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Introduction to Spark Dataset

Spark Dataset is one of the basic data structures by SparkSQL. It helps in storing the intermediate data for spark data processing. Spark dataset with row type is very similar to Data frames that work as a tabular form on the Resilient distributed dataset(RDD). The Datasets in Spark are known for their specific features such as type-safety, immutability, schemas, performance optimization, lazy evaluation, Serialization, and Garbage Collection. The Datasets are supported through Scala and Java programming APIs. Spark’s dataset supports both compile-time safety and optimizations, making it a preferred choice for implementation in the spark framework.

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Why do we need Spark Dataset?

RDD is the core of Spark. Inspired by SQL and to make things easier, Dataframe was created on top of RDD. Dataframe is equivalent to a table in a relational database or a DataFrame in Python.

RDD provides compile-time type safety, but there is an absence of automatic optimization in RDD.

Dataframe provides automatic optimization, but it lacks compile-time type safety.

Dataset is added as an extension of the Dataframe. Dataset combines both RDD features (i.e. compile-time type safety ) and Dataframe (i.e. Spark SQL automatic optimization ).

As Dataset has compile-time safety, it is only supported in a compiled language( Java & Scala ) but not in an interpreted language(R & Python). But Spark Dataframe API is available in all four languages( Java, Scala, Python & R ) supported by Spark.

Language supported by Spark. Dataframe API Dataset API

Compiled Language (Java & Scala) YES YES

Interpreted Language (R & Python) YES NO

How to Create a Spark Dataset?

There are multiple ways of creating a Dataset based on the use cases.

1. First Create SparkSession


To create a dataset using basic data structure like Range, Sequence, List, etc.:

To create a dataset using the sequence of case classes by calling the .toDS() method :

To create dataset from RDD using .toDS():

To create the dataset from Dataframe using Case Class:

To create the dataset from Dataframe using Tuples :

2. Operations on Spark Dataset

1. Word Count Example

2. Convert Spark Dataset to Dataframe

We can also convert Spark Dataset to Datafame and utilize Dataframe APIs as below :

Features of Spark Dataset

1. Type Safety: Dataset provides compile-time type safety. It means that the application’s syntax and analysis errors will be checked at compile time before it runs.

2. Immutability: Dataset is also immutable like RDD and Dataframe. It means we can not change the created Dataset. Every time a new dataset is created when any transformation is applied to the dataset.

3. Schema: Dataset is an in-memory tabular structure that has rows and named columns.

4. Performance and Optimization: Like Dataframe, the Dataset also uses Catalyst Optimization to generate an optimized logical and physical query plan. 

5. Programming language: The dataset api is only present in Java and Scala, which are compiled languages but not in Python, which is an interpreted language.

6. Lazy Evaluation: Like RDD and Dataframe, the Dataset also performs the lazy evaluation. It means the computation happens only when action is performed. Spark makes only plans during the transformation phase.

7. Serialization and Garbage Collection: The spark dataset does not use standard serializers(Kryo or Java serialization). Instead, it uses Tungsten’s fast in-memory encoders, which understand the internal structure of the data and can efficiently transform objects into internal binary storage. It uses off-heap data serialization using a Tungsten encoder, and hence there is no need for garbage collection.


Dataset is the best of both RDD and Dataframe. RDD provides compile-time type safety, but there is an absence of automatic optimization. Dataframe provides automatic optimization, but it lacks compile-time type safety. Dataset provides both compile-time type safety as well as automatic optimization. Hence, the dataset is the best choice for Spark developers using Java or Scala.

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This is a guide to Spark Dataset. Here we discuss How to Create a Spark Dataset in multiple ways with Examples and Features. You may also have a look at the following articles to learn more –

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Learn Top 6 Amazing Spark Components

Overview of Spark Components

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Top Components of Spark

Currently, we have 6 components in Spark Ecosystem: Spark Core, Spark SQL, Spark Streaming, Spark MLlib, Spark GraphX, and SparkR. Let’s see what each of these components do.

1. Spark Core

As the name suggests, Spark Core is the core unit of a Spark process. It handles task scheduling, fault recovery, memory management, input-output operations, etc. Think of it as something similar to a CPU to a computer. It supports programming languages like Java, Scala, Python, and R and provides APIs for respective languages using which you can build your ETL job or do analytics. All the other Spark components have their APIs built on Spark Core. Spark can handle any workload because of its parallel processing capabilities and in-memory computation.

