View Function in R

The View() function in R is a built-in function that allows users to view the contents of data structures interactively in a spreadsheet-like format. When we use the View() function, it opens a separate window or tab (depending on your R environment) displaying the data in a table format, making it easier to explore and understand the structure and contents of the data.

Features of the View() function

• Displays data in a table format similar to a spreadsheet.
• Allow sorting, filtering, and searching in the data.
• Provides variable types and basic statistics information.
• Provides export data options (e.g., CSV, Excel).

How to Use View()?

1. Creating a Data Frame

First, create a data frame by combining vectors

# Define vectors for each column
ID <- c(1, 2, 3, 4, 5)
Name <- c("Alice", "Bob", "Charlie", "David", "Eva")
Age <- c(25, 30, 22, 35, 28)
Score <- c(85, 92, 78, 95, 89)

# Combine vectors into a data frame
data <- data.frame(ID, Name, Age, Score)
data

Output:

ID Name Age Score
1 1 Alice 25 85
2 2 Bob 30 92
3 3 Charlie 22 78
4 4 David 35 95
5 5 Eva 28 89

2. View the Data Frame

View the created data frame using the View() function to see its contents in a viewer window.

View(data)

Output:

3. Exploring Data

Here we take a Iris dataset and filtering the data based on their Sepal Length which are greater than 5.0 cm.

# Load required packages
library(dplyr)

# Load the Iris dataset
d <- iris

# Filter rows 
fil_d <- d %>% filter(Sepal.Length > 5.0)

View(fil_d)

Output:

4. Sort and Search Data

• Sorting: Sort data in ascending or descending order by clicking column headers.
• Search: Search for certain values or patterns in the search box in the viewer

5. Export Data

Utilize options within the viewer window/tab to export data to CSV, Excel, or other formats if needed.

write.csv(filtered_data, "filtered_iris_data.csv", row.names = FALSE)

6. Interacting with Data

Explore and interact with the data dynamically in the viewer window/tab. We can scroll through the data, sort columns, filter rows, or search for specific values interactively.

7. Closing the Viewer

Once finished exploring the data, now close the viewer window/tab manually by clicking on the close button or using the appropriate option provided by the R environment.

Advantages

1. Quick and easy data exploration
2. Interactive features for insights
3. Seamless integration with R environment

Disadvantages

1. Limited functionality for complex tasks
2. Resource-intensive with large datasets
3. Platform-dependent appearance

Related Articles:

• R Programming Language – Introduction
• Functions in R Programming

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Article Tags :

• R Language
• R Functions