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Spend an hour with A Gentle Introduction to Tidy Statistics In R. You may also enjoy the Basic Basics lesson unit from R-Ladies Sydney, which provides an opinionated tour of RStudio for new users and a step-by-step guide to installing and using R packages. For beginner-friendly installation instructions, we recommend the free online ModernDive chapter Getting Started with R and RStudio. These three installation steps are often confusing to first-time users. Install, RStudio, and R packages like the tidyverse.

The information appears here are generally important if any problem occurs.No one starting point will serve all beginners, but here are 6 ways to begin learning R. it is generally running on a remote server.īasically Console pane is the communication interface between you and systems. Jobs is th interface between you and your job execution system. R markdown 1 is interface between you and the markdown compiler, if authoring a markdown file, every time you compile ( knitr 2) the code, system will report status in that window. Terminal is the interface between you and operating system, where you can have a direct interaction with OS, in our case it is the Windows.

Apart from console, Other three, as their name suggested, they are the interface between you and other systems. The rest of the document will be largely concerned with working in the Console, with occasional references to other panes.īy default it also has 4 tabs: Console, Terminal, R markdown and Jobs.

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Console pane is the most important pane – the Console! This is where you enter your commands to be executed or your R code to do everything in the curriculum.

TitanicDataAnalysis_Model_Cross_Validation.Rīy default console pane appears at the bottom left.

TitanicDataAnalysis_Model_Construction.R.

(APPENDIX) Apendix: The R code of the entire project.

Support Vector Machine Model for Titanic.

Cross Validation on Random Forest Models.

Cross Validation on Decision Tree Models.

10.1 Model’s Underfitting and Overfitting.

Comparision the Three Random Forest Models.

9.2 Titanic prediciton with a Random Forest.

9 Titiannic Prediction with Random Forest.

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8.5 The Decision Tree with Full Predictors.

8.4 The Decision Tree with More Predictors.

8.3 The Decision Tree with Core Predictors.

8.2 The Simplest Decision Tree for Titanic.

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How to Determine the Best Split Condition?.

6.3 Classification as A Specific Prediction.

6.2 Process of Predcitive Data Analysis.

Extract ticket class from ticket number.

4.5 Actual Data Attributes Value Examination.

4.4 Actual Attributes Types Examination.

2.1 Brief introductiuon about R and RStudio.

1.4 Tools used in Doing a Data Science Project.

Step 5: Results Interpretation and Evaluation.

Step 4: Data Analyese - Building Models.

Step 3: Data Preprocess - Get your Data Ready.

Step 2: Undertand Data - Knowing your Raw Materials.

Step 1: Understand the Problem - Define Objectives.

Data Science as Development of Data Product.

Data Science as Discovery of Data Insight.