Spark Core comes with a special kind of data structure called RDD (Resilient Distributed Dataset) which distributes the data across all the nodes within a cluster. RDDs work on a Lazy evaluation paradigm where the computation is memorized and only executed when necessary. This helps in optimizing the process by only computing the necessary objects.

2. Spark SQL

If you have worked with Databases, you understand the importance of SQL. Wouldn’t it be extremely overwhelming if the same SQL code works N times faster, even on a larger dataset? Spark SQL helps you manipulate data on Spark using SQL. It supports JDBC and ODBC connections that connect Java objects and existing databases, data warehouses, and business intelligence tools. Spark incorporates something called Dataframes, which are structured collections of data in the form of columns and rows.

Spark allows you to work on this data with SQL. Dataframes are equivalent to relational tables, and they can be constructed from any external databases, structured files, or existing RDDs. Dataframes have all the features of RDD, such as immutable, resilient, and in-memory, but with the extra feature of being structured and easy to work with. Dataframe API is also available in Scala, Python, R, and Java.

3. Spark Streaming

Streaming is Netflix, Pinterest, and Uber. Apache Kafka can integrate with Spark Streaming, allowing for the decoupling and buffering of input streams. Spark Streaming algorithms process real-time streams using Kafka as the central hub.

4. Spark MLLib

Spark’s major attraction is scaling up the computation massively, and this feature is the most important requirement for any Machine Learning Project. Spark MLLib is Spark’s machine learning component, which contains Machine Learning algorithms such as classification, regression, clustering, and collaborative filtering. It also offers a place for feature extraction, dimensionality reduction, transformation, etc.

You can also save and run your models on larger datasets without worrying about sizing issues. It also contains utilities for linear algebra, statistics, and data handling. Because of Spark’s in-memory processing, fault tolerance, scalability, and ease of programming, with the help of this library, you can run iterative ML algorithms easily.

5. GraphX

It finds the distance between two locations and gives an optimal route suggestion. Another example can be Facebook friends’ suggestions. GraphX works with both graphs and computations. Spark offers a range of graph algorithms like page rank, connected components, label propagation, SVD++, strongly connected components, and triangle count.

6. SparkR

More than 10,000 packages are available for different purposes in R, making it the most widely used statistical language. It uses data frames API, which makes it convenient to work with and provides powerful visualizations for data scientists to analyze their data thoroughly. R does not support parallel processing and limits itself to the memory available in a single machine. This is where SparkR comes into the picture.

Spark developed a package known as SparkR, which solves the scalability issue of R. It is based on distributed data frames and also provides the same syntax as R. Spark’s distributed Processing engine and R’s unparalleled interactivity, packages; visualization combines to give Data Scientists what they want for their analyses.


Since Spark is a general-purpose framework, it finds itself in many applications. Spark is extensively used in most big data applications because of its performance and reliability. The developers update all these components of Spark with new features in every new release, making our lives easier.

Recommended Articles

This is a guide to Spark Components. Here we discuss the basic concept and top 6 components of spark with a detailed explanation. You may also look at the following articles to learn more –

Examples On How To Create Redis Api To Access Database

Introduction to Redis API

Redis API helps us to automate common tasks. Authentication for redis enterprise software API which occurs by using basic auth. At the time of using it, we need to provide a username and password for the basic auth credentials. If suppose username and password are incorrect then the request is failing with an unauthorized status code. By default, the admin user is used to authorize for accessing all the endpoints.

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Key Takeaways

To use it, we must execute the docker command. All Redis APIs will be versioned in order to minimize the impact of API changes for coordinating different versions of operations.

We need to specify the version in the request of URI, which was defined in versions.

How to Create Redis API?

It enables to access the database of redis using API. To create it we are creating the redis database on the redis enterprise server. The below image show we are creating the api database on the redis server. After creating the database we also create the user for accessing the database as follows. In the below example, we can see that the public endpoint is created for accessing the database.

Database name – api





It follows the same command with API which we execute with redis-cli. The below example shows how we can execute the command as follows. The below example shows the REST api as follows.





In the above example, $VALUE is sent into the request body, which is appended to the command that we provided. The body of the post request is appended as the final parameter in the redis command.


