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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
Customize R studio
RStudio Debugger
RStudio History and Environment variables
R Syntax
R Variables
R Data Types & Structures
R Arithmetic Operators
R Logical Operators
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
Data Extraction
Data Frame Expansion
R Built-in Data frames
R Factors
Manage Factor levels
Factor Functions
R Contingency Tables
R Data Visualization
R – Charts and Graphs
R Density Plot
R Strip Charts
R Boxplots
R Violin Plots
R Bar Charts
R Pie Charts
R Area Plots
R Time Series
Graphics with ggplot2
Ggplot2 Structure
ggplot2 Bar Charts
ggplot2 Pie Chart
ggplot2 Area Plot
ggplot2 Histogram
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
Analysis of external data
This is a complete example of working with an external CSV file. First, we will import the data file from CSV, then check the structure of the data and pre-process the data. After that, we will do some basic data frame operations like creating a data frame from the stored data file and create a subset of data.
Export data to CSV file
In this section, we are going to learn how to export the data frame to a CSV file. We have to use specifically write.csv() to export the data. In this function, the mandatory arguments are data and file name.
Importing Excel Data
Microsoft Excel is a spreadsheet developed by Microsoft for Windows, macOS, Android, and iOS. Usually, we receive the data set either in CSV format or Excel format. In this section, we will Import Dataset from Excel file using the R default Import Dataset option.
Step 1: In the initial phase, we have an Excel file with two data sheets ageData and employee, which is available in our current working directory.
Step 2: Now, we will go to the Import Dataset option and select From Excel. If we run this first time, R Studio will install and attach the required libraries.
Step 3: After step 2, we will be landed into the Import Excel Data option. Please follow the below steps to continue:
1. Browse and select the file, which we have planned to work with.
2. Select the worksheet. In our case, we have selected ageData.
3. Now we treat Null values from this NA option. In our worksheet, there were a few ## as Null values. Our NA option will treat them as Null Values.
Finally, we Import the dataset in our R environment. R will store the data in a variable for future work.
Step 4: Now, we will work with the employee datasheet. In this sheet, data is only available in the range B3 to G11. At the time of import this sheet, we have to mention the specific date range.
Step 5: Now again, we go to the Import Data option and follow the below actions:
4. Browse and select the file, which we have planned to work with.
5. Select the worksheet. In our case, we have selected employee.
6. We will select the data range B3 to G11 as data is only available in this range.
7. Finally, Import the dataset in the R environment. R will store the data in a variable for future work.
Direct Import from R code:
We can directly import them using the following code snippet.
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