Every tasty fruit basket needs oranges, so you decide to add six oranges. As a data analyst, your reflex is to immediately create the variable my_oranges and assign the value 6 to it. Next, you want to calculate how many pieces of fruit you have in total. Since you have given meaningful names to these values, you can now code this in a clear way:
my_apples + my_oranges# Assign a value to the variables my_apples and my_oranges
my_apples <- 5
my_oranges <- 6
# Add these two variables together
my_apples + my_oranges
# Create the variable my_fruit
my_fruit <- my_apples + my_oranges> # Assign a value to the variables my_apples and my_oranges
> my_apples <- 5
> my_oranges <- 6
>
> # Add these two variables together
> my_apples + my_oranges
[1] 11
>
> # Create the variable my_fruit
> my_fruit <- my_apples + my_oranges
> # Assign a value to the variables my_apples and my_oranges
> my_apples <- 5
> my_oranges <- 6
>
> # Add these two variables together
> my_apples + my_oranges
[1] 11
>
> # Create the variable my_fruit
> my_fruit <- my_apples + my_oranges
>
> # see result
> my_fruit
[1] 11
my_oranges the value 6.my_apples and my_oranges and have R simply print the result.my_apples and my_oranges to a new variable my_fruit.First read the algorithm, then study the program code line by line. After that, compare the code with the output and finally go through the explanation. This approach helps learners understand both the logic and the implementation properly.
After understanding this example, try to rewrite the same program without looking at the code. Then change some values or logic and run it again. This helps improve confidence and keeps learners engaged on the page for longer.