Get_Field() Function In R

R is a powerful Programming Language that is widely used by data scientists and analysts. This language helps statistical analysis by providing a wide range of libraries and packages. These packages and libraries provide functions that make work easier and improve accuracy as well. One such function is the Get_Field() function in R Programming Language used to extract specific values from a data frame. In this article, we will understand the use of this function with the help of multiple examples.

get_function is a custom function in R. It is not a built-in function and therefore we have to define it first. This function is used to extract specific values from the dataset. This function makes our work easier because we don’t need to go through the entire dataset to get certain information, instead, we can use get_field() to get the required value. The basic syntax to use this function is given below:

#syntax to use get_field function

get_field(data, field_name)

data: it is the dataset or the list from which the value is to be extracted.
field_name: the name of the field or column which is to be extracted.

How get_field() Works

This function makes the code more readable and easy to access. get_field helps use the arguments to locate the field and return the required value which also reduces the time consumption. The function is designed to simplify the process of extracting specific values from a dataset. It also works when a certain field name is removed, added, or moved, it can still locate the field and return the specific value. We can understand this in a better way with the help of examples.

Extracting a Column from a Data Frame

This is not an in-built function therefore we need to define it first. To define the get_function we can use the below given code.

R

# Custom get_field() function 
get_field <- function(data, field_name) { 
  return(data[[field_name]]) 
} 

After defining the function we can use this custom function for its use. In this example, we will use a fictional small dataset.

R

# Sample data frame 
my_data <- data.frame( 
  Name = c("Alice", "Bob", "Charlie"), 
  Age = c(25, 30, 22), 
  Score = c(95, 87, 92) 
) 
my_data 
  
# Using get_field() to extract the "Age" column 
ages <- get_field(my_data, "Age") 
print(ages)

Output:

     Name Age Score
1   Alice  25    95
2     Bob  30    87
3 Charlie  22    92

[1] 25 30 22

Our output is the required age that we got using the get_field function for the respective names and scores of our dataset.

Retrieving a List Element

We can also extract a list element with the help of this function. In this example, we will take another fictional dataset.

R

# Sample list 
my_list <- list( 
  Name = c("David", "Eva", "Frank"), 
  Grade = c("A", "B", "C"), 
  Status = c("Pass", "Fail", "Pass") 
) 
my_list  
  
# Using get_field() to extract the "Status" list element 
status <- get_field(my_list, "Status") 
print(status) 

Output:

$Name
[1] "David" "Eva"   "Frank"

$Grade
[1] "A" "B" "C"

$Status
[1] "Pass" "Fail" "Pass"

[1] "Pass" "Fail" "Pass"

In this case, the get_field() function is applied to a list (my_list), and it extracts the “Status” element. The resulting status vector contains the pass/fail statuses.

Extracting a specific value

To find a specific value from a large dataset we can use this function instead of going through the entire dataset. This will save us time as well. Here we will use a large in-built dataset in R called the “mtcars” dataset. This dataset has information related to different cars of different brands and their features such as ‘horsepower’, ‘mpg’, ‘cylinder’ etc.
We will try to extract the value of the horsepower of the Ford Mustang car for a specific mpg value.

R

# Custom get_field_value() function for extracting a single value 
get_field_value <- function(data, row_index, col_name) { 
  return(data[row_index, col_name]) 
} 
  
# Load the mtcars dataset 
data(mtcars) 
  
# Find the row index for the Ford Mustang based on the model name 
mustang_row_index <- which(mtcars$mpg == "15.8") 
  
# Check if the Mustang is found and print the result 
if (length(mustang_row_index) > 0) { 
  mustang_horsepower <- get_field_value(mtcars, mustang_row_index, "hp") 
  cat("Horsepower of the Ford Mustang:", mustang_horsepower, "\n") 
} else { 
  cat("Ford Mustang not found in the dataset.\n") 
} 

Output:

Horsepower of the Ford Mustang: 264

This code gave us the specific value of HP for the brand we needed. This saved our time and effort and is more accurate as well.

