R Programmer By DataCamp

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Learn Path Description

Gain the career-building programming skills you need to successfully develop software, wrangle data, and perform advanced data analysis in R. No prior coding experience required. In this track, you'll learn how to manipulate data, write efficient R code, and work with challenging data, including date and time data, text data, and web data using APIs. As you become more comfortable with these skills, you'll move on to learn about writing functions and object-oriented programming—an essential skill when working with large and complex programs. Through interactive exercises, you'll also gain experience working with powerful R libraries, including devtools, testthat, and rvest, that will help you perform key programmer tasks, such as web development, data analysis, and task automation. Start this track and embark on your journey to becoming a R programmer.

Skills You Will Gain

Courses In This Learning Path

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Total Duration

4 hours

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Level

Beginner

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Learn Type

Certifications

Introduction to the Tidyverse

This article will introduce R programming language and Tidyverse, an extremely powerful tool set. The tools dplyr and ggplot will help you understand the interconnected process of data manipulation, visualization, and how they work together. You'll learn how to sort and filter historical data in order to answer exploratory questions. The ggplot2 package can convert these data into informative line-and-bar plots, histograms and other plots. This introduction will provide a glimpse of exploratory data analysis and Tidyverse's power. This introduction is for those who are not familiar with R but are interested data analysis.

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Level

Beginner

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Certifications

Dr. Semmelweis and the Discovery of Handwashing

Hungarian physician Ignaz Semmelweis discovered handwashing in 1847. He discovered that childbed fever was caused by contamination of the hands. By requiring handwashing in his hospital, he saved hundreds of children's lives.

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Total Duration

4 hours

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Level

Beginner

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Learn Type

Certifications

Data Manipulation with dplyr

Let's say you have a great dataset that you want to know more about. What can you do to begin answering the questions about the data? To answer these questions, you can use dplyr. It can also assist with basic data transformations. Learn how to add, subtract, or modify variables and aggregate data. You'll also be able to explore a dataset that contains information about the United States. These tools will be applied to the dataset babynames to examine trends in baby names in the United States.

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Total Duration

4 hours

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Level

Intermediate

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Learn Type

Certifications

Writing Efficient R Code

R's strength lies in its ability to do data analysis. Sometimes R can be slow and cause problems in our analysis. You should be familiar with the best techniques to speed up analysis in order to reduce computation time and gain insight as quickly as possible.

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Total Duration

4 hours

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Level

Intermediate

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Learn Type

Certifications

Working with Dates and Times in R

The data available in dates and times is vast. These are crucial for answering questions starting with when, how frequently, or how long. Because they can come in many formats, and behave in surprising ways, it can be confusing. This course will show you how to manipulate and parse dates and times in R. It will also teach you how to find out the birthplace of R, Auckland's weather, and how long Britain has been ruled by monarchs.

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Level

Intermediate

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Learn Type

Certifications

Drunken Datetimes in Ames, Iowa

The [Who is drunk and when in Ames, Iowa project]. You looked at breathalyzer test data from the State of Iowa. This dataset contained a lot date-time manipulation. This project will help you uncover temporal trends within the Ames breath alcohol data. This project is easy for anyone who has experience with the lubridate and dplyr packages. To answer questions like "What day has the highest number of tests?", you will need to manipulate the dates and times contained in the breath alcohol data. ", "What hour are the most frequent breath alcohol tests?" ", "Is blood alcohol content (BAC), higher on days when Iowa State University's football teams play?"

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Total Duration

4 hours

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Level

Intermediate

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Certifications

String Manipulation with stringr in R

Character strings can be found at all stages of data science projects. It is possible to extract text data, clean up strings and turn numeric results into sentences that can be used in reports. It is possible to match strings with patterns. This course will show you how to disassemble strings and put them back together. You can also use stringr to match strings and extract strings using regular expressions. This is an effective way to express patterns.

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Total Duration

20 hours

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Level

Beginner

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Certifications

Data Manipulation with R

Data in real-world situations is messy. Packages like data.table and dplyr are valuable because they make it easy to organize real-world data. These packages make data manipulation easy by extracting, filtering and transforming data. This makes it possible to perform reliable and fast data analysis. This is the right track if you are looking to improve your data manipulation skills. As you learn to work with multiple tables, you'll also be able to create real-world data and improve your skills. Additionally, you will get hands-on experience in creating visualizations, combining, merging, and combining data. Your new data manipulation skills will be applied using dplyr for analysis of voting data from the United Nations. This track will help you save time when manipulating data.

