R intro 20140716-basic
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本文介绍了R统计软件的基本信息，包括其历史、特点和功能。R是一个开源的统计分析和绘图软件，拥有强大的数据处理能力，但也有学习曲线陡峭、缺乏图形界面等缺点。此外，文中还讲述了如何安装R、操作界面以及变量类型等相关内容。
![數學運算
> 3 - 4
[1] -1
> 5 * 6
[1] 30
> 7 / 8
[1] 0.875
> 1 + 2 * 3
[1] 7
> (1 + 2) * 3
[1] 9
> 15 / 4
[1] 3.75
> 15 %% 4
[1] 3
> 2^2
[1] 4
> 2^0.5
[1] 1.414214
> 2^ 4.3
[1] 19.69831
> 2^-0.5
[1] 0.7071068
log(4) # natural log
log10(4) # log in base 10
log(4,10) # same as above
sqrt(9) # square root
abs(3-4) # absolute value
exp(1) # exponential](https://image.slidesharecdn.com/rintro20140716a-150322034136-conversion-gate01/85/R-intro-20140716-basic-14-320.jpg)
![vectors(1)
> a = rnorm(100, mean = 50, sd = 5)
> str(a)
num [1:100] 49.2 48.7 46.1 43.9 44.3 ...
> head(a) ### 列出前面六項
[1] 49.17096 48.71730 46.13608 43.89396 44.28375 43.36800
> tail(a) ### 列出最後六項
[1] 52.86879 50.79304 49.25390 41.76034 44.41400 49.13812
> summary(a)
Min. 1st Qu. Median Mean 3rd Qu. Max.
38.77 46.33 49.00 49.29 52.24 61.16
> fix(a)](https://image.slidesharecdn.com/rintro20140716a-150322034136-conversion-gate01/85/R-intro-20140716-basic-28-320.jpg)
![vectors(2)
> b = c(1,2,3,4,5,6) #### 使用 c() 建立 vector，包含數字項
> str(b)
num [1:6] 1 2 3 4 5 6
> c = c(“1”,“2”,“3”,“4”,“5”,“6”) #### 使用 c() 建立 vector，由字串構成
> str(c)
chr [1:6] "1" "2" "3" "4" "5" "6"
> summary(b)
Min. 1st Qu. Median Mean 3rd Qu. Max.
1.00 2.25 3.50 3.50 4.75 6.00
> summary(c)
Length Class Mode
6 character character](https://image.slidesharecdn.com/rintro20140716a-150322034136-conversion-gate01/85/R-intro-20140716-basic-29-320.jpg)
![vectors(3)
> mean(b)
[1] 3.5
> mean(c)
[1] NA
警告訊息：
In mean.default(c) : argument is not numeric or logical: returning NA
> d = as.numeric(c) ### help(as)
> str(d)
num [1:6] 1 2 3 4 5 6
> mean(d)
[1] 3.5](https://image.slidesharecdn.com/rintro20140716a-150322034136-conversion-gate01/85/R-intro-20140716-basic-30-320.jpg)
![vectors(4)
> colors <- c("red", "orange", "yellow", "green", "blue", "indigo", "violet")
> length(colors) ### 計算 vector 長度
[1] 7
> colors[7] ### 回傳第七項
[1] "violet"
> colors[7] <- "purple" ### 把第七項換成 "purple"
> x = rnorm(10, 0, 1)
> idx = x > 1
> idx ### str(idx)
[1] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE
> x.select = x[idx]
> x.select
[1] 1.355631 1.165835](https://image.slidesharecdn.com/rintro20140716a-150322034136-conversion-gate01/85/R-intro-20140716-basic-31-320.jpg)
![matrix
• 呼叫特定欄位的資料
USPersonalExpenditure[1, 3]
USPersonalExpenditure["Food and Tobacco", "1950"]
USPersonalExpenditure[1, "1950"]](https://image.slidesharecdn.com/rintro20140716a-150322034136-conversion-gate01/85/R-intro-20140716-basic-39-320.jpg)
![matrix
• 呼叫特定欄位的資料
USPersonalExpenditure[1, c(5, 3, 1)]
USPersonalExpenditure["Food and Tobacco", c("1960",
"1950", "1940")]](https://image.slidesharecdn.com/rintro20140716a-150322034136-conversion-gate01/85/R-intro-20140716-basic-40-320.jpg)
![list()
m1 <- list(title = "Lawrence of Arabia", year = 1962)
m2 <- list(title = "Stardust", year = 2007)
m3 <- list(title = "Troy", year = 2004)
pete$movies <- list(m1, m2, m3)
pete[["roles"]] <- c("T.E. Lawrence", "King", "Priam")
str(pete)](https://image.slidesharecdn.com/rintro20140716a-150322034136-conversion-gate01/85/R-intro-20140716-basic-44-320.jpg)
R intro 20140716-basic
- 2. R 統計軟體發展歷史 • R 統計軟體最初是由Ross Ihaka及Robert Gentleman兩人以統計分析及繪圖為目的， 仿S語言的架構為基礎而發展出來的統計軟 體，可視為改進版本的S語言。大部分的S語 言程式碼可直接或稍做修改後就在R上面執 行 • R屬於GNU計畫中的一個項目，目前是由 R Development Core Team維護及發展 • 目前R最新的版本為3.1.1版(2014.07.10)
- 4. R的特色及功能 • 有效的資料處理及存取能力 • 方便的矩陣操作與運算能力 • 簡單且發展完善的程式語言環境(S 語言) – open source, 開放原始碼 • 活躍的咨詢社群 • 強迫使用者理解統計分析的步驟 •免費
- 5. R的缺點 • 沒有圖形化界面 • 需要較長的學習時間 • 沒有公司/企業支援(不能客訴)，使用者必 須自行克服問題(容易有挫折感) • 常常在不知不覺中犯錯 – 要反覆檢查指令、輸出結果 • help 訊息幫助不大
- 6. 軟體下載 • Google 搜尋 “R” 第一個顯示即是R統 計軟體網頁 • The R Project for Statistical Computing • CRAN • 選擇下載點 :http://cran.csie.ntu.edu.tw/ • Windows baseR-3.0.0-win32.exe
- 7. • R 的主要操作界面 • 兩種視窗: – Document (scripts) – Console
- 13. Seriously, how it works?