We can send all of the commands using the request body and a single JSON array. The first name array contains the command name and parameters, which were appended in the same order.



The HTTP code that we defined using the curl command is shown below:

Curl supports a variety of HTTP codes.

200 ok – When the request is accepted successfully, this code returns.

400 bad request – When a syntax error occurs, this code returns.

401 unauthorized – This code is returned when authentication fails or the authentication token is missing.

The Redis rest API provides a JSON response. When the execution is successful, the JSON response will return a single result. The following are the responses from it. The following example shows pipelining.

Null value



Array value



curl -X POST chúng tôi -H "Authorization: Bearer A4lxhllu169je1oqnszexyyqxryoyvq36ecxm5ywehrf02yxoh6" -d ' [ ["SET", "API1", "val1"], ["SETEX", "API2", 13, "val2"], ["INCR", "API1"], ["ZADD", "myset", 11, "val3", 22, "val4"] ] '


It supports transactions for automatically executing multiple commands. The transactions shown in the example below are as follows.


curl -X POST chúng tôi -H "Authorization: Bearer A4lxhllu169je1oqnszexyyqxryoyvq36ecxm5ywehrf02yxoh6" -d ' [ ["SET", "API1", "val1"], ["SETEX", "API2", 13, "val2"], ["INCR", "API1"], ["ZADD", "myset", 11, "val3", 22, "val4"] ] '

We need to add the header for our API requests in redis or we need to set the token as a parameter.


curl -X POST chúng tôi -H "Authorization: Bearer A4lxhllu169je1oqnszexyyqxryoyvq36ecxm5ywehrf02yxoh6"


We are using performance optimization in it. The below example shows performance optimization as follows.


-H “Authorization: Bearer A4lxhllu169je1oqnszexyyqxryoyvq36ecxm5ywehrf02yxoh6”



Given below are the FAQs mentioned:

Q1. What is the use of rest API in redis?

Answer: It enables us to access our redis database by using REST. We can access the database and execute the command by using the curl command.

Q3. What is the use of response in redis API?

Answer: Redis rest API returns the JSON response. The null, integer, string, and array responses are used in it.

Conclusion Recommended Articles

This is a guide to Redis API. Here we discuss the introduction, how to create Redis API, and FAQ for better understanding. You may also have a look at the following articles to learn more –

Learn How To Speed Read With Syllable

Subvocalization is the act of “sounding out” words as you read them. Nearly everyone does it. It happens on a subconscious level. It is how we were taught to read and makes it possible to visualize the sound of words in order to interpret and comprehend them.

Syllable is an app that trains you to stop subvocalizing so you can read faster. It starts you off at a slow, comfortable pace, and works you up to 1,500 words per minute. You’ll be able to read an entire book during your lunch break with the help of this app…


With the most minimalist of designs, this app features two things: a white background and black text. There is a bright green border around your reading window, but there is very little else.

When you add an article to read in Syllable, it will be listed with the amount of time it will take to read based on how many words per minute (WPM) you’ve selected. For example, if you choose to read a particular article at 150 words per minute, it will show you that the article will take you four minutes and 36 seconds to read. If you speed up your WPM time to 250, it will shorten the time it takes to get through the same article to two minutes and 45 seconds.

You can add articles from your Instapaper or Pocket account by setting each app to allow access from Syllable. When you save an article in either app, it will automatically be populated into Syllable. You can also add articles by copying and pasting a URL link or write or paste your own text into a readable post.

In the settings section, you can change the font style, increase or decrease the size of the letters, switch from day to night mode, adjust the WPM speed, add more words to the screen from one to five, and change the brightness level of the screen.

App Use

To get started, log into your Pocket or Instapaper account. You don’t have to have these apps to use Syllable, but it makes it much easier to add articles. Once logged in, go back to your Pocket or Instapaper app and start grabbing articles to read. Then, open Syllable to see the generated list that will have all of your offline reading articles.

Pick a post by tapping it. When you do, you will be taken to a page that has a white background and the first word of the article. You can either start reading right away by tapping the word, or make the necessary changes to fine-tune your reading experience. I found that default starting WPM speed of 50 is much too slow and found that 225 WPM was a comfortable speed to begin my training.

