R-Data Frames

R Programming Language is an open-source programming language that is widely used as a statistical software and data analysis tool. Data Frames in R Language are generic data objects of R that are used to store tabular data. 

Data frames can also be interpreted as matrices where each column of a matrix can be of different data types. R data frame is made up of three principal components, the data, rows, and columns. 

R Data Frames Structure

As we can see in the image below, this is how a data frame is structured. The data is presented in tabular form, which makes it easier to operate and understand.

1. Create Data Frame in R Programming Language

To create an R data frame use data.frame() function and then pass each of the vectors we have created as arguments to the function.

friend.data <- data.frame(
    friend_id = c(1:5), 
    friend_name = c("Sachin", "Sourav", 
                    "Dravid", "Sehwag", 
                    "Dhoni"),
    stringsAsFactors = FALSE
)
print(friend.data)

Output:

2. Printing Structure of the R Data Frame

One can get the structure of the R data frame using str() function in R. It can display even the internal structure of large lists which are nested. It provides one-liner output for the basic R objects letting the user know about the object and its constituents. 

friend.data <- data.frame(
    friend_id = c(1:5), 
    friend_name = c("Sachin", "Sourav", 
                    "Dravid", "Sehwag", 
                    "Dhoni"),
    stringsAsFactors = FALSE
)
print(str(friend.data))

Output:

3. Summary of Data in the R data frame

In the R data frame, the statistical summary and nature of the data can be obtained by applying summary() function. It is a generic function used to produce result summaries of the results of various model fitting functions. The function invokes particular methods which depend on the class of the first argument. 

friend.data <- data.frame(
    friend_id = c(1:5), 
    friend_name = c("Sachin", "Sourav", 
                    "Dravid", "Sehwag", 
                    "Dhoni"),
    stringsAsFactors = FALSE
)
print(summary(friend.data))

Output:

4. Extract Data from Data Frame in R 

Extracting data from an R data frame means that to access its rows or columns. One can extract a specific column from an R data frame using its column name. 

friend.data <- data.frame(
    friend_id = c(1:5), 
    friend_name = c("Sachin", "Sourav", 
                    "Dravid", "Sehwag", 
                    "Dhoni"),
    stringsAsFactors = FALSE
)

result <- data.frame(friend.data$friend_name)
print(result)

Output:

5. Expand Data Frame in R

A data frame in R can be expanded by adding new columns and rows to the already existing R data frame. 

friend.data <- data.frame(
    friend_id = c(1:5), 
    friend_name = c("Sachin", "Sourav", 
                    "Dravid", "Sehwag", 
                    "Dhoni"),
    stringsAsFactors = FALSE
)

friend.data$location <- c("Kolkata", "Delhi", 
                          "Bangalore", "Hyderabad",
                          "Chennai")
resultant <- friend.data
print(resultant)

Output:

In R, one can perform various types of operations on a data frame like accessing rows and columns, selecting the subset of the data frame, editing data frames, delete rows and columns in a data frame, etc.

Please refer to DataFrame Operations in R to know about all types of operations that can be performed on a data frame.

6. Access Items in R Data Frame

We can select and access any element from data frame by using single $ ,brackets [ ] or double brackets [[]]  to access columns from a data frame.

friend.data <- data.frame(
  friend_id = c(1:5), 
  friend_name = c("Sachin", "Sourav", 
                  "Dravid", "Sehwag", 
                  "Dhoni"),
  stringsAsFactors = FALSE
)

print(friend.data[1])

friend.data[['friend_name']]

print(friend.data$friend_id)

Output:

7. Amount of Rows and Columns in R Data Frame

We can find out how many rows and columns present in our data frame by using dim function.

friend.data <- data.frame(
  friend_id = c(1:5), 
  friend_name = c("Sachin", "Sourav", 
                  "Dravid", "Sehwag", 
                  "Dhoni"),
  stringsAsFactors = FALSE
)

dim(friend.data)

Output:

[1] 5 2

8. Add Rows and Columns

We can easily add rows and columns in a data frame. Insertion helps in expanding the already existing data frame, without needing a new one.

