R programming language is a widely used language for statistical computing and graphics. It is a powerful language for data analysis, data visualization, and machine learning.
R is designed for data analysis and visualization. It has a wide range of tools and packages for statistical analysis, data manipulation, and visualization. It is the language of choice for many data scientists and statisticians.
R is an open-source language, which means it is free to use and modify. This makes it accessible to anyone who wants to learn data analysis, regardless of their budget.
R has a wide range of packages for machine learning, making it a popular language for data scientists and machine learning engineers.
R is widely used in the industry, particularly in the fields of finance, healthcare, and tech. Learning R can increase your job opportunities in data analysis, data science, and machine learning.
R has a large and active community of users who share their knowledge and expertise. This means that there are plenty of resources available for learning R, including online tutorials, forums, and user groups.
General Introduction into the R Ecosystem
Downloading and installing R
History of R, R packages, CRAN
R community, R-bloggers
Stack Overflow, Coursera, DataCamp
R User Groups & meetups
Demonstration of a Data Analysis Project in R
Brief Overview on R Coding Tools RStudio
R Syntax Basics
Constants, operators, functions, variables
Vectors and vector indexing
Simple descriptive stats
The Power of R
Applying PCA on an image for outlier-detection
Visualizing MDS on a distance matrix
A Systematic Introduction into Data Types
Levels of measurement (nominal, ordinal, interval, ratio scale)
data. frame objects, rows and columns, indexing
Characteristics of tidy data
Basic Data Transformations Create new variables in a data. frame
Filter rows and columns
Introduction to data. table for More Complex Data Transformations
Filtering and ordering data
Summaries and aggregates
Joins on Keys
Introduction into fuzzy joins
Transforming wide and long tables
EDA - First Steps with Data Visualization
Why not Use Pie Charts
Plots outside of Excel: dot chart and violin plot examples
The Grammar of Graphics in R with ggplot2
Using labels for variable names
Introduction to Non-tabular Data Types
Big Data Problems: What is Big Data
4V: volume, variety, velocity, veracity
Data Transformations: Converting Numeric Variables into Factors
Dirty Data Problems: missing values
4 forms of data dates
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