Intro to Programming in R

Unit 1 – Intro to R and RStudio

Unit 2 – Data Types and Objects in R

Unit 3 – Vectors: Basics and Operations in R

Unit 4 – Matrices and Matrix Operations in R

Unit 5 – Lists and Data Frames

Unit 6 – Subsetting Data

Unit 7 – Control Structures: Conditionals in R

Unit 8 – Control Structures: Loops in R Programming

Unit 9 – Functions: Building Blocks of R Programming

Unit 10 – String Manipulation & Regular Expressions

Unit 11 – Importing & Exporting Data in R

Unit 12 – Data Manipulation with dplyr

Unit 13 – Data Visualization with ggplot2

Unit 14 – Exploring Data: Analysis Techniques

Unit 15 – Basic Statistical Tests

Unit 16 – Linear Regression Models

Unit 17 – Logistic Regression Models

Unit 18 – Clustering and Classification in R

Unit 19 – Reproducible Research & Reporting in R

Unit 20 – Advanced R Topics and Applications

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

You'll get the lowdown on R programming basics, focusing on its applications in statistics. The course covers data types, functions, loops, and conditional statements. You'll learn to import, manipulate, and visualize data using R packages like dplyr and ggplot2. By the end, you'll be able to perform basic statistical analyses and create snazzy data visualizations.

## Is Introduction to Programming in R hard?

It can be a bit of a challenge if you're new to programming. The syntax might seem weird at first, and there's a lot to remember. But honestly, once you get the hang of it, it's not too bad. The trickiest part is usually wrapping your head around the different data structures and how to manipulate them. Practice makes perfect though, so don't sweat it too much.

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

1. Use [Fiveable Study Guides](https://fiveable.me/cram-mode) to help you cram 🌶️
2. Practice coding regularly, even if it's just for 30 minutes a day
3. Use RStudio's built-in help function and cheat sheets
4. Join study groups to tackle tricky concepts like data manipulation with dplyr
5. Work on mini-projects to apply what you're learning (e.g., analyze your Spotify listening history)
6. Check out "R for Data Science" by Hadley Wickham for extra practice
7. Watch YouTube tutorials for visual explanations of complex topics

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

1. Introduction to Statistics: This course covers basic statistical concepts and methods. You'll learn about probability, distributions, and hypothesis testing.

2. Calculus I: This class introduces differential and integral calculus. It's essential for understanding more advanced statistical concepts.

## Classes similar to Introduction to Programming in R

1. Introduction to Python Programming: Learn another popular programming language used in data analysis. Python is known for its simplicity and versatility.

2. Data Visualization: Dive deeper into creating effective and visually appealing graphs and charts. You'll explore various tools and techniques for presenting data.

3. Statistical Computing: This course expands on R programming, focusing on more advanced statistical methods and simulations.

4. Machine Learning with R: Learn how to implement machine learning algorithms using R. You'll cover topics like regression, classification, and clustering.

## Majors related to Introduction to Programming in R

1. Statistics: Focuses on collecting, analyzing, and interpreting data. Students learn various statistical methods and their applications in real-world problems.

2. Data Science: Combines statistics, computer science, and domain expertise to extract insights from data. Students learn to use various tools and techniques for data analysis and visualization.

3. Applied Mathematics: Involves using mathematical methods to solve real-world problems. Students study advanced math concepts and their applications in various fields.

4. Bioinformatics: Applies computational techniques to analyze biological data. Students learn to use programming and statistical methods to study genomics and molecular biology.

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

1. Data Analyst: Examines data to identify trends and patterns. Data analysts use tools like R to clean, analyze, and visualize data for decision-making.

2. Statistician: Designs studies, collects data, and interprets results. Statisticians use R for complex statistical analyses and to create models for predicting outcomes.

3. Business Intelligence Analyst: Helps organizations make data-driven decisions. They use R and other tools to analyze business data and create reports for management.

4. Quantitative Researcher: Applies mathematical and statistical methods to financial and economic problems. They use R for modeling and analyzing complex financial data.

## Introduction to Programming in R FAQs

1. Do I need prior programming experience? Not necessarily, but it can be helpful. The course is designed for beginners, but having some coding experience might make the learning curve a bit smoother.

2. Can I use R on my personal computer? Absolutely! R is free and open-source. You can easily download and install it on your laptop or desktop.

3. How often will I use R in other statistics courses? Pretty frequently. Many upper-level stats courses use R for data analysis and visualization, so the skills you learn here will definitely come in handy.

Statistics

Computer Science

1.1 Overview of R and its applications

1.3 RStudio interface and basic functionality

1.2 Installing R and RStudio

1.4 Writing and executing R code

2.1 Numeric, character, and logical data types

2.2 Variables and assignment

2.3 Basic arithmetic and logical operations

3.1 Creating and manipulating vectors

3.2 Vector arithmetic and recycling

3.3 Vector indexing and slicing

4.1 Creating and manipulating matrices

4.2 Matrix operations and algebra

4.3 Applying functions to matrices

5.1 Creating and working with lists

5.2 Data frame structure and creation

5.3 Manipulating data frames

6.1 Subsetting vectors and matrices

6.2 Subsetting data frames

6.3 Logical indexing and filtering

7.1 If-else statements

7.2 Nested conditionals

7.3 Switch statements

8.1 For loops

8.2 While loops

8.3 Apply family of functions

9.1 Function syntax and structure

9.2 Arguments and return values

9.3 Scoping rules and environments

9.4 Writing efficient and reusable functions

10.3 Pattern matching and replacement

10.1 Basic string operations

10.2 Regular expression syntax

11.1 Reading and writing CSV files

11.3 Connecting to databases

11.2 Working with Excel files

12.3 Joining data frames

12.2 Grouping and summarizing data

12.1 dplyr verbs: select, filter, mutate, arrange

13.1 Grammar of graphics concept

13.2 Creating basic plots (scatter, line, bar)

13.3 Customizing plot aesthetics and themes

13.4 Faceting and multi-layer plots

14.1 Descriptive statistics and summary measures

14.2 Data distribution and outlier detection

14.3 Correlation analysis

15.1 t-tests and ANOVA

15.2 Chi-square tests

15.3 Non-parametric tests

16.1 Simple linear regression

16.2 Multiple linear regression

16.3 Model diagnostics and assumptions

17.1 Binary logistic regression

17.2 Multinomial logistic regression

17.3 Model evaluation and interpretation

18.1 K-means clustering

18.2 Hierarchical clustering

18.3 Decision trees and random forests

19.1 Introduction to R Markdown

19.2 Creating dynamic reports

19.3 Version control with Git and GitHub

20.1 Time series analysis

20.2 Spatial data analysis

20.3 Machine learning with caret

20.4 Shiny for interactive web applications

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