Microsoft PowerPoint _cp08_檔案資料輸入與輸出.pptx

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1 105-2: EE4052 計算機程式設計 Computer Programming 連豊力臺大電機系 Feb Jun 2017 課程主題進度 U08: 檔案資料輸入與輸出 2

2 大綱取得檔案的資料 : 輸入與輸出取得內建資料庫取得其他資料庫的資料取得網頁的資料 3 大綱檔案資料輸入與輸出 4

3 設定工作目錄 rm( list = ls() ) ls() mywd <- "L:/DataWD" setwd( mywd ) getwd( ) 5 資料儲存 dump, source # 儲存物件的名稱與其值 ( 內容 ) x <- 1:10 y <- matrix( 1:6, nrow = 2, ncol = 3 ) ls() dump( c("x", "y"), file = "mydump.txt") rm(x); rm(y) ls() source( file = "mydump.txt" ) ls() mydump.txt x <- 1:10 y <- structure(1:6,.dim = 2:3) 6

4 資料儲存 dput, dget # 儲存物件的值 ( 內容 ) x <- 1:10 y <- matrix( 1:6, nrow = 2, ncol = 3 ) dput( y, file = "mydput.txt" ) newy <- dget( "mydput.txt" ) newy mydput.txt structure(1:6,.dim = 2:3) 7 資料儲存 sink # 螢幕輸出轉到檔案 sink( "mysink.txt" ) x y sink( ) x y mysink.txt [1] [,1] [,2] [,3] [1,] [2,]

資料儲存 read.table, write.table dataf <- iris[c(1, 2, 51, 52, 101, 102), c(1, 2, 5)] dataf <- edit( dataf ) write.table( dataf, "mydataf.txt" ) > dataf Sepal.Length Sepal.Width Species 1 5.1 3.

5 資料儲存 read.table, write.table dataf <- iris[c(1, 2, 51, 52, 101, 102), c(1, 2, 5)] dataf <- edit( dataf ) write.table( dataf, "mydataf.txt" ) > dataf Sepal.Length Sepal.Width Species setosa setosa versicolor versicolor virginica virginica df <- read.table( "mydataf.txt" ) mydataf.txt "Sepal.Length" "Sepal.Width" "Species" "1" "setosa" "2" "setosa" "3" "versicolor" "4" "versicolor" "5" "virginica" "6" "virginica" > dataf Sepal.Length Sepal.Width Species setosa setosa versicolor versicolor virginica virginica 9 資料儲存 read.table, write.table mydataf.txt df <- read.table( "mydataf.txt" ) sf0 <- read.table( "mydataf0.txt" ) % 去掉 "" 的資料 "Sepal.Length" "Sepal.Width" "Species" "1" "setosa" "2" "setosa" "3" "versicolor" "4" "versicolor" "5" "virginica" "6" "virginica" sf1 <- read.table( "mydataf1.txt", header = FALSE ) % 去掉標題的資料 sf2 <- read.table( "mydataf2.txt", header = TRUE, row.names = NULL ) % 去掉列名稱的資料 sf3 <- read.table( "mydataf3.txt", header = FALSE, row.names = NULL ) % 去掉標題與列名稱 mydataf0.txt mydataf1.txt mydataf2.txt mydata3.txt Sepal.Length Sepal.Width Species setosa setosa versicolor versicolor virginica virginica setosa setosa versicolor versicolor virginica virginica Sepal.Length Sepal.Width Species setosa setosa versicolor versicolor virginica virginica setosa setosa versicolor versicolor virginica virginica 10

