都已經採用 R 語言進行資料分析，許多全球一流大學如 Stanford、Johns Hopkins 和 UCLA 也將 R 視為資料分析課程的先修科目。R 語言具有免費、跨平台、佔有率高、可塑性高等優勢，各式各樣的 R 社群蓬勃發展。在國際知名的 KDnuggets 論壇統計當中，R 語言已經連續三年獲得資料科學家最常使用的資料分析語言第一名。

IEEE Spectrum Rank

Let's try it!

If you do not finish RStudio downloading, use this link.

RStudio Interface

Try it!

> x <- 10
> y <- "IBM"
>

Try it!

> x <- 10
> y <- "IBM"
> z <- rnorm(1000, mean = 100, sd = 5)
>

rnorm?

> z <- rnorm(1000, mean = 100, sd = 5)
> ?rnorm
>

rnorm

> z <- rnorm(1000, mean = 100, sd = 5)
> ?rnorm
> z

rnorm

> z <- rnorm(1000, mean = 100, sd = 5)
> ?rnorm
> z
> hist(z)
>

Exercise 1

Display the histogram of normal distribution with 1000 numbers, average 10 and standard deviation 10

Exercise 1

> hist(rnorm(1000, mean = 10, sd = 10))
> 

R Data Structure

R's Basic Data Types

• Integer
• Numeric
• Complex
• Character
• Logical

Data Types

> class(x)
[1] "numeric"
> class(y)
[1] "character"
>

Data Types

> class(x)
[1] "numeric"
> class(z)
[1] "numeric"
> class(z)
[1] "numeric"
> 

Data Types

> x <- 10
> class(x)
[1] "numeric"
> x <- as.integer(x)
> class(x)
[1] "integer"
> 

Data Types

> x <- 10
> class(x)
[1] "numeric"
> x <- as.integer(x)
> class(x)
[1] "integer"
> y <- as.integer(y)
Warning message:
NAs introduced by coercion 

General Data Structures

• Vector
• Matrix
• Array
• List
• Data Frame

Vector

The basic data object in R,

consisting of one or more values of

a single data type.

Matrix

A two-dimensional of a single data type.

Array

A multi-dimensional object of a single data type.

Data Frame

A special kind of named list where all elements has the same length.

List

A list can contain (multi) dimensional objects of any data type.

Vector

Matrix

Array

DataFrame

List

Practice

Create Vector

> V <- c(10, 5, 3, 1, 0)
> class(V)
[1] "numeric"
> 

Create Vector

> V <- c(10, 5, 3, 1, 0)
> class(V)
[1] "numeric"
> V <- as.integer(V)
> class(V)
[1] "integer"
> 

Create Vector

> V2 <- c(1, 2, NA, NA, 5)
> V2[1]
[1] 1
> V2[4]
[1] NA
> 

Create Array

> A <- 1:24
> dim(A) <- c(3, 4, 2)
> A
, , 1

     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12

, , 2

     [,1] [,2] [,3] [,4]
[1,]   13   16   19   22
[2,]   14   17   20   23
[3,]   15   18   21   24

> 

Create Array

> A <- array(1:24, c(3, 4, 2))
> A
, , 1

     [,1] [,2] [,3] [,4]
[1,]    1    4    7   10
[2,]    2    5    8   11
[3,]    3    6    9   12

, , 2

     [,1] [,2] [,3] [,4]
[1,]   13   16   19   22
[2,]   14   17   20   23
[3,]   15   18   21   24

> 

Create DataFrame

> age <- c(27, 18, 25, 40, 25)
> sex <- c("Male", "Female", "Female", "Male", "Female")
> name <- c("Shawn", "Luna", "Asu", "Alex", "Claire")
> X <- data.frame(id, age, sex, name)
> X
  id age    sex   name
1  1  27   Male  Shawn
2  2  18 Female   Luna
3  3  25 Female    Asu
4  4  40   Male   Alex
5  5  25 Female Claire
> 

Edit DataFrame

> X <- edit(X)
> 

Edit DataFrame

> X$age[4] <- 39
> X
  id age    sex   name
1  1  27   Male  Shawn
2  2  18 Female   Luna
3  3  25 Female    Asu
4  4  39   Male   Alex
5  5  25 Female Claire
> 

Exercise2

> install.packages("swirl")
> library(swirl)
> install_course_github("shiyoubun","CDL-TW_BDA-R")
> swirl()
>

R Data Import/Export

Working Directory

> setwd("/Users/shawn/r language/workspace/demo/")
> getwd()
[1] "/Users/shawn/r language/workspace/demo"
> 

Working Directory

> setwd("/Users/shawn/r language/workspace/demo/")
> getwd()
[1] "/Users/shawn/r language/workspace/demo"
> setwd("folder")
> getwd()
[1] "/Users/shawn/r language/workspace/demo/folder"
> 

Import CSV files

csv download

Download csv to working directory

> getwd()
[1] "/Users/shawn/r language/workspace/demo/folder"
> 

read.csv

> Y <- read.csv("city-of-chicago-salaries.csv")
> View(Y)
>

write.csv

> write.csv(Y,"output.csv")
> View(Y) 
>

write.csv

> write.csv(Y,"output.csv")
> View(Y) 
>

write.csv

> write.csv(Y,"output2.csv", row.names=FALSE)
> View(Y) 
>

Let's do some data process

aggregate

> ?aggregate
>

Remove $ in data frame

> Z <- Y
> Z$Employee.Annual.Salary = as.numeric(gsub("[\\$,]","",Z$Employee.Annual.Salary))

New dataframe

> AGGR <- aggregate(Z$Employee.Annual.Salary,
by = list(Z$Position.Title), FUN = mean)
> View(AGGR)
>

New dataframe with column name

> AGGR <- aggregate(Z$Employee.Annual.Salary,
by = list(Z$Position.Title), FUN = mean)
> View(AGGR)
> AGGR <- setNames(AGGR, c("Position.Title",
"Annual.Salary"))
> View(AGGR)
>

Draw histrogram

> hist(AGGR$Annual.Salary)
> 

Draw histrogram

> hist(AGGR$Annual.Salary, main="Histogram", 
xlab = "Salary")
>

Write to csv file

> write.csv(AGGR,"survey.csv",row.names = FALSE)
> 

Try it!

• plot()
• boxplot()

plot

boxplot

Exercise3

> install.packages("swirl")
> library(swirl)
> install_course_github("shiyoubun","CDL-TW_BDA-R")
> swirl()
>