R Programming

R is an open-source programming language that was created by Roass Ihaka and Robert Gentleman in 1995. The purpose of developing this language was to focus on delivering a more user-friendly and better way to perform statistics, data analysis, and graphical modules.
R was designed by statisticians and was specialized for statistical computing, and thus is known as the lingua franca (m
other tongue) of statistics. As technology improves, the data companies or research institutions collect has become more and more complex, and R has been adopted by many as the language of choice to analyze data.
R is great for machine learning, data visualization and analysis, and some areas of scientific computing.

Main Features

  • Get Started and navigate in the RStudio interface
  • Perform Data Preparation in R
  • Identify and locate missing records in data frames
  • Apply the Median Imputation method to replace missing records
  • Work with the gsub() and sub() functions for replacing strings
  • Create, use, append, modify, rename, access and subset data frames in R
  • Understand when to use [] and when to use [[]] or the $ sign when working with Lists and data frames
  • Understand how the Apply family of functions works
  • Create complex nested for() loops to automate tasks
  • Use apply() when working with matrices
  • Use lapply() and sapply() when working with lists and vectors
  • Add user defined functions into apply statements
  • Create data visualizations using ggplot2, gvisgeochart and plot() function

What is R?

1
Birth and Rise of R
2
Links for the necessary software
3
Comparison with SAS
4
GUI of R: IDE and Statistical Analysis Interfaces
5
R Workspace
6
GUI of RStudio

Basic Operations in R

1
Expressions: Basic Idea
2
Constant Values: Numeric and Non-Numeric
3
Arithmetic: Operations and BODMAS
4
Conditions: Equality, Greater Than, Less Than, etc
5
Function Calls: Introduction to R Functions
6
Symbols and Assignment
7
Keywords: NA, Inf, NaN, NULL, TRUE, FALSE
8
Naming a Variable: Generally accepted conventions

Data Types and Data Structures

1
Basic data types
2
Basic data structures: Vector, Factor, Matrices, Data Frame, List

Subsetting in R

1
Vector Subsetting
2
c() function: Creation of Vectors
3
Using rep() and seq() functions
4
Using factor() to covert vectors to factors
5
Using data.frame() to create data frames
6
Meta data access: dimnames(), rownames(), colnames()
7
Using matrix() to create matrices
8
Using array() to create arrays
9
Subsetting data frames: row subset, column subset, using subset() function
10
Assigning to a subset
11
Using is.na() to detect NA
12
Subsetting factors

Additional Topics on Data structures

1
The recycling rule: Uneven arithmetic operation on vectors
2
Type coercion: Character to Numeric
3
Automatic Type coercion
4
Coercing factors: Using as.factor() function
5
Changing factor levels
6
Attributes: attribute(), attr(), names() functions
7
Classes: Idea of OOP in R
8
Dates: As a special class
9
Formulas: As a special class
10
Exploring Objects: summary(), str(), dim() functions
11
Generic functions

Data Import and Export

1
Text formats: Reading Delimited Files
2
read.table() function
3
Using read.fwf() function for fixed width files
4
Using readLines() for reading lines
5
Using write.csv() function to store data as CSV files
6
Reading Excel file: Package XLConnect
7
Reading SPSS file: Package Foreign
8
Reading SAS data file: Package sas7bdat
9
Database connection: The ideas of ODBC connecting in Windows
10
RODBC package: Create and Query database from R
11
Basic SQL

Control Structures and User Defined Functions

1
Conditional Statements
2
If statement: The Structure
3
If Else statement: The Structure
4
Ifelse() function
5
Iteration
6
The for loop
7
The while loop
8
The repeat statement
9
lapply() function
10
sapply() function
11
apply() function
12
User defined function
13
Variable scooping: Global and Local Variables
14
Using user defined functions inside function definition

Data Visualisation: Charting with R

1
The plot function
2
plot.new() function: Generating new plot object
3
plot.window() function: Creating window
4
points() function: Plotting points
5
axis() function: Generating Axis
6
box() function: Creating enclosure
7
title() function: Assigning title
8
par() function: Fixing plotting parameters
9
lines() function: Adding connector lines
10
Multi figure layout: Creating multiple charts in the same window
11
hist() function: Plotting histograms
12
Kernel Density Plot: The non-parametric probability distribution
13
Comparing Groups via Kernel Density: Comparing two different probability distributions
14
Simple Bar Plot: Visualizing categorical data
15
Staked Bar Plot: Understating category composition
16
Grouped Bar Plot
17
Line Charts
18
Pie Charts
19
Boxplots: Understanding data distributions and outliers
20
Using Google Chart Tools with R (Package googleVis)
21
Geo Charts
22
Motion Charts

Visualisation on R using Google Vis

1
Visualisation in R using GoogleVis
2
Introduction to the package GoogleVis
3
Line chart
4
Bar chart and Column chart
5
Stepped chart and Combo chart
6
Bubble chart
7
Candle-stick Chart
8
Pie chart and Gauge chart
9
Geo charts
10
Annotation charts
11
Histogram

Visualization in R using GGPLOT2

1
Introduction and understanding the GGPLOT2 syntax
2
Scatter Plot and Jitter Plot
3
Counts chart
4
Maginal Histogram/ Boxplot
5
Correlogram
6
Ordered Bar chart
7
Histogram on continuous and categorical variable
8
Density Plot and Box Plot
9
Pie chart and Bar chart
10
Spatial Map
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Enrolled: 34 students
Duration: 40 hours
Lectures: 107
Video: 9 hours
Level: Advanced

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