You can also increase or decrease the size of the font. It is helpful to have large letters when trying to read fast, but not particularly realistic since the average font size of an article is 12.

After adjusting the speed and size of the words, try reading your first article. You will notice that you are not able to sound out the words in your head the way you normally do. This is because your brain is being forced to keep pace with the words that appear on the screen.

You can switch the screen to night mode in the settings section for a black background with white font. This works better when you are in a dimly lit environment because the bright white background can be harsh on the eyes.

While you are reading an article, you can tap the word to pause it, rewind a few seconds, and fast-forward a few seconds. When you’ve finished a whole article, you can tap it again to restart it.

When you are done reading an article, go back to the list to mark it as read. Swipe the post from left to right to bring up a check mark. Or, if you want to remove it from the list, swipe the post from right to left to bring up an “X” mark. This will delete the article from your list.

To add a new post without going through Instapaper or Pocket, copy the link to the article you want to read and then open Syllable. It will automatically ask you if you want to add the copied link to the list. Select “yes” to add it.

You can also write your own post to read in Syllable. Tap the plus (+) symbol at the top right of the screen to call up the options. Then, tap the “Text” tab and begin writing. You can either type your own words, or add an email that was sent to you, or some other text that you’ve copied. Just paste it into the Text post and tap the “Done” tab in the upper right corner of the screen and you can find it in the list and read it whenever you like. You’ll even be able to tell how long it will take to read at your current WPM pace.

The Good

If you are trying to learn how to speed-read, this is a great app for helping you retrain your brain to not sound out words. I love being able to add articles from my Pocket account. Plus, being able to copy and paste emails into the Text feature makes reading messages from others a more focused event. I am less distracted while reading and more engaged in what is in front of me.

The Bad

It would be nice to be able to add more reading apps to the list of compatible connections. I’d like to be able to add articles from the New York Times app or Flipboard to my reading list.

There were a few times when I was unable to restart an article that I had finished reading. I had to exit back to the article list and then select the post again in order to read it.


Syllable is on sale for $0.99, which is the perfect price for an app like this. Other speed-reading apps can be downloaded for free, but require an in-app purchase in order to access all of the features. Some of them cost $4.99 to unlock. I don’t know the full price of the app, but I can say it is definitely worth $0.99. However, anything higher makes it a niche app that would only be worth buying if you are really into the idea of learning to speed-read.


I’ve always wanted to learn how to read faster. I’m actually kind of slow at it. This app is great for slowly teaching you how to increase your WPM speed while helping you retrain your brain to stop subvocalizing when you read. It’s like Couch to 5K for your brain. If you are interested in learning how to speed read, download this app while it is on sale for $0.99.

Related Apps

ReadQuick is one app that also offers access to Instapaper and Pocket and lets you set the speed of words. Another similar app is Speed Reader, which has a more skeuomorphic look to it.

How fast do you read? Have you ever tried speed reading?

How To Create A Pop


The pop-up div can be created with the help of HTML, CSS and the functioning of which can be done with the help of ‘Javascript’ library ‘jQuery’. To make the mouseover and stay functionality to the div jQuery has a built in pre defined function.

The two functions which are mainly used in this task are −

mouseover − This function triggers when the mouse is over the selected element.

mouseout − This function triggers when the mouse leaves the are of the selected element for mouse over.


Step 1 − Create a HTML boilerplate in the text editor.

Step 2 − Add the jQuery CDN link to the head tag of the HTML code. On adding the CDN link it gives the functionality to the HTML code to use jQuery methods.

Step 4 − Create a div container which contains the popup of the page.


Step 5 − Now create the jQuery function inside the script tag. $(‘.container’).css(“display”, “block”) }) $(‘.container’).css(“display”, “none”) })

− Now create the jQuery function inside the script tag.

Step 6 − The popup functionality is ready to use on browsers.


In the given example we have created a HTML button and we have created the popup div container which is displayed on screen when the mouse is hovering over the button. We had also styled the popup using the inline css. The jQuery function is created in which using the jQuery selector syntax the button element is selected with the mouseover event attached to it. In the mouseover event it is passed with the callback function that is triggered on entering the mouse over div.

Tutorialspoint $(‘.container’).css(“display”, “block”) }) $(‘.container’).css(“display”, “none”) })

In the given below images it shows the output of the above example. In the first image it shows the static simple output. Which contains only a single button on the page.

