try R learning


Outline
  1. 1. 1. Using R
  2. 2. 2. Vectors 向量
  3. 3. 3. Matrice 矩陣
  4. 4. 4. 統計 Summary Statistics
  5. 5. 5. Factors
  6. 6. 6. Data Frames
  7. 7. 7. Real-World Data
  8. 8. 8. Next
  9. 9. Referance
  • codeschool

    • R 是一個程式語言、統計計算與繪圖的整合環境，是 GNU 的一個project萌生於貝爾實驗室（Bell Laboratories），主要作者為 John Chambers。其語法與 S 語言（S-Plus）非常相似，提供非常多的統計工具，包含線性與非線性模型（linear and nonlinear modelling）、統計檢定（statistical tests）、時間序列分析（time series analysis）、分類分析（classification）、群集分析（clustering）等相關工具。

    R 擁有 CRAN， a network of ftp and web servers around the world that store identical, up-to-date, versions of code and documentation for R.（目前有 4435 多種packages，幾乎由統計學家更新）

    1. Using R

    • Expressions
    • Logical Values
    • Variables
    • Functions
    • repeat print
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    > rep("123", times=3)
    [1] "123" "123" "123"
    • Help & Example(functionname)* 
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    > help(sum)
    > example(min)
    • Files
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    > list.files()
     [1] "Applications"    "ASUS"            "Desktop"        
     [4] "Documents"       "Downloads"       "Dropbox"        
     [7] "Google 雲端硬碟" "Library"         "Movies"         
    [10] "Music"           "NTU Space"       "Pictures"       
    [13] "Public"          "tmp"    
    > read.csv("file.csv") # read csv file
    • Run R script
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    > source("file.R")
    • na.rm 是否移除 NA （default = FALSE）
    1
    > sum(a, na.rm = TRUE)

    2. Vectors 向量

    Mixing type，自動轉成 string

    • c Combine 
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    > c("a", TRUE, 1)
    [1] "a"    "TRUE" "1"
    • Sequence 序列向量
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    > 5:9
    [1] 5 6 7 8 9
    > seq(5,9,0.5) # 間隔 0.5
    [1] 5.0 5.5 6.0 6.5 7.0 7.5 8.0 8.5 9.0
    • [V] Access
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    > a =c("a", TRUE, 1)
    > a[3]
    [1] "1"
    > a[-3] # execluding
    [1] "a"    "TRUE"
    • names(v) 命名
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    > b = 5:7
    > names(b) <- c("first", "second", "third")
    > b
     first second  third  
         5      6      7    
         
    > b["first"]
    first 
        5
    • 繪圖 Plotting
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    > barplot(v) # 長條圖
    > x <- seq(1,20,0.1)
    > y <- sin(x)
    > plot(x, y) # Scatter Plots 分散圖
    • 計算
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    > a = a(1,2,3)
    > a+1
    [1] 2 3 4
    • 判別
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    > a == c(1,2,5)
    [1]  TRUE  TRUE FALSE

    3. Matrice 矩陣

    • 給值＋給維度
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    > matrix(0,3,4)
         [,1] [,2] [,3] [,4]
    [1,]    0    0    0    0
    [2,]    0    0    0    0
    [3,]    0    0    0    0
    > matrix(1:12,3,4) # 一個一個填入
         [,1] [,2] [,3] [,4]
    [1,]    1    4    7   10
    [2,]    2    5    8   11
    [3,]    3    6    9   12
    • dim 後給維度
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    > a <- 1:12
    > dim(a) <- c(3,4) 
    > a
         [,1] [,2] [,3] [,4]
    [1,]    1    4    7   10
    [2,]    2    5    8   11
    [3,]    3    6    9   12
    • [M] Access
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    > a
         [,1] [,2] [,3] [,4]
    [1,]    1    4    7   10
    [2,]    2    5    8   11
    [3,]    3    6    9   12
    > a[2,3]
    [1] 8
    > a[ ,3]
    [1] 7 8 9
    • 繪圖 Plotting
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    > contour(a)
    > persp(a) # 3D
    > persp(e,expand=0.2) # 擴大
    > image(volcano) # 內建火山 heat map

    4. 統計 Summary Statistics

    • mean() 平均 
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    > a <- 1:12
    > mean(a)
    [1] 6.5
    > median(a)
    [1] 6.5
    > sd(a) # Standard Deviation
    [1] 3.605551

    5. Factors

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    > a =c('gold', 'silver', 'gems', 'gold', 'gems')
    > type=factor(a)> 
    > type
    [1] gold   silver gems   gold   gems  
    Levels: gems gold silver
    > as.integer(type)
    [1] 2 3 1 2 1
    > levels(type)
    [1] "gems"   "gold"   "silver"
    • 繪圖
    1
    暫略 try R 5.2

    6. Data Frames

    • data.frame() 把同樣的 data structures 用表格呈現
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    > a=c(1:3)
    > b=c(2:4)
    > c=c(3:5)
    > f=data.frame(a,b,c)
      a b c
    1 1 2 3
    2 2 3 4
    3 3 4 5
    • Access
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    > f
      a b c
    1 1 2 3
    2 2 3 4
    3 3 4 5
    > f[2]
      b
    1 2
    2 3
    3 4
    > f[[2]]
    [1] 2 3 4
    > f[["b"]] 
    or
    > f$b
    [1] 2 3 4
    • merge
    1
    暫略 try R 6.4

    7. Real-World Data

    8. Next

    slide: Ruby & R

    Referance

    codeschool

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