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Importing Formatted Text Files
R Playbook
Justin Flett
Justin Flett
R is a widely used programming language for statistical computing. This course will teach you the fundamentals of understanding how to import formatted text files within R.
Read more
R is a widely used programming language for statistical computing. This course will teach you the fundamentals of understanding how to import formatted text files within R.
Read more
R is a widely used programming language for statistical computing. This course will teach you the fundamentals of understanding how to import formatted text files within R.
R is a widely used programming language for statistical computing. In this course, you will learn foundational knowledge importing formatted text files R. First, you will learn the basics of importing text files using default R functions. Next, you will learn how to import CSV and tab delimited files. You will learn how to import JSON files using the rjson package. Finally, you will learn how to import XML files within R. When you’re finished with this course, you will have the skills and knowledge of importing some of the most commonly used data files into R.
Register for this course and see more details by visiting:
OpenCourser.com/course/42vexn/importing
What's inside
Syllabus
Course Overview
Importing Text Files in R
Importing CSV Files in R
Importing Delimited Files in R
Read more
Syllabus
Course Overview
Importing Text Files in R
Importing CSV Files in R
Importing Delimited Files in R
Read more
Importing JSON Files in R
Importing XML Files in R
Good to know
Good to know
Know what's good ,
what to watch for , and
possible dealbreakers
Explores R, which is standard in statistical computing
Teaches R, which helps learners do statistical computing
Develops foundational skills for importing formatted text files into R
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Activities
Be better prepared
before
your course. Deepen your understanding
during
and
after
it. Supplement your coursework and achieve mastery of the topics covered
in Importing Formatted Text Files: R Playbook with these
activities:
Brush up on your data manipulation skills
Show steps
Reviewing data manipulation skills will refresh your knowledge of essential techniques for working with and preparing data in R, ensuring a stronger foundation for this course's content.
Browse courses on
Data Manipulation
Show steps
-
Revisit R functions for data import, filtering, sorting, and summarizing.
-
Practice data manipulation techniques on sample datasets.
Review computer science basics
Show steps
Reviewing computer science basics will strengthen your foundation for success in this course and refresh your understanding of key terms and concepts.
Show steps
-
Review your notes from a previous computer science course or online materials.
-
Take practice quizzes or solve coding challenges to test your knowledge.
Engage in peer study groups or online discussions
Show steps
Participating in peer study groups or discussions will provide you with opportunities to exchange knowledge, clarify concepts, and learn from others' perspectives.
Show steps
-
Join or create a study group with classmates or fellow learners.
-
Meet regularly to discuss course material, solve problems together, and provide support.
-
Engage in online discussions forums to ask questions, share insights, and connect with a wider community.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete practice exercises and coding challenges
Show steps
Regular practice through exercises and coding challenges will enhance your proficiency in R programming and solidify your understanding of the concepts.
Show steps
-
Locate online resources or textbooks with practice exercises.
-
Solve the exercises and compare your solutions with provided answers or ask for assistance on forums.
-
Participate in online coding challenges or competitions to test your skills.
Kickstart a personal data analysis project
Show steps
Initiating a personal data analysis project will enable you to apply your R programming skills to a topic you are passionate about, further developing your capabilities and building your portfolio.
Show steps
-
Identify a topic or research question that interests you.
-
Gather and prepare your data using R's data import and manipulation functions.
-
Analyze your data using appropriate statistical techniques and visualizations.
-
Interpret your results and draw meaningful conclusions.
-
Share your findings and insights through a blog post, presentation, or report.
Contribute to R open-source projects
Show steps
Contributing to R open-source projects will allow you to actively engage with the R community, learn from experienced developers, and enhance your understanding of R's ecosystem.
Show steps
-
Identify R open-source projects that align with your interests.
-
Familiarize yourself with the project's codebase and documentation.
-
Propose and implement improvements or bug fixes through pull requests.
Create a small R programming project
Show steps
Working on a mini project will allow you to apply your R programming knowledge to a practical scenario, deepening your understanding and solidifying your skills.
Show steps
-
Identify a small-scale problem or task that can be solved using R.
-
Plan and design your project, including data collection, analysis methods, and visualization techniques.
-
Implement your project and write clean, efficient R code.
-
Document your project and share it with others for feedback and improvement.
Brush up on your data manipulation skills
Show steps
Reviewing data manipulation skills will refresh your knowledge of essential techniques for working with and preparing data in R, ensuring a stronger foundation for this course's content.
Browse courses on
Data Manipulation
Show steps
Show steps
Show steps
- Revisit R functions for data import, filtering, sorting, and summarizing.
- Practice data manipulation techniques on sample datasets.
Review computer science basics
Show steps
Reviewing computer science basics will strengthen your foundation for success in this course and refresh your understanding of key terms and concepts.
Show steps
Show steps
Show steps
- Review your notes from a previous computer science course or online materials.
- Take practice quizzes or solve coding challenges to test your knowledge.
Engage in peer study groups or online discussions
Show steps
Participating in peer study groups or discussions will provide you with opportunities to exchange knowledge, clarify concepts, and learn from others' perspectives.