In the below second image it shows the popup div container. So when the user hover over the button the mouseover event is triggered and it performs the display block action to the div container which shows the popup div. As soon as the mouse is over the button the div container containing the popup displays on the screen. When the mouse leaves the button the popup disappears from the browser’s screen.


These types of popover are used in the web application such as a mcq web app, in this we can make a button to function popover which will pop out the hint to the answer of the question. In this we had only used the two mouse events but there are more mouse events such as: mouse down, enter, leave these all have their own functionality. The popup is like a dialogue box which tells us certain information about any topic or it can be a confirmation box also to confirm the choice of the end user in terms of yes or no. In the mouseover and mouseout event a callback must be passed so that a particular action triggers. Do not forget to add the CDN link to the head tag otherwise the jQuery function will not execute and the page will remain static with some errors in the console.

How To Use $ In R: A Complete Guide (With 6 Examples)

To efficiently deal with dataframes and lists in R, you need to be able to access the columns or values in an easy and readable way. This is where you can use the dollar sign operator ($). This operator retrieves the value or column by a name after which you can easily read, modify, or remove the accessed objects.

This is a comprehensive guide to using the dollar sign operator $ in R. You will learn what the $ operator does and what are its main use cases for both lists and dataframes.

What Is $ in R?

In R, one of the fundamental operators is the dollar sign operator ($). With the $ operator, you can access a specific part of data. The most notable use cases for the $ operator are related to lists and dataframes:

With lists, you can use the $ operator to access a particular variable of the list.

With dataframes, you can use the $ operator to access a specific column.

The $ operator in R gives you easy access to a subset of data. This makes it easy to access, modify, and remove parts of your data.

Let’s take a look at the most common use cases and examples for the $ operator.

6 Use Cases for $ in R

The main use cases for the $ in R are associated with lists and dataframes. This section is split into two parts:

Using the $ operator with lists

Select a variable from a list

Add a new variable to a list

Select a variable with white spaces

Using the $ operator with dataframes

Select a column in a dataframe

Add a new column to a dataframe

Remove a column from a dataframe

Let’s jump into it!

Using $ on Lists

Lists are a common data type in R. You can use the $ operator to access, modify, and remove list values.

1. Select a Variable on a List

The most basic usage of the $ operator in R is to access a variable in a list. To do this, add a $ after the list name and specify the variable name you’re interested in.


For example, given a list of names associated with ages, let’s access the age of “Bob”:

ages <- list('Alice'=30, 'Bob'=25) ages$Bob


[1] 25 2. Add New Variable to a List

In the previous section, you learned how to access a variable in a list using the $ operator.

Adding a new variable to a list happens in a similar fashion. But instead of accessing a variable, you need to specify the name of the new variable after the dollar sign.

For example, let’s add a new variable Charlie=32 to the list of names and ages:

ages <- list('Alice'=30, 'Bob'=25) ages$Charlie <- 32 ages


$Alice [1] 30 $Bob [1] 25 $Charlie [1] 32

Now there are three names each associated with age in the list.

Notice that you can also update an existing value in a list with the $ operator.

For example, let’s update the age of “Alice”:

ages <- list('Alice'=30, 'Bob'=25) ages$Alice <- 60 ages


$Alice [1] 60 $Bob [1] 25 3. Select an Object with White Spaces

Accessing a list variable with names that contain blank spaces is a bit trickier. This is because blank spaces are a no-go in R (similar to other programming languages) as they mess up the compiler.

For example:

ages <- list('Alice Smith'=30, 'Bob Jones'=25) ages$Bob Jones


[1] Error: unexpected symbol in "ages$Bob Jones" Execution halted

Here the blank space between Bob Jones caused the R compiler to think that Jones is an invalid and unrelated symbol that is there by accident.

But because the list has a variable with the name Bob Jones, this is not a mistake. To fix the issue with variables that have blank spaces, wrap the name around backticks after the $ operator.

For example:

ages <- list('Alice Smith'=30, 'Bob Jones'=25) ages$`Bob Jones`


[1] 25

Now the compiler successfully knows that it should look for a multi-part variable name in the list.

Notice that a better approach is to name the columns of the table so that there would be no blank spaces! If your table has columns with blank spaces, to begin with, you can replace the blank spaces with something else, such as underscores.

