Advanced dplyr Verbs in the R tidyverse Master Class 2

"You see, R tidyverse is like pageant; everybody has to bring their A-game. In coding terms, it’s like combining different colors to create a masterpiece. Much like mixing paints on a palette, you need to blend the right functions and codes to reveal the full picture. Strategy is key: it’s about choosing the right variables and using them like building blocks." 🎨

# R tidyverse 마스터 클래스 2강 - dplyr 중급동사들

Table of Contents
=================

   * [Introduction](#introduction)
   * [Understanding the Basics of dplyr](#understanding-the-basics-of-dplyr)
   * [Mastering Data Manipulation with dplyr](#mastering-data-manipulation-with-dplyr)
   * [Use of SQL Verbs in dplyr](#use-of-sql-verbs-in-dplyr)
   * [Advanced Techniques with dplyr Functions](#advanced-techniques-with-dplyr-functions)
   * [Joining and Grouping with dplyr](#joining-and-grouping-with-dplyr)
   * [Conclusion](#conclusion)
   
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## Introduction 🚀

The R tidyverse 마스터 클래스 2강 introduces intermediate level functions of dplyr, a key component in the R programming language.

## Understanding the Basics of dplyr 💡

Start by getting familiar with the basic functions and commands used in dplyr. It's essential to understand and apply these fundamentals before moving on to more advanced techniques.


| Commands     | Functions |
|:-------------|:-----------|
| filter()     | Selecting rows based on conditions |
| arrange()    | Ordering rows |
| select()     | Selecting or dropping columns |
| mutate()     | Adding new variables |
| summarise()  | Creating summary statistics |
| slice()      | Selecting rows by position |
| distinct()   | Getting unique rows |
| group_by()   | Grouping observations |

Apply these functions to different datasets to understand their practical applications.

## Mastering Data Manipulation with dplyr 📈

Once you are comfortable with the basic commands, move on to more complex data manipulation techniques. Start exploring more extensive datasets and practice performing complex operations effectively.

> "The ability to manipulate and analyze data efficiently is a critical skill for any data scientist or analyst."

**Key Takeaways:**
- Data manipulation using dplyr is a vital skill in R programming.
- Mastering these techniques can significantly enhance your data analysis capabilities.

## Use of SQL Verbs in dplyr ⚙️

dplyr allows you to execute SQL verbs on datasets, providing a powerful set of tools for data manipulation. This section provides an overview of how to apply SQL verbs effectively to perform complex operations on your data.

select(country, gdp, population) %>%
filter(year == 2021) %>%
arrange(desc(gdp)) %>%
head(10)

#### Technical Query:

SELECT country, gdp, population
FROM dataset
WHERE year = 2021
ORDER BY gdp DESC
LIMIT 10;

## Advanced Techniques with dplyr Functions 🌟

Explore the advanced functionality available in dplyr functions. Dive into more complex and powerful manipulations that will allow you to handle diverse, real-world datasets with ease and efficiency.

**Important Note:**
- Continue practicing these functions with a variety of datasets to understand their diverse applications.

## Joining and Grouping with dplyr 🤝

Learn how to join and group data using dplyr functions. Understanding these techniques will enable you to combine datasets effectively and extract valuable insights from relational data.

> "Applying the correct join strategies is a crucial skill for data handling and analysis."

**Frequently Asked Questions**

| Question             | Answer                      |
|:---------------------|:----------------------------|
| How to perform a left join in dplyr? | Use the `left_join()` function. |
| What is the process of grouping data?  | Use the `group_by()` function followed by the specific column names you want to group by. |

## Conclusion 🎯

In conclusion, the dplyr package provides powerful tools for data manipulation in R, making it an essential skill for any data analyst or scientist. To become proficient in dplyr, consistent practice and experimentation with data is essential.

Keep honing your skills through regular practice and exploration of diverse datasets to become a master of data manipulation with dplyr.

Key Takeaways Table: