Course Outline:- R Programming Training In Nepal
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
- git, Github
R Syntax Basics :
- Constants, operators, functions, variables
- Random numbers
- Vectors and vector indexing
- Simple descriptive stats
- Loops
- Conditional expressions
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)
- Vector types
- 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
- Merging datasets
- Introduction to data. table for More Complex Data Transformations
Filtering and ordering data:
- Summaries and aggregates
- New variables
- Relational data
- 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:
- Time-series
- Spatial data
- Network data
- Big Data Problems: What is Big Data
- 4V: volume, variety, velocity, veracity
- Data Transformations: Converting Numeric Variables into Factors
- Date Operations
- String Parsing
- Geo coding
- Dirty Data Problems: missing values
- data imputation
- duplicates
- 4 forms of data dates
- outliers
- spelling
