Getting Started with JARI

We will create a report using JARI

RPubs – Testing use of JARI – Just Another R Interface to generate Report

Download JARI.zip at

JARI – Just Another R Interface

2. Extracted the files in D:/ drive. It is preferred to extract in D:/ drive or E:/ drive other than C:/ drive due to windows security. Start JARI.exe

3. Create a New Project

Click Create New Folder. It is preferred to save the project in D:/ also because of Windows security.

In project folder, you should see Identify folder, Collect Folder, Clean Folder, Analyze Folder, and Interpret Folder. JARI follows the Data Analysis Process. You should be spending time in these folders for the project.

4. Identify

Click on Problem, or Questions or Stakeholders to write your Problems, SMART Questions and Stakeholders for your Data Analysis projects.

For here,

Problem Statement:

Our company recently measured the iris flower and get the iris dataset.We would like to get some plots and exploration on the dataset.

SMART Problems

How are the variables correlated to each other?

What is the distribution of each variable?

How are the variables same to each other in mean or median?

Stakeholder Analysis

Eric Goh Ming Hui holds Master of Technology and MBA. Eric Goh knows technical and business. Eric Goh is founder of EGMHAcademy.com, hence, he is more interested on performance and improvements.

5. Collect

You can use the

Image to Data: JATI software. The software OCR images to text.

Video to Data: JAVT software. The software convert video to text. It uses Microsoft SAPI and CMU sphinx.

MySQL to Data: YADBS Software/ The software convert MySQL to text.

Webscraping: JAWS Software. The software get text from html.

Put Data together with Gnumeric. Gnumeric is opensource software like Microosft Excel.

6. Clean

Assume we have collected the data by measuring iris flower, and put in iris dataset. Go File > Import Data

You can download the iris data from https://gist.github.com/netj/8836201

Put the iris.csv in D:/ also because of Windows security.

We can

Duplicate Button: to remove Duplicates

Missing Button: Remove missing values

Standardization Button: Standardization/Normalization

Select Variables Button: Select variables for the dataset

Filter Rows Button: Filter the rows for dataset

Mutate Button: Create Variables

R and dplyr Button: Use R and dplyr to Clean

The Data Created on the right side shows the data created from removing missing values, removing duplicates, and the data are in the clean folder. Click on the data to change data.

7. Analyze

Click on View Variables, and we can see the Column names and Column Class. Column Names are the Variable Names.

SMART Problems

How are the variables correlated to each other?

What is the distribution of each variable?

How are the variables same to each other in mean or median?

So, how are the variables correlated to each other?

Click Correlation Matrix and select variables. Click OK.

We only select three variables because if we select four variables, there will be errors because of stargazer library. We can however, click on the R Code to edit the R codes and error.

JARI generate the R Codes, it allows you to edit the R Codes.

At the right side, there is Results Saved. Name CorrelationMatrix (no spacing) and click save. We save the results.

Click Correlation Test. Select Sepal Length and Sepal Width. Click Ok.

Save results. Name: CorrelationSepalLengthSepalWidth.

We can infer that the correlation is low.

Do the same for Petal Width and Petal Length.

Plot a Scatterplot

Click Data Visualization

Edit the x, y, x-axis, y-axis

Click Generate

Click Add to Plots

Repeat that for

Petal Length and Petal Width

Sepal Length and Petal Length

Sepal Width and Petal Width

Click Multiple Graphs

Click Generate

Click OK

Save results. Name CorrelationSepalLengthSepalWidth.

SMART Problems

How are the variables correlated to each other?

What is the distribution of each variable?

How are the variables same to each other in mean or median?

So, what is the distribution of each variable?

Click descriptives

Sepal Length

Click Normality Test

Click OK

Save Results. Name NormalTestSepalLength. Save.

Repeat that for

Petal Length

Sepal Width
Petal Width

How are the variables correlated to each other?

What is the distribution of each variable?

How are the variables same to each other in mean or median?

So, How are the variables same to each other in mean or median?

Since the variable are not normal distribution, Click Unpaired Wilcoxon Test

Select variables

Click OK

Save Results. Name WilcoxonSepalLengthSepalWidth Save

Repeat for Petal Length Petal Width

8. Intepret

Go to Intepret

For now

Executive Summary:

JARI – Just Another R Interface, is a software developed by Eric Goh Ming HUi, author of the book – Learn R for Applied Statistics, published at Apress. This is a report generated ffrom JARI, for testing. You can download JARI at EGMHacademy.com. Executive Summary: Our company recently measured the iris flower and get the iris dataset.We would like to get some plots and exploration on the dataset. The company has three questions – how are the variables correlated to each other, what is the distribution of each variable, and how are the variables same to each other in mean or median. We will use the correlation matrix for the first question, the normality test for the second question, and Wilcoxon test for third questions.

Introduction

Our company recently measured the iris flower and get the iris dataset.We would like to get some plots and exploration on the dataset.

Data Source

Our company recently measured the iris flower and get the iris dataset.We would like to get some plots and exploration on the dataset.

Key Findings

How are the variables correlated to each other?
What is the distribution of each variable?
How are the variables same to each other in mean or median?

Conclusion

We can infer that there is correlation between Petal Length and Petal Width, Sepal Length and Petal Length, Petal Width and Sepal Width. For Normality Test, Only Sepal Width is normal distribution. Both wilcoxon test showed P-Value < 0.05, hence, we can reject null hypothesis and the median of variable A is different of median of variable B. So, median of Petal Length and median of Petal Width is different. So, median of Sepal Length and median of Sepal Width is different

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