Show steps
Show steps
Show steps
- Join or create a study group with classmates or fellow learners.
- Meet regularly to discuss course material, solve problems together, and provide support.
- Engage in online discussions forums to ask questions, share insights, and connect with a wider community.
Four other activities
Expand to see all activities and additional details
Show all seven activities
Complete practice exercises and coding challenges
Show steps
Regular practice through exercises and coding challenges will enhance your proficiency in R programming and solidify your understanding of the concepts.
Show steps
Show steps
Show steps
- Locate online resources or textbooks with practice exercises.
- Solve the exercises and compare your solutions with provided answers or ask for assistance on forums.
- Participate in online coding challenges or competitions to test your skills.
Kickstart a personal data analysis project
Show steps
Initiating a personal data analysis project will enable you to apply your R programming skills to a topic you are passionate about, further developing your capabilities and building your portfolio.
Show steps
Show steps
Show steps
- Identify a topic or research question that interests you.
- Gather and prepare your data using R's data import and manipulation functions.
- Analyze your data using appropriate statistical techniques and visualizations.
- Interpret your results and draw meaningful conclusions.
- Share your findings and insights through a blog post, presentation, or report.
Contribute to R open-source projects
Show steps
Contributing to R open-source projects will allow you to actively engage with the R community, learn from experienced developers, and enhance your understanding of R's ecosystem.
Show steps
Show steps
Show steps
- Identify R open-source projects that align with your interests.
- Familiarize yourself with the project's codebase and documentation.
- Propose and implement improvements or bug fixes through pull requests.
Create a small R programming project
Show steps
Working on a mini project will allow you to apply your R programming knowledge to a practical scenario, deepening your understanding and solidifying your skills.
Show steps
Show steps
Show steps
- Identify a small-scale problem or task that can be solved using R.
- Plan and design your project, including data collection, analysis methods, and visualization techniques.
- Implement your project and write clean, efficient R code.
- Document your project and share it with others for feedback and improvement.
Career center
Learners who complete Importing Formatted Text Files: R Playbook will develop knowledge and skills
that may be useful to these careers:
Data Analyst
Data Analyst
Data Analysts use their specialized skills to mine data for patterns and trends, which they then communicate to stakeholders to help them make better decisions. This course may be useful for someone interested in building a foundation for importing data into R, a skill often required by Data Analysts. The course may be especially relevant for Analysts who specifically wish to work with data in a structured format such as CSV, JSON, and XML.
Data Scientist
Data Scientist
Data Scientists use their specialized skills to solve complex problems using data. They develop models and algorithms to analyze data and provide insights that can help organizations make better decisions. This course may be useful for Data Scientists who want to build a foundation for importing data into R, a valuable tool for data management and analysis. The course may be especially relevant for Scientists who specifically wish to work with data in a structured format such as CSV, JSON, and XML.
Statistician
Statistician
Statisticians collect, analyze, and interpret data to help organizations make informed decisions. They use statistical methods to draw conclusions from data and identify trends. This course may be useful for Statisticians who want to build a foundation for importing data into R, a powerful tool for statistical analysis and modeling. The course may be especially relevant for Statisticians who specifically wish to work with data in a structured format such as CSV, JSON, and XML.
Machine Learning Engineer
Machine Learning Engineer
Machine Learning Engineers build and maintain machine learning models to solve business problems. They use data to train and evaluate models, and deploy them into production. This course may be useful for Machine Learning Engineers who want to build a foundation for importing data into R, a language often used for machine learning tasks. The course may be especially relevant for Engineers who specifically wish to work with data in a structured format such as CSV, JSON, and XML.
Data Engineer
Data Engineer
Data Engineers build and maintain data pipelines to ensure that data is available for analysis and modeling. They work with data from a variety of sources, and use tools and technologies to process and transform data. This course may be useful for Data Engineers who want to build a foundation for importing data into R, a language often used for data engineering tasks. The course may be especially relevant for Engineers who specifically wish to work with data in a structured format such as CSV, JSON, and XML.
Software Engineer
Software Engineer
Software Engineers design, develop, and maintain software applications. They work with a variety of programming languages and technologies to build software that meets the needs of users. This course may be useful for Software Engineers who want to build a foundation for importing data into R, a language that can be used for a variety of software development tasks. The course may be especially relevant for Engineers who specifically wish to work with data in a structured format such as CSV, JSON, and XML.
Data Architect
Data Architect
Data Architects design and oversee the implementation of data management systems. They work with data from a variety of sources, and use tools and technologies to ensure that data is available for analysis and modeling. This course may be useful for Data Architects who want to build a foundation for importing data into R, a language often used for data architecture tasks. The course may be especially relevant for Architects who specifically wish to work with data in a structured format such as CSV, JSON, and XML.
Business Analyst
Business Analyst
Business Analysts use data to help organizations make better decisions. They work with stakeholders to identify business needs, and develop solutions that meet those needs. This course may be useful for Business Analysts who want to build a foundation for importing data into R, a language that can be used for a variety of business analysis tasks. The course may be especially relevant for Analysts who specifically wish to work with data in a structured format such as CSV, JSON, and XML.
