Exploring R
Evolution of R
Programming Features of R
R for Machine Learning
R for Data Analysis
Application of R
R vs. Python vs. SAS
R vs. Excel vs.Tableau
Install R base on Windows
Install R Studio on Windows
Install R base on Ubuntu
Install R Studio on Ubuntu
R Starter
First R Program
Working with R Packages
R Workplace and R Sessions
Manage working directory
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RStudio Debugger
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R Syntax
R Variables
R Data Types & Structures
R Arithmetic Operators
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R If Statement
R - If…Else Statement
If…else if…Else Statement
R for loop
R while loop
R repeat loop
R String Construction
R String Manipulation Functions
Creating Character Strings
R Functions
R built-in functions
Working with Vector
R Vector Indexing
R Vector Modification
R Arithmetic Vector Operations
R Lists
Access List elements (List Slicing)
List modification
R Matrix construction
Access Matrix elements
R Matrix Modification
R Matrix Operations
R Array Construction
Accessing Array Elements
Manipulating Array Elements
R Data Frames
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R Built-in Data frames
R Factors
Manage Factor levels
Factor Functions
R Contingency Tables
R Data Visualization
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R Density Plot
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R Time Series
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ggplot2 Scatter Plot
ggplot2 Box Plot
Mean & Median
Standard Deviation
Normal Distribution
Correlation
T-Tests
Chi-Square Test
ANOVA Test
Survival Analysis
Data Pre-processing and Missing Value Analysis
Missing data treatment
Missing value analysis with mice package
Outlier Analysis
Problems with outliers
Outlier Detection
Outlier Treatment
Simple Linear Regression
Mathematical Computation
Linear Regression in R
A complete Simple Regression Analysis
Multiple Linear Regression
Mathematical Analysis
Model Interpretation
A complete Multiple Regression Analysis
Logistic Regression
Mathematical Computation in R
Logistic Regression in R
Heart Risk Analysis using LR
Support Vector Machine
Heart Risk Analysis using SVM
Decision Trees
Random Forest
K means Clustering
Big data Analytics using R-Hadoop
RHADOOP Packages:
rJava: Low-Level R to Java Interface
rhdfs: Integrate R with HDFS
rmr2: MapReduce job in R
plyrmr: Data Manipulation with MapReduce job
rhbase: Integrate HBase with R
Environment setup for RHADOOP
Getting Started with RHADOOP
The standard deviation (σ) measures the dispersion of a dataset relative to its mean (μ) and is calculated as the square root of the variance. Standard deviation is a measure of how spread out numbers are. More specifically, it measures the dispersion of a data set relative to its mean and is calculated by the square root of the variance.
We can calculate the standard deviation using the formula below. Where μ is the mean, xi is the sample points.
Formula:
If the data is being considered a population on its own, we divide by the number of data points, N.
If the data is a sample from a larger population, we divide by one fewer than the number of data points in the sample, N-1.
Steps to calculate the Standard deviation:
Step 1: Calculate the mean (μ).
Step 2: For each data point, calculate the square of its distance to the mean.
Step 3: Aggregate the values from Step 2.
Step 4: Divide the aggregated result by the number of data points (N).
Step 5: Take the square root.
Example: Calculate manually standard deviation and compare this with R function.
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