6 資料儲存 read.table, read.csv # csv: comma separated values read.table( "mydataf2.csv" ) mydata2.csv Sepal.Length,Sepal.Width,Species 5.1,3.5,setosa 4.9,3,setosa 7,3.2,versicolor 6.4,3.2,versicolor 6.3,3.3,virginica 5.8,2.7,virginica read.table( "mydataf2.csv", header = TRUE ) read.table( "mydataf2.csv", header = TRUE, sep = "," ) dt1 <- read.table( "mydataf2.csv", sep = ",", header = TRUE ) dt2 <- read.csv( "mydataf2.csv" ) 11 資料儲存 read.table, read.csv mydata2.csv Sepal.Length,Sepal.Width,Species 5.1,3.5,setosa 4.9,3,setosa 7,3.2,versicolor 6.4,3.2,versicolor 6.3,3.3,virginica 5.8,2.7,virginica > read.table( "mydataf2.csv" ) V1 1 Sepal.Length,Sepal.Width,Species 2 5.1,3.5,setosa 3 4.9,3,setosa 4 7,3.2,versicolor 5 6.4,3.2,versicolor 6 6.3,3.3,virginica 7 5.8,2.7,virginica > read.table( "mydataf2.csv", header = TRUE ) Sepal.Length.Sepal.Width.Species 1 5.1,3.5,setosa 2 4.9,3,setosa 3 7,3.2,versicolor 4 6.4,3.2,versicolor 5 6.3,3.3,virginica 6 5.8,2.7,virginica > read.table( "mydataf2.csv", header = TRUE ) Sepal.Length.Sepal.Width.Species 1 5.1,3.5,setosa 2 4.9,3,setosa 3 7,3.2,versicolor 4 6.4,3.2,versicolor 5 6.3,3.3,virginica 6 5.8,2.7,virginica > dt1 <- read.table( "mydataf2.csv", sep = ",", header = TRUE ) > dt1 Sepal.Length Sepal.Width Species setosa setosa versicolor versicolor virginica virginica > dt2 <- read.csv( "mydataf2.csv" ) > dt2 Sepal.Length Sepal.Width Species setosa setosa versicolor versicolor virginica virginica > read.table( "mydataf2.csv", header = TRUE, sep = "," ) Sepal.Length Sepal.Width Species setosa setosa versicolor versicolor virginica virginica 12

7 資料儲存 read.table, read.csv # 設定不同的目錄 tphdata <- read.table( "L:/DataWD/Typhoon-01.txt", header = TRUE ) tphdata > tphdata x1 x2 x3 x4 d1 d2 d 資料儲存 scan # 讀取大量資料的文字檔 scandata <- scan( "scanlist.txt", list( Sepal.Length = 0, Sepal.Width = 0, Species = "") ) scanlist.txt setosa setosa versicolor versicolor virginica virginica > scandata $Sepal.Length [1] $Sepal.Width [1] $Species [1] "setosa" "setosa" "versicolor" [4] "versicolor" "virginica" "virginica" 14

8 讀取資料 Insurance 例子 # 讀取資料 Ins_c1 <- read.csv( "Insurance.csv" ) head( Ins_c1 ) Ins_c2 <- read.table( "Insurance.csv" ) head( Ins_c2 ) Ins_c3 <- read.table( "Insurance.csv", header = TRUE, sep="," ) head( Ins_c3 ) Ins_t1 <- read.table( "Insurance.txt" ) head( Ins_t1 ) Ins_t2 <- read.table( "Insurance.txt", header = TRUE, sep="" ) head( Ins_t2 ) 15 資料儲存 read.table, read.csv District Group Age Holders Claims 1 1 <1l < <1l <1l <1l > l < l l l > l < l > Ins_c1 <- read.csv( "Insurance.csv" ) > head( Ins_c1 ) X District Group Age Holders Claims <1l < <1l <1l <1l > l < l > Ins_c2 <- read.table( "Insurance.csv" ) > head( Ins_c2 ) V1 V2 1 NA,"District","Group","Age","Holders","Claims" 2 1,"1","<1l","<25",197,38 3 2,"1","<1l","25-29",264,35 4 3,"1","<1l","30-35",246,20 5 4,"1","<1l",">35",1680, ,"1","1-1.5l","<25",284,63 > Ins_t1 <- read.table( "Insurance.txt" ) > head( Ins_t1 ) District Group Age Holders Claims 1 1 <1l < <1l <1l <1l > l < l > Ins_t2 <- read.table( "Insurance.txt", header = TRUE, sep="" ) > head( Ins_t2 ) District Group Age Holders Claims 1 1 <1l < <1l <1l <1l > l < l > Ins_c3 <- read.table( "Insurance.csv", header = TRUE, sep="," ) > head( Ins_c3 ) X District Group Age Holders Claims <1l < <1l <1l <1l > l < l

9 讀取資料從其他檔案格式 R Programming/Importing and exporting data Getting Data From An Online Source 17 大綱內建資料庫 18

10 Datasets 資料集 #R 內建資料集 :datasets # 近百個資料集, 涵蓋 : 醫學自然社會人體等資料 data( package = "datasets" ) help( AirPassengers )?AirPassengers AirPassengers % Monthly Airline Passenger Numbers summary( AirPassengers ) data( package =.packages( all.available = TRUE )) #CO2 Carbon Dioxide Uptake in Grass Plants #uspop Populations Recorded by the US Census #Titanic Survival of passengers on the Titanic #women Average Heights and Weights for American Women 19 Datasets 資料集 #CO2 Carbon Dioxide Uptake in Grass Plants CO2 summary( CO2 ) #uspop Populations Recorded by the US Census uspop summary( uspop ) #Titanic Survival of passengers on the Titanic Titanic summary( Titanic ) #women Average Heights and Weights for American Women women summary( women ) 20

