Difference between revisions of "R/C2/Indexing-and-Slicing-Data-Frames/English"

Opening slide

Learning Objectives

• Extract rows or columns from a data frame
• Create a subset from a data frame
• Retrieve data using double square brackets

Pre-requisites

• Data frames in R
• R script in RStudio and
• How to set Working directory in RStudio

If not, please locate the relevant tutorials on R on this website.

System Specifications

• Ubuntu Linux OS version 16.04
• R version 3.2.3
• RStudio version 1.1.456

Install R version 3.2.0 or higher.

Download Files

Download these files from the Code files link of this tutorial.

Highlight CaptaincyData.csv and mydataframe.R in the folder myProject

I have also set this folder as my Working Directory.

Click mydataframe.R in RStudio

Point to mydataframe.R in Rstudio.

For this, click on the script mydataframe.R

Script mydataframe.R opens in Rstudio.

captaincy <- read.csv("CaptaincyData.csv")

View(captaincy)

Also, View function is being used to see the contents of the file.

However, we recommend less than symbol followed by hyphen.

For this, there is a shortcut in RStudio.

Suppose we want to assign a value of 2 to a variable named testvar.

Alt + -

testvar <- 2

Alt and -(hyphen) keys simultaneously.

Then type 2 and press Enter.

Run the script mydataframe.R by clicking on the Source button.

Highlight third row of captaincy

captaincy[3,]

Type capt and press Enter to make the variable captaincy autocomplete. Now within square brackets 3 followed by comma. And press Enter.

Press Enter key at the end of every command.

Remember one of the most important features of RStudio include intelligent auto-completion of function names, packages, and R objects.

captaincy[3]

When we extract data using row number or column number, it is known as numeric indexing.

Highlight second and third rows of captaincy

captaincy[c(2,3),]

In the Source window, type the following command and press Enter.

Extracting this type of information is known as logical indexing.

captaincy[captaincy$played==25,]

Within square brackets captaincy dollar sign played equal to equal to 25 comma. Press Enter.

Highlight two equal to signs

Please note that there is no space between the two equal to signs.

We will fetch the names of all the captains.

Highlight first column of captaincy

captaincy[1]

Click on the script mydataframe.R

In the Source window, type captaincy and within square brackets 1.

captaincy["names"]

Within square brackets inside double quotes names

Extracting data by column names is known as name indexing.

captaincy[c("names", "won")]

We will create a subset of captains, who have won more than 30% of their matches.

This is called slicing a data frame.

Highlight victory column of captaincy in the Source window

For required subset of captains, their victory should be greater than 0.3.

Highlight names, played and won columns of captaincy

• number of matches played
• number of matches won

subData <- subset(captaincy, victory > 0.3, select = c("names", "played", "won"))

Now, press Enter after the comma followed by 0 point 3.

Highlight select

print(subData )

In the Source window, type print within parentheses subData .

Press Ctrl + S >> Press Ctrl+Enter keys.

Highlight the third value of fourth column in captaincy

We will extract the third value in the fourth column of the captaincy data frame.

captaincy[[4]][3]

Summary

• Extract rows or columns from a data frame
• Create a subset from a data frame
• Retrieve data using double square brackets

Assignment

• Create a subset from captaincy data frame with the captains who have played > 20 matches and lost < 14 matches.

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Acknowledgement

Thank You

This is Sudhakar Kumar from IIT Bombay signing off. Thanks for watching.