[Reading Notes]R in Action

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• R in Action

• By: Robert Kabacoff

• Publisher: Manning Publications

• Pub. Date: August 24, 2011

• Print ISBN-10: 1-935182-39-0

• Print ISBN-13: 978-1-935182-39-9

• Pages in Print Edition: 472

• Subscriber Rating: [5 Ratings] Subscriber Reviews

• 
  

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关键词：reading Action notes DING READ Robert

这是最新第2版的
https://bbs.pinggu.org/thread-3914012-1-1.html

1. Listing 2.2. Using matrix subscripts
   
2. 
   
3. > x <- matrix(1:10, nrow=2)
   
4. > x
   
5.       [,1] [,2] [,3] [,4] [,5]
   
6. [1,]     1    3    5    7    9
   
7. [2,]    2    4    6    8    10
   
8. > x[2,]
   
9.   [1]  2  4  6  8 10
   
10. > x[,2]
    
11. [1] 3 4
    
12. > x[1,4]
    
13. [1] 7
    
14. > x[1, c(4,5)]
    
15. [1] 7 9

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Listing 2.4. Creating a data frame
A data frame is more general than a matrix in that different columns can contain different modes of data (numeric, character, etc.). It’s similar to the datasets you’d typically see in SAS, SPSS, and Stata. Data frames are the most common data structure you’ll deal with in R.

The patient dataset in table 2.1 consists of numeric and character data. Because there are multiple modes of data, you can’t contain this data in a matrix. In this case, a data frame would be the structure of choice.

A data frame is created with the data.frame() function:

1. > patientID <- c(1, 2, 3, 4)
   
2. > age <- c(25, 34, 28, 52)
   
3. > diabetes <- c("Type1", "Type2", "Type1", "Type1")
   
4. > status <- c("Poor", "Improved", "Excellent", "Poor")
   
5. > patientdata <- data.frame(patientID, age, diabetes, status)
   
6. > patientdata
   
7.   patientID age  diabetes     status
   
8. 1         1  25     Type1       Poor
   
9. 2         2  34     Type2   Improved
   
10. 3         3  28     Type1  Excellent
    
11. 4         4  52     Type1       Poor

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1. Entering data from the keyboard
   
2. 
   
3. mydata <- data.frame(age=numeric(0),
   
4.   gender=character(0), weight=numeric(0))
   
5. mydata <- edit(mydata)

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1. Importing data from a delimited text file
   
2. 
   
3. mydataframe <- read.table(file, header=logical_value,
   
4.   sep="delimiter", row.names="name")

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1. Importing data from Excel
   
2. library(RODBC)
   
3. channel <- odbcConnectExcel("myfile.xls")
   
4. mydataframe <- sqlFetch(channel, "mysheet")
   
5. odbcClose(channel)

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1. library(xlsx)
   
2. workbook <- "c:/myworkbook.xlsx"
   
3. mydataframe <- read.xlsx(workbook, 1)

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1. Import Data from SPSS
   
2. library(Hmisc)
   
3. mydataframe <- spss.get("mydata.sav", use.value.labels=TRUE)

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1. Import Data From SAS
   
2. mydata <- read.table("mydata.csv", header=TRUE, sep=",")

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1. Importing data from Stata
   
2. 
   
3. Importing data from Stata to R is straightforward. The necessary code looks like this:
   
4. 
   
5. library(foreign)
   
6. mydataframe <- read.dta("mydata.dta")

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