Extracting Sales Value using the get_field function

In this example, we will create a fictional dataset using different columns ‘Transaction_ID’, ‘Product_Name’, ‘Category’, ‘Quantity’, ‘Price’, and ‘Date’. We will use the get_field function to extract the total sales value.

R

# Custom get_field_value() function for extracting a single value 
get_field_value <- function(data, row_index, col_name) { 
  return(data[row_index, col_name]) 
} 
  
# Create a larger dataset for sales data 
set.seed(123)  # Set seed for reproducibility 
num_transactions <- 1000 
  
sales_data <- data.frame( 
  Transaction_ID = seq(1, num_transactions), 
  Product_Name = sample(c("Laptop", "Phone", "Tablet", "Printer", "Monitor"),  
                        num_transactions, replace = TRUE), 
  Category = sample(c("Electronics", "Office Supplies", "Accessories"),  
                    num_transactions, replace = TRUE), 
  Quantity = sample(1:10, num_transactions, replace = TRUE), 
  Price = round(runif(num_transactions, 50, 1000), 2), 
  Date = sample(seq(as.Date('2022-01-01'), as.Date('2022-12-31'), by = 'day'),  
                num_transactions, replace = TRUE) 
) 
  
# Display the first few rows of the sales data 
print(head(sales_data)) 

Output:

  Transaction_ID Product_Name        Category Quantity  Price       Date
1              1       Tablet     Accessories        1 964.35 2022-07-21
2              2       Tablet Office Supplies        4 692.00 2022-01-05
3              3        Phone     Accessories        3 861.58 2022-03-18
4              4        Phone     Accessories        2 461.16 2022-09-22
5              5       Tablet Office Supplies        3 929.32 2022-06-23
6              6      Monitor Office Supplies        7 766.66 2022-04-03

Extract Values

R

# Find the row indices for transactions in the "Electronics" category 
electronics_indices <- which(sales_data$Category == "Electronics") 
  
# Check if Electronics transactions are found and calculate total sales 
if (length(electronics_indices) > 0) { 
  total_sales_electronics <- sum(sales_data$Quantity[electronics_indices]  
                                 * sales_data$Price[electronics_indices]) 
  cat("Total Sales for Electronics category:", total_sales_electronics, "\n") 
} else { 
  cat("No transactions found for Electronics category.\n") 
}

Output:

Total Sales for Electronics category: 955358.9

Extracting Laptop Features using the get_field function

In this example, we will use an external dataset that has laptop features for different models.

DataSet Link: Customizing function and Loading the dataset

Make sure you replace the path dataset with the original dataset.

R

# Custom get_field_value() function for extracting a single value 
get_field_value <- function(data, row_index, col_name) { 
  return(data[row_index, col_name]) 
} 
  
# Load the laptop dataset from a CSV file 
laptop_data <- read.csv("path/to/your/laptop_dataset.csv") 
  
# Display the first few rows of the loaded laptop dataset 
print(head(laptop_data)) 

Output:

   Brand Processor_Speed RAM_Size Storage_Capacity Screen_Size   Weight     Price
1   Asus        3.830296       16              512    11.18515 2.641094 17395.093
2   Acer        2.912833        4             1000    11.31137 3.260012 31607.606
3 Lenovo        3.241627        4              256    11.85302 2.029061  9291.024
4   Acer        3.806248       16              512    12.28036 4.573865 17436.728
5   Acer        3.268097       32             1000    14.99088 4.193472 32917.991
6     HP        1.881348       16              256    11.94396 4.840268  9543.720

Extracting values for specific models

R

acer_laptop_index <- which(laptop_data$Brand == "Acer" &  
                             laptop_data$RAM_Size == 16) 
  
# Print the result 
print(acer_laptop_index)

Output:

 [1]   4  22  36  48  75  81  83 113 129 130 286 288 297 310 328 358 360 382 397 453 474 530 534
[24] 536 547 556 582 586 621 655 667 707 740 763 764 768 771 785 797 810 835 841 845 922 923 930
[47] 954

A laptop with certain features are present in these indexes.

Conclusion

In this article, we learned the use of the get_field() function and how it can be used in different ways to extract different values with the help of examples.

Reffered: https://www.geeksforgeeks.org

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