When doing data science, the content is often found in various data structures. This is particularly true when you are getting it from r packages. In such cases, you are often accessing the information in large data frames. As a result, if you want to access individual columns or even single values, then you need to pull it out of the data frame. But what is in r that will let you do this?
Description – $ in R
The dollar operator in r is the operation used to pull individual columns out of a data frame. The dollar sign operator has the form of Dataframe$Columnname. Dataframe is the name of the data frame, and Columnname is the name of the column you are accessing. This operation will also work on a list. It turns an entire column into a vector allowing you to process it as one. It also allows you to change the content of a column. This also allows you to access individual values within the column. You do this like any other vector with the expression vector[x], where x is the position of the value in the vector you want to access.
Explanation – Dollar Operator in R
The dollar sign operator works the way it does because data frames are essentially rows of atomic vectors. These vectors can have any data type. When entering a character string, it does not matter if you use a double quoted string or a single quoted string. This operation simply produces a vector of the selected column, allowing you to use it as a vector. You can use this operator in any place that you would use a vector. For clarity, you may want to place it into a separate vector. It has the advantage of protecting the original content. However, accessing the column directly is a perfect way of changing one of the values in a column of a data frame.
Examples of $ in R
Here are three examples of this operation in action.
> A11crew = list(“Commander” = “Neil Armstrong”,”Lem pilot” = “Buzz Aldrin”,”CM pilot” = “Michael Collins”)
 “Neil Armstrong”
 “Buzz Aldrin”
 “Michael Collins”
> First = A11crew$Commander
 “Neil Armstrong”
Here is an example of the dollar sign operator being used with a list. Note that in this case, this operation provides direct access to the data point.
+ hrv = data.frame(Roket=c(‘Saturn V’, ‘STS’, ‘Falcon 9’, ‘Falcon H’, ‘SLS’, ‘SLS B2’, ‘Star Ship’),
+ Thrust=c(3440, 2000, 776, 2327, 3992, 4309, 6400),
+ Orbit=c(140, 27.5, 22.8, 63.8, 95, 130, 150),
+ Hight=c(110.6, 56.1, 70, 70, 98.1, 111.3, 120))
Roket Thrust Orbit Hight hrv.Roket hrv.Thrust hrv.Orbit hrv.Hight
1 Saturn V 3440 140.0 110.6 Saturn V 3440 140.0 110.6
2 STS 2000 27.5 56.1 STS 2000 27.5 56.1
3 Falcon 9 776 22.8 70.0 Falcon 9 776 22.8 70.0
4 Falcon H 2327 63.8 70.0 Falcon H 2327 63.8 70.0
5 SLS 3992 95.0 98.1 SLS 3992 95.0 98.1
6 SLS B2 4309 130.0 111.3 SLS B2 4309 130.0 111.3
7 Star Ship 6400 150.0 120.0 Star Ship 6400 150.0 120.0
> t = hrv$Thrust
 3440 2000 776 2327 3992 4309 6400
Here is an example of the dollar sign operator being used with a data frame. It shows a column being extracted and equated to another vector.
> Staff = data.frame(Employee = c(“Samuel”, “Elizabeth”, “Tim”, “Charles”, “James”),
+ salary = c(15000, 130000, 55000, 100000, 75000),
+ Years = c(15, 10, 4, 10, 5),
+ Age = c(75,50,35,57,45))
Employee salary Years Age
1 Samuel 15000 15 75
2 Elizabeth 130000 10 50
3 Tim 55000 4 35
4 Charles 100000 10 57
5 James 75000 5 45
> a = Staff$Employee
> b = match(“Elizabeth”,a)
Here is an example of the dollar sign operator being used with a data frame. It shows a column being extracted and the location of a search term is found. That location is then used to extract an individual value from another column.
Applications of the $ operator in R
The most obvious application of the dollar operator in r is to use an item in one column to find a corresponding term in another column. When doing r programming with data frames this is something you are likely to need to do. All you need to do is use the match function on the first column and apply it like a vector to the second column.
The dollar sign is a useful operator that will allow you to extract data from a data frame. Being able to do this is important for being able to analyze individual values. Learning how to use this operation appropriately will give you a handy tool.