8.1 Add Rows in R Data Frame

To add rows in a Data Frame, we can use a built-in function rbind(). Following example demonstrate the working of rbind() in R Data Frame.

Products <- data.frame(
  Product_ID = c(101, 102, 103),
  Product_Name = c("T-Shirt", "Jeans", "Shoes"),
  Price = c(15.99, 29.99, 49.99),
  Stock = c(50, 30, 25)
)

cat("Existing dataframe (Products):\n")
print(Products)

New_Product <- c(104, "Sunglasses", 39.99, 40)
Products <- rbind(Products, New_Product)

cat("\nUpdated dataframe after adding a new product:\n")
print(Products)

Output:

8.2 Add Columns in R Data Frame

To add columns in a Data Frame, we can use a built-in function cbind(). Following example demonstrate the working of cbind() in R Data Frame.

Products <- data.frame(
  Product_ID = c(101, 102, 103),
  Product_Name = c("T-Shirt", "Jeans", "Shoes"),
  Price = c(15.99, 29.99, 49.99),
  Stock = c(50, 30, 25)
)

cat("Existing dataframe (Products):\n")
print(Products)

Discount <- c(5, 10, 8)
Products <- cbind(Products, Discount)

colnames(Products)[ncol(Products)] <- "Discount"

cat("\nUpdated dataframe after adding a new column 'Discount':\n")
print(Products)

Output:

9. Remove Rows and Columns 

A data frame in R removes columns and rows from the already existing R data frame. 

9.1 Remove Row in R Data Frame

We first created a data frame called data with three columns: friend_id, friend_name, and location. To remove a row with friend_id equal to 3, we used the subset() function and specified the condition friend_id != 3. This removed the row with friend_id equal to 3.

library(dplyr)

data <- data.frame(
  friend_id = c(1, 2, 3, 4, 5),
  friend_name = c("Sachin", "Sourav", "Dravid", "Sehwag", "Dhoni"),
  location = c("Kolkata", "Delhi", "Bangalore", "Hyderabad", "Chennai")
)

print(data)

data <- subset(data, friend_id != 3)

print(data)

Output:

9.2 Remove Column in R Data Frame

To remove the location column, we used the select() function and specified -location. The - sign indicates that we want to remove the location column. The resulting data frame data will have only two columns: friend_id and friend_name.

library(dplyr)

data <- data.frame(
  friend_id = c(1, 2, 3, 4, 5),
  friend_name = c("Sachin", "Sourav", "Dravid", "Sehwag", "Dhoni"),
  location = c("Kolkata", "Delhi", "Bangalore", "Hyderabad", "Chennai")
)

print(data)

data <- select(data, -location)

print(data)

Output:

10. Combining Data Frames in R

There are 2 way to combine data frames in R. we can either combine them vertically or horizontally. Let's look at both cases with example:

10.1 Combine Data Frame Vertically

If we want to combine 2 data frames vertically, we can use rbind() function. This function works for combination of two or more data frames.

df1 <- data.frame(
  Name = c("Alice", "Bob"),
  Age = c(25, 30),
  Score = c(80, 75)
)

df2 <- data.frame(
  Name = c("Charlie", "David"),
  Age = c(28, 35),
  Score = c(90, 85)
)

cat("Dataframe 1:\n")
print(df1)

cat("\nDataframe 2:\n")
print(df2)

combined_df <- rbind(df1, df2)

cat("\nCombined Dataframe:\n")
print(combined_df)

Output:

10.2 Combine Data Frame Horizontally

If we want to combine 2 data frames horizontally, we can use cbind() function. This function works for combination of two or more data frames.

df1 <- data.frame(
  Name = c("Alice", "Bob"),
  Age = c(25, 30),
  Score = c(80, 75)
)

df2 <- data.frame(
  Height = c(160, 175),
  Weight = c(55, 70)
)

cat("Dataframe 1:\n")
print(df1)

cat("\nDataframe 2:\n")
print(df2)

combined_df <- cbind(df1, df2)

cat("\nCombined Dataframe:\n")
print(combined_df)

Output:

In this article we have covered R Data Frames, and all basic operations like create, access, summary, add and remove.

Also Read:

• R - Objects
• Data Structures in R Programming