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R Data Analysis Cookbook, Second Edition

You're reading from   R Data Analysis Cookbook, Second Edition Customizable R Recipes for data mining, data visualization and time series analysis

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Product type Paperback
Published in Sep 2017
Publisher Packt
ISBN-13 9781787124479
Length 560 pages
Edition 2nd Edition
Languages
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Authors (3):
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Kuntal Ganguly Kuntal Ganguly
Author Profile Icon Kuntal Ganguly
Kuntal Ganguly
Shanthi Viswanathan Shanthi Viswanathan
Author Profile Icon Shanthi Viswanathan
Shanthi Viswanathan
Viswa Viswanathan Viswa Viswanathan
Author Profile Icon Viswa Viswanathan
Viswa Viswanathan
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Toc

Table of Contents (14) Chapters Close

Preface 1. Acquire and Prepare the Ingredients - Your Data 2. What's in There - Exploratory Data Analysis FREE CHAPTER 3. Where Does It Belong? Classification 4. Give Me a Number - Regression 5. Can you Simplify That? Data Reduction Techniques 6. Lessons from History - Time Series Analysis 7. How does it look? - Advanced data visualization 8. This may also interest you - Building Recommendations 9. It's All About Your Connections - Social Network Analysis 10. Put Your Best Foot Forward - Document and Present Your Analysis 11. Work Smarter, Not Harder - Efficient and Elegant R Code 12. Where in the World? Geospatial Analysis 13. Playing Nice - Connecting to Other Systems

Reading data from R files and R libraries

During data analysis, you will create several R objects. You can save these in the native R data format and retrieve them later as needed.

Getting ready

First, create and save the R objects interactively, as shown in the following code. Make sure you have write access to the R working directory.

> customer <- c("John", "Peter", "Jane") 
> orderdate <- as.Date(c('2014-10-1','2014-1-2','2014-7-6'))
> orderamount <- c(280, 100.50, 40.25)
> order <- data.frame(customer,orderdate,orderamount)
> names <- c("John", "Joan")
> save(order, names, file="test.Rdata")
> saveRDS(order,file="order.rds")
> remove(order)

After saving the preceding code, the remove() function deletes the object from the current session.

How to do it...

To be able to read data from R files and libraries, follow these steps:

  1. Load data from the R data files into memory:
> load("test.Rdata") 
> ord <- readRDS("order.rds")
  1. The datasets package is loaded in the R environment by default and contains the iris and cars datasets. To load these datasets data into memory, use the following code:
> data(iris) 
> data(list(cars,iris))

The first command loads only the iris dataset, and the second loads both the cars and iris datasets.

How it works...

The save() function saves the serialized version of the objects supplied as arguments along with the object name. The subsequent load() function restores the saved objects, with the same object names that they were saved with, to the global environment by default. If there are existing objects with the same names in that environment, they will be replaced without any warnings.

The saveRDS() function saves only one object. It saves the serialized version of the object and not the object name. Hence, with the readRDS() function, the saved object can be restored into a variable with a different name from when it was saved.

There's more...

The preceding recipe has shown you how to read saved R objects. We see more options in this section.

Saving all objects in a session

The following command can be used to save all objects:

> save.image(file = "all.RData") 

Saving objects selectively in a session

To save objects selectively, use the following commands:

> odd <- c(1,3,5,7) 
> even <- c(2,4,6,8)
> save(list=c("odd","even"),file="OddEven.Rdata")

The list argument specifies a character vector containing the names of the objects to be saved. Subsequently, loading data from the OddEven.Rdata file creates both odd and even objects. The saveRDS() function can save only one object at a time.

Attaching/detaching R data files to an environment

While loading Rdata files, if we want to be notified whether objects with the same names already exist in the environment, we can use:

> attach("order.Rdata") 

The order.Rdata file contains an object named order. If an object named order already exists in the environment, we will get the following error:

The following object is masked _by_ .GlobalEnv: 

order

Listing all datasets in loaded packages

All the loaded packages can be listed using the following command:

> data() 
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