R Data Analysis Cookbook and R Data Visualization Cookbook

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Data analytics with R has emerged as a very important focus for organizations of all kinds. R enables even those with only an intuitive grasp of the underlying concepts, without a deep mathematical background, to unleash powerful and detailed examinations of their data.Over 80 recipes to help you breeze through your data analysis projects using R

About This Book

• Analyse data with ready-to-use and customizable recipes
• Discover convenient functions to speed-up your work and data files
  

R Data Analysis Cookbook - Viswa Viswanathan.rar (5.91 MB, 需要: 50 个论坛币) 本附件包括：

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• Demystifies several R packages that seasoned data analysts regularly use
  

Who This Book Is For
This book is ideal for those who are already exposed to R, but have not yet used it extensively for data analytics and are seeking to get up and running quickly for analytics tasks. This book will help people who aspire to enhance their skills in any of the following ways:

• Perform advanced analyses and create informative and professional charts
• Become proficient in acquiring data from many sources
• Apply supervised and unsupervised data mining techniques
• Use R's features to present analyses professionally
  

In Detail
This book empowers you by showing you ways to use R to generate professional analysis reports. It provides examples for various important analysis and machine-learning tasks that you can try out with associated and readily available data. The book also teaches you to quickly adapt the example code for your own needs and save yourself the time needed to construct code from scratch.

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关键词：Cookbook Analysis Analysi Visual Analys background convenient important concepts detailed

1. Reading data from CSV files
   
2. 
   
3. CSV formats are best used to represent sets or sequences of records in which each record has an identical list of fields. This corresponds to a single relation in a relational database, or to data (though not calculations) in a typical spreadsheet.
   
4. 
   
5. Getting ready
   
6. 
   
7. If you have not already downloaded the files for this chapter, do it now and ensure that the auto-mpg.csv file is in your R working directory.
   
8. 
   
9. How to do it...
   
10. 
    
11. Reading data from .csv files can be done using the following commands:
    
12. 
    
13. Read the data from auto-mpg.csv, which includes a header row:
    
14. > auto <- read.csv("auto-mpg.csv", header=TRUE, sep = ",")
    
15. Verify the results:
    
16. > names(auto)

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1. Reading XML data
   
2. 
   
3. You may sometimes need to extract data from websites. Many providers also supply data in XML and JSON formats. In this recipe, we learn about reading XML data.
   
4. 
   
5. Getting ready
   
6. 
   
7. If the XML package is not already installed in your R environment, install the package now as follows:
   
8. 
   
9. > install.packages("XML")
   
10. How to do it...
    
11. 
    
12. XML data can be read by following these steps:
    
13. 
    
14. Load the library and initialize:
    
15. > library(XML)
    
16. > url <- "http://www.w3schools.com/xml/cd_catalog.xml"
    
17. Parse the XML file and get the root node:
    
18. > xmldoc <- xmlParse(url)
    
19. > rootNode <- xmlRoot(xmldoc)
    
20. > rootNode[1]
    
21. Extract XML data:
    
22. > data <- xmlSApply(rootNode,function(x) xmlSApply(x, xmlValue))
    
23. Convert the extracted data into a data frame:
    
24. > cd.catalog <- data.frame(t(data),row.names=NULL)
    
25. Verify the results:
    
26. > cd.catalog[1:2,]

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1. Reading JSON data
   
2. 
   
3. Several RESTful web services return data in JSON format—in some ways simpler and more efficient than XML. This recipe shows you how to read JSON data.
   
4. 
   
5. Getting ready
   
6. 
   
7. R provides several packages to read JSON data, but we use the jsonlite package. Install the package in your R environment as follows:
   
8. 
   
9. > install.packages("jsonlite")
   
10. If you have not already downloaded the files for this chapter, do it now and ensure that the students.json files and student-courses.json files are in your R working directory.
    
11. 
    
12. How to do it...
    
13. 
    
14. Once the files are ready and load the jsonlite package and read the files as follows:
    
15. 
    
16. Load the library:
    
17. > library(jsonlite)
    
18. Load the JSON data from files:
    
19. > dat.1 <- fromJSON("students.json")
    
20. > dat.2 <- fromJSON("student-courses.json")
    
21. Load the JSON document from the Web:
    
22. > url <- "http://finance.yahoo.com/webservice/v1/symbols/allcurrencies/quote?format=json"
    
23. > jsonDoc <- fromJSON(url)
    
24. Extract data into data frames:
    
25. > dat <- jsonDoc$list$resources$resource$fields
    
26. Verify the results:
    
27. > dat[1:2,]
    
28. > dat.1[1:3,]
    
29. > dat.2[,c(1,2,4:5)]

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1. Replacing missing values with the mean
   
2. 
   
3. When you disregard cases with any missing variables, you lose useful information that the nonmissing values in that case convey. You may sometimes want to impute reasonable values (those that will not skew the results of analyses very much) for the missing values.
   
4. 
   
5. Getting ready
   
6. 
   
7. Download the missing-data.csv file and store it in your R environment's working directory.
   
8. 
   
9. How to do it...
   
10. 
    
11. Read data and replace missing values:
    
12. 
    
13. > dat <- read.csv("missing-data.csv", na.strings = "")
    
14. > dat$Income.imp.mean <- ifelse(is.na(dat$Income), mean(dat$Income, na.rm=TRUE), dat$Income)
    
15. After this, all the NA values for Income will now be the mean value prior to imputation.

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