Advanced R Programming

Unit 1 – Introduction to R Programming

Unit 2 – Data Structures in R

Unit 3 – Control Structures & Functions in R

Unit 4 – Data Manipulation in R

Unit 5 – Exploring Data: Analysis Techniques

Unit 6 – Probability & Statistical Inference

Unit 7 – Machine Learning Basics in R

Unit 8 – Advanced ML Techniques in R

Unit 9 – Time Series Analysis in R

Unit 10 – Text Mining & NLP in R

Unit 11 – Parallel Computing in R for Big Data

Unit 12 – Advanced Graphics & Reporting in R

Unit 13 – R Software Dev & Reproducibility

Unit 14 – Case Studies in Advanced R Programming

## What do you learn in Introduction to Advanced Programming in R

You'll get into the nitty-gritty of R programming, focusing on advanced data manipulation, visualization, and statistical modeling. The course covers topics like efficient coding practices, creating custom functions, working with large datasets, and implementing machine learning algorithms. You'll also dive into package development and learn how to optimize R code for better performance.

## Is Introduction to Advanced Programming in R hard?

It can be pretty challenging, especially if you're not already comfortable with basic R programming. The course moves fast and covers a lot of ground. That said, if you've got a solid foundation in R and enjoy problem-solving, you'll probably find it more exciting than difficult. Just be prepared to put in some serious coding time.

## Tips for taking Introduction to Advanced Programming in R in college

1. Use [Fiveable Study Guides](https://fiveable.me/cram-mode) to help you cram 🌶️
2. Practice, practice, practice. Coding is like a muscle - the more you use it, the stronger it gets
3. Get comfortable with RStudio and learn keyboard shortcuts
4. Join or form study groups to tackle complex problems together
5. Don't just copy-paste code; try to understand how each function works
6. Explore the "R for Data Science" book by Hadley Wickham for extra insights
7. Check out the "Advanced R" website by Hadley Wickham for in-depth explanations
8. Watch DataCamp's R programming tutorials for visual learning

## Common pre-requisites for Introduction to Advanced Programming in R

1. Introduction to R Programming: This course covers the basics of R syntax, data types, and simple data manipulation. You'll learn how to create variables, work with vectors and data frames, and write basic functions.

2. Statistics for Data Science: This class introduces fundamental statistical concepts and their applications in R. You'll explore descriptive statistics, probability distributions, hypothesis testing, and linear regression.

## Classes similar to Introduction to Advanced Programming in R

1. Data Visualization with R: Focuses on creating stunning and informative graphics using ggplot2 and other R packages. You'll learn how to communicate complex data through effective visual representations.

2. Machine Learning in R: Covers popular machine learning algorithms and their implementation in R. You'll work on projects involving classification, regression, and clustering techniques.

3. Big Data Analytics with R: Teaches techniques for handling and analyzing large-scale datasets in R. You'll learn about parallel processing, database connections, and working with distributed computing frameworks.

4. Time Series Analysis in R: Explores methods for analyzing and forecasting time-dependent data. You'll use R packages like forecast and tseries to model and predict trends in sequential data.

## Majors related to Introduction to Advanced Programming in R

1. Data Science: Combines statistics, computer science, and domain expertise to extract insights from data. Students learn to collect, analyze, and interpret complex datasets using various tools and techniques.

2. Statistics: Focuses on the collection, analysis, interpretation, and presentation of data. Students develop skills in probability theory, statistical inference, and experimental design.

3. Computer Science: Deals with the theory, design, and application of computer systems. Students learn programming languages, algorithms, data structures, and software development principles.

4. Bioinformatics: Applies computational techniques to analyze biological data. Students learn to process and interpret genomic, proteomic, and other large-scale biological datasets.

## What can you do with a degree in Introduction to Advanced Programming in R?

1. Data Scientist: Analyzes complex datasets to extract meaningful insights and inform business decisions. They use statistical techniques and machine learning algorithms to solve real-world problems.