11 其他軟體套件的資料集 #MASS library(mass) data( Insurance )?Insurance head( Insurance ) tail( Insurance ) dim( Insurance ) names( Insurance ) attributes( Insurance ) class( Insurance$District ) class( Insurance$Age ) class( Insurance$Holders ) levels( Insurance$Age ) > head( Insurance ) District Group Age Holders Claims 1 1 <1l < <1l <1l <1l > l < l > tail( Insurance ) District Group Age Holders Claims l l > >2l < >2l >2l >2l > > dim( Insurance ) [1] 64 5 > names( Insurance ) [1] "District" "Group" "Age" "Holders" "Claims" > attributes( Insurance ) $names [1] "District" "Group" "Age" "Holders" "Claims" $class [1] "data.frame" $row.names [1] [23] [45] > class( Insurance$District ) [1] "factor" > class( Insurance$Age ) [1] "ordered" "factor" > class( Insurance$Holders ) [1] "integer" > levels( Insurance$Age ) [1] "<25" "25-29" "30-35" ">35" 21 其他軟體套件的資料集 # arules 軟體套件中,Groceries 資料集 install.packages( "arules" ) library( arules ) data( Groceries )?Groceries Groceries[1:10] inspect( Groceries[1:10] ) # 10 位消費者購物車中的商品 > Groceries[1:10] transactions in sparse format with 10 transactions (rows) and 169 items (columns) > inspect( Groceries[1:10] ) items [1] {citrus fruit, semi-finished bread, margarine, ready soups} [2] {tropical fruit, yogurt, coffee} [3] {whole milk} [4] {pip fruit, yogurt, cream cheese, meat spreads} [5] {other vegetables, whole milk, condensed milk, long life bakery product} [6] {whole milk, butter, yogurt, rice, abrasive cleaner} [7] {rolls/buns} [8] {other vegetables, UHT-milk, rolls/buns, bottled beer, liquor (appetizer)} [9] {pot plants} [10] {whole milk, cereals} 22

12 大綱其他資料庫的資料 23 網路上的資料庫 The R Datasets Package UC Irvine Machine Learning Repository The Free Datasets at r-dir.com Rdatasets: An archive of datasets distributed with R Datasets in R packages (IAState) 24

13 大綱網頁的資料 25 讀取資料從網路的檔案 Reading a CSV-file from an URL The number of police officers in Scotland over time read.csv(" E_42.csv") Date Value " 26

14 讀取資料從網路的檔案 # 和訊網萬科股票的相關金融資料 install.packages("xml") library(xml) url = " &type=1&date= " tales1 = readhtmltable( url ) tales1 27 讀取資料從網路的檔案 > tales1 $`NULL` V1 1 Annual $`NULL` Period End Date June March Operating Income 41,390,345, ,999,905, Net Profit 4,556,304, ,613,904, Total Profit 7,133,412, ,394,885, Net Profit Excluding Extraordinary Items 4,536,753, ,615,472, Total Assets 432,241,960, ,894,248, Shareholders' Equity 66,644,627, ,578,003, Net Cash Flows From Operating Activities -9,792,399, ,383,260, Basic Earnings Per Share The Rate Of Return On Equity Net Cash Flows From Operating Activities Per Share Net Assets Value Per Share Net Assets Per Share After Adjusted Foreign Financial Accounting Standard Net Profit EPS Excluding Extraordinary Items Report Start Time Report End Time $`NULL` Period End Date December December December December Operating Income 240,477,236, ,549,130, ,388,004, ,418,791, Net Profit 21,022,606, ,119,406, ,745,454, ,118,549, Total Profit 39,253,611, ,802,617, ,252,363, ,291,011, Net Profit Excluding Extraordinary Items 20,929,278, ,615,950, ,576,596, ,113,721, Total Assets 830,674,213, ,295,567, ,408,755, ,205,323, Shareholders' Equity 113,444,766, ,183,517, ,164,569, ,895,983, Net Cash Flows From Operating Activities 39,566,129, ,046,020, ,724,819, ,923,868, Basic Earnings Per Share The Rate Of Return On Equity Net Cash Flows From Operating Activities Per Share Net Assets Value Per Share Net Assets Per Share After Adjusted Foreign Financial Accounting Standard Net Profit EPS Excluding Extraordinary Items Report Start Time Report End Time $`NULL` NULL 28

num [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] 460168401 400504099 59664302 626094 171240103 148604471 22635633 534352332 [2,] 224449710 189673422 34776287 0