- 14. 數學運算 > 3 - 4 [1] -1 > 5 * 6 [1] 30 > 7 / 8 [1] 0.875 > 1 + 2 * 3 [1] 7 > (1 + 2) * 3 [1] 9 > 15 / 4 [1] 3.75 > 15 %% 4 [1] 3 > 2^2 [1] 4 > 2^0.5 [1] 1.414214 > 2^ 4.3 [1] 19.69831 > 2^-0.5 [1] 0.7071068 log(4) # natural log log10(4) # log in base 10 log(4,10) # same as above sqrt(9) # square root abs(3-4) # absolute value exp(1) # exponential
- 15. • Beginner's notes: > q() ## just quit R ## ctl + alt + esc > getwd() > setwd(“C:/”) > help(function) or > help(library) > dir() > ls() > rm(list = ls())
- 16. R 的學習目標 • 讀入外部資料，直接使用別人寫好的 packages/functions • 讀入外部資料作資料處理/轉換/彙整 • 讀入外部資料，並作資料處理/轉換，再使 用現有的packages/functions 計算 • 修改他人所寫的packages/functions，以適用 於自己的資料分析任務 • 依照自己的特殊需求寫出全新的程式
- 17. • 「不算」複雜的R程式: • 不需資料處理，直接使用他人的套件與函數, e.g.: babies=read.txt("c:/mydata/babies/babies.txt",header=TRUE) mean(babies$bwt); sd(babies$age); hist(babies$bwt); plot(babies$bwt, babies$age) library(lme4) lmer.fit1=lmer(bwt ~.,data=babies); summary(lmer.fit1)
- 18. 複雜 R 程式的可能架構 • 資料處理: – 條件過濾、轉換、新增變數、分割、合併、變 數轉碼, etc. • 使用者自訂函數 • 重新改寫他人的函數或套件 • 自製套件(package)
- 19. 如何撰寫 R 程式？ • 和撰寫論文一樣： – 研究動機與文獻探討 • 從R社群找過去的Q&A; 尋找可用的 package – 方法 • 演算程序 (e.g., 公式、參數、ANOVA) – 材料 • 讀取外部資料、自製模擬資料 – 程序 • 轉換資料、各種想像不到的細節… – 結果與結論
- 20. Path of R program (and packages) in Windows system: Program Files C:Program FilesRR-n.n.netcRprofile.site
- 21. Path of R program (and packages) in Mac: Apps and Frameworks (X11 or terminal) ~/.Rprofile
- 22. Rprofile (optional) library(languageR) library(lme4) library(ggplot2) library(R.matlab) library(MASS) library(effects) library(ez) library(car) library(sciplot) setwd("/Users/Kevin/Documents/R_docs") ### google: what's in your .Rprofile?
