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R Data Visualization Cookbook

R Data Visualization Cookbook

By : Gohil
4.2 (6)
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R Data Visualization Cookbook

R Data Visualization Cookbook

4.2 (6)
By: Gohil

Overview of this book

If you are a data journalist, academician, student or freelance designer who wants to learn about data visualization, this book is for you. Basic knowledge of R programming is expected.
Table of Contents (12 chapters)
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11
Index

Editing a data frame in R

Once we have generated a data and converted it into a data frame, we can edit any row or column of a data frame.

How to do it...

We can add or extract any column of a data frame using the dollar ($) symbol, as depicted in the following code:

data = data.frame(x = c(1:4), y = c("tom","jerry","luke","brian"))
data$age = c(2,2,3,5)
data

In the preceding example, we have added a new column called age using the $ operator. Alternatively, we can also add columns and rows using the rbind() and cbind() functions in R as follows:

age = c(2,2,3,5)
data = cbind(data, age)

The cbind and rbind functions can also be used to add columns or rows to an existing matrix.

To remove a column or a row from a matrix or data frame, we can simply use the negative sign before the column or row to be deleted, as follows:

data = data[,-2]

The data[,-2] line will delete the second column from our data.

To re-order the columns of a data frame, we can type the following lines in the R command window:

data = data.frame(x = c(1:4), y = c("tom","jerry","luke","brian"))
data = data[c(2,1)]# will reorder the columns
data

To view the column names of a data frame, we can use the names() function:

names(data)

To rename our column names, we can use the colnames() function:

colnames(data) = c("Number","Names")
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