15 讀取資料從網路的檔案 # 和訊網萬科股票的相關金融資料 ls() names(tales1) tales1[[1]] tales1[[2]] tales1[[3]] tales1[[4]] aa <- tales1[[2]] aa class(aa) aa[1] aa[2] aa[3] 29 讀取資料從網路的檔案 # 2016 年美國總統選舉 # load packages source( "U08_2016USA.R" ) > data.main.num [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [1,] [2,] [3,] [4,] [5,] [6,] [7,] [8,] [9,] [10,] > 30

16 大綱作業 31 HW07: 檔案資料輸入與輸出 On 4/25, 2017 假設您預計購買一個手機, 從網路上找到一些手機的資料, 如下表所示 : 廠牌 Brand Apple HTC HTC ASUS ASUS 型號 Model iphone7 OneM8 OneS9 ZenFoneDeluxe ZenFoneZoom 價格 ( 元 ) Price 螢幕 ( 吋 ) Screen 重量 ( 克 ) Weight 記憶體 (GB) GB 日期 ( 年月日 ) Date 這筆資料, 已經整理成四個檔案 :( 請到課程網站下載到您的工作目錄 ) HW07_Phone.txt, HW07_Phone.csv, HW07_Phone.web, HW07_Phone.html 32

17 HW07: 檔案資料輸入與輸出編輯一個程式於.R 檔, 完成下面的工作 : 用 read.table 取讀取 HW07_Phone.txt 中的資料, 指定到 :myphonetxt 用 read.csv 取讀取 HW07_Phone.csv 中的資料, 指定到 :myphonecsv 用 read.table 去取讀取下面網址的檔案, 指定到 :myphonewebtab 用 read.csv 去取讀取下面網址的檔案, 指定到 :myphonewebcsv 用 readlines( ) 去取讀取檔案, 指定到 :myphonehtml 比較一下 :myphonetxt,myphonecsv,myphonewebtab 與 myphonewebcsv,myphonehtml, 這幾個物件, 看看 : 內容是否相同? 格式是否相同? 如果不同, 可否重新讀入 ( 例如 : 設定不同的參數 ), 或者重新指定到新的物件, 使得這四個的內容與格式都相同請利用讀進來的數據, 建立一個 5x3 的矩陣 (matrix):number, 放置五個手機的價格, 螢幕, 重量三種資料把執行的過程, 以及產生的數據等, 整理到報告檔 (pdf or pptx) On 4/25, HW07: 檔案資料輸入與輸出繳交下面檔案, 檔案名稱 :HW07_ 學號 _ 關鍵字.xxx 主要指定檔案 : HW07_B _ReadData.R 報告檔案 : HW07_B _ReadData.pdf 或者.pptx 或者是 :R Markdown 等整合式的檔案,.Rmd 與.pdf 繳交方式與期限 : 有關 R Markdown 的使用方式, 可以參考下面說明 : 上面兩個檔案到 :ntucp105s@gmail.com 主旨 :HW07_B _ReadData ( 就是, 作業編號 _ 您的學號 _ 關鍵字 ) 繳交期限 :4/30 (Sun), 2017, 11pm 以前學習方式 : 請至下面網址輸入此次的學習方式所花的時間 : On 4/25,

HW07: 加分題課程學習時間資料分析 On 4/25, 2017 2016 年美國總統選舉 35 HW07:

xxx 主要指定檔案 : HWPlus_B01921001_LearnTime.

18 HW07: 加分題課程學習時間資料分析 On 4/25, 年美國總統選舉 35 HW07: 加分題 On 4/25, 2017 繳交下面檔案, 檔案名稱 :HWPlus_ 學號 _ 關鍵字.xxx 主要指定檔案 : HWPlus_B _LearnTime.R 報告檔案 : HWPlus_B _LearnTime.pdf 或者.pptx 主要指定檔案 : HWPlus_B _USA2016.R 報告檔案 : HWPlus_B _USA2016.pdf 或者.pptx 或者是 :R Markdown 等整合式的檔案,.Rmd 與.pdf 繳交方式與期限 : 上面兩個檔案到 :ntucp105s@gmail.com 主旨 :HWPlus_B _LearnTime or HWPlus_B _USA2016 ( 就是, 作業編號 _ 您的學號 _ 關鍵字 ) 繳交期限 : 無 36

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PowerPoint Presentation 通識課程 : 106-2: EE4052 計算機程式設計之旅 Computer Programming 連豊力臺大電機系 Feb 2018 - Jun 2018 大綱 R 的故事安裝軟體 : R 軟體 The R Project for Statistical Computing https://www.r-project.org/ RStudio 軟體 RStudio https://www.rstudio.com/