- 23. 套 件 install.packages() 自動下載與安裝指定的套件，例如要安裝 lme4套件： >install.packages(”lme4") > install.packages(”lme4", repos = "http://cran.csie.ntu.edu.tw") 更新 R 的套件： > update.packages() 查看目前環境中已經安裝的套件： > library()
- 24. 套 件 尋找可安裝的套件可使用： > available.packages() 載入指定的套件： > library(lme4) 查看指定套件的相關資訊可使用 help 參數，例如： > library(help = lme4)
- 27. 變數類型 • R有很多種資料形式。較常使用的有： vectors, matrices, lists, and data frames • 列出記憶體裡面暫存的資料： > ls() • 辨別資料類型的指令： > str() > class()
- 28. vectors(1) > a = rnorm(100, mean = 50, sd = 5) > str(a) num [1:100] 49.2 48.7 46.1 43.9 44.3 ... > head(a) ### 列出前面六項 [1] 49.17096 48.71730 46.13608 43.89396 44.28375 43.36800 > tail(a) ### 列出最後六項 [1] 52.86879 50.79304 49.25390 41.76034 44.41400 49.13812 > summary(a) Min. 1st Qu. Median Mean 3rd Qu. Max. 38.77 46.33 49.00 49.29 52.24 61.16 > fix(a)
- 29. vectors(2) > b = c(1,2,3,4,5,6) #### 使用 c() 建立 vector，包含數字項 > str(b) num [1:6] 1 2 3 4 5 6 > c = c(“1”,“2”,“3”,“4”,“5”,“6”) #### 使用 c() 建立 vector，由字串構成 > str(c) chr [1:6] "1" "2" "3" "4" "5" "6" > summary(b) Min. 1st Qu. Median Mean 3rd Qu. Max. 1.00 2.25 3.50 3.50 4.75 6.00 > summary(c) Length Class Mode 6 character character
- 30. vectors(3) > mean(b) [1] 3.5 > mean(c) [1] NA 警告訊息： In mean.default(c) : argument is not numeric or logical: returning NA > d = as.numeric(c) ### help(as) > str(d) num [1:6] 1 2 3 4 5 6 > mean(d) [1] 3.5
- 31. vectors(4) > colors <- c("red", "orange", "yellow", "green", "blue", "indigo", "violet") > length(colors) ### 計算 vector 長度 [1] 7 > colors[7] ### 回傳第七項 [1] "violet" > colors[7] <- "purple" ### 把第七項換成 "purple" > x = rnorm(10, 0, 1) > idx = x > 1 > idx ### str(idx) [1] FALSE FALSE TRUE FALSE FALSE FALSE TRUE FALSE FALSE FALSE > x.select = x[idx] > x.select [1] 1.355631 1.165835
- 32. useful commands on vectors sample(x, 4) # randomly picks four states sample(x) # randomly permute the entire vector of state names sample(x, replace=TRUE) # selection with replacement
- 33. useful commands on vectors rev(x) # reverses the vector sum(x) # sums all the elements in a numeric or logical vector cumsum(x) # returns a vector of cumulative sums (or a running total) diff(x) # returns a vector of differences between adjacent elements max(x) # returns the largest element min(x) # returns the smallest element range(x) # returns a vector of the smallest and largest elements mean(x) # returns the arithmetic mean
- 34. factors
- 35. colors 和 colors.fc 差在哪裡？
- 36. 試試看下列的指令有什麼作用？ pony.colors <- sample(colors, size=500, replace=TRUE) str(pony.colors) pony.colors.f <- factor(pony.colors) str(pony.colors.f) plot(pony.colors.f)
- 38. matrix
- 39. matrix • 呼叫特定欄位的資料 USPersonalExpenditure[1, 3] USPersonalExpenditure["Food and Tobacco", "1950"] USPersonalExpenditure[1, "1950"]
- 40. matrix • 呼叫特定欄位的資料 USPersonalExpenditure[1, c(5, 3, 1)] USPersonalExpenditure["Food and Tobacco", c("1960", "1950", "1940")]
- 41. matrix apply(matrix, dimension, function, function_arguments) apply(USPersonalExpenditure, 2, sum) apply(USPersonalExpenditure, 1, sum) apply(USPersonalExpenditure, 2, mean)
- 42. list() • Lists can hold data structures of different types, and of different sizes. • Each component in a list can be (optionally, but commonly) separately named.
- 43. list() • A list of Peter O’Toole pete <- list("Peter", "O'Toole", 1932, FALSE) print(pete) pete <- list(first.name = "Peter", last.name = "O'Toole", yob = 1932, oscar.winner = FALSE) print(pete)
- 44. list() m1 <- list(title = "Lawrence of Arabia", year = 1962) m2 <- list(title = "Stardust", year = 2007) m3 <- list(title = "Troy", year = 2004) pete$movies <- list(m1, m2, m3) pete[["roles"]] <- c("T.E. Lawrence", "King", "Priam") str(pete)
- 45. Data frame • Data frames are two-dimensional data structures like matrices, but unlike matrices can have multiple different data types. • You can think of a data frame as a list of vectors, where all the vector lengths are the same.
- 46. Data frame test1<-read.table("c:/test.csv", header=T, sep=",") #讀取C:test.csv檔案，有標題，分隔符號為 “,” test2<-read.csv("c:/test.csv", header=T , col.names=c("M","F")) #讀取C:test.csv檔案，有標題，將column 1 ,2分別命名為 “M” 及 “F”
- 47. Data frame library(foreign) x=read.spss("d:/somedir/x4.sav") # not a data.frame names(mydata) #"id" "score" "age" mydata2=data.frame(id=x$id,score=x$score,age=x$age) library(R.matlab) dataMat <- readMat("my_matlab_file.mat") data <-data.frame(dataMat$var)
- 48. help(state) state.db <- data.frame(state.name, state.abb, state.area, state.center, stringsAsFactors=FALSE) names(state.db) names(state.db) <- c("name", "abb", "area", "long", "lat") state.db$division <- state.division plot(area ~ division, data=state.db) plot(lat ~ long, data=state.db)