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Total Duration

4 hours

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Level

Intermediate

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Certifications

Web Scraping in R

Websites that have a lot of data (e.g. product reviews and prices) may not be suitable for data analysis. Many data providers and authorities publish their data in tables format. If you do not see a download link on these sites, don't be discouraged. This course will show you how to quickly extract data from any website using R. It will also teach you how to use CSS and HTML through hands-on activities. This will make data harvesting more efficient and less error-prone.

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Total Duration

4 hours

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Level

Beginner

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Certifications

Introduction to Writing Functions in R

By being able create your own functions, your analyses will be more easy to read and more precise. Function writing is more efficient than any other skill. This course will cover the basics of function writing. This course will cover both the arguments and the return values. Your focus will be on data science functions that are useful and use real-world data to analyze Wyoming's stock prices/earnings rates and grain yields.

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Level

Intermediate

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Certifications

Clustering Bustabit Gambling Behavior

Ever wondered what the gambling behavior at the casino was like? Some people seem to win more, while others can be risky and reckless with their bets. Others are more casual about the experience. While collecting this data from the casino might be a challenge, there is an online platform called [Bustabit](https://www.bustabit.com/play) in which gamblers can bet Bitcoin. We have data from thousands of Bustabit gambling sessions. We tracked the user's amount of bet, winnings, and other properties. You will use this data to perform a cluster analysis, which is a grouping of gamblers according to their gambling habits. Students should be familiar with R programming and the Tidyverse package, which will be used for data manipulation and summarization. This dataset includes 10,000 Bustabit games. Each game records the name of the individual gambler, the BustedAt-value of the game and the multiplier at the time the gambler cashed out.

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Total Duration

4 hours

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Level

Beginner

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Certifications

Introduction to Shell

The Unix command line is a versatile tool that allows users to perform complex tasks in a matter of keystrokes. It has been around almost 50 years. Because it allows users to run programs on clouds or clusters that might exist halfway around the world, and combine programs in new ways, the Unix command line is often called "universal glue" of programming. This course will cover its key elements and show you how to use them.

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Total Duration

4 hours

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Level

Intermediate

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Learn Type

Certifications

Defensive R Programming

R scripts are easy to write. Good R code is not easy. This course will cover defensive programming. It is a set of standard techniques that can be used in order to reduce bugs and improve team work. We will talk about how to avoid common mistakes and how to deal with those that do occur. The course will conclude with a discussion about when to move from script to package and project.

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Total Duration

4 hours

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Level

Intermediate

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Certifications

Developing R Packages

This course provides a comprehensive guide on how to create an R Package from scratch. The course begins by teaching you how to establish the basic structure of your package and add metadata. It emphasizes the importance of documenting your package, as this is crucial for creating high-quality packages that others can use, as well as for your own future reference. The course also covers how to verify the functionality of the components in your package by running tests and building your own package. By the end of the course, you will have the skills necessary to create and share your own R packages.

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Total Duration

4 hours

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Level

Intermediate

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Learn Type

Certifications

Object-Oriented Programming with S3 and R6 in R

Object-oriented programming (OOP) allows you to define relationships between functions and objects. This allows you to manage complexity in your code. This course is designed for intermediate students. It introduces OOP using R3 and R6. The S3 R programming tool is great for everyday use. Many of the functions that you write are simplified by it. R6 is especially useful for industry-specific analysis and web APIs work. Winston Chang, who is the inventor of R6, will be interviewed after the event.

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Total Duration

22 hours

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Level

Beginner

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Learn Type

Certifications

R Programming by DataCamp

This beginner's course will teach you how to program like a programmer. You'll first learn how to use common data structures in R such as vectors, data frames, and matrices. Next, you will master conditional statements and loops. Then, you'll learn how to optimize your code through code benchmarking and code profiling. You'll also learn how to write functions and object-oriented program (OOP). You'll be able to handle more complicated tasks, such as advanced data visualization or machine learning, by the end of this track.

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