Using $ on Dataframes

Another main use case for the $ operator is when dealing with dataframes. Similar to lists, the $ operator makes it easy to access, modify, and remove columns in the dataframe.

This section shows you how to use the $ operator on data frames.

In the following sections, you are going to work with the following data frame:

shopping_list = data.frame(PRODUCT_GROUP = c("Fruit","Fruit","Fruit","Fruit","Fruit","Vegetable","Vegetable","Vegetable","Vegetable","Dairy","Dairy"), PRODUCT_NAME = c("Banana","Apple","Mango","Orange","Papaya","Carrot","Potato","Cucumber","Tomato","Milk","Yogurt"), Price = c(1,0.8,0.7,0.9,0.7,0.6,0.8,0.75,0.15,0.3,1.1), Tax = c(NA,NA,24,3,20,30,NA,10,NA,12,15))

This dataframe is a table of products that looks like this when printed out:

PRODUCT_GROUP PRODUCT_NAME Price Tax 1 Fruit Banana 1.00 NA 2 Fruit Apple 0.80 NA 3 Fruit Mango 0.70 24 4 Fruit Orange 0.90 3 5 Fruit Papaya 0.70 20 6 Vegetable Carrot 0.60 30 7 Vegetable Potato 0.80 NA 8 Vegetable Cucumber 0.75 10 9 Vegetable Tomato 0.15 NA 10 Dairy Milk 0.30 12 11 Dairy Yogurt 1.10 15

Let’s jump into it!

4. Select a Column

Selecting a particular column in a dataframe is easy with the $ operator. All you need to do is specify the name of the dataframe, followed by the $ operator and the name of the specific column you’d like to access.

For example, let’s access the PRODUCT_NAME column in the shopping_list dataframe:



[1] "Banana" "Apple" "Mango" "Orange" "Papaya" "Carrot" [7] "Potato" "Cucumber" "Tomato" "Milk" "Yogurt"

The result is the names of the individual products.

Notice how the idea of accessing a column in a dataframe is exactly the same as accessing a variable in a list.

5. Add a New Column to a Dataframe

To add a new column to a dataframe, use the $ operator as an accessing operator by specifying the new column name to it.

The idea is the same as adding a new variable to a list with the $ operator.

For example, let’s add a new column, “Available” to the shopping_list and make it default to the value “Yes“.

shopping_list$Available <- rep('Yes', length(shopping_list$PRODUCT_GROUP)) shopping_list


PRODUCT_GROUP PRODUCT_NAME Price Tax Available 1 Fruit Banana 1.00 NA Yes 2 Fruit Apple 0.80 NA Yes 3 Fruit Mango 0.70 24 Yes 4 Fruit Orange 0.90 3 Yes 5 Fruit Papaya 0.70 20 Yes 6 Vegetable Carrot 0.60 30 Yes 7 Vegetable Potato 0.80 NA Yes 8 Vegetable Cucumber 0.75 10 Yes 9 Vegetable Tomato 0.15 NA Yes 10 Dairy Milk 0.30 12 Yes 11 Dairy Yogurt 1.10 15 Yes

If you’re wondering what this part does: rep(‘Yes’, length(shopping_list$PRODUCT_GROUP)), it’s just to make the new column as long as the PRODUCT_GROUP column in the table.

6. Delete a Column

Deleting a specific column from a dataframe is possible by using the $ operator and the NULL object. To remove a column, access it with the $ operator and assign a NULL value to it.

For example, let’s remove the “Tax” column from the shopping_list table:

shopping_list$Tax <- NULL shopping_list


PRODUCT_GROUP PRODUCT_NAME Price 1 Fruit Banana 1.00 2 Fruit Apple 0.80 3 Fruit Mango 0.70 4 Fruit Orange 0.90 5 Fruit Papaya 0.70 6 Vegetable Carrot 0.60 7 Vegetable Potato 0.80 8 Vegetable Cucumber 0.75 9 Vegetable Tomato 0.15 10 Dairy Milk 0.30 11 Dairy Yogurt 1.10 Summary

Today you learned how to use the $ operator in R.

To recap, you can use the $ operator in R to access columns of dataframes or variables of a list. The $ operator makes it easy to access, modify, and delete values from dataframes and lists.

Thanks for reading. Happy coding!

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