Data Frame In R - A Practical ✍️ Approach For Creating And Using Data Frame In R Language

This article focuses on the R Data Frame definition, creation, and practical uses in real-world examples.

Introduction

 
So far, we have learned about Vector in R which is similar to a one-dimensional array. It contains only elements of only one data type, i.e., all elements of a vector will be of a similar data type.
 
Now, we will learn about Data Frame in R. Let's start now.
 

Data Frame in R 

 
Data Frame in R is a kind of data type similar to other data types, such as numeric, character, vector, etc. It is a two-dimensional object. It can contain different data type elements like numeric, character or logical in different columns. Here, one thing we need to remember is that while creating it, in any single column, all the elements should be of the same type i.e., either numeric, character, logic or something else.
 
In general, we can say the data frame is the more general form of the matrix as well as the collection of different vectors.
 
Let's see the below example of Data Frame in the image. Here, as you can see, there are seven columns containing different data type elements. But, in any single column, all elements are of the same type which verifies the definition of Data Frame in R as said above.
 
Data Frame in R - A Practical ✍️ Approach For Creating And Using Data Frame In R Language
 

Creating Data Frame in R Studio

 
To create a Data Frame in R, we use the method data.frame(). Now, we shall see the below steps to create a Data Frame.
 
First, we create a vector of Rank, Country, 2019 Population, 2018 Population, and Growth Rate and then, we will use these vectors to create the above-shown vector.
 
We will use c() function to create vector as shown below. Other way of creating vector in R using colon (:). We will use both methods here.
  1. #Creating Rank Vector  
  2. Rank <- 1:10  
  3. #Creating Country Vector  
  4. Country <- c("China""India""United States""Indonesia""Pakistan""Brazil",  
  5. "Nigeria""Bangladesh""Russia""Mexico")  
  6. #Creating 2019 Population Vector  
  7. Population.2019 <- c(14337836861366417754329064917270625568216565318,  
  8.                     211049527200963599163046161145872256127575529)  
  9. #Creating 2018 Population Vector  
  10. Population.2018 <- c(14276477861352642280327096265267670543212228286,  
  11.                      209469323195874683161376708145734038126190788)  
  12. #Creating Growth Rate Vector  
  13. Growth.Rate <- c("0.43%""1.02%""0.60%""1.10%""2.04%""0.75%""2.60%",  
  14.                  "1.03%""0.09%""1.10%")  
  15.   
  16. #Creating Data Frame using above vectors  
  17. DataFrame.WorldPopulation <- data.frame(Rank, Country, Population.2019, Population.2018,  
  18.                                         Growth.Rate)   
  19. #Printing the above Data Frame  
  20. DataFrame.WorldPopulation  
Below image shows the above steps involved in R studio for creating Data Frame.
 
Data Frame in R - A Practical ✍️ Approach For Creating And Using Data Frame In R Language
 
The below image is the output of newly created Data Frame in R studio. It shows same as shown in first image which was our goal to create as a Data Frame in R.
 
Data Frame in R - A Practical ✍️ Approach For Creating And Using Data Frame In R Language
 

Filtering/Selection Elements of R Data Frame

 
So, far we have already created Data Frame in R. Now we shall see and learn how to filter elements of Data Frame in R. We can filter elements of Data Frame in R in the same way as we select elements of matrix in R.
 

Filtering Element By Index

 
If we want to filter all countries then we can use the index 2 which can be passed in square bracket [] as shown below. The index in R Data Frame start with 1 not 0. So, to select all country we will pass 2 as index. 
  1. DataFrame.WorldPopulation[2]  
Data Frame in R - A Practical ✍️ Approach For Creating And Using Data Frame In R Language

 
Filtering Element By Column Name

 
To filter Data Frame elements in R using column name we simply pass the column name in square brackets as shown below. It is shown below.
 
To select all the elements of Growth Rate we use the following code.
  1. DataFrame.WorldPopulation["Growth.Rate"]  
Data Frame in R - A Practical ✍️ Approach For Creating And Using Data Frame In R Language
 

Filtering Elements Of Multiple Columns By Column Name

 
To filter multiple columns element we can pass vector in square bracket as shown below. Select country, 2019 population and growth rate. 
  1. > DataFrame.WorldPopulation [c("Country""Population.2019""Growth.Rate")]  
Data Frame in R - A Practical ✍️ Approach For Creating And Using Data Frame In R Language
 

Filtering Elements Of Particular Row By Passing Criteria

 
To select all the elements i.e., all columns of Rank 2 we use the following condition.
  1. DataFrame.WorldPopulation [DataFrame.WorldPopulation$Rank == 2, ]  
The output is as shown below.
 
Data Frame in R - A Practical ✍️ Approach For Creating And Using Data Frame In R Language
 

Filtering Data Frame Elements By And (&) Condition

 
We pass single ampersand (&) for supplying and providing and condition to fulfill multiple data selection criteria as shown below. The below code shows that we select all the elements of Data Frame whose growth rate is equal to 1.10% and rank is greater than 5.
  1. DataFrame.WorldPopulation [DataFrame.WorldPopulation$Growth.Rate == "1.10%" & DataFrame.WorldPopulation$Rank > 5, ]  
You can see the output as shown in the below image.
 
Data Frame in R - A Practical ✍️ Approach For Creating And Using Data Frame In R Language
 
Remember to create Data Frame in R the number of elements in each column should be the same otherwise it will give the following error which is a differing number of rows. When I created Rank using 1:11 and used all the other vector for creating Data Frame, it shows the error as shown below.
 
Data Frame in R - A Practical ✍️ Approach For Creating And Using Data Frame In R Language
 

Summary

 
In this article, we have learned about the Data Frame in R. We have seen what is the Data Frame in R, how to create Data Frame and also how to filter Data Frame elements in R.
 
I hope you learned and enjoyed it. I look forward to seeing your feedback.