2. Quantitative Analyst: Develops and implements complex mathematical models to solve financial and risk management problems. They use R and other tools to analyze market trends and create trading strategies.

3. Biostatistician: Applies statistical methods to biological and medical research. They design experiments, analyze clinical trial data, and help interpret results for healthcare professionals.

4. Business Intelligence Analyst: Transforms raw data into actionable insights for companies. They create dashboards, reports, and visualizations to help businesses make data-driven decisions.

## Introduction to Advanced Programming in R FAQs

1. How much coding experience do I need before taking this course? It's best to have at least one semester of R programming under your belt. Familiarity with basic data structures and functions will give you a good starting point.

2. Can I use RStudio Cloud instead of installing R and RStudio on my computer? Yes, RStudio Cloud is a great option for this course. It provides a consistent environment and makes it easy to share code with classmates and instructors.

3. Are there any good online resources for extra practice? Absolutely! Websites like DataCamp, Coursera, and edX offer great R programming courses that can supplement your learning. Many of these platforms offer free or discounted access for students.

Statistics

Computer Science

1.1 Overview of R and its applications

1.2 Installing R and RStudio

1.5 Basic arithmetic and logical operations

1.3 Basic syntax and data types

1.4 Variables and assignment operators

2.1 Vectors and matrices

2.2 Lists and data frames

2.4 Subsetting and indexing data structures

2.3 Factors and arrays

3.2 Loops (for, while, repeat)

3.4 Scope and environments

3.5 Recursion and memoization

3.1 Conditional statements (if-else, switch)

3.3 Writing user-defined functions

4.1 Reading and writing data (CSV, Excel, SQL)

4.4 Data manipulation with dplyr

4.5 Handling missing data and outliers

4.2 Data preprocessing and cleaning

4.3 Merging and reshaping data (tidyr)

5.1 Summary statistics and descriptive analysis

5.2 Data visualization with base R graphics

5.3 Advanced plotting with ggplot2

5.4 Interactive visualizations with plotly and shiny

6.1 Probability distributions in R

6.2 Sampling and hypothesis testing

6.3 Confidence intervals and p-values

6.4 ANOVA and regression analysis

6.5 Bayesian inference with MCMC

7.1 Introduction to machine learning concepts

7.2 Supervised learning: classification and regression

7.4 Model evaluation and selection

7.5 Regularization and cross-validation

7.3 Unsupervised learning: clustering and dimensionality reduction

8.1 Decision trees and random forests

8.2 Support vector machines (SVM)

8.3 Neural networks and deep learning

8.4 Ensemble methods and boosting

8.5 Handling imbalanced datasets

9.1 Time series data manipulation (xts, zoo)

9.2 Decomposition and seasonality

9.3 ARIMA and SARIMA models

9.4 Forecasting and model evaluation

10.1 Text preprocessing and feature extraction

10.2 Sentiment analysis and topic modeling

10.4 Word embeddings and language models

10.3 Named entity recognition and part-of-speech tagging

11.1 Parallel processing with foreach and parallel packages

11.2 Distributed computing with Spark and SparkR

11.3 Handling big data with data.table and dplyr

11.4 Memory management and profiling

12.1 Creating publication-quality graphics

12.2 Customizing plots and themes in ggplot2

12.3 Dynamic reporting with RMarkdown and knitr

12.4 Interactive dashboards with flexdashboard

13.1 Version control with Git and GitHub

13.2 Package development and documentation

13.3 Unit testing and continuous integration

13.4 Reproducible research and data provenance

14.2 Web scraping and API integration

14.3 Geospatial analysis and mapping

14.4 Network analysis and graph theory

14.1 Data science projects and workflows

14.5 Bioinformatics and genomic data analysis

Browse all Advanced R Programming Key Terms

About Us

Resources

Stay Connected

© 2024 Fiveable Inc. All rights reserved.

AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.

About Us

Resources

© 2024 Fiveable Inc. All rights reserved.

AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.

Back

Next