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Statistical Consulting CentreIntroduction to R and Reproducible Research Monday, 23 September to Tuesday, 24 September 2024 Enrolment form for feepaying participants Title:Full Name:Employer: Department:
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01
Start with a brief overview of R as a programming language used for statistical computing and graphics.
02
Explain the installation of R and RStudio, detailing the steps and links to download pages.
03
Provide instructions on setting up the R environment, including opening RStudio and creating a new script.
04
Introduce basic R syntax, covering data types, variables, and basic operations.
05
Discuss how to import and export data in R, including reading CSV files and writing outputs.
06
Demonstrate basic data manipulation using packages like dplyr and tidyr.
07
Explain basic plotting using the ggplot2 library, highlighting how to create simple visualizations.
08
Include resources for further learning, such as online courses, documentation, and books.

Who needs introduction to r and?

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Students studying statistics or data science who require knowledge of data analysis tools.
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Researchers in various fields looking to utilize statistical software for their analyses.
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Professionals seeking to improve their data analysis skills for better decision-making.
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Anyone interested in gaining programming skills to work with data more effectively.
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Hobbyists and enthusiasts who want to explore data visualization and statistical projects.
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Introduction to R and refers to an educational context or course that provides the basics of the R programming language, focusing on statistical computations and data analysis.
Typically, students or professionals looking to enhance their data analysis skills or anyone interested in learning how to use the R programming language effectively would be required to engage with such an introduction.
To fill out an introduction to R and, one would typically begin by enrolling in a course, following course materials, and practicing coding exercises to build proficiency in R.
The purpose of introduction to R and is to equip learners with the foundational skills necessary to use R for data analysis, visualizations, and statistical computing.
Individuals must report their progress in learning R, tasks completed, projects undertaken, and any relevant data analysis outcomes achieved during the course.
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