Information Systems Manager
Information Systems Manager
Information Systems Managers plan, implement, and manage information systems for organizations. They work with a variety of technologies, and ensure that systems are running smoothly and meeting the needs of users. This course may be useful for Information Systems Managers who want to build a foundation for importing data into R, a language that can be used for a variety of information systems management tasks. The course may be especially relevant for Managers who specifically wish to work with data in a structured format such as CSV, JSON, and XML.
Database Administrator
Database Administrator
Database Administrators design, implement, and maintain databases. They work with a variety of database technologies, and ensure that databases are running smoothly and meeting the needs of users. This course may be useful for Database Administrators who want to build a foundation for importing data into R, a language that can be used for a variety of database administration tasks. The course may be especially relevant for Administrators who specifically wish to work with data in a structured format such as CSV, JSON, and XML.
IT Project Manager
IT Project Manager
IT Project Managers plan, implement, and manage IT projects. They work with a variety of stakeholders, and ensure that projects are completed on time and within budget. This course may be useful for IT Project Managers who want to build a foundation for importing data into R, a language that can be used for a variety of IT project management tasks. The course may be especially relevant for Managers who specifically wish to work with data in a structured format such as CSV, JSON, and XML.
Data Quality Analyst
Data Quality Analyst
Data Quality Analysts ensure that data is accurate, complete, and consistent. They work with data from a variety of sources, and use tools and technologies to identify and correct errors. This course may be useful for Data Quality Analysts who want to build a foundation for importing data into R, a language that can be used for a variety of data quality analysis tasks. The course may be especially relevant for Analysts who specifically wish to work with data in a structured format such as CSV, JSON, and XML.
Data Governance Analyst
Data Governance Analyst
Data Governance Analysts develop and implement policies and procedures to ensure that data is managed in a consistent and compliant manner. They work with a variety of stakeholders, and ensure that data is used in a responsible and ethical way. This course may be useful for Data Governance Analysts who want to build a foundation for importing data into R, a language that can be used for a variety of data governance tasks. The course may be especially relevant for Analysts who specifically wish to work with data in a structured format such as CSV, JSON, and XML.
Data Security Analyst
Data Security Analyst
Data Security Analysts protect data from unauthorized access, use, disclosure, disruption, modification, or destruction. They work with a variety of security technologies, and ensure that data is secure and compliant with regulations. This course may be useful for Data Security Analysts who want to build a foundation for importing data into R, a language that can be used for a variety of data security tasks. The course may be especially relevant for Analysts who specifically wish to work with data in a structured format such as CSV, JSON, and XML.
Data Visualization Analyst
Data Visualization Analyst
Data Visualization Analysts use data to create visual representations of information. They work with data from a variety of sources, and use tools and technologies to create visualizations that are informative and easy to understand. This course may be useful for Data Visualization Analysts who want to build a foundation for importing data into R, a language that can be used for a variety of data visualization tasks. The course may be especially relevant for Analysts who specifically wish to work with data in a structured format such as CSV, JSON, and XML.
For more career information including salaries, visit:
OpenCourser.com/course/42vexn/importing
Reading list
We've selected ten books
that we think will supplement your
learning. Use these to
develop background knowledge, enrich your coursework, and gain a
deeper understanding of the topics covered in
Importing Formatted Text Files: R Playbook.
Save
Provides a comprehensive introduction to R for data science, covering topics such as data import, cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about using R for data science.
Provides a comprehensive introduction to data analysis using R, covering topics such as data import, cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about using R for data analysis.
Provides a comprehensive introduction to R programming, covering topics such as data import, cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about using R for data analysis.
Provides a comprehensive introduction to R programming, covering topics such as data import, cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about using R for data analysis.
Save
Provides a comprehensive introduction to R programming, covering topics such as data import, cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about using R for data analysis.
Provides a comprehensive introduction to R programming for machine learning, covering topics such as data import, cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about using R for machine learning.
Save
Provides a comprehensive introduction to advanced R programming, covering topics such as data import, cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about using R for data analysis.
Save
Provides a comprehensive introduction to R programming, covering topics such as data import, cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about using R for data analysis.
Provides a comprehensive introduction to R programming for SAS and SPSS users, covering topics such as data import, cleaning, analysis, and visualization. It valuable resource for anyone who wants to learn more about using R for data analysis.
This journal provides a forum for the publication of original research in the field of R programming. It valuable resource for anyone who wants to learn more about the latest developments in R programming.
For more information about how these books relate to this course, visit:
OpenCourser.com/course/42vexn/importing
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Time
76 hours
Level
Introductory
Via
Pluralsight
Instructor
Justin Flett
Language
English
This course belongs to a
collection
called
Importing Data with R. It's comprised of two courses:
- Importing Formatted Text Files: R Playbook (viewing)
- Importing Common Data File Formats: R Playbook
Good to know
Explores R, which is standard in statistical computing
Teaches R, which helps learners do statistical computing
Develops foundational skills for importing formatted text files into R
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