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_field() Function in R
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.
# 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.
# 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.
# 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"
$Status
[1] "Pass" "Fail" "Pass"
[1] "Pass" "Fail" "Pass"
In this case, the get_field() function is applied to a list (my_list), and it extracts the “Status” element. The resulting status vector contains the pass/fail statuses.
Extracting a specific value
To find a specific value from a large dataset we can use this function instead of going through the entire dataset. This will save us time as well. Here we will use a large in-built dataset in R called the “mtcars” dataset. This dataset has information related to different cars of different brands and their features such as ‘horsepower’, ‘mpg’, ‘cylinder’ etc.
We will try to extract the value of the horsepower of the Ford Mustang car for a specific mpg value.
# Custom get_field_value() function for extracting a single value get_field_value <- function (data, row_index, col_name) {
return (data[row_index, col_name])
} # Load the mtcars dataset data (mtcars)
# Find the row index for the Ford Mustang based on the model name mustang_row_index <- which (mtcars$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.
# 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
# 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$Price[electronics_indices])
cat ( "Total Sales for Electronics category:" , total_sales_electronics, "\n" )
} else {
cat ( "No transactions found for Electronics category.\n" )
} |
Output:
Total Sales for Electronics category: 955358.9
Extracting Laptop Features using the get_field function
In this example, we will use an external dataset that has laptop features for different models.
DataSet Link: Customizing function and Loading the dataset
Make sure you replace the path dataset with the original dataset.
# Custom get_field_value() function for extracting a single value get_field_value <- function (data, row_index, col_name) {
return (data[row_index, col_name])
} # Load the laptop dataset from a CSV file laptop_data <- read.csv ( "path/to/your/laptop_dataset.csv" )
# Display the first few rows of the loaded laptop dataset print ( head (laptop_data))
|
Output:
Brand Processor_Speed RAM_Size Storage_Capacity Screen_Size Weight Price
1 Asus 3.830296 16 512 11.18515 2.641094 17395.093
2 Acer 2.912833 4 1000 11.31137 3.260012 31607.606
3 Lenovo 3.241627 4 256 11.85302 2.029061 9291.024
4 Acer 3.806248 16 512 12.28036 4.573865 17436.728
5 Acer 3.268097 32 1000 14.99088 4.193472 32917.991
6 HP 1.881348 16 256 11.94396 4.840268 9543.720
Extracting values for specific models
acer_laptop_index <- which (laptop_data$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.