Renaming the data variable

The use of a data frame enables the user to select and filter data by row names and column names. As not all imported datasets contain row names and column names, we need to rename this dataset with a built-in naming function.

Getting ready

In this recipe, you need to prepare your environment with R installed and a computer that can access the Internet.

How to do it…

Perform the following steps to rename data:

1. First, download employees.csv from the GitHub link https://github.com/ywchiu/rcookbook/raw/master/chapter3/employees.csv:

   > download.file("https://github.com/ywchiu/rcookbook/raw/master/chapter3/employees.csv", " employees.csv")
   

2. Additionally, download salaries.csv from the GitHub link https://github.com/ywchiu/rcookbook/raw/master/chapter3/salaries.csv:

   > download.file("https://github.com/ywchiu/rcookbook/raw/master/chapter3/salaries.csv", "salaries.csv")
   

3. Next, read the file into an R session with the read.csv function:

   > employees <- read.csv('employees.csv', head=FALSE)
   > salaries <- read.csv('salaries.csv', head=FALSE)
   

4. Use the names function to view the column names of the dataset:

   > names(employees)
   [1] "V1" "V2" "V3" "V4" "V5" "V6"
   > names(salaries)
   [1] "V1" "V2" "V3" "V4"
   

5. Next, rename columns with a given names vector:

   > names(employees) <- c("emp_no", "birth_date", "first_name", "last_name", "gender", "hire_date")
   > names(employees)
   [1] "emp_no" "birth_date" "first_name" "last_name" 
   [5] "gender" "hire_date"
   

6. Besides using names, you can also rename columns with the colnames function:

   > colnames (salaries) <- c("emp_no", "salary", "from_date", "to_date")
   > colnames (salaries)
   [1] "emp_no" "salary" "from_date" "to_date"
   

7. In addition to revising the column names, we can also revise row names with the rownames function:

   > rownames (salaries) <- salaries$emp_no
   

How it works…

In this recipe, we demonstrated how to rename datasets with the names function. First, we used the download.file function to download both salaries.csv and employees.csv from GitHub. Then, we used the names function to examine the column names of these two datasets. To revise the column names of these two datasets, we simply assigned a character vector to the name of the dataset. We can also revise column names with the colnames function. Finally, we can revise the row names of the dataset to emp_no with the rownames function.

There's more…

To avoid having to specify column names and row names separately with the colnames and rownames functions, we can use the dimnames function to specify both column names and row names in one operation:

> dimnames(employees) <- list(c(1,2,3,4,5,6,7,8,9,10), c("emp_no", "birth_date", "first_name", "last_name", "gender", "hire_date"))

In this code, the first input vector within the list indicates the row names, and the second input vector points to the column names.