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(Completed in 2 hours) R Programming Basics for Data Analysis

Welcome to the R Programming Basics course! This course provides an easy and systematic guide to the core concepts of R programming and practical data handling and visualization methods for those starting out in data analysis. Starting with R installation, it covers data structures, basic syntax, and real analysis cases, aiming to help you successfully take your first step as a data analyst.

5 learners are taking this course

  • sdj0831
실습 중심
R
Big Data

What you will gain after the course

  • R and RStudio

  • Data Analysis

  • Data Cleaning

  • Data Processing

Getting started with R programming is easy.

  • R, the first step in data analysis! 🚀

  • Data Analysis Magic Starting with R

  • Let's Play with Data! Introduction to R Programming

  • I do it too! Conquer the basics of R data analysis

  • R Programming, Opening the Door to Data Analysis

I recommend this to these people

In data analysis

For beginners

R programming

For beginners

Data related

People preparing for certification

After class

  • Acquire basic data analysis capabilities

  • Improve your data-driven problem-solving skills

  • Expanding future growth and usability

Features of this course

Please introduce the key features and differentiating factors.

2nd period_R programming basics_lecture_

Insert rich graphic examples

This lecture contains many related pictures to make it easy to understand.

Easy explanation with a picture analogy

It is structured so that even beginners can easily access it.

Learn about these things

1. R Basics:

  • R, RStudio

  • Variable

  • Data Types (Numeric, Character, Logical, etc.)

  • Data Structures (Vector, List, Matrix, Data Frame, Tibble)

  • Function, User Defined Function

  • Package (Package: Installation and Loading)

  • Conditional Statement: if , else )

  • Loop ( for , while )

  • Comment, Help

2. Data Manipulation & Preprocessing:

  • Data Import/Export (CSV, Excel, etc.)

  • dplyr package

  • Select and extract rows/columns ( select , filter )

  • arrange

  • Create and mutate derived variables

  • Data summary ( summarise / summarize )

  • Grouping data ( group_by )

  • Data merging/connection ( join family functions: left_join , inner_join etc., bind_rows , bind_cols )

  • Missing Value: NA , is.na , na.omit

  • Outlier

  • Data Cleaning

  • Pipe operator ( %>% )

3. Data Visualization:

  • ggplot2 package

  • Base R plotting

  • Bar chart (Bar chart: geom_bar , geom_col )

  • Line graph (Line graph: geom_line )

  • Scatter plot: geom_point

  • Histogram (Histogram: geom_histogram )

  • Box plot (Box plot: geom_boxplot )

  • Aesthetic Mapping ( aes() )

  • Setting the Axis, Title, and Legend

  • Control visual elements such as color, shape, and size

4. Basic Statistics & Analysis:

  • Descriptive Statistics (mean, median, mode, variance, standard deviation)

  • Frequency Analysis (Frequency Analysis: table )

  • Correlation Analysis ( cor )

  • Introduction to (simple) hypothesis testing (t-test, etc.)

  • (Simple) Introduction to Regression Analysis

  • Exploratory Data Analysis (EDA)

Who created this course

  • It was difficult for me when I first started studying, but now I have acquired the know-how of R programming easily.

  • I created this in an easy lecture format so that anyone can easily study and learn R programming.

Do you have any questions?

R Basics

  • What is R? What is it used for?

  • What are the differences between R and Python?

  • How to install and use R?

Handling data

  • What is a data frame?

  • How to import an Excel file?

  • How to handle missing values (NA)?

Data Visualization

  • How to draw a graph with the ggplot2 package?

  • How to draw a bar graph, line graph, histogram?

📐 Statistical Analysis

  • How to find mean, median, variance, and standard deviation?

  • How do I do t-test and ANOVA analysis?

Things to note before taking the class

Practice environment

  • Operating System and Version (OS): Windows

  • Tools used: Install R Studio

  • PC Specs: Applicable

Learning Materials

  • The format of the learning materials provided (PPT, cloud link, text, source code, assets, programs, example problems, etc.)

  • Quantity and capacity, features and notes on other learning materials, etc.

Player Knowledge and Notes

  • None applicable

Recommended for
these people

Who is this course right for?

  • Data Beginner

  • R programming beginner

  • Data Visualization Enthusiast

Hello
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Learners

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Reviews

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Answers

4.3

Rating

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Curriculum

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10 lectures ∙ (1hr 52min)

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