Sign In Start Free Trial
Account

Add to playlist

Create a Playlist

Modal Close icon
You need to login to use this feature.
  • Book Overview & Buying R Data Science Essentials
  • Table Of Contents Toc
R Data Science Essentials

R Data Science Essentials

By : Raja B. Koushik, Sharan Kumar Ravindran
3 (3)
close
close
R Data Science Essentials

R Data Science Essentials

3 (3)
By: Raja B. Koushik, Sharan Kumar Ravindran

Overview of this book

With organizations increasingly embedding data science across their enterprise and with management becoming more data-driven it is an urgent requirement for analysts and managers to understand the key concept of data science. The data science concepts discussed in this book will help you make key decisions and solve the complex problems you will inevitably face in this new world. R Data Science Essentials will introduce you to various important concepts in the field of data science using R. We start by reading data from multiple sources, then move on to processing the data, extracting hidden patterns, building predictive and forecasting models, building a recommendation engine, and communicating to the user through stunning visualizations and dashboards. By the end of this book, you will have an understanding of some very important techniques in data science, be able to implement them using R, understand and interpret the outcomes, and know how they helps businesses make a decision.
Table of Contents (10 chapters)
close
close
9
Index

Reading data from different sources

Importing data to R is quite simple and can be done from multiple sources. The most common method of importing data to R is through the comma-separated values (CSV) format. The CSV data can be accessed through the read.csv function. This is the simplest way to read the data as it requires just a single line command and the data is ready. Depending on the quality of the data, it may or may not require processing.

data <- read.csv("c:/local-data.csv")

The other function similar to read.csv is read.csv2. This function is also used to read the CSV files but the difference is that read.csv2 is mostly used in the European countries, where comma is used as decimal point and semicolon is used as a separator. Also, the data can be read from R using a few more parameters, such as read.table and read.delim. By default, read.delim is used to read tab-delimited files, and the read.table function can be used to read any file by supplying suitable parameters as the input:

data  <- read.delim("local-data.txt", header=TRUE, sep="\t")
data  <- read.table("local-data.txt", header=TRUE, sep="\t")

Tip

Downloading the example code

You can download the example code files for all Packt books you have purchased from your account at http://www.packtpub.com. If you purchased this book elsewhere, you can visit http://www.packtpub.com/support and register to have the files e-mailed directly to you.

All the preceding functions can take multiple parameters that would explain the data source's format at best. Some of these parameters are as follows:

  • header: This is a logical value indicating the presence of column names in the file. When it is set to TRUE, it indicates that the column names are present. By default, the value is considered as TRUE.
  • sep: This defines the separator in the file. By default, the separator is comma for read.csv, tab for read.delim, and white space for the read.table function.
  • nrows: This specifies the maximum number of rows to read from the file. By default, the entire file will be read.
  • row.names: This will specify which column should be considered as a row name. When it is set as NULL, the row names will be forced as numbers. This parameter will take the column's position (one represents the first column) as input.
  • fill: This parameter when set as TRUE can read the data with unequal row lengths and blank fields are implicitly added.

These are some of the common parameters used along with the functions to read the data from a file.

We have so far explored reading data from a delimited file. In addition to this, we can read data in Excel formats as well. This can be achieved using the xlsx or XLConnect packages. We will see how to use one of these packages in order to read a worksheet from a workbook:

install.packages("xlsx")
library(xlsx)
mydata <- read.xlsx("DTH AnalysisV1.xlsx", 1)
head(mydata)

In the preceding code, we first installed the xlsx package that is required to read the Excel files. We loaded the package using the library function, then used the read.xlsx function to read the excel file, and passed an additional parameter, 1, that specifies which sheet to read from the excel file.

CONTINUE READING
83
Tech Concepts
36
Programming languages
73
Tech Tools
Icon Unlimited access to the largest independent learning library in tech of over 8,000 expert-authored tech books and videos.
Icon Innovative learning tools, including AI book assistants, code context explainers, and text-to-speech.
Icon 50+ new titles added per month and exclusive early access to books as they are being written.
R Data Science Essentials
notes
bookmark Notes and Bookmarks search Search in title playlist Add to playlist font-size Font size

Change the font size

margin-width Margin width

Change margin width

day-mode Day/Sepia/Night Modes

Change background colour

Close icon Search
Country selected

Close icon Your notes and bookmarks

Confirmation

Modal Close icon
claim successful

Buy this book with your credits?

Modal Close icon
Are you sure you want to buy this book with one of your credits?
Close
YES, BUY

Submit Your Feedback

Modal Close icon
Modal